Search Results (10,956)

Snakebite envenoming causes an estimated 138,000 deaths annually worldwide, with approximately 75% of fatalities occurring prior to arrival at definitive medical care. Even in regions where antivenom is available in hospitals, the absence of treatment options before a victim can reach definitive care results in delays of many hours before therapy is initiated. Manufacturing complexity, region-specific products, and the risk of anaphylaxis further limit the availability and use of antivenom in many regions. Reducing the persistently high mortality of snakebite envenoming requires both novel scientific approaches and partnerships that extend beyond traditional disciplinary and funding silos. This article describes the collaboration between Ophirex, a Public Benefit Corporation developing the oral secretory phospholipase A2 (sPLA2) inhibitor varespladib, and the United States military, which has identified a capability gap in snakebite treatment for forward-deployed personnel. The partnership was driven by a shared requirement for a shelf-stable, easy-to-administer, snake-species-agnostic therapy suitable for use prior to definitive medical care. A central insight of the program was that military operational requirements and global public health needs converged around the same product characteristics, enabling a strategically aligned development effort. From early proof-of-concept studies through regulatory pathway definition and advanced development, the Military–Ophirex partnership integrated operational requirements, regulatory planning, and iterative risk mitigation to advance manufacturing, nonclinical, and clinical development. This work provides both practical insights into complex drug development and a case study in how structured partnerships can carry innovation through translation in underfunded and operationally challenging conditions. Full article

Background: Chronic Kidney Disease-associated Pruritus (CKD-aP) is a frequent, debilitating, and often underestimated symptom in clinical practice, with significant impacts on quality of life, sleep, mental health, and therapeutic adherence. This study aimed to develop a structured, person-centered nursing care overview for the management of CKD-aP. Methods: A comprehensive narrative review of the recent scientific literature on CKD-aP was conducted, adapting the conceptual domains of the European Specialist Nurses Organisation (ESNO) Common Training Framework (CTF) to nephrology nursing practice. The theoretical model guiding the work was Virginia Henderson’s paradigm, selected for its consistency with care models focused on promoting independence and meeting fundamental human needs. The study would answer the main research question “Which nursing evidence, tools, and strategies can support integrated, patient-centered management of CKD-aP?”. Results: A structured nursing care process was developed, articulated in sequential phases (assessment, problem definition, planning, intervention, and re-evaluation), visually represented in an operational flowchart and supported by validated clinical tools. The model emphasizes the nurse’s role in the multidimensional management of the symptom, incorporating educational, relational, therapeutic, and coordination-focused interventions. Conclusions: This proposal contributes to nephrology nursing practice by providing a theoretical and practical framework to standardize the management of CKD-aP. It promotes a holistic, evidence-based approach tailored to individual care needs, establishing a foundation for future clinical, educational, and research developments. Full article

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

Objectives: The objective of the study was to investigate the mRNA expression of critical gene patterns, including IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1, and their associations with adverse postoperative outcomes in reconstructive and plastic surgery patients, depending on age. Methods: A total of 95 women participated in this prospective longitudinal cohort study and underwent reconstructive/plastic surgery. The mean age was 35.48 ± 6.61 years (range: 19–57). mRNA expression of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 genes was evaluated in peripheral blood leukocytes using a PCR-based method with reverse transcription of cDNA. Results: The risk of postoperative complications significantly increased with elevated expression levels of IL-6 and COL3A1 (7.5-fold, p = 0.007), CCL2 (6.2-fold, p = 0.012), and MAPK1 (25.5-fold, p < 0.001). Increased expression of MAPK8, IL-10, and MMP9 was associated with a 13.2-fold higher risk (p < 0.001). The strongest association was observed for COL1A1 overexpression, which increased complication risk by 58.33-fold (p < 0.001). Risk stratification using the Molecular Complication Risk Index (MCRI), incorporating weighted gene contributions, identified an unfavorable molecular profile predominantly among women aged ≥ 40 years. Receiver operating characteristic analysis confirmed the model’s discriminative ability (AUC = 0.78; 95% CI 0.68–0.87), with an optimal cut-off value of MCRI ≥ 8.5 (sensitivity 76%, specificity 71%, p < 0.05). Conclusions: The transcriptional activity of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 is associated with postoperative wound healing risk. Women aged over 40 years are at the highest risk of complications. Implementation of the MCRI model may enable early identification of high-risk patients, support targeted preventive strategies, and improve personalized surgical planning. Full article

This study aims to automate the generation of execution plans for university projects by transforming BPMN-based process models into hierarchical planning representations that can be executed by HTN planners. Effective implementation of university extension projects requires explicit management of objectives, dependencies, and operational constraints, yet this process is often carried out manually and without formal planning support. To address this problem, the paper proposes PlanProjU, a web-based platform that captures project knowledge through BPMN and translates it into HDDL domain and problem files for execution with SHOP2 and PyHOP. The system was evaluated through real university project cases and a comparative analysis of alternative generated plans. The results show that BPMN-based project knowledge can be operationalized into executable hierarchical planning structures and that different planners may produce distinct plan alternatives depending on project characteristics. The originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning the originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning in a domain that has received limited attention in prior research. In this sense, the proposal serves both as an innovative research contribution and as a practical alternative for structuring implementation decisions in institutional settings. Full article

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Sewage sludge management remains a critical challenge in Greece, where increasing regulatory pressure, environmental constraints, and limited stakeholder participation complicate regional decision-making. In particular, the revision of regional Waste Management Plans requires decision-support approaches that are both technically robust and socially legitimate. This study develops and applies a participatory, data-driven multi-criteria decision analysis framework to evaluate sustainable sewage sludge management strategies in the Region of Eastern Macedonia and Thrace. The framework combines structured stakeholder participation with quantitative performance assessment, enabling transparent, reproducible, and systematic comparison of alternative sewage sludge management options. Four realistic sludge management alternatives—composting fr agriculture, forestry use, land restoration, and thermal drying with energy recovery were assessed against fifteen economic, environmental, and social sub-criteria. Data were collected through structured questionnaires administered to forty-four representatives from five stakeholder groups: utilities (water and sewerage service providers), local authorities, scientists/experts, end-users, and citizens. Group preferences were aggregated using equal group weighting to ensure balanced representation. The results show that environmental and economic criteria outweigh social aspects. The highest mean weights were assigned to compliance with environmental requirements for products derived from the disposal method (0.105) and compliance with stricter national environmental legislation (0.104), followed by energy intensity (0.097), installation cost (0.065), and operation and maintenance (O&M) cost (0.061). Overall rankings identified composting and thermal drying as the most preferred options, followed by land restoration and forestry use; sensitivity analysis (±10% variation in sub-criterion weights) confirmed ranking stability. The proposed framework enhances decision transparency by embedding measurable criteria and stakeholder inputs within a structured analytical process. From a policy perspective, it addresses participation gaps in Greek waste planning and offers a transferable decision-support tool for future regional planning. Further extensions may include integration with life cycle assessment and cost–benefit analysis to support adaptive updates under circular economy objectives. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article

Background: Contemporary coronary artery bypass grafting (CABG) is often performed in patients with diffuse atherosclerosis, severe calcification, prior percutaneous coronary intervention (PCI), and fragile myocardium, creating intraoperative scenarios that can compromise target selection, anastomotic quality, and completeness of revascularization. We synthesize operative strategies and outcomes across five predefined “complex CABG” scenarios. Methods: A focused literature review was performed targeting intraoperative CABG challenges in adult patients. Two reviewers independently screened titles/abstracts and selected studies describing operative details, technical considerations, or outcomes relevant to (1) intramyocardial/embedded coronaries, (2) severely calcified or diffuse disease requiring reconstruction, (3) small-caliber targets/flow-limited grafting, (4) iatrogenic right ventricular (RV) injury, and (5) failed PCI/stent-related surgical management. Disagreements were resolved through discussion and consensus. Results: Thirty core publications were synthesized across five complex intraoperative CABG scenarios (intramural/embedded coronaries n = 7; calcified/diffuse disease n = 7; small-caliber/flow-limited targets n = 7; iatrogenic RV injury n = 5; failed PCI/stent-related management n = 5). Intramural/embedded targets: reported intramyocardial LAD prevalence ranged from 2.2–13%, and studies emphasized structured localization strategies with a small but real risk of ventricular injury depending on technique. Severely calcified/diffuse disease: reconstructive approaches (endarterectomy, patch angioplasty, long-segment LAD reconstruction) were used to create graftable beds when standard anastomosis was not feasible, with series reporting acceptable early mortality and generally high early-to-midterm patency when paired with planned antithrombotic and imaging follow-up strategies. Small-caliber targets: vessel size alone did not preclude durable grafting when flow was optimized, with evidence supporting flow-augmenting designs (e.g., sequential grafting) and intraoperative flow verification to reduce low-flow failure in limited runoff beds. Iatrogenic RV injury: bailout techniques prioritized rapid hemostasis while preserving LAD/graft patency using buttressed closure concepts designed for constrained exposure and ongoing bleeding risk. Failed PCI/stent-related pathology: long stented segments shifted operative planning from distal target selection to target reconstruction (stentectomy/endarterectomy with long-segment LAD reconstruction), with angiographic follow-up cohorts demonstrating feasible revascularization but variable patency by territory and lesion extent. Conclusions: Complex CABG is best approached as structured, anatomy-driven problem-solving: deliberate target localization, creation of a graftable bed when needed, flow-augmenting graft design, and predefined bailout options. Standardized comparative studies are needed to define optimal strategies across these common clinically important scenarios. Full article

Objective: We sought to compare common neonatal and maternal morbidity outcomes amongst operative vaginal delivery (OVD) versus cesarean delivery performed in the setting of failed attempt at labor. We planned to stratify outcomes by type of OVD (vacuum-assisted vaginal delivery (VAVD) and forceps-assisted vaginal delivery (FAVD)). Methods: This was a retrospective cohort study of singleton live births in the United States, using the 2023 National Vital Statistics birth certificate dataset. The primary outcome of interest was the risk of neonatal morbidity, as listed on the birth certificate. The secondary outcome of interest was the risk of maternal morbidity. Neonatal morbidities were planned to be analyzed independently (i.e., risk of NICU admission, need for antibiotics) as well as in aggregate (i.e., the risk of any morbidity occurring). Three groups were planned: FAVD, VAVD, and cesarean in the setting of attempted labor or attempted induction of labor (referent group). Differences in demographic and clinical characteristics were compared and subsequently adjusted for, and odds ratios (aOR) were calculated using multivariable logistic regression. Results: Of the 3,605,081 births from 2023, there were 15,384 FAVDs; 83,134 VAVDs; and 325,310 cesareans after failed labor. Neonatal morbidity was lower in FAVD (aOR 0.71, 95% CI 0.66–0.76) and VAVD (aOR 0.57, 95% CI 0.55–0.59) compared to cesarean delivery, with VAVD showing the lowest rates, in particular, the need for assisted ventilation (aOR 0.52 95% CI 0.48–0.57 with VAVD and aOR 0.74 95% CI 0.68–0.81 with FAVD) and NICU admissions aOR 0.66, 95% CI 0.60–0.71 with FAVD and aOR 0.48, 95% CI 0.46–0.51 with VAVD) were reduced with operative vaginal delivery. Antibiotic usage was only reduced in VAVD, not FAVD. Maternal morbidity was highest FAVD; however, this was driven by perineal lacerations. ICU admission, hysterectomy, and ruptured uterus were all higher in cesarean delivery than FAVD or VAVD. Conclusions: Operative vaginal delivery, particularly VAVD, is associated with reduced neonatal morbidity compared to cesarean delivery in the setting of labor. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

With the rapid growth of passenger demand, the imbalance between transport capacity and passenger flow has become increasingly severe. Existing studies seldom consider the impacts induced by passenger demand uncertainty under a flexible train composition mode. To address this issue, this study investigates the integrated optimization of train timetabling and rolling stock circulation planning under a flexible train composition mode. The objective is to minimize the number of stranded passengers and operational costs. A scenario-based robust optimization framework is introduced, and a mean risk objective is formulated by combining the expected objective value with the expected absolute deviation of each scenario’s objective value from the expectation. By using linearization techniques, the model is transformed into a mixed integer programming (MIP) problem, which balances the operating cost and robustness while satisfying safety and service level requirements. The model is validated through a case study of Shanghai Metro Line 16. Numerical experimental results indicate that, in a single scenario, compared with the fixed train composition scheme, the proposed scheme reduces the objective function value by 28.3%. Simultaneously, it can enhance the robustness of the train timetable and rolling stock circulation plan under the condition of uncertain passenger demands. The related findings provide decision support for the design of urban rail transit operating plans. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article