Search Results (44)

Multiphase electric drives have gained significant attention in recent years due to their enhanced efficiency and inherent fault-tolerant capability, making them a promising solution for modern high-performance applications. In this context, finite control set model predictive control (FCS-MPC) has emerged as an effective control strategy due to its flexibility in handling multivariable systems and multiple control objectives. Among its recent developments, variable-sampling-time approaches introduce an additional degree of freedom that enables more efficient adaptation of the control action, particularly reducing switching frequency. This variant of FCS-MPC schemes is based on a sequential structure, in which the direction of the desired current response is prioritized over its magnitude, even when implementation constraints limit its achievement. This work proposes an adaptive sampling time multivector model predictive control strategy (AST-MPC) for six-phase induction motor (6ph-IM) drives. The proposed AST-MPC combines multivector control actions with a threshold-based mechanism to incorporate magnitude information into the selection of control actions, typically governed by directional criteria. The designed approach is experimentally validated and compared under steady-state and transient conditions using multiple performance metrics. Results demonstrate that AST-MPC achieves improved current quality and reduced switching frequency, maintaining suitable dynamic performance and providing natural fault tolerance. Full article

Natural and human-made disasters can severely impair terrestrial communication infrastructures and disrupt emergency response coordination in modern smart cities. To address these challenges, this paper introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme, an adaptive, secure, and energy-efficient drone-assisted solution for post-disaster network recovery and emergency response. WAY-CR integrates three main components: First, a novel WAY-based metaheuristic optimizer incorporates the concept of Yo-Yo Motion into the conventional Weighted Average Algorithm (WAA), improving the balance between exploration and exploitation during CH selection and clustering. Second, a secure communication model combines the Paillier Homomorphic Cryptosystem (PHC) with a trust evaluation model to provide end-to-end security and authenticity, ensuring that only authenticated and trustworthy drones participate in communication and routing. Third, a Trust-Aware Boltzmann Path Selection method introduces probabilistic decision-making into routing, allowing adaptive selection of secure and energy-efficient routing paths. WAY-CR formulates a multi-objective optimization model that minimizes communication cost and energy consumption while maximizing trust, link stability, and coverage. Stage 1 addresses secure intra-Ground Control Station (GCS) clustering, authentication, and trust management, whereas Stage 2 restores inter-GCS connectivity through a Secure Relay Discovery and Verification procedure based on Boltzmann Path Selection. An adaptive maintenance mechanism further supports dynamic reconfiguration in response to CH failures, mobility, or trust degradation, thereby preserving stable network performance under disaster-induced disruptions. Extensive simulation results show that WAY-CR outperforms state-of-the-art Flying Ad Hoc Network (FANET) baselines in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery, highlighting its potential as a resilient, scalable, and secure solution for post-disaster smart-city environments. Full article

Graphitic carbon nitride (g-C₃N₄), an emerging metal-free semiconductor material, has attracted considerable attention in the field of photoelectrochemical (PEC) sensing due to its unique electronic structure, excellent chemical stability, and visible-light responsiveness. This article systematically reviews recent advances in research on g-C₃N₄-based PEC sensors applied to water environment monitoring. First, the fundamental physicochemical properties of g-C₃N₄ are introduced, along with its advantages and limitations in PEC sensing applications. Subsequently, four main performance enhancement strategies are outlined: heterojunction construction (including type II, Z-scheme, and S-scheme heterojunction), elemental doping and defect engineering, morphology control and nanostructure design, as well as various signal amplification approaches such as self-powered systems, dual-mode detection, and cyclic amplification. Furthermore, the current application status of these sensors in detecting typical water pollutants, including heavy metal ions (e.g., Pb²⁺, Cu²⁺, Cd²⁺, Hg²⁺), antibiotics (e.g., tobramycin, norfloxacin, kanamycin), pesticide residues (e.g., chlorpyrifos, atrazine, glyphosate), and pathogenic microorganisms (e.g., Salmonella, Candida albicans), is comprehensively reviewed, with particular emphasis on detection sensitivity, selectivity, and real-sample performance. Finally, the remaining challenges in terms of long-term stability, anti-interference capabilities in complex matrices, portability, and multifunctional integration are analyzed, and future development directions are proposed, including smartphone-based intelligent sensing, CRISPR/Cas12a-assisted signal amplification, and multi-target high-throughput detection. This review aims to provide a reference for the rational design and practical application of g-C₃N₄-based PEC sensors in the field of water environment monitoring. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

To address the limitations of traditional runoff prediction methods—namely, the oversimplification of meteorological factor selection, ambiguous interactions among core variables, and the disruptive influence of redundant inputs—this study focuses on the Zijiang River Basin as a representative case. A suite of machine learning models, including Long Short-Term Memory Neural Network (LSTM), Convolutional Neural Network (CNN)-LSTM, Temporal Convolutional Network (TCN), and Gradient Boosting Regression Tree (GBRT), was constructed and trained using 13 distinct combinations of meteorological variables. These configurations were systematically evaluated to assess their compatibility with each model in simulating daily runoff patterns. Additionally, the Shapley Additive Explanations (SHAP) algorithm was employed to quantitatively assess the contribution of each factor to predictive accuracy. Among the models tested, the TCN model consistently demonstrated superior performance, particularly in mitigating the effects of irrelevant or redundant features. The GBRT model showed distinctive strengths in accurately predicting peak flow timings. Of all input configurations, the combination of “runoff + precipitation + evaporation + temperature” emerged as the most effective. Findings indicate that the predictive value of individual meteorological variables hinges primarily on their direct correlation with runoff, while the effectiveness of multi-factor schemes depends on the degree of functional integration—specifically, the coupling of hydrological recharge, consumption, and regulatory processes. The presence of redundant variables was found to impair model performance unless they contributed to a meaningful synergistic relationship with core inputs. The SHAP analysis further reinforced these insights: precipitation-related variables proved to be the most critical to prediction accuracy, whereas temperature and evaporation served more complementary roles. Notably, the inclusion of relative humidity tended to suppress runoff responses and increased deviation in peak timing estimates. These findings shed light on the nuanced interplay between meteorological input design and model selection, offering a robust foundation for optimizing data-driven runoff prediction frameworks. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience. Full article

Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions. Full article

Postharvest operations are cost intensive in microalgae production, and when electrocoagulation–electroflotation (EC/EF) with aluminum anodes is used, aluminum can remain associated with biomass and wash streams; hence, a selective postwash process is needed. Accordingly, this study defined an operational window for aluminum desorption that preserves the energetic advantage of EC/EF. A response-surface design (I-optimal/CCD) was used to evaluate the effects of the EDTA concentration (1–100 mM), contact time (5–20 min), mixing speed (100–300 rpm), and pH (6–10) on EC/EF-harvested Chlorella sp. biomass, with ANOVA and model diagnostics supporting adequacy. EDTA concentration and mixing emerged as significant factors, whereas time and pH acted mainly through interactions; moreover, quadratic terms for EDTA and mixing indicated diminishing returns at high levels. Consequently, the surface predicted an optimum near EDTA ≈ 65 mM, time ≈ 20 min, pH 10, and 100 rpm, corresponding to ~97% aluminum removal. Importantly, a confirmation run under these conditions across eight chlorophyte strains consistently achieved >95% removal, revealing narrow dispersion yet statistically distinguishable means. Taken together, coupling EC/EF with an EDTA postwash operation in the identified window effectively limits aluminum carry-over in microalgal biomass and, therefore, provides a reproducible basis for downstream conditioning and potential recirculation within biorefinery schemes. Full article

This study presents a comprehensive overview of recent advancements in fire protection technologies for reinforced concrete (RC) structures, with a focus on sustainable and high-performance solutions. As climate change and urban densification continue to shape modern construction, the need for fire-resilient and environmentally responsible building systems has never been more urgent. This study examines traditional fire protection practices and contrasts them with emerging innovations. Emphasis is placed on their thermal performance, structural integrity post-exposure, and long-term durability. Case studies and laboratory findings highlight the effectiveness of these systems under standard and severe fire scenarios. This paper will present the results of a research study on the assessment of different fire protection systems for RC columns retrofitted with fiber-reinforced polymer (FRP) jacketing. To quantify how insulation can preserve confinement, three commercial fire protection schemes were tested on small-scale CFRP- and GFRP-confined concrete cylinders: (i) a thin high-temperature cloth + blanket (DYMAT™-RS/Dymatherm), (ii) an intumescent epoxy-based coating (DCF-D + FireFree 88), and (iii) cementitious mortar (Sikacrete™ 213F, 15 mm and 30 mm). Specimens were exposed to either 60 min of soaking at 200 °C and 400 °C or to a 30 min and 240 min ASTM E119 standard fire; thermocouples recorded interface temperatures and post-cooling uniaxial compression quantified residual capacity. All systems reduced FRP–interface temperatures by up to 150 °C and preserved 65–90% of the original confinement capacity under moderate fire conditions ($\le$400 °C and $\le$30 min ASTM E119) compared to 40–55% for unprotected controls under the same conditions. The results provide practical guidance on selecting insulation types and thicknesses for fire-resilient FRP retrofits. Full article

China’s urbanisation has transitioned from an era of rapid, coarse expansion to one of refined and targeted development. In accordance with China’s “dual-carbon” strategy, the building sector—presently the third-largest source of domestic carbon emissions—is compelled to pursue emission optimisation in its forthcoming evolution. Photovoltaic-building technologies offer an effective response to this imperative. Within the context of accelerating high-rise residential construction, the architectural integration of scientifically configured photovoltaic façades has emerged as a critical challenge. Employing an integrated methodology of urban surveying and simulation, this study examines the façade characteristics of residential buildings in northern Chinese cities, selecting Xi’an as the representative case. Three PV-facade integration strategies for existing stock are presented: window retrofitting, wall retrofitting, and full-façade renovation. Utilising the EnergyPlus platform, the manuscript simulates the electrical demand profiles and clean-electricity generation of typical dwellings under varying photovoltaic materials and configuration schemes, while concurrently assessing economic performance. It demonstrates that a judicious determination of photovoltaic installation scale and layout strategy markedly amplifies energy-saving efficacy, diminishes aggregate energy consumption and carbon emissions, and simultaneously reduces the capital expenditure of photovoltaic systems. For multi-story buildings, a full façade retrofit yielded the highest annual electricity generation of 514,703.56 kWh and an annual carbon reduction of 15,521.50 kgCO₂. For high-rise buildings, installing PV modules only above the 20th floor increased the effective generation ratio from 45.24% to 87.17%, while the carbon reduction efficiency per unit investment improved from 0.05 to 0.22 kgCO₂/¥. Full article

Enhancement and restoration algorithms are widely used in the exploration of coral reefs for improving underwater images. However, by selecting an improvement algorithm based on image quality metrics, image processing key factors such as the execution time are not considered. In response to this issue, herein is presented a novel method built on multicriteria decision analysis that evaluates the processing time and feature point increase with respect to the original image. To set the Decision Matrix (DM), both the processing time and keypoint increase criteria of the evaluated algorithms are normalized. The criteria weights in the DM are set in accordance with the application, and the quantitative metric used to select the best alternative is the highest Weighted Sum Method (WsuM) score. In this work, the DM of six scenarios is shown, since the setting of weights could completely change the decision. For this research’s target application of generating underwater photomosaics, the Dark Channel Prior (DCP) algorithm emerged as the most suitable under a weighting scheme of 75% for processing time and 25% for keypoint increase. This proposal represents a solution for evaluating improvement algorithms in applications where computational efficiency is critical. Full article

It is particularly important to improve the emergency rescue response ability of heavy-haul railways to ensure the safety of personnel and the efficiency of material transportation. The current research has achieved some results for multi-objective material dispatching, but it does not consider the impact of accident response level and material type on material dispatching scheme. In this study, a heavy-haul railway in China was selected as the research object. By designing a dual-objective material scheduling model, an optimal material scheduling scheme was obtained, and the optimal solution was solved by a non-dominated sorting genetic algorithm (NSGA-II). Under the condition of keeping the station unchanged and ensuring that the total amount of materials remained unchanged, an optimization scheme of relief material reserves that match the risk characteristics of the line is proposed. The results show that, based on the utility theory, the minimum distance of the improved dual-objective material dispatching is reduced by 34.8% (single accident point) and 62.99% (multiple accident points), and the total distance of material dispatching is reduced by 37.92% and 70.57%, respectively, indicating that the optimized reserve scheme can effectively shorten the response time and improve the rescue efficiency. The material reserve optimization scheme for emergency rescue stations proposed in this study has important reference value for improving the emergency rescue efficiency of heavy-haul railways. Full article

Nested, or cascade speed and torque control has been widely used for DC motors over recent decades. Simultaneously, fractional-order control schemes have emerged, offering additional degrees of control. However, adopting fractional-order controllers, particularly in cascade schemes, does not inherently guarantee better performance. Poorly paired fractional exponents for inner and outer PI controllers can worsen the DC motor’s behavior and controllability. Finding appropriate combinations of fractional exponents is therefore crucial to minimize experimental costs and achieve better dynamic response compared to integer-order cascade control. Additionally, mitigating adverse couplings between speed and current loops remains an underexplored area in fractional-order control design. This paper develops a computational model for fractional-order cascade control of DC motor speed (external) and current (internal) loops to derive appropriate combinations of internal and external fractional orders. Key metrics such as overshoot, rise time, and peak current values during speed and torque changes are analyzed, along with coupled variables like speed drop during torque steps and peak torque during speed steps. The proposed maps guide the selection of effective combinations, enabling readers to deduce robust or adaptive designs depending on specific performance needs. The methodology employs Oustaloup’s recursive approximation to model fractional-order elements, with MATLAB–SIMULINK simulations validating the proposed criteria. Full article

This paper presents an innovative methodology for the dynamic emergency response scheme selection (ERSS) problem in post-major natural disasters. It employs a combination of subjective and objective composite weights and the integrated ELECTRE-score approach. The study aims to provide a practical approach for continuously determining optimal decision schemes at various time points during the decision period in the aftermath of significant natural disasters while accommodating evolving real-world scenarios. Firstly, the probabilistic T-spherical hesitant fuzzy set (Pt-SHFS) captures decision-makers’ ambivalence and hesitation regarding diverse evaluation attributes of different schemes. Subsequently, Pt-SHFS is integrated with the best–worst method (BWM) to determine subjective weights, followed by the structured CRITIC method to amalgamate subjective weights and derive the final combination weights of criteria. Additionally, this paper proposes applying a penalty-incentive mechanism to establish dynamic attribute weights during scenario evolution. Furthermore, the ELECTRE-score method, which may fully exploit the advantages of non-compensation situations, is adopted to obtain more reliable dynamic optimal decision outcomes. Consequently, based on these foundations, an integrated dynamic ERSS approach is formulated to determine optimal dynamic emergency response schemes. Finally, a case study on the Gansu Jishishan earthquake, sensitivity analysis, comparative analysis, and continuous analysis are conducted to verify the practicality, stability, and effectiveness of the proposed approach. The result shows that the proposed comprehensive approach can depict variances among experts’ information, dynamically adjust attribute weights in response to evolving scenarios, and assign a score range and a representative score to each scheme at each decision state. Sensitivity and comparative analyses show this model has strong stability and dynamics. Furthermore, the proposed approach can effectively deal with the complex dynamic situation in the earthquake rescue process, such as the secondary collapse of buildings after the earthquake, the damage of materials caused by heavy rain, and the occurrence of aftershocks. The model can continuously optimize decision-making and provide scientific and reliable support for emergency decision-making. Full article

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires. Full article