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Against the backdrop of the global integration of green transformation and the digital economy, how manufacturing enterprises leverage digitalisation to drive green innovation has become a focal point for both academic and industrial sectors. Based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study constructs a moderated mediation model to explore the internal mechanism through which digital transformation influences green innovation via green dynamic capabilities and examines the boundary role of environmental munificence. Questionnaire data, collected in two stages from 312 Chinese manufacturing enterprises using SPSS 27.0 and AMOS 24.0, was analysed, and the empirical results indicate that digital transformation not only directly promotes green innovation but also exerts an indirect influence through the three dimensions of green dynamic capabilities: insights into the capability of green opportunities, green resource integration, and green resource reconstruction. Furthermore, environmental munificence significantly and positively moderates the relationship between green dynamic capabilities and green innovation, suggesting that this relationship is strengthened in resource- and opportunity-rich environments. Path analysis of the three green dynamic capability dimensions reveals that back-end capabilities (resource integration and reconfiguration) have a more pronounced impact on green innovation than front-end capabilities (opportunity insights). From the dual perspectives of capability building and contextual fit, this study elucidates the mechanism and boundary conditions of digital transformation driving green innovation, enriches green innovation theory, and offers practical insights into the digital-green transformation of manufacturing enterprises. Full article

Background: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical framework examining how knowledge process capabilities and dynamic capabilities interact to enable successful artificial intelligence adoption in logistics organizations within emerging market contexts. Methods: Through comprehensive literature review and theoretical synthesis, we propose a hybrid capability framework that integrates knowledge-based view perspectives with dynamic capabilities theory. Results: Theoretical analysis suggests that knowledge combination capabilities may be the strongest predictor of artificial intelligence implementation success, while dynamic reconfiguring capabilities could mediate the relationship between artificial intelligence adoption and performance outcomes. The proposed framework indicates that organizations with hybrid capability architecture may achieve superior implementation success compared to traditional approaches. Environmental uncertainty is theorized to strengthen the knowledge process capabilities—artificial intelligence adoption relationship. Conclusions: The framework suggests that successful artificial intelligence integration requires simultaneous development of knowledge-based and adaptive capabilities rather than sequential capability building. The hybrid capability framework provides theoretical guidance for managers in emerging markets, while highlighting the critical role of environmental context in shaping transformation strategies. Full article

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how GVC reconfiguration mediates their impact on manufacturing supply chain resilience (MSCR). To address this gap, this study conducts an enhanced bibliometric–systematic literature review (B-SLR) of 120 peer-reviewed articles. The findings reveal that macro-level shocks induce GVC reconfigurations along geographical, value, and governance dimensions, which in turn trigger MSCR through node- and link-level mechanisms. MSCR represents a manufacturer-centered capability that enables MSCs to preserve, realign, and enhance value amid shocks. Building on these insights, this research proposes a multi-tier strategy encompassing firm-level practices, inter-firm collaborations, and policy interventions. This study outlines three key contributions. First, at the theoretical level, it embeds MSCR within a GVC framework, clarifying how GVC reconfiguration mediates SCR under macro-level shocks. Second, at the methodological level, it ensures corpus completeness through snowballing and refines bibliometric mapping with multi-dimensional visualization. Third, at the managerial level, it provides actionable guidance for firms, industry alliances, and policymakers to align MSCR strategies with the dynamics of global production networks. Full article

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article

Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it is common that users cannot accurately specify their applications’ low-level requirements, and they tend to overestimate them—a problem further intensified by their lack of detailed knowledge on the infrastructure’s characteristics. In this context, resource orchestration mechanisms perform allocations based on the provided worst-case assumptions, with a direct impact on the performance of the whole infrastructure. In this work, we propose a resource orchestration mechanism based on intents, in which users provide their high-level workload requirements by specifying their intended preferences for how the workload should be managed, such as prioritizing high capacity, low cost, or other criteria. Building on this, the proposed mechanism dynamically assigns resources to applications through a Reinforcement Learning method leveraging the feedback from the users and infrastructure providers’ monitoring system. We formulate the respective problem as a discrete-time, finite horizon Markov decision process. Initially, we solve the problem using a tabular Q-learning method. However, due to the large state space inherent in real-world scenarios, we also employ Deep Reinforcement Learning, utilizing a neural network for the Q-value approximation. The presented mechanism is capable of continuously adapting the manner in which resources are allocated based on feedback from users and infrastructure providers. A series of simulation experiments were conducted to demonstrate the applicability of the proposed methodologies in intent-based resource allocation, examining various aspects and characteristics and performing comparative analysis. Full article

This study uses the CREATE^TM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the technical build-up, which includes aircraft, paratrooper and static line modeling, plus preliminary functional checkouts executed to verify simulation performance. This research and simulation development effort is driven by the need to meet the analysis demands required to support the US Army Personnel Airdrop with static line length studies and the North Atlantic Treaty Organization (NATO) Joint Airdrop Capability Syndicate (JACS) with airdrop interoperability assessments. Each project requires the use of various aircraft types, static line lengths and exit procedures. To help meet this need and establish a baseline proof of concept (POC) simulation, simulation setups were developed for a towed jumper from both the C-130J and C-17 using a 20-ft static line to support US Army Personnel Airdrop efforts. Concurrently, the JACS is requesting analysis to support interoperability testing to help qualify the T-11 parachute from an Airbus A400M Atlas aircraft, operated by NATO nations. Due to the lack of an available A400M geometry, the C-17 was used to demonstrate the POC, and plans to substitute the geometry are in order when it becomes available. The results of a nominal Computational Fluid Dynamics (CFD) simulation run using a C-17 and C-130J will be reviewed with a sample of the output to help characterize performance differences for the aircraft settings selected. The US Army Combat Capabilities Development Command Soldier Center (DEVCOM-SC) Aerial Delivery Division (ADD) has partnered with the US Air Force Academy (USAFA) High Performance Computing Research Center (HPCRC) to enable Modeling and Simulation (M&S) capabilities that support the Warfighter and NATO airdrop interoperability efforts. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

Water pumping stations are essential components of national water infrastructure, yet their construction involves complex, high-risk processes, and traditional risk management approaches often show significant limitations in practice. To address this challenge, this study proposes a Building Information Modeling (BIM)-based approach that integrates structured risk information into an interactive nD BIM environment. We first developed an extended Risk Breakdown Matrix (eRBM), which systematically organizes risk factors, assessment levels, and causal relationships. This is linked to the BIM model through a customized BIM–risk integration framework. Subsequently, the framework is further implemented and quantitatively validated via a Navisworks plug-in. The system incorporates three core components: (1) a structured risk information model, (2) a visualization mechanism for dynamic, spatiotemporal risk representation and (3) risk influence path analysis using the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL–ISM) method. The plug-in allows users to access risk information on demand and monitor its evolution over time and space during the construction process. This study makes contributions by innovatively integrating risk information with BIM and developing a data-driven visualization tool for decision support, thereby enhancing project managers’ ability to anticipate, prioritize, and mitigate risks throughout the construction lifecycle of water pumping station projects. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

In increasingly turbulent environments, organizations must go beyond generic robustness and develop Requisite Resilience, the capacity to align internal variety with environmental variety to sustain core functions during crises. This study situates Requisite Resilience within organizational theory and strategic management, assessing how major theories of the firm contribute to its development. The analysis groups these perspectives into foundational/diagnostic theories, which clarify environmental, structural and institutional constraints and correspond to passive resilience frameworks, and enabling/capability-building theories, which emphasize managerial agency, resource orchestration and adaptive learning, corresponding to active resilience frameworks. Findings indicate that while foundational perspectives offer essential diagnostics, they are insufficient on their own to foster Requisite Resilience. A composite configuration provides the strongest fit: co-evolutionary views offer an integrative backbone, dynamic capabilities and organizational learning enhance sensing, seizing and acting, and resource dependence theory informs the design of permeable boundaries. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

Internal insulation plasters enable historic building renovation without altering the external appearance of the wall. However, the use of internal insulation must be verified case-by-case through dynamic hygrothermal simulation, and the influence of input parameters on the results is not always clear. This paper aims to (i) characterize a new lime-based insulating plaster with expanded recycled glass and aerogel through laboratory measurements, (ii) assess the damage criteria of the plaster under different boundary conditions through dynamic simulations, and (iii) identify the most impactful design conditions on the relative humidity behind insulation. This innovative plaster combines highly insulating properties (thermal conductivity of 0.0463 W/mK) with good capillary activity while also integrating recycled components without compromising performance. The relative humidity behind insulation remains below 95% in most simulated scenarios, with cases above this threshold found only in cold climates, particularly under high internal moisture loads. The parametric study shows that (i) in the analyzed stones, the thermal conductivity variation of the existing wall has a greater effect on the relative humidity behind insulation than the variation of the vapor resistance factor, (ii) the effect of insulation thickness on the relative humidity behind insulation depends on the difference in thermal resistance of the insulation and existing masonry layers, and (iii) internal moisture load and external climate directly impact the relative humidity behind insulation. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article