Search Results (4,162)

Background: Undergraduate nursing students (UNSs) often enter clinical training just as they are still mastering the emotional labor of the profession. In Nepal, where teaching hierarchies discourage upward dialogue and hospitals routinely struggle with overcrowding, supply shortages, and outward nurse migration, these learners confront a distinct, under-documented burden of psychological distress. Objective: This study examines how UNSs interpret, negotiate, and cope with the mental health challenges that arise at the intersection of cultural deference, resource scarcity, and migration-fueled uncertainty. Methods: A qualitative design employing reflexive thematic analysis (RTA), guided by the Reflexive Thematic Analysis Reporting Guidelines (RTARG), was used. Fifteen second-, third-, and fourth-year Bachelor of Science in Nursing students at a major urban tertiary institution in Nepal were purposively recruited via on-campus digital flyers and brief in-class announcements that directed students (by QR code) to a secure sign-up form. Participants then completed semi-structured interviews; audio files were transcribed verbatim and iteratively analyzed through an inductive, reflexive coding process to ensure methodological rigor. Results: Four themes portray a continuum from silenced struggle to systemic constraint. First, Shrouded Voices, Quiet Connections captures how students confide only in trusted peers, fearing that formal disclosure could be perceived as weakness or incompetence. Second, Performing Resilience: Masking Authentic Struggles describes the institutional narratives of “strong nurses” that drive students to suppress anxiety, adopting scripted positivity to satisfy assessment expectations. Third, Power, Hierarchy, and the Weight of Tradition reveals that strict authority gradients inhibit questions in classrooms and clinical placements, leaving stress unvoiced and unaddressed. Finally, Overshadowed by Systemic Realities shows how chronic understaffing, equipment shortages, and patient poverty compel students to prioritize patients’ hardships, normalizing self-neglect. Conclusions: Psychological distress among Nepalese UNSs is not an individual failing but a product of structural silence and resource poverty. Educators and policymakers must move beyond resilience-only rhetoric toward concrete reforms that dismantle punitive hierarchies, create confidential support avenues, and embed collaborative pedagogy. Institutional accountability—through regulated workloads, faculty-endorsed wellbeing forums, and systematic mentoring—can shift mental health care from a private struggle to a shared professional responsibility. Multi-site studies across low- and middle-income countries are now essential for testing such system-level interventions and building a globally resilient, compassionate nursing workforce. Full article

Net primary productivity (NPP) is a key metric for assessing ecosystem functionality and sustainability. This study utilized MOD17A3 NPP data in conjunction with trend analysis, a gravity center model, and the Geodetector method to examine the spatiotemporal evolution and driving mechanisms of NPP across the Yellow River Basin (Shaanxi section) from 2000 to 2022. Results revealed: (1) The average NPP over the study period was 353.01g C m⁻² with an upward trend of 9.7 g C m⁻²yr⁻¹; spatially, NPP increased from north to south, with significant variability in northern Shaanxi and a 17.89 km northeastward shift in NPP’s gravity center. (2) Areas exhibiting significant NPP increases (slope > 0, p < 0.01) comprised 97.83% of the region, while declines were mainly observed in Guanzhong. (3) Normalized Difference Vegetation Index (NDVI) was the dominant factor, with the strongest synergistic, nonlinear interaction with land use type reflecting human activities (q = 0.831), indicating that the combined influence of climate factors, land surface factors, and human activities amplifies the explanatory effect on NPP variability. The study demonstrates an overall improvement in NPP, although local declines occurred, and its spatial distribution was influenced by a combination of natural and human factors. These findings will provide data support for the high-quality development of the Yellow River Basin. Full article

This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single future trajectory per agent, our algorithm outputs three distinct trajectories for each surrounding road user, capturing different interaction scenarios (e.g., yielding, non-yielding, and aggressive driving behaviors). We design a GNN-based predictor with bi-directional gated recurrent unit (Bi-GRU) encoders for agent histories, VectorNet-based lane encoding for map context, an interaction-aware attention mechanism, and multi-head decoders to predict trajectories for each mode. The MPC-based planner employs a bicycle model and solves a constrained optimal control problem using CasADi and IPOPT (Interior Point OPTimizer). All three predicted trajectories per agent are fed to the planner; the primary prediction is thus enforced as a hard safety constraint, while the alternative trajectories are treated as soft constraints via penalty slack variables. The designed motion planning algorithm is examined in real-world intersection scenarios from the INTERACTION dataset. Results show that the multi-modal trajectory predictor covers possible interaction outcomes, and the planner produces smoother and safer trajectories compared to a single-trajectory baseline. In high-conflict situations, the multi-modal trajectory predictor anticipates potential aggressive behaviors of other drivers, reducing harsh braking and maintaining safe distances. The innovative method by integrating the GNN-based multi-modal trajectory predictor with the MPC-based planner is the backbone of the effective motion planning algorithm for robust, safe, and comfortable autonomous driving in complex intersections. Full article

Achieving sustainable development in the aviation industry increasingly relies on the synergistic operation of complex product innovation ecosystems. These ecosystems not only drive technological breakthroughs, but also serve as crucial enablers of resource efficiency, ecological resilience, and long-term industrial competitiveness. This study explores how specific configurations of synergistic factors within innovation ecosystems support sustainable innovation outcomes in the aviation sector. Drawing on the innovation ecosystem theory and principles of sustainable development, we employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine 15 representative aviation equipment R&D cases, including AVIC Tongfei and AVIC Xifei. The analysis centers on five key dimensions: core enterprise leadership, value chain collaboration, cross-organizational innovation, technology–market feedback loops, and institutional policy support. These dimensions interact to shape multiple synergy pathways that facilitate sustainable transformation. The results reveal that no single factor alone is sufficient to ensure high innovation sustainability. Instead, three distinct synergy configurations emerge: (1) core enterprise-led model, which reduces resource redundancy through optimized value chain governance; (2) the industry chain collaboration model, which enhances environmental performance via modular design and lifecycle management; and (3) cross-organization innovation collaboration model, which improves material reuse and infrastructure sharing through collaborative mechanisms. Together, these pathways form a reinforcing cycle of innovation–efficiency–sustainability, offering a practical framework for aligning technological advancement with ecological goals. This study deepens the understanding of how innovation ecosystem mechanisms contribute to sustainable development, particularly in high-integration industries. It offers actionable insights into achieving the Sustainable Development Goals (SDGs) through collaborative innovation and systemic resource optimization. Full article

As environmental conservation and community development gain importance, ecotourism has emerged as a significant segment of the global tourism industry. However, the cultural factors that drive tourist behavior in this domain remain underexplored. This research examined how power distance belief (PDB), interacts with the type of tourism content shared on social media (conspicuous versus experiential) to influence travelers’ ecotourism intentions. To test our hypotheses, we conducted two experimental studies using a 2 (PDB: high vs. low) × 2 (tourism content type: conspicuous vs. experiential) between-subjects design. Participants for both experiments (N = 480) were recruited through an online survey platform. In the experiments, participants’ PDB was situationally primed, and tourism content type was manipulated using specifically created fictitious posts adapted from a real social media platform. Other key variables were measured using validated multi-item scales. Data were analyzed using analysis of variance (ANOVA) and moderated mediation analysis (PROCESS Model 15). The findings reveal that travelers with high PDB show higher ecotourism intentions when exposed to conspicuous content, whereas travelers with low PDB exhibit higher intentions when exposed to experiential content. This interactive effect is mediated by travelers’ social comparison motives. These findings offer novel insights into the motivations underlying ecotourism behavior by identifying distinct pathways through which social media can promote sustainable tourism behaviors, and provide practical guidance for eco-destination managers to design targeted marketing strategies that encourage sustainable tourism practices across different consumer segments. Full article

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic CAE software integrated development platform to examine how open-source strategies shape collaborative mechanisms and innovation outcomes. The analysis reveals that firms adopt both formal (behavioral and outcome coordination) and informal (relationship and empowerment coordination) strategies, fostering high-level complementary collaboration in data, technology, institution, and human resources. These mechanisms significantly enhance R&D efficiency and quality, drive technological innovation, and create new market innovation, thereby improving collaborative performance. The study contributes to theory by linking open-source-driven digital–real integration with industrial software innovation catch-up and offers practical governance recommendations for strengthening China’s industrial software autonomy and ecosystem sustainability. Full article

Variability in the Laptev Sea–East Siberian Sea circulation system modulates freshwater circulation in the Arctic Ocean, yet details of these wind-driven mechanisms remain poorly understood. Based on in situ observations from the 2018 Sino-Russian joint Arctic expedition, this study investigates the modulatory influence of wind on circulation structures and freshwater transport in the study area and examines the long-term variation characteristics of this circulation and its inherent connection with the Arctic wind. In situ measurements confirm two freshwater transport pathways: a coastal-current route and a geostrophic slope-current route. As the Beaufort High moves toward the Canadian Basin, it shifts wind patterns from anticyclonic to cyclonic, which regulates the transport of shelf water by influencing the prevailing wind direction. Furthermore, our analysis identifies two main modes of long-term changes in summer surface circulation: the first mode characterizes the coastal-current architecture, while the second mode delineates slope-current configurations. Crucially, large-scale modes of the Arctic wind play an important role in regulating circulation. Its first mode corresponds to the summer anticyclonic circulation pattern of the Arctic Ocean Oscillation, which drives the eastward strengthening of the coastal current, while the third mode presents a mechanism similar to the Arctic Dipole, which promotes the development of the slope current by enhancing the convergence of the polar current and wind. This has led to the long-term strengthening of the slope current. Full article

The use of the Industrial Internet of Things within the domain of electric vehicles signifies a paradigm shift toward advanced, integrated, and optimized transport systems. This study thoroughly investigates the pivotal role of the Industrial Internet of Things in elevating various features of electric vehicle technology, comprising predictive maintenance, vehicle connectivity, personalized user management, energy and fleet optimization, and independent functionalities. Key IIoT applications, such as Vehicle-to-Grid integration and advanced driver-assistance systems, are examined alongside case studies highlighting real-world implementations. The findings demonstrate that IIoT-enabled advanced charging stations lower charging time, while grid stabilization lowers electricity demand, boosting functional sustainability. Battery Management Systems (BMSs) prolong battery lifespan and minimize maintenance intervals. The integration of the IIoT with artificial intelligence (AI) optimizes route planning, driving behavior, and energy consumption, resulting in safer and more efficient autonomous EV operations. Various issues, such as cybersecurity, connectivity, and integration with outdated systems, are also tackled in this study, while emerging trends powered by artificial intelligence, machine learning, and emerging IIoT technologies are also deliberated. This study emphasizes the capacity for IIoT to speed up the worldwide shift to eco-friendly and smart transportation solutions by evaluating the overlap of IIoT and EVs. Full article

The fashion industry is facing increasing challenges related to textile waste and environmental pollution, driving the need for sustainable material innovations. Bacterial cellulose (BC), a biodegradable and non-polluting biomaterial, has emerged as a promising alternative for the sustainable transformation of fashion materials. Investigations into printing and dyeing techniques are expected to provide methodological frameworks for the design and functional application of BC materials, promoting their adoption and development in the fashion sector. This study, using the kombucha culture method, systematically investigated the cultivation, purification, plasticization, and drying processes of BC as a fashion material, examined its color characteristics using plant and reactive dyeing, and evaluated the effects of pattern printing and the feasibility of traditional plant pigment stencil printing, digital printing, and cyanotype printing on BC. Based on these printing and dyeing methods, digital printing combined with reactive dyeing—offering richer print effects, a wider color gamut, and higher rubbing fastness—was selected to realize the fashion design series Photosynthesis using BC as the primary material. This research contributes methodological insights into the integration of bio-based materials in fashion design and promotes the advancement of sustainable practices within the textile and apparel industries. Full article

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning. Full article

This study investigates the impact of minimalism on ethical consumption within the framework of the Value–Identity–Personal Norms (VIP) model. Data were collected from 340 Turkish consumers using a convenience sampling method and an online survey. Analyses were conducted with SmartPLS, employing the structural equation modeling (SEM) approach to examine the relationships among the constructs. The results reveal that minimalism, conceptualized as a value, significantly shapes environmentally responsible behavior by strengthening environmental identity and personal norms, which in turn drive ethical consumption choices. By extending the VIP model, the research positions minimalism not only as a lifestyle but also as a value-based orientation that aligns with biospheric values in encouraging pro-environmental actions. Furthermore, the study underscores the importance of sustainability communication as a crucial mechanism for reinforcing the connection between minimalistic values and ethical consumer behavior. It also highlights the mediating role of environmental identity between values (both biospheric and minimalistic) and personal norms, supporting the view that values influence behavior indirectly through psychological constructs. Overall, the findings demonstrate that minimalism positively affects environmental identity and personal norms, thereby fostering ethical consumption in line with the theoretical perspectives of the Value–Belief–Norm (VBN) and pro-environmental behavior models. This research provides valuable insights into how minimalism can be integrated into sustainability communication strategies to promote sustainable consumption, particularly in emerging economies. Full article

Antibiotics have dramatically reduced the burden of infectious diseases since their discovery, but the accelerating rise in antimicrobial resistance (AMR) now threatens these gains. AMR was responsible for nearly 5 million deaths in 2023 and continues to undermine the efficacy of existing treatments, particularly in low- and middle-income countries. While efforts to address AMR have focused heavily on antibiotic stewardship and new drug development, vaccines represent a powerful yet underutilized tool for prevention. By reducing the incidence of bacterial infections, vaccines lower antibiotic consumption, interrupt transmission of resistant strains, and minimize the selective pressures that drive resistance. Unlike antibiotics, vaccines offer long-lasting protection, rarely induce resistance, and confer indirect protection through herd immunity. This review examines the global burden and drivers of AMR, highlights the unique advantages of vaccines over antibiotics in mitigating AMR, and surveys the current development pipeline of vaccines targeting key multidrug-resistant bacterial pathogens. Full article

In recent years, the irregular wave characteristics of ocean dynamics have often been overlooked in the study of the driving mechanism of groundwater movement in coastal aquifers. To clarify the propagation mechanisms of groundwater fluctuations driven by irregular waves in beach aquifers, we employed spectral analysis based on numerical simulations to examine the energy migration processes and evolution characteristics of wave signals at different frequencies. It elucidates the response mechanism of groundwater movement characteristics (head, velocity) to irregular waves in the sea. The energy density in the low-frequency region is enhanced compared to the incident wave and continuously increases in the direction away from the sea within the aquifer. The wavelet power corresponding to the 1/2 spectral peak frequency is significantly enhanced. The energy density in the high-frequency region is generally weaker than that of the incident waves, and the wavelet power corresponding to double spectral peak frequency is enhanced. The correlation between incident waves and groundwater fluctuations is highest near the spectral peak period. This study addresses some problems in modeling surface water–groundwater interactions under irregular wave conditions and provides a theoretical reference for investigating the impacts of extreme climate events (such as typhoon waves and low-frequency offshore oscillations generated by storm surges) on seawater intrusion into coastal groundwater systems. Full article

This study examined the spatial distribution and environmental determinants of hairtail (Trichiurus lepturus) catch volumes in the southern coastal waters of South Korea, employing a Spatial Durbin Model (SDM) based on grid-level data collected from 2020 to 2022. Key explanatory variables included chlorophyll-a concentration, dissolved oxygen, salinity, sea surface temperature, and fishing effort. Spatial autocorrelation was confirmed through Moran’s I test, justifying the application of a spatial econometric framework. Among the environmental factors, salinity exhibited the strongest positive direct effect on catch volumes, whereas dissolved oxygen consistently showed a negative effect. Chlorophyll-a concentration exhibited significant positive effects both within local grids and in neighboring areas. Sea surface temperature also had a modest but significant direct effect on catch volumes. Additionally, higher fishing effort was associated with increased catch volumes, emphasizing the spatial impact of human activities on fishery resources. These findings reveal that hairtail tend to aggregate in high-salinity, low-oxygen environments and respond to seasonal oceanographic variations. Overall, the results highlight the value of spatial econometric models in fisheries research by revealing how environmental and anthropogenic factors influence fish catch through both direct and indirect effects. The spatial framework offers deeper insight into the mechanisms driving hairtail distribution, particularly in ecologically complex regions like the Jeju Strait. Full article

Traditional polyurethanes have gained widespread application due to their excellent mechanical properties, wear resistance, and processability. However, these materials are susceptible to cracking or fracture under environmental stresses. In recent years, self-healing polyurethanes have garnered significant attention as a critical research field owing to their key capabilities, such as repairing physical damage, restoring mechanical strength, structural adaptability, and cost-effective manufacturing. This review systematically examines the healing mechanisms, structural characteristics, and performance metrics of self-healing polyurethanes, with in-depth analysis of their repair efficacy across various applications—particularly in flexible electronic devices. It demonstrates that self-healing polyurethanes overcome traditional failure modes in flexible electronics through self-repair-function integration mechanisms. Their stimuli-responsive healing behavior is driving the evolution of this field toward an intelligent regenerative electronics paradigm. Full article