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TH Nguyen Pham,1 Nigel A Lengkeek,2 Ivan Greguric,2 Byung J Kim,1 Paul A Pellegrini,2 Stephanie A Bickley,3 Marcel R Tanudji,3 Stephen K Jones,3 Brian S Hawkett,1 Binh TT Pham1 1Key Centre for Polymers and Colloids, School of Chemistry, University of Sydney, 2Radioisotopes and Radiotracers, NSTLI, Australian Nuclear Science and Technology Organisation, Sydney, 3Sirtex Medical Limited, North Sydney, NSW, Australia Abstract: Physiologically stable multimodality imaging probes for positron emission tomography/single-photon emission computed tomography (PET/SPECT)-magnetic resonance imaging (MRI) were synthesized using the superparamagnetic maghemite iron oxide (γ-Fe2O3) nanoparticles (SPIONs). The SPIONs were sterically stabilized with a finely tuned mixture of diblock copolymers with either methoxypolyethylene glycol (MPEG) or primary amine NH2 end groups. The radioisotope for PET or SPECT imaging was incorporated with the SPIONs at high temperature. 57Co2+ ions with a long half-life of 270.9 days were used as a model for the radiotracer to study the kinetics of radiolabeling, characterization, and the stability of the radiolabeled SPIONs. Radioactive 67Ga3+ and Cu2+-labeled SPIONs were also produced successfully using the optimized conditions from the 57Co2+-labeling process. No free radioisotopes were detected in the aqueous phase for the radiolabeled SPIONs 1 week after dispersion in phosphate-buffered saline (PBS). All labeled SPIONs were not only well dispersed and stable under physiological conditions but also noncytotoxic in vitro. The ability to design and produce physiologically stable radiolabeled magnetic nanoparticles with a finely controlled number of functionalizable end groups on the SPIONs enables the generation of a desirable and biologically compatible multimodality PET/SPECT-MRI agent on a single T2 contrast MRI probe. Keywords: magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, SPIONs, radiolabeling

Binh TT Pham,1 Nirmesh Jain,1 Philip W Kuchel,2 Bogdan E Chapman,2,† Stephanie A Bickley,3 Stephen K Jones,3 Brian S Hawkett11School of Chemistry, University of Sydney, 2School of Molecular Bioscience, University of Sydney, 3Sirtex Medical Limited, Sydney, NSW, Australia†Bogdan E Chapman passed away on August 16, 2014Abstract: Sterically stabilized superparamagnetic iron oxide nanoparticles (SPIONs) were incubated with fresh human erythrocytes (red blood cells [RBCs]) to explore their potential application as magnetic resonance imaging contrast agents. The chemical shift and linewidth of 133Cs+ resonances from inside and outside the RBCs in 133Cs nuclear magnetic resonance spectra were monitored as a function of time. Thus, we investigated whether SPIONs of two different core sizes and with three different types of polymeric stabilizers entered metabolically active RBCs, consuming glucose at 37°C. The SPIONs broadened the extracellular 133Cs+ nuclear magnetic resonance, and brought about a small change in its chemical shift to a higher frequency; while the intracellular resonance remained unchanged in both amplitude and chemical shift. This situation pertained over incubation times of up to 90 minutes. If the SPIONs had entered the RBCs, the intracellular resonance would have become broader and possibly even shifted. Therefore, we concluded that our SPIONs did not enter the RBCs. In addition, the T2 relaxivity of the small and large particles was 368 and 953 mM-1 s-1, respectively (three and nine times that of the most effective commercially available samples). This suggests that these new SPIONs will provide a superior performance to any others reported thus far as magnetic resonance imaging contrast agents.Keywords: 133Cs NMR spectroscopy, bulk magnetic susceptibility, fresh human erythrocyte, particle stability, paramagnetic shift, superparamagnetic iron oxide nanoparticles, SPIONs

The present work aimed to investigate the effect of film-forming solution pH on characteristics of chitosan (CH) - Piper betel L. leaf extract (PBe) coating films and their potential applications in mango preservation. The coating films were fabricated from CH-PBe solutions in the pH range of 3-5 using a solvent evaporation technique. The analysis results (DSC and FTIR) demonstrate higher miscibility, better compatibility, and tighter intermolecular interactions between CH and active compounds in the film matrix prepared at low pH. The mechanical and barrier properties of the CH-PBe film significantly decreased with increasing pH value. Varying the film-forming pH insignificantly affected the antioxidant activity and antibacterial inhibition against Staphylococcus aureus (Gram-positive) of the resultant films. However, Escherichia coli (Gram-negative) was less vulnerable to the blend film prepared at the higher pH medium. The coating solution at pH 4 proved suitable for preserving 'Tu Quy' mangoes, according to the observation of color changes, accumulated decay rate, respiration rate, ripening index, and other related factors. The findings of this work reveal the importance of pH control in producing CH-PBe coating films for mango preservation and provide more insights into pH-affected interactions between CH matrix and polyphenols in PBe., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Apathy is a behavioral symptom prevalent both in neuropsychiatric pathologies and in the healthy population. However, the knowledge of the cognitive and neural mechanisms underlying apathy is still very limited, even if clinical and fMRI data support the existence of three forms of apathy (executive, emotional, initiative). These forms could be explained by the alteration of specific mechanisms. This present study's aim is to specify the cognitive and neuronal mechanisms of executive and emotional apathy. We used an EEG study conducted on 68 subjects comprising two groups of young people with specific executive or emotional phenotypes of apathy and one group with no apathy. Despite having symptom of apathy, participants were free of any neurological, metabolic, or psychiatric diagnoses and with high education. Two tasks were used: the DPX for cognitive control and the MID for motivation. Our results showed that distinct mechanisms underlie these two forms of apathy, and, for the first time, we specified these mechanisms. A deficit of the proactive control mode, reflected by a reduced probe-N2 amplitude in AY trials, underlies the executive form of apathy (p < .03), whereas liking motivational blunting, highlighted by a reduced LPP amplitude for financial loss, characterizes the emotional form (p < .04). The main limit of the results is that generalizability to the general population may be reduced since the apathetic samples were chosen for having a specific form of apathy. To conclude, better knowledge of these mechanisms informs new, more targeted treatments, both pharmacological and non-pharmacological, necessary for reducing the debilitating consequences of apathy., Competing Interests: Declaration of competing interest The authors declare no competing interests to report., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities., (© 2023 Society for Risk Analysis.)

Novel active coating from gelatin/agarose (GA) functionalized with Ocimum gratissimum L. essential oil (OGO) had been developed as a medium to evaluate their properties before being applied for avocado preservation. The resultant coating films showed enhanced mechanical, water-barrier, bactericidal, antioxidant, and UV-shielding properties by adding OGO. The best tensile strength (2.91 MPa) and flexibility (45.82 %) was found in the GA film containing 5 % (w/w) of OGO (GA-OGO-5). Furthermore, this coating formulation presented moderate antibacterial activities against Listeria, Pseudomonas, Salmonella, and Escherichia. The GA-OGO-5 coating film also divulged the highest hydrophobicity and adequate antioxidant function (30.75 μg/mL) and thus, was chosen to coat on 'Booth 7' avocados by dipping method. The GA-OGO-5 coating layers were to be efficient to decline the respiration rate of avocado during 6-day storage at 25 °C and 64 %RH. Peel color, weight loss (5.22 %), total soluble solids (8.14 %), and solution pH (6.79) at the end of storage also indicated that the GA-OGO-5 coating presented the best effectiveness for enhancing the storability of avocado as compared to uncoated and GA-treated fruit. Therefore, the GA-OGO coating has been considered as an alternative post-harvest technique to enhance the avocado storability and could be further commercialized for industry application., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective: This study aims to describe the diagnostic performance of alpha-fetoprotein (AFP), alpha-fetoprotein L3 isoform (AFP-L3), protein induced by vitamin K absence II (PIVKA-II), and combined biomarkers for non-B non-C hepatocellular carcinoma (NBNC-HCC)., Results: A total of 681 newly-diagnosed primary liver disease subjects (385 non-HCC, 296 HCC) who tested negativity for the hepatitis B surface antigen (HBsAg) and hepatitis C antibody (anti-HCV) enrolled in this study. At the cut-off point of 3.8 ng/mL, AFP helps to discriminate HCC from non-HCC with an area under the curve (AUC) value of 0.817 (95% confidence interval [CI]: 0.785-0.849). These values of AFP-L3 (cut-off 0.9%) and PIVKA-II (cut-off 57.7 mAU/mL) were 0.758 (95%CI: 0.725-0.791) and 0.866 (95%CI: 0.836-0.896), respectively. The Bayesian Model Averaging (BMA) statistic identified the optimal model, including patients' age, aspartate aminotransferase, AFP, and PIVKA-II combination, which helps to classify HCC with better performance (AUC = 0.896, 95%CI: 0.872-0.920, P < 0.001). The sensitivity and specificity of the optimal model reached 81.1% (95%CI: 76.1-85.4) and 83.2% (95%CI: 78.9-86.9), respectively. Further analyses indicated that AFP and PIVKA-II markers and combined models have good-to-excellent performance detecting curative resected HCC, separating HCC from chronic hepatitis, dysplastic, and hyperplasia nodules., (© 2023. The Author(s).)

Ensemble learning techniques have shown promise in improving the accuracy of landslide models by combining multiple models to achieve better predictive performance. In this study, several ensemble methods (Dagging, Bagging, and Decorate) and a radial basis function classifier (RBFC) were combined to predict landslide susceptibility in the Trung Khanh district of the Cao Bang Province, Vietnam. The ensemble models were developed using a geospatial database containing 45 historical landslides (1074 points) and thirteen influencing variables characterizing the topography, geology, land use/cover, and human activities of the study area. The performance of the models was evaluated based on the area under the receiver operating characteristic curve (AUC) and several other performance metrics, including positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), and root mean square error (RMSE). The Bagging-RBFC model with PPV = 86%, NPV = 95%, SST = 95%, SPF = 87%, ACC = 91%, RMSE = 0.297, and AUC = 98% was found to be the most accurate model for the prediction of landslide susceptibility, followed by the Dagging-RBFC, Decorate-RBFC, and single RBFC models. The study demonstrates the efficacy of ensemble learning techniques in developing reliable landslide predictive models, which can ultimately save lives and reduce infrastructure damage in landslide-prone regions worldwide., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Prior research suggests that R&D intensive firms are especially vulnerable during crises due to their narrow specialization, high adjustment costs, increased distress risks, and higher sensitivity to financial distress. This paper exploits the difference in the research and development intensity as a quasi-natural experiment to examine the impact of the coronavirus pandemic on firm performance. Our study finds that the adverse consequences of COVID-19 on firms' profitability have been less pronounced for R&D intensive firms. R&D intensive firms are also able to record more positive changes in cash holdings as a response to the COVID-19 pandemic. As a result, R&D intensive firms are less likely to rely on external financing and record a lower level of increase in financing. Our results further highlight the potential function of R&D investment as a panacea for firm's growth during economic downturns., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas., (© 2022 Society for Risk Analysis.)

Initiative apathy is the most disabling form of apathy, prevalent both in neuropsychiatric pathologies and in the healthy population. This apathy has been specifically associated with functional abnormalities of the anterior cingulate cortex, a key structure underlying Effort-based Decision-Making (EDM). The main aim of the present study was to explore, for the first time, the cognitive and neural effort mechanisms of initiative apathy, by distinguishing the steps of effort anticipation and effort expenditure and the potential modulating effect of motivation. We conducted an EEG study in 23 subjects with specific subclinical initiative apathy and 24 healthy subjects with no apathy. The subjects had to complete two effort tasks. The analysis of behavioral choices, CNV, and mPFC theta power highlighted that initiative apathy is associated with effort avoidance and impairments of effort anticipation and effort expenditure that suggest EDM deficits. Better knowledge of these impairments should aid the development of new, more targeted therapeutic interventions necessary for reducing the debilitating consequences of initiative apathy., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods.

Shortly after its emergence, Omicron and its sub-variants have quickly replaced the Delta variant during the current COVID-19 outbreaks in Vietnam and around the world. To enable the rapid and timely detection of existing and future variants for epidemiological surveillance and diagnostic applications, a robust, economical real-time PCR method that can specifically and sensitively detect and identify multiple different circulating variants is needed. The principle of target- failure (TF) real-time PCR is simple. If a target contains a deletion mutation, then there is a mismatch with the primer or probe, and the real-time PCR will fail to amplify the target. In this study, we designed and evaluated a novel multiplex RT real-time PCR (MPL RT-rPCR) based on the principle of target failure to detect and identify different variants of SARS-CoV-2 directly from the nasopharyngeal swabs collected from COVID-19 suspected cases. The primers and probes were designed based on the specific deletion mutations of current circulating variants. To evaluate the results from the MPL RT-rPCR, this study also designed nine pairs of primers for amplifying and sequencing of nine fragments from the S gene containing mutations of known variants. We demonstrated that (i) our MPL RT-rPCR was able to accurately detect multiple variants that existed in a single sample; (ii) the limit of detection of the MPL RT-rPCR in the detection of the variants ranged from 1 to 10 copies for Omicron BA.2 and BA.5, and from 10 to 100 copies for Delta, Omicron BA.1, recombination of BA.1 and BA.2, and BA.4; (iii) between January and September 2022, Omicron BA.1 emerged and co-existed with the Delta variant during the early period, both of which were rapidly replaced by Omicron BA.2, and this was followed by Omicron BA.5 as the dominant variant toward the later period. Our results showed that SARS-CoV-2 variants rapidly evolved within a short period of time, proving the importance of a robust, economical, and easy-to-access method not just for epidemiological surveillance but also for diagnoses around the world where SARS-CoV-2 variants remain the WHO's highest health concern. Our highly sensitive and specific MPL RT-rPCR is considered suitable for further implementation in many laboratories, especially in developing countries.

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

Background: There is a lack of published research on the impact of the first wave of the COVID-19 pandemic in Taiwan. We investigated the mortality risk factors among critically ill patients with COVID-19 in Taiwan during the initial wave. Furthermore, we aim to develop a novel AI mortality prediction model using chest X-ray (CXR) alone., Method: We retrospectively reviewed the medical records of patients with COVID-19 at Taipei Tzu Chi Hospital from May 15 to July 15 2021. We enrolled adult patients who received invasive mechanical ventilation. The CXR images of each enrolled patient were divided into 4 categories (1st, pre-ETT, ETT, and WORST). To establish a prediction model, we used the MobilenetV3-Small model with "Imagenet" pretrained weights, followed by high Dropout regularization layers. We trained the model with these data with Five-Fold Cross-Validation to evaluate model performance., Result: A total of 64 patients were enrolled. The overall mortality rate was 45%. The median time from symptom onset to intubation was 8 days. Vasopressor use and a higher BRIXIA score on the WORST CXR were associated with an increased risk of mortality. The areas under the curve of the 1st, pre-ETT, ETT, and WORST CXRs by the AI model were 0.87, 0.92, 0.96, and 0.93 respectively., Conclusion: The mortality rate of COVID-19 patients who receive invasive mechanical ventilation was high. Septic shock and high BRIXIA score were clinical predictors of mortality. The novel AI mortality prediction model using CXR alone exhibited a high performance., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2022 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.)

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

This study aims to investigate the influence of all the mixture components of high-performance concrete (HPC) on its early compressive strength, ranging from 1 to 14 days. To this purpose, a Gaussian Process Regression (GPR) algorithm was first constructed using a database gathered from the available literature. The database included the contents of cement, blast furnace slag (BFS), fly ash (FA), water, superplasticizer, coarse, fine aggregates, and testing age as input variables to predict the output of the problem, which was the early compressive strength. Several standard statistical criteria, such as the Pearson correlation coefficient, root mean square error and mean absolute error, were used to quantify the performance of the GPR model. To analyze the sensitivity and influence of the HPC mixture components, partial dependence plots analysis was conducted with both one-dimensional and two-dimensional. Firstly, the results showed that the GPR performed well in predicting the early strength of HPC. Second, it was determined that the cement content and testing age of HPC were the most sensitive and significant elements affecting the early strength of HPC, followed by the BFS, water, superplasticizer, FA, fine aggregate, and coarse aggregate contents. To put it simply, this research might assist engineers select the appropriate amount of mixture components in the HPC production process to obtain the necessary early compressive strength., Competing Interests: The authors have declared that no competing interests exist.

We bring the notion of connectedness (Diebold and Yilmaz, Int J Forecast 28(1):57-66 2012) to a set of two critical macroeconomic variables as inflation and unemployment. We focus on the G7 economies plus Spain, and use monthly data-high-frequency data in a macro setting-to explore the extent and consequences of total and directional volatility spillovers across variables and countries. We find that total connectedness is larger for prices (58.28%) than for unemployment (41.81%). We also identify asymmetries per country that result in higher short-run Phillips curve trade-offs in recessions and lower trade-offs in expansions. Besides, by exploring time-varying connectedness (resulting from country-specific shocks), we find that volatility spillovers magnify in periods of common economic turmoil such as the Global Financial Crisis. Our results call for an enhancement of international macroeconomic policy coordination., Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-021-02052-0., Competing Interests: Conflict of interest Binh Thai Pham declares that he has no conflict of interest. Hector Sala declares that he has no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.

The ability to detect errors, which derives from the medial prefrontal cortex (mPFC), is crucial to maintain attention over a long period of time. While impairment of this ability has been reported in patients with sustained attention disruption, the role mPFC-mediated processes play in the intra-individual fluctuation of sustained attention remains an open question. In this context, we computed the variance time course of reaction time (RT) of 42 healthy individuals to distinguish intra-individual periods of low and high performance instability, assumed to represent optimal and suboptimal attentional states, when performing a sustained Go/NoGo task. Analysis of the neurophysiological mechanisms of response monitoring revealed a specific reduction in the error-related negativity (ERN) amplitude and frontal midline theta power during periods of high compared to low RT variability, but only in individuals with a higher standard deviation of reaction time (SD-RT). Concerning post-error adaptation, an increase in the correct-related negativity (CRN) amplitude as well as the frontal lateral theta power on trials following errors was observed in individuals with lower SD-RT but not in those with higher SD-RT. Our results thus show that individuals with poor sustained attention ability exhibit altered post-error adaptation and attentional state-dependent efficiency of error monitoring. Conversely, individuals with good sustained attention performances retained their post-error adaptation and response monitoring regardless of the attentional periods. These findings reveal the critical role of the action-monitoring system in intra-individual behavioral stability and highlight the importance of considering attentional states when studying mPFC-mediated processes, especially in subjects with low sustained attention ability., Competing Interests: Declaration of competing interest None of the authors have potential conflicts of interest to be disclosed., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation., (© 2021 National Ground Water Association.)

Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R 2 ), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R 2  = 0.9415, RMSE = 0.0589, and MAE = 0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Impaired cognitive control has been associated with the occurrence of attentional errors in those with schizophrenia. However, the extent of altered proactive or reactive control underlying such errors is still unknown. Twenty-two patients with schizophrenia and 21 healthy matched controls performed a detection task (i.e., the continuous temporal expectancy task). Electrophysiological measures of proactive and reactive control were based on two periods of interest: during the target presentation (the critical window) and four trials before the critical window. Regarding the proactive mode, patients with schizophrenia exhibited a specific decrease in frontal midline theta power during the critical window before a miss compared to a correct detection. In contrast, the contingent negative variation amplitude was altered regardless of the response type, four trials before the critical window. Regarding the reactive mode, a reduced P3 amplitude was revealed later before a miss than a correct detection with differences apparent only two trials before the critical window in patients with schizophrenia, whereas it was observable up to four trials prior in healthy controls. Moreover, only the P3 amplitude reduction in patients with schizophrenia predicted the miss rate and was anti-correlated with the clinical symptoms. Thus, our results revealed a specific impairment of the proactive goal-updating process before an error and an altered implementation of the endogenous proactive mode engagement regardless of the response type. The results also highlighted the strong relationship between the disrupted reactive mode and the increased rate of attentional errors and severity of the clinical symptoms of schizophrenia., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network-Frequency Ratio (DLNN-FR), Deep Learning Neural Network-Weights of Evidence (DLNN-WOE), Alternating Decision Trees-Frequency Ratio (ADT-FR) and Alternating Decision Trees-Weights of Evidence (ADT-WOE). The model's performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPI DLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

Aims: To assess a cost-effective in-house selective plate formula for actively screening carbapenem-resistant Enterobacteriaceae (CRE)., Methodology and Results: The in-house formula included CHROMagar TM Orientation, meropenem, and ingredients present in the Mac-Conkey formula, such as bile salts and crystal violet (pH 6.9-7.2). American Type Culture Collection strains and 200 clinical strains were used to validate the plate formula. The CRE plates had a sensitivity of 97.4% and a specificity of 98.8% with ATCC andor clinical strains used in the quality control procedure. A point prevalence survey among the 18 inpatients at Viet-Tiep hospital ICU using fecal swabs plated at the in-house agar plate showed a CRE prevalence of 44.4%., Conclusion: The in-house plate had high sensitivity and specificity, particularly for Escherichia coli and the KESC group (Klebsiella spp., Enterobacter spp., Serratia marscescens, and Citrobacter spp.), and it may be widely applied as an alternative to other ready-to-use commercial plates., Significance and Impact of the Study: The formula developed in the present study may facilitate the early detection and isolation of CRE and decrease transmission, particularly in low- and middle-income countries with a high rate of CRE colonization and limited access to ready-to-use commercial plates., (© 2020 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.)

Due to their excellent electrocatalytic properties, transition metal phosphides have been considered as desirable and cost-effective electrocatalysts in recent years. However, in many cases, the synthesis of phosphide-based nanostructures requires expensive conditions and toxic phosphorous-containing compounds. Therefore, the emergence of an economical and eco-friendly method for creating phosphides-based nanostructures can be very effective. Here, S-doped Ni-P nanospheres were created using a novel pulse electrochemical deposition method, and its hydrogen evolution reaction (HER), as well as oxygen evolution reaction (OER) electrocatalytic activity and stability were investigated. Inspired by the high electrochemically active surface area, the synergistic effect between S and P, the rapid detachment of the gasses from the surface and thus the lowered resistance resulted from bubbles pinning, and ultimately the increased wettability due to nanostructuring, the electrode exhibited outstanding electrocatalytic activity for HER and OER processes. The electrode requires only 55 mV and 229 mV overproduction to be able to afford the current density of 10 mA.cm -2 for HER and OER processes, respectively. Furthermore, at a current density of 100 mA.cm -2 , these electrodes showed minor changes in potential, indicating the superb electrocatalytic stability of the synthesized electrode. Moreover, in the overall water splitting process, the S-doped Ni-P electrode requires only 1.51 V to generate a current of 10 mA.cm -2 . The results of this study indicate the successful use of pulse electrochemical deposition method to create active electrocatalysts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca 2 + , Mg 2 + , Na + , K + , HCO 3 - , CO 3 2 - , SO 4 2 - , and Cl - concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na + and Ca 2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and -0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources., (© 2019, National Ground Water Association.)

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM 2.5 (particles with an aerodynamic diameter smaller than 2.5 μm), PM 10 (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF&#95;PSO was presented and the predictive performance was analyzed. The results show that the RF&#95;PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM 2.5 , PM 10 and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination ( R 2 ), root mean square error ( RMSE ), and mean absolute error ( MAE ). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.

Warm mix asphalt (WMA) technology, taking advantage of reclaimed asphalt pavements, has gained increasing attention from the scientific community. The determination of technical specifications of such a type of asphalt concrete is crucial for pavement design, in which the asphalt concrete dynamic modulus (E*) of elasticity is amongst the most critical parameters. However, the latter could only be determined by complicated, costly, and time-consuming experiments. This paper presents an alternative cost-effective approach to determine the dynamic elastic modulus (E*) of WMA based on various machine learning-based algorithms, namely the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and ensemble boosted trees (Boosted). For this, a total of 300 samples were fabricated by warm mix asphalt technology. The mixtures were prepared with 0%, 20%, 30%, 40%, and 50% content of reclaimed asphalt pavement (RAP) and modified bitumen binder using Sasobit and Zycotherm additives. The dynamic elastic modulus tests were conducted by varying the temperature from 10 °C to 50 °C at different frequencies from 0.1 Hz to 25 Hz. Various common quantitative indications, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used to validate and compare the prediction capability of different models. The results showed that machine learning models could accurately predict the dynamic elastic modulus of WMA using up to 50% RAP and fabricated by warm mix asphalt technology. Out of these models, the Boosted algorithm (R = 0.9956) was found as the best predictor compared with those obtained by ANN-LMN (R = 0.9954), SVM (R = 0.9654), and GPR (R= 0.9865). Thus, it could be concluded that Boosted is a promising cost-effective tool for the prediction of the dynamic elastic modulus (E*) of WMA. This study might help in reducing the cost of laboratory experiments for the determination of the dynamic modulus (E*).

Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC: 0.904 - 0.919). Whereas, testing with LR (AUC: 0.825 - 0.785) and NNET (AUC: 0.882 - 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not characterized properly in an inventory., Competing Interests: Declaration of competinginterest No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In this paper, the main objectives are to investigate and select the most suitable parameters used in particle swarm optimization (PSO), namely the number of rules (n rule ), population size (n pop ), initial weight (w ini ), personal learning coefficient (c 1 ), global learning coefficient (c 2 ), and velocity limits (f v ), in order to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. This is an important mechanical property in terms of the safety of structures under subjected loads. An available database of 3645 data samples was used for generation of training (70%) and testing (30%) datasets. Monte Carlo simulations, which are natural variability generators, were used in the training phase of the algorithm. Various statistical measurements, such as root mean square error (RMSE), mean absolute error (MAE), Willmott's index of agreement (IA), and Pearson's coefficient of correlation (R), were used to evaluate the performance of the models. The results of the study show that the performance of ANFIS optimized by PSO (ANFIS-PSO) is suitable for determining the buckling capacity of circular opening steel beams, but is very sensitive under different PSO investigation and selection parameters. The findings of this study show that n rule = 10, n pop = 50, w ini = 0.1 to 0.4, c 1 = [1, 1.4], c 2 = [1.8, 2], f v = 0.1, which are the most suitable selection values to ensure the best performance for ANFIS-PSO. In short, this study might help in selection of suitable PSO parameters for optimization of the ANFIS model.

Background: Intestinal fibrosis is a hallmark of Crohn's disease. Here, we investigated the impact of several putative antifibrotic compounds on the expression of fibrosis markers using murine precision-cut intestinal slices., Methods: Murine precision-cut intestinal slices were cultured for 48 hours in the presence of profibrotic and/or antifibrotic compounds. The fibrotic process was studied on gene and protein level using procollagen 1a1 (Col1α1), heat shock protein 47 (Hsp47), fibronectin (Fn2), and plasminogen activator inhibitor-1 (Pai-1). The effects of potential antifibrotic drugs mainly inhibiting the transforming growth factor β (TGF-β) pathway (eg, valproic acid, tetrandrine, pirfenidone, SB203580, and LY2109761) and compounds mainly acting on the platelet-derived growth factor (PDGF) pathway (eg, imatinib, sorafenib, and sunitinib) were assessed in the model at nontoxic concentrations., Results: Murine precision-cut intestinal slices remained viable for 48 hours, and an increased expression of fibrosis markers was observed during culture, including Hsp47, Fn2, and Pai-1. Furthermore, TGF-β1 stimulated fibrogenesis, whereas PDGF did not have an effect. Regarding the tested antifibrotics, pirfenidone, LY2109761, and sunitinib had the most pronounced impact on the expression of fibrosis markers, both in the absence and presence of profibrotic factors, as illustrated by reduced levels of Col1α1, Hsp47, Fn2, and Pai-1 after treatment. Moreover, sunitinib significantly reduced Hsp47 and Fn2 protein expression and the excretion of procollagen 1., Conclusions: Precision-cut intestinal slices can successfully be used as a potential preclinical screening tool for antifibrotic drugs. We demonstrated that sunitinib reduced the expression of several fibrosis markers, warranting further evaluation of this compound for the treatment of intestinal fibrosis., (© 2020 Crohn’s & Colitis Foundation. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

: The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Polymer-assisted flocculation-dewatering of mineral processing tailings (MPT) is crucial for its environmental disposal. To reduce the number of laboratory experiments, this study proposes a novel and hybrid machine learning (ML) method for the prediction of the flocculation-dewatering performance. The proposed ML method utilizes principle component analysis (PCA) for the dimension-reduction of the input space. Then, ML prediction is performed using the combination of particle swarm optimisation (PSO) and adaptive neuro-fuzzy inference system (ANFIS). Monte Carlo simulations are used for the converged results. An experimental dataset of 102 data instances is prepared. 17 variables are chosen as inputs and the initial settling rate (ISR) is chosen as the output. Along with the raw dataset, two new datasets are prepared based on the cumulative sum of variance, namely PCA99 with 9 variables and PCA95 with 7 variables. The results show that Monte Carlo simulations need to be performed for over 100 times to reach the converged results. Based on the statistic indicators, it is found that the ML prediction on PCA99 and PCA95 is better than that on the raw dataset (average correlation coefficient is 0.85 for the raw dataset, 0.89 for the PCA99 dataset and 0.88 for the PCA95 dataset). Overall speaking, ML prediction has good prediction performance and it can be employed by the mine site to improve the efficiency and cost-effectiveness. This study presents a benchmark study for the prediction of ISR, which, with better consolidation and development, can become important tools for analysing and modelling flocculate-settling experiments., Competing Interests: Conflicts of interest None., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P u ) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P u , with a value of R 2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R 2 ), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R 2 value of 0.976 on the training part and an R 2 of 0.972 on the testing part, using the optimized C-ANN-[3, 4 , 5 , 1 ] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO 2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO 2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO 2 and CO.

The quality of digital elevation models (DEMs), as well as their spatial resolution, are important issues in geomorphic studies. However, their influence on landslide susceptibility mapping (LSM) remains poorly constrained. This work determined the scale dependency of DEM-derived geomorphometric factors in LSM using a 5 m LiDAR DEM, LiDAR resampled 30 m DEM, and a 30 m ASTER DEM. To verify the validity of our approach, we first compiled an inventory map comprising of 267 landslides for Sihjhong watershed, Taiwan, from 2004 to 2014. Twelve landslide causative factors were then generated from the DEMs and ancillary data. Afterward, popular statistical and machine learning techniques, namely, logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented to produce the LSM. The accuracies of models were evaluated by overall accuracy, kappa index and the receiver operating characteristic curve indicators. The highest accuracy was attained from the resampled 30 m LiDAR DEM derivatives, indicating a fine-resolution topographic data does not necessarily achieve the best performance. Additionally, RF attained superior performance between the three presented models. Our findings could contribute to opt for an appropriate DEM resolution for mapping landslide hazard in vulnerable areas.

In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R 2 ). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The main objective of the present study was to provide a novel methodological approach for flash flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative flash-flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for flash flood susceptibility mapping., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R 2 ), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R 2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R 2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models' performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R 2 ). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.

Carbapenemase-producing Enterobacteriaceae (CPE) are well known to cause many serious infections resulting in increasing mortality rate, treatment cost, and prolonged hospitalization. Among the widely recognized types of carbapenemases, New Delhi β -lactamase (NDM) and Klebsiella pneumoniae carbapenemase (KPC) are the most important enzymes. However, in Vietnam, there are only scattered reports of CPE due to the lack of simple and affordable methods that are suitable to laboratory conditions. This study aims to survey the characteristics of carbapenem-resistant E. coli and K. pneumoniae (CR-E/K) at two hospitals in Southern Vietnam and perform some simple methods to detect the two enzymes. A total of 100 CR-E/K strains were collected from clinical isolates of Gia Dinh People's Hospital and Dong Nai General Hospital, Vietnam, from November 2017 to May 2018. The patient-related information was also included in the analysis. We conducted real-time polymerase chain reaction (PCR), Modified Hodge Test (MHT), and combined disk test (CDT) on all isolates. Carbapenemase-encoding genes were detected in 47 isolates (36 NDM, 10 KPC, and one isolate harboring both genes). The E. coli strain carrying simultaneously these two genes was the first case reported here. Most of isolates were collected from patients in ICU, Infectious Disease Department, and Department of Urologic Surgery. Urine and sputum were two common specimens. The true positive rate (sensitivity, TPR) and specificity (SPC) of the imipenem-EDTA (ethylen diamine tetra acetic acid) for NDM detection and the imipenem-PBA (phenylboronic acid) for KPC detection on E. coli were 93.8%, 97.1% and 66.7%, 95.7%, respectively. Meanwhile, the imipenem-EDTA for NDM detection and the imipenem-PBA for KPC detection among K. pneumonia achieved 90.5%, 100% and 100%, 92.9% TPR and SPC, respectively. However, MHT showed low sensitivity and specificity. Our findings showed that CP-E/K were detected with high prevalence in the two hospitals. We suggest that CDT can be used as a low-priced and accurate method of detection.

Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%-3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model's performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( R ²). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R ² = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R ² = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

Introduction: TNF-α-neutralizing antibodies, such as infliximab (IFX) and adalimumab (ADA), are effective in the treatment of inflammatory bowel diseases (IBD), but they are expensive and become ineffective when patients develop anti-IFX or anti-ADA antibodies (ATI and ATA, respectively). Second-generation anti-TNF-α antibodies, such as Golimumab, Etanercept, Certolizumab-pegol and IFX biosimilars, may solve these issues., Aim: To determine the neutralizing capacity of first- and second generation anti-TNF-α antibodies and to determine whether ATI show cross-reactivity with the IFX biosimilar CT-P13 (Inflectra)., Methods: TNF-α neutralization was measured using a quantitative TNF-α sensor assay consisting of HeLa 8D8 cells that express the Green Fluorescence Protein (GFP) under control of a NF-кB response element. All available anti-TNF-α drugs and the IFX biosimilar CT-P13 (Inflectra) were tested for their TNF-α-neutralizing capacity. In addition, patient sera with ATI were tested for their potential to block the activity of IFX, IFX (F)ab2-fragment, biosimilar CT-P13 (Inflectra) and ADA., Results: TNF-α strongly induced GFP expression in Hela 8D8 cells. Higher concentrations of first-generation anti-TNF-α drugs were required to neutralize TNF-α compared to the second-generation anti-TNF-α drugs. Serum of IBD patients with proven ATI blocked TNF-α-neutralizing properties of IFX biosimilar CT-P13 (Inflectra), whereas such sera did not block the effect of ADA., Conclusion: The second-generation anti-TNF-α drugs show increased TNF-α-neutralizing potential compared to first-generation variants. ATI show cross-reactivity toward IFX biosimilar CT-P13 (Inflectra), consequently patients with ATI are unlikely to benefit from treatment with this IFX biosimilar., Competing Interests: The authors have declared that no competing interests exist.