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The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator. Full article

Against the backdrop of the dual-carbon strategy promoting the green and low-carbon transformation of the shipping industry, pollutant emissions generated during vessel berthing operations have become a critical challenge in port environmental governance. To address the combined effects of the priority berthing policy for new energy vessels and time-of-use electricity pricing, a joint optimization model for berth and shore power allocation is developed with the objectives of minimizing the total economic cost of vessels and the environmental tax cost associated with pollutant emissions. An improved Adaptive Large Neighborhood Search algorithm (ALNS-II) is further designed to solve the model. Numerical experiments based on actual port data verify the effectiveness of the proposed model and the superiority of the algorithm. The results indicate that, under time-of-use electricity pricing, the priority berthing policy for new energy vessels can shorten their waiting time at anchorage and encourage fuel-powered vessels to shift toward electrification. When the peak-to-valley electricity price ratio increases from 4.1:1 to 7.5:1, the environmental tax cost of pollutant emissions decreases slightly, whereas the total economic cost of vessels rises by 4.17%, suggesting that the peak-to-valley electricity price ratio should not be set excessively high. In addition, increasing the proportion of new energy vessels to 70% is more conducive to improving the overall economic and environmental performance of ports. The findings provide a theoretical basis and decision support for the optimal allocation of port resources under the coordination of multiple policies. Full article

During the adaptive reuse of industrial heritage buildings, existing opening systems and envelope performance often pose major constraints. These restrictions make it difficult for the building to meet the requirements of the updated indoor environment, resulting in insufficient daylight and increased energy consumption. Therefore, optimizing lighting and energy performance has become the primary goal of the retrofit design. However, with limited interventions, the retrofit of heritage buildings to achieve significant overall performance improvement is still a challenge. From a sustainability perspective, improving daylight utilization and reducing energy demand are essential strategies for achieving low-carbon and resource-efficient building retrofit. This study proposes a grid-based parametric multi-objective optimization approach to optimize the window openings of the building envelope. The approach defines the position, size and material properties of the roof and facade openings as design variables. Implemented via the Honeybee and Octopus platforms, it integrates a genetic algorithm with EnergyPlus and Radiance simulations to co-optimize daylight performance, circadian frequency, and energy use intensity. Taking a single-story typical industrial heritage building in China’s cold climate zone as a case study, it is shown that coordinated multi-objective constraints significantly improve the overall performance across various evaluation metrics. The optimization results also provide interpretable window configuration strategies and recommended parameter ranges, which fully consider the climate adaptability of the surrounding environment. These findings offer useful guidance for sustainable retrofit design decision-making in similar single-story industrial heritage buildings. Full article

Background: Circular economy (CE) practices, such as glass bottle refilling, are critical to the beverage industry’s sustainability. However, coordinating manufacturer marketing efforts with collector reverse logistics investment remains a strategic challenge. Methods: This study develops a Stackelberg game-theoretic model featuring a manufacturer and a collector. The model incorporates communication effort as a demand driver and analyzes the role of bottle quality (damage rates) and the reusable bottle unit cost on the optimal decisions of the players and the collection rate. Results: Equilibrium analysis shows that the quality of the reusable bottle and the rate of bottle damage are crucial in reducing the operational costs of the refilling program. Additionally, these factors significantly influence the decisions made by manufacturers and collectors regarding their investments in communication and collection systems. Conclusions: The study demonstrates that successful refilling requires strategic coordination between manufacturers and collectors, particularly in terms of communication and investment in reverse logistics. Managerial insights indicate that investing in the quality of bottles is the key factor for achieving joint profitability. Full article

In the context of global energy system transformation and the pursuit of regional sustainability, China’s Air Pollution Control and Prevention Action Plan (APPA) targets both pollution reduction and carbon mitigation, serving as a critical policy instrument for coordinating the energy-economy-environment nexus in the “2+26” cities. This study employs a quasi-natural experiment with a difference-in-difference (DID) method to assess the synergistic impact of this energy-related policy on these cities. Results show that APPA significantly reduces PM2.5 and carbon emissions by 5.56% and 9.89%, respectively, demonstrating a successful alignment of short-term environmental targets with long-term decarbonization goals. Heterogeneity analysis reveals that large cities with higher institutional capacity are more effective in reducing both pollutants, while resource-based cities achieve more PM2.5 reduction, and non-resource-based cities excel in low-carbon energy transition. Mechanism analysis indicates that APPA promotes these outcomes by optimizing the energy-intensive industrial structure and fostering green technological innovation. This study highlights the effectiveness of integrated governance frameworks in enhancing air quality and reducing carbon emissions, providing crucial insights for redesigning sustainable energy policies and managing the socio-economic disruptions of just transitions in rapidly developing regions. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

This paper proposes a novel covert communication paradigm in which covertness emerges from network-induced structural uncertainty, eliminating the traditional reliance on pre-shared secret pilots in multi-user cooperative networks. Unlike conventional schemes that create information asymmetry through secret training sequences, we show that structural uncertainty naturally arises from user selection in spatially dispersed networks. Specifically, we consider a public pilot aided system under a worst-case adversarial assumption where Willie possesses full knowledge of all individual channel state information (CSI) but remains uncertain about the active subset of cooperative users. We prove that this selection-induced structural uncertainty renders different transmission states statistically indistinguishable from Willie’s perspective, thereby forcing the optimal detector to reduce to an energy-based test. The proposed framework demonstrates that robust covertness can be achieved without secrecy-based coordination, providing a scalable and practically viable alternative to secret pilot management in future wireless networks. Full article

Environmental sustainability has emerged as a strategic requirement of those organizations that want to remain competitive in the long run, but most companies continue to adopt green human resource management (GHRM) practices and organizational change initiatives individually, thus restraining their potential transformation. This paper constructs and confirms a combined approach of how the fit between GHRM practices and adaptive change management processes results in high performance in sustainable innovation. In this study, 83 organizations from both the manufacturing and service sectors were selected using a purposive sampling method, to ensure diversity across developed and developing countries and varying levels of GHRM integration (low, moderate, and high). The sample was chosen to represent a broad spectrum of sustainability maturity levels, allowing for a comprehensive analysis of how GHRM practices influence green product, process, and business model innovation. This selection, alongside 30 peer-reviewed studies published between 2020 and 2025, underpins the conceptual framework used to activate change preparedness and link GHRM dimensions with innovation outcomes. I demonstrate that organizations with a high GHRM–change management fit have much higher levels of innovation performance—both in terms of the number of green product innovations (485%) and more sustainable performance improvement (90.5 on average)—than low-integration organizations. Findings also reveal that leadership commitment, employee engagement, organizational learning, and systemic reinforcement are key mediating processes that enhance the effect of GHRM activities. Temporal trajectory analysis demonstrates that integrated organizations go through deployment, consolidation, and optimization phases, as well as increasing returns to performance, with an accelerating trend of 36 months. This paper is important in management research as it fills in gaps in the literature, providing an explanation of how human resource practices facilitate organizational change at the system level. In practice, this study offers evidence-based recommendations to managers who want to establish sustainability-oriented innovation capability by implementing a coordinated GHRM and adaptive change management approach. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge. We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks and on-load tap changers, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness ($p<0.001$, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure, which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading. Finally, five ablation studies yield three broader insights. First, structured domain context engineering produces synergistic quality gains that exceed any single knowledge component, demonstrating that prompt composition matters more than the choice of foundational model. Second, even an open source 8B-parameter model outperforms templates given the same prompt, confirming the framework’s backbone-agnostic value. Most importantly, counterfactual faithfulness increases alongside load severity, indicating that post hoc attributions are most reliable in the high-stakes regimes where trustworthy explanations matter most. Full article

A structured optimization approach was applied to ultrasonic surface rolling process (USRP) parameters, aiming to enhance the material surface characteristics of TC4 titanium alloy. To overcome the premature convergence and limited exploration capability of the standard Whale Optimization Algorithm (WOA), three enhancement strategies were introduced, including population initialization based on an optimal point set, a sinusoidal nonlinear convergence factor, and an adaptive inertia-based position update strategy. By optimizing the structural parameters of the RBF neural network with the improved WOA, an IWOA–RBF predictive model for surface performance evaluation was developed and rigorously validated in terms of prediction accuracy. Using the developed IWOA–RBF model, a multi-criteria decision-making framework integrating the CRITIC weighting method and the TOPSIS ranking approach was constructed to evaluate surface quality. This framework was further combined with a multi-objective Grey Wolf Optimization (MOGWO) algorithm to perform Pareto-based optimization and determine the optimal USRP parameter set. Experimental validation showed that the optimized parameters resulted in a significant reduction in surface roughness, while enhancing both surface hardness and residual compressive stress. The results confirm the robustness and effectiveness of the proposed IWOA–RBF and MOGWO optimization framework, providing a reliable strategy for high-precision parameter optimization and coordinated enhancement of surface properties in the TC4 titanium alloy USRP. Full article

Acinetobacter baumannii is a leading cause of healthcare-associated infections and is classified among the highest-priority antimicrobial-resistant pathogens. Its clinical success reflects the convergence of antimicrobial resistance (AMR) and biological traits that promote environmental persistence and transmission. Acinetobacter baumannii has undergone a remarkable transformation over the past few decades, evolving from a relatively obscure environmental bacterium into a globally recognized multidrug-resistant pathogen. Its prevalence in healthcare settings, particularly intensive care units, has made it a leading cause of ventilator-associated pneumonia, bloodstream infections, wound infections, and urinary tract infections. Beyond its antibiotic resistance, the bacterium’s ability to persist in hospital environments and adapt to host defences has amplified its clinical significance. Recent research has uncovered complex networks of virulence factors, regulatory systems, and metabolic strategies that enable A. baumannii to thrive in hostile environments and evade host immunity, providing new insights into its pathogenesis and potential therapeutic vulnerabilities. This review summarizes the main mechanisms underlying its pathogenicity, including desiccation tolerance, biofilm formation, disinfectant resistance, metal acquisition, motility, and the ability to enter viable but non-culturable states. In A. baumannii, AMR functions as a pathogenesis-adjacent trait, enhancing survival and clonal dissemination through genomic plasticity, resistance islands, efflux systems, and envelope remodeling. Key resistance pathways involve carbapenem-hydrolyzing oxacillinases, metallo-β-lactamases, permeability defects, and multidrug efflux, often coexisting within high-risk clones. From a clinical perspective, management of carbapenem-resistant strains requires accurate infection diagnosis, reliable susceptibility testing, site-specific and PK/PD-optimized therapy, and early reassessment. Overall, the success of A. baumannii reflects the integration of resistance and persistence within healthcare ecosystems, highlighting the need for coordinated strategies combining stewardship, infection control, improved diagnostics, and anti-biofilm or anti-virulence approaches. Full article

Rutin is a naturally occurring flavonoid with well-documented antioxidant and pharmacological properties. In this study, a manganese(II) complex with rutin (Mn(II)-Rut) was synthesized in a solid state and characterized using FT-IR, Raman spectroscopy, thermogravimetric and elemental analysis, confirming its composition as C₂₇H₂₇O₁₆Mn₂·5H₂O. The IR spectra indicated that rutin coordinates manganese ions through the carbonyl group at the C4 position and the hydroxyl group at the C5 atom, as well as the catecholic system. The antioxidant potential of both Mn(II)-Rut and rutin was evaluated using several spectrophotometric assays. The Mn(II)-Rut complex showed stronger activity in most spectrophotometric assays than rutin, i.e., in ABTS assay, 50.37 ± 2.64% vs. 41.49 ± 1.38%; in CUPRAC assay, 0.468 ± 0.006 mM Trolox vs. 0.379 ± 0.007 mM Trolox; and FRAP assay, 0.201 ± 0.002 µM vs. 0.189 ± 0.003 µM. However, the DPPH assay complex showed a diminished effect compared with ligand (IC₅₀ 2.78 ± 0.13 µM vs. 0.98 ± 0.04 µM for rutin). Quantum-chemical calculations were also performed using the Gaussian09 program to determine the optimized geometric structures, electron charge distribution, and the energies of the HOMOs and LUMOs in the analyzed molecules in order to discuss the antioxidant mechanism of the molecules. Enzymatic assays demonstrated that the Mn(II) complex with rutin exhibited a stronger α-amylase inhibitory effect compared to free rutin, which showed the potential antidiabetic activity of the compound. The results suggest that the Mn(II) complex of rutin possesses better antioxidant and α-amylase inhibitory activity than the ligand alone. Full article

Surgical wards constitute a significant contributor to global medical waste (MW), accounting for over one-third of total healthcare sector trash. Medical interventions produce hazardous, infectious, and potentially toxic byproducts, making effective MW management crucial, especially where current mechanisms are insufficient. Substantial disparities persist between high-income and low- and middle-income countries regarding MW infrastructure, enforcement, and adoption of safe, sustainable treatment technologies. Proper segregation, recycling, treatment, and disposal are key to protecting public health, environmental integrity, and promoting healthcare sustainability. Waste treatment technologies divide into thermal and physico-chemical processes, requiring thorough evaluation of advantages, disadvantages, and suitability for each waste type. This narrative review updates MW knowledge by synthesizing data from scientific literature, institutional documents, and regulatory sources. Key quantitative data indicate operating rooms generate up to 30% of total hospital waste, with recyclable materials representing over 40% of that volume. Improper segregation rates remain high, and incineration remains dominant despite sustainability concerns. The Romanian case study highlights progressive EU alignment, enforcing standardized MW classification, color-coded segregation, and specialized disposal protocols in surgical wards. Despite legal compliance, Romania is advancing incrementally, with systematic audits, digital tracking, and national outcome-based evaluations yet to be fully established. The Plastic Surgery Unit at Oradea County Emergency Clinical Hospital demonstrates good protocol adherence; however, strengthening data feedback mechanisms would enhance hospital-wide performance optimization and strategic waste reduction. Training and monitoring represent important areas for continued development. Coordinated professional engagement, modernized infrastructure, and enforceable audits are identified as critical priorities for improving MW handling in surgical environments. Future research should emphasize management innovation, evidence-based policy formulation, and a systematic strategy to achieve sustainable MW. Full article