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Artificial Intelligence (AI) techniques leverage the justice system in terms of effectiveness and efficiency. AI-empowered multi-label crime classification can facilitate the precise and expedient categorization of various legal documents. Multi-label classification within the justice system has an indispensable role in achieving accurate legal categorization that invigorates case analysis, optimizes resource distribution and refines the contours of legal processes. To support this critical function, this paper proposes a temporal convolutional network (TCN)-bidirectional encoder representations from transformers (BERT)-coupled model for multi-label crime classification. The proposed method fuses the temporal formation and semantic information in the model to obtain a high-quality result. The experimental results show that the proposed method achieved the best accuracy in comparison to existing methods on a public dataset. [ABSTRACT FROM AUTHOR]

This study aims to address debate in previous studies on whether AI has a positive or negative effect on carbon emission reduction. We used quantile regression and PSTR models to study the diverse impacts of AI on carbon emissions in 66 countries from 1993–2019. There were three main findings in this paper. First, the impact of AI on carbon emissions varies across countries, and its effect on carbon reduction is mainly found in high-carbon emission and high-income countries. Second, the industrial structure environment of different countries affects the role of AI in carbon reduction, with its marginal effect in limiting emissions decreasing with the rise of secondary industrial structures. Third, the impact of AI varies in countries based on their different demographic structures. The marginal effect of AI on carbon emission reduction increases in places with older populations. This study offers unique insight into the heterogeneous impact of AI on CO2 emissions. Our analysis confirms the importance of industrial and demographic structures in promoting carbon emission reduction. We provide effective policy recommendations for economic development and environmental governance. [ABSTRACT FROM AUTHOR]

Addressing the reproducibility crisis in artificial intelligence through the validation of reported experimental results is a challenging task. It necessitates either the reimplementation of techniques or a meticulous assessment of papers for deviations from the scientific method and best statistical practices. To facilitate the validation of reported results, we have developed numerical techniques capable of identifying inconsistencies between reported performance scores and various experimental setups in machine learning problems, including binary/multiclass classification and regression. These consistency tests are integrated into the open-source package mlscorecheck , which also provides specific test bundles designed to detect systematically recurring flaws in various fields, such as retina image processing and synthetic minority oversampling. [ABSTRACT FROM AUTHOR]

Medical devices have increased in complexity where there is a pressing need to consider design thinking and specialist training for manufacturers, healthcare and sterilization providers, and regulators. Appropriately addressing this consideration will positively inform end-to-end supply chain and logistics, production, processing, sterilization, safety, regulation, education, sustainability and circularity. There are significant opportunities to innovate and to develop appropriate digital tools to help unlock efficiencies in these important areas. This constitutes the first paper to create an awareness of and to define different digital technologies for informing and enabling medical device production from a holistic end-to-end life cycle perspective. It describes the added-value of using digital innovations to meet emerging opportunities for many disposable and reusable medical devices. It addresses the value of accessing and using integrated multi-actor HUBs that combine academia, industry, healthcare, regulators and society to help meet these opportunities. Such as cost-effective access to specialist pilot facilities and expertise that converges digital innovation, material science, biocompatibility, sterility assurance, business model and sustainability. It highlights the marked gap in academic R&D activities (PRISMA review of best publications conducted between January 2010 and January 2024) and the actual list of U.S. FDA's approved and marketed artificial intelligence/machine learning (AI/ML), and augmented reality/virtual reality (AR/VR) enabled-medical devices for different healthcare applications. Bespoke examples of benefits underlying future use of digital tools includes potential implementation of machine learning for supporting and enabling parametric release of sterilized products through efficient monitoring of critical process data (complying with ISO 11135:2014) that would benefit stakeholders. This paper also focuses on the transformative potential of combining digital twin with extended reality innovations to inform efficiencies in medical device design thinking, supply chain and training to inform patient safety, circularity and sustainability., Competing Interests: Declaration of competing interest The author declares no conflict of interest., (Copyright © 2024 The Author. Published by Elsevier B.V. All rights reserved.)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., 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 Ltd. All rights reserved.)

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO 2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset., 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 Inc. All rights reserved.)

With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling., 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 Inc. All rights reserved.)

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized. [Display omitted] • The WTPs have been increasing applying AI techniques in lab scale. • Practical applications of AI techniques are limited in the real WTPs. • Significant gap in systematic data collection/preservation. • Learning process/activation functions have effects on AI based solutions. • Impact of missing data needs better understanding. [ABSTRACT FROM AUTHOR]