Search Results (1,448)

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Johannes Althusius (1563–1638) was a pioneer of early modern federalism. Opposing Jean Bodin’s theory of absolute sovereignty, his theory, centered on association and symbiosis, laid the groundwork for later concepts such as associationalism, consociationalism, and the principle of subsidiarity. While his ideas have been rediscovered and reinterpreted by scholars since the 20th century, systematic research on his federalist framework, especially contrasting it with rival theories of that time, remains insufficient. This article addresses this research gap by systematically exploring Althusius’s federalism. It argues that Althusius’s covenant-based, multi-level associational/federal framework provided a counter-theory to the concept of absolute sovereignty. Systematically studying his federalism not only helps to restore his federalist ideas to their rightful place in the history of federalist thought, but also provides insights for contemporary governance paradigms struggling with modern pluralism. Full article

The escalating climate crisis and global disruptions have prompted a critical re-evaluation of operations management within manufacturing and supply systems. This conceptual article addresses the theoretical and strategic gap in aligning resilience and sustainability by proposing an Integrated Sustainable Operational Strategy (ISOS) framework. Drawing on systems theory, circular economy principles, and sustainability science, the framework synthesizes multiple operational domains—circularity, localization, digital adaptation, and workforce flexibility—across macro (policy), meso (organizational), and micro (process) levels. This study constructs a conceptual model that explains the interdependencies and trade-offs among strategic operational responses in the Anthropocene era. Supported by multi-level logic and a synthesis of domain constructs, the model provides a foundation for empirical investigation and strategic planning. Key propositions for future research are developed, focusing on causal relationships and boundary conditions. The novelty of ISOS lies in its simultaneous integration of three strategic pillars—circularity, localization, and digital resilience—within a unified, multi-scalar architecture that bridges fragmented operational theories. The article advances theory by redefining operational excellence through regenerative logic and adaptive capacity, responding directly to SDG 9 (industry innovation), SDG 12 (responsible consumption and production), and SDG 13 (climate action). This integrative framework offers both theoretical insight and practical guidance for transforming operations into catalysts of sustainable transition. Full article

Background/Objectives: A growing body of literature has demonstrated that living with food insecurity (FI) increases risk for mental health concerns in addition to nutritional deficits (e.g., suboptimal micronutrient consumption, excessive macronutrient consumption, malnutrition). Yet, research is needed to improve our understanding of subpopulations potentially at increased risk for mental health concerns when living in the context of FI. The current study examined psychosocial health across women of different developmental life stages all living with FI. Methods: Female clients of a large, urban food bank (N = 680) living with FI completed measures of mental health and health-related quality of life (HRQOL) in a cross-sectional design conducted on site at the food bank. Results: Consistent with past research, FI severity was correlated with poorer psychosocial health across all variables. A multivariate analysis of covariance compared women living with FI across 4 developmental life stages (young adult, early midlife, late midlife, and older adult; age range = 18–94 years), controlling for FI severity and race/ethnicity, on outcomes related to mental health and HRQOL. Women in early and late midlife reported higher anxiety, eating disorder symptoms, and eating-related psychosocial impairment than younger and older women. Conclusions: The mental health toll of living with FI is profound; midlife may comprise a developmental period of increased vulnerability to experience this mental health burden of living with FI for women. Thus, efforts are needed to develop innovative pathways for interventions to support the mental health of midlife women living with FI, likely involving multi-level and/or multicomponent approaches to resource access. Full article

This study proposes a design method for near-slope roads along multi-level slopes that integrates excavation requirements and post-construction ecological restoration through sprayed eco-protection. Firstly, the design principles and procedural steps for near-slope roads are established. The planar layouts of multi-level slopes are categorized, including mixing areas, turnaround areas, berms, and access ramps. Critical technical parameters, such as curve radii and widths of berms and ramps, as well as dimensional specifications for turnaround areas, are systematically formulated with corresponding design formulas. The methodology is applied to the ecological restoration project of multi-level slopes in the Huamahu mountainous area, and a comparative technical-economic analysis is conducted between the proposed design and the original scheme. Results demonstrate that the optimized design reduces additional maintenance costs caused by near-slope roads by 6.5–8.0% during the curing period. This research advances the technical framework for multi-level slope governance and enhances the ecological design standards for slope protection engineering. Full article

Existing research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will reduce employees’ organization-based self-esteem (OBSE), which in turn increases their subsequent defensive silence to avert further damage to relationships. In addition, we also expect a moderating role of the sense of power in mitigating the negative impact of workplace ostracism on OBSE. Based on the multi-wave, multi-source data of 345 employees and their 82 immediate supervisors, we tested all the hypotheses. Results from multilevel modeling indicated that OBSE mediated the indirect effect of workplace ostracism on defensive silence, and also supported the moderation role of sense of power. Our theoretical model provides a novel perspective that deepens the understanding of defensive silence and suggests implications for managerial practices. Full article

Using common corn stalks as raw materials, a functional dense-structured carbon microsphere with good elastic deformation and certain rigid support was modified from biomass through a step-by-step hydrothermal method. The composition, thermal stability, fluid-loss reduction performance, and reservoir protection performance of the modified carbon microspheres were studied. Research indicates that after hydrothermal treatment, under the multi-level structural action of a small amount of proteins in corn stalks, the naturally occurring cellulose, polysaccharide organic compounds, and part of the ash in the stalks are adsorbed and encapsulated within the long-chain network structure formed by proteins and cellulose. By attaching silicate nanoparticles with certain rigidity from the ash to the relatively stable chair-type structure in cellulose, functional dense-structured carbon microspheres were ultimately prepared. These carbon microspheres could still effectively reduce fluid loss at 200 °C. The permeability recovery value of the cores treated with modified biomass carbon microspheres during flowback reached as high as 88%, which was much higher than that of the biomass itself. With the dense network-like chain structure supplemented by small-molecule aldehydes and silicate ash, the subsequent invasion of drilling fluid was successfully prevented, and a good sealing effect was maintained even under high-temperature and high-pressure conditions. Moreover, since this functional dense-structured carbon microsphere achieved sealing through a physical mechanism, it did not cause damage to the formation, showing a promising application prospect. Full article

The subject of ship structural dynamics has faced new technological obstacles due to scientific and technological advancements, and one of the main concerns in related sectors is how to effectively reduce the vibration levels of different ships. This article focuses on the application scenarios of ship floating raft isolation systems, establishing a wave propagation model for acoustic black hole (ABH) structures based on the idea of the ABH effect. Then, a transfer matrix model for serially connected ABH structures is derived, which serves as a basis for subsequent structural designs. Second, the finite element method is used to study the energy distribution and vibration characteristics of a symmetrically distributed periodic non-uniform multi-level ABH structure. Meanwhile, it examines its bandgap distribution under a one-dimensional periodic arrangement and then investigates the vibration properties of non-uniform multi-level ABH thin-plate constructions with different periods from the perspective of engineering applications. Moreover, parameter optimization studies of non-uniform multi-level ABH structures with finite periods are carried out with an emphasis on engineering applications. The first step is to use the design space to determine the range of values for the parameters that need to be optimized. The hyper Latin cubic sampling method is then employed to select samples, and the EI criterion and PSO optimization algorithm are applied to add new samples to improve the Kriging surrogate model’s accuracy. When the optimal structural parameters have been determined, they are applied to the raft rib plate to verify the isolation effect of the non-uniform multi-level ABH structure by analyzing the vibration level difference at specific raft positions before and after embedding it. Full article

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article

Coal miners’ unsafe behavior is the primary reason for accidents. This research aims to examine the effect of male coal miners’ shared work crew identity on their safety behavior. A 2-2-1 multilevel mediation model is established based on social identity theory and safety climate theory. To validate the model, a paper-and-pencil survey with male coal miners was carried out in Henan Province, China. A total of 212 valid responses from male coal miners nested in 53 work crews were secured, and Mplus was used to analyze the data. Results show that work crew safety climate fully mediates the effect of male coal miners’ shared work crew identity on their safety behavior. In theory, the findings support that social identity brings a safety climate. In practice, the findings highlight that making safety part of work crew norms improves male coal miners’ safety behavior. Limitations and future research are also discussed. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

The mental health of prekindergarten (PK)-12th grade students is of extreme importance. Therapy dogs are a unique intervention to address mental health needs in educational frameworks. The purpose of this pilot study is to examine the components of a PK-12 district-wide therapy dog program and the implications for the mental health and well-being of students, faculty, and staff. This research used qualitative methods for semi-structured interviews with therapy dog handlers, observations including photographs, and researcher field notes of therapy dogs and handlers in their education environment. Thematic analysis of transcripts from semi-structured interviews was used for qualitative analysis. The results indicated that the strategic implementation of a district-wide therapy program supported a sustainable program that is integrated into the educational ecosystem. The participants perceived that the therapy dogs impacted mental health in the domains of social, emotional, and behavioral support. A district-wide systematic approach to addressing mental health and well-being needs of students, faculty, and staff with therapy dogs’ hinges on a comprehensive training and evaluation process, district-level support, and consistent implementation across educational settings. Therapy dogs can be embedded within educational structures to create a multilevel impact from the classroom to school to district level for a sustainable program. Therapy dogs positively contribute to the mental health and well-being of students, faculty, and staff in PK-12 education settings. Full article

In the realm of recommender systems, the prediction of diverse customer preferences has emerged as a compelling research challenge, particularly for multi-state business organizations operating across various geographical regions. Collaborative filtering, a widely utilized recommendation technique, has demonstrated its efficacy in sectors such as e-commerce, tourism, hotel management, and entertainment-based customer services. In the item-based collaborative filtering approach, users’ evaluations of purchased items are considered uniformly, without assigning weight to the participatory data sources and users’ ratings. This approach results in the ‘relevance problem’ when assessing the generated recommendations. In such scenarios, filtering collaborative patterns based on regional and local characteristics, while emphasizing the significance of branches and user ratings, could enhance the accuracy of recommendations. This paper introduces a turnover-based weighting model utilizing a big data processing framework to mine multi-level collaborative filtering patterns. The proposed weighting model assigns weights to participatory data sources based on the turnover cost of the branches, where turnover refers to the revenue generated through total business transactions conducted by the branch. Furthermore, the proposed big data framework eliminates the forced integration of branch data into a centralized repository and avoids the complexities associated with data movement. To validate the proposed work, experimental studies were conducted using a benchmarking dataset, namely the ‘Movie Lens Dataset’. The proposed approach uncovers multi-level collaborative pattern bases, including global, sub-global, and local levels, with improved predicted ratings compared with results generated by traditional recommender systems. The findings of the proposed approach would be highly beneficial to the strategic management of an interstate business organization, enabling them to leverage regional implications from user preferences. Full article

Urban systems are the spatial carriers of social and economic relations at the regional level, and their relational and structural resilience are key to regional coordination and sustainable development, attracting widespread attention from scholars. In order to analyze the internal relationships of urban agglomerations in underdeveloped mountainous regions and optimize their spatial resource allocation and resilience, this study takes the urban agglomeration of Qiandongnan in China as an example and researches their internal relationships, development potential, and influencing factors based on quantitative methods such as social network analysis. The results show that the urban cluster in Qiandongnan presents “large dispersion and small aggregation” distribution characteristics, with the karst landscape as the main influencing factor; the spatial network exhibits a scale-free morphology with an obvious core–periphery structure, demonstrating moderate stability but poor completeness, weak equilibrium, and low overall resilience; only 15.61% of nodes demonstrate high competitiveness; urban units with functional roles serve as critical network nodes; urban units’ development potential is divided into three tiers (with 47.31% being medium-high), although overall levels remain low; and the development potential, overall network, individual network, and network resilience of urban units are all positively correlated, with economic and transportation development conditions being the main influencing factors. Based on the abovementioned findings, this study proposes a “multi-level resilience promotion path for network structure optimization”, which provides a theoretical basis and optimization control methods for the reconstruction and synergistic development of urban agglomerations. It also serves as a reference for the development planning of urban systems in other underdeveloped mountainous regions. Full article

Through the energy conversion of building skins, building-integrated photovoltaic (BIPV) technology, the core carrier of the smart city energy system, encourages the conversion of buildings into energy-generating units. However, the decommissioning of the module faces the challenge of physical dismantling and financial environmental damage because of the close coupling with the building itself. As the first tranche of BIPV projects will enter the end of their life cycle, it is urgent to establish a multi-dimensional collaborative recycling mechanism that meets the characteristics of building pv systems. Based on the theory of reverse logistics network, the research focuses on optimizing the reverse logistics network during the decommissioning stage of BIPV modules, and proposes a dual-objective optimization model that considers both cost and carbon emissions for BIPV. Meanwhile, the multi-level recycling network which covers “building points-regional transfer stations-specialized distribution centers” is designed in the research, the Pareto solution set is solved by the improved NSGA-II algorithm, a “1 + 1” du-al-core construction model of distribution center and transfer station is developed, so as to minimize the total cost and life cycle carbon footprint of the logistics network. At the same time, the research also reveals the driving effect of government reward and punishment policies on the collaborative behavior of enterprise recycling, and provides methodological support for the construction of a closed-loop supply chain of “PV-building-environment” symbiosis. The study concludes that in the process of constructing smart city energy system, the systematic control of resource circulation and environmental risks through the optimization of reverse logistics network can provide technical support for the sustainable development of smart city. Full article