Search Results (2,689)

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are a class of synthetic organic compounds with extremely high chemical stability and environmental persistence that are widely used in the industrial sector and in consumer goods. Their strong C-F bonds make them difficult to degrade, meaning they can migrate through the atmosphere and settle over long distances, posing long-term risks to the global ecological environment and human health. This article systematically reviews the classification, physicochemical properties, concentration levels, spatial distribution, migration and transformation behaviors, and health and ecological impacts of PFASs in the atmosphere, along with related analytical detection techniques and pollution control methods. Studies show that short-chain PFASs are more likely to migrate through the atmosphere due to their high water solubility and volatility, while long-chain PFASs tend to be adsorbed onto particulate matter and display stronger bioaccumulation. Although atmospheric research on PFASs lags behind that focused on their dynamics in water and soil, the existing data still reveal a difference in their distribution and regional pollution characteristics in the gas and particle phases. Toxicological studies have confirmed that PFAS exposure is associated with liver injury, immunosuppression, developmental toxicity, and cancer risk and can threaten ecological security through the food chain. Currently, governance technologies are confronted with the challenges of low efficiency and high cost. In the future, it will be necessary to combine multi-media models, new analytical techniques, and international collaboration to promote the development of source control and innovative governance strategies. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

The proliferation of diverse multi-stakeholder partnering arrangements that seek to achieve the 2030 Agenda for Sustainable Development has prompted calls for overarching standards to enhance their governance, legitimacy and effectiveness. This conceptual article critically examines the limitations of applying universal partnership standards across complex and context-sensitive collaborative arrangements. Drawing on a purposive sampling of approximately 115 academic, policy-oriented and practitioner sources, identified through targeted database searches, we explore the historical development of sustainability-related partnership norms and identify some of the tensions in their alignment with socio-historic, institutional and relational dynamics. We examine the concept of partnership meta-governance as a way of both ensuring and enabling effective collaborative initiatives working to meet the targets of the 2030 Agenda’s 17 Sustainable Development Goals. Using a methodology that combines conceptual analysis with practitioner-tested support mechanisms, we propose a principles-based approach to enrich the enabling dimension of partnership meta-governance by privileging contextual responsiveness, co-creation and relational values over prescriptive compliance. This approach seeks to reinforce the transformational intent of the 2030 Agenda by offering a foundation for more inclusive and adaptive collaboration that supports the long-term aspirations of the United Nations’ Pact for the Future. Full article

The partnership between government and market plays a crucial role in allocating green resources and fostering collaboration across organizations and departments. It integrates diverse knowledge types into the green innovation process and offers multifaceted insights into enterprises’ responses to green governance decisions. However, existing research predominantly examines the interplay among government green governance instruments, with insufficient exploration of the synergistic impacts of government and market in green governance. This study constructs a capacity coupling coefficient model to measure the synergy degree of “government–market” green governance (GMGG). Exploiting a balanced dynamic panel of 28,451 firm-year observations for 3807 Chinese listed companies from 2010 to 2020, we estimate the causal effect of GMGG synergy on corporate green innovation (CGI) and further dissect the underlying transmission mechanisms as well as the moderating channels through which the effect operates. Empirical results reveal that the effect of GMGG synergy on CGI is subject to diminishing marginal returns, with the effect being significantly more pronounced for substantive green innovation. Heterogeneity analysis indicates that non-state-owned firms, eastern-region firms, and those in non-heavy-polluting industries respond with markedly greater sensitivity. Mechanism analysis further demonstrates that the extent of marketization serves as a mediating channel, whereas an elevated level of digital-economy development mitigates the impact of GMGG synergy on CGI. This study delineates the effective boundary of GMCC synergy in stimulating CGI, providing empirical benchmarks for the synergistic implementation of effective government and efficient market actions in green governance. It further corroborates the positive roles of marketization and the digital economy as novel governance instruments, thereby offering critical policy insights for the coordinated advancement of the “dual-carbon” goals and high-quality economic development. Full article

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct research topics. These topics reveal both well-established and emerging areas, such as p-adic differential equations, harmonic analysis, and their connections to theoretical physics and cryptography. This study highlights key contributors, including Robert Coleman, Alain M. Robert, and Jean-Pierre Serre, whose work has shaped the development of the field. Temporal patterns observed in the topic distribution indicate dynamic shifts in research focus, while the interdisciplinary nature of recent contributions highlights the growing relevance of p-adic theory beyond pure mathematics. This analysis provides a data-driven overview of the intellectual structure of p-adic research, identifies underexplored areas, and suggests future directions for inquiry. The findings aim to support researchers in understanding historical trends, recognizing influential work, and identifying opportunities for further advancement and collaboration in the field. Full article

Augmented reality features, such as overlaying information in real time, modifying the projected scene, or dynamically adjusting parameters like contrast, zoom, and brightness, show promise in addressing the specific challenges faced by people with low vision. These tailored solutions enhance their visual experiences. When combined with mobile technology, these features significantly improve the personalization of visual aids and the monitoring of patients with low vision. Retiplus emerges as a personalized visual aid and rehabilitation system, utilizing smart glasses and augmented reality technology for visual aid functions, along with a mobile app for visual assessment, aid customization, and usage monitoring. This wearable system quickly assesses visual conditions, providing deep insights into the visual perception of patients with low vision. Designed to enhance autonomy and quality of life, Retiplus seamlessly integrates into indoor and outdoor environments, enabling the programming of rehabilitation exercises for both static and ambulatory activities at home. In collaboration with specialists, the system meticulously records patient interaction data for subsequent evaluation and feedback. A clinical study involving 30 patients with low vision assessed the effect of Retiplus, analyzing its impact on visual acuity, contrast sensitivity, visual field, and ambulation. The most notable finding was an average increase of 61% in visual field without compromising ambulation safety. Retiplus introduces a new user-centered approach that emphasizes collaboration among a multidisciplinary team for the customization of visual aids, thereby minimizing the gap between the perceptions of low vision specialists and technologists regarding user needs and the actual requirements of users. Full article

Recently, the market for miniaturized Unmanned Agricultural Robots has experienced rapid development worldwide, driven by advances in robotics, artificial intelligence and precision agriculture. These technologies are no longer confined to highly industrialized countries but are increasingly penetrating emerging economies, including Romania, where they hold significant potential for transforming farming practices and entrepreneurial competitiveness. The purpose of the present paper is to present strategies for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. This is achieved by leveraging competitive advantage through value creation, specifically by deepening strategies for the rapid integration of new miniaturized robotic products. The research employed a mixed-methods approach, combining qualitative and quantitative techniques to investigate the ability of key stakeholders—agricultural entrepreneurs, precision agriculture product/service providers, institutional representatives, and investors—to dynamically adapt to evolving market conditions. The study’s findings reveal a strong interest and readiness among precision agriculture stakeholders to adopt advanced technologies, supported by robust operational knowledge management practices including external knowledge acquisition, strategic partnerships and data protection. Although agricultural entrepreneurs exhibit considerable adaptive and absorptive capacities—evidenced by their openness to innovation and collaboration—persistent barriers such as high equipment costs and limited financing access continue to impede the broad adoption of miniaturized robotic solutions. The study concludes by emphasizing the need for supportive policies and collaborative financing models and it suggests future research on adoption dynamics, cross-country comparisons and the role of education in accelerating agricultural robotics. Full article

WSNs are an important component of the Internet of Things (IoT), and the research on their routing protocols has always been a hot topic in academia. However, in ARTFMRs’ collaborative operation along railway lines, there are common problems such as energy holes, high latency, and uneven energy consumption in LWSNs. To address these issues, this paper proposes a genetic algorithm-optimized energy-aware routing protocol (GAECRPQ). Firstly, a non-uniform deployment strategy of three-line isosceles triangles is constructed to enhance coverage and balance node distribution. Secondly, an energy–distance adaptive weighting mechanism based on a genetic algorithm is introduced for cluster head (CH) selection to reduce energy consumption in hotspots and extend the network lifetime. Finally, a task-aware TDMA dynamic time slot allocation method is proposed, which incorporates the real-time task status of ARTFMRs into communication scheduling to achieve priority transmission under latency constraints. The simulation results show, that compared with six unequal clustering protocols—EADUC, EAUCA, EBUC, EEUC, LEACH, and LEACH-C—the three-line isosceles triangle deployment has a wider coverage area, and the GAECRPQ protocol increases the network lifetime by 7.4%, the lifetime by 40%, and reduces the average latency by 55.77%, 53.07%, 47.61%, 39.87%, 52.08%, and 50.48%, respectively. This verifies that GAECRPQ has good performance in terms of network lifetime and energy utilization efficiency, providing a practical solution for the collaborative operation of ARTFMRs in railway maintenance scenarios. Full article

This study proposes a multi-stage collaborative design framework integrating sensitivity analysis, response surface methodology (RSM), and topology optimization for synergistic lightweighting and performance enhancement of micromanufacturing systems using ultra-precision computer numerical control (CNC) machine tools. Overall sensitivity analysis identified the base and column as stiffness-critical components, while the spindle box exhibited significant weight-reduction potential. Using spindle box wall and bottom thickness as variables, RSM models for mass and stress were constructed. Multi-objective optimization via a genetic clustering algorithm achieved a 57.2% (590 kg) weight reduction under stress constraints (<45 MPa). Subsequent variable-density topology optimization (SIMP model) reconfigured the rib layouts of the base and column under volume constraints, reducing their weights by 38.5% (2844 kg) and 41.5% (1292 kg), respectively. Whole-machine validation showed that maximum static deformation decreased from 0.17 mm to 0.09 mm, maximum stress reduced from 58 MPa to 35 MPa, and first-order natural frequency increased from 50.68 Hz to 84.08 Hz, significantly enhancing dynamic stiffness. Cumulative weight reduction exceeded 3000 kg, achieving a balance between lightweighting and static/dynamic performance improvement. This work provides an effective engineering pathway for a structural design of high-end micromanufacturing systems. Full article

With the aggravation of global climate change and the increasing frequency and intensity of extreme weather events, power systems with a high proportion of renewable energy are under threat. In response, in traditional wind–solar–storage–hydrogen multi-energy systems, it is difficult to balance power supply resilience, economy, and environmental protection, and such systems cannot meet actual demand due to the lack of a dynamic meteorological integration mechanism. Therefore, a dynamic collaborative optimization model of a multi-energy system driven by meteorological risk perception is proposed. The dynamic meteorological risk factor integrating various meteorological elements is introduced, and the risk response mechanism is established based on the system’s energy storage state to realize the adaptive adjustment of coupled weight parameters and achieve the goal of collaborative optimization of power supply resilience, economy, and environmental protection. The case analysis results show that, compared with other models, the proposed model can reduce the power supply shortage by 23.1% in extreme weather periods, and the system’s survival probability can reach 97.1% at most. The proposed model minimizes the assembly while ensuring that carbon emissions meet standards, and achieves the collaborative optimization of power supply toughness, economy, and environmental protection. It provides a theoretical tool for solving the collaborative optimization problem that energy systems with a high proportion of renewables face in coping with climate risks. Full article

As a key device for precise feeding in aquaculture, feeders directly affect feed utilization efficiency and farming profitability; however, pneumatic pond feeders commonly exhibit poor spreading uniformity and low feed utilization. In this study, a dual-sided air intake structure incorporating a triangular flow-splitter plate was added inside the feed chamber, and the spreading process was simulated using a coupled computational fluid dynamics–discrete element method approach to analyze the motion mechanisms of feed pellets within the feeding device. A rotatable orthogonal composite experimental design was employed for the multiparameter collaborative optimization of the feed chamber height ($h$), the triangular flow-splitter plate width ($d$), and its inlet angle ($\alpha$). The results demonstrated that the triangular flow-splitter plate renders the velocity field within the device chamber more uniform and reduces the coefficient of variation ($CV$) of circumferential pellet distribution to 18.27%, a 22.19% decrease relative to the unmodified design. Experimental validation using the optimal parameter combination confirmed a mean $CV$ of 17.02%, representing a 24.45% reduction compared with the original structure. This study provides a theoretical foundation and reliable technical solution for precise feeding equipment in aquaculture. Full article

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

The integration of the industrial Internet and big data analytics is reshaping enterprise decision-making models and providing new momentum for the transformation and upgrading of traditional manufacturing industries. In this study, a decision support system based on multi-source heterogeneous data fusion was established. The system carries out data collection, storage, and processing, as well as visualization analysis. The system also performs time-series data feature extraction and unstructured data processing in a three-layer architecture model to train models and generate decision-making. In case studies, the effectiveness of the system in predictive maintenance of equipment, dynamic optimization of supply chains, and product quality traceability was verified. A fault prediction model was developed based on an improved random forest algorithm, and it showed a high level of accuracy. Optimization strategies, such as modular system design, dynamic knowledge base updating, and human–machine collaborative decision-making, can be formulated using the system. To evaluate the system, a three-dimensional evaluation index system was built, including technology maturity, application adaptability, and benefit–output ratio. The developed system effectively improved the efficiency of enterprise resource allocation, shortened abnormality response times, and enhanced market adaptability. By using edge computing and digital twin technologies, a more flexible distributed decision-making architecture can be created in the system, promoting data-driven and intelligent decision-making in manufacturing industry. Full article

V658 Car is the first known eclipsing binary system involving a classical Be star and an sdOB companion, offering a unique opportunity to study disk physics and binary interactions in unprecedented detail. From TESS data and multi-color observations from the comissão para a colaboração entre profissionais e amadores collaboration, we analyze the system’s color–magnitude diagram and compare it with radiative transfer models that include the Be star, its circumstellar disk, and the sdOB companion. While the stellar eclipses are well reproduced, two features observed in the multi-color photometry challenge the current modeling paradigm: the discrepancy between the observed reddening and the modeled blueing during the first attenuation phase and the complete lack of modeled attenuation around the second stellar eclipse. These issues highlight the need for more sophisticated modeling approaches to capture the complex interplay between disk opacity and binary dynamics. Full article

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article