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BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL algorithms in predicting HT post-AIS, benchmarking them against conventional models. METHODS: A systematic search was conducted across PubMed, Embase, Web of Science, Scopus, and IEEE, initially yielding 1421 studies. After screening, 24 studies met the inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the quality of these studies, and a qualitative synthesis was performed due to heterogeneity in the study design. RESULTS: The included studies featured diverse ML and DL algorithms, with Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) being the most common. Gradient boosting (GB) showed superior performance. Median Area Under the Curve (AUC) values were 0.91 for GB, 0.83 for RF, 0.77 for LR, and 0.76 for SVM. Neural networks had a median AUC of 0.81 and convolutional neural networks (CNNs) had a median AUC of 0.91. ML techniques outperformed conventional models, particularly those integrating clinical and imaging data. CONCLUSIONS: ML and DL models significantly surpass traditional scoring systems in predicting HT. These advanced models enhance clinical decision-making and improve patient outcomes. Future research should address data expansion, imaging protocol standardization, and model transparency to enhance stroke outcomes further.

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

BackgroundPatients find technology tools to be more approachable for seeking sensitive health-related information, such as reproductive health information. The inventive conversational ability of artificial intelligence (AI) chatbots, such as ChatGPT (OpenAI Inc), offers a potential means for patients to effectively locate answers to their health-related questions digitally.ObjectiveA pilot study was conducted to compare the novel ChatGPT with the existing Google Search technology for their ability to offer accurate, effective, and current information regarding proceeding action after missing a dose of oral contraceptive pill.MethodsA sequence of 11 questions, mimicking a patient inquiring about the action to take after missing a dose of an oral contraceptive pill, were input into ChatGPT as a cascade, given the conversational ability of ChatGPT. The questions were input into 4 different ChatGPT accounts, with the account holders being of various demographics, to evaluate potential differences and biases in the responses given to different account holders. The leading question, "what should I do if I missed a day of my oral contraception birth control?" alone was then input into Google Search, given its nonconversational nature. The results from the ChatGPT questions and the Google Search results for the leading question were evaluated on their readability, accuracy, and effective delivery of information.ResultsThe ChatGPT results were determined to be at an overall higher-grade reading level, with a longer reading duration, less accurate, less current, and with a less effective delivery of information. In contrast, the Google Search resulting answer box and snippets were at a lower-grade reading level, shorter reading duration, more current, able to reference the origin of the information (transparent), and provided the information in various formats in addition to text.ConclusionsChatGPT has room for improvement in accuracy, transparency, recency, and reliability before it can equitably be implemented into health care information delivery and provide the potential benefits it poses. However, AI may be used as a tool for providers to educate their patients in preferred, creative, and efficient ways, such as using AI to generate accessible short educational videos from health care provider-vetted information. Larger studies representing a diverse group of users are needed.

A special design, based on the Bridgman technique, was used in our laboratory for preparing single crystals of . The structure of in powder form was examined using X-ray diffraction. at room temperature was found to be a tetragonal system with lattice parameters of Å and Å. The structural parameters, such as crystallite size D, micro strain , dislocation density , and unit cell parameters were determined from XRD spectra. Thermo gravimetric analysis (TGA) was employed to study the thermal behavior of , showcasing its significance in solid state physics. The TGA curve of exhibited distinct weight loss events corresponding to thermal decomposition processes. The frequency and temperature dependence of Ac-conductivity in a single crystal was studied by assessing the permittivity () and dielectric loss () over a broad frequency range. The dependence of AC conductivity and dielectric properties on the frequency and temperature for in pellet form obtained from single crystal were studied in the frequency range of (40 Hz–3 MHz) and temperature range of K. The AC conductivity of the was found to obey the power law, i.e., . AC conductivity of was dominated by the correlated barrier hopping (CBH) model. The obtained activation energy values of the AC conductivity have confirmed that the hopping conduction is the dominant one. A decrease in these values has noticed with the increase in frequency. The density of localized states close to Fermi level for was obtained in the range of cm ) for various temperatures and frequency. The frequencies corresponding to maxima of the imaginary electric modulus at different temperatures were found to satisfy an Arrhenius law with activation energy of 0.32 eV. A decrease in the relaxation time was observed with the increase in temperature. The average hopping distance R and the average time of charge carrier hoping between localized states t were found in the range of 6.10–11.95 nm and s respectively, for the investigated range of frequency and the value of the binding energy was 0.52 eV. We report on the preparation, characterization, and analysis of semiconductor single crystals, focusing on the influence of frequency and temperature on AC conductivity. Utilizing X-ray diffraction, thermo gravimetric analysis, and dielectric property measurements, we delineate the material's structural and electrical properties. Complementing our experimental findings, Machine Learning (ML) models, including Random Forest and Gradient Boosting, were employed to predict AC conductivity, revealing significant predictors and corroborating the experimental insights with high accuracy. This interdisciplinary approach enhances our understanding of 's properties and demonstrates the potential of ML in materials science research. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICS) are vital parts of the physical infrastructure for many industrial assets, such as oil and gas fields, water stations, and power generation plants. Inadequate protection of such critical assets may lead to disruption of vital services and substantial monetary losses. Therefore, the safety of these assets is prioritized as national security. Operational technology networks have a unique nature and different requirements than conventional enterprise networks as they seek tailored security solutions to detect and prevent cyberattacks on such attractive targets. Motivated by a necessary need from industry and academia, this paper aims to present a broad survey of the research works related to developing Intrusion Detection Systems in ICS networks focusing on using recent machine learning techniques. A proposed review methodology is presented and applied to the relevant selected literature. The paper offers a comparative analysis to provide better insights into this domain, where it identifies several unresolved challenges that present intriguing research prospects for the industry and academic community. [ABSTRACT FROM AUTHOR]

Advancement of intelligent and computational techniques like internet of things (IoT), machine learning (ML), artificial intelligence (AI), blockchain etc., gradually transforming the existing traditional manufacturing process to a smart process by developing a heterogeneous distributed environment. Considering the lack of transparency in the present manufacturing system with respect to information traceability of products, this paper discusses the IoT, ML and blockchain-based product traceability system. Two uses cases are considered in this study: market demand and buyers' priority-based manufacturing process. To start with the market demand-based manufacturing process, ML algorithms are framed to forecast the market demand of the product and to predict the supplier's profitability. Based on the prediction, the manufacturing process starts, and all the stakeholders (manufacturing organizations, shipping companies, dealers, and consumers) involved in the supply chain process engages in product information certification via blockchain. The paper also validates that with the implementation of blockchain, even the end user of the product knows the product manufacturing process and can effectively tracks the product information like quality of the material out of which the product is made, product delivery period, warranty, etc. [ABSTRACT FROM AUTHOR]

Recognition technology based on millimeter wave radar (MMW) can operate in all-weather conditions and has received much attention in the field of intelligent connected vehicles (ICVs). However, the label information of the targets cannot be directly obtained from the original radar point clouds, making it necessary to develop advanced recognition algorithms. This paper proposes a target recognition algorithm based on machine learning that utilizes radar point clouds and leverages the radar reflection intensity to improve target recognition accuracy. Firstly, regional division and density clustering techniques are employed to preprocess the original point clouds from the MMW and segment them into meaningful regions, thereby reducing the computational burden; Secondly, relevant features are extracted from the processed radar point cloud, including radar scattering cross section and its related features. Finally, to improve target recognition accuracy, this paper proposes a grid search optimization principal component analysis support vector machine (GS-PCA-SVM) classification algorithm. The algorithm uses PCA to reduce the dimensionality of the data while preserving key information; then, it optimizes the parameters and kernel function of SVM by using the GS method to improve the performance of the classifier. The experimental results indicate that the recognition algorithm proposed in this paper achieves accuracies of 80%, 93%, and 95% on static, dynamic, and mixed datasets, respectively. Real vehicle experiments also prove that this algorithm has high accuracy and reliability when applied to ICV. [ABSTRACT FROM AUTHOR]

The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and inductively coupled plasma spectrometry (ICP-OES and ICP-MS) are effective but time-consuming and costly, mainly due to sample preparation and lab consumables, respectively. In the present study, a laser-based spectroscopic approach is proposed, laser-induced breakdown spectroscopy (LIBS), which, combined with machine learning (ML), can provide a tool for rapid assessment of soil contamination by heavy metals. A dataset comprising 523 soil samples, from the areas of Mati, Kineta, Varympompi, and Evia (Greece) after the wildfires of 2018 and 2021, was employed to train and validate various ML models. The analysis focused on Cr, Ni, Zn, and Pb concentrations, utilizing environmental and human health screening values for soil classification. Two classification schemes were employed: the first identified samples "outside the danger zone" of contamination, while the second focused on samples "inside the safe zone". The models achieved over 93% performance for Cr, Ni, and Zn in the first scheme and 97% for Pb in the second. These findings demonstrate that LIBS, coupled with ML, can provide a reliable and efficient solution for preliminary assessment of soil contamination, particularly suited for large-scale operations of environmental monitoring and remediation efforts. [ABSTRACT FROM AUTHOR]

Next-generation non-invasive biochemical wearables hold promise in transforming healthcare by providing real-time, continuous monitoring of biochemical markers. Non-invasive methods include smart tattoos, microneedle patches, wearable biosensors, flexible bioelectronics, implantable sensors, smart textiles, and smart contact lenses. A comprehensive picture of an individual's health can be detected via critical markers such as glucose, lactate, cortisol, and volatile organic compounds (VOCs) from sweat, saliva, tears, breath and interstitial fluid (ISF)-based, non-invasive and minimally invasive biosensors using these advanced technologies. Personalized insights for enhancing their functionality are possible by integrating them with AI and big data analytics for early disease detection and proactive health management. This study explores the potential of futuristic biochemical wearables, their current status, underlying technologies, potential associated applications and challenges, and their positioning as transformative solutions in personalized healthcare for redefining the future of healthcare monitoring. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology. [ABSTRACT FROM AUTHOR]

Soils may not always be suitable to fulfill their intended function. Soil improvement can be achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as chemical improvement additives. In this study, two natural clay samples with extreme and very high plasticity were improved by using L and FA admixtures, and their properties under static and repeated loads were investigated by ML methods. Two soil samples from two different sites were analyzed. In this study, eight datasets were used. There are 14 inputs, including specific gravity (Gs), void ratio (eo), sieve analysis (+No.4, −No.200), clay size, LL, plastic limit (PL), plasticity index (PI), linear shrinkage (Ls), shrinkage limit (SL), cure day, agent, clay type, and agent percentage. The outputs are index and swelling properties (compressive, percent), compressive strengths, modulus of elasticity, and compressibility properties in soaked and non-soaked conditions. Prediction is attempted with different ML (ML) techniques. ML techniques used for regression (such as Decision Tree Regression (DTR) and K-nearest neighbors (KNN)). SHapley Additive Explanations (SHAP), the impact of inputs on outputs were observed, and it was generally found that PL and LL had the highest impact on outputs. Different performance metrics are used for evaluation. The results showed that these ML techniques can predict the static and cyclic properties of extremely high plasticity clays with high performance (R2 > 0.99). These results highlight the general applicability of the used ML models on different datasets containing soil properties. [ABSTRACT FROM AUTHOR]

Machine learning (ML) based on remote video has shown ideal diagnostic value in autism spectrum disorder (ASD). Here, we conducted a meta-analysis of the diagnostic value of home video–based ML in ASD. Relevant articles were systematically searched in PubMed, Cochrane, Embase, and Web of Science from inception to September 2023 with no language restriction, and the literature search was updated in September 2024. The overall risk of bias and suitability of the ML prediction models in the included studies were assessed using PROBAST. Nineteen articles involving 89 prediction models and 9959 subjects were included. The mean video duration was 5.63 ± 1.23 min, and the mean number of behavioral features during initial modeling was 23.53. Among the 19 included studies, 13 models had been trained. Seven of the 13 models were not cross-validated (c-index = 0.92, 95% CI 0.88–0.96), while 6 of the 13 models were tenfold cross-validated (c-index = 0.95, 95% CI 0.94–0.97). There were 8 validation cohorts (c-index = 0.83, 95% CI 0.77–0.89). The pooled sensitivity and specificity were 0.87 (95% CI 0.77–0.93) and 0.79 (95% CI 0.76–0.81) in the training cohort, 0.90 (95% CI 0.85–0.94) and 0.87 (95% CI 0.72–0.94) in the cross-validation, and 0.81 (95% CI 0.74–0.86) and 0.72 (95% CI 0.68–0.75) in the validation cohort, respectively. These results indicated that this model is a highly sensitive and user-friendly tool for early ASD diagnosis. Conclusion: Remote video–based ML may improve clinical practice and future research, particularly by combining advanced technologies such as facial recognition. It is a potential tool for diagnosing ASD in children. What is Known: • The incidence of pediatric ASD has increased in recent years. • ML based on remote video has shown ideal early diagnostic value. What is New: • The first systematic review and meta-analysis evaluating the diagnostic performance of remote video–based ML for ASD. • Home video–based ML is a valuable diagnostic tool for the early diagnosis of ASD. • Remote video–based ML is convenient and simple to utilize. [ABSTRACT FROM AUTHOR]

The Healthcare Internet of Things (H-IoT) is a rapidly developing problem solving model with significant potential to improve patient care and healthcare outcomes. This study focuses on integrating cryptographic platforms into H-IoT systems to enable secure data access. We present insights on how to manage challenges such as cyber risks, resource constraints, latency, and energy consumption by exploring cryptographic approaches in diverse H-IoT applications. We explore important topics including big data management, blockchain, machine learning, edge computing, and software-defined networks. Additionally, we examine real-time operational challenges and emerging trends, such as remote patient monitoring and predictive analytics. Our research underscores the need for strong encryption mechanisms, access controls, device authentication, and proactive threat detection to improve the safety and efficiency of healthcare services. By combining cryptographic approaches with the architecture of the H-IoT system, this work provides the foundation for resilient healthcare infrastructures. Addressing current challenges and anticipating future opportunities contribute to strategic healthcare planning that prioritizes patient privacy and data security while anticipating future strategic healthcare planning opportunities. This article aims to provide valuable information to researchers by covering energy-efficient and resource-optimized healthcare system approaches that are integrated into the development of H-IoT systems. [ABSTRACT FROM AUTHOR]

Background: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL algorithms in predicting HT post-AIS, benchmarking them against conventional models. Methods: A systematic search was conducted across PubMed, Embase, Web of Science, Scopus, and IEEE, initially yielding 1421 studies. After screening, 24 studies met the inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the quality of these studies, and a qualitative synthesis was performed due to heterogeneity in the study design. Results: The included studies featured diverse ML and DL algorithms, with Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) being the most common. Gradient boosting (GB) showed superior performance. Median Area Under the Curve (AUC) values were 0.91 for GB, 0.83 for RF, 0.77 for LR, and 0.76 for SVM. Neural networks had a median AUC of 0.81 and convolutional neural networks (CNNs) had a median AUC of 0.91. ML techniques outperformed conventional models, particularly those integrating clinical and imaging data. Conclusions: ML and DL models significantly surpass traditional scoring systems in predicting HT. These advanced models enhance clinical decision-making and improve patient outcomes. Future research should address data expansion, imaging protocol standardization, and model transparency to enhance stroke outcomes further. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions. [ABSTRACT FROM AUTHOR]

Decentralized Finance (DeFi) is a recent advancement of the cryptocurrency ecosystem, giving plenty of opportunities for financial inclusion, innovation, and growth domains by providing services such as lending, borrowing, and trading without traditional intermediaries. However, inadequate regulatory oversight and technological vulnerabilities raise pressing concerns around market manipulation, fraud, and regulatory compliance, exposing a clear research gap in effective DeFi risk management. This paper addresses this gap by proposing a utility-based framework to evaluate six leading DeFi tracking platforms—Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan—focusing on two critical metrics: transaction accuracy and real-time responsiveness. Applying a mixed methods approach that combines a quantitative survey (n = 138) with qualitative interviews (n = 12), we identified critical platform features and found significant differences across these platforms with respect to compliance features, advanced analytics, and user experience. We used a utility-based model that links accuracy and responsiveness metrics, allowing us to adjust differing priorities and risk management needs for users. The results show the need for balanced, user-centric solutions that accommodate regulatory, technological efficiency and affordability requirements. Our study contributes to the growing knowledge base by providing a structured evaluation model and empirical insights, offering clear directions for practitioners, platform developers, and policymakers aiming to strengthen the DeFi ecosystem. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this research is to integrate wavelet analysis and machine learning (ML) algorithms to improve the accuracy and dependability of roller element bearing defect diagnostics. Roller element bearings are vital parts of many industrial machinery, and identifying problems early on is crucial to avoiding expensive maintenance and downtime. Method: The process entails taking vibration signals from rolling element bearings and using wavelet analysis to extract pertinent features. Wavelet transformations are a good choice for defect diagnostic jobs because of their advantages in capturing both the time and frequency domain features of signals. Also, this project incorporates a variety of machine learning methods to classify faults according to the features that are extracted. Result: It is investigated how wavelets can be used to denoise vibrational data. The vibrational data is subjected to a variety of machine learning algorithms, and their performance is compared in terms of accuracy and training time. The efficacy of machine learning (ML) methods including Support Vector Machines (SVM), Decision Tree, KNN, and Neural Networks in precisely diagnosing various kinds of bearing defects is investigated. Although the neural network’s training period varies according to the system parameters, it provides us with a clear understanding of its intended use. The decision tree model demonstrated a balance between speed and accuracy, with a training time of 8.583 s and an accuracy of 93.3%. On the other hand, the ANN model obtained a better accuracy of 93.8% and needed 10 s to train. With a training time of 72 s, the DNN model showed the best accuracy of all the models, at 98.5%, highlighting its potential for accurate fault classification at the expense of more computing effort. The KNN model demonstrated quick training in 6.59 s with a 93.5% accuracy rate, which makes it a good option in situations where training speed is prioritised without requiring a large trade-off in accuracy. The trade-offs between model complexity, training time, and accuracy were highlighted by the SVM model, which demonstrated a high accuracy of 94.1% but needed a longer training period of 44.8 s. Conclusion: We used the ANN and DNN neural network models, together with three machine learning methods. It is discovered that the DNN classifies the data the most precisely, while the ANN strikes a balance between accuracy and training time. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility. [ABSTRACT FROM AUTHOR]

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science. Advances in AI and omics enable the creation of AI virtual cells (AIVCs)—multi-scale, multimodal neural network models that simulate molecules, cells, and tissues across diverse states. This vision outlines their design and collaborative development, promising to transform biological research through high-fidelity simulations, accelerating discoveries, and fostering interdisciplinary open science collaborations. [ABSTRACT FROM AUTHOR]

• Case study feasibility of real-time computer vision-based scrap metal separation. • Analysis of neural network architectures for recognition of forbidden scrap. • Proposal of a neural classifier analyzing a non-standardized stream of metal scrap. • Dataset augmentation for improved model robustness of a neural classifier. • Eliminating human in the detection of prohibited objects in copper-based scrap. Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes. [ABSTRACT FROM AUTHOR]

In the dynamic field of ophthalmology, artificial intelligence (AI) is emerging as a transformative tool in managing complex conditions like uveitis. Characterized by diverse inflammatory responses, uveitis presents significant diagnostic and therapeutic challenges. This systematic review explores the role of AI in advancing diagnostic precision, optimizing therapeutic approaches, and improving patient outcomes in uveitis care. A comprehensive search of PubMed, Scopus, Google Scholar, Web of Science, and Embase identified over 10,000 articles using primary and secondary keywords related to AI and uveitis. Rigorous screening based on predefined criteria reduced the pool to 52 high-quality studies, categorized into six themes: diagnostic support algorithms, screening algorithms, standardization of Uveitis Nomenclature (SUN), AI applications in management, systemic implications of AI, and limitations with future directions. AI technologies, including machine learning (ML) and deep learning (DL), demonstrated proficiency in anterior chamber inflammation detection, vitreous haze grading, and screening for conditions like ocular toxoplasmosis. Despite these advancements, challenges such as dataset quality, algorithmic transparency, and ethical concerns persist. Future research should focus on developing robust, multimodal AI systems and fostering collaboration among academia and industry to ensure equitable, ethical, and effective AI applications. The integration of AI heralds a new era in uveitis management, emphasizing precision medicine and enhanced care delivery. [ABSTRACT FROM AUTHOR]

Evaluating groundwater potential is critical for the socioeconomic development of Vietnam. This research aims to assess the underground water potential in the country's Mekong Delta using the machine learning (ML) such as support vector machines (SVM), CatBoost (CB), K-nearest neighbors (KNN), random forest (RF) and AdaBoost (ADB). The problem of exploitation of groundwater resources in the delta is aggravated due to global warming and growth of population. In total, 146 groundwater points and 14 drivers (namely elevation, aspect, curvature, slope distance to river and river density, land use, normalized difference built-up index, flow accumulation, rainfall, soil type, normalized difference vegetation index, stream power index, terrain roughness index, and topographic wetness index) were used to assess groundwater potential. Each proposed model was evaluated utilizing area under curve (AUC), root mean square error, coefficient of determination (R2), and mean absolute error. The findings showed that the RF outperformed the others in building of a groundwater potential map. In which, AUC value was estimated at 0.99 and R2 value was estimated at 0.63 then came CB (AUC = 0.98, R2 = 0.56), ADB (AUC = 0.92, R2 = 0.50), SVM (AUC = 0.91, R2 = 0.57), and KNN (AUC = 0.75, R2 = 0.45). The results illustrate the power of ML in assessing groundwater potential and can support decision makers, planners, and local authorities responsible for sustainable groundwater planning in the Mekong Delta and beyond. [ABSTRACT FROM AUTHOR]