Search Results (1,165)

Invasive candidiasis (IC), characterized by the most common clinical manifestation of candidemia, is a fungal infection with a high mortality rate and a significant impact on global public health. It is estimated that each year there are between 227,000 and 250,000 hospitalizations related to IC, with more than 100,000 associated deaths. In Latin America and the Caribbean (LA&C), the absence of a standardized surveillance system has led to multicenter studies documenting incidences ranging from 0.74 to 6.0 cases per 1000 hospital admissions, equivalent to 50,000–60,000 hospitalizations annually, with mortality rates of up to 60% in certain high-risk groups. Armed conflicts and structural violence in LA&C cause forced displacement, the collapse of health systems, and poor living conditions—such as overcrowding, malnutrition, and lack of sanitation—which increase vulnerability to opportunistic infections, such as IC. Insufficient specialized laboratories, diagnostic technology, and trained personnel impede pathogen identification and delay timely initiation of antifungal therapy. Furthermore, the empirical use of broad-spectrum antibiotics and the limited availability of echinocandins and lipid formulations of amphotericin B have promoted the emergence of resistant non-albicans strains, such as Candida tropicalis, Candida parapsilosis, and, in recent outbreaks, Candidozyma auris. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

Asthma is a persistent ailment that impacts the respiratory system and stands as a formidable public health challenge globally. Inhaled corticosteroids and bronchodilators, while effective in asthma management, are accompanied by side effects and high costs. Recently, nutraceuticals have gained significant attention as adjuvant therapy due to their promising outcomes. Given the antioxidant properties, nutrient richness, and an array of health benefits, beetroot and its bioactive compounds have been tested as an adjuvant therapy for asthma management. Although its main bioactive compound, betalains (betanin), has demonstrated promising results in mouse studies, beetroot juice has been found to worsen asthma. This review investigated the full spectrum of active compounds associated with beetroots to understand the underlying factors contributing to the conflicting findings. The finding suggests that individual bioactive compounds, such as phenolic compounds, flavonoids, nitrates, betalains, saponins, vitamins, fiber, and carotenoids, possess asthma-managing properties. However, the consumption of juice may exacerbate the condition. This discrepancy may be attributed to the presence of sugars and oxalates in the juice, which could counteract the beneficial effects of the bioactive compounds. Full article

The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. Third, the head implements a shared feature detection head (SFDH) structure, incorporating shared convolution modules for efficient cross-scale feature sharing and detail enhancement branches for improved texture and edge modeling. Extensive experiments validate the effectiveness of LEAD-YOLO: on the nuImages dataset, the method achieves 3.8% and 5.4% improvements in mAP@0.5 and mAP@[0.5:0.95], respectively, while reducing parameters by 24.1%. On the VisDrone2019 dataset, performance gains reach 7.9% and 6.4% for corresponding metrics. These findings demonstrate that LEAD-YOLO achieves an excellent balance between detection accuracy and model efficiency, thereby showcasing substantial potential for applications in autonomous driving. Full article

Background and Objectives: Diabetes is a global public health challenge, with increasing prevalence worldwide. The implementation of artificial intelligence (AI) in the management of this condition offers potential benefits in improving healthcare outcomes. This study primarily investigates the barriers and facilitators perceived by healthcare professionals in the adoption of AI. Secondarily, by analyzing both quantitative and qualitative data collected, it aims to support the potential development of AI-based programs for diabetes management, with particular focus on a possible bottom-up approach. Materials and Methods: A scoping review was conducted following PRISMA-ScR guidelines for reporting and registered in the Open Science Framework (OSF) database. The study selection process was conducted in two phases—title/abstract screening and full-text review—independently by three researchers, with a fourth resolving conflicts. Data were extracted and assessed using Joanna Briggs Institute (JBI) tools. The included studies were synthesized narratively, combining both quantitative and qualitative analyses to ensure methodological rigor and contextual depth. Results: The adoption of AI tools in diabetes management is influenced by several barriers, including perceived unsatisfactory clinical performance, high costs, issues related to data security and decision-making transparency, as well as limited training among healthcare workers. Key facilitators include improved clinical efficiency, ease of use, time-saving, and organizational support, which contribute to broader acceptance of the technology. Conclusions: The active and continuous involvement of healthcare workers represents a valuable opportunity to develop more effective, reliable, and well-integrated AI solutions in clinical practice. Our findings emphasize the importance of a bottom-up approach and highlight how adequate training and organizational support can help overcome existing barriers, promoting sustainable and equitable innovation aligned with public health priorities. Full article

Achieving sustainable development goals remains a core issue in global development. In response, China has proposed the development of new quality productive forces (NQPFs) through innovative thinking, emphasizing that fostering NQPFs is both an intrinsic requirement and a pivotal focus for advancing high-quality development. Concurrently, the intelligent transformation of the manufacturing sector serves as a critical direction for China’s economic restructuring and upgrading. This paper places “new quality productive forces” and “intelligent transformation of manufacturing” within the same analytical framework. Starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation of manufacturing,” it concretizes the value implications of new quality productive forces into a systematic conceptual framework driven by the synergistic interaction of three major mechanisms: the mechanism of revolutionary technological breakthroughs, the mechanism of innovative allocation of production factors, and the mechanism of deep industrial transformation and upgrading. This study constructs a “3322” evaluation index system for NQPFs, based on three formative processes, three driving forces, two supporting systems, and two-dimensional characteristics. Simultaneously, it builds an evaluation index system for the intelligent transformation of manufacturing, encompassing intelligent technology, intelligent applications, and intelligent benefits. Using national time-series data from 2012 to 2023, this study assesses the development levels of both NQPFs and the intelligent transformation of manufacturing during this period. The study further analyzes the impact of NQPFs on the intelligent transformation of the manufacturing sector. The research results indicate the following: (1) NQPFs drive the intelligent transformation of the manufacturing industry through the three mechanisms of innovative allocation of production factors, revolutionary breakthroughs in technology, and deep transformation and upgrading of industries. (2) The development of NQPFs exhibits a slow upward trend; however, the outbreak of the pandemic and Sino-US trade frictions have caused significant disruptions to the development of new-type productive forces. (3) The level of intelligent manufacturing continues to improve; however, from 2020 to 2023, due to the impact of the COVID-19 pandemic and Sino-US trade conflicts, the level of intelligent benefits has slightly declined. (4) NQPFs exert a powerful driving force on the intelligent transformation of manufacturing, exerting a significant positive impact on intelligent technology, intelligent applications, and intelligent efficiency levels. Full article

Departure punctuality (KPI01) and ATFM slot adherence (KPI03) have been emphasized by the International Civil Aviation Organization as key performance indicators (KPIs) in the Global Air Navigation Plan. To address the inherent conflict between these two objectives in departure scheduling, a multi-objective optimization model is proposed that aims to simultaneously enhance departure punctuality, ATFM slot adherence, and taxiing efficiency. A simulated annealing algorithm based on a resource transmission mechanism was developed to solve the model effectively. Based on full-scale operational data from Nanjing Lukou International Airport in June 2023, the empirical results confirm the model’s effectiveness in two primary dimensions: (1) Significant improvement in taxiing efficiency: The average unimpeded taxi-out time was reduced by 6.4% (from 17.2 to 16.1 min). The number of flights with taxi-out times exceeding 30 min decreased by 58%. For representative taxi routes (e.g., stand 118 to runway 6), the excess taxi-out time was reduced by 82.3% (from 5.61 to 1.10 min). (2) Enhanced operational punctuality: Departure punctuality improved by 10.7% (from 67.9% to 78.7%), while ATFM slot adherence increased by 31.2% (from 64.6% to 95.8%). This study presents an innovative departure scheduling approach and offers a practical framework for improving collaborative operational efficiency among airports, air traffic management units, and airlines. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

Russia’s invasion of Ukraine has profoundly altered the global geopolitical landscape. Owing to its geographical proximity, the conflict has had a considerable impact on Europe. Marked by the professionalisation and democratisation of technology, it has underscored the growing significance of hybrid warfare, in which disinformation and propaganda serve as additional instruments of war. Within this context, the aim of this article is to examine the characteristics of false information related to the war between Russia and Ukraine in four European countries between 2022 and 2023. To this end, a content analysis of 297 hoaxes was conducted across eight fact-checking platforms, complemented by ten in-depth interviews with specialised professionals. The findings indicate that disinformation is characterised by viral audiovisual hoaxes, particularly on Facebook and X (formerly Twitter), with a notable surge in disinformation flows at the onset of the invasion. In the early months, misleading content predominantly consisted of decontextualised images of the conflict, whereas a year later, the focus shifted to narratives concerning international support and alliances. The primary objective of this disinformation is to polarise public opinion against a perceived common enemy. The conclusions provide a broader and more nuanced understanding of wartime disinformation within the European context. Full article

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

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

Global supply chains face unprecedented challenges from geopolitical conflicts, climate change, economic volatility, and technological disruptions, highlighting the critical role of supply chain resilience as a core strategy for firms to maintain stability and competitive advantage. Grounded in the resource-based view and dynamic capability theory, this study examines how supply chain capability—that is, entrepreneurial leadership, collaborative capability, and digital transformation—enhances resilience, which mediates its impact on performance. Using structural equation modeling on survey data from Chinese firms, we find that resilience, comprising absorptive, reactive, and recovery capability, significantly mediates the relationship between supply chain capability and performance. Environmental uncertainty moderates this relationship, particularly in highly uncertain contexts, where resilience becomes a key driver of competitive advantage. Theoretically, this study extends dynamic capability theory by disaggregating resilience and exploring its mediating role. Practically, it emphasizes strengthening entrepreneurial leadership, collaborative capability, and digital transformation to improve resilience and performance in uncertain environments. Full article

National parks represent a global initiative for biodiversity conservation and environmentally sustainable societal development, with China having launched its own national park program. The establishment and operation of these parks significantly impact local residents’ livelihoods. Based on DFID’s Sustainable Livelihoods Framework, an assessment tool introduced by the UK Department for International Development (DFID) for evaluating the livelihood standards of residents, this study constructs a structural equation modeling (SEM) framework to analyze how national parks affect residents’ livelihoods, discussing livelihood risk management and feasible capacity-building interventions. Focusing on the Northeast Tiger and Leopard National Park as a case study, the research reveals that indirect wildlife-inflicted damage poses more pronounced negative impacts on local communities than park establishment policies. Both regulatory land-use restrictions and wildlife conflicts disrupt land-based livelihood activities, ultimately affecting residents’ livelihood stability. Mitigation requires comprehensive measures, including retaining essential farmland; providing vocational skill training; offering specialized loans; diversifying employment channels; and improving compensation mechanisms to safeguard residents’ livelihood security. Full article