Search Results (43)

This systematic review examines how organisational resilience is conceptualised, enacted, and enabled in the Digital Age, characterised by Artificial Intelligence (AI), Generative AI, the Internet of Things (IoT), Big Data, and Robotics. Despite their transformative potential, these technologies are often treated as peripheral tools rather than core mechanisms in resilience architectures. Adopting a critical realist paradigm, we conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol to review thirty (30) peer-reviewed empirical studies (2017–present). A pre-SLR conceptual framework, linking Business Intelligence and Responsiveness constructs, guided data extraction and synthesis. Building on this, we propose a conceptual framework and explanatory model grounded in the Context–Mechanism–Outcome logic. The model distinguishes generative mechanisms (real domain), organisational responses (actual domain), and observable indicators (empirical domain). The review identifies Collective Capability (CC), Adaptive Capability (AC) and Dynamic Capability (DC) mechanisms as key generative powers, with Digital Age enablers embedded within Adaptive Capability (AC) and Dynamic Capability (DC). Together, these mechanisms contribute to Systemic Preparedness (SP), Rapid Recovery (RR) and Generative Stability (GS), thereby supporting the emergence of Organisational Resilience (OR). This reconceptualises resilience as an emergent, non-linear outcome of mechanism interactions, offering a unified direction. Future research should prioritise longitudinal multi-case studies and quantitative testing of Context–Mechanism–Outcome configurations, supported by mixed-method designs to validate and refine the proposed framework. Full article

This paper investigates the multi-objective drone routing problem for on-demand delivery considering hybrid delivery modes. Unlike previous studies that assume a single delivery strategy or statically bind modes to heterogeneous drone types, real-world operations require a hybrid framework where homogeneous drones dynamically switch between exclusive and sharing modes to accommodate orders with distinct logics. We formulate a multi-objective mixed-integer programming model that minimizes operational costs while maximizing order revenue, explicitly accounting for dynamic order arrivals, drone battery swapping, and resource conflicts at shared lockers. To solve this problem under dynamic conditions, we propose an online optimization framework, the reinforcement learning knowledge-driven multi-objective evolutionary algorithm based on decomposition (RL-KMD-MOEA/D), which integrates Q-learning for adaptive operator selection within a rolling-horizon scheme. Comprehensive experiments demonstrate that RL-KMD-MOEA/D achieves competitive performance on small-scale instances and exhibits superior scalability and robustness on large-scale, highly constrained dynamic scenarios, outperforming other compared algorithms. Full article

Against the backdrop of global industrial sustainable transition and the advancement of UN Sustainable Development Goals (SDGs), higher education―a core carrier of sustainable human capital supply―plays a pivotal role in adjusting majors to meet labor market demands, resolving education–industry structural mismatch, and boosting regional sustainable development. From the perspective of “higher education supporting industrial sustainable transition,” this study explores how government Policy Mix Intensity enhances universities’ Major–Industry Alignment and its transmission mechanism, aiming to reveal higher education governance’s sustainable development path. Using panel data from 30 Chinese provinces (2012–2023), we constructed a PMI quantitative index and conducted empirical analysis via a two-way fixed-effects model. The results show the following: (1) high-intensity policy mixes significantly improve alignment, overcoming university organizational inertia and laying an institutional foundation for sustainable education–industry synergy; (2) Policy Mix Intensity acts through three pathways―optimizing capital allocation, deepening industry–education integration, and enhancing dynamic responsiveness―forming a “sustainable factor allocation—sustainable industry-education alignment” logic; (3) policy efficacy is more pronounced in highly marketized Eastern regions and via regulatory tools, reflecting the moderating effect of regional sustainable endowments and policy tool types. This study provides empirical evidence for the “policy mix intensity–sustainable efficacy” transformation mechanism, offers theoretical references and empirical insights from China for the global collaborative realization of SDG4, SDG8, and SDG9 through higher education policy optimization, and proposes that policy design should shift toward factor integration-based sustainable comprehensive governance. Full article

Compared with rule-based and optimization energy management strategies, online optimal energy management control strategies for a dual-mode power-split hybrid electric vehicles (PSHEVs) are able to achieve better fuel economy and real-time performance. Global online optimization of a finite time domain energy management strategy based on the hybrid model predictive control (HMPC) algorithm is proposed in this study. To reduce the computing time, a linearized predictive model is built; because dual-mode PSHEVs can be considered hybrid systems that include continuous and discrete states, the hybrid states can be expressed uniformly. Therefore, a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory, and an HMPC energy management strategy is proposed based on the MLD predictive model. To solve the optimal control problem online to obtain the optimal control sequence, the optimal control problem is converted into a mixed-integer linear programming (MILP) problem. The HMPC-based energy management strategy is compared with dynamic programming (DP)-based and rule-based energy management strategies over two different driving cycles. Simulation results indicate that the HMPC-based EMS achieves 80.60% and 83.79% of the fuel economy performance obtained by the DP-based EMS. In comparison, the rule-based EMS only achieves 66.46% and 70.51% of the DP-based control performance. Therefore, the HMPC-based energy management strategy is favorable for real-time control while effectively improving fuel economy. Full article

The collaboration between economic and ecological departments in land-use planning is crucial for advancing sustainable development. However, existing research has largely focused on macro-level policies and technical instruments, paying insufficient attention to the micro-level logics of behavior and strategic interactions between these two departments. This research employs a rigorous mixed-methods approach to bridge empirical depth with analytical rigor. The qualitative phase, encompassing 41 semi-structured interviews and analysis of 327 internal documents, examines the departments’ real-world motivations, strategic behaviors, and the cost–benefit structures underlying their decision-making. Based on these empirical findings, a tailored evolutionary game theory model is constructed to formally simulate the dynamic pathways and stable equilibria of collaboration between the Economic and Ecological Departments. Our analysis reveals that the evolutionary game system converges toward a dichotomy of stable states: a non-cooperative equilibrium characterized by development-oriented land-use planning with adaptive regulation, and a cooperative equilibrium underpinned by green-coordinated planning supported by stringent regulatory enforcement. A cooperative equilibrium is more readily achieved when both departments demonstrate a willingness to simultaneously increase their cost investment parameters in sustainable land-use planning. Conditions contrary to this mutual commitment lead to a non-cooperative equilibrium. Building on these findings, the study synthesizes this interplay into a novel “Institutional-Situational-Behavioral” (ISB) framework. This framework provides a cohesive theoretical lens for diagnosing and fostering interdepartmental collaboration in sustainable land governance. The research thus offers a theoretical foundation for analyzing the evolutionary dynamics of interdepartmental collaboration and delivers mechanism-informed policy guidance for enhancing sustainable land-use planning. Full article

Automated assessment of coronary artery (CA) lesions via Coronary Computed Tomography Angiography (CCTA) is essential for the diagnosis of coronary artery disease (CAD). However, current deep learning approaches confront several challenges, primarily regarding the modeling of long-range anatomical dependencies, the effective decoupling of plaque texture from stenosis geometry, and the utilization of clinically prevalent mixed-grained annotations. To address these challenges, we propose a novel mixed-grained hierarchical geometric-semantic learning network (MG-HGLNet). Specifically, we introduce a topology-aware dual-stream encoding (TDE) module, which incorporates a bidirectional vessel Mamba (BiV-Mamba) encoder to capture global hemodynamic contexts and rectify spatial distortions inherent in curved planar reformation (CPR). Furthermore, a synergistic spectral–morphological decoupling (SSD) module is designed to disentangle task-specific features; it utilizes frequency-domain analysis to extract plaque spectral fingerprints while employing a texture-guided deformable attention mechanism to refine luminal boundary. To mitigate the scarcity of fine-grained labels, we implement a mixed-grained supervision optimization (MSO) strategy, utilizing anatomy-aware dynamic prototypes and logical consistency constraints to effectively leverage coarse branch-level labels. Extensive experiments on an in-house dataset demonstrate that MG-HGLNet achieves a stenosis grading accuracy of 92.4% and a plaque classification accuracy of 91.5%. The results suggest that our framework not only outperforms state-of-the-art methods but also maintains robust performance under weakly supervised settings, offering a promising solution for label-efficient CAD diagnosis. Full article

Write amplification factor (WAF) is a critical performance and endurance bottleneck in flash-based solid-state drives (SSDs). Multi-streamed SSDs mitigate WAF by enabling logical data streams to be written separately, thereby improving the efficiency of garbage collection. However, despite the architectural potential of multi-streaming, prior research has largely overlooked the design of write buffer management schemes tailored to this model. In this paper, we propose a stream-aware block-level write buffer management technique that leverages both spatial and temporal locality to further reduce WAF. Although the write buffer operates at the granularity of pages, eviction is performed at the block level, where each block is composed exclusively of pages from the same stream. All pages and blocks are tracked using least recently used (LRU) lists at both global and per-stream levels. To avoid mixing data with disparate hotness and update frequencies, pages from the same stream are dynamically grouped into logical blocks based on their recency order. When space is exhausted, eviction is triggered by selecting a full block of pages from the cold region of the global LRU list. This strategy prevents premature eviction of hot pages and aligns physical block composition with logical stream boundaries. The proposed approach enhances WAF and garbage collection efficiency without requiring hardware modification or device-specific extensions. Experimental results confirm that our design delivers consistent performance and endurance improvements across diverse multi-streamed I/O workloads. Full article

Fuel cell commercial vehicles are widely used in commercial transport for their high efficiency and long range. However, in mixed operating scenarios, their energy economy and fuel cell operational stability cannot be fully balanced. Traditional strategies lack adaptability in mixed operating scenarios. Therefore, based on the equivalent factor regulation formula of the Adaptive Equivalent Hydrogen Consumption Minimization Strategy (A-ECMS) and the improved Sparrow Search Algorithm-Long Short-Term Memory (SSA-LSTM) hybrid model, short-term speed prediction and three-stage speed interval division are embedded into the equivalent factor regulation logic. A dynamic equivalent factor regulation strategy integrating SOC deviation is constructed, and an improved Predictive Equivalent Hydrogen Consumption Minimization Strategy (P-ECMS) is finally derived. The SSA-LSTM algorithm is optimized via constrained hyperparameter tuning for short-term speed prediction. A time-decay weighting mechanism enhances recent speed data weight, with weighted results as inputs to boost accuracy. Moving Average Residual Correction (MARC) is used to verify the speed prediction model accuracy and correct residuals. Multi-scenario tests show that the SSA-LSTM model outperforms the Gated Recurrent Unit (GRU) model in prediction accuracy and generalization ability, providing reliable data support for segmented regulation. With battery SOC deviation and the SSA-LSTM-predicted speed trend as core inputs, combined with three-stage speed interval division, A-ECMS’s equivalent factor regulation formula is improved. The model adopts a segmented dynamic regulation logic to integrate dual factors into equivalent factor adjustment, and it reasonably adjusts the energy output ratio of fuel cells and power batteries according to speed intervals and operating condition changes. In scenarios with significant speed fluctuations and frequent operating condition transitions, power shocks are mitigated by the power battery’s peak-shaving and valley-filling function. Simulation results for C-WTVC and NREL2VAIL show that, compared with traditional A-ECMS, the improved P-ECMS has notable energy benefits, with equivalent hydrogen consumption reduced by 3.41% and 5.48%, respectively. The fuel cell’s state is significantly improved, with its high-efficiency share reaching 63%. The output power curve is smoother, start–stop losses are reduced, and the fuel cell’s service life is extended, balancing the energy economy and component durability. Full article

A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for hybrid OC and SC faults. To address these issues, this paper proposes a diagnosis method for cascaded switch open/short-circuit fault in a three-phase inverter based on a two-stage interval sliding mode observer (ISMO). First, by establishing a mixed logic dynamic (MLD) model considering open- and short-circuit faults, the different fault operating states of the three-phase inverter can be fully characterized. Furthermore, a two-stage cascaded ISMO was designed. The pre-stage ISMO rapidly detects abnormal status and fault phase, while the post-stage ISMO accurately isolates OC and SC faults. After diagnosis, the corresponding fault identification of the observer is set for the next fault diagnosis, achieving the sequential diagnosis of cascaded faults. The proposed diagnosis method was tested to validate its effectiveness. Full article

With the rapid transformation of the automotive industry towards electric vehicles, how to achieve efficient mixed-line production of electric vehicles and fuel vehicles has become a key challenge for modern assembly systems. This study investigated the balancing problem of a mixed-model multi-manned assembly line, considering workstation sharing (MMuALBP-WS), and developed a deterministic multi-objective model that integrates the heterogeneity of tasks and the coordination of shared workstations. An improved genetic algorithm was proposed, whose decoding mechanism enables different types of electric vehicle and fuel vehicle tasks to achieve dynamic collaboration within the shared workstations. A real case study from the chassis assembly line of Company W demonstrated the effectiveness of the proposed method, achieving a 25% reduction in the number of workstations, a 27% decrease in the total number of workers, and a 23.56% increase in average workstation utilization. The results confirmed that the workstation sharing mechanism significantly improved production balance, labor utilization, and flexibility, providing a practical and scalable optimization framework for the mixed-model assembly system in the era of the transition from electric vehicles to fuel vehicles. In addition to its practical significance, this study enhances the understanding of mixed-model multi-manned line balancing by incorporating workstation-sharing logic into both the mathematical modeling and optimization process, offering a theoretical basis for future extensions to more complex production environments. Full article

Modeling complex non-physical systems is essential for understanding the interdependent dynamics of human-centered adaptive environments. This study extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework to represent and analyze these systems beyond their traditional engineering applications. A mixed-methods approach, combining a systematic literature review, expert interviews, and survey-based validation, was employed to test the framework using the teaching–learning process in Higher Education (HE) as an illustrative case study. The results show how function-centered modeling within the GTST-MLD structure decomposes the complexity of the system and reveals pedagogical bottlenecks, providing a structured basis for designing adaptive strategies. Rather than measuring learning gains directly, the model offers a structured representation of the conceptual and methodological pathways that influence learner engagement, conceptual integration, and adaptability. Within this bounded context, this study demonstrates a reproducible GTST-MLD modeling procedure for non-physical systems, an auditable dependency structure, based on explicitly defined nodes and edges, and a coherent alignment between Threshold Concepts (TCs), Learning Outcomes (LOs), and methodological strategies. Together, these contributions offer a basis for diagnosing and optimizing complex non-physical systems and form a foundation for future empirical evaluation. Full article

Background: Kazakhstan’s transport system plays a key role in Eurasian logistics due to its position along the Middle Corridor. However, multimodal freight transport remains under-optimized due to infrastructure bottlenecks, uneven cargo flows, and limited digital tools for forecasting and planning. Methods: This study presents the development of an agent-based simulation model for analyzing multimodal transportation in Kazakhstan. The model integrates railway, road, and maritime components, simulating cargo flows across export, import, and transit scenarios. Key agents include orders, transport vehicles, logistics hubs, and border checkpoints. The model is implemented in AnyLogic 8.9 and calibrated using a mix of official statistics, industry data, and field estimates. Results: The simulation replicates key logistics processes, identifies congestion points, and evaluates delivery performance under different scenarios. Experiments demonstrate how bottlenecks at terminals and border crossings affect delivery times, vehicle utilization, and hub load. The model allows testing infrastructure development options and scheduling policies. Conclusions: The approach enables a dynamic assessment of logistics efficiency under uncertainty and can support decision-making in transport planning. The novelty lies in the integrated simulation of multimodal freight flows with infrastructure constraints. The model serves as a foundation for digital twin applications and scenario-based planning. Full article

Urban growth models often prioritize environmental and accessibility factors while underestimating behavioral and functional dynamics. This study develops a POI-enhanced Cellular Automata (CA) framework to simulate urban expansion by incorporating three semantic indicators derived from Point-of-Interest (POI) data—density (PD), diversity (PDI), and functional centrality (FC). Taking Yan’an, China, as a case, the model integrates these indicators with terrain and infrastructure variables via logistic regression to estimate land-use transition probabilities. To ensure robustness, spatial block cross-validation was adopted to reduce spatial autocorrelation bias. Results show that the POI-based model outperforms the baseline in both Kappa and Figure of Merit metrics. High-density and mixed-function POI zones correspond with compact infill growth, while high-centrality zones predict decentralized expansion beyond administrative cores. These findings highlight how functional semantics sharpen spatial prediction and uncover latent behavioral demand. Policy implications include using POI-informed maps for adaptive zoning, ecological buffer protection, and growth hotspot management. The study contributes a transferable workflow for embedding behavioral logic into spatial simulation. However, limitations remain: the model relies on static POI data, omits vertical (3D) development, and lacks direct comparison with alternative models like Random Forest or SVM. Future research could explore dynamic POI trajectories, integrate 3D building forms, or adopt agent-based modeling for richer institutional representation. Overall, the approach enhances both the accuracy and interpretability of urban growth modeling, providing a flexible tool for planning in functionally evolving and ecologically constrained cities. Full article

Emerging hybrid governance models are transforming conventional approaches to land-use regulation by simultaneously enabling urban–rural development and enforcing ecological safeguards. This study investigates the regulatory mechanisms underpinning China’s urban–rural land-use policies through an innovative mixed-methods approach, integrating systematic text analysis and the Institutional Grammar Tool (IGT). Drawing on a comprehensive dataset of 62 national policy documents (2012–2024), we employ textual coding and thematic clustering to identify seven core policy pathways, ranging from territorial spatial planning to ecological protection. These pathways are further deconstructed using IGT to assess their regulatory intensity, revealing a tripartite governance model: (1) flexible AIC-strategies (e.g., land market mechanisms), which enable local experimentation by specifying actors, aims, and conditions without rigid obligations; (2) adaptive ADIC-norms (e.g., collective land reforms), which balance central directives with localized discretion through conditional deontic rules; and (3) rigid ADICO-rules (e.g., ecological redlines), which enforce absolute compliance through binding sanctions. Through systematic analysis of land use policy regulations, we reveal how China’s hybrid governance system operationalizes a tripartite institutional logic—maintaining rigid regulatory control (ADICO-rules) in ecologically critical zones, adaptive policy experimentation (ADIC-norms) in transitional areas, and flexible market-based instruments (AIC-strategies) in development zones—thereby dynamically reconciling environmental conservation with socioeconomic diversification. The study advances both institutional theory through its grammatical analysis of policy instruments and governance theory by transcending the traditional command-and-control versus flexible governance dichotomy. Practically, the research offers actionable insights for policymakers in emerging economies, emphasizing spatially differentiated regulation, dynamic monitoring system, and strategic coupling of binding rules with flexible implementation mechanisms. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article