Search Results (502)

Based on Conservation of Resources (COR) theory and a socio-technical systems perspective, this study examines how technology-enacted abusive supervision (TAS) influences employees’ counterproductive work behavior (CWB) in digitalized organizational contexts. Conceptualizing TAS as a system-embedded form of digitally mediated control, we argue that technology-amplified supervisory power constitutes a persistent resource threat that reshapes employees’ behavioral regulation strategies. Using three-wave time-lagged survey data from 428 employees working in digital-intensive enterprises in China, we develop and test a moderated mediation model. The results indicate that TAS is positively associated with CWB, with defensive silence serving as a critical mediating mechanism. Although defensive silence may temporarily reduce interpersonal risk, it disrupts feedback and resource replenishment processes, leading to cumulative resource depletion and a higher likelihood of counterproductive behavior over time. Moreover, power distance significantly moderates this indirect effect, such that the mediating role of defensive silence is stronger among employees with higher-power-distance orientations. By integrating leadership research, COR theory, cultural value orientations, and a socio-technical systems perspective, this study advances our understanding of covert resistance and behavioral risk in technology-driven work systems and offers important implications for digital governance and sustainable organizational performance. Full article

Marine tourism embodies a sustainability paradox: high economic value coexists with ecological harm. Yet how macro-level policies shape pro-environmental intentions among transient, socially disembedded tourists remains unclear. Integrating institutional signaling theory with the Theory of Planned Behavior, we analyze survey data from 413 coastal visitors in China. The results reveal an asymmetric mediation pattern: marine policy influences intention primarily through behavioral attitudes and, to a lesser extent, subjective norms, while perceived behavioral control, though a direct predictor, does not mediate this relationship. This highlights a key boundary condition of TPB in low-embeddedness contexts, where institutional signals substitute for absent social ties by activating cognitive pathways. Practically, we propose a tiered governance pathway—attitude-focused messaging at digital touchpoints, normative feedback at entry points, and visible on-site infrastructure—to translate policy into observable actions, advancing both theory and practice in sustainable marine tourism. Full article

Cross-regional mega infrastructure (CrMI) is vital for sustaining economic vitality and social connectivity but is increasingly threatened by climate extremes and fragmented management. This study develops a targeted and interpretable evaluation system integrating 5 dimensions and 37 indicators. And social network analysis (SNA) with clustering methods is applied to the Hong Kong–Zhuhai–Macao Bridge as a representative case. Key indicators are classified into “Management Focuses,” “Management Challenges,” and “Management Sensitives,” reflecting varying levels of influence, feedback efficiency, and control capacity. The results reveal that the sustainable operation and maintenance management of CrMI should prioritize economic development while simultaneously strengthening resilience and intelligence. However, environmental protection remains a major challenge, and public attention and inter-regional cooperation are critical for management sensitivity. By embedding resilience intelligence into sustainable evaluation, this study advances sustainability theory and offers a more feasible and forward-looking pathway to sustaining CrMI under conditions of accelerating uncertainty. Full article

Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports efficient sensing, aggregation, and analysis, but energy constraints from transmission (over 60% of consumption), idle listening, and dynamic conditions like body motion hinder adoption. Challenges include minimizing energy waste while ensuring data reliability, Quality of Service (QoS), and adaptation to channel variations, alongside algorithm complexity and privacy concerns. This paper reviews recent power control mechanisms in WBANs, encompassing feedback control, dynamic and convex optimization, graph theory-based path optimization, game theory, reinforcement learning, deep reinforcement learning, hybrid frameworks, and emerging architectures such as federated learning and cell-free massive MIMO, adopting a systematic review approach with a focus on healthcare and IoT application scenarios. Achieving energy savings ranging from 6% (simple feedback control) to 50% (hybrid frameworks with emerging architectures), depending on method complexity and application scenario, with prolonged network lifetime and improved reliability while preserving QoS requirements in healthcare and IoT applications. Full article

This study explores finite-time synchronization (FTS) in fractional-order quaternion-valued neural networks (FQVNNs) characterized by discontinuous activation functions and uncertainties in parameters. Initially, leveraging the properties of the Mittag-Leffler function along with fractional-order (F-O) delayed differential inequalities, a novel finite-time stability theorem for F-O systems is established, building upon previous research findings. Next, based on norm definitions, two state feedback controllers employing quaternion 1-norm and quaternion 2-norm are devised to ensure FTS for the system under consideration. Following this, by utilizing differential inclusion theory, examining the quaternion sign function, employing advanced inequality methods, applying principles of F-O differential equations, and using the Lyapunov functional approach, new criteria for achieving FTS in FQVNNs are formulated. Additionally, precise estimates for the settling time are presented. In conclusion, two carefully designed numerical examples are included to corroborate the theoretical results derived. Full article

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article

Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind–wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%. These findings demonstrate the framework’s capability to ensure safe personnel transfer by effectively isolating complex vessel motions and validate the reliability of the coupled dynamic model for offshore operational forecasting. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article

With the advancement of the “dual carbon” goals, power transmission and transformation projects face complex challenges arising from the construction of new power systems. Traditional cost management models struggle to meet dynamic management demands, necessitating the establishment of analytical methods that systematically reflect the relationship between cost management levels and cost dynamics. This paper introduces system dynamics theory and methodology to construct a cost management model applicable to all phases of transmission and transformation projects. It aims to deeply analyze the relationship between project cost levels and expenses from the perspectives of system structure, feedback mechanisms, and dynamic behavior. Research indicates that pathways such as controlling cost deviations and optimizing resource allocation significantly impact total project costs. Specifically, enhancing design accuracy can effectively mitigate cost shocks caused by carbon price fluctuations, while timely implementation of cost control measures can significantly improve cost management levels. The system dynamics approach effectively reveals the dynamic interaction mechanism between cost management levels and costs in power transmission and transformation projects, providing theoretical foundations and methodological support for enhancing project cost control efficiency. Full article

In the construction of large cruise ships, the restricted deck space and dense obstacles create a strongly coupled problem between path planning and sequence optimization during prefabricated cabin boarding operations, significantly impairing overall installation efficiency. To coordinately optimize the boarding sequence of multiple cabins and minimize operational conflicts, this study proposes a dual-layer coordinated planning methodology. The lower layer generates feasible paths satisfying kinematic and contour-based obstacle avoidance constraints through optimal control theory, while the upper layer introduces a dynamic priority evaluation mechanism based on grid mapping and an “enclosure factor”, combined with a reverse planning strategy to dynamically adjust the cabin boarding sequence. Through iterative feedback between path feasibility and sequence efficiency, the proposed method effectively resolves the strong coupling between sequencing and path planning. Case validation demonstrates that the proposed approach significantly reduces total installation time compared to conventional sequence planning methods, proving its effectiveness and practical value in enhancing the efficiency of coordinated multi-cabin installation. Full article

This paper presents a conceptual model for creating digital educational games that aim to reduce mathematics anxiety (MA) and promote positive emotional engagement in mathematics education. No empirical data were collected or analyzed; the proposed model is based on a synthesis of theory and empirical findings from prior studies. Drawing on Control-Value Theory and recent meta-analyses and systematic reviews, the model identifies key psychological mechanisms underlying MA and proposes game features that address both cognitive and emotional domains. Adaptive difficulty and feedback, safe error handling, narrative, collaborative play, emotional regulation tools, mastery-oriented low-stakes practice, and non-competitive progress tracking are all discussed in terms of their theoretical foundation and empirical support. The paper explains how these features can improve learners’ perceived control and value, reducing anxiety while increasing motivation, self-efficacy, and engagement. The proposed model combines game design principles with evidence-based intervention strategies to provide guidance for the future development and evaluation of anxiety-reducing digital math games. This framework is intended to help researchers and practitioners create digital games that effectively support students with high math anxiety and improve mathematics education outcomes. Full article

Food supply chains play a critical role in advancing sustainability within today’s food systems. In this work, we construct a differential equation-based model with a four-layer supply chain framework that captures the intricate relationships among producers, manufacturers, distributors, and retailers while considering resource optimization, waste minimization, and supply–demand equilibrium. To better understand and predict supply chain behavior, we perform a series of model analyses. By applying chaos theory, we analyze the system’s equilibrium states and evaluate their local stability. Our findings reveal that manufacturers and retailers encounter significant difficulties when the system shifts into chaotic behavior. This can be made worse by future uncertainties. This entails formulating tailored strategies to mitigate risks. Hence, we design a set of nonlinear feedback control strategies to synchronize two chaotic supply chain networks. Theoretical validity is established using Lyapunov theory. Our simulation results confirm that the proposed strategy can eliminate synchronization errors. Furthermore, it allows for swift alignment and coordination between the networks. Overall, this synchronization method is both effective and easy to implement for managing risks and enhancing sustainability in food supply chains affected by chaotic dynamics. Full article

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results. Full article

Background: Neurofeedback training (NFT) enhances athletic performance through alpha modulation, but optimal inter-session intervals and individual response variability remain poorly understood. Objective: This is the first randomized controlled trial to systematically compare neurofeedback periodization (2-day vs. 3-day inter-session intervals) on neurophysiological adaptations, strength performance, and retention in elite judo athletes. Methods: Thirty-one national-level judokas completed 15 alpha enhancement sessions in 2-day (n = 12), 3-day (n = 12), or control (n = 7) groups, receiving pseudo-neurofeedback with randomized, non-contingent feedback. Primary outcomes included Frontal Alpha Index changes (ΔFAI; frontal alpha power modulation ratio) and squat performance (35–100% 1RM), with secondary assessment of 48/72 h retention and response phenotypes. Results: Mean ΔFAI was modest (E15G-2d: 0.005 ± 0.205; E15G-3d: 0.052 ± 0.202), with early peak responses followed by stabilization. E15G-3d demonstrated superior retention (90.2 ± 3.4% at 72 h vs. 76.8 ± 4.1% at 48 h; p < 0.001) despite similar peaks. Both training groups showed significant strength improvements versus controls (E15G-2d: 2.37 ± 0.66 reps; E15G-3d: 2.00 ± 0.53 reps), yet neurophysiological-performance correlations were non-significant (p > 0.072), indicating strength adaptations via mechanisms independent of alpha modulation. Three response phenotypes emerged (high: 29.0%, moderate: 51.6%, low: 19.4%), representing the first empirical classification of neurofeedback responsiveness in athletes. Conclusions: Three-day intervals uniquely optimize retention through enhanced consolidation, establishing evidence-based periodization guidelines for elite athletes. The dissociation between neural and performance adaptations challenges traditional neurofeedback theory, while individual heterogeneity necessitates personalized protocols for optimal NFT periodization. Full article