Search Results (14,390)

With the rapid development of social media, the speed and breadth of information dissemination have increased substantially, leading to more complex patterns in the emergence and evolution of online public opinion. Compared to unsigned networks, signed networks more accurately capture supportive and adversarial relationships among users. Although the traditional Polarity-Related Independent Cascade model (IC-P) can describe opinion propagation in signed networks, its capability remains limited when applied to complex social environments. To address this issue, this paper improves the IC-P model by incorporating a Prisoner’s Dilemma game to establish a user propagation-choice mechanism. Furthermore, activation probability and activation thresholds are redesigned from the perspectives of authority effect, homophily, and temporal decay, resulting in an Independent Cascade model incorporating Communication Choice and Polarity (ICC-P). Using three real-world negative public opinion datasets collected from the Sina Weibo platform spanning from March to April 2024, Monte Carlo simulations were conducted and compared with the main baseline models. Experimental results indicate that, relative to the best existing baselines, ICC-P reduces the mean absolute error of the prediction of the propagation scale by approximately 43% and reduces the mean absolute error of the prediction of the sentiment distribution of the nodes by approximately 57%, demonstrating significant improvements in both propagation fitting accuracy and sentiment prediction performance. Full article

Urban–rural fringes within contaminated regions frequently exhibit severe socio-environmental fragmentation and territorial stigmatization. This study evaluates the implementation of a Successional Agroforestry System (SAFS) in the “Land of Fires” (Southern Italy), which is conceptualized as a multifunctional socio-ecological infrastructure. Adopting a six-year longitudinal case study design (2019–2025), the research utilizes the Gioia methodology to triangulate retrospective field records and systematic monitoring with iterative qualitative narratives. Semi-quantitative and retrospective ecological evaluations indicate that the established multi-layered vertical stratification improved proxy indicators of structural complexity and soil functionality. Estimated soil surface coverage increased from 5.0 ± 1.2% to 85.0 ± 4.3%, while proxy vegetation density rose from 4.8 ± 1.2 to 36.4 ± 4.7 plants/m² (p < 0.001). Beyond these biophysical trends, the intervention catalyzed a “narrative inversion,” transitioning the site from a stigmatized wasteland to a socio-ecological hub that fostered a significant increase in community engagement (from 6.2 ± 1.4 to 34.8 ± 6.5 participants per event). By integrating agroecological practices with the EcoFoodFertility framework, the project highlights the potential of localized interventions to support primary environmental prevention strategies aligned with a One Health paradigm. The findings suggest that this SAFS represents a scalable model for territorial re-signification, offering transferable insights for aligning ecological restoration with social innovation in degraded peri-urban landscapes in accordance with Nature-Based Solutions (NBSs) and European Green Deal objectives. Full article

Accurate and consistent self-localization is essential for multi-UAV aerial missions in complex dynamic environments. However, communication constraints and heterogeneous sensor reliability variations often lead to cumulative localization errors and degraded robustness in conventional fusion frameworks. To address these challenges, this paper proposes a distributed cooperative localization framework integrating deep temporal feature learning, heterogeneous multi-sensor fusion, and consistency-aware distributed state estimation. First, an LSTM-based staged fusion strategy is designed to integrate VIO, GPS, and UWB measurements for accurate single-UAV localization. Second, a Squeeze-and-Excitation LSTM Self-Attention (SE-LSTM-SA) network is developed to adaptively recalibrate heterogeneous sensor channels and enhance temporal feature extraction under dynamic sensing conditions. Finally, a consistency-aware distributed fusion mechanism based on the Labeled Multi-Bernoulli (LMB) framework is introduced to improve inter-UAV state consistency through iterative local-neighbor information exchange. Experiments conducted on the XTDrone platform demonstrate that the proposed framework achieves superior localization accuracy compared with traditional EKF and conventional LSTM-based methods. Specifically, the proposed method achieves lower RMSE, MAE, and Maximum Prediction Error (MaxPE), while significantly improving global consistency performance. Experimental results demonstrate that the proposed framework provides accurate and consistent localization performance for multi-UAV systems in complex dynamic environments. Full article

Small-scale vertical-axis wind turbines (VAWTs) are increasingly essential for the “blue economy,” providing autonomous power to remote coastal communities, offshore platforms, and marine industries. However, the design of efficient Darrieus-type rotors is complicated by complex unsteady aerodynamics, particularly the phenomenon of dynamic stall. This study aims to establish and validate a cost-effective yet accurate mathematical modeling approach for simulating unsteady turbulent flow around a Darrieus H-rotor to support practical engineering applications. The research methodology integrates computational fluid dynamics (CFD) with physical experiments in a hydrodynamic channel. The numerical model utilizes the unsteady Reynolds-averaged Navier–Stokes (URANS) equations closed with the Strain-Adaptive Linear Spalart–Allmaras (SALSA) turbulence model, chosen for its efficiency in capturing flow separation. The system of initial equations was being devised relatively to an arbitrary curvilinear coordinate system. The pressure and velocity fields have been coordinated using the artificial compressibility method adapted to calculate non-stationary problems. Experimental verification was conducted in the GT-400 hydrodynamic tube using a three-bladed H-rotor model, where flow structures were visualized via the colored jet method at tip speed ratios λ ranging from 2 to 5 and Reynolds number 1470. The findings reveal that dynamic stall occurs over a significant portion of the blade trajectory, characterized by vortex generation at the leading edge and subsequent advection along the chord. Qualitative comparison demonstrates a high degree of correlation between the calculated vortex dynamics and physical flow spectra. These results confirm that the URANS-SALSA approach provides a rational compromise between computational cost and physical accuracy. Full article

A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has been introduced as a promising multiple access scheme. This study investigates the energy efficiency (EE) and spectral efficiency (SE) performance of NOMA in comparison with Orthogonal Multiple Access (OMA) under varying bandwidth conditions. In addition to conventional analytical and simulation-based evaluations, artificial intelligence (AI) techniques, including Deep Learning (DL), Decision Tree (DT), K-Nearest Neighbours (KNN), and Logistic Regression (LR), are employed to model and predict system performance. The AI models are trained using simulation-generated datasets to capture complex relationships between bandwidth, transmit power, and user distribution. Simulation results demonstrate improvement in SE and EE of NOMA across different bandwidth scenarios. Furthermore, DL and DT models achieve higher prediction accuracy. The consistency between AI predictions and simulation outcomes confirms the robustness of the proposed framework. These findings highlight the superiority of NOMA over OMA and demonstrate the effectiveness of integrating AI techniques for performance optimization in 5G and beyond wireless networks. Full article

Addressing critical bottlenecks in traditional hydrogen refueling station operations—specifically supply–demand imbalances and suboptimal scheduling—this paper presents a systematic review of the advancements and practical implementations of intelligent transportation platforms (ITPs). We explore how these platforms catalyze enhancing operational efficiency within the hydrogen ecosystem. This paper first outlines the technical foundations of Vehicle-to-Everything communication, edge computing, and multi-source data fusion, and provides an in-depth analysis of core challenges, such as demand uncertainty and resource scheduling complexity, as well as existing optimization algorithms. Through typical case studies, the significant value of such platforms in breaking down data silos, reducing equipment idle rates, and achieving end-to-end energy efficiency optimization is demonstrated. This study notes that current bottlenecks include fragmented standards, difficulties in implementing algorithms, commercial challenges, and the retrofitting of existing infrastructure. Moving forward, efforts should shift from isolated technological breakthroughs to ecosystem development. This includes improving demand forecasting accuracy in low-penetration regions, implementing lightweight retrofits to revitalize the existing market, establishing cross-domain data collaboration standards, building a trustworthy cross-platform settlement system, and exploring innovative pathways that integrate “hydrogen, carbon, and computing.” Full article

Background/Objectives: Emerging viral zoonoses represent a growing threat to global public health, with most newly emerging infectious diseases originating from animal reservoirs. Recent outbreaks of monkeypox, Ebola virus disease, Marburg virus disease, Rift Valley fever, and avian influenza highlight the capacity of zoonotic viruses to cross species barriers, spread internationally, and generate substantial health, social, and economic consequences. This review examines the ecological, epidemiological, and biological determinants of viral zoonotic emergence and transmission, with particular emphasis on vaccination and outbreak prevention strategies. Methods: A structured narrative review was conducted using a predefined literature search strategy across major scientific databases. Peer-reviewed epidemiological, clinical, and public health publications published between January 2000 and February 2026 were screened and selected according to predefined relevance criteria. Results: The emergence of viral zoonoses is driven by complex interactions among animal reservoirs, environmental and climatic changes, human behavior, and viral adaptation. Although transmission pathways and clinical outcomes differ among pathogens, common determinants of spillover and outbreak amplification were identified. Current evidence supports the importance of integrated surveillance, genomic monitoring, vaccination strategies, and community engagement as key components of preparedness and response. Emerging preventive approaches targeting pathogen transmission, including transmission-blocking strategies and vector-associated microbiota interventions, may provide additional opportunities for disease control. Conclusions: Strengthening preparedness for emerging viral zoonoses requires coordinated One Health approaches integrating human, animal, and environmental health. Future priorities include the development of next-generation vaccines, expansion of digital and genomic surveillance systems, improved equitable access to vaccines, and innovative interventions aimed at reducing zoonotic spillover and interrupting pathogen transmission. Full article

Background/Objectives: Community-based drug rehabilitation is a key component of public health strategies in China, with anti-drug social workers playing a frontline role in relapse prevention, social reintegration, and long-term recovery. However, the sustainability and effectiveness of this workforce remain uncertain due to complex organisational and structural conditions. This study aims to examine the professional roles, work-related challenges, and coping strategies of anti-drug social workers within community-based rehabilitation systems. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and was registered in PROSPERO (Registration ID: 1381833). The literature published between 2009 and 2025 was identified through Google Scholar, PubMed, Web of Science, and the Electronic Library. A total of 35 Chinese and English-language studies met the inclusion criteria and were analysed to synthesise evidence on social work practice in drug rehabilitation contexts. Results: The findings identify three core professional roles: information provider, resource linker, and relationship repairer. These roles highlight the multifaceted contribution of social workers in bridging institutional systems and client needs. However, their effectiveness is constrained by fragmented governance structures, role conflict, professional identity ambiguity, administrative burden, limited training, and sustained emotional labour. These conditions contribute to occupational stress, burnout risk, and workforce instability, which weaken service continuity and client-centred care. Conclusions: Strengthening community-based drug rehabilitation requires addressing workforce and system-level constraints. Clearer role definition, targeted interdisciplinary training, reduced administrative demands, and structured organisational support are essential to enhance professional capacity, improve service delivery, and support long-term recovery outcomes. Full article

Obesity is increasingly recognized as a complex systemic disorder rather than a simple consequence of excess energy intake and fat accumulation. This review presents a systems biology framework that examines how obesity-driven disruption of inter-organ communication networks contributes to chronic disease susceptibility, with particular emphasis on colorectal cancer (CRC). Disrupted signaling among the brain, adipose tissue, liver, skeletal muscle, gut, and immune system generates maladaptive feedback loops that promote chronic metabolic inflammation (metaflammation), loss of physiological resilience, and progressive metabolic dysfunction. Within this framework, obesity is redefined as a network disease characterized by neuroendocrine dysregulation, adipose tissue remodeling, immune dysfunction, impaired organ crosstalk, and alterations in the gut microbiome. A central feature of this dysregulation is persistent low-grade inflammation driven by immune-metabolic reprogramming and sustained activation of inflammatory pathways. Obesity-associated metaflammation is further linked to accelerated biological aging through mechanisms involving cellular senescence, mitochondrial dysfunction, oxidative stress, and impaired metabolic resilience. These interconnected processes create a tumor-promoting environment by enhancing oncogenic signaling, disrupting intestinal barrier integrity, altering microbial and metabolic signaling, impairing immune surveillance, and promoting epithelial dysfunction, thereby increasing susceptibility to CRC. The review also examines how behavioral, circadian, environmental, and socioeconomic factors influence metabolic health and cancer risk. Finally, emerging translational opportunities, including biomarker-guided risk stratification, precision prevention, metabolic network restoration, and integrative lifestyle and pharmacological interventions, are discussed. Collectively, this review reframes obesity as a whole-body regulatory disorder and provides an integrated conceptual framework linking metabolism, inflammation, aging, and colorectal carcinogenesis to inform future prevention and therapeutic strategies. Full article

Direct sea discharge outlets served as critical conduits for urban sewage and industrial wastewater disposal, playing dual roles as pollutant dilution channels and hotspots for pathogens and antibiotic resistance genes. Traditional monitoring approaches relying on physicochemical parameters and fecal indicator bacteria failed to capture the latent and cumulative risks posed by complex microbial communities. In this review, a holistic microbiome perspective was adopted to systematically synthesize current knowledge on the bacterial community dynamics, assembly mechanisms, resistome–virulome coupling patterns, mobilome-associated risk characteristics, and emerging biomonitoring strategies in direct sea discharge outlets. By integrating high-throughput multi-omics technologies with ecological network analysis and machine learning, we delineated a paradigm shift from cataloging microbial presence to deciphering functional interactions, risk propagation dynamics, and proactive surveillance strategies. Furthermore, under the “One Health” framework, we discussed emerging research frontiers and future challenges in managing pollution at discharge outlets, aiming to provide a scientific basis for environmental risk management in coastal zones. Full article

Wavefront sensing (WFS) is fundamental to adaptive optics, astronomical observation, biological microscopy, and free-space optical communications. However, conventional approaches—including Shack–Hartmann sensors, shearing interferometers, and transport of intensity equation-based methods—are inherently limited by trade-offs among spatial sampling density, angular dynamic range, and device compactness and have rarely been extended to the mid-infrared range. Here, we propose an on-chip mid-infrared wavefront sensing scheme operating based on vectorial photocurrent manipulation and analyze the properties of the proposed device through finite-element simulations. The proposed device comprises a hexagonal array of antenna-integrated graphene pixels, each equipped with three contacts and a microlens. Based on the antenna-induced vectorial photocurrent manipulation, angle-dependent absorption is translated into photocurrent signals, potentially enabling unambiguous recovery of both the elevation and azimuth angles of the incident light over an effective angular dynamic range of ±28°. The hexagonal layout provides a high spatial sampling density of 11,547 mm⁻². Southwell algorithm-based wavefront reconstruction and numerical simulations yield faithful recovery of parabolic, conical, and quadrangular pyramidal wavefronts. In addition, simulation results indicate that this approach can enable high-fidelity reconstruction of both the phase and intensity distributions of an object based on angular-spectrum diffraction theory. Overall, this work theoretically demonstrates a new route toward high-density wavefront measurement and complex light field imaging in the mid-infrared range without a conventional imaging lens. Full article

Brachymystax lenok tsinlingensis is an endangered salmonid endemic to China. Traditional trapping methods frequently fail to detect this rare fish in low-density mountain streams, hampering evidence-based conservation. Here, we employed environmental DNA quantitative PCR (eDNA-qPCR) with species-specific primers to assess the spatial biomass distribution of this species in the Zhouzhi Heihe River. Concurrently, we surveyed plankton, benthic macroinvertebrates, and physicochemical water parameters. eDNA detected the target species at 12 of 14 sites, with reliable quantification achieved at 9 sites, suggesting that the method may be more effective than conventional trapping for detecting this species under the studied low-density conditions. eDNA-derived relative biomass exhibited pronounced spatial heterogeneity, ranging from 6.0 × 10⁻⁴ to 1.5 × 10⁻² g/cm³. Water depth showed a significant positive association with biomass (r = 0.5347), whereas phytoplankton Shannon diversity (a measure of species richness and evenness) was significantly negatively correlated (r = −0.5447). Flow velocity displayed a negative trend that did not reach statistical significance (r = −0.5009). Plankton and benthic communities indicated overall ecological conditions but did not directly explain the observed spatial variation in fish biomass. These findings indicate that the spatial pattern of B. lenok tsinlingensis is primarily shaped by local physical habitat structure, with deeper, hydraulically more complex channel units serving as key microhabitats. eDNA-qPCR thus represents an effective, low-disturbance monitoring tool for this endangered cold-water fish and provides a scientific basis for targeted habitat protection and restoration. Full article

An emerging environmental pollutant, microplastics have garnered global attention due to their widespread presence in soil and aquatic ecosystems. Early research primarily treated microplastics as single pollutants, focusing on their individual toxic effects. However, microplastics in the environment exist as a complex mixture, comprising various polymer types, sizes, shapes, and aging states. This diversity influences how microplastics regulate ecosystem carbon and nitrogen cycles and intervene through pathways such as direct carbon input, physical disturbance, microbial community restructuring, and coupled effects. This paper systematically reviews the characteristics of microplastic diversity and its mechanisms influencing carbon and nitrogen cycles: the chemical structure of polymers determines bioavailability and degradation rate, with biodegradable plastics altering carbon and nitrogen transformations more significantly than conventional plastics; microplastics of different sizes affect nitrogen transformation dynamics by modulating specific surface area and microbial colonization, with small-sized biodegradable microplastics particularly inhibiting plant nitrogen uptake; aging modifies surface properties and dissolved organic carbon release, thereby enhancing their role in promoting greenhouse gas emissions. Existing studies are largely confined to short-term laboratory simulations, leaving a gap in understanding the comprehensive effects of microplastic diversity under long-term, field conditions. Future research should focus on standardized methods and long-term experiments with multi-factor coupling to provide a scientific basis for ecological risk assessment of microplastic pollution. Full article

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Introduction/Objectives: Patient-centered communication is essential in oncology care, where healthcare professionals often manage emotionally demanding conversations, uncertainty, complex decisions, and patient involvement in care. However, the relationship between communication knowledge, empathic listening, and practical communication skills remains insufficiently examined. This study aimed to examine the associations between communication knowledge, empathic listening, and interpersonal communication skills among healthcare professionals involved in oncology care. Methods: A cross-sectional study was conducted in Croatia from May to November 2025 on a convenience sample of 138 healthcare professionals involved in oncology care. Communication knowledge was assessed using a study-specific questionnaire, empathic listening using an adapted Active Empathic Listening Scale, and interpersonal communication skills using an adapted Interpersonal Communication Skills Inventory. Because the instruments were adapted to the oncology care context, their dimensions were examined using exploratory factor analysis and interpreted as sample-specific exploratory constructs. Descriptive statistics, correlation analyses, and multiple linear regression analyses were performed. Results: Clear message delivery and assertiveness had the highest self-reported score, whereas emotional interaction management had the lowest. Communication knowledge was not an independent predictor of communication skills dimensions. Processing and responding positively predicted clear message delivery and assertiveness (β = 0.361; p = 0.001; R² = 13.4%), while noticing emotional and nonverbal cues negatively predicted emotional interaction management (β = −0.234; p = 0.032; R² = 7.6%). The explained variance of the models was low. Conclusions: The findings suggest limited but potentially relevant associations between selected dimensions of empathic listening and self-reported communication skills in oncology care. Communication knowledge, measured using a study-specific exploratory instrument, was not independently associated with communication skills. Because of the exploratory design, self-report measures, adapted instruments, and convenience sampling, the results should be interpreted with caution. Full article