Search Results (940)

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

The transition towards active-learning pedagogies represents an important challenge in STEM (Science, Technology, Engineering, and Mathematics) education, particularly in Agricultural Engineering courses characterised by mathematically demanding content and complex engineering problem solving. Although gamification and Student Response Systems (SRSs) have shown promising educational potential, empirical evidence regarding their combined application in Agricultural Engineering remains limited. This quasi-experimental repeated-measures study evaluated an Educational Innovation Project integrating the Wooclap Student Response System with structured gamification activities—including educational escape rooms, forensic engineering simulations, collaborative challenges, and peer discussion—in two undergraduate Agricultural Engineering courses at the University of Almería. Academic performance was assessed through short-term (one week) and medium-term (one month) knowledge-retention tests, while students’ perceptions were explored using repeated questionnaire administrations throughout the intervention. Quantitative analyses included descriptive statistics, assessment of normality, paired-samples Student’s t-tests, 95% confidence intervals, and Cohen’s d effect sizes. The intervention was associated with statistically significant improvements in short-term and medium-term academic performance, with medium to large effect sizes, while students also reported high levels of engagement, perceived knowledge retention, technological usability, and reduced academic stress. These findings suggest that integrating a Student Response System with structured gamification may support active learning and knowledge retention in technically demanding Agricultural Engineering courses. Nevertheless, because of the quasi-experimental repeated-measures design and the absence of a parallel control group, the findings should be interpreted as evidence of an association between the instructional approach and the observed educational outcomes rather than as proof of causal effectiveness. Further controlled studies involving larger and more diverse engineering cohorts are warranted. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Improving the low-carbon operational performance of wastewater treatment facilities (WWTFs) is important for coordinated pollution reduction and carbon mitigation in China. The interprovincial carbon emission efficiency (CEE) of WWTFs reflects both the low-carbon performance of regional wastewater governance systems and cross-regional linkages shaped by technology diffusion, governance experience, and spatial adjacency. Using operational data from WWTFs in 30 provincial-level regions of China from 2010 to 2023, this study first measures provincial CEE with a super-efficiency slack-based measure (SBM) model incorporating undesirable outputs. It then integrates a modified gravity model, social network analysis, and the quadratic assignment procedure (QAP) to examine the evolution, structure, and formation mechanisms of the association network. The results show that the CEE of provincial WWTFs remained below the efficiency frontier overall, with periodic fluctuations and persistent structural differences. The model-inferred association network first contracted and then expanded, showing dense connections in eastern and central China and sparse connections in the northwest. Its node structure evolved from a dispersed multicore pattern to hub-centered concentration and then back toward a multicore configuration, while inter-block linkages showed a hierarchical transmission structure involving core spillovers, absorptive transmission, and peripheral reception. The QAP results indicate that interprovincial adjacency is the most stable correlate of network formation, whereas differences in technological innovation and capacity utilization show only stage-specific associations. These findings suggest that the CEE of WWTFs is not only a local performance outcome, but also a relational outcome shaped by regional linkages and structural positions. Therefore, low-carbon governance of WWTFs should move beyond single-region efficiency improvement and place greater emphasis on cross-regional collaboration, role-specific policy design, and leadership by core regions. Full article

This study examines how Female-Associated Leadership Practices (FALPs) are associated with firm performance in Ecuadorian small and medium-sized enterprises (SMEs) through organizational governance and strategic orientation. Drawing on Upper Echelons Theory, social role theory, expectation states theory, and gender and leadership research, FALPs are conceptualized as perceived leadership practices frequently linked to participative decision-making, collaboration, ethical consideration, innovation stimulation, and long-term orientation. This conceptualization avoids treating leader gender, gender diversity, women’s representation in management, and leadership style as interchangeable constructs. Using a quantitative cross-sectional design, firm-level data from 300 SMEs were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that FALPs are not directly associated with firm performance. Instead, they are positively associated with governance quality and strategic orientation, both of which are, in turn, positively associated with firm performance. Strategic orientation represents the strongest indirect pathway, while governance is associated with performance both directly and indirectly through strategic orientation. These findings provide a process-based explanation for inconsistent evidence on gender-related leadership practices and firm outcomes. Practically, the study suggests that participative, ethical, collaborative, innovation-oriented, and long-term leadership practices may support SMEs by strengthening governance quality and strategic capabilities. Full article

Long-term bridge temperature monitoring data are often affected by random noise, outliers, and sensor drift, which may reduce the reliability of structural thermal-response analysis. This study proposes a collaborative framework based on an improved adaptive cubature Kalman filter (IACKF) for multi-anomaly mitigation. First, the process- and observation-noise covariance matrices are updated online using innovation and residual statistics to suppress time-varying noise. Second, a dual-Gaussian contaminated observation model is incorporated to develop an outlier-resistant improved adaptive cubature Kalman filter (OR-IACKF) for isolated and patch-type outliers. Third, an improved particle swarm optimization–backpropagation–IACKF (IPSO-BP-IACKF) scheme is used to predict a drift-free reference from adjacent monitoring points and recursively estimate sensor drift. The framework is evaluated using simulated data, constant-temperature chamber measurements, field-monitored bridge data, and a jointly contaminated dataset. The results indicate that the staged framework improves the mitigation of different anomaly types while maintaining relatively stable performance under different parameter settings. The proposed method provides a practical data-processing approach for long-term bridge structural health monitoring. Full article

The European Union Framework Programmes constitute one of the main instruments for promoting research and innovation at a supranational scale. Although their linkages with scientific and technological output are widely recognized, empirical evidence regarding the mechanisms connecting institutional participation, innovative performance, and territorial innovation dynamics remains limited. This study investigates these relationships using data from the 27 European Union member states and indicators associated with Horizon 2020. The analysis considers three dimensions of institutional participation—funding, projects, and participating organizations—and four innovative performance indicators related to traditional and green patents. The empirical strategy combines regression models, panel data models, feedback analyses, and spatial econometric diagnostics based on Moran’s I statistics and LM tests, using a collaboration matrix derived from Horizon 2020 project networks. The results indicate that project participation and organizational involvement show more consistent associations with innovative activity than funding volumes considered in isolation. However, the panel models reveal that these associations are concentrated in structural differences between countries rather than short-term temporal variations. The feedback analyses point to a bidirectional relationship between institutional participation and innovative performance, suggesting mutually reinforcing associations between technological performance and integration into research networks over time. The results also show that the observed associations are particularly relevant for green technologies. Finally, the spatial diagnostics indicate persistent patterns of relational concentration in both innovative capabilities and collaboration networks, though without robust evidence of residual spatial dependence in the estimated models. The study contributes to the literature on innovation systems and innovation policy by suggesting that supranational programmes operate less as direct mechanisms for resource transfer and more as institutional structures for coordination, integration, and the reinforcement of previously accumulated innovative capabilities. Full article

Sustainable mobility is undergoing a profound transformation driven by emerging technologies, increasingly stringent government regulations, and rising societal expectations, challenging traditional mobility models and fostering the emergence of new collaborative ecosystems. Actors from diverse sectors are converging to co-create innovative solutions that support a more sustainable mobility paradigm. Given the novelty of this phenomenon, this study proposes a theory-based taxonomy of innovation ecosystem initiatives for sustainable mobility. To this end, an exploratory literature review was conducted on 760 peer-reviewed articles published over the past twenty years, linking publications on innovation ecosystems with those on sustainable mobility. Topic analysis, performed using Latent Dirichlet Allocation (LDA), identified ten key themes. These themes were then grouped into three main categories during an expert roundtable: smart transportation systems, sustainable mobility planning, and logistics optimization. Furthermore, the analysis of author networks reveals a segmented and specialized collaborative structure, characterized by strong cohesion within thematic groups but limited connectivity between them. This fragmentation reinforces thematic concentration while limiting inter-domain interactions. Overall, the proposed taxonomy offers a structured framework for better understanding and supporting the design, coordination, and orchestration of innovation ecosystems in sustainable mobility. Full article

Against the rapid expansion of the digital economy in Southeast Asia, improving firms’ digital capability has become increasingly important for sustaining employee innovation in the IT services sector. However, existing research has mainly emphasized organizational or performance outcomes and has paid less attention to the internal mechanisms through which corporate digital capability shapes employees’ sustainable innovation behavior, especially in the heterogeneous institutional and cultural context of ASEAN. Drawing on Conservation of Resources theory and Social Information Processing Theory, this study examines the relationship between corporate digital capability and sustainable employee innovation behavior, with innovation atmosphere and knowledge sharing as mediating mechanisms. Using cross-sectional survey data collected from 257 employees in IT services firms across five ASEAN countries, and employing hierarchical regression analysis with bootstrap mediation testing, this study finds that corporate digital capability positively promotes sustainable employee innovation behavior (β = 0.38, p < 0.01). The results further show that innovation atmosphere and knowledge sharing serve as important mediating pathways through which digital capability is translated into employee-level innovation outcomes (indirect effects: 0.20, 95% CI [0.10, 0.31] for IA; 0.19, 95% CI [0.11, 0.28] for KS). In addition, innovation atmosphere strengthens knowledge sharing, forming a sequential mechanism (indirect effect = 0.26, 95% CI [0.15, 0.37]) that further supports sustainable innovation behavior. This study contributes to the literature by clarifying how digital capability affects sustainable employee innovation at the micro level, by distinguishing the organizational climate and knowledge exchange mechanisms involved in this process, and by providing context-sensitive evidence from ASEAN’s diverse digital transformation environment. The findings also provide practical insights for firms and policymakers seeking to strengthen sustainable innovation capacity through digital capability building, supportive organizational climates, and stronger knowledge-sharing practices. Specifically, policymakers should align DEFA-funded digital skill programs with organizational climate interventions that foster psychological safety and cross-border collaboration, while managers in resource-constrained SMEs can leverage low-cost measures such as monthly idea recognition schemes, public endorsement of reasonable failure, and designated cross-border knowledge brokers to amplify the innovation returns of digital investments. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

Hyperspectral image classification is a core task in remote sensing image analysis and understanding. Existing Transformer-based methods have achieved excellent performance but are limited by the quadratic computational complexity of the self-attention mechanism, while the high-dimensional redundancy of hyperspectral data and the difficulty in deeply integrating spatial–spectral features also restrict further performance improvement. To address these issues, we introduce the Mamba architecture based on state-space models into hyperspectral image classification and propose the DFMamba model. The main innovations include (1) constructing a Hyperspectral Spatial Attention Embed (HSAE) to achieve efficient channel compression and feature extraction via adaptive grouped convolution, depth-wise separable convolution, and spatial attention; (2) proposing a spatial–spectral dual-branch collaborative modeling mechanism, EnhancedBothMamba, which separately models global dependencies in the spatial and spectral branches and integrates their outputs through softmax-normalized learnable global weights together with a learnable residual scaling factor; and (3) building an improved classification head, ClsHead, with a multi-scale branch fusion strategy to fully exploit local and global feature information. The experimental results on four standard hyperspectral datasets demonstrate that DFMamba achieves overall accuracy (OA) of 97.41% on the Pavia University dataset, 92.25% on the HanChuan dataset, 95.12% on the HongHu dataset, and 94.98% on the Houston dataset. Under the adopted evaluation protocol, DFMamba obtains higher mean OA than MambaHSI and the other compared methods while retaining favorable computational efficiency. Full article

Against the backdrop of the “Rural Revitalization” strategy, contract farming has emerged as a new mechanism and a growing research focus. This study employs differential game theory to systematically analyze the dynamic decision-making and operational performance differences among supply chain members by comparing scenarios with and without research institution participation, as well as different government subsidy policies. The innovative contribution lies in introducing a dual-effect index to capture consumers’ quality utility from agricultural products and psychological utility derived from supporting farmers. Furthermore, we explore the synergistic effects of research institutions’ technological empowerment and government subsidy strategies on supply chain efficiency. Through the above research and analysis, the study draws the following conclusions: (1) The collaborative agricultural supporting model significantly enhances both farmer yields and overall supply chain profits through technology diffusion and brand premium effects. However, revenue distribution conflicts may lead enterprises to limit their cooperation depth. (2) Government subsidies can alleviate benefit allocation conflicts. Subsidizing a research institution proves more effective at stimulating long-term technological dividends, while subsidizing an enterprise primarily ensures short-term market stability. (3) Brand advantage, technological leadership, and consumer preferences exhibit nonlinear driving effects on supply chain efficiency. Specifically, the positive impact progressively strengthens with wider brand advantages, greater technological leadership, and more intense consumer preferences. The study provides a theoretical foundation for tripartite collaboration, revealing the pivotal role of policy precision and dynamic benefit allocation equilibrium in agricultural supply chain upgrading. Full article

Cyber wargaming serves as a core tool for simulating cyber confrontations and supporting operational decision verification. Existing multi-agent methods face issues such as the absence of cross-level mechanisms and poor adaptability to dynamic environments when applied to cyber wargaming. To address these challenges, we innovatively propose HC-MARL, a general hierarchical cascaded multi-agent reinforcement learning architecture tailored for cyber wargaming. Within this architecture, agents are modeled as hierarchical cascaded units to achieve structural decoupling and preserve scalability in both horizontal and vertical dimensions. Specifically, we devise a cross-level bidirectional information passing and intrusion alert sharing mechanism to accommodate cross-level command characteristics; design a Transformer-based message transformation function that fuses variable-length observation vectors from lower-level agents into fixed-length vectors, adapting to dynamic changes in agent structures and numbers; and design a policy function that integrates neural networks with empirical knowledge, incorporating an intrusion-alert-based action mask to enhance threat response efficiency. To the best of our knowledge, the proposed HC-MARL framework is a novel general hierarchical collaborative multi-agent architecture for cyber wargaming. To validate the effectiveness of our framework, we instantiate two-layer and three-layer agent clusters and conduct experiments in the CybORG CC4 environment. The results demonstrate that our architecture can effectively accommodate cross-level information transfer and dynamic changes in agents in cyber wargaming scenarios. Furthermore, incorporating intrusion-alert-based action masking into the policy function significantly improves defensive performance. Compared with the state-of-the-art (SOTA) methods, the average reward improves by approximately 12.3%, and compared with mainstream methods such as Singh et al., the average reward improves by approximately 27.5%. Full article