Search

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO 2 , SO 2 , O 3 , PM 10 , and PM 2.5 ) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O 3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves. These images undergo data augmentation techniques, such as rotation, flipping, and contrast adjustment, to form the scattered and stacked tea leaves datasets. Subsequently, the YOLOv8x model was enhanced by Space pyramid pooling improvements (SPPCSPC) and the concentration-based attention module (CBAM). The established YOLOv8x-SPPCSPC-CBAM model is evaluated by comparing it with popular DL models, including Faster R-CNN, YOLOv5x, and YOLOv8x. The experimental findings reveal that the YOLOv8x-SPPCSPC-CBAM model delivers the most impressive results. For the scattered tea leaves, the mean average precision, precision, recall, and number of images processed per second rates of 98.2%, 95.8%, 96.7%, and 2.77, respectively, while for stacked tea leaves, they are 99.1%, 99.1%, 97.7% and 2.35, respectively. This study provides a robust framework for accurately classifying the quality grade of fresh tea leaves., (© 2024. The Author(s).)

Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S 11 ) spectrum, and the applied pressure changes the magnitude of the S 11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S 11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Introduction: Tumor-infiltrating lymphocytes are both prognostic and predictive biomarkers for immunotherapy response. However, less is known about the survival benefits oftheir subpopulations. Methods: Using machine learning models, we assessed the clinical association of the CD8+, PD1+, TCF1+ cel l subset by multiplex immunohistochemistry using tissue microarrays in 553 non-small cell lung cancer (NSCLC) patients and its correlation with other immune cell biomarkers. Results: We observed positive correlations between TCF1 and CD20 (r=0.37), CD3 (r=0.45)and CD4 (r=0.33). Notably, triple positive (CD8+PD1+TCF1+) were rare, only observed in 29 of 553 patients (5%). Our analysis revealed that cells coexpressing TCF1 with either CD8+ or PD1+ were independent prognostic markers of disease-specific survival in multivariable analysis (HR=0.728, p=0.029 for CD8+TCF1+, and HR=0.612, p=0.002 for PD1+TCF1+). To pilot the subtype of abundant CD8-TCF1+ cells, we explored an immune cell infiltrated whole slideimage and found the majority to be CD4+. Discussion: Overall, these findings suggest that assessment of CD8+, PD1+, TCF1+ could serve as a potential prognostic biomarker in NSCLC. [ABSTRACT FROM AUTHOR]

The flash point (FP) modeling of ternary mixtures containing partially miscible regions, even by known models, is still challenging. This work proposes a practical and easy-to-use hybrid machine learning method, which combines artificial neural networks (ANNs) with the genetic algorithm (GA), to calculate the FPs of complex alcohol-based mixtures, particularly aqueous solutions of low-carbon alcohols. For this purpose, multilayer perceptron (MLP), radial basis function (RBF), and group method of data handling (GMDH) ANNs, were trained using the FPs of totally miscible region while the FPs of the partially miscible region were predicted by the trained ANN. By applying this approach, the FPs of partially miscible region of water-ethanol-1-butanol, water-ethanol-2-butanol, water-1-butanol-2-butanol, and methanol + toluene + 2,2,4-trimethylpentane were predicted. Another method was implemented by which the FPs of water-1-butanol-2-butanol mixture were predicted by only those of other mixtures, i.e., water-ethanol-1-butanol and water-ethanol-2-butanol. To the best of our knowledge, two applied approaches have not been previously used. Moreover, the FPs of two totally miscible aqueous solutions, namely water-methanol-ethanol and water-ethanol-isopropanol, were evaluated by different proposed ANNs in a usual way. The highest and lowest absolute average errors obtained by the applied methods for testing data were 5.09 and 0.45 °C, respectively. [ABSTRACT FROM AUTHOR]

Exploring the spatiotemporal dynamics, spatial mismatch, and complex influencing mechanism of grain production and cropland productivity in the black soil region of northeast China (BSRNC) is essential for the synergistic protection and utilization of black soil cropland and sustainable grain production. The BSRNC has realized cropland expansion and grain production increases in the past decades. This implied a substantial investment has been made in the region's agriculture. However, at present, knowledge on the spatial mismatch and influencing factors of grain production and cropland productivity is still unclear. This study analyzed the spatial–temporal mismatch characteristics of grain production and cropland net primary productivity (CNPP) using the gravity center model, spatial autocorrelation analysis, and spatial mismatch index (SMI), and identified the spatial heterogeneity and prediction–response relationships of influencing factors based on a geographically and temporally weighted regression (GTWR) model and boosted regression tree (BRT) machine learning algorithm. The findings indicated that grain production and CNPP have been increasing, but the overall spatial pattern of cold hotspots has not changed obviously in the BSRNC from 2000 to 2020. The SMI has shown a decreasing trend, indicating that the synergistic development of grain production and CNPP has been obvious, which plays an important role in sustainable food supply capacity. Agricultural production and the natural environment have always been critical factors influencing the spatial mismatch. Specifically, the marginal impact of fertilizer application has undergone a shift. This study may provide new clues for the formulation of regional strategies for sustainable food supply and black soil cropland system protection. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model's fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas. [ABSTRACT FROM AUTHOR]

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network. [ABSTRACT FROM AUTHOR]

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal–oxide–semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications. [ABSTRACT FROM AUTHOR]

Against the backdrop of the gradually exposed credit bond default risk in China, how to accurately identify and efficiently warn of corporate bond default risk has become a key concern for both academia and practice. To effectively solve a series of key problems in the traditional credit risk warning model, such as insufficient warning performance, single optimization target of hyperparameters, and weak model interpretability, this study integrates machine learning algorithms such as LightGBM, NSGA- II, and SHAP to constructs a LightGBM-NSGA- II-SHAP for early warning of corporate bond default risk, and empirically analyzes and tests the warning performance of the proposed model. The research results show that the warning accuracy of the proposed model exceed 85%, and compared with traditional machine learning models, the warning performance of the proposed model in this study is more excellent. In addition, the impact of visualization of warning features on warning results is demonstrated through the SHAP algorithm, and it is found that coupon interest rate, profit margin on fixed assets, total issuance, and receivable turnover etc. are the key features for identifying corporate bond defaults. [ABSTRACT FROM AUTHOR]

Digital twins represent a promising approach for sustainable building operations and management in the context of the carbon neutrality goals of the European Union (EU). Using OpenStudio, an opensource platform for building energy modeling, we demonstrated the creation and editing of building digital twins. OpenStudio provides a user-friendly interface and extensive simulation capabilities, allowing detailed and accurate modeling of building components and systems. Using OpenStudio Measures, users can automate tasks and customize simulation models to optimize the building performance. The process of creating a building digital twin involves collecting historical data and accurately representing the building geometry; materials; schedules; and heating, ventilation, and air conditioning (HVAC) systems. Challenges such as data availability and model accuracy highlight the importance of modeling practices. Editing the digital twin involves modifying the OpenStudio model files and EnergyPlus weather files to simulate different building operation scenarios. Python programming language opportunities were considered for digital twin file modification. The potential of digital twins lies in their ability to simulate future building conditions and optimize building system settings. By integrating digital twins with machine learning algorithms and connecting them directly to building management systems, optimal building control strategies can be automated, thereby reducing energy consumption and improving occupant comfort levels. [ABSTRACT FROM AUTHOR]

As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world's largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. [ABSTRACT FROM AUTHOR]

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels. [ABSTRACT FROM AUTHOR]

Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline–alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE–RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE–GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline–alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline–alkali soil ecological protection. [ABSTRACT FROM AUTHOR]

One of the key priorities of the Ministry of Agriculture Jihad is the mapping of croplands to estimate crop acreage and annual yield. In recent decades, remote sensing technology has proven to be highly effective in estimating the extent of crop cultivation through the use of timely images and synchronized data with diverse spatial, temporal, and spectral resolutions, leveraging advanced machine-learning algorithms. This study presented a framework for crop mapping in Marvdasht, Fars Province, by utilizing time series of Landsat-8 satellite images and advanced machine-learning algorithms. The employed algorithms included Decision Tree (DT), Random Forest (RF), Rotation Forest (RoF), Support Vector Machine (SVM), and Dynamic Time Warping (DTW) analysis. The results indicated that the dynamic time warping and random forest methods outperformed others, achieving significantly higher accuracy (with an overall accuracy improvement of 10-12%) in generating the agricultural land-use map of the study area. Furthermore, this research demonstrated the effectiveness of Bands 2-5 of Landsat-8 satellite in confidently identifying all crops in this region using the mentioned methods. [ABSTRACT FROM AUTHOR]

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare the performances of different machine learning and parametric techniques for differentiating highly mixed urban land cover classes in Ulaanbaatar, the capital city of Mongolia, using multisource data sets. For data sources, 17 features are chosen, including the original 10 spectral bands of the Sentinel-2 data; VV, VH, average of HH & HV, and simple ratio of Sentinel-1 data; and normalized difference vegetation index (NDVI), Bare soil index (BSI), and modified soil adjusted vegetation index (MSAVI). Six different feature combinations are used to identify available urban land cover classes. To discriminate existing classes, a support vector machine (SVM), artificial neural network (ANN), random forest (RF), and a statistical maximum likelihood classifier (MLC) are employed and compared. In all six feature combinations, the RF method outperforms the others with an overall accuracy ranging from 81.72% to 90.71%. The SVM has an overall accuracy ranging from 77.77%-83.50%, with the second-highest performance in four combinations and the lowest in two. The ANN has an overall accuracy ranging from 74.83%-83.31%, with poorer performance than the SVM. The MLC's performance varies across feature combinations, with an overall accuracy ranging from 70.96%-85.92%, and the second-highest performance in two feature combinations. Overall, the study shows that multisource information along with additional features/indices can significantly improve the classification of mixed urban land cover types, and for the given test site, the RF technique is the best option for producing a dependable land cover map. [ABSTRACT FROM AUTHOR]

Over half of the world's population resides in urban areas. We anticipate that this pattern will become more evident, notably in South Africa. Therefore, research on urban spirals, both past and projected, is necessary for efficient urban land use planning and management. This study aims to assess the spatio-temporal urban sprawl dynamics from 2003 to 2033 in Mbombela, South Africa. We employed robust approaches such as machine learning, the cellular automata-Markov chain, and the Shannon entropy model to look at how urban sprawl changes over time using both the Landsat 4–5 Thematic Mapper and the 8 Operational Land Imagers. We conducted this study to bridge the gaps in existing research, which primarily focuses on past and current urban growth trends rather than future trends. The findings indicated that the coverage of built-up areas and vegetation has expanded by 1.98 km2 and 13.23 km2 between the years 2003 and 2023. On the other hand, the amount of land continues to decrease by -12.56 km2 and − 2.65 km2 annually, respectively. We anticipate an increase in the built-up area and vegetation to a total of 7.60 km2 and 0.57 km2, respectively, by the year 2033. We anticipate a total annual decline of -7.78 km2 and − 0.39 km2 in water bodies and open land coverage, respectively. This work has the potential to assist planners and policymakers in improving sustainable urban land-use planning. [ABSTRACT FROM AUTHOR]

Student engagement is positively related to learning outcomes. The student engagement measurement is studied in varied settings, from the traditional classroom to online learning. Artificial intelligence and machine learning advancements have fueled automatic student engagement analysis. The automated student engagement measurement employed several sensor data such as audio, video, and physiological signals in different settings. This paper presents a literature review of automatic student engagement measurement in the classroom and online learning settings, including data collection and annotation techniques, methods, and evaluation metrics. First, a generalized methodology for automatic student engagement analysis is discussed. Then we describe various data collection techniques and annotation methods widely used in the literature and detail the limitations and advantages. The state-of-the-art machine learning methods and the evaluation metrics used to test those methods are reviewed. Additionally, we extend our literature review to the insight into the existing datasets for evaluating the automatic student engagement methods and recent developments in the machine learning methods on open-source datasets. Finally, we present a comprehensive comparison of the methods proposed on various public datasets based on evaluation metrics and engagement types. [ABSTRACT FROM AUTHOR]

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short‐term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine‐tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root‐mean‐square error of only 2.12%. [ABSTRACT FROM AUTHOR]

This document discusses the importance of arable land quality in China and its implications for global food security and sustainable development. It highlights the negative impacts of damaging practices such as excessive land reclamation and over-cultivation on soil, ecosystems, and water resources. The document emphasizes the need for sustainable land use to address hunger, combat soil erosion, mitigate climate change, and achieve the UN's Sustainable Development Goals. It includes research articles that explore topics such as soil degradation, arable land quality assessment and management, and arable land utilization and spatiotemporal changes. The document also presents methods for assessing changes in cropland quantity and quality, evaluating cultivated land quality, and managing cropland big data. The special issue aims to provide insights for policymakers, researchers, and practitioners involved in arable land utilization and management. [Extracted from the article]

The aim of the current study is to determine which models are most suited for forecasting a property's rental price given a variety of provided characteristics and to develop a predictive model using machine learning techniques to estimate the rental prices of apartments in Cluj-Napoca, Romania, in relation to market dynamics. Given the absence of a comprehensive dataset tailored for this specific purpose, a primary focus was placed on data acquisition, cleaning, and transformation processes. By leveraging this dataset, the model aims to provide accurate predictions of fair rental prices within the Cluj-Napoca real estate market. Additionally, the research explores the factors influencing rental prices and evaluates the model's performance against real-world data to assess its practical utility and effectiveness in aiding rental market stakeholders. [ABSTRACT FROM AUTHOR]

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics. [ABSTRACT FROM AUTHOR]

The classical reliability analysis methods, due to the ever-increasing complexity of engineering structure, may lead to higher and higher calculation errors and costs. The adaptive surrogate-model-based reliability evaluation method strikes a desirable balance between computational efficiency and accuracy, making it a prevalent technique in the domain of reliability evaluation. Learning function is the core of this reliability evaluation method. In this study, a novel learning function is proposed to adaptively choose the best update sample. This learning function does not depend on the prediction variance provided by the Kriging model. Therefore, this learning function is not limited to the Kriging model. In theory, it can be combined with different surrogate models. Four comparative cases are used to illustrate the computational efficiency and accuracy of the proposed method, including series system case with four branches, highly nonlinear two-dimensional numerical example, and two practical engineering case. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) technology with the cinema and television sectors has resulted in significant transformations in the programming and production of television shows and the emergence of a novel cohort of AI-driven media. The ubiquity of AI-enabled technology enhances film and television production quality. Conversely, there has been a notable expansion in the animation sector in recent years, characterized by a growing number of film productions annually. Finding the user's preferred animated films within the large array of information about animated movies has emerged as a notable obstacle. This article examines the sophisticated visual effects of computer vision in animated films, focusing on using artificial intelligence and machine learning technologies. This article proposes a critical perspective on fostering the advancement of cinematic visual effects through strategic means, utilizing computer vision and machine learning technology as fundamental tools for investigating novel methodologies and frameworks for achieving visual effects. This article explores new methodologies and methods for creating visual effects in moving images, using the film industry's digitalization, intelligent advancement, and enhancement as a starting point. This article examines the application of convolutional neural algorithms in analyzing the visual effects of the Hollywood anime film "Coco." The study's findings indicate that the test set's accuracy remained relatively constant at approximately 59% even after determining the model's parameters. This outcome significantly enhances film productions' audiovisual quality and creative standards while fostering healthy and sustainable growth in the film industry. [ABSTRACT FROM AUTHOR]

Data-driven fault detection and diagnosis (FDD) approaches rely on operational data even when in-depth knowledge of the studied system is lacking. Simulated data is affordable and can cover numerous fault types and severities. Developing FDD models with a single fault severity has limitations on the model's generalisability owing to evolution of faults with severities. Thus, machine learning models must be individually trained on diverse scenarios. Current studies on building FDD are often at system levels, thereby leaving significant knowledge gaps when there is interest in energy performance of an entire building. This research presents a data-driven FDD approach for classifying several building faults using different ensemble multi-class classification approaches. Additionally, the impact of noise on training datasets is investigated. The XGBoost classifier achieved the highest classification accuracy for the considered faults during validation and testing stages, with low sensitivity to noise, thereby demonstrating a promising potential for wider scale deployment. [ABSTRACT FROM AUTHOR]

Image classification is an essential factor for crop mapping and identification. In recent years, many researchers focused on improving data mining/machine learning algorithms to more accurately deal with image classification and predictive problems. The publicly availability of geospatial datasets and free access to cloud-based geo-computing platforms such as Google Earth Engine (GEE) are widely being used for robust mapping and monitoring of crop phenology, acreage estimation and crop yield forecasting. In the present study, the maize (Zea mays) crop has been identified and acreage estimated using integrated Sentinel-2A/B and PlanetScope satellite data for the Rabi/ winter season of 2022 in the Indo-Gangetic Plain. In which, we have assessed and compared the performance of classification and regression trees (CART), support vector machine (SVM) and random forest (RF) algorithms of machine learning (ML) for acreage estimation of maize crops using Google's GEE cloud computing platform. Wherein, we found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (90.17% Overall Accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 95.53%, Kappa 0.91). But, CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 88.59%, Kappa 0.85). We have also developed a web-based JavaScript code that can be tested anywhere in the world for robust mapping of crops under various climatic conditions. We expected that this study will be helpful for crop cultivation management, precision agriculture, crop insurance and for making a decision support system to prioritise the input subsidy for farmers. [ABSTRACT FROM AUTHOR]

In some developing countries, particularly China, a significant number of individual farmers manage small field scale of cultivated land. However, the existing research on cultivated land quality assessment mainly focuses on large-scale regions, establishing comprehensive index systems from a macro perspective, while lacking evaluations customized to individual farmers, who constitute a crucial component in agricultural production, and a demand-driven field-scale assessment of cultivated land quality. Therefore, we developed a field-scale index system that meets the needs of individual farmers in the black soil region of Northeast China. Additionally, we proposed a machine learning model for field-scale cultivated land quality assessment. The experimental results showed that our model achieved an R 2 value of 0.9660 and an RMSE of 1.94 × 10 - 2 under fourfold cross-validation, which represents an improvement of 5.19% and a reduction of 1.13%, respectively, relative to the XGBoost model. Ultimately, we conducted obstacle factor diagnosis, aiming to assist individual farmers in identifying the existing issues in their cultivated land fields. This study not only provides guidance to individual farmers but also addresses the research gap in cultivated land quality assessment by offering an individual farmer demand-driven index system for field-scale studies. [ABSTRACT FROM AUTHOR]

Global crop productivity is reduced by 20–40% annually due to agricultural pests. Mass trapping with insect sex-pheromones have been the major option for the pest control because of non-negative impact on both natural enemy and environment. In addition to this, we can lure and record a wide variety of insect pests with low concentrations of the insect sex pheromone which is released by female insects. However, the lack of understanding regarding the mechanism of how insect sex pheromone lures and traps work in the field has negatively affected the application of these techniques in countries like China and Pakistan. This lack of understanding hinders growers' ability to accurately comprehend the effectiveness and proper utilization of insect sex pheromone lures in pest management. This review summarizes recent advancements in molecular understanding of insect sex pheromones (their relevant structures and compositions), mass trapping, mating disruption and peptide biosensors, various forms of insect sex pheromone traps with a focus on the integration of artificial intelligence, machine learning, deep learning, and smart trap technologies used in Asia. According to this review, the insect sex pheromone technique is not only well understood but can also be effectively utilized for agricultural pest management. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers fabricated on an air clad 220 nm silicon on insulator single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm can cover a 150 nm bandwidth with less than 10.2 dB of loss at their lowest point. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid deep learning framework that combines graph neural networks with convolutional neural networks to predict porous media properties. This approach capitalizes on the capabilities of pre-trained convolutional neural networks to extract ndimensional feature vectors from processed three dimensional micro computed tomography porous media images obtained from seven different sandstone rock samples. Subsequently, two strategies for embedding the computed feature vectors into graphs were explored: extracting a single feature vector per sample (image) and treating each sample as a node in the training graph, and representing each sample as a graph by extracting a fixed number of feature vectors, which form the nodes of each training graph. Various types of graph convolutional layers were examined to evaluate the capabilities and limitations of spectral and spatial approaches. The dataset was divided into 70/20/10 for training, validation, and testing. The models were trained to predict the absolute permeability of porous media. Notably, the proposed architectures further reduce the selected objective loss function to values below 35 mD, with improvements in the coefficient of determination reaching 9%. Moreover, the generalizability of the networks was evaluated by testing their performance on unseen sandstone and carbonate rock samples that were not encountered during training. Finally, a sensitivity analysis is conducted to investigate the influence of various hyperparameters on the performance of the models. The findings highlight the potential of graph neural networks as promising deep learning-based alternatives for characterizing porous media properties. The proposed architectures efficiently predict the permeability, which is more than 500 times faster than that of numerical solvers. [ABSTRACT FROM AUTHOR]

One of the inevitable consequences of the ever-increasing growth of the world’s population is the expansion of urbanization. So, it is very important to provide a vision of the spatial development of cities with the aim of understanding the correct pattern of city growth and providing the necessary infrastructure. Since Qom metropolis faced urban growth and has recorded 95% urbanization, this research focused on the spatial development of urban lands around this metropolis. First, the land use/cover and the urban growth merit maps were produced. Land use/ land cover maps of the region for the years 2000, 2010 and 2020 were produced using the random forest method in the Google Earth Engine, and the urban growth merit map for the years 2000 and 2010 was produced using MCDM analyses based on GIS. Finally, based on the ANN-CAMarkov and SVM-CA-Markov algorithms, 2020’s land use/cover maps were simulated. The validation of the models showed that the SVM-CA-Markov algorithm with the ROC (0.96) was more accurate and was chosen as the optimal algorithm for modeling the horizon of 2040. The results indicate the increasing spatial development of this metropolis. The area of urban land in this region will increase from 139.62 square kilometers in 2020 to more than 183 square kilometers in 2040. The evaluation of the results can help the relevant managers in order to take necessary policies to manage the situation as best as possible. This importance can be realized through planning for the regular development of the road network, the expansion of urban green spaces, etc. official organizations and local officials should have a purposeful monitoring of this issue, while having full control over the development directions of this metropolis. [ABSTRACT FROM AUTHOR]

Biomicrofluidics, a subdomain of microfluidics, has been inspired by several ideas from nature. However, while the basic inspiration for the same may be drawn from the living world, the translation of all relevant essential functionalities to an artificially engineered framework does not remain trivial. Here, we review the recent progress in bio-inspired microfluidic systems via harnessing the integration of experimental and simulation tools delving into the interface of engineering and biology. Development of "on-chip" technologies as well as their multifarious applications is subsequently discussed, accompanying the relevant advancements in materials and fabrication technology. Pointers toward new directions in research, including an amalgamated fusion of data-driven modeling (such as artificial intelligence and machine learning) and physics-based paradigm, to come up with a human physiological replica on a synthetic bio-chip with due accounting of personalized features, are suggested. These are likely to facilitate physiologically replicating disease modeling on an artificially engineered biochip as well as advance drug development and screening in an expedited route with the minimization of animal and human trials. [ABSTRACT FROM AUTHOR]

Enormous amount of earth information, gathered from satellite sensors, simulations, and other resources, are collectively referred to as Big Earth Observation Data (BEOD). The data contains remarkable insights and spatio-temporal stamps of pertinent Earth phenomena for enhancing our knowledge, responding, and addressing demanding situations of earth sciences and observations. However, However, traditional data mining algorithms are generally time-inefficient, making it difficult to process and analyze BEOD. To address this challenge, we explore two ways to enhance scalability: 1) improving the algorithm with specific parameters or data modifications when run on a single machine, and 2) making the algorithm parallel through distributed execution on multiple machines, such as with cluster-based implementations. We also suggest improvements for existing techniques and widely used algorithms for processing BEOD. In this review, we conduct a systematic review of data mining techniques for classification, clustering, prediction, regression, association rules, pattern mining, and anomaly detection to determine their scalability and performance on various Earth observation use cases. We explored advanced mining techniques, including statistical, machine learning, and deep learning approaches, for handling BEOD at scale. We also identified potential challenges, open research issues, and provide future directions for the development of scalable algorithms for mining BEOD. We observed that applying data mining techniques on BEOD introduces serious concerns since the data has both spatial and temporal components. Statistical and machine learning models such as ARIMA, SARIMA, Naive Bayes, Bayesian networks, KNN, and K-means, as well as SVM, are not suitable for working with the volume and heterogeneity present in the data, but it can be improved by employing big data environments. Nowadays, deep learning-based techniques are popularly used for working with large amounts of data, but it requires specialized systems and upgrades as the data volume increases. This issue can be addressed through a big data platform. A unified deep learning architecture can be employed to handle both the spatial and temporal components of BEOD, and performance can be improved by deploying the architecture on a big data environment. Therefore, this study also reveals that, although deep learning architectures are efficient and trending, traditional statistical methods and machine learning can achieve competitive or sometimes improved performance with the involvement of big data technologies and/or internal data representation. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning techniques are successfully applied to many problems in industrial and academic fields with classical computers. With the introduction of quantum simulators, the idea of using quantum-computing speed to solve these problems has become widespread. Many researchers are experimenting with using quantum circuits in various machine-learning methods to solve different problems. Due to the limited number of qubits, experiments are on simpler problems. In this study, a variational quantum circuit (VQC) was proposed using amplitude encoding to overcome the limited qubit number barrier and use the advantages of quantum computing more efficiently. The proposed amplitude encoding method and VQC were explained. Generalized by exemplifying how they can be applied to different problems. The proposed approach was applied to a navigation problem. The performance of the proposed approach was evaluated with the number of parameters, the number of qubits needed, and the success rate. As a result, the performance of the proposed approach has been verified. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the global issues of extreme climate and urbanization, the ecological security patterns (ESPs) in the Qinling Mountains are facing prominent challenges. As a crucial ecological barrier in China, understanding the characteristics of ESPs in the Qinling Mountains is vital for achieving sustainable development. This study focuses on Yangxian and employs methods such as machine learning (ML), remote sensing (RS), geographic information systems (GISs), analytic hierarchy process and principal component analysis (AHP–PCA), and the minimum cumulative resistance (MCR) model to construct an ecological security network based on multi-factor ecological sensitivity (ES) and conduct quantitative spatial analysis. The results demonstrate that the AHP–PCA method based on ML overcomes the limitations of the single-weighting method. The ESPs of Yangxian were established, consisting of 21 main and secondary ecological sources with an area of 592.81 km2 (18.55%), 41 main and secondary ecological corridors with a length of 738.85 km, and 33 ecological nodes. A coupling relationship among three dimensions was observed: comprehensive ecological sensitivity, ESPs, and administrative districts (ADs). Huangjinxia Town (1.43 in C5) and Huayang Town (7.28 in C4) likely have significant areas of ecological vulnerability, while Machang Town and Maoping Town are important in the ESPs. ADs focus on protection and management. The second corridor indicated high-quality construction, necessitating the implementation of strict protection policies in the study area. The innovation lies in the utilization of quantitative analysis methods, such as ML and RS technologies, to construct an ecological spatial pattern planning model and propose a new perspective for the quantitative analysis of ecological space. This study provides a quantitative foundation for urban and rural ecological spatial planning in Yangxian and will help facilitate the sustainable development of ecological planning in the Qinling region. [ABSTRACT FROM AUTHOR]

Knowing each other is obligatory in a multi-agent collaborative environment. Collaborators may develop the desired know-how of each other in various aspects such as habits, job roles, status, and behaviors. Among different distinguishing characteristics related to a person, personality traits are an effective predictive tool for an individual’s behavioral pattern. It has been observed that when people are asked to share their details through questionnaires, they intentionally or unintentionally become biased. They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves. Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments, job interviews, mental health diagnoses, etc. Stream of consciousness, collected by James Pennbaker and Laura King, is one such way of writing, referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator. Moreover, computationally, various attempts have been made in an individual’s personality traits assessment through deep learning algorithms; however, the effectiveness and reliability of results vary with varying word embedding techniques. This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents. Bidirectional Encoder Representations from Transformers (BERT) word embedding technique is used for word vector generation to enhance the contextual meanings. [ABSTRACT FROM AUTHOR]

The rate of global urbanization is constantly increasing. As a result of the massive population growth, there is an increasing demand for further urban development, especially in developing regions such as Aswan city. This paper aims to examine the usage official land-use data in predicting future urban growth until 2046, moreover, to define urban driving forces in case study area. This was done using Similarity weighted model, a machine learning based model to simulate future urban growth. The results show that official land-use data produce a slightly better results’ accuracy than remote sensing sources within small to medium scales. The results although reveal that for study region, urban area is expected to expand to cover an area of almost 4460 Feddan by year 2046. The outcome of this research assesses decision makers to accurately predict future urban sprawl areas using available official land-use data. [ABSTRACT FROM AUTHOR]

Background: Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods: A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effectsmodel was preferred formeta-analysis using R4.2.0 because of the different variables and parameters. Results: A total of 44 studies were included in thismeta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion: ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. [ABSTRACT FROM AUTHOR]

This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm in that (a) it does not require manual annotation of images to learn how to count targeted pests, and (b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server, which configures its operation, while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of dehydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in the literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep learning approaches (YOLOv7/8, crowd counting, deep learning regression). Interestingly, there is no one optimal clear-cut counting approach that can cover all situations involving small and large insects with overlap. By weighting the pros and cons we suggest that YOLOv7/8 provides the best embedded solution in general. We open-source the code and a large database of Lepidopteran plant pests. [ABSTRACT FROM AUTHOR]

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system's operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency. [ABSTRACT FROM AUTHOR]

In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information. [ABSTRACT FROM AUTHOR]

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on “suitability” and “potential improvement in farmland productivity”, we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) are extensively studied because of their great potential in energy harvesting and sensing. Traditional chemical batteries cannot meet the requirements of wearable electronic products because of their inherent limitations such as large weight, large size, limited endurance, and limited lifespan. To address the issue, the self‐powered energy harvester has attracted considerable interest among researchers. Here, the woven structure TENG consists of flexible silicone mixed with polyvinylidene fluoride as dielectric layers, silver fabric, and conductive tape as electrodes. The electrical output performance of the pattern structure processed by tip design is nearly three times higher. The whole TENG and flexible printed circuit board are encapsulated by polydimethylsiloxane film, reducing inconvenient, and discomfort sensation during wearing. The woven structure TENG can not only harvest energy to power wearable electronic devices but also be used to recognize human motion as the waveforms changing when people are in different states. Taking the knee bending angle data as an example to conduct machine learning, it is able to classify different states of human motion. This work extends the application of the woven TENG as an integrated device for sensing and energy harvesting. [ABSTRACT FROM AUTHOR]