Search Results (14,896)

This paper presents a dual-layered computational framework for the robust trajectory planning and active stabilization of a robotic manipulator transporting a non-fixed payload. The primary challenge addresses the transport of a tray containing multiple objects prone to sliding, exacerbated by significant uncertainties in the system’s dynamic parameters, such as objects’ mass and inertia. The first contribution is an optimal closed-loop sensitivity-based trajectory planning algorithm that generates energy-efficient paths while minimizing the possibility of object sliding. The second contribution is an active sliding control strategy based on Nonlinear Model Predictive Control (NMPC). This algorithm dynamically adjusts the orientation of the tray, mounted to the robot’s end effector, usefully exploiting a dynamic model including inertial forces and gravity to move the object to given positions. Simulation and experimental results demonstrate that the integrated approach allows the robot to set the objects in desired positions with an average steady-state error of $6.3\times {10}^{-3}$ m across a set of ten experiments, while limiting the sliding to less than 3% of the tray dimensions during successive transportation in face of a 10% uncertainty about objects’ masses. The synergy between the uncertainty-aware planner and the NMPC controller supports robust tray-transport tasks in unstructured environments where precise dynamic modeling is unavailable. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Background/Objectives: Clinical pharmacists contribute pharmacotherapy expertise to antimicrobial stewardship (AMS), but antimicrobial prescribing remains a multidisciplinary process led by treating clinicians. In physician-centered hospital systems, pharmacists’ contributions may be integrated into prescribing decisions to varying degrees depending on the organizational structures, interprofessional relationships, workflow integration, and prescribing cultures. This study aimed to explore how contextual and interprofessional factors shape the integration of clinical pharmacists’ antimicrobial stewardship contributions within multidisciplinary prescribing decisions in Jordanian hospitals. Methods: A multi-site qualitative study was conducted across nine Jordanian hospitals. Semi-structured interviews were conducted with 26 clinical pharmacists involved in antimicrobial reviews in intensive care and general medical units. Interviews incorporated the critical incident technique to elicit examples of accepted and rejected stewardship recommendations. Data were analyzed using a realist-informed approach to develop context–mechanism–outcome configurations explaining variations in pharmacists’ reported AMS contribution. Results: Pharmacotherapy expertise was necessary but not sufficient for pharmacists’ recommendations to shape antimicrobial prescribing. Leadership endorsement, structured multidisciplinary rounds, and formal documentation pathways activated mechanisms of legitimacy, credibility, and workflow visibility, supporting reported uptake of dose optimization, therapeutic drug monitoring, and selected de-escalation recommendations. In contrast, prescriber-led hierarchies, limited documentation pathways, workload pressures, and defensive prescribing cultures activated mechanisms of self-limitation, risk aversion, and limited recommendation uptake, particularly for discontinuation, duration control, and narrowing of broad-spectrum therapy. Conclusions: Strengthening AMS requires not only formal committees and guidelines but also team-based structures that integrate pharmacists’ pharmacotherapy expertise into antimicrobial review while preserving clinicians’ ultimate prescribing responsibility. Full article

Human African trypanosomiasis (HAT) or sleeping sickness is a neglected tropical disease with a progressive central nervous system (CNS) involvement and marked sleep and circadian rhythm abnormalities. Even though this is a prominent feature of HAT, the connection between parasite neuroinvasion, neuroinflammation, circadian dysfunction, and neurological impairment is not fully understood. This narrative review aims to summarize the most up-to-date knowledge about sleep and circadian disturbance in HAT and proposes an integrated approach for the Trypanosome-Associated Sleep Disorder (TASD). The relevant literature was identified by searching major biomedical databases for HAT, sleep disorders, circadian rhythms, neuroinflammation, and CNS invasion. The review covers the steps by which the CNS becomes invaded, how the barriers are disrupted, how the CNS becomes activated by inflammatory responses, and how the hypothalamic and circadian regulatory networks are disrupted. The evidence suggests that excessive daytime sleepiness, fragmented nocturnal sleep, circadian misalignment, and neuropsychiatric manifestations are related to the activation of inflammatory cytokines, altered neurotransmitter signaling, activation of the kynurenine pathway, dysregulation of clock genes, and disruption of the suprachiasmatic nucleus. We also discuss TASD as a syndrome-like phenotype of CNS involvement and propose a three-stage model of sleep–wake dysfunction in HAT. The review unites these integral mechanisms in a single mechanistic framework to offer a unified understanding of the sleep pathology associated with HAT. There are still important gaps in our knowledge of biomarkers, disease staging, and irreversible neuronal damage, which indicate priorities for future research and better clinical management. Full article

Cyber–Physical Systems (CPS), including smart grids and industrial control networks, must maintain secure and stable operations despite increasingly adaptive cyber threats. Existing moving target defense (MTD) approaches often rely on fixed reconfiguration strategies or flat learning architectures that fail to scale and do not explicitly ensure operational resilience under real-time constraints. This study proposes a resilience-oriented hierarchical multi-agent reinforcement learning (MARL) framework for adaptive MTD in CPS environments. The attacker–defender interaction is modeled as a partially observable stochastic game, enabling defenders to learn adaptive strategies with incomplete information. The proposed architecture consists of three layers: a strategic MARL layer that optimizes high-level defense parameters, a distributed k-winner-take-all coordination layer for low-latency defender selection, and a robust execution layer based on sliding-mode control to preserve physical system stability during reconfiguration. By decoupling strategic adaptation from real-time control, the framework improves scalability and supports resource-aware defense through selective agent activation. Extensive simulations with up to 50 defender agents demonstrate that the proposed approach achieves a defense success rate of 92.4%, reduces the response time by 15% compared with the random MTD, and lowers the energy consumption by 34% on average (up to 52% at N = 50) relative to the flat MARL. These results indicate that hierarchical MARL can significantly enhance CPS resilience by enabling adaptive, efficient, and operationally safe defenses against dynamic cyber-attacks. The proposed framework is particularly suitable for edge-enabled CPS environments with strict, real-time, and safety constraints. Full article

Port operations play a strategic role in global trade but are associated with significant environmental impacts due to intensive energy use, equipment operation, and cargo handling activities. In this context, Life Cycle Assessment (LCA) emerges as an essential tool to quantify these impacts and support decarbonization strategies in the maritime-port sector. This study aims to evaluate the environmental performance and energy transition implications of fertilizer import operations in a multi-cargo port by comparing semi-automated and non-automated scenarios through a Life Cycle Assessment (LCA) approach. The methodology followed the standard LCA framework, including goal and scope definition, inventory analysis, impact assessment, and interpretation. Primary data collected in situ were combined with secondary data from the Ecoinvent database, ensuring consistency and representativeness. The results indicate that post-port logistics is the main driver of environmental impacts. In the semi-automated scenario, rail transport consumes approximately 18,800 L of diesel, showing higher efficiency due to its greater load capacity. In contrast, the non-automated scenario relies on 100 trucks, each consuming about 238.75 L per trip, resulting in higher total fuel consumption and emissions. It is concluded that the non-automated system presents higher environmental impacts across all categories analyzed, highlighting the importance of modal choice and operational efficiency in reducing emissions and supporting the energy transition in the port sector. Full article

The high-rate contact stabilization–activated sludge (HiCS–AS) process, which integrates an upstream high-rate contact stabilization process with a downstream activated sludge process, is a promising approach for enhancing energy recovery from municipal wastewater. This 16-week continuous study evaluated the organic matter recovery capacity and process stability of a HiCS–AS process, configured as a sequencing batch reactor, under realistic conditions where water temperatures decreased from 29 °C to 14 °C. The upstream HiCS process, operating at a solids retention time of 0.60 days, exhibited stable sludge settling behavior and achieved a stable carbon recovery rate of 0.23 ± 0.04 g-COD/g-COD. The overall COD removal rate for the system was 86% ± 3%, and the HiCS–AS process demonstrated a statistically significant increase in organic matter recovery compared to a parallel single activated sludge process (paired Student’s t-test, p < 0.05). These findings indicate that the HiCS–AS process provides a consistent and effective approach to organic matter recovery compared to conventional processes. Full article

This paper presents an automatic classification system for pulmonary diseases in chest X-rays based on geometric normalization. The proposed method consists of three main modules. Module 1: A landmark detector: A ResNet-18 convolutional neural network with coordinate attention mechanism is trained to predict 15 landmarks defining the lung contour, achieving a mean error of 3.61 pixels (median 3.07 pixels) through an ensemble of four models with test-time augmentation. Module 2: Geometric normalizer: a set of landmarks surrounding the lung region is used to geometrically normalize each image. This normalization involves: Generalized Procrustes Analysis used once to obtain a standard lung shape, Delaunay triangulation to build a deformation mesh, and a piecewise affine transformation (warping) to map the original lung region to a standardized region. This process eliminates variations in position, scale, and orientation in the original set. Module 3: Classifier: normalized images are classified into three categories (COVID-19, Viral Pneumonia, and Normal) using a ResNet-18 classifier with transfer learning and a contrast adjustment (using the SAHS (Statistical Asymmetrical Histogram Stretching) method). The classifier was evaluated through five-fold cross-validation on the COVID-19 Radiography Database, demonstrating high stability with 98.60 ± 0.26% accuracy, and 98.00% F1-Macro, confirming the robustness of the approach. Although the classifier trained with original images reached a higher accuracy than using normalized images, Gradient-weighted Class Activation Mapping (Grad-CAM) analysis and the cropping experiment suggest that this advantage is partly driven by acquisition artifacts rather than lung pathology. In contrast, geometrically normalized images outperform their non-aligned artifact-masked/cropped counterparts: 98.60% vs. 96.24% on the COVID-19 Radiography Database and 94.67% vs. 94.17% on a balanced adult–pediatric mixed dataset including pediatric cases from the Kermany dataset, suggesting that anatomical alignment can yield a more reliable and artifact-resistant representation for pulmonary disease recognition. Full article

Hypertension threatens the health of aging captive giant pandas, yet its molecular signatures remain poorly characterized. Here, multi-omics sequencing was applied to explore the molecular regulatory process of hypertension and the pharmacodynamic effect of levamlodipine on this endangered species. Six aged giant pandas were divided into hypertensive and normotensive groups (three hypertensive and three normotensive pandas) based on clinical phenotypes and blood pressure measurements. A multi-omics approach was employed, including blood RNA-seq, Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq), single-cell RNA-seq (scRNA-seq) of peripheral blood mononuclear cells, and time-series RNA-seq following levamlodipine administration. Our transcriptomic analysis revealed a statistically significant decline in ACE2 transcript abundance (Padj < 0.05), which suggests a possible shift in renin–angiotensin system signaling linked to hypertensive status in giant pandas. Single-cell analysis of 88,693 cells revealed that hypertension-associated genes were predominantly enriched in monocytes and T cells, implicating immune cell activation. Time-dependent transcriptional changes after levamlodipine administration. Temporal gene dynamics showed early activation of metabolic pathways followed by delayed inhibition of ion channels and calcium signaling. This study provides a transcriptional molecular perspective into the pathogenesis of hypertension in giant pandas, which is conducive to developing more effective antihypertensive treatment strategies, thereby protecting the health of this endangered species. Full article

Background: Traditional orthopaedic casting has remained the cornerstone of non-surgical fracture management for more than a century. Although plaster and fiberglass casts reliably stabilize fractures, they are associated with physical, psychological, emotional, social, and economic burdens that extend beyond bone healing. Children, older adults, and individuals with pre-existing vulnerabilities may be disproportionately affected. Despite increasing recognition of these complications, existing orthopaedic literature has historically prioritized radiographic healing and biomechanical stability, with limited synthesis of the broader multidimensional patient impact of traditional casting. Emerging technologies such as light-cured polymer mesh (LCPM) systems and 3D-printed lattice immobilizers have been developed to address these limitations and better align fracture care with patient-centered principles. Methods: A narrative review was conducted using a structured and transparent literature identification approach informed by PRISMA reporting principles; however, this study was not conducted as a formal systematic review and did not include risk-of-bias assessment or quantitative synthesis. A broad search of PubMed, Scopus, Web of Science, and Google Scholar was performed for studies published between January 2000 and July 2025. Search strategies combined MeSH terms and free-text keywords relating to orthopaedic casting, complications, psychosocial impacts, LCPM, and 3D-printed immobilizers. Following duplicate removal and a structured review process, 87 studies were included in the final narrative synthesis. Eligible studies included randomized controlled trials, observational studies, qualitative research, case series, and systematic reviews. Data were synthesized narratively across five domains: physical, psychological, emotional/social, economic, and technological alternatives. Results: Traditional plaster and fiberglass casts were consistently associated with musculoskeletal deterioration, joint stiffness, dermatological complications, and hygiene challenges. Psychological and emotional consequences included cast-induced anxiety, claustrophobia, depressive symptoms, and diminished autonomy. Social participation was frequently reduced due to mobility restrictions and perceived stigma, while economic impacts included hidden out-of-pocket expenses, caregiver burden, lost wages, and disparities in access to follow-up care. In contrast, emerging alternatives demonstrated promising advantages. LCPM systems improved ventilation, comfort, and hygiene, while reducing saw-related anxiety. Preliminary evidence suggests that both LCPM and 3D-printed systems may support improved patient experience and earlier return to selected activities, although larger comparative studies are needed to confirm effects on complication rates and long-term outcomes. Over time, these benefits may help offset higher upfront material costs. Conclusions: Fracture care should be evaluated not only by radiographic healing but also by patient-centered outcomes such as comfort, independence, and quality of life. Traditional casting imposes significant multidimensional burdens, whereas newer technologies such as LCPM and 3D-printed systems may offer a more holistic approach to immobilization while maintaining acceptable fracture stability in appropriately selected patient populations. While current evidence indicates potential physical, psychological, and economic advantages, large-scale comparative trials remain necessary to confirm long-term clinical, psychosocial, and cost-effectiveness outcomes across diverse populations. Future integration of emerging immobilization technologies into clinical practice may support more patient-centered, function-oriented, and cost-conscious approaches to fracture care. Full article

Measuring platelet function is important for patient stratification to judge bleeding vs. thrombotic risk and for research into antiplatelet drugs to prevent cardiovascular disease. Variability in platelet function is not fully understood, and large studies of inter-individual variation are making gradual progress using laboratory measurements, but rapid and high-performance hematological tests are also needed. We present here a novel microfluidic technology for platelet function analysis that images light scatter by platelets, using a low-cost, open-source, high-throughput and customizable darkfield imaging system called Dark-Pi. The hardware consists of a camera and a simple LED light source controlled by a Raspberry Pi, with 3D-printed parts. Using the Dark-Pi, platelet aggregation was imaged within adenosine 5′-diphosphate-loaded microcapillaries, revealing clearly visible patterns. This darkfield cellular light scatter approach was previously developed for bacterial cells, and here we adapted and optimized it for directly monitoring platelet aggregation. Capturing high-quality time-resolved images of platelets undergoing activation within microcapillaries allowed us to measure changes in light scattering in platelet-rich plasma that correspond with aggregation measured using conventional laboratory methods. This novel prototype system shows that this approach may have potential for use in large-scale studies of platelet function, combining simplicity with low-cost components and using a disposable dip-and-test microfluidic format. Full article

Background: Insect stings, particularly those caused by Hymenoptera such as wasps and bees, are frequent triggers of severe allergic reactions and anaphylaxis, yet the ability to predict short-term risk periods based on environmental conditions has not been systematically evaluated. Meteorological factors influence both insect activity and human exposure, highlighting a relevant gap in preventive risk assessment. Methods: This exploratory single-center study was conducted in Bochum, Germany, an urban region within the Rhine-Ruhr metropolitan area. A 17-year retrospective dataset (2005–2022) of clinically treated Hymenoptera-related sting events was analyzed to explore time-resolved, weather-informed patterns using artificial intelligence (AI)-based machine learning. The study emphasizes methodological feasibility and pattern identification rather than clinical prediction. Daily weather parameters were transformed into expert-informed indicators capturing current-season and carry-over environmental conditions. A multilayer perceptron (MLP) was trained to identify periods of increased sting occurrence, and model performance was evaluated primarily using recall to capture rare-event signals. Results: A total of 346 clinically significant sting events were recorded. Weather variables showed strong spatial coherence across four stations and were associated with intra-seasonal clustering of sting events rather than absolute annual incidence. Exploratory analyses suggested that earlier seasonal onset correlated with higher sting counts (Pearson R = −0.52; p = 0.037). Weekly aggregation improved model performance compared with daily prediction. The cross-validated MLP showed moderate recall (0.431) and high specificity (0.86), supporting exploratory risk stratification; however, post hoc benchmarking did not demonstrate consistent superiority over simpler baseline approaches. Conclusions: This study combines a long-term clinical insect sting dataset with high-resolution meteorological data to explore time-resolved, weather-informed risk patterns using machine learning. The findings demonstrate the technical feasibility of exposure-based risk stratification in a rare-event setting. However, benchmarking showed that the MLP did not consistently outperform simpler baseline approaches for binary warning of elevated-risk periods. This proof-of-concept should therefore be interpreted as exploratory and not as a stand-alone warning system, supporting further external validation in larger, multi-center cohorts before clinical, public health, or digital health implementation can be considered. Full article

Background and Objectives: Anterior cruciate ligament reconstruction (ACLR) is a very common procedure in young, active individuals. One of the rarest, but potentially life-threatening complications is symptomatic venous thromboembolism (VTE). Despite that, international guidelines offer conflicting advice on routine thromboprophylaxis. This review aims to summarize key international recommendations to support clinical decision-making. Materials and Methods: The role of thromboprophylaxis after ACL reconstruction remains controversial. Rather than performing a systematic review, relevant documents were identified from official publications, consensus statements, and registry-based recommendations issued by recognized orthopaedic and thrombosis-related organizations. Documents were included if they represented formal guidelines, consensus statements, or national/registry-based recommendations with direct clinical relevance to ACLR and thromboprophylaxis, and if they reflected contemporary practice across different healthcare systems. Recommendations from CHEST, NICE, BOA/BASK/BOSTAA, AAOS, ICM-VTE, ICS, SFAR, HAOST, and the Swedish Knee Ligament Registry were included. These were analyzed with respect to indications for pharmacological and mechanical prophylaxis, as well as risk stratification strategies. Results: This narrative review identified nine major international guidelines addressing thromboprophylaxis in ACLR. Most guidelines, including CHEST and NICE, advise against routine anticoagulation for low-risk patients, reserving it for those with specific risk factors. BOA recommends prophylaxis only when multiple comorbidities are present. The Swedish registry indicates that anticoagulation is primarily used in patients with prior VTE or oral contraceptive use. While ICM-VTE supports the use of aspirin or LMWH in high-risk cases, SFAR stands out by recommending routine LMWH for all patients. HAOST emphasizes early mobilization and selective prophylaxis, including aspirin for low-risk groups. Conclusions: Current evidence does not support universal thromboprophylaxis following ACLR. A risk-stratified approach is recommended, with mechanical measures or aspirin for low-to-moderate-risk patients and LMWH or DOACs for high-risk individuals. Future research may focus on genetic and patient-specific factors to better explain existing heterogeneity. Full article

A frequent and sudden two-phase (solid–liquid) geological hazard in mountainous areas, the evolution of debris flows involves the coupling of multiple physical fields, making monitoring and early warning particularly challenging. To accurately reveal the dynamic patterns of debris flow evolution and improve early-warning accuracy, this study focused on the Ni Chang Valley area in Shimian County, Ya’an City, Sichuan Province. Based on the COMSOL Multiphysics coupling simulation platform, a multiphysics bidirectionally strongly coupled numerical model was proposed and constructed, integrating the SPH (smoothed particle hydrodynamics) meshless particle method, FLO-2D shallow-water dynamics, and the MassFlow full-process simulation approach. Using COMSOL as a unified framework, this model employs MassFlow’s deep-integration, continuous medium method to simulate rainfall triggering and material source activation, FLO-2D’s shallow-water equations to describe macroscopic flow-deposition processes, and SPH’s mesh-free particle method to accurately capture large deformations and free-surface flow. The model fully reproduces the entire dynamic chain of debris flow processes, from rainfall triggering and soil mobilization to fluid transport and channel deposition. The reliability and accuracy of the model were verified by comparing it with field measurements from the 20 September 2022 historical debris flow event at Ni Chang Valley. Quantitative analysis indicates that when the viscosity coefficient increases from 0.1 Pa·s to 100 Pa·s, the flow velocity decreases by approximately 47% and the flow depth increases by approximately 62%. When the yield stress increases from 1 Pa to 100 Pa, the deposition area shrinks from 269,900 m² to approximately 109,000 m², a reduction of about 60%. Combining the results of the dynamic analysis, daily maximum temperature, daily precipitation, moisture content, mud-water level, and ground surface displacement were selected as core monitoring indicators. The analytic hierarchy process (AHP) was used to determine the weights of each indicator, and a data- and physics-driven weighted summation model for debris flow monitoring and early warning was constructed to achieve a five-level debris flow monitoring and early-warning system. Historical disaster cases demonstrate that this early-warning model can provide advance predictions of debris flow disasters up to 2 h and 40 min in advance. The warning lead time is sufficient, the grading logic is clear, and the model is capable of accurately capturing precursor information on disasters. Full article

Canarii Fructus (Canarium album) is a rich source of polyphenols with significant potential for functional food applications. This review summarizes recent advances in the composition, extraction technologies, biological activities, and utilization prospects of Canarii Fructus polyphenols (CFPs). More than 30 polyphenolic compounds have been identified, with gallic acid and ellagic acid as the major constituents, accounting for approximately 38.8% and 14.3% of the total phenolics, respectively. The fruit contains about 300 mg/100 g fresh weight of phenolic compounds. Emerging extraction technologies, including ultrasound-assisted extraction, microwave-assisted extraction, and ultrasound–microwave-assisted extraction (UMAE), have improved extraction efficiency, with UMAE achieving yields of up to 6.33% within 4.4 min. Unlike previous studies focusing primarily on phytochemical characterization or pharmacological activities, this review provides a comprehensive food-oriented perspective by integrating chemical diversity, extraction strategies, molecular mechanisms, bioavailability challenges, and functional food applications of CFPs. CFPs exhibit antioxidant, anti-inflammatory, antiviral, antibacterial, antitumor, anti-aging, hepatoprotective, and metabolic regulatory activities through pathways including Nrf2/ARE, NF-κB, PI3K/Akt, and AMPK. Representative bioactivities include α-glucosidase inhibition (IC₅₀ = 9.914 × 10⁻³ μg/mL) and regulation of lipid metabolism via AMPK activation. Particular attention is given to emerging approaches, including green extraction technologies, nanodelivery systems, and AI-assisted target discovery. Current limitations related to low bioavailability, unclear structure–activity relationships, and insufficient in vivo evidence are also discussed. Overall, CFPs represent a promising natural resource for the development of functional foods and nutraceuticals. Full article