Search Results (3,851)

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

Radiotherapy is an effective treatment for cancer; however, radioresistant cancer cells result in recurrence. Therefore, elucidating the mechanisms of radioresistance is urgently needed. Super-enhancers (SEs) are clusters of enhancers occupied by a high density of master transcription factors, mediators, and bromodomain protein BRD4. Recently, we reported that ΔNp63, an oncogenic transcription factor, promotes radioresistance in human head and neck squamous cell carcinoma (HNSCC) cells. As ΔNp63 establishes SEs in HNSCC cells, SEs may be involved in radioresistance. Here, we investigated the role of the SE component BRD4 in the radiation responses of HNSCC cells using a BRD4 degrader ARV-771 or BRD4 knockdown. First, Western blotting confirmed that ARV-771 decreased BRD4 protein expression. ARV-771 treatment resulted in reduced cell proliferation and enhanced apoptosis in irradiated HNSCC cells. Moreover, colony formation assays revealed that both ARV-771 and BRD4 knockdown enhanced the radiosensitivity of HNSCC cells, suggesting BRD4 contributes to the radioresistance of HNSCC cells. Furthermore, fluorescence immunostaining revealed distinct localization patterns of γH2AX, a marker of DNA double-strand breaks, compared with BRD4 and ΔNp63 in irradiated cells. Notably, ARV-771 and BRD4 knockdown decreased ΔNp63 and BRD4 protein expression, whereas ΔNp63 knockdown had minimal impact on BRD4 expression. Taken together, these findings suggest that BRD4-dependent maintenance of ΔNp63 expression may contribute, at least in part, to the regulation of radioresistance in HNSCC cells. Full article

The evolution of tennis equipment is fundamentally linked to advances in materials science and engineering, which have enabled enhanced player performance through optimised racquet and string designs. This review comprehensively examines the critical role of modern composite materials, manufacturing methods, and string technologies in tennis equipment, focusing on how these elements influence mechanical performance and player experience. It first explores the contributions of matrix and reinforcing materials, particularly carbon fibre and aramid composites, to racquet stiffness, strength, and vibration damping. Next, it details advanced manufacturing techniques such as prepreg layup, autoclave curing, and hollow moulding, which enable precise control over mechanical properties and quality assurance. This paper further evaluates various string materials including natural gut, Kevlar, polyester, nylon, and emerging hybrid setups, analysing their mechanical characteristics, tension maintenance, and impact on ball response and player comfort. Special attention is given to the interaction between design choices and playing conditions, such as court surfaces and player sensitivity, underscoring the complex interplay between equipment mechanics and gameplay dynamics. Through an interdisciplinary lens, this paper synthesises current scientific knowledge and experimental findings, providing a critical foundation for optimising tennis equipment design. By integrating materials science with practical application, this paper provides a comprehensive understanding of tennis equipment design, identifying gaps in current research and offering insights to guide future innovation for manufacturers, coaches, and players. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and adaptive control of multi-robot teams performing tasks such as crop monitoring and autonomous field maintenance. This paper introduces a cohesive Potential Linked Nodes (PLNs) framework, an adjustable formation structure that employs Artificial Potential Fields (APFs), and virtual node–link interactions to regulate swarm cohesion and coordinated motion (CM). The proposed model governs swarm formation, modulates structural integrity, and enhances responsiveness to external perturbations. The PLN framework facilitates swarm stability, maintaining high cohesion and adaptability while the system’s tunable parameters enable online adjustment of inter-agent coupling strength and formation rigidity. Comprehensive simulation experiments were conducted to assess the performance of the model under multiple swarm conditions, including static aggregation and dynamic flocking behavior using differential-drive mobile robots. Additional tests within a simulated cropping environment were performed to evaluate the framework’s stability and cohesiveness under agricultural constraints. Swarm cohesion and formation stability were quantitatively analyzed using density-based and inter-robot distance metrics. The experimental results demonstrate that the PLN model effectively maintains formation integrity and cohesive stability throughout all scenarios. Full article

Modernizing public security risk governance demands a paradigm shift from reactive response to proactive, systems-oriented prevention. Prevailing governance models, with their focus on institutions and technology, often neglect the micro-foundational mechanisms of risk generation: the internal psychological processes of individuals. To address this gap, this study develops a novel theoretical model—the F(T, P, C, R) framework—which integrates self-organization theory with a psychological gaming perspective. We conceptualize an individual’s behavioral choice (F_behavior) as an emergent outcome of the dynamic interplay among four constitutive factors: the situational context of Time (T) and Place (P), and the cognitive assessments of perceived Risk Control power (C) and perceived Risk Destructive power (R). Employing automotive driving behavior—specifically decisions regarding safe distance maintenance and the adoption of autonomous driving technologies—as our primary analytical scenario, we derive a dynamic risk-decision matrix. This matrix categorizes behavioral outcomes into four distinct quadrants (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) based on the subjective calculus between C and R, thereby elucidating the internal logic of risk-related choices. The study’s main contribution is constituted by this novel micro-behavioral analytical framework that integrates cognitive science with systems-based governance principles. It offers theoretical insights for behavioral public policy and provides a structured toolkit for diagnosing and designing targeted interventions, ultimately aiming to enhance proactive risk management and systemic resilience. Full article

With the intensification of global population aging, the co-morbidity rate of periodontitis and osteoporosis has significantly increased. The two are pathologically intertwined, forming a vicious cycle characterized by bone immunoregulatory dysfunction in the periodontal microenvironment, abnormal accumulation of reactive oxygen species (ROS), and disruption of bone homeostasis. Conventional mechanical debridement and anti-infective therapy can reduce the pathogen load, but in some patients, it remains challenging to achieve long-term stable control of inflammation and bone resorption. Furthermore, abnormal bone metabolism in the context of osteoporosis further weakens the osteogenic response during the repair phase, limiting the efficacy of these treatments. Bioinspired cell membrane-coated nanoparticles (CMNPs) have emerged as an innovative technological platform. By mimicking the biointerface properties of source cells—such as red blood cells, platelets, white blood cells, stem cells, and their exosomes—CMNPs enable targeted drug delivery, prolonged circulation within the body, and intelligent responses to pathological microenvironments. This review systematically explores how biomimetic design leverages the advantages of natural biological membranes to address challenges in therapeutic site enrichment and tissue penetration, in vivo circulation stability and effective exposure maintenance, and oxidative stress and immune microenvironment intervention, as well as functional regeneration supported by osteogenesis and angiogenesis. Additionally, we conducted an in-depth analysis of the key challenges encountered in translating preclinical research findings into clinical applications within this field, including issues such as the feasibility of large-scale production, batch-to-batch consistency, and long-term biosafety. This review lays a solid theoretical foundation for advancing the clinical translation of synergistic treatment strategies for periodontitis with osteoporosis and provides a clear research and development pathway. Full article

Background: Chronic low back pain (cLBP) with kinesiophobia is difficult to treat, and traditional graded activity approaches often show limited adherence and short-term effects. Virtual reality (VR) may enhance treatment engagement by providing immersive game-based environments that encourage therapeutic movement. This randomized controlled trial aimed to examine the effects of VR interventions designed to promote lumbar spine flexion in individuals with cLBP and elevated movement-related fear. Methods: Participants were randomized to one of two nine-week VR game conditions that differed only in the amount of lumbar flexion required. Primary outcomes were changes in pain intensity and disability from baseline to one-week post-treatment. Secondary analyses examined lumbar flexion and expectations of pain/harm as potential mediators. Follow-up assessments were conducted at multiple time points through 48 weeks to assess maintenance of treatment gains. Results: Both VR groups showed significant and clinically meaningful reductions in pain and disability at post-treatment. Improvements were maintained throughout the 48-week follow-up period. Depression symptoms continued to improve during follow-up. Expectations of pain and harm decreased significantly during treatment and remained reduced, whereas objective lumbar flexion did not change appreciably over time. Mediator analyses indicated that improved expectations of pain/harm, rather than increased lumbar flexion, were more closely associated with treatment response. Conclusions: Immersive VR gaming produced sustained reductions in pain, disability, and movement-related fear in individuals with cLBP and kinesiophobia. Findings suggest that VR may enhance rehabilitation outcomes by modifying maladaptive expectations rather than altering lumbar motion. VR-based interventions represent a promising and engaging approach for long-term cLBP management. Full article

This research has successfully addressed the challenges attributed with SMS, including the fragmented data, heavy reliance on experience, and lack of life cycle integration. This study presents the development and validation of a novel sustainable material selection (SMS) model using Artificial Intelligence (AI). The proposed model structures the process around four core life cycle phases—design, construction, operation and maintenance, and end of life—and incorporates a dual-interface system. This includes a main credits interface for high-level tracking of 100 total credits to trace the dynamics of SMS in relation to energy efficiency, indoor air quality, site selection, and efficient use of water. Further, it includes a detailed credit interface for granular assessment of specific material properties. A key innovation is the formalization of closed-loop feedback mechanisms between phases, ensuring that practical insights from construction and operation inform earlier design choices. The model’s functionality is demonstrated through a proof of concept for SMS considering thermal properties, showcasing its ability to contextualize benchmarks by climate, map properties to building components via a weighted networking system, and rank materials using a comprehensive database sourced from the academic literature. Automated scoring aligns with green building certification tiers, with an integrated alert system flagging suboptimal performance. The proposed model was validated through a structured practitioner survey, and the collected responses were analysed using descriptive and inferential statistical analysis. The result presents a scalable quantitative AI-assisted decision-making support model for optimizing material selection across different project phases. This work paves the way for further research with additional assessment criteria and better integration of AI and Machine Learning for SMS. Full article

Drought, high temperature, salinity, waterlogging, and nutrient deficiency, along with metal toxicity, are among the environmental factors that have resulted in much alteration of many ecosystems by climate change. Such stresses have dramatically lowered the global average human harvest of core crops, which, in turn, has driven an overall decrease in worldwide agricultural productivity. Plants have developed a variety of defense strategies against biotic and abiotic stress. Evidence of the successful roles of phytohormone-like neurotransmitters in ameliorating the response to stress has already been established. One neurotransmitter accumulated by the plants is gamma-aminobutyric acid (GABA), a non-protein amino acid that is essential for signaling in plant growth regulation and development via the control of physiological and biochemical processes. Plant tissues demonstrate rapid accumulation of GABA when exposed to various abiotic stresses. Consequently, it is imperative to understand how this accumulation affects the resistance and productivity of crops in challenging environmental conditions. Previously, different application methods and doses of GABA on different plant species were used under various abiotic stress conditions. The research findings exhibited that the method and concentration of GABA depend on the type of crop. Furthermore, the GABA dose depends on the methods of GABA application. The present review summarizes the potential doses and methods of applications of GABA under different abiotic stress conditions to ameliorate deficiencies in plant growth, yield, and stress tolerance through the avoidance of oxidative damage and maintenance of cell organelle structures. This review will also describe the complex mechanism by which GABA contributes to the attenuation of the effects of abiotic stresses by regulating some important physiological, molecular, and biochemical processes in crops. Full article

Background: Iatrogenic cerebral amyloid angiopathy (iCAA) is a rare form of CAA occurring decades after neurosurgical procedures involving cadaveric dural grafts. While typically associated with recurrent lobar intracerebral hemorrhages, recent reports suggest a possible overlap with CAA-related inflammation (CAAri). We report a case of iCAA with features indicative of active neuroinflammation that demonstrated a positive response to immunosuppressive therapy. Methods: Over a 12-year natural history, the patient underwent a comprehensive work-up, including serial clinical assessments, brain MRIs, core CSF biomarker analysis, amyloid PET imaging, and next-generation sequencing panel testing. Results: Previous clinical charts confirmed the use of cadaveric graft (Lyodura) in a neurosurgical intervention thirty years before. During hospitalization for seizures, brain MRI revealed, along with a severe form of CAA, an area of vasogenic edema. Given the suspicion of an active inflammatory process, corticosteroid and subsequent methotrexate maintenance therapy were introduced, leading to clinical and radiological improvement. Over 30 months of follow-up, the patient has remained clinically and radiologically stable, with no new hemorrhagic or inflammatory events. Conclusions: This case highlights the potential interplay between iCAA and neuroinflammation. The absence of new hemorrhages following immunosuppression suggests a possible disease-modifying effect, warranting further investigation into the role of neuroinflammation in iCAA and its therapeutic implications. Full article

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article

Background: Primary plasma cell leukemia (pPCL) represents the most aggressive plasma cell dyscrasia with a poor prognosis and survival of <3 years. The International Myeloma Working Group (IMWG) adopted more inclusive diagnostic criteria for pPCL in 2021, including patients with 5% or more circulating plasma cells (down from 20%). Most published studies of pPCL do not include patients who meet the criteria for pPCL based on the newer diagnostic guidelines, and the data on the optimal treatment of pPCL is scarce. In our multi-center retrospective analysis, we report data on treatment regimens used in 67 pPCL patients to characterize outcomes in this population. Methods: We included patients with newly diagnosed pPCL between 2010 and 2023 based on the 2021 IMWG definition at one of three academic centers. Results: Our results suggest significant improvement in overall response rate (ORR) and progression-free survival (PFS) with the use of autologous stem cell transplant, but without additional benefit for a tandem transplant. The presence of high-risk cytogenetics was an independent risk factor for progression in the cohort. Conclusions: Our dataset represents one of the largest cohorts to date using the expanded definition of pPCL adopted by the IMWG in 2021 and stresses the importance of taking pPCL patients to transplant. Unfortunately, our study was not powered to determine the efficacy of individual induction and maintenance regimens, and many patients diagnosed with pPCL are ineligible for transplant based on end-organ damage at diagnosis or from disease that is refractory to induction therapy, underscoring the need for early diagnosis and treatment in hopes of preserving transplant eligibility. Full article

To address the issues of poor geometric dimension retention, short component lifespan, and heavy maintenance workload of the 1520 mm gauge 50 kg/m rail No.9 turnout, a new design was proposed for the 1520 mm gauge 60 kg/m rail No.9 turnout. Based on the new design’s plane alignment, structural features, and other requirements, dynamic models of the vehicle–turnout system, the turnout conversion model, and the continuous welded rail turnout (CWR turnout) model were established. The focus was on analyzing the dynamic response of the vehicle when passing through the 1520 mm gauge 60 kg/m rail No.9 turnout, as well as its switching performance. The feasibility of applying CWR technology to this turnout was also explored. The results indicate that the dynamic indicators of the vehicle passing through the 1520 mm gauge 60 kg/m rail No.9 turnout meet the regulatory requirements; the maximum switching force at the traction point is 1.807 kN, which is less than the rated power of the switch machine; and the rail strength and track stability of the CWR turnout model all meet the design specifications. Full article