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[Display omitted] • A flexible pressure sensor was constructed with MAPbBr 3 in the matrix of PVDF. • A porous polymer composite with superhydrophobicity was synthesized in situ. • The mechanism of the sensor's response is explained by the DFT and MEPs. • The sensor can monitor human physiological signals and wind speed very well. • Feature engineering and machine learning improved detection accuracy in 60 tests. The application of pressure sensors based on perovskite in high-humidity environments is limited by the effect of water on their stability. Endowing sensors with superhydrophobicity is an effective strategy to overcome the issue. In this work, MAPbBr 3 /Polyvinylidene Fluoride-TFSI composite was prepared by a one-step in-situ strategy to form a flexible superhydrophobic pressure sensor, which exhibited a contact angle of 150.25°. The obtained sensor exhibited a sensitivity of 0.916 in 1 kPa, a detection limit of 0.2 Pa, a precision of 0.1 Pa, and a response/recovery of ∼100 ms, along with good thermal stability. Through density functional theory calculations, it is revealed that the formation of the porosity is attributed to the interaction between the polymer and EMIM TFSI, which further leads to superhydrophobicity. And, the perovskite structure is easy to change under pressure, affecting the carrier transport and electrical signals output, which explains the sensing mechanism. In addition, the sensor performed well in monitoring facial expression, pulse, respiration, finger bending, and wind speed ranging from 1 m/s to 6 m/s. With both the Linear Regression and the Random Forest algorithm, the sensor can monitor the wind speed with an R2 greater than 0.977 in 60 tests. [ABSTRACT FROM AUTHOR]

• A computational frame was proposed to predict diabetes that collected from hospitals and health center by leveraging blood routine data. • The contributions of different blood routine indicators for diabetes were identified by our frame. • A nomogram was constructed for assessing the influence of blood routine indicators on prediction outcomes. Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked until individuals manifest noticeable symptoms of the condition. This study aimed to address this gap by collecting comprehensive datasets, including 1000 instances of blood routine data from diabetes patients and an equivalent dataset from healthy individuals. To differentiate diabetes patients from their healthy counterparts, a computational framework was established, encompassing eXtreme Gradient Boosting (XGBoost), random forest, support vector machine, and elastic net algorithms. Notably, the XGBoost model emerged as the most effective, exhibiting superior predictive results with an area under the receiver operating characteristic curve (AUC) of 99.90% in the training set and 98.51% in the testing set. Moreover, the model showcased commendable performance during external validation, achieving an overall accuracy of 81.54%. The probability generated by the model serves as a risk score for diabetes susceptibility. Further interpretability was achieved through the utilization of the Shapley additive explanations (SHAP) algorithm, identifying pivotal indicators such as mean corpuscular hemoglobin concentration (MCHC), lymphocyte ratio (LY%), standard deviation of red blood cell distribution width (RDW-SD), and mean corpuscular hemoglobin (MCH). This enhances our understanding of the predictive mechanisms underlying diabetes. To facilitate the application in clinical and real-life settings, a nomogram was created based on the logistic regression algorithm, which can provide a preliminary assessment of the likelihood of an individual having diabetes. Overall, this research contributes valuable insights into the predictive modeling of diabetes, offering potential applications in clinical practice for more effective and timely diagnoses. [ABSTRACT FROM AUTHOR]

• Identification of DHSs can help to understanding the mechanism of disease development and the treatment of the disease. • Multi-dimensional feature fusion strategy was used for feature extraction from DNase samples. • An overall prediction accuracy of 0.859 was achieved with an AUC value of 0.837. DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis -regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F -score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites. [ABSTRACT FROM AUTHOR]

• RDscan employs Bioformer-8L to identify RD information in sentences. • RDscan Utilizes BioBERT for RNA and disease entity extraction. • RDscan curates a 2082-sentence RRI corpus for RD text mining research. • RDscan's web tool facilitates RD relationship extraction from texts. With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/. [ABSTRACT FROM AUTHOR]

• A random forest model is proposed for cell-type-specific cohesin-mediated chromatin loops prediction. • Chromatin state is responsible for cell-type-specificity of chromatin loops. • Chromatin state combines with the binding of key factors can predict cell-type-specific loops accurately. Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation. [ABSTRACT FROM AUTHOR]

Acidification is a well-known treatment to reduce NH 3 emissions from livestock slurries by lowering their pH, but its application at the farm scale is still limited. The acid dosage depends on the acid strength and slurries composition. Acidification does not have a lasting effect and after the acid addition the slurry pH tends to rise due to its buffer system. By studying 54 samples of pig slurry, dairy cattle slurry and digestate, this study aimed to: (i) identify the chemical-physical parameters related to the sulphuric acid (98% w/w) dosage necessary to reduce pH at 5.5, and pH variation over time; (ii) develop predictive models for the acid dosage and the pH after one (pH1w) and two weeks (pH2w) of storage based on slurry composition by using regression trees (RT) and random forests (RF). Acid dosage ranged between 0.8 and 11.7 ml kg−1 increasing with slurry alkalinity, with digestate requiring significantly higher dosage than slurries. Pig slurry showed significantly higher pH increase than the other two slurries. Finally, the pH trend over time was negatively correlated with the solids content. The RF identified the alkalinity and the initial slurry pH as the most important variables in explaining the required acid dosage, while for pH1w and pH2w it identified the total organic carbon and volatile solids. Based on RF results, RT models accurately predicted required acid dosage (r2 = 0.881), the pH1w (r2 = 0.728) and pH2w (r2 = 0.667). Therefore, these simple models can have practical applications for reducing NH 3 emissions. • Acidification treatment reduces ammonia emissions from livestock slurry and digestate. • Correlations among slurry composition variables, acid dosage and pH trends were studied. • Needed acid dosage and pH trends were predicted with regression trees and random forests. • Needed acid dosage was accurately predicted by alkalinity and initial pH of the slurry. • PH trends over time were accurately predicted by total organic carbon and volatile solids. [ABSTRACT FROM AUTHOR]

Risk stratification for poor outcomes is not currently age-specific. Risk stratification of older patients based on observational cohorts primarily composed of young patients may result in suboptimal clinical care and inaccurate quality benchmarking. We assessed two hypotheses. First, we hypothesized that risk factors for poor outcomes after trauma are age-dependent and, second, that the relative importance of various risk factors are also age-dependent. A cohort study of severely injured adult trauma patients admitted to the intensive care unit 2014-2018 was performed using trauma registry data. Random forest algorithms predicting poor outcomes (death or complication) were built and validated using three cohorts: (1) patients of all ages, (2) younger patients, and (3) older patients. Older patients were defined as aged 55 y or more to maintain consistency with prior trauma literature. Complications assessed included acute renal failure, acute respiratory distress syndrome, cardiac arrest, unplanned intubation, unplanned intensive care unit admission, and unplanned return to the operating room, as defined by the trauma quality improvement program. Mean decrease in model accuracy (MDA), if each variable was removed and scaled to a Z-score, was calculated. MDA change ≥4 standard deviations between age cohorts was considered significant. Of 5489 patients, 25% were older. Poor outcomes occurred in 12% of younger and 33% of older patients. Head injury was the most important predictor of poor outcome in all cohorts. In the full cohort, age was the most important predictor of poor outcomes after head injury. Within age cohorts, the most important predictors of poor outcomes, after head injury, were surgery requirement in younger patients and arrival Glasgow Coma Scale in older patients. Compared to younger patients, head injury and arrival Glasgow Coma Scale had the greatest increase in importance for older patients, while systolic blood pressure had the greatest decrease in importance. Supervised machine learning identified differences in risk factors and their relative associations with poor outcomes based on age. Age-specific models may improve hospital benchmarking and identify quality improvement targets for older trauma patients. [ABSTRACT FROM AUTHOR]

Accelerometry and machine learning are powerful tools for gaining in-depth information about animal behaviour. However, such methods are rarely used to quantify behaviours that contribute to mating success, such as intrasexual competition, courtship and copulation. In part, this is probably due to the inherent challenges of classifying behaviours that can be brief and infrequent, and that are not necessarily exhibited by all individuals. In this study, we used triaxial accelerometry and machine-learning methods to quantify the mating behaviours of captive male ruffs, Calidris pugnax. The ruff is a polymorphic, lekking shorebird with highly skewed mating success. Mating behaviour in ruffs includes ritualized postures that can be distinguished during observations. Using this system as a case study, we (1) describe possible approaches to classifying mating behaviour; (2) compare the classification performance of three supervised machine-learning methods: random forests, hidden Markov models and neural networks; (3) highlight potential pitfalls that can cause overestimation of model performance; and (4) offer suggestions for avoiding these pitfalls. In our study, some models distinguished mating behaviours from nonmating behaviours with high precision and sensitivity (>75%), but only when trained and tested on the same individuals within the same timeframe. Estimates of model performance were much poorer when models were tested on future data or data from different individuals. Nevertheless, even when tested on different individuals, the hidden Markov model provided a reasonably accurate estimate of which males invested more time in mating behaviour overall. Here, we provide an end-to-end workflow for classifying behaviour from accelerometry, including recommendations, considerations and code. Although posture-based mating behaviours in ruffs proved challenging to distinguish, the methods that we describe show promise for displays associated with distinctive, dynamic movements. • Accelerometry and machine learning are powerful tools for quantifying behaviour. • 'Mating' behaviours, such as courtship, can be challenging to classify reliably. • We describe and compare methods, using a lekking shorebird as a case study. • Models were used to rank males by time spent on mating behaviours with high accuracy. • Performance is overestimated when data are split randomly for training and testing. [ABSTRACT FROM AUTHOR]

A complex field environment and differences in maize density cause feed rate fluctuations in the combine. Traditional threshing methods do not adjust multiple operating parameters based on the feed rate, resulting in low threshing performance and high power consumption. A multi-parameter control method for maize threshing based on the feed rate was proposed to overcome these problems. Random forest, support vector machine, and multiple linear regression machine learning algorithms were used to estimate the threshing performance indices for different feed rates. The algorithm (random forest) was used to construct a control model for the rotor speed, threshing gap, and top cover guide vane angle. The performance of the control system was compared with a threshing system with constant parameters to verify the control model's accuracy. The results demonstrated that the threshing unit with the control system outperformed the one without. The broken grain rate and the power consumption were 2.18% and 36.22% lower, respectively, whereas the unthreshed grain rate was not significantly different. Random forest was more suitable than the support vector machine and multiple linear regression models for establishing a multi-parameter control model for maize threshing. The adjustment of multiple threshing parameters significantly improved the operating performance for different feed rates. • The threshing performance will decrease due to the fluctuation of feed rate. • Threshing multi-parameter control can effectively improve operation performance. • Artificial intelligence algorithm is conducive to the decision of harvest parameters. [ABSTRACT FROM AUTHOR]

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner. • Different representation learning strategies are developed to learn the semantic graph and function similarity representations of drugs and diseases. • SFRLDDA, a potent drug repositioning algorithm, is introduced to accurately discern novel DDAs by learning multifaceted representations of drugs and diseases. • Experimental results demonstrate that SFRLDDA yields a best performance when compared with several state-of-the-art drug repositioning algorithms under ten-fold cross-validation. [ABSTRACT FROM AUTHOR]

This study explores the possibility of using routinely taken blood tests in the diagnosis and triage of patients with suspected musculoskeletal malignancy. A retrospective study was performed on results of patients who had presented for assessment to a regional musculoskeletal tumour unit. Blood results of patients with a histologically confirmed diagnosis between 2010 and 2020 were retrieved. 33 distinct blood tests were available for model forming. Results were standardised by calculating z-scores. Data were split into a training set (70%) and a test set (30%). The training set was balanced by resampling underrepresented classes. The random forest algorithm performed best and was selected for model forming. Receiver operating characteristic curves were used to find the optimum threshold. Models were calibrated and performance metrics evaluated with confusion tables. 2371 patients formed the study population. 1080 had a malignant diagnosis in one of three categories: sarcoma, metastasis, or haematological malignancy. 1291 had a benign condition. Metastasis could be predicted with an accuracy of 79% (AUC 87%, sensitivity 79%, specificity 80% NPV 91%). Haematological malignancy accuracy 79% (AUC 81%, sensitivity 77%, specificity 79%, NPV 97%). Sarcoma accuracy 64% (AUC 73%, sensitivity 76%, specificity 61%, NPV 88%) and all malignancy accuracy 74% (AUC 80%, sensitivity 72%, specificity 75%, NPV 76%). Routinely performed blood tests can be useful in triage of musculoskeletal tumours and can be used to predict presence of musculoskeletal malignancy. [ABSTRACT FROM AUTHOR]

The aim was to evaluate if maize (Zea mays L.) grain and silage yield can be estimated from unmanned aerial systems (UAS) imagery. A fixed-wing UAS was used to collect imagery throughout the growing season (emergence to reproductive growth stages) at eight commercial fields, each implemented with a nitrogen-rich (NRich) strip. Exponential regression models were fitted between yield and seven vegetation indices (VIs) from NRich strip, whilst two machine learning (ML) regression models (random forest [RF] and support vector regression [SVR]) were fitted with VIs, elevation, and landform layers. The results showed that the normalised difference vegetation index (NDVI) was the most reliable indicator for yield using exponential models when the images were collected at late vegetative stages before tasseling for silage and late reproductive stage before senescence for grain. The exponential models performed better with NRich strip than those derived with the statistical reference strips (SRS) for fields without NRich strips, but the differences were minimal when sensed at a vegetative stage before tasselling. The Boruta algorithm identified elevation, landform, and three VIs as the top five important features for yield estimation. Using these five features, the RF model produced a lower error and was faster to train than the SVR. The ML models outperformed the exponential models with SRS but underperformed compared to the models with NRich strip. It was concluded that if a field does not have an NRich strip, using RF models with additional data layers can provide reliable yield estimations for maize grain and silage. • Normalised difference vegetation index from UAS is a reliable indicator of yield. • Implementing NRich strip in the field helps in reliable yield prediction. • Sensing at V8–V10 growth stage was the best time for maize silage yield prediction. • Sensing at R4 was the best time for maize grain yield prediction. • Random forest model with five features can be used if NRich is not available. [ABSTRACT FROM AUTHOR]

Crabs have a high nutritional and economic value and there is an increased demand for live crabs. However, live crabs have limited shelf life and they are susceptibility to death and spoilage during transportation. Monitoring live crab vitality during transportation in the supply chain to meet consumer acceptability is vital. In this study, an information fusion enabled live crab viability monitoring system was developed. Hazard analysis and critical control point (HACCP) analysis was used to identify potential hazards and critical control points in the transportation supply chain that affect live crab vitality. Multi-source information during live crab transportation was collected by integrating temperature, relative humidity, oxygen, alcohol, aldehyde, and impedance sensors. The predictive modelling of live crab vitality based on information fusion effectively improved, the utilisation of information and the accuracy of vitality prediction. An ensemble learning based soft-voting classifier outperformed the individual performances of other models (i.e. support vector machine, random forest, k-nearest neighbour) and it achieved accuracy above 99% and 86% at 4 °C and 25 °C. The system evaluation indicated that the developed information fusion-based vitality monitoring offers the possibility of ubiquitous monitoring of the vitality of aquatic products in the transportation supply chain and improves the economic efficiency of the supply chain. • Development of a vitality monitoring system for live crab vitality control. • HACCP based critical hazard control points in live crab transportation supply chain. • Processing multi-sensor data based on information fusion model. • System application improved quality of live crab transportation supply chain. [ABSTRACT FROM AUTHOR]

[Display omitted] • CO 2 adsorption onto porous carbons was modelled by machine learning (ML). • Six ML models used, and feature importance and instance plots explained by SHAP. • GBDT showed the best prediction, however other models were also performed well. • CO 2 uptake more governed by adsorption conditions followed by adsorbent properties. Adsorption of CO 2 on porous carbons has been identified as one of the promising methods for carbon capture, which is essential for meeting the sustainable developmental goal (SDG) with respect to climate action, i.e., SDG 13. This research implemented six supervised machine learning (ML) models (gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), light boost gradient machine (LBGM), random forest (RF), categorical boosting (Catboost), and adaptive boosting (Adaboost)) to understand and predict the CO 2 adsorption mechanism and adsorption uptake, respectively. The results recommended that the GBDT outperformed the remaining five ML models for CO 2 adsorption. However, XGB, LBGM, RF, and Catboost also represented the prediction in the acceptable range. The GBDT model indicated the accurate prediction of CO 2 uptake onto the porous carbons considering adsorbent properties and adsorption conditions as model input parameters. Next, two-factor partial dependence plots revealed a lucid explanation of how the combinations of two input features affect the model prediction. Furthermore, SHapley Additive exPlainations (SHAP), a novel explication approach based on ML models, were employed to understand and elucidate the CO 2 adsorption and model prediction. The SHAP explanations, implemented on the GBDT model, revealed the rigorous relationships among the input features and output variables based on the GBDT prediction. Additionally, SHAP provided clear-cut feature importance analysis and individual feature impact on the prediction. SHAP also explained two instances of CO 2 adsorption. Along with the data-driven insightful explanation of CO 2 adsorption onto porous carbons, this study also provides a promising method to predict the clear-cut performance of porous carbons for CO 2 adsorption without performing any experiments and open new avenues for researchers to implement this study in the field of adsorption because a lot of data is being generated. [ABSTRACT FROM AUTHOR]

For targeted spraying in urban streets using mobile laser scanning (MLS), it is crucial to continuously detect tree crowns in real time from MLS data collected online. Crown detection based on a pointwise classification method, which predicts whether a point belongs to a crown using a binary classifier composed of a set of local features extracted from the neighbourhood of the point, is suitable for online data processing. However, the computational complexity of pointwise detection methods needs to be reduced to meet the real-time requirements of online targeted spraying. This paper proposes a real-time detection method for street tree crowns using MLS based on pointwise classification. A cubic neighbourhood is adopted from which a set of statistical features are extracted. A coarse-to-fine neighbourhood search method based on an MLS data grid storage structure is proposed to quickly query the neighbours. The classification performance and computational time of single features are evaluated. Then, the features are ranked using the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, a subset of features that are easy to calculate and have high discrimination are selected and fused into a crown detector using a random forest algorithm. During online detection, the point cloud is sampled in terms of the vehicle movement and LiDAR measurement before being detected. An MLS point cloud collected from a 451 m urban street is used in the experiments. When the neighbourhood size is set to 0.3 m, the detection time per scanline is 24.84 ms on the unsampled test set with an F 1 score of 0.9927. When the test set is sampled with a total sampling rate of 40, the detection time is reduced to 1% with an F 1 score of 0.9906. The experimental results show that the proposed method can provide real-time and accurate detection of street tree crowns for online targeted spraying. • Street tree crowns are detected in real-time using mobile laser scanning (MLS). • A cubic neighbourhood with a fast neighbourhood search method is adopted. • A feature subset with low computational cost and high discrimination is selected. • The method is speeded up by sampling the point cloud from two dimensions. [ABSTRACT FROM AUTHOR]

Aflatoxin poisoning can harm broiler health and cause economic loss. Traditional diagnoses rely on human assessment, which is laborious and time-consuming. The objective of this study was to identify aflatoxin-poisoned broilers by analyzing three-dimensional accelerations collected by wearable accelerometers using five machine learning models, including K-Nearest Neighbor (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). A total of 75 yellow feather broilers were reared in 3 pens (25 birds/pen) provided with 3 types of feed, including Normal (no Aflatoxin B 1 , control), AF50 (50 μg/kg Aflatoxin B 1 "AFB 1 "), and AF100 (100 μg/kg AFB 1). Lightweight tri-axial accelerometers were attached to 9 randomly selected birds (3 birds/pen) to continuously monitor the movement of broilers. Broiler behaviors were manually labelled as six categorises (sitting, standing, drinking, feeding, walking, and other) by observing the videos recorded by the camera installed in each pen. Different behavioral patterns were observed for AFB 1 poisoned birds as compared to that for sound birds. The poisoned broilers performed less standing, walking, feeding, and drinking behaviors, but more sitting behaviors. Five models could identify poisoned birds (AF100) with good accuracies (>86%). Higher misclassification rates were observed for all models in classifying AF50 and Normal groups. Among different models, the GBDT model performed best (97%) in differentiating Normal and AF50 broilers. In conclusion, three-dimensional accelerations and machine learning models could be used to identify aflatoxin-poisoned broilers with high accuracies. The findings could help in the development of viable systems that can identify diseased birds. • Poisoned broilers spent less time on feeding, drinking, walking, and standing. • Performances of models differ in identifying healthy and mild poisoning broilers. • The GBDT model performed better in identifying poisoned broilers. [ABSTRACT FROM AUTHOR]

• The coding capacity or their potential expression of the queried sORF can be evaluated by our method. • We demonstrated that the features that we used between eukaryotes and prokaryotes have significant differences. • A web server was developed to allow users easily identify potential coding sORFs. Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html. [ABSTRACT FROM AUTHOR]

Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However, for accuracy, random feature methods require more measurements than trainable parameters, limiting their use for data-scarce applications. We introduce the sparse random feature expansion to obtain parsimonious random feature models. We leverage ideas from compressive sensing to generate random feature expansions with theoretical guarantees even in the data-scarce setting. We provide generalization bounds for functions in a certain class depending on the number of samples and the distribution of features. By introducing sparse features, i.e. features with random sparse weights, we provide improved bounds for low order functions. We show that our method outperforms shallow networks in several scientific machine learning tasks. [ABSTRACT FROM AUTHOR]

The production of terpenoids from engineered microbes contributes markedly to the bioeconomy by providing essential medicines, sustainable materials, and renewable fuels. The mevalonate pathway leading to the synthesis of terpenoid precursors has been extensively targeted for engineering. Nevertheless, the importance of individual pathway enzymes to the overall pathway flux and final terpenoid yield is less known, especially enzymes that are thought to be non-rate-limiting. To investigate the individual contribution of the five non-rate-limiting enzymes in the mevalonate pathway, we created a combinatorial library of 243 Saccharomyces cerevisiae strains, each having an extra copy of the mevalonate pathway integrated into the genome and expressing the non-rate-limiting enzymes from a unique combination of promoters. High-throughput screening combined with machine learning algorithms revealed that the mevalonate kinase, Erg12p, stands out as the critical enzyme that influences product titer. ERG12 is ideally expressed from a medium-strength promoter which is the 'sweet spot' resulting in high product yield. Additionally, a platform strain was created by targeting the mevalonate pathway to both the cytosol and peroxisomes. The dual localization synergistically increased terpenoid production and implied that some mevalonate pathway intermediates, such as mevalonate, isopentyl pyrophosphate (IPP), and dimethylallyl pyrophosphate (DMAPP), are diffusible across peroxisome membranes. The platform strain resulted in 94-fold, 60-fold, and 35-fold improved titer of monoterpene geraniol, sesquiterpene α-humulene, and triterpene squalene, respectively. The terpenoid platform strain will serve as a chassis for producing any terpenoids and terpene derivatives. • A combinatorial yeast strain library tested the promoter space of MVA pathway enzymes. • Random forest modeling revealed the contribution of MVA enzymes to terpenoid production. • The mevalonate kinase, Erg12p, is critical for determining terpenoid yield. • Mevalonate and IPP/DMAPP are diffusible across peroxisome membranes in vivo. • A yeast platform strain is constructed as a chassis to produce any terpenoids. [ABSTRACT FROM AUTHOR]

Postoperative atrial fibrillation (POAF) occurs in up to 65% of cardiac surgery patients and is associated with an increased risk for stroke and mortality. Electrolyte disturbances in sodium (Na+), potassium (K+), total calcium (Ca2+), chloride (Cl−), and magnesium (Mg2+) are predisposing factors for POAF, but these imbalances are yet to be used to predict POAF. The purpose of this study is to determine whether the development of POAF can be predicted by blood plasma ionic composition. Metabolic panels of patients with no prior history of atrial fibrillation who did (n = 763) and did not develop POAF (n = 2144) after cardiac surgery were obtained from the Carilion Clinic electronic medical record system. We initially evaluated serum Na+, K+, Ca2+, Cl−, and Mg2+ in the two groups using descriptive statistics via scatter and spaghetti plots and then with predictive modeling via logistic regression and random forest models. Neither scatter nor spaghetti plots of electrolyte data revealed a significant difference between those who did and did not develop POAF. Two logistic regression models and two random forest models with POAF status as the outcome were generated using the first observation for each electrolyte and the coefficient of the linear regression, which was obtained from a linear fit of the scatter plot. The random forest model using the first observation had a sensitivity of only 12.2%, but all four models had specificities more than 97%. Neither of the two logistic regression nor two random forest models were able to effectively predict the development of POAF from plasma ionic concentrations, but the random forest models effectively classified patients who would not develop POAF. [ABSTRACT FROM AUTHOR]

Understanding and managing the health effects of Nitrogen Dioxide (NO 2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO 2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO 2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO 2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO 2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO 2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO 2 health effects in epidemiological studies. [Display omitted] • Modeling daily NO2 concentrations at a 1 km resolution in France 2005–2022. • Calculating NO2 concentrations at a 200 m resolution in large urban areas. • Ensembling predictions from three decision-tree based algorithms. • Assessing performance using a spatio-temporal blocking procedure. • Cross-validated R2 of 0.83 for the 1 km model, and of 0.69 for the 200 m model. [ABSTRACT FROM AUTHOR]

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 μg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations. [Display omitted] • Assessed cropland use impact on groundwater arsenic (As) by RF and SHAP. • AUC of 0.86 for As hazards (>50 μg/L) prediction considering cropland use. • The importance of crop areas was as follows: sunflower > melon > wheat > maize. • Transitioning from sunflower to wheat or melon may mitigate As hazards. [ABSTRACT FROM AUTHOR]

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH 3 , CO, NO 2 , CH 4 , CO 2 , SO 2 , O 3 , PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings. • Novel IoT-based system for real-time air quality monitoring in chrome plating industry. • IoT sensors monitor pollutants like NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, PM10. • AI models (LSTM, Random Forest, Linear Regression) predict pollution levels accurately. • System activates exhaust fans based on predicted pollution, preventing hazards. • LSTM model achieved 99 % R² and 0.33 MAE for temperature and humidity forecasting. [ABSTRACT FROM AUTHOR]

Spectral analyses have become dependable and promising analytical methods for objective evaluation of coffee bean quality. However, although commercial instruments are available, financially accessible device solutions still need to be provided for this technique to become more widely available, particularly among emerging small and medium-scale coffee enterprises. This study developed an innovative multichannel spectral acquisition system integrated with an Internet of Things (IoT) platform for evaluating roasted coffee beans. The system was built using commercially available low-cost components with a total cost of approximately 114 USD. A study on the roasting degree classification combined with five machine learning algorithms was conducted based on 18 channels of spectral data (410–940 nm) acquired from the proposed device. Original spectral datasets were directly used for model development to minimize data processing and simplify the implementation of the best machine learning model on the device. The results revealed that the Random Forest (RF) model demonstrated a satisfactory performance (validation accuracy values reaching 0.988, precision 0.988, recall 0.988, and F1-score 0.987). Therefore, the proposed system can effectively classifiy roasted coffee beans and might have important applications in assisting roasteries during the roasting process in a real-time, non-subjective, and non-invasive manner. [Display omitted] • A low-cost IoT-based multichannel spectral acquisition device was developed. • A study on 18 spectral data was employed for four roast coffee beans degrees. • Roast coffee degree classification was performed using machine learning. • The best machine learning algorithm was found with a validation accuracy of 0.988. • This prototype has the potential to be used for broad agri-food product assessment. [ABSTRACT FROM AUTHOR]

The determination of moisture and vanillin content significantly influences the quality of vanilla. Currently, conventional chemical methods employed for assessing these parameters are time-consuming, involve complex sample preparation, are expensive, and environmentally unfriendly due to the use of chemical solutions. Portable Near-Infrared (NIR) spectroscopy emerges as a promising alternative, characterized by smaller dimensions and lower costs. This study investigates the performance of two portable NIR spectrometers with distinct wavelengths at 740–1070 nm and 1350–2550 nm, in conjunction with Random Forest (RF) and Partial Least Square (PLS) regression, and preprocessing techniques including min-max normalization, 1st derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), 1st derivative + SNV, and 1st derivative + MSC for predicting moisture and vanillin content. At the wavelength range of 1350–2550 nm, RF coupled with 1st derivative produced the best moisture content prediction model with an R2 of 0.971, and RF paired with 1st derivative+SNV yielded the best vanillin content prediction with an R2 of 0.983. This work highlights that the integration of portable NIR and RF allows for rapid and non-destructive detection of moisture and vanillin content. This methodology provides a novel regression method for predicting vanilla qualities. [Display omitted] • Portable NIR were used for moisture and vanillin content assessment in vanilla. • Comparison between two portable spectrometers at different wavelength ranges. • Predictive performance of Partial Least Square and Random Forest was evaluated. • Random Forest outperformed conventional approaches in outcome reliability. [ABSTRACT FROM AUTHOR]

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO 2 and PM 2.5 , for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO 2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM 2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO 2 and PM 2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO 2 hourly R2 = 0.35–0.70; PM 2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data. • Hourly LUR models for NO 2 and PM 2.5 are developed with SLR and RF. • Models' performance and structure differ between hours, seasons and weekday type. • Hourly LUR models outperformed temporal adjustment of annual surfaces. [ABSTRACT FROM AUTHOR]

The excessive application and loss of pesticides poses a great risk to the ecosystem, and the environmental safety assessment of pesticides is time-consuming and expensive using traditional animal toxicity tests. In this work, a pesticide acute toxicity dataset was created for silkworm integrating extensive experiments and various common pesticide formulations considering the sensitivity of silkworm to adverse environment, its economic value in China, and a gap in machine learning (ML) research on the toxicity prediction of this species, which addressed the previous limitation of only being able to predict toxicity classification without specific toxicity values. A new comprehensive voting model (CVR) was developed based on ML, combined with three regression algorithms, namely, Bayesian Ridge (BR), K Neighbors Regressor (KNN), Random Forest Regressor (RF) to accurately calculate lethal concentration 50 % (LC 50). Three conformal models were successfully constructed, marking the first combination of conformal models with confidence intervals to predict silkworm toxicity. Further, the mechanism by analyzing structural alerts was summarized, and identified 25 warning structures, 24 positive compounds and 14 negative compounds. Importantly, a novel comprehensive prediction system was constructed that can provide LC 50 and confidence intervals, structural alerts analysis, lipid-water partition coefficient (LogP) and similarity analysis, which can comprehensively evaluate the ecological toxicity risk of substances to make up for the incomplete toxicity data of new pesticides. The validity and generalization of the CVR model were verified by an external validation set. In addition, five new, low-toxic and green pesticide alternatives were designed through 50,000 cycles. Moreover, our software and ST Profiler can provide low-cost information access to accelerate environmental risk assessment, which can predict not only a single chemical, but also batches of chemicals, simply by inputting the SMILES / CAS / (Chinese / English) name of chemicals. [Display omitted] • A high-quality pesticide acute toxicity dataset for silkworm was created. • A new comprehensive voting model was developed combined with conformal models. • The previous limitation of only predicting toxicity classification was broken. • A comprehensive prediction system and software can evaluate the ecotoxicity risk. • Five low-toxic green pesticide alternatives were designed and screened. [ABSTRACT FROM AUTHOR]

Machine learning models are often viewed as black boxes in landslide susceptibility assessment, lacking an analysis of how input features predict outcomes. This makes it challenging to understand the mechanisms and key factors behind landslides. To enhance the interpretability of machine learning models in wide-area landslide susceptibility assessments, this study uses the Shapely method to explore the contributions of feature factors from local, global, and spatial perspectives. Landslide susceptibility assessments were conducted using random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, focusing on the geologically complex Sichuan–Tibet region. Initially, the study revealed the contributions of specific key feature factors to landslides from a local perspective. It then examines the overall impact of interactions among feature factors on landslide occurrence globally. Finally, it unveils the spatial distribution patterns of the contributions of various feature factors to landslide occurrence. The analysis indicates the following: (1) The XGBoost model excels in landslide susceptibility assessment, achieving accuracy, precision, recall, F1-score, and AUC values of 0.7815, 0.7858, 0.7962, 0.7910, and 0.86, respectively; (2) The Shapely method identifies the leading factors for landslides in the Sichuan–Tibet region as Elevation (3000–4000 m), PGA (1–2 g), NDVI (<0.5), and distance to rivers (<3 km); (3) Using the Shapely method, the study explains the contributions, interaction mechanisms, and spatial distribution patterns of landslide susceptibility feature factors across local, global, and spatial perspectives. These findings offer new avenues and methods for the in-depth exploration and scientific prediction of landslide risks. • Utilizing spatial distribution patterns of landslide susceptibility in Sichuan and Tibet using SHAP values. • Analysis of the effect of evaluation factors on landslide susceptibility was analyzed. • Explored the Shapley method's feasibility for assessing landslide susceptibility. • Explained the process of employing the XGBoost model for evaluating landslide susceptibility. [ABSTRACT FROM AUTHOR]

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan—under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041–2060 and 2081–2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region. • Machine learning algorithms were applied to predict flood susceptibility in South Asia. • The RRF model had the highest accuracy (AUC = 0.94) in flood susceptibility modeling. • CMIP6 data used to predict flood susceptibility under SSP1-2.6 and SSP5-8.5 scenarios. • Projected flood susceptibility maps for 2041–2060 and 2081–2100 periods under climate change impacts. [ABSTRACT FROM AUTHOR]

Facial Feature Point Detection (FFPD) plays a significant role in several face analysis tasks such as feature extraction and classification. This paper presents a Fully Automatic FFPD system using the application of Random Forest Regression Voting in a Constrained Local Model (RFRV-CLM) framework. A global detector is used to find the approximate positions of the facial region and eye centers. A sequence of local RFRV-CLMs are used to locate a detailed set of points around the facial features. Both global and local models use Random Forest Regression to vote for optimal positions. The system is evaluated in the task of facial expression localization using five different facial expression databases of different characteristics including age, intensity, 6-basic expressions, 22 compound expressions, static and dynamic images, and deliberate and spontaneous expressions. Quantitative results of the evaluation of automatic point localization against manual points (ground truth) demonstrated that the results of the proposed approach are encouraging and outperform the results of alternative techniques tested on the same databases. [ABSTRACT FROM AUTHOR]

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010–2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development. • The supply and demand of three critical ecosystem services were evaluated. • The spatiotemporal supply-demand relationships of ecosystem services were identified. • The natural and human drivers of these relationship shifts were examined. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization have greatly contributed to boosting regional economic growth and mitigating the problem of poverty, but blind expansion of cities and towns has not only caused the inefficient use of urban land resources but also caused the deterioration in the urban ecological environment. Within the current context of emphasizing high-quality development, achieving synergy between the efficient use of urban land and ecological environmental protection is an urgent task for promoting new urbanization construction. In this study, cities in the upper reaches of the Yangtze River (URYR) were adopted as the research object, a theoretical analysis framework for the urban land use efficiency (ULUE) and ecological environment quality (EEQ) was established, the ULUE was measured by using the Slacks-Based Measure (SBM) model, the coupling coordination and interactive corresponding response relationship between the ULUE and EEQ were analyzed, and the influencing factors of the coupling coordination between these two systems were explored by using the random forest model. The following conclusions can be obtained: in 2020, compared with those in 2006, both the ULUE and EEQ were improved, and the two systems exhibited interactions and significant spatiotemporal heterogeneity. The coupling coordination degree (CCD) between the ULUE and EEQ could facilitate maintaining the original state, and the transfer of the CCD exhibited a significant spatial correlation with the state of neighbouring cities. The effect of the ULUE on the EEQ indicated nonlinear characteristics, while the effect of the EEQ on the ULUE was manifested as inhibition initially and then promotion. The random forest regression results showed that the population density, landscape agglomeration and connectivity, market conditions, government intervention, and industrial institutions are the key influencing factors of the CCD. Finally, this study provides policy implications for innovative urban land use modelling, environmental regulation, and industrial transformation and upgrading. • Put forward the framework of coupling coordination and interactive response between urban land use efficiency (ULUE) and ecological environment quality (EEQ) firstly. • Socio-economic and natural data were used to assess the ULUE. • The influencing factors of the coupling coordination between ULUE and EEQ were explored by using the random forest model. • Results can guide the implementation of land utilization and ecological protection. [ABSTRACT FROM AUTHOR]

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils. • The effectiveness of a variety of auxiliary variables was assessed in predicting EC and SAR. • PLS latent variables (LVs) derived from Vis-NIR led to the highest performance for predicting EC and SAR. • Among the various ML models, Random Forest (RF) demonstrated the highest efficiency. [ABSTRACT FROM AUTHOR]

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use. [Display omitted] • Machine learning models were explored in groundwater quality assessment. • The model RF-CV outperformed RF-B, ANN-CV, and ANN-B. • The normalized EWQI was used to decipher groundwater quality zones. • The role of variables was different in machine learning models. [ABSTRACT FROM AUTHOR]

Thyroid nodules are the main indicators of thyroid cancer, their malignancy is evaluated by cytological analysis and imaging technology, however, there are still cases where the result is not enough to classify thyroid cancer. Therefore, there is a necessity for accurate molecular biomarkers to collaborate in the diagnosis. Here, we analyzed the mRNA relative expression of CLDN1, TIMP1, and KRT19 genes in FNA of malignant (n = 48) and benign (n = 49) thyroid nodules by RT-qPCR analysis to assess their predictive value as cancer biomarkers. We identified a significant overexpression of the three transcripts in malignant nodules, therefore, the evaluation of their predictive capacity to distinguish between benign and malignant nodule as individual biomarkers were evaluated by logistic regression tests, obtaining promising prediction results to rule out cancer; later by random forest to create a stronger model, we included expression results with clinicopathological characteristics, the best model consists of the three-mRNA level expression with patient's history of cancer (AUC = 0.821, accuracy = 85.4% and sensitivity of 81.1%). These results demonstrate a dysregulated expression of CLDN1, KRT19 and TIMP1 in thyroid cancer, thus, represent a promising panel of biomarkers to be evaluated in indeterminate thyroid nodules. • CLDN1, KRT19 and TIMP1 were significantly overexpressed in malignant thyroid nodules. • Strong correlation may suggest functional interactions during cancer progression. • High predictive value to rule out cancer as individual biomarkers. [ABSTRACT FROM AUTHOR]

Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have enabled large data to be analyzed using machine learning (ML) algorithms. A national database of colon cancer patients was selected to determine whether ML methods better predict outcomes following surgery compared to conventional methods. Surgical colon cancer patients were identified using the 2013 National Cancer Database (NCDB). The negative outcome was defined as a composite of 30-d unplanned readmission and 30- and 90-d mortality. ML models, including Random Forest and XGBoost, were built and compared with conventional logistic regression. For the accounting of unbalanced outcomes, a synthetic minority oversampling technique (SMOTE) was implemented and applied using XGBoost. Analysis included 528,060 patients. The negative outcome occurred in 11.6% of patients. Model building utilized 30 variables. The primary metric for model comparison was area under the curve (AUC). In comparison to logistic regression (AUC 0.730, 95% CI: 0.725-0.735), AUC's for ML algorithms ranged between 0.748 and 0.757, with the Random Forest model (AUC 0.757, 95% CI: 0.752-0.762) outperforming XGBoost (AUC 0.756, 95% CI: 0.751-0.761) and XGBoost using SMOTE data (AUC 0.748, 95% CI: 0.743-0.753). We show that a large registry of surgical colon cancer patients can be utilized to build ML models to improve outcome prediction with differential discriminative ability. These results reveal the potential of these methods to enhance risk prediction, leading to improved strategies to mitigate those risks. • Adverse outcomes remain common after surgery for colon cancer. • Logistic regression is a common tool for predictive modeling of surgical outcomes. • Machine learning methods may improve predictions compared to logistic regression. • Improved risk quantification, applied broadly, may greatly reduce morbidity and cost. [ABSTRACT FROM AUTHOR]

• Improved mapping of 2′-O-methylated (Nm) and pseudouridine (ψ) residues by RiboMethSeq. • Random Forest (RF) model including scores for neighboring positions gives the best results. • The model was trained using human rRNA datasets and successfully validated on other rRNAs. Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several analytical approaches have been proposed, including antibody-driven enrichment, analysis of RT-signatures and specific chemical treatments. However, analysis and interpretation of these massive datasets, especially for low abundant cellular RNAs (e.g. mRNA and lncRNA) is not easy nor straightforward, since the insufficient specificity and selectivity are leading to massive false-positive and false-negative identifications. The main issue in the application of these methods relies on a subjective classification of potentially modified positions, mostly based on arbitrarily defined threshold values for different scores. Such approach using pre-defined scores' values was revealed to be appropriate for limited complexity datasets (for tRNA and/or rRNA analysis), but application to longer reference sequences requires much better classification algorithms. In this work we applied a machine learning algorithm (Random Forest, RF) to create a predictive model for analysis of 2′-O-methylated sites in RNA using RiboMethSeq datasets. Model's training was performed on a large collection of human rRNA datasets with well-known modification profiles and the performance of the prediction was assessed using experimentally defined profiles for other eukaryotic rRNAs (S. cerevisiae and A. thaliana). Application of this Random Forest prediction model for detection of other RNA modifications and to more complex datasets is discussed. [ABSTRACT FROM AUTHOR]

• A novel method was proposed to identify RNA m2G sites using hybrid features. • The over-sample method SMOTE was adopted to deal with the problem of data imbalance. • After using MRMD to select features, the performance of the model is improved. • The RFhy-m2G is superior to other methods, which can effective identify m2G sites. N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors. [ABSTRACT FROM AUTHOR]

• Microscopic hyperspectral imaging technology is used for Cholangiocarcinoma diagnosis. • A multidimensional cholangiocarcinoma benchmark dataset is set up. • Spectral interval convolution and normalization scheme is proposed to fully exploiting the spatial-spectral information. This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method. The first benchmark based on the microscopic hyperspectral pathological images is set up. Particularly, 880 scenes of multidimensional hyperspectral Cholangiocarcinoma images are collected and manually labeled each pixel as either tumor or non-tumor for supervised learning. Moreover, each scene from the slide is given a binary label indicating whether it is from a patient or a normal person. Different from traditional RGB images, the HSI acquires pixels in multiple spectral intervals, which is added as an extension on the channel dimension of 3-channel RGB image. This work aims at fully exploiting the spatial-spectral HSI data through a deep Convolution Neural Network (CNN). The whole scene is first divided into several patches. Then they are fed into CNN for the tumor/non-tumor binary prediction and the tumor area regression. The further diagnosis on the scene is made by random forest based on the features from patch prediction. Experiments show that HSI provides a more accurate result than RGB image. Moreover, a spectral interval convolution and normalization scheme are proposed for further mining the spectral information in HSI, which demonstrates the effectiveness of the spatial-spectral data for CC diagnosis. [ABSTRACT FROM AUTHOR]

To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random forest (RF) and naive bayes (NB)) for six feature schemes were performed to classify the ECG recordings. The ECG recordings were initially filtered with a 0.1 Hz − 12 Hz band pass filter. Then 80 features, including 48 time domain, 18 frequency domain, 12 time–frequency and two principle component analysis (PCA) features, were extracted to construct six feature schemes. The RF, SVM, LS-SVM and NB were employed to discern a binary-classification task (i.e., normal and AF ECG recordings) and a tri- classification task (i.e., the normal, AF and ST change ECG recordings) for the six feature schemes. The results revealed that time domain, frequency domain features and PCA features can provide relatively reliable feature combinations to the RF and SVM. In addition, the RF yielded the highest F1-scores (0.8908 and 0.7535) for the binary-classification task and the tri-classification task than the SVM, LS-SVM and NB. [ABSTRACT FROM AUTHOR]

• The devices and sensors in blockchain-enabled IoT network produce a lot of data, so AI is applied as analytical tool to provide consistent results in decision-making. • Fog computing paradigm is used to decentralize cloud based centralized security mechanism, thus data analysis and security related concerns are handled at edge of networks. • A distributed IDS is designed using fog computing to detect DDoS attacks against memory pool in blockchain-enabled IoT network. • To evaluate the proposed detection system two well-known machine learning algorithms, random forest and XGBoost are used in distributed architecture. • An actual IoT based BoT-IoT dataset is used to analyze the performance of the model. As it contains various recent Botnet related attacks such as DoS, DDoS, theft. • Different evaluation metrics such as accuracy, detection rate, false alarm rate, and precision are used to thoroughly investigate performance of the proposed. The Internet of Things (IoT) is emerging as a new technology for the development of various critical applications. However, these applications are still working on centralized storage architecture and have various key challenges like privacy, security, and single point of failure. Recently, the blockchain technology has emerged as a backbone for the IoT-based application development. The blockchain can be leveraged to solve privacy, security, and single point of failure (third-part dependency) issues of IoT applications. The integration of blockchain with IoT can benefit both individual and society. However, 2017 Distributed Denial of Service (DDoS) attack on mining pool exposed the critical fault-lines among blockchain-enabled IoT network. Moreover, this application generates huge amount of data. Machine Learning (ML) gives complete autonomy in big data analysis, capabilities of decision making and therefore is used as an analytical tool. Thus, in order to address above challenges, this paper proposes a novel distributed Intrusion Detection System (IDS) using fog computing to detect DDoS attacks against mining pool in blockchain-enabled IoT Network. The performance is evaluated by training Random Forest (RF) and an optimized gradient tree boosting system (XGBoost) on distributed fog nodes. The proposed model effectiveness is assessed using an actual IoT-based dataset i.e., BoT-IoT, which includes most recent attacks found in blockchain-enabled IoT network. The results indicate, for binary attack-detection XGBoost outperforms whereas for multi-attack detection Random Forest outperforms. Overall on distributed fog nodes RF takes less time for training and testing compared to XGBoost. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is recognized as a serious global health problem due to its high prevalence and all-cause mortality. The aim of this research was to identify critical biomarkers and construct an integrated model for the early prediction of CKD. By using existing RNA-seq data and clinical information from CKD patients from the Gene Expression Omnibus (GEO) database, we applied a computational technique that combined the random forest (RF) and artificial neural network (ANN) approaches to identify gene biomarkers and construct an early diagnostic model. We generated ROC curves to compare the model with other markers and evaluated the associations of selected genes with various clinical properties of CKD. Moreover, we highlighted two biomarkers involved in energy metabolism pathways: pyruvate dehydrogenase kinase 4 (PDK4) and zinc finger protein 36 (ZFP36). The downregulation of the identified key genes was subsequently confirmed in both unilateral ureteral obstruction (UUO) and ischemia reperfusion injury (IRI) mouse models, accompanied by decreased energy metabolism. In vitro experiments and single-cell sequencing analysis proved that these key genes were related to the energy metabolism of proximal tubule cells and were involved in the development of CKD. Overall, we constructed a composite prediction model and discovered key genes that might be used as biomarkers and therapeutic targets for CKD. • An integrated predictive model for chronic kidney disease was constructed by machine learning. • PDK4 and ZFP36 were identified as biomarkers for predicting CKD. • PDK4 and ZFP36 were mainly expressed in proximal tubular cell and were downregulated in CKD. [ABSTRACT FROM AUTHOR]

This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved to be the most accurate model in terms of determination coefficient, mean squared error, and mean absolute error. The analysis revealed that the harvested area of permanent crops is the most influential variable in predicting deforestation, followed by the area of temporary crops. Furthermore, our findings indicate a significant inverse relationship between public spending and deforestation rates. These results contribute to understanding deforestation dynamics and offer potential strategies for improving conservation efforts. • We analyze deforestation in the Brazilian Amazon from 1999 to 2020. • We employ machine learning to assess 16 critical factors. • Random Forest was the most accurate of five machine learning models. • Harvested permanent crop area dominates deforestation predicted in most models, followed by temporary crop area. • Higher public spending was linked to reduced deforestation rates, suggesting conservation interventions. [ABSTRACT FROM AUTHOR]

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments. We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods. The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome. The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%. The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised. • Exposure to obesogenic environments was assessed using a neighbourhood-level composite index comprising 17 components. • The index had a modest, non-linear association with BMI at the individual level. • This association was modified by age, sex, socioeconomic status and urbanicity. • Methods of improvement by adding, adjusting or weighting components did not improve this association. • The index can serve as a useful tool to identify neighbourhoods with clustered risk factors. [ABSTRACT FROM AUTHOR]

Reliable nitrogen (N) fertilizer management indicators are essential for improving crop yields and minimizing environmental impacts for sustainable production. The objectives of this study were to assess the importance of major N management indicators (NMIs) for higher yield with low risks of environmental pollution in an intensive potato system under drip irrigation. Six drip-irrigated field experiments with no N application (Control), farmer practice (FP), and optimized N management (OM) based on N-balance, soil mineral N (N min), and target yield were conducted from 2018 to 2020 in Inner Mongolia, China. The response of NMIs to potato yield and yield-based environment impact indices (EI Y) was evaluated by the random forest algorithm. The N input, N losses from N leaching, ammonia (NH 3) volatilization, nitrous oxide (N 2 O) emission, N use efficiency (NUE), N surplus, and soil residual N after harvest were obtained to identify the best NMIs for high yield and minimal ecological impact. The N management practices in field experimental sites affected the importance of the order of NMIs on potato yield and EI Y. The NUE and N leaching were identified as the highest importance scores and the most essential controlling variables to potato yield and EI Y , respectively. The integrated NUE and N leaching indicator played a vital role in improving potato yield and reducing ecological impact. The OM treatment achieved 46.0%, 63.6%, and 64.6% lower in N application rate, N surplus, and reactive N loss, and 62.4% higher in NUE than the FP treatment while achieving equal potato yields, respectively. Those key NMIs can guide farmers in understanding their practice short comes to achieve both high productivity and environmental sustainability in intensive potato production systems under drip irrigation. • We assess the response of nitrogen (N) indicators to drip-irrigated potato system. • Yield and yield-base environment indices (EI Y) were affected by NUE and leaching. • The N surplus was an effective indicator for controlling N losses. [ABSTRACT FROM AUTHOR]

This article presents a novel method for deterministic finite element model updating that is based on an "inverse surrogate model". The latter is a regression model that uses structural responses as independent variables and structural properties as dependent ones. Such regressor is trained on structural responses using a finite element model having in input the structural properties to be updated. The training set is obtained by executing multiple finite element model instances where each instance is formed by assigning random, though realistic, values to the structural properties. Experimentally measured structural responses can then be given as input to the trained "inverse surrogate model", and the structural properties, which are used to update the finite element model, are obtained as output. Random forest algorithm is identified as a good candidate to perform this type of regression, and the performance offered by this method is compared to that of the "standard" finite element model updating based on the particle swarm optimization algorithm on both a numerical and an experimental case study where the structural responses are represented by a set of modal parameters. The results show that the proposed approach offers higher accuracy on the estimation of the structural properties, at the cost of a slightly inferior matching of the modal parameters. This indicates that the "inverse surrogate model" is less susceptible to the ill-conditioning issue that plagues optimization-based finite element model updating, where similar structural responses can arise from various combinations of structural properties. The results also show a good repeatability and that the computational costs are similar or better than those of the particle swarm optimization algorithm. • A novel FEM updating method based on a so-called inverse surrogate model is proposed. • It uses a random forest trained for regression between structural modes and properties. • The method is compared to the widely used particle swarm optimization (PSO) algorithm. • The new method is shown to outperform PSO on the estimation of structural properties. • The results show good repeatability, and the computational costs are similar to PSO. [ABSTRACT FROM AUTHOR]

This study explores how well various machine learning classifiers can identify mean-motion resonances in the main belt using supervised learning. The most popular classifiers are assessed: k-Nearest Neighbours, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, and Naïve Bayes. In contrast to previous studies that often relied on default ML configurations, this research conducts a detailed investigation, fine-tuning, and testing of each classifier across various parameters. The results show that simpler models, especially k-Nearest Neighbours and Decision Tree, perform better than more complex ones, particularly in terms of F 1 scores. The paper provides guides on selecting features, parameters, and training set sizes for optimal classifier performance and outlines a method for developing effective machine-learning models for asteroid classification. • Fine-tuning classifiers boosts performance in asteroid classification. • kNN and Decision Trees outperform in mean-motion resonance identification. • Naïve Bayes is effective with minimal initial dataset. [ABSTRACT FROM AUTHOR]

In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensively assess the ESG performance of the solid waste treatment industry and identify company types, this study constructs a targeted ESG evaluation index system based on existing literature, SASB industry standards, and company reports and utilizes a random forest approach combined with K-means clustering to determine indicator weights. Based on this index system, the paper evaluates the ESG performance of 71 solid waste disposal companies (SWDCs) from 2013 to 2021 and identifies their ESG types from static and dynamic perspectives. In the static view, company types are determined based on annual ESG performance, while the dynamic view considers time-series changes to observe the evolution of company ESG types. The results show that the overall ESG performance of SWDCs falls within the 2-8-point range, indicating a noticeable high-low imbalance. Key initiatives to improve ESG performance in this industry include enhancing waste management measures, developing emergency plans, and reinforcing ESG disclosure. From a static perspective, this paper can identify companies into three categories: delayed development, single-wheel-driven, and coordinated development. Finally, from a dynamic perspective considering the time factor, companies are further subdivided into five types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia. This study not only provides targeted recommendations for different types of ESG companies but also helps various sectors of society better understand the ESG conditions of this high environmental risk industry, thereby enhancing the regulation and support for its sustainable development. [Display omitted] • ESG evaluation index system for solid waste disposal companies is proposed. • A random forest method based on k-means clustering is used to construct the index. • Comparing E, S, and G scores with industry averages to identify static ESG types. • Analyzing trends in annual static ESG types to identify dynamic ESG types. • Identify companies as three static ESG types and five dynamic ESG types. [ABSTRACT FROM AUTHOR]

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability. • Study the impact of green finance and green innovation on corporate credit rating. • Use Chinese A-share listed firms from 2018 to 2021 and machine learning techniques. • A 1% increase in green finance diminishes credit rating by 0.26%. • Impact is more pronounced in heavily polluting firms and non-state-owned firms. • Green innovation promotes credit rating performance by 0.15%. [ABSTRACT FROM AUTHOR]

Chlorophyll fluorescence is the long-wave light released by the residual energy absorbed by vegetation after photosynthesis and dissipation, which can directly and non-destructively reflect the photosynthetic state of plants from the perspective of the mechanism of photosynthetic process. Moso bamboo has a substantial carbon sequestration ability, and leaf-expansion stage is an important phenological period for carbon sequestration. Gross primary production (GPP) is a key parameter reflecting vegetation carbon sequestration process. However, the ability of chlorophyll fluorescence in moso bamboo to explain GPP changes is unclear. The research area of this study is located in the bamboo forest near the flux station of Anji County, Zhejiang Province, where an observation tower is built to monitor the carbon flux and meteorological change of bamboo forest. The chlorophyll fluorescence physiological parameters (Fp) and fluorescence yield (Fy) indices were measured and calculated for the leaves of newborn moso bamboo (I Du bamboo) and the old leaves of 4- to 5-year-old moso bamboo (Ⅲ Du bamboo) during the leaf-expansion stage. The chlorophyll fluorescence in response to the environment and its effect on carbon flux were analyzed. The results showed that: F v /F m , Y (II) and α of Ⅰ Du bamboo gradually increased, while Ⅲ Du bamboo gradually decreased, and FY int and FY 687 /FY 738 of Ⅰ Du bamboo were higher than those of Ⅲ Du bamboo; moso bamboo was sensitive to changes in air temperature(Ta), relative humidity(RH), water vapor pressure(E), soil temperature(ST) and soil water content (SWC), the Fy indices of the upper, middle and lower layers were significantly correlated with Ta, E and ST; single or multiple vegetation indices were able to estimate the fluorescence yield indices well (all with R2 greater than 0.77); chlorophyll fluorescence (Fp and Fy indices) of Ⅰ Du bamboo and Ⅲ Du bamboo could explain 74.4% and 72.7% of the GPP variation, respectively; chlorophyll fluorescence and normalized differential vegetation index of the canopy (NDVIc) could estimate GPP well using random forest (Ⅰ Du bamboo: r = 0.929, RMSE = 0.069 g C·m−2; Ⅲ Du bamboo: r = 0.899, RMSE = 0.134 g C·m−2). The results of this study show that chlorophyll fluorescence can provide a basis for judging the response of moso bamboo to environmental changes and can well explain GPP. This study has important scientific significance for evaluating the potential mechanisms of growth, stress feedback and photosynthetic carbon sequestration of bamboo. • Over the course of leaf spreading, moso bamboo's photosynthetic capability varied. • Moso bamboo is sensitive to temperature and moisture during leaf-expansion stage. • Chlorophyll fluorescence can explain more than 70% of GPP variation of moso bamboo. • Chlorophyll fluorescence can be used to estimate GPP well using random forests. [ABSTRACT FROM AUTHOR]