Search Results (3,011)

This study systematically investigates the influence of CeO₂ content (0–0.6 wt.%) on the microstructure and mechanical properties of Stellite6/WC composite coatings fabricated by plasma spray welding. The phase composition and microstructure of the coatings were characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM), while microhardness and tribological performance were evaluated using a semi-automatic Vickers microhardness tester and a ball-on-disk tribometer. The results indicate that the coating with 0.4 wt.% CeO₂ exhibits the optimal combination of mechanical and tribological properties, achieving a maximum microhardness of 1107.62 HV_0.3—a 50.5% improvement over the unmodified coating—and a minimum wear mass loss of 1.4 mg, corresponding to a 78.1% reduction compared to the CeO₂-free counterpart. These findings demonstrate that appropriate CeO₂ addition significantly enhances both the microhardness and wear resistance of Stellite6/WC coatings, offering an effective strategy to mitigate surface degradation and extend the service life of 45 steel substrates under demanding operating conditions. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Background: This study evaluated the independent predictive value of preoperative Systemic Immune–Inflammation Index (SII) for postoperative wound infection (WI) in diabetic patients undergoing isolated Coronary Artery Bypass Grafting (CABG). Methods: A retrospective cohort of 300 diabetic patients (2024–2025) was analyzed. The primary outcome was 30-day postoperative WI. Preoperative SII was calculated from blood counts within 24 h before surgery. Multivariable logistic regression was performed using both a primary model (adjusting for age, BMI, and comorbidities) and an extended model including glycemic control (HbA1c), smoking status, operative duration, and transfusion requirements. Model discrimination was evaluated via Area Under the ROC Curve (AUC). Statistical power and sensitivity analyses were conducted to ensure the robustness of the findings. Results: WI occurred in 7% (n = 21). Preoperative SII was significantly lower in the WI group (958.48 ± 493.49 vs. 1293.56 ± 758.15, p = 0.047). SII remained an independent predictor in the adjusted model (Adjusted OR per 100-unit increase: 0.93; 95% CI: 0.86–1.00; p = 0.048). ROC analysis confirmed an inverse predictive pattern (AUC: 0.374, 95% CI: 0.312–0.436). Comparative analysis showed that SII provided superior additional insight compared to NLR and PLR in this population. Conclusions: Preoperative SII is an independent predictor for WI in diabetic CABG patients. However, given the modest discriminative performance (AUC: 0.374), it should be integrated into a broader clinical risk assessment. Contrary to conventional expectations, lower SII values indicated increased susceptibility, suggesting that immune exhaustion rather than hyperinflammation may drive infectious risk in diabetic patients. Full article

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary. Full article

Resilient wheels are widely employed in metro vehicles to mitigate vibration and noise, in which rubber damping components play a critical role in load transmission and fatigue resistance. However, stress concentration and cyclic loading can significantly compromise their durability and service life. In this study, the structural optimization and fatigue life of rubber damping components in resilient wheels are systematically investigated based on finite element analysis and in-service metro operational data. A three-dimensional finite element model incorporating hyperelastic material behavior is developed to evaluate stress distributions under three representative conditions: press-fit assembly, straight-line operation, and curved-track operation. Based on the resulting stress fields, critical high-stress regions within the rubber component are identified and selected as targets for structural optimization. The Design of Experiments (DOE) methodology, integrated with the Isight 2022 optimization platform, is employed to determine the optimal geometric parameters that minimize the von Mises equivalent stress. Furthermore, a fatigue life prediction framework is established using actual metro service mileage data. Fatigue performance is assessed using Fe-safe 2022 software in conjunction with rubber fatigue crack propagation theory, and the results before and after optimization are systematically compared. This study demonstrates that stress concentrations in resilient wheel rubber damping components predominantly occur at fillet transition regions, governed by load transfer characteristics under press-fitting and service conditions. Through DOE-based structural optimization, the critical geometric parameters are effectively refined, leading to a significant reduction in stress levels in key regions. As a result, the proposed approach markedly improves fatigue performance, extending the minimum fatigue life from 1300 days to 24,322 days, thereby substantially enhancing the durability and reliability of the resilient wheel system. Full article

Heterogeneous photocatalysis is one of the most versatile and widely studied photochemical approaches for the degradation of recalcitrant pollutants. Owing to its favorable physicochemical properties, titanium dioxide (TiO₂) remains one of the most investigated semiconductor photocatalysts. However, its wide band-gap energy (3.2 eV) restricts its photoactivity to the UV region, which represents only a small fraction of the solar spectrum. A major challenge in this field is therefore the development of TiO₂-based materials capable of operating efficiently under visible light irradiation, enabling the use of solar energy as a sustainable primary source. Several strategies have been explored to extend the optical response of TiO₂, among which elemental doping remains one of the most effective and commonly applied. In this work, we conducted systematic comparative analysis to evaluate the photocatalytic performance of TiO₂ modified through different doping approaches. Sixty-one scientific reports published between 2015 and 2025 were analyzed, comparing three categories of dopants: (i) metal dopants, (ii) non-metal dopants, and (iii) co-doping systems. In the first section, we discuss fundamental concepts of photocatalysis and recent advances in doping strategies and surface modifications aimed at enhancing the photocatalytic performance of TiO₂. In the second section, we present a comparative analysis based on 61 scientific reports focusing on TiO₂ doping and co-doping processes. Finally, this study summarizes the different categories of doped TiO₂ photocatalysts by comparing the photocatalytic performance employing an alternative performance metric. Full article

In this study, a novel direct torque control (DTC) strategy is proposed to mitigate the torque ripple issue inherent in switched reluctance motors (SRMs), which is caused by the double salient pole configuration and the pulse power supply mode. The strategy is based on the prediction and optimization of a long-time-domain model. Central to this method is the development of a multi-step predictive optimization framework. By incorporating hysteresis control, the conventional approach of minimizing instantaneous error in predictive control is shifted towards minimizing tracking error over an extended time frame. A dual-objective evaluation function is also introduced, which simultaneously optimizes both torque smoothness and switching frequency, ensuring their collaborative enhancement. To validate the proposed method, a 6/4-pole SRM simulation model was implemented using MATLAB/Simulink 2024B, and comparisons were made with traditional methods. The results demonstrate that this strategy significantly reduces torque pulsation and lowers the system’s switching frequency, even under varying operational conditions such as different rotational speeds and sudden load variations. Consequently, this approach not only guarantees improved dynamic performance but also enhances the motor’s efficiency and stability. Full article

We developed two distinct automatic packet reporting system (APRS) payload designs to establish a reliable backup communication system for disaster risk reduction and management. The payloads are designed to perform a significant key operation, primarily APRS digital repeating (DP), enabling continuous communication access even in areas where conventional ground-based infrastructure is damaged by natural disasters through the relay of APRS packets to extend communication coverage. A detailed framework is designed using the standard amateur packet radio (AX.25 protocol). It specifies the structure of APRS data frames and packets, which are used to transmit alerts, emergency status updates, and text messages. This structure ensures that important information is transmitted reliably and effectively during an emergency. The designs for the APRS payloads share a common overall operating system architecture but differ in their very high frequency transceiver modules used for the amateur radio (Radiometrix BiM1H very high frequency (VHF) Narrowband Transceiver and Dorji DRA818V VHF Band Voice Transceiver Module). Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R²), with R² reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production. Full article

A triple phase shift (TPS) modulation strategy is proposed for a three-port active bridge (TAB) converter in shipboard zonal DC systems. Unlike traditional multi-port converters, the TAB realizes voltage conversion and bidirectional power conversion under TPS modulation. It exhibits superior performance in reducing control complexity, enhancing fault-tolerant capability, and extending the zero-voltage switching (ZVS) region under normal and fault operation modes. To further enhance its conversion efficiency, a deep reinforcement learning optimization approach based on the deep deterministic policy gradient (DDPG) algorithm is introduced to adaptively optimize TPS control parameters and minimize the overall power losses of the converter. To verify the proposed TPS modulation and DDPG-based optimization strategy for the TAB converter topology, a corresponding hardware prototype is built and experimentally tested under different operating conditions. Experimental results demonstrate that the TAB architecture with DDPG optimization effectively reduces current stress and power loss, boosting the converter’s maximum efficiency to 96.9% under normal mode and a 3% efficiency gain after fault isolation. Full article

A method for integrated brand analysis and strategy is developed in this work. The foundation of this method is market research, through which the relevance of brand attributes, their evaluation for competing brands and the market performance of these brands on the steps of the buying process are determined. On this basis, the overall evaluation of brands and their number of brand attributes with the best evaluation are calculated so that strategic decision guidelines for overall brand positioning can be deduced. These strategic decision guidelines are securing the brand based on the existing identity/image, developing the brand based on the existing identity/image, developing (pivoting to) a new brand identity/image, whilst securing the strengths of the existing identity/image, and developing a new brand identity/image. On the level of brand attributes, the weighted relevance of attributes and their evaluation difference to the best competitor are calculated so that, again, strategic decision guidelines can be deduced. The strategic decision guidelines on brand attribute level are securing the attributes as the core brand identity (first priority), selecting and developing the attributes to the core brand identity (second priority), securing the attributes as the extended brand identity (third priority), and selecting and developing the attributes as the extended brand identity (fourth priority). Based on the market performance of brands across the stages of the buying process, the conversions between these steps are determined. On this basis, strategic decision guidelines for market cultivation are deduced, i.e., awareness, image, sales, and loyalty strategies. To gain first indications of the validity of the method for integrated brand analysis and strategy, it is applied to food retail and chocolate brands in the German market. Future research should focus on further validating the method and enhancing it by integrating segmenting and targeting processes and, potentially, marketing measures on an operational level. Full article

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

Boundary Layer Ingestion propulsors operate in an adverse aerodynamic environment with high levels of distortion. With the purpose of extending the operating range of transonic fan rotors for BLI applications, in this paper we present an optimisation study focused on blade profiles design under different working conditions. Quasi-2D blade sections are optimised using a genetic algorithm and numerical simulations, by varying the camberline and thickness distribution. A method to efficiently achieve a combination of total pressure ratio at a given relative inlet Mach number is devised. The isentropic efficiency is optimised at the design point, concurrently with the stall total pressure ratio at a lower inlet Mach number, in a multi-objective fashion. Pareto-optimal profiles exhibit a moderate leading edge concavity for high efficiency and a straighter fore part with increased trailing edge deflection for higher compression at stall. Optimised airfoils are used in a preliminary three-dimensional evaluation with a realistic BLI inflow, in which the unsteady full-annulus analysis corroborates the approach of the sectional optimisation, also showing the possibility of estimating the integral performance of the machine with a simplified approach based on a single-passage simulation with a circumferential-averaged inflow distribution. Full article