Search Results (6,300)

The rapid rise of inverter-based resources (IBRs) such as solar, wind, and battery energy storage is transforming power grids and creating new challenges for protection. Unlike synchronous generators, many IBRs are interfaced through grid-following (GFL) inverters that operate as controlled current sources and rely on an external voltage reference, resulting in fault responses that are current-limited and controller-shaped. These characteristics reduce fault current magnitude and can undermine conventional protection schemes. In contrast, emerging grid-forming (GFM) inverters behave as voltage sources that establish local voltage and frequency, offering improved disturbance support but still transitioning to current-limited operation under severe faults. This review summarizes GFL versus GFM operating principles and deployments, compares their behavior under balanced and unbalanced faults, and evaluates protection impacts using a protection-relevant taxonomy supported by illustrative electromagnetic transient (EMT) case studies. Key challenges, including underreach/overreach of impedance-based elements, reduced overcurrent sensitivity, and directional misoperation, are identified. Mitigation options are discussed, spanning adaptive/supervised relaying, communication-assisted and differential protection, and inverter-side fault current shaping and GFM integration. The implications of IEEE 1547-2018 and IEEE 2800-2022 are reviewed to clarify ride-through and support requirements that constrain protection design in high-IBR systems. Full article

Background: Intrauterine Growth Restriction (IUGR) is associated with a distinct neonatal metabolic profile, attributable to chronic intrauterine nutritional deprivation and suboptimal placental nutrient exchange. Upon delivery, IUGR neonates typically present with depleted nutrient stores, dysregulated endocrine activity, and a spectrum of micronutrient deficiencies, factors that collectively compromise metabolic homeostasis and significantly influence subsequent health trajectories. Methods: This narrative review systematically synthesizes the current body of evidence from clinical, biochemical, and translational investigations pertaining to the micronutrient status and pivotal endocrine markers in neonates affected by intrauterine growth restriction. The collected findings were integrated to elucidate metabolic adaptation mechanisms, immediate clinical ramifications, and the potential pathways linking neonatal biochemical patterns to long-term metabolic programming. Results: IUGR neonates consistently exhibit reduced cord-blood concentrations of essential micronutrients, including vitamin D, iron (Fe), zinc (Zn), magnesium (Mg), folate (vitamin B9), and cobalamin (vitamin B12), reflecting compromised placental nutrient transfer and limited fetal reserves. Concomitantly, endocrine alterations—most notably reduced insulin (INS) and C-peptide (C-pep) levels—indicate suppressed pancreatic β-cell activity and a prevailing hypoanabolic adaptive state. In parallel, disturbances in mineral metabolism, characterized by lower calcium (Ca) concentrations and increased alkaline phosphatase (ALP) activity, suggest impaired bone mineralization during the critical phase of early postnatal adaptation. Collectively, these biochemical patterns increase vulnerability to early clinical complications such as neonatal hypoglycemia and bone demineralization, disrupt early growth trajectories, and are associated with an elevated long-term risk of insulin resistance and adverse cardiometabolic programming. Conclusions: IUGR neonates consistently demonstrate a synergistic interplay of micronutrient deficiencies and adaptive endocrine responses, profoundly impacting immediate postnatal metabolic stability and predisposing them to long-term health challenges. Therefore, early biochemical screening, followed by tailored nutritional and hormonal interventions, may assist restore metabolic balance, promote growth and decrease long term risk for metabolic diseases. Full article

Background/Objectives: Vertical root fractures (VRFs) present significant diagnostic challenges due to their subtle radiographic features and variability across imaging modalities. Artificial intelligence (AI) offers potential to improve detection accuracy, yet evidence regarding its performance across different imaging systems remains fragmented. To critically evaluate current evidence on AI-assisted detection of VRFs across periapical radiography, panoramic radiography, and cone-beam computed tomography (CBCT) and to compare diagnostic performance, methodological strengths, and limitations. Methods: A systematic review of literature up to January 2025 was carried out using databases such as PubMed, Scopus, Web of Science, and the Cochrane Library. The studies included in this review utilized AI-based techniques for detecting VRF through periapical, panoramic, or CBCT imaging. Extracted data encompassed study design, AI models, dataset sizes, preprocessing methods, imaging parameters, validation techniques, and diagnostic metrics. The risk of bias in these studies was evaluated using the QUADAS-2 tool. Results: Ten studies met inclusion criteria; CNN-based models predominated, with performance highly dependent on imaging modality. CBCT-based AI systems achieved the highest diagnostic accuracy (91.4–97.8%) and specificity (90.7–100%), followed by periapical radiography models with accuracies up to 95.7% in controlled settings. Panoramic radiography models demonstrated lower sensitivity (0.45–0.75) but maintained high precision (0.93) in certain contexts. Most studies reported improvements over human performance, yet limitations included small datasets, heterogeneous methodologies, and risk of overfitting. Conclusions: AI-assisted VRF detection shows promising accuracy, particularly with CBCT imaging, but current evidence is constrained by methodological variability and limited clinical validation. Full article

Tantalum pentoxide (Ta₂O₅) films deposited on fused silica substrates are critical components of high-power laser systems. Ion-assisted electron beam evaporation (IAD-EBE) is the mainstream technique for fabricating Ta₂O₅ films. However, it commonly requires extensive experimental efforts for deposition quality optimization, while each coating cycle is extremely time-consuming. To solve this issue, this work establishes a dataset targeting the surface roughness (Rq) and refractive index (n) of Ta₂O₅ films using atomic force microscopy, as well as ellipsometer and deposition experiments. Influence of assisting ion source beam voltage (V)/current (I) and Ar (Q₁)/O₂ (Q₂) flow rate on the n and Rq of Ta₂O₅ films are analyzed. Combining energy-field mechanism analysis with a Bayesian optimization approach (PI-BO), both deposition quality prediction and feature analysis of process parameters are achieved. The determination coefficient/mean absolute error for the prediction models of n and Rq reach 0.927/0.013 nm and 0.821/0.049 nm, respectively. Based on sensitivity analysis, the weight factors of V, I, Q₁, and Q₂ affecting n/Rq of Ta₂O₅ films are determined to be 0.616/0.274, 0.199/0.144, 0.113/0.582, and 0.072/0.000. V and Q₂ are identified as the core factors for regulating deposition quality. The optimal ranges for V and Q₂ are 600~700 V and 70~80 sccm, respectively. This study proposes a PI-BO method for predicting Rq and n of Ta₂O₅ films under small-data conditions, while determining the preferred parameter ranges and their sensitivity weight factors. These findings provide effective theoretical support and technical guidance for IAD-EBE strategy design and optimization of optical films in high-power laser systems. Full article

The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions. Full article

Lung cancer in never-smokers (LCINS) represents a distinct clinical entity driven by dominant oncogenic alterations and characterized by a low tumor mutational burden. Although tyrosine kinase inhibitors (TKIs) achieve high initial response rates, their long-term efficacy is limited by suboptimal pharmacokinetics, restricted central nervous system (CNS) penetration, tumor microenvironment barriers, and acquired resistance. In this review, we critically assess the current state of nanotechnology-assisted drug delivery systems for LCINS, with a primary focus on how rationally designed nanocarriers can overcome biological barriers, enable molecular subtype-specific therapeutic strategies, and address mechanisms that limit clinical efficacy and durability of response. We conducted a structured literature search using PubMed and Web of Science (January 2022 to November 2025), focusing on primary studies reporting the preparation, physicochemical properties, and therapeutic performance of nanocarriers in in vitro and in vivo models, as well as available pharmacokinetic and clinical data. LCINS is characterized by inefficient vasculature, high extracellular matrix density, active efflux transporters, and immunosuppressive niches, and is frequently complicated by brain metastases. Nanocarrier-based platforms can enhance aqueous solubility, prolong systemic circulation, and improve tumor or CNS targeting. Co-delivery systems combining TKIs with nucleic acid-based therapeutics, together with stimuli-responsive platforms, offer the potential for simultaneous modulation of multiple oncogenic pathways and partial mitigation of resistance mechanisms. In summary, nanotechnology provides a promising strategy to improve both the efficacy and specificity of targeted therapies in LCINS. Successful clinical translation will depend on biologically aligned carrier–payload combinations, scalable and reproducible manufacturing processes, and biomarker-guided patient selection. Full article

The relentless rise in global plastic consumption has intensified the challenge of managing plastic waste pollution. Current conventional recycling technologies face significant limitations in processing efficiency and environmental compatibility, hindering the effective recovery of plastic resources. Against this background, microwave pyrolysis technology has emerged as a promising solution, leveraging its dual advantages of thermal and non-thermal effects. This technology enables uniform and rapid heating, substantially reducing processing time and energy consumption. Its characteristics open new pathways for the high-value conversion of waste plastics. Through this approach, waste plastics can be efficiently transformed into valuable products such as pyrolysis oil, hydrogen gas, and solid carbon, demonstrating broad application prospects. This paper first systematically reviews the shortcomings of existing plastic pyrolysis technologies. It then delves into the operational mechanisms, process characteristics, and key influencing factors of microwave-assisted pyrolysis. Finally, it examines current challenges and issues while outlining future research directions, offering insights for the sustainable resource utilisation of waste plastics. Full article

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article

Polychrome sculptures are complex, multilayered artifacts that embody the intersection of artistic craftsmanship, material science, and cultural heritage. Over the past two decades, the study of material identification in polychrome sculptures has shown marked interdisciplinary development, driven by advances in analytical technologies that have transformed how these objects are studied, enabling high-resolution identification of pigments, binders, and structural substrates. This review synthesizes key developments in the identification of polychrome sculpture materials, focusing on the integration of non-destructive and molecular-level techniques such as XRF, FTIR, Raman, LIBS, GC-MS, and proteomics. It highlights regional and historical variations in materials and craft processes, with case studies from Brazil, China, and Central Africa demonstrating how multi-modal methods reveal both technical and ritual knowledge embedded in these artworks. The review also examines evolving research paradigms—from pigment identification to stratigraphic and cross-cultural interpretation—and discusses current challenges such as organic material degradation and the need for standardized protocols. Finally, it outlines future directions including AI-assisted diagnostics, multimodal data fusion, and collaborative conservation frameworks. By bridging scientific analysis with cultural context, this study offers a comprehensive methodological reference for the conservation and interpretation of polychrome sculptures worldwide. Full article

Bt Cry toxins remain the cornerstone of transgenic crop protection against Lepidopteran pests, yet field-evolved resistance, particularly in invasive species such as Spodoptera frugiperda and Helicoverpa armigera, can threaten their long-term efficacy. This review presents a comprehensive and unified mechanistic framework that synthesizes current understanding of Bt Cry toxin modes of action and the complex, multilayered regulatory mechanisms of field-evolved resistance. Beyond the classical pore-formation model, emerging evidence highlights signal transduction cascades, immune evasion via suppression of Toll/IMD pathways, and tripartite toxin–host–microbiota interactions that can dynamically modulate protoxin activation and receptor accessibility. Resistance arises from target-site alterations (e.g., ABCC2/ABCC3, Cadherin mutations), altered midgut protease profiles, enhanced immune regeneration, and microbiota-mediated detoxification, orchestrated by transcription factor networks (GATA, FoxA, FTZ-F1), constitutive MAPK hyperactivation (especially MAP4K4-driven cascades), along with preliminary emerging findings on non-coding RNA involvement. Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants. Future sustainability hinges on system-level integration of single-cell transcriptomics, midgut-specific CRISPR screens, microbiome engineering, and AI-accelerated protein design to preempt resistance trajectories and secure Bt biotechnology within integrated resistance and pest management frameworks. Full article

Serum and urinary steroid profiles are altered in hormone-producing adrenal adenomas, Cushing’s or Conn’s syndrome, or adrenocortical carcinoma. Definitive diagnosis of inherited congenital adrenal hyperplasia is usually accomplished by measuring the blood levels of adrenal hormones and precursor steroids. Neonatal diagnosis of congenital adrenal hyperplasia is complicated. Alternative methods such as gas chromatography/mass spectrometry or liquid chromatography/mass spectrometry have been used for the diagnosis of congenital adrenal hyperplasia. This review covers the current application of gas chromatography/mass spectrometry or liquid chromatography/mass spectrometry in the interpretation of steroid profiles in different clinical and diagnostic settings. In the future, mass spectrometry may provide more information to assist in the choice of routine DNA analysis. Full article

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis. Full article

Aquaculture has grown rapidly worldwide and has become a key source of food and employment opportunities. However, its expansion faces environmental, health, reproductive, and technological challenges that threaten its long-term sustainability. In this context, biologists play a crucial role in promoting sustainable practices and integrated management of aquaculture systems. This article reviews their main contributions to animal health, genetic improvement, assisted reproduction, and resource conservation. They also highlight their leadership in applying advanced technologies, including biotechnology, nanotechnology, and genetic engineering. Moreover, this study explores emerging research trends and emphasizes the importance of interdisciplinary training to address the evolving demands of the sector. This underscores the need to strengthen collaboration between science, technology, and public policy to ensure sustainable aquaculture. Enhancing the role of biologists is essential for overcoming current challenges and advancing efficient, ethical, and environmentally responsible aquaculture systems that meet global demand. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article