Search Results (4,053)

Federated learning (FL) enables multiple participants to collaboratively train models while efficiently mitigating the issue of data silos. However, large-scale heterogeneous data distributions result in inconsistent client objectives and catastrophic forgetting, leading to model bias and slow convergence. To address the challenges under non-independent and identically distributed (non-IID) data, we propose DPAO-PFL, a Dynamic Parameter-Aware Optimization framework that leverages continual learning principles to improve Personalized Federated Learning under non-IID conditions. We decomposed the parameters into two components: local personalized parameters tailored to client characteristics, and global shared parameters that capture the accumulated marginal effects of parameter updates over historical rounds. Specifically, we leverage the Fisher information matrix to estimate parameter importance online, integrate the path sensitivity scores within a time-series sliding window to construct a dynamic regularization term, and adaptively adjust the constraint strength to mitigate the conflict overall tasks. We evaluate the effectiveness of DPAO-PFL through extensive experiments on several benchmarks under IID and non-IID data distributions. Comprehensive experimental results indicate that DPAO-PFL outperforms baselines with improvements from $5.41\%$ to $30.42\%$ in average classification accuracy. By decoupling model parameters and incorporating an adaptive regularization mechanism, DPAO-PFL effectively balances generalization and personalization. Furthermore, DPAO-PFL exhibits superior performance in convergence and collaborative optimization compared to state-of-the-art FL methods. Full article

The state path optimization model, alongside strategies like slowing down and waiting, aims to identify optimal aircraft routes that minimize the total taxi time and prevent conflicts. Optimization reduces taxiing times for aircraft YZR7537, CES2558, and CSZ9806, while slightly increasing the times for CSN6310 and CSN3210 due to conflict hotspot avoidance measures. This approach also decreases the number of aircraft passing through key conflict hotspots, effectively reducing both conflicts and risk levels in these areas. Consequently, the total taxiing time for the optimized aircraft is cut by 53 s, enhancing airport operational efficiency. The proposed model serves as a theoretical foundation for developing an intelligent airport operation management system. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

The maritime industry faces growing pressure to reduce greenhouse gas (GHG) emissions, reflected in the progressive adoption of stricter international energy regulations. Wind-Assisted Propulsion Systems (WAPS) offer a promising solution by significantly contributing to decarbonization. This paper presents a versatile simulation and optimization platform that supports the conceptual design of WAPS-equipped vessels and evaluates the viability of such investments. The platform uses a steady-state force equilibrium model to simulate vessel performance along predefined routes under realistic weather conditions, incorporating regulatory frameworks and economic assessments. A multi-objective optimization framework identifies optimal designs across user-defined criteria. To demonstrate its capabilities, the platform is applied to a bulk carrier operating between China and the USA, optimizing for capital expenditure, net present value (NPV), and CO₂ emissions. Results show the platform can effectively balance conflicting objectives, achieving substantial emissions reductions without compromising economic performance. The final optimized design achieved a 12% reduction in CO₂ emissions, a 7% decrease in capital expenditure, and a 6.6 million USD increase in net present value compared to the reference design with sails, demonstrating the platform’s capability to deliver balanced improvements across all objectives. The methodology is adaptable to various ship types, WAPS technologies, and operational profiles, offering a valuable decision-support tool for stakeholders navigating the transition to zero-carbon shipping. Full article

Acute pancreatitis (AP) is among the most frequent gastroenterology emergencies, with hospital admission rates on the rise in recent decades. However, a specific treatment for this condition is still lacking. Mitochondrial damage induced by oxidative stress is regarded as the key event in the pathophysiology and initiation of cellular damage in AP. In the early stages of AP, the oxidant–antioxidant balance changes rapidly, and there are significant data regarding the reduced serum levels of antioxidants, with this event being correlated with the clinical severity of pancreatitis. Therefore, addressing oxidative stress could represent a potential therapeutic target in AP. In this comprehensive review, we aimed to provide an update on current evidence regarding clinical and experimental data on antioxidant use in AP, focusing on human studies investigating the effects of single and combined antioxidant supplementation. Although a multitude of animal studies demonstrated that antioxidant therapy has beneficial effects in experimental AP by reducing oxidative injury, inflammatory markers, and ameliorating histological outcomes, human trials showed predominantly conflicting results, with some studies suggesting benefit while others showed no effect, or even potential harm, when antioxidants were administered in high doses or in combination. Moreover, some antioxidants with beneficial results in experimental settings did not show the same efficacy when translated to human studies, which may be a consequence of either inappropriate dosage, route of administration and duration of therapy, or altered pharmacodynamics in vivo. In conclusion, oxidative stress plays a key role in the pathophysiology of AP by enhancing acinar cell injury, inflammation, and systemic complications. Future studies should be centered on optimized dosing strategies, early administration protocols, targeted patient selection, and delivery methods of proper pharmaceutical forms. Full article

Hydrogen is a promising zero-carbon fuel for internal combustion engines; however, the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NO_x emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs, the injector featuring three holes at a 15.24° injection angle outperformed the baseline, delivering improved mixture uniformity, reduced knock tendency, and lower NO_x emissions. These results demonstrate the potential of geometry-based optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies. Full article

Aircraft, as electromagnetically complex targets, have radar cross-sections (RCSs) that are influenced by various factors, with the inlet duct being a critical component that often serves as a primary source of electromagnetic scattering, significantly impacting the scattering characteristics. In light of the conflict between aerodynamic performance and electromagnetic characteristics in the design of aircraft engine inlet grilles, this paper proposes a metasurface radar-absorbing inlet grille (RIG) solution based on a NACA symmetric airfoil. The RIG adopts a sandwich structure consisting of a polyethylene terephthalate (PET) dielectric substrate, a copper zigzag metal strip array, and an indium tin oxide (ITO) resistive film. By leveraging the principles of surface plasmon polaritons, electromagnetic wave absorption can be achieved. To enhance the design efficiency, a multi-objective Bayesian optimization framework driven by the expected hypervolume improvement (EHVI) is constructed. The results show that, compared with a conventional rectangular cross-section grille, an airfoil-shaped grille under the same constraints will reduce both aerodynamic losses and the absorption bandwidth. After 100-step EHVI–Bayesian optimization, the optimized balanced model attains a 57.79% reduction in aerodynamic loss relative to the rectangular-shaped grille, while its absorption bandwidth increases by 111.99%. The RCS exhibits a reduction of over 8.77 dBsm in the high-frequency band. These results confirm that the proposed optimization design process can effectively balance the conflict between aerodynamic performance and stealth performance for RIGs, reducing the signal strength of aircraft engine inlets. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

In June 2025, a two-year-old female brown bear (Ursus arctos) appeared in the streets of Vilnius, the capital city of Lithuania. This sparked significant public, institutional, and media responses. This paper analyzes the event through ecological, social, and symbolic lenses to explore how large carnivores are perceived and managed at the wildland–urban interface. Through an examination of media reports, policy responses, and theoretical perspectives from environmental sociology and narrative studies, we explore how the bear’s presence became a public safety concern and a culturally significant symbol. Public discourse revealed tensions between institutional authority and local ethical values, as evidenced by hunters’ refusal to carry out a kill permit. This case also illustrates the growing use of technology, such as drones, in urban wildlife management. The bear’s peaceful departure reinforced the effectiveness of nonlethal conflict resolution. This case underscores the importance of integrating ecological realities with social perceptions, media framing, and symbolic interpretations in large carnivore conservation. It emphasizes the need for interdisciplinary approaches that address the emotional and cultural aspects of human–wildlife interactions in rapidly urbanizing areas. Full article

Background/Objectives: Gestational diabetes (GD), which affects approximately 15% of pregnancies worldwide, poses significant risks to both maternal and fetal health, underscoring the need for effective prevention and management strategies. This umbrella review aims to evaluate the role of probiotics in the prevention of GD. Methods: The review was conducted in accordance with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. A comprehensive literature search was performed in November 2024 across four databases: PubMed/Medline, Cochrane Library, Embase, and CINAHL. A total of 307 articles were identified, of which 6 met the inclusion criteria and were included in the final synthesis. Results: Probiotic supplementation was associated with a significant reduction in the incidence of GD in selected populations, particularly in women with a body mass index (BMI) < 26, age < 30 years [Relative Risk (RR): 0.58], and p < 0.05 in the other studies included, alongside improvements in several metabolic parameters. However, consistent benefits on maternal or neonatal complications were not observed but a 33% reduction in GD was confirmed (RR 0.67). The combination of probiotics with healthy lifestyle behaviors appeared to exert a stronger protective effect against GD and its potential complications. Conclusions: This umbrella review suggests that probiotics—particularly multi-strain formulations—may have a potential role in reducing the risk of GD in certain populations. However, the findings across the included studies are inconsistent and sometimes conflicting. While probiotics are generally considered safe and have recognized benefits for metabolic health, their efficacy as an adjunct intervention for GD prevention remains not fully clear. Further well-designed research is needed to clarify which specific probiotic interventions may be effective and to better guide clinical practice. Full article

The m-Polar Fuzzy Incidence Graph (m-PFIG) is an extension of the m-Polar Fuzzy Graph (m-PFG), which provides information on how vertices affect edges. This study explores the concept of matching within both bipartite and general m-polar fuzzy incidence graphs (m-PFIGs). It extends various results and theorems from fuzzy graph theory to the framework of m-PFIGs. This research investigates various operations within m-PFIGs, including augmenting paths, matching principal numbers, and the relationships among them. It focuses on identifying the most suitable employees for specific roles and achieving optimal outcomes, particularly in situations involving internal conflicts within an organization. To address fuzzy maximization problems involving vertex–incidence pairs, this study outlines key properties of maximum matching principal numbers in m-PFIGs. Ultimately, the matching concept is applied to attain these maximum principal values, demonstrating its effectiveness, particularly in bipartite m-PFIG scenarios. Full article

Background/Objectives: The prognostic uncertainty of Mild Cognitive Impairment (MCI) imposes comprehensive neuropsychological evaluations beyond mere memory assessment. However, previous investigations into other cognitive domains, such as attention, have yielded divergent findings. Furthermore, while evidence suggests the presence of sex differences across the spectrum of dementia-related conditions, no study has systematically explored attentional disparities between genders within this context. The current study aims to investigate differences in the attentional subcomponents, i.e., alerting, orienting, and executive control, between patients with MCI and healthy older controls (HOCs), emphasizing interactions between biological sex and cognitive impairment. Methods: Thirty-six participants (18 MCI, and 18 HOCs) were evaluated using the Attention Network Test (ANT). Raw RTs as well as RTs corrected for general slowing were analyzed using Generalized Mixed Models. Results: Both health status and sex influenced ANT performance, when considering raw RTs. Nevertheless, after adjusting for the baseline processing speed, the effect of cognitive impairment was no longer evident in men, while it persisted in women, suggesting specific vulnerabilities in females not attributable to general slowing nor to the MCI diagnosis. Moreover, women appeared significantly slower and less accurate when dealing with conflicting information. Orienting and alerting did not differ between groups. Conclusions: To the best of our knowledge, this is the first study investigating sex differences in attentional subcomponents in the aging population. Our results suggest that previously reported inconsistencies about the decline of attentional subcomponents may be attributable to such diversities. Systematically addressing sex differences in cognitive decline appears pivotal for informing the development of precision medicine approaches. Full article

Based on attachment theory and the McMaster family functioning model, this study explores the protective role and mechanisms of a caring parenting style in protecting adolescents from bullying, from the perspective of the family environment. Study 1, conducted in Southwest China with middle school students (n = 4582), investigates the relationship between a caring parenting style and adolescent bullying victimization through a large-scale cross-sectional survey. The results show that both parents’ caring parenting styles are significantly negatively correlated with adolescent bullying victimization. Study 2, a two-wave study (n = 302), explores the protective mechanisms of a caring parenting style in adolescent bullying victimization. We not only observed again that a caring parenting style significantly negatively predicts bullying victimization but also found that family functioning and constructive conflict resolution play a chain-mediating role in this relationship. This finding not only supports the core hypothesis of attachment theory regarding the role of a secure base but also expands the theoretical model of bullying protection from a family ecological perspective by revealing a three-level transmission mechanism of parenting style–family system–individual capability, providing a theoretical anchor for the construction of a “family–school” collaborative intervention framework. Full article

This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. On the algorithmic level, an adapted NSGA-III was implemented on the MATLAB platform (version R2022b), introducing a dynamic reference point mechanism and hybrid constraint-handling strategy to enhance convergence and solution diversity. Taking the northern residential area of the western campus of Dalian University of Technology as a case study, multiple Pareto-optimal solutions were generated. Five representative alternatives were selected and evaluated through the AHP–TOPSIS method to determine the optimal scheme. The results indicated significant improvements in energy, economic, spatial, and ecological dimensions, while also achieving quantifiable control over cultural expression. On this basis, an integrated optimization strategy targeting “form–function–environment–culture” was proposed, offering data-informed support and procedural reference for systematic campus planning. This study demonstrates the effectiveness, adaptability, and practical value of the proposed approach in addressing multi-objective conflicts in university planning. Full article