Search Results (6,601)

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R² values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions. Full article

Optical fiber biosensors have evolved into powerful tools for non-invasive biomedical analysis. While foundational principles are well-established, recent years have marked a paradigm shift, driven by advancements in nanomaterials, fabrication techniques, and data processing. This review provides a focused overview of these emerging trends, critically analyzing the innovations that distinguish the current generation of optical fiber biosensors from their predecessors. We begin with a concise summary of fundamental sensing principles, including Surface Plasmon Resonance (SPR) and Fiber Bragg Gratings (FBGs), before delving into the latest breakthroughs. Key areas of focus include integrating novel 2D materials and nanostructures to dramatically enhance sensitivity and advancing synergy with Lab-on-a-Chip (LOC) platforms. A significant portion of this review is dedicated to the rapid expansion of clinical applications, particularly in early cancer detection, infectious disease diagnostics, and continuous glucose monitoring. We highlight the pivotal trend towards wearable and in vivo sensors and explore the transformative role of artificial intelligence (AI) and machine learning (ML) in processing complex sensor data to improve diagnostic accuracy. Finally, we address the persistent challenges—biocompatibility, long-term stability, and scalable manufacturing—that must be overcome for widespread clinical adoption and commercialization, offering a forward-looking perspective on the future of this dynamic field. Full article

This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of MSMEs, limited access to finance, and vulnerability to macroeconomic volatility, which make financial risk assessment particularly challenging. Traditional statistical and econometric methods often fail to capture the complex, nonlinear interdependencies among financial and operational indicators, resulting in the inaccurate classification of high-risk MSMEs. By applying advanced machine learning (ML) techniques, neural networks (NNs) can identify intricate patterns in multidimensional financial data, significantly improving the accuracy and reliability of risk classification. In this research, a predictive model was developed using key financial and operational variables of MSMEs, enabling the accurate classification of MSMEs in terms of financial instability and insolvency. Empirical validation shows that NNs outperform conventional methods in accuracy, sensitivity, and generalisation. This approach offers tangible benefits for investors, credit institutions, and MSME managers, supporting improvements in early warning systems, optimisation of credit decision-making, and strengthening MSMEs’ financial resilience and sustainability. The methodology also advances risk quantification tools, providing robust indicators for strategic planning and resource management. By focusing the analysis on Montenegro and the Western Balkans, this study demonstrates that regional economic and structural similarities support the adaptation of NN models for precise financial risk classification, offering actionable insights to enhance MSME performance and regional economic stability. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

Chlorophyll content is a key physiological indicator reflecting photosynthetic capacity, and the Soil–Plant Analysis Development (SPAD) meter is a commonly used tool for its rapid and non-destructive estimation. Hyperspectral imaging (HSI) is a non-destructive technique that captures fine spectral characteristics and thus holds great potential for high-throughput phenotyping and early stress detection. This study aimed to explore the potential of HSI combined with ensemble learning (EL) to estimate SPAD of rapeseed seedlings under different durations of waterlogging. Hyperspectral images and corresponding SPAD values were collected from six rapeseed cultivars at 0, 2, 4 and 6 days of waterlogging. The mutual information was employed to select the top 30 most relevant spectral and vegetation index features. The EL model was constructed using partial least squares, support vector machine, random forest, ridge regression and elastic net as the first-layer learners and a multiple linear regression as the second-layer learner. The results showed that the EL model showed superior stability and higher prediction accuracy compared to single models across various genotypes and waterlogging treatment datasets. As waterlogging duration increased, the overall model accuracy improved; notably, under 6 days of waterlogging, the EL model achieved an R² of 0.79 and an RMSE of 3.27, indicating strong predictive capability. This study demonstrated that combining EL with HSI enables stable and accurate estimation of SPAD values, therefore providing an effective approach for early stress monitoring in crops. Full article

Background: Accurately predicting difficult airways is essential to ensuring patient safety in anesthesiology and emergency medicine. However, traditional assessment tools often lack sufficient sensitivity and specificity, particularly in high-pressure or resource-limited settings. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for enhancing airway assessment. Objective: This review evaluates the performance of AI- and ML-based models for predicting difficult airways and compares them with traditional clinical methods. The review also analyzes the models’ methodological robustness, clinical applicability, and ethical considerations. Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Scopus to identify studies published between 2020 and 2025 that employed AI/ML models to predict difficult airways. Both original research and review articles were included. Key metrics, such as the area under the curve (AUC), sensitivity, and specificity, were extracted and compared. A qualitative analysis was performed to focus on dataset characteristics, validation strategies, model interpretability, and clinical relevance. Results: AI models demonstrated superior performance compared to traditional assessment tools. The MixMatch semi-supervised deep learning (DL) model achieved the highest performance (area under the curve [AUC] of 0.9435, sensitivity of 89.58%, and specificity of 90.13%). Models that used facial imaging combined with deep learning consistently outperformed those that relied solely on clinical parameters. However, methodological heterogeneity, a lack of standardized evaluation metrics, and limited population diversity impeded cross-study comparability. Few studies incorporated interpretability frameworks or addressed ethical challenges related to data privacy and algorithmic bias. Conclusions: AI and ML models have the potential to transform the assessment of difficult airways by improving diagnostic accuracy and enabling real-time clinical decision support. Full article

Crashes on roadways continue to represent a major global public health concern due to high rates of death and injury, underscoring the need for predictive tools that can identify high-risk conditions and guide prevention strategies. This study develops a framework that combines structured crash records and road information with unstructured police narratives to predict injury severity using machine learning and natural language processing (NLP). The dataset is used to train, validate, and test nine models, combining three algorithms (Random Forest, AdaBoost, and XGBoost) with two NLP methods (TF-IDF and Word2Vec). Model performance is evaluated using macro-average F1-scores to address severe class imbalance. Results show that XGBoost with TF-IDF achieves the best performance (macro-F1 = 0.644), demonstrating measurable improvements from incorporating narrative features compared to structured data alone. Beyond prediction, a simulation-based sensitivity analysis is conducted on the top 100 features, identifying 11 variables with the greatest impact on severity outcomes in Kentucky. Seatbelt non-use, occupant entrapment, and impaired driver control emerge as the most influential factors, with simulated improvements leading to notable reductions in fatalities and major injuries. The study introduces a “prediction-to-prevention” framework that links injury severity prediction with simulation-based sensitivity analysis. By integrating structured and narrative crash data, the framework identifies how changes in key behavioral and roadway factors can shift injury outcomes toward less severe levels. These findings highlight the dual contribution of this study: improving predictive accuracy through narrative integration and offering actionable insights to support evidence-based traffic safety interventions. Full article

Chikungunya virus (CHIKV) poses a significant public health threat, and its continuous evolution necessitates high-resolution genomic surveillance. Current methods lack the speed and resolution to efficiently discriminate sub-lineages. To address this, we developed CHIKVGenotyper, an interpretable machine learning framework for high-resolution CHIKV lineage classification. This study leveraged a comprehensive dataset of 6886 CHIKV genome sequences, from which a high-quality set of 3014 sequences was established for model development. A hierarchical assignment pipeline that integrated a probability-based sequence matching model, machine learning refinement, and phylogenetic validation was developed to assign high-confidence labels across eight CHIKV lineages, thereby constructing a reliable dataset for subsequent analysis. Multiple machine learning models were trained and evaluated, with the optimal Random Forest model achieving near-perfect accuracy (F1-score: 99.53%) on high-coverage whole-genome test data and maintaining robust performance (F1-score: 96.50%) on an independent low-coverage set. The E2 glycoprotein alone yielded comparable accuracy (F1-score: 99.52%), highlighting its discriminative power. SHapley Additive exPlanations (SHAP) analysis identified key lineage-defining amino acid mutations, such as E1-K211E and E2-V264A, for the Indian Ocean Lineage, which were corroborated by established biological knowledge. This work provides an accurate, scalable, and interpretable tool for CHIKV molecular epidemiology, offering insights into viral evolution and aiding outbreak response. Full article

Dyslexia is not easily observed from outward appearance alone; differences typically emerge through learning performance and certain behavioral indicators. This study introduces the Direction Game, a computer-based task that uses mouse-tracking to capture behavioral signals related to directional perception, a common challenge among children at risk for dyslexia. The prototype consists of language-independent mini-games targeting three main types of directional confusion and was piloted with 102 primary school students. Analyses showed that concentration-related variables, particularly attentional control and visuo-motor planning, may provide more informative indicators of risk than simple accuracy scores. Machine learning models demonstrated promising classification performance relative to standardized school screening protocols. Additionally, an exploratory analysis of mouse trajectories revealed five tentative interaction profiles: hesitation, impulsivity, deliberate processing, fluent performance, and disengagement. Together, these findings highlight the potential of a simple, game-based mouse-tracking tool to support accessible and preliminary dyslexia risk assessment in classroom environments. Full article

The applications of machine learning (ML) are rapidly expanding across various fields to reduce their complexity and improve efficiency. In power electronics, where design tasks require complex analytical computations and accurate predictions, ML techniques are becoming increasingly important for reliable device design and robust manufacturing. With the growing demand of power density of high-power semiconductor devices, such as diodes and thyristors, the electrical parameters critically influence the physical dimensions and geometry of the chip. In this article, a comprehensive survey of high-power thyristors is conducted, analyzing the influence of chip diameter and thickness on both electrical and thermal performance. Moreover, a dedicated dataset is developed by extracting electrical parameters from the leading semiconductor manufacturer’s datasheet of multiple models. Furthermore, multiple machine learning algorithms, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Ensemble methods, are implemented and compared. The developed models provide manufacturers with efficient predictive tools to determine optimal chip dimensions for specific power ratings, thereby supporting efficient and reliable device design. Full article

Underground gas storage (UGS), encompassing hydrogen, natural gas, and compressed air, is a cornerstone of large-scale energy transition strategies, offering seasonal balancing, security of supply, and integration with renewable energy systems. However, the complexity of geological conditions, multiphysics coupling, and operational uncertainties pose significant challenges for UGS design, monitoring, and optimization. Artificial intelligence (AI)—particularly machine learning and deep learning—has emerged as a powerful tool to overcome these challenges. This review systematically examines AI applications in underground storage types such as salt caverns, depleted hydrocarbon reservoirs, abandoned mines, and lined rock caverns using bibliometric and knowledge-graph analysis of 176 publications retrieved from the Web of Science Core Collection. The study revealed a rapid surge in AI-related research on UGS since 2017, with underground hydrogen storage emerging as the most dynamic and rapidly expanding research frontier. The results reveal six dominant research frontiers: (i) AI-assisted geological characterization and property prediction; (ii) physics-informed proxy modeling and multi-physics simulation; (iii) gas–rock–fluid interaction, wettability, and interfacial behavior prediction; (iv) injection-production process optimization; (v) intelligent design and construction of underground storage, especially salt caverns; and (vi) intelligent monitoring, optimization, and risk management. Despite these advances, challenges persist in data scarcity, physical consistency, and generalization. Future efforts should focus on hybrid physics-informed AI, digital twin-enabled operation, and multi-gas comparative frameworks to achieve safe, efficient, and intelligent underground storage systems aligned with global carbon neutrality. Full article

The manufacturing sector is undergoing a paradigm shift driven by the integration of the Internet of Things (IoT), robotics, and advanced technologies such as Digital Twins (DTs), machine learning (ML), and edge computing within the Industry 4.0 framework. This scoping review systematically explores the breadth and depth of research on the disruptive potential of these technologies in manufacturing. Drawing on 14 empirical studies published between 2019 and 2025, we highlight the often-overlooked synergies between IoT and robotics. Following PRISMA guidelines, a comprehensive search of SpringerLink, Science Direct, and Google Scholar was conducted, with data extraction and quality appraisal guided by the Mixed-Methods Appraisal Tool (MMAT). Three thematic areas emerged: IoT-driven optimization, robotics and human–robot collaboration (HRC), and emerging technologies. Findings reveal IoT-enabled cycle time improvements (0.44–1.71%), robotics achieving 9% cycle time reductions with safety metrics (mAP 0.605–0.789), and DTs reporting predictive performance (AUC 0.916). However, challenges persist in data heterogeneity, standardization gaps, and limited real-world validations. This review offers critical insights for manufacturers, researchers, and policymakers to foster scalable and resilient manufacturing ecosystems. Full article

Artificial intelligence (AI) is transforming pharmaceutical science by shifting drug delivery research from empirical experimentation toward predictive, data-driven innovation. This review critically examines the integration of AI across formulation design, smart drug delivery systems (DDSs), and sustainable pharmaceutics, emphasizing its role in accelerating development, enhancing personalization, and promoting environmental responsibility. AI techniques—including machine learning, deep learning, Bayesian optimization, reinforcement learning, and digital twins—enable precise prediction of critical quality attributes, generative discovery of excipients, and closed-loop optimization with minimal experimental input. These tools have demonstrated particular value in polymeric and nano-based systems through their ability to model complex behaviors and to design stimuli-responsive DDS capable of real-time therapeutic adaptation. Furthermore, AI facilitates the transition toward green pharmaceutics by supporting biodegradable material selection, energy-efficient process design, and life-cycle optimization, thereby aligning drug delivery strategies with global sustainability goals. However, challenges persist, including limited data availability, lack of model interpretability, regulatory uncertainty, and the high computational cost of AI systems. Addressing these limitations requires the implementation of FAIR data principles, physics-informed modeling, and ethically grounded regulatory frameworks. Overall, AI serves not as a replacement for human expertise but as a transformative enabler, redefining DDS as intelligent, adaptive, and sustainable platforms for future pharmaceutical development. Compared with previous reviews that have considered AI-based formulation design, smart DDS, and green pharmaceutics separately, this article integrates these strands and proposes a dual-framework roadmap that situates current AI-enabled DDS within a structured life-cycle perspective and highlights key translational gaps. Full article

Emerging artificial intelligence (AI) and robotic surgical technologies have the potential to influence digital implant dentistry substantially. As a narrative review, and building on the foundations outlined in Part I, which described current digital tools and workflows alongside their persistent interface-related limitations, this second part examines how AI and robotics may overcome these barriers. This synthesis is based on peer-reviewed literature published between 2020 and 2025, identified through searches in PubMed, Scopus, and Web of Science. Current evidence suggests that AI-based approaches, including rule-based systems, traditional machine learning, and deep learning, may achieve expert-level performance in diagnostic imaging, multimodal data registration, virtual patient model generation, implant planning, prosthetic design, and digital smile design. These methods offer substantial improvements in efficiency, reproducibility, and accuracy while reducing reliance on manual data handling across software, datasets, and workflow interfaces. In parallel, robotic-assisted implant surgery has advanced from surgeon-guided systems to semi-autonomous and fully autonomous platforms, with the potential to provide enhanced surgical precision and reduce operator dependency compared with conventional static or dynamic navigation. Several of these technologies have already reached early stages of clinical deployment, although important challenges remain regarding interoperability, standardization, validation, and the continuing need for human oversight. Together, these innovations may enable the gradual convergence of digital technologies, real-time-assisted, unified, end-to-end implant prosthodontic workflows, and gradual automation, while acknowledging that full automation remains a longer-term prospect. By synthesizing current evidence and proof-of-concept applications, this review aims to provide clinicians with a comprehensive overview of the AI and robotics toolkit relevant to implant dentistry and to outline both the opportunities and remaining limitations of these disruptive technologies as the field progresses towards seamless, fully integrated treatment pathways. Full article

Mine water inrush poses a severe threat to coal mine safety, making rapid and accurate identification of water sources essential. Existing methods, including conventional hydrochemical diagrams and machine learning, struggle with high-dimensional, nonlinear hydrogeochemical data characterized by implicit temporal dynamics. This study proposes an intelligent identification model integrating convolutional neural networks (CNNs), long short-term memory (LSTM), and an attention mechanism (CNN–LSTM–Attention). The model employs a CNN to extract local fingerprint features from hydrochemical indicators (K⁺+Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻), uses LSTM to model evolutionary patterns, and leverages an attention mechanism to adaptively focus on critical discriminative features. Based on 76 water samples from the Tangjiahui Coal Mine, the model achieved 91% accuracy on the test set, outperforming standalone CNN, LSTM, and CNN–LSTM models. Visualization of attention weights further revealed key diagnostic indicators, enhancing interpretability and bridging data-driven methods with hydrogeochemical mechanisms. This study provides a powerful and interpretable tool for water inrush source identification, supporting the transition toward intelligent and transparent coal mine water hazard prevention. Full article