Search Results (1,338)

Combinatorial optimization is a key component in critical decision problems such as routing, scheduling, network design, and graph optimization. Although combinatorial optimization methods, including exact algorithms, approximation methods, constraint programming, mixed integer programming, and metaheuristics, are widely available, they often face obstacles, such as limited scalability and adaptability in various applications. In this study, a systematic critical review of machine learning for combinatorial optimization is provided to characterize the usage and evaluation of learning-based approaches. A detailed analysis is used to infer and determine findings and limitations. The paper emphasizes how machine learning for computational optimization has changed over time, moving from end-to-end neural solvers to hybrid systems. Learning components are essential for directing, speeding up, or enhancing traditional solver backbones such as constraint programming and metaheuristics in hybrid systems. The review also critically examines current limits that impact performance in general, including scalability, deployment readiness, generalization, and benchmark consistency. Even though using large language models for problem formulation and heuristic synthesis has potential, more work needs to be done to ensure reliable validation. As a conclusion, this article examines recent studies’ findings, emphasizes the growing trend toward hybrid learning-driven optimization frameworks, and underlines important methodological limits and unresolved issues. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Hierarchical Public Sector Organizations (PSOs), particularly military organizations, face persistent challenges in sustaining innovation due to structural rigidity, hierarchical control, and embedded resistance to change. While existing literature explains why innovation emerges and why it is resisted, significantly less attention has been devoted to understanding how innovation becomes institutionalized as a sustained organizational capability. This study addresses this gap by introducing the Bi-focal Innovation Contagion Model (BICM), an agent-based framework that conceptualizes innovation diffusion and resistance as a co-evolutionary “tug-of-war” between competing organizational forces. The model integrates top-down governance mechanisms and bottom-up innovation processes, capturing how heterogeneous actors interact within hierarchical systems to shape the diffusion, assimilation, and stabilization of innovation over time. Using the Israel Defense Forces (IDF) as an empirical source case, the model explores how Innovation Management Systems (IMS) may be designed to support the institutionalization of innovation as a self-sustaining organizational capability within hierarchical PSOs. Simulation results suggest that hybrid innovation architectures may better sustain innovation across varying leadership conditions. This occurs when centralized strategic coordination is combined with decentralized innovation activity and supported by mature innovation agents with sufficient centrality and hierarchical reinforcement. The findings highlight the critical role of IMS as an organizational architecture for achieving anti-fragility, enabling innovation dynamics to persist, adapt, and strengthen in the face of uncertainty, leadership turnover, and shifting strategic priorities. By integrating agent-based modeling with organizational theory, this study contributes a dynamic framework for understanding and designing sustainable innovation systems in hierarchical PSOs. Full article

Antimicrobial resistance and biofilm-associated contamination continue to pose critical challenges in food safety and biomedical applications, necessitating the development of advanced antimicrobial materials with enhanced efficacy, safety, and functional adaptability. Antimicrobial nanomaterials offer versatile solutions due to their tunable physicochemical properties, surface engineering capabilities, and controlled release behaviors, enabling improved antimicrobial and antibiofilm performance across diverse systems. This review highlights the main advancements in AI-assisted design of antimicrobial nanomaterials, demonstrating how data-driven approaches are increasingly used to predict antimicrobial activity, optimize synthesis parameters, model nanotoxicity, integrate multimodal datasets, and improve interpretability through explainable AI frameworks. Key findings indicate that machine learning-guided strategies and autonomous experimental platforms significantly accelerate material optimization while reducing reliance on traditional trial-and-error methods. The review further summarizes the performance and mechanisms of major antimicrobial nanomaterial systems, including metal and metal oxide nanoparticles, metal–organic frameworks, polymeric nanocarriers, nanoemulsions, and hybrid nanostructures, with emphasis on their translational applications in food preservation, antimicrobial coatings, wound healing, implant protection, and drug delivery. Despite these advances, challenges remain in data quality, model generalizability, toxicity prediction, reproducibility, and regulatory translation. AI-enabled and data-driven frameworks provide a powerful pathway for accelerating the rational design and practical implementation of next-generation antimicrobial nanomaterials. Full article

As High-Performance Computing (HPC) architectures evolve towards the Exascale, storage infrastructure reliability has emerged as a critical operational challenge, with traditional reactive and static preventive maintenance strategies proving increasingly insufficient. This study addresses this gap by proposing a comprehensive methodological framework for the transition from predictive to predictive-prescriptive maintenance in large-scale storage environments. By integrating the CRISP-DM industry standard with a multi-layered eXplainable Artificial Intelligence (XAI) suite, we develop a system capable of isolating hardware degradation signals amidst massive volumes of routine telemetry. To validate our approach, we leveraged a publicly available disk failure dataset to evaluate multiple Machine Learning configurations, addressing the challenge of severe class imbalance through optimized oversampling and Gradient Boosting algorithms. The methodology employs global and local XAI techniques, including Permutation Feature Importance, SHAP, and surrogate decision trees, to translate probabilistic risk assessments into auditable hardware engineering rules. Our results demonstrate that this hybridization of robust predictive modeling with multi-layered explainability provides a transparent, evidence-based decision support system. Ultimately, we conclude that converting opaque risk predictions into technical justifications enables infrastructure managers to optimize hardware lifecycle management and minimize system downtime in mission-critical environments, establishing a viable pathway toward more resilient and auditable storage management. Full article

Despite rapid growth in the AI endorser market, the psychological mechanisms governing their effectiveness remain theoretically fragmented. This study proposes the AHICC (AI–Human Interface in Consumer Cognition) model—integrating the Stereotype Content Model, Uncanny Valley hypothesis, anthropomorphism theory, Source Credibility Model, and Parasocial Interaction theory—to explain consumer responses to AI endorsers. A fully crossed 3 (endorser type: AI vs. hybrid vs. human) × 3 (anthropomorphism level: low vs. moderate vs. high) × 2 (technological transparency: low vs. high) between-subjects factorial experiment (n = 252) was conducted. Twenty-one sub-hypotheses were tested using MANOVA, polynomial regression, SEM, and bootstrap mediation analysis. All 21 sub-hypotheses were supported. AI endorsers outperformed human counterparts on brand attitude and purchase intention. Polynomial regression confirmed an inverted U-shaped Uncanny Valley effect with an optimal anthropomorphism level of 4.7 (7-point scale). High technological transparency attenuated the Uncanny Valley effect by approximately 60%. Dual-pathway mediation through cognitive and affective routes was confirmed, and TRI and product complexity emerged as significant boundary conditions. The AHICC model offers the first comprehensive framework for the AI endorser context, providing theoretically grounded guidance on anthropomorphism calibration, transparency strategy, and product-category-specific endorser selection. Full article

Blood typing remains a manual, subjective procedure when not reliant on centralized laboratory infrastructure. This study presents an automated blood typing system for point-of-care deployment, developed in collaboration with CRIAM, whose portable device captures reaction images for in vitro diagnostics. The system integrates computer vision and artificial intelligence to classify these reactions automatically. Fourteen classification pipelines were trained and evaluated with a 3090-image dataset, encompassing fine-tuned convolutional neural networks, raw pixel-based classifiers, and hybrid architectures pairing pretrained embeddings from DINOv2 and EfficientNet-B4 with lightweight classifiers. Embedding-based approaches consistently outperformed alternatives in accuracy and cross-fold stability. The best pipeline, in terms of performance and suitability for low-power devices, combined DINOv2-small embeddings with logistic regression, achieving 99.87 ± 0.12% mean accuracy. After 8-bit integers (INT8) quantization and retraining with data augmentation, accuracy improved to 99.97 ± 0.03%, surpassing the uncompressed baseline. All misclassifications were traced to borderline weak-positive Rh/D reactions, confirming errors are localized and explainable. Held-out validation on 856 images yielded 99.53% accuracy, with the single error attributed to a lighting artifact. While deployment on a legacy 32-bit CPU prototype processes four images in approximately 4.7 min, hardware benchmarking confirmed feasibility, from a Raspberry Pi Zero 2W to high-end mobile processors. These results establish quantized embedding-driven architectures as a solution for automated blood typing in point-of-care and resource-limited settings. Full article

Thyroid tumors rank among the most frequently occurring endocrine cancers because early detection helps doctors deliver effective treatments that lead to better patient results. Ultrasound imaging enables the detection of thyroid nodules, yet medical professionals struggle to differentiate between benign and malignant nodules through their diagnostic tests. This study introduces a new medical framework that enables thyroid nodule diagnosis through ultrasound imaging. The proposed model combines advanced segmentation with feature extraction, classification, and reasoning components to create a complete system. The specialized segmentation method shows accurate results when it detects nodule boundaries, which leads to better analysis of specific regions. The Hybrid Vision Transformer (HVT) operates to capture detailed textural information together with complete environmental patterns, which boosts its ability to classify different elements. The proposed framework incorporates a Large Language Model (LLM), specifically Med-PaLM, to provide context-aware clinical reasoning and interpretation. The structured evaluation process uses Thyroid Imaging Reporting and Data System (TI-RADS)-based feature scoring to compare model results with designated clinical standards. The diagnostic process is enhanced through the use of a language model, which delivers contextual understanding and produces valuable information from features that have been extracted. The proposed model achieves excellent performance with accuracy at 98.5%, precision at 98.7%, recall at 98.4%, and F1-score at 98.5%, which demonstrates its capacity for accurate and equivalent performance across different classifications. The experimental results demonstrate that the model achieves better results than existing methods. The combination of multimodal data with clinical reasoning improves both the accuracy and the user experience of the system. The proposed framework provides an efficient, interpretable, and scalable solution for thyroid nodule diagnosis. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

The design of cushioning packaging for flat-screen television (TV) products relies heavily on repeated simulations, resulting in high development costs and low design efficiency. In this study, we propose a hybrid framework integrating finite element (FE) simulation, data augmentation and interpretable machine learning (ML) for rapid peak acceleration prediction and optimization of TV cushioning packaging. First, a total of 216 FE drop-impact simulation samples of TV cushioning packaging systems were generated using ANSYS Workbench, covering TV dimensions, liner type, liner density, liner thickness, drop height and peak acceleration. Mixup-based data augmentation and Bayesian optimization were then employed to develop and tune six ML models. All ML models trained on the original dataset achieved coefficients of determination (R²) ranging from 0.797 to 0.990. The Mixup-augmented XGBoost model achieved the best prediction performance, yielding R² values of 0.998 and 0.983 for the training and testing datasets, respectively. SHAP analysis revealed that liner material type, liner density and liner thickness were the dominant factors affecting the protective performance of TV cushioning packaging. In addition, a web-based platform was developed based on the proposed FE–ML strategy to support the design exploration of feasible schemes for new TV products. The predictive capability of the proposed FE-ML framework was further evaluated using 22 independent cushioning packaging schemes, achieving an R² of 0.926 and an average prediction error of 4.490 g. These results suggest that the proposed workflow can support the performance evaluation and optimization of TV cushioning packaging. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

Artificial intelligence (AI) and digital platforms are increasingly influencing how universities manage experiential learning, interdisciplinary collaboration, and innovation-oriented educational activities. Challenge-based capstone and graduation projects play an important role in this context because they connect technical learning with teamwork, stakeholder engagement, project management, and applied innovation. However, many universities still rely on fragmented and highly manual coordination processes, which can limit scalability, transparency, and effective alignment between project requirements and participant capabilities. This study presents Smart Innovation Hub, an AI-enabled information system developed to support challenge-based innovation and capstone-project coordination in higher education. The platform brings together challenge intake, participant profiling, AI-supported recommendations, mentor coordination, workflow governance, and human review within a shared educational innovation environment. The system operationalizes an Innovation Bridge ecosystem model that connects students, faculty mentors, research centers, and external partners through a data-supported coordination framework. A Design Science Research (DSR) methodology guided the development and pilot evaluation of the platform within a public university environment. The pilot evaluation relied on several evidence sources, including platform logs, coordinator records, stakeholder surveys, milestone documentation, and partner feedback collected during implementation activities. Early pilot observations suggested an approximate 60% reduction in average team-formation cycle time, together with positive stakeholder perceptions regarding workflow usability and recommendation quality. These findings should be interpreted as preliminary implementation indicators within a single-institution pilot environment. The study contributes an AI-enabled educational innovation ecosystem architecture, a hybrid semantic-structured recommendation framework for challenge-based coordination, and a structured workflow model that integrates explainability and human oversight into educational innovation management. The findings further suggest that AI-enabled information systems may improve the transparency and coordination of challenge-based innovation workflows while preserving institutional governance and human decision-making. Full article

For microbiological diagnostics, accurately counting and segmenting microbial colonies is extremely important. However, manual methods are labor-intensive and yield inconsistent results. We develop a hybrid model using swarm intelligence, combining a convolutional transformer with nested skip connections and global context with channel and spatial attention. Parameter tuning is supported by a variety of swarm optimization algorithms (e.g., Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, and Differential Evolution Particle Swarm Optimization). Morphological refinement, including a further watershed transform, an attention graph, and post-processing, enhances colony boundaries by separating them. Grad-CAM++, Integrated Gradients, and temperature scaling provide a transparent and trustworthy model through explainability and post hoc calibration. The proposed model was extensively tested on the Microbial Colony Recognition and Circular Bacterial Colony Datasets, achieving a Dice score of 94.2%, an Intersection over the Union of 88.6%, and a mean absolute counting error of 2.7 colonies. These results significantly outperform several baseline models, including U-Net (88.1%), U-Net++ (89.7%), Attention U-Net (90.6%), and Swin-Unet (91.4%). Statistically significant improvements were confirmed (p < 0.01). A cross-dataset analysis demonstrates the framework’s robustness and cross-domain applicability, and positions it as a trustworthy, explainable automated model for assessing microbial colonies in laboratory and clinical settings. Full article

Background: The rapid advancement of generative artificial intelligence is transforming music composition from an exclusively human-centric activity into a hybrid human–algorithmic domain. Despite technological progress and growing commercial integration, consumer acceptance of AI-generated music remains empirically underexplored. Methods: This study formulates and empirically evaluates a multidimensional theoretical model integrating nine frameworks—including UTAUT2, parasocial interaction theory, anthropomorphism theory, authenticity theory, and innovation resistance theory—through a quantitative cross-sectional survey of 466 young adults aged 17–28. Confirmatory factor analysis and multiple regression analysis (with robust standard errors) were employed. Results: The model explained 63.6% of the variance in behavioral intention (R² = 0.636). Five constructs emerged as significant predictors: hedonic motivation (β = 0.136, p = 0.017), parasocial relationships (β = 0.121, p = 0.002), social influence (β = 0.126, p = 0.002), performance expectancy (β = 0.102, p = 0.019), and innovation resistance (β = −0.089, p = 0.029). Authenticity concerns, ethical AI concerns, anthropomorphic perceptions, and technological substitution fears were non-significant in the multivariate model. Conclusions: Young consumers’ acceptance of AI-generated music is primarily driven by experiential, social, and relational factors rather than ethico-cultural concerns. These findings have substantive implications for creative industries navigating algorithmic cultural production. Full article

Deep learning-based automated classification of dermatoscopic skin lesions has exhibited promising potential in diagnostics. However, two prominent issues need to be addressed before achieving high-quality diagnostic tools: inconsistent performance in the case of imbalanced classes and poor clinical interpretability of models. Even though some studies have attempted to leverage both deep and shallow learning by combining pretrained convolutional neural networks (CNNs)-based feature extraction with classical machine learning (ML) models, very few of them systematically explore several model combinations based on various clinically important metrics, such as F1-score, precision, recall, accuracy, etc., and utilize decision threshold calibration techniques. In this research, we present an evaluation of a systematic framework with threshold calibration for the comparison of several hybrid models on seven-class skin lesion classification (multi-class) on the HAM10000 dataset. In particular, we used deep features extracted from three pretrained CNN architectures, i.e., DenseNet201, InceptionV3 and EfficientNet-B4. These deep features were used as inputs for six different classical classifiers. As a result, we obtained 18 comparable hybrid models that were then systematically compared by multiple clinically relevant metrics: accuracy, macro-precision, macro-recall, macro-F1, ROC-AUC, Precision-Recall-AUC, and log loss. Also, fold-wise optimization of decision thresholds was performed, which was based on the maximization of the macro-F1 score. Finally, we found out that DenseNet201 with an SVM-RBF classifier yielded the highest performance among all 18 tested models, showing 90.88% accuracy, 90.7% macro-precision, and 0.921 ROC-AUC. To analyze the clinical plausibility, top-performing models were further explained with explainable artificial intelligence (XAI) techniques: Grad-CAM, LIME and Occlusion Sensitivity. Results show that the most successful models concentrated mostly on lesion-specific areas. Overall, this study contributes a reproducible hybrid-XAI model-selection framework rather than a single black-box classifier, supporting more transparent and clinically meaningful skin lesion diagnosis. Full article