Search Results (7,148)

Artificial intelligence is increasingly used in asset management, although evidence that greater model complexity consistently improves investment performance remains limited. This study examines whether machine-learning methods generate incremental value in European equity selection beyond transparent investment rules. The analysis used 562 eligible monthly price series drawn from the March 2026 STOXX Europe 600 constituents and applied retrospectively as a common ex post reference universe. The models were trained on observations from January 2011 to December 2020 and evaluated out of sample using signals formed from January 2021 to March 2026, with corresponding portfolio returns realized from February 2021 to April 2026. Logistic regression, random forest, XGBoost, and an equal-probability ensemble are compared with momentum, low-volatility, and equal-weight strategies under common portfolio-construction rules. Logistic regression recorded the strongest classification results and the highest gross cumulative portfolio return. The transparent momentum strategy recorded the highest observed Sharpe ratio and substantially lower target-weight turnover, while the three-model ensemble underperformed both logistic regression and momentum. Newey–West inference did not establish a statistically significant difference in mean monthly returns between logistic regression and momentum. The results indicate that additional algorithmic complexity did not generate incremental investment value under the restricted technical information set and portfolio framework considered. The findings support evaluating model complexity jointly through predictive quality, gross performance, statistical uncertainty, implementation sensitivity, interpretability, and governance requirements. Full article

Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4063 SME and 1903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in two of four SME sectors, matched it in a third, and was marginally outperformed in the fourth; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: an architecture for AI-driven audit planning that is internally validated against verified audit outcomes within the historical Lebanese dataset examined, built entirely from data tax administrations already collect, and potentially transferable to comparable jurisdictions, though not yet operationally deployed. Full article

Background/Objectives: The programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) immune checkpoint is a key therapeutic target in cancer immunotherapy, but small-molecule inhibition remains challenging due to its shallow and dynamic interaction interface. This study aimed to develop an artificial intelligence (AI)-guided workflow to identify novel small-molecule inhibitors targeting the PD-L1 dimer interface. Methods: A combined computational and experimental approach was established. A support vector regression-genetic algorithm (SVR-GA) model was trained on a dataset of 1385 known PD-L1 inhibitors to predict activity and guide molecular generation. From 470 AI-generated candidates, docking and molecular dynamics (MD) simulations were used for virtual screening. Selected compounds were synthesized and evaluated for PD-1/PD-L1 binding disruption using homogeneous time-resolved fluorescence (HTRF) assays. Cytotoxicity was tested in MDA-MB-231 and 4T1 cell monocultures, and in vivo efficacy was assessed in an immunocompetent 4T1 tumor model. Results: Two hits, PD-L1-Ser and PD-L1-Ser-OEt, were identified. Both disrupted PD-1/PD-L1 binding in HTRF assays, with PD-L1-Ser-OEt showing higher potency (IC₅₀ = 0.2068 μM). Both compounds exhibited limited direct cytotoxicity in cancer cell monocultures, suggesting an immune-mediated mechanism. In the 4T1 syngeneic mouse model, both inhibitors suppressed tumor growth without causing body weight loss. PD-L1-Ser-OEt demonstrated superior antitumor efficacy and elevated serum levels of IFN-γ and IL-4. Conclusions: This AI-guided workflow combining machine-learning-based molecular generation with structure validation is feasible for discovering PD-L1 dimer-interface inhibitors. PD-L1-Ser-OEt represents a promising lead compound for further development as an immune checkpoint inhibitor. Full article

Background: Artificial intelligence-derived parameters hold substantial promise as indicators for tumor prognosis prediction and treatment guidance. However, existing studies have not sufficiently addressed the application of these parameters across different cancer types. Methods: We employed a deep learning algorithm to conduct a multi-cancer analysis for disease-free survival (MC-DFS) prediction using 8856 cases with associated whole-slide images and clinical data. The training cohort consisted of 7392 cases from the TCGA set (24 cancer types), and the independent external validation cohort comprised 1464 cases from the CPTAC and General Hospital sets (9 cancer types). A nomogram prediction signature for disease-free survival (NOMO) was developed by integrating the MC-DFS, tumor stage, and patient age. The prognostic model’s performance was validated in an independent cohort. Results: In the training and validation cohorts, the MC-DFS model achieved area under the curve (AUC) values of 0.750 and 0.682, respectively. It effectively differentiated patients with poorer disease-free survival, with hazard ratios of 4.823 (95% CI: 4.343–5.356, p < 0.0001) in the training cohort and 2.092 (95% CI: 1.472–2.971, p < 0.0001) in the validation cohort. Each cancer subtype’s analysis confirmed the model’s robust performance. Additionally, using nomogram analysis, we developed a multi-model prediction signature for disease-free survival across multiple cancer types based on MC-DFS and the clinicopathologic features in the training cohort. This enhanced model offers more precise risk stratification for stage I malignancies and complements the existing tumor staging systems by identifying high-risk patients. Conclusions: The newly developed MC-DFS shows marked improvements in prognostic predictions across multiple cancer types. With further validations across multiple centers, this nomogram prediction system could become a valuable practical tool for managing various cancers. Full article

The accurate placement of lines, drains, and airways (LDAs) is essential for the safe management of critically ill patients, as malpositioned medical devices can result in severe complications ranging from ineffective treatment to life-threatening injury. Chest radiography (CXR) is the primary imaging modality for confirming the placement of many LDAs; however, the growing volume and complexity of bedside CXRs have created increasing demand for rapid, reliable interpretation. Recent advances in artificial intelligence (AI) have enabled automated detection, localization, and position assessment of LDA devices, supporting opportunities to improve clinical workflow and patient safety. This review summarizes recent developments in AI algorithms for automated LDA assessment on CXRs, emphasizing their clinical applications, technical approaches, performance, and limitations. The review begins with clinical characteristics, radiographic appearance, and placement criteria of common LDA categories, followed by a survey of AI methods for presence detection, device localization, and position classification. Although some models have achieved performance approaching that of radiologists, most do not perform at the level needed for clinical deployment. Future research should prioritize multicenter validation, standardized annotation protocols, pediatric-specific datasets, and anatomically informed models capable of simultaneously evaluating multiple LDA devices. Advances in these areas will facilitate the integration of AI-assisted LDA assessment into clinical decision support and ultimately improve patient care. Full article

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

The aquatic products industry has experienced rapid development due to the growing global demand for high-quality proteins. However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities. Full article

As the crisis of antibiotic resistance escalates, phage therapy has regained attention as an alternative strategy. Artificial intelligence (AI) technologies offer new avenues to overcome the bottlenecks inherent in traditional bacteriophage research. This review summarizes the multi-dimensional innovative applications of machine learning, deep learning, and large biological models in phage studies. In the fields of phage recognition and genomics, support vector machines (SVMs), convolutional neural networks (CNNs), and pre-trained protein language models can all achieve recognition accuracy rates of over 90%. Furthermore, tools such as DeepHost and VirSorter2 can efficiently identify phage sequences, annotate functional genes, and predict hosts at the species or strain levels. For clinical translation, AI integrates patient characteristics, bacterial phenotypes, and phage profiles to customize cocktail regimens for individualized phage therapy. Graph neural network-based models like DeepPBI-KG integrate multi-omics knowledge graphs to precisely predict phage-host interactions (PHIs), whereas agent-based simulation and defense protein predictors forecast phage resistance evolution. Additionally, generative AI can support the de novo design of functional phage genomes and mine massive unannotated virome dark matter. Nevertheless, this cross-disciplinary field faces significant constraints, including uneven and biased sequencing datasets, insufficient model interpretability, and dual-use biosafety ethical risks accompanied by unclear algorithm accountability and incomplete global supervision systems. Future research should optimize standardized multimodal databases, develop explainable AI algorithms, and establish cross-disciplinary ethical governance frameworks to facilitate closed-loop verification between computational prediction and wet-lab experiments. In conclusion, the deep integration of AI and phage biology provides revolutionary strategies to tackle multidrug-resistant infections and advances the clinical transformation of phage precision medicine. Full article

Although traditional machine learning models demonstrate high accuracy in agricultural prediction scenarios, their “black box” nature prevents them from transparently presenting decision-making mechanisms and limits their reliability in integrated pest management (IPM) processes. In this study, the Cherry Fruit Fly (Rhagoletis cerasi) was investigated. A rule-based, explainable artificial intelligence (XAI) framework is proposed for characterizing bio-edaphic profiles associated with observed Cherry Fruit Fly Pupal Count (CfPC) density levels and classifying microclimatic aspects (Aspects) using measurable edaphic and biological parameters. The developed hierarchical rule inference engine parses the decision trees of the LightGBM classifier, which achieved the highest performance when benchmarked against 10 baseline machine learning algorithms (11 models in total), and extracts human-interpretable results that can be directly interpreted by experts. In Experiment 1, the analysis characterized the combinations of observed CfPC and edaphic conditions associated with Low, Medium, and High pupal-density profiles, whereas Experiment 2 evaluated the classification of canopy aspect from the measured bio-edaphic variables. According to the derived rules, continuous biological counts (CfPC) serve as the primary biological reference, while edaphic parameters such as soil temperature, pH, lime content, and water saturation percentage characterize additional soil conditions associated with the observed pupal-density profiles. These synthesized rules provide an interpretable representation of the bio-edaphic patterns observed within the studied orchards and may support the development of future precision crop-protection strategies following independent validation. Full article

Mycotoxins are widespread contaminants in animal feeds and continue to pose serious threats to animal welfare, production efficiency, food security, and sustainable economic growth worldwide. Chickens are highly susceptible to multiple mycotoxins, such as aflatoxins (AFLs), deoxynivalenol (DON), fumonisins (FBs), zearalenone (ZEA), ochratoxin A (OTA), and T-2 toxin. Exposure to these toxins can damage intestinal integrity, compromise immune functions, impair nutrient absorption, and ultimately reduce production efficiency even at subclinical concentrations. Conventional mycotoxin detection methods, such as enzyme-linked immunosorbent assays (ELISA) and chromatographic techniques, provide accurate quantification but remain expensive, labor-intensive, time-consuming, and inefficient for large-scale screening, particularly when masked mycotoxins are present. The frequent co-occurrence of multiple mycotoxins further complicates risk assessment and effective management. Emerging analytical technologies, including hyperspectral imaging, biosensors, Internet of Things (IoT)-based platforms, and machine-learning algorithms, offer promising advancements for rapid detection and predictive risk forecasting. This review summarizes the toxicological impacts of major mycotoxins on poultry, outlines critical challenges in detection and prevention, and evaluates current artificial intelligence (AI) and machine learning (ML) approaches for mycotoxin identification, prediction, and management. A systematic literature search conducted across PubMed, ScienceDirect and Google Scholar identified 176 unique studies published between 2010 and 2026. Key knowledge gaps include limited availability of high-quality datasets and source code, inconsistent model interpretability, and poor reproducibility across production environments. By integrating conventional toxicology with data-driven approaches, this review highlights how predictive modeling can strengthen mycotoxin surveillance and support proactive mitigation strategies in modern poultry production systems. Full article

Monumental heritage sites must be protected as rigorously as critical infrastructures, but under aesthetic and architectural constraints that limit where protection technologies can be installed; the purpose of this study is to reconcile effective security with minimal impact on the historical fabric. The paper presents the integrated security and safety system realized for the Royal Palace and Gardens of Caserta, a UNESCO World Heritage Site visited by around one million people per year. This system includes an Internet of Things/Internet of Everything framework integrating a 3D supervision platform, a resilient park-wide network, video surveillance, emergency communications, an artificial-intelligence engine and visitor services. Perimeter video-surveillance design is formulated as a constrained multi-objective optimization problem (coverage, camera count, overlap, reuse of existing installation points) solved with a purpose-built genetic algorithm and characterized through 311 optimization runs and 216 baseline runs on synthetic perimeter instances. The algorithm reached 95–98% perimeter coverage while reusing 95–100% of existing installation points, converging within 120–410 generations. Two greedy baselines were respectively quantified: the price of the aesthetic objectives (a 29–41% camera overhead with respect to a coverage-only design) and the specific contribution of the joint optimization (an order-of-magnitude reduction in coverage redundancy at equal reuse of existing installation points). Sensitivity analysis exposed the coverage–cost trade-off. The framework and method provide a reproducible, quantitatively characterized approach to minimally invasive heritage security design that is transferable to comparable sites. Full article

Closed-loop insulin delivery, or the artificial pancreas, has evolved from an ambitious engineering concept into one of the most consequential advances in diabetes technology. By integrating continuous glucose monitoring, insulin pumps, and control algorithms into a single feedback system, these platforms aim to shift diabetes care from repeated manual correction toward more anticipatory and adaptive glucose regulation. The field has progressed from early proof-of-concept systems to contemporary hybrid closed-loop platforms, reflecting a deeper shift in diabetes management itself: from treating glucose excursions after they occur to trying to blunt them in real time. Its clinical relevance is greatest in type 1 diabetes, where the burden of self-management is high and the consequences of glycemic instability are immediate. This review introduces the major technologies and evidence shaping the field, with emphasis on the control strategies that drive system behavior, and asks which currently available closed-loop systems offer the best balance of glycemic benefit, usability, and translational readiness for routine diabetes care. Across randomized trials and real-world studies, automated insulin delivery has consistently improved time in range, reduced hypoglycemia, and enhanced patient experience, particularly in pediatric populations. At the same time, important limitations remain: sensor lag, the physiologic constraints of subcutaneous insulin, device complexity, cost, and unequal access limit full autonomy and widespread adoption. Looking ahead, the next phase of progress will likely depend on more adaptive artificial intelligence, improved meal detection, multimodal wearable data, and multi-hormone systems that move the field closer to truly physiologic glucose control. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied across spinal pain care, by improving diagnostic accuracy, optimizing clinical workflow, and advancing translational research. However, AI’s role and its integration into rural spine care face challenges. Therefore, the current narrative review synthesizes the role of AI in rural spine care, spanning diagnosis, clinical decision support, imaging, natural-language processing, and remote monitoring. Methods: We searched the peer-reviewed literature on AI in spinal pain care across six databases from inception to June 2026. Results: In the United States, AI models have shown promise in strengthening clinical decision support that assists clinicians in identifying spinal pain disorders and providing evidence-based treatment recommendations; however, most were developed and validated in urban healthcare settings. Additionally, evidence on external validation, dataset representativeness, robustness to incomplete or low-quality data, interoperability, prospective clinical utility, and implementation feasibility in rural spine care remains limited. Therefore, we propose a translational framework in which de-identified data from rural and urban healthcare settings are aggregated, harmonized, and used to develop a multimodal AI model. AI models further require rigorous technical and external validation, prospective validation in rural settings, explainability, and continuous post-implementation monitoring. Despite its benefits, key limitations, including data scarcity and algorithmic bias, are also highlighted. Conclusions: AI has emerged as a promising tool for strengthening clinical decision support in spinal pain disorders in rural settings. The most realistic role of AI in rural settings is not to replace specialist expertise, but to act as one component of a multidisciplinary care team. Full article

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a transformative technology for optimizing the operation, control, monitoring, and maintenance of battery storage systems. This review provides a comprehensive overview of AI-driven BESS technologies for renewable energy applications. The study examines recent advances in machine learning, deep learning, reinforcement learning, and hybrid intelligent algorithms applied to battery state estimation, energy management, fault diagnosis, predictive maintenance, thermal management, and lifetime prediction. Furthermore, the integration of AI-based BESSs with photovoltaic systems, wind farms, microgrids, and smart grids is critically analyzed. The review highlights the advantages of AI techniques in improving system efficiency, reliability, adaptability, and decision-making capabilities under uncertain operating conditions. Current challenges, including data quality, model interpretability, computational requirements, cybersecurity concerns, and real-time implementation issues, are also discussed. Finally, emerging research directions such as digital twins, explainable artificial intelligence, federated learning, and edge intelligence are explored to provide insights into the future development of intelligent battery storage systems. This review aims to serve as a valuable reference for researchers, engineers, and practitioners working at the intersection of artificial intelligence, battery technologies, and renewable energy systems. Full article