Search Results (686)

Compound faults in electric motors are difficult to diagnose because simultaneous-fault recordings are scarce and fault multiplicity is unknown at inference. We propose a compound-sample-free, cardinality-free framework for induction motors running continuously varying speed profiles at several load levels. Synchronized key-phase, triaxial vibration and three-phase current signals are converted to order-domain representations by anti-aliased computed order tracking, augmented by a band-pass envelope order spectrum and normalized with a scale-invariant, noise-floor-removed representation. Nine fault primitives are evaluated by modality-specific experts, combined through physics-regularized routing, and decoded by maximum a posteriori inference over 19 feasible machine states. Six leave-one-speed-load-combination-out folds and three seeds evaluate every held-out recording. Exact condition accuracy counts a recording as correct only when the predicted set of fault primitives matches the true set exactly; exact compound recovery applies the same criterion to the unseen compound recordings, requiring both constituent primitives and nothing else. Without a fault-count prior, the pipeline reaches 76.7% and 45.7%, against 62.5% and 11.1% for a conventional order-domain front end and 59.2–62.0% and 0.0–2.5% for three re-implemented baselines. A source-domain modality-selection control reduces compound recovery from 59.3% to 27.8%. Physically aligned representation, sensing specialization and cardinality-aware decoding are therefore critical to compositional motor-fault diagnosis. Full article

Accurate regulation of combustion temperature is critical for objectively evaluating the fire-resistance performance of cables. However, existing temperature control strategies mainly rely on centralized regulation methods, which struggle to simultaneously address the nonlinear coupling among multiple heat sources, spatial thermal non-uniformity, and dynamic temperature fluctuations. To address these challenges, a multi-channel self-adaptive temperature control method based on distributed optimization and computational modeling is proposed in this study. First, a data-driven computational model based on an attention-enhanced multi-channel convolutional neural network is developed to characterize the complex nonlinear relationship between distributed heat inputs and the resulting temperature field, enabling accurate thermal state perception and prediction. Subsequently, a data-driven NSGA-III optimization algorithm is introduced to achieve dynamic allocation and coordinated optimization of heat flux among multiple independent heating channels. Furthermore, a deep reinforcement learning-based adaptive decision framework is established to realize autonomous adjustment of heating strategies under varying testing conditions. The proposed framework integrates thermal modeling, distributed optimization, and intelligent decision-making to achieve real-time adaptive control of multi-source heating systems. Experimental validation on practical fire-resistance testing equipment demonstrates that the proposed framework achieves an R² of 0.9745 with an MAE of 19.90 °C in the closed-loop control evaluation and provides improved spatial thermal uniformity compared with conventional control strategies. Full article

Soybean (Glycine max) is a globally important source of protein and oil, and its capacity for biological nitrogen fixation (BNF) underpins its strategic role in sustainable agriculture. However, BNF efficiency is highly sensitive to abiotic stresses, and conventional agronomic interventions struggle to simultaneously optimize nodulation, nitrogenase activity, and stress resilience. Agricultural nanotechnology offers a unique avenue to address this bottleneck by modulating the tripartite nanomaterial–rhizobium–soybean interaction at multiple scales. This review systematically examines (1) nanomaterial design strategies tailored to the rhizosphere and nodule microenvironments; (2) the ‘Rhizosphere–Nodule–System’ (RNS) cascade model, which integrates rhizosphere interfacial events, nodule metabolic reprogramming, and systemic stress signaling; and (3) field-level efficacy, genotype-dependent responses, and barriers to scalable application. By elucidating the mechanistic logic by which nanotechnology coordinates BNF enhancement with abiotic stress tolerance, this framework provides theoretical support for the targeted deployment of nano-agricultural technologies in soybean production systems. Full article

Background/Objectives: Serratia marcescens is an opportunistic pathogen that causes severe hospital-acquired infections, notable for its biofilm formation abilities and development of extensive antibiotic resistance. Here, we aim to evaluate the efficacy of bacteriophages, antibiotics, and antimicrobial peptides (BAP), alone and in combination, against fourteen multidrug-resistant (MDR) S. marcescens isolates sourced from hospitals and other environmental settings. Methods: S. marcescens was grown planktonically or in surface-associated biofilms, and biofilm biomass was measured via changes in absorbance and colony-forming units or live/death staining. Results: Combining bacteriophage with a low-dose cocktail of penicillin–streptomycin, kanamycin, and ciprofloxacin enhanced antimicrobial activity compared with antibiotics alone. Across the isolate panel, responses to BAP treatment varied according to determined antibiotic resistance profiles. The highly resistant AR-0517 isolate was selected for detailed mature biofilm analysis, where the BAP treatment reduced biofilm biomass by 97.8% and recoverable bacteria by 99.99%. Microscopy and viability assays further confirmed extensive biofilm disruption and bacterial killing. Conclusions: These findings demonstrate that simultaneous targeting of multiple bacterial pathways can enhance antimicrobial activity against MDR S. marcescens in vitro and support further evaluation of BAP as a potential strategy for biofilm-associated infections. Full article

Traditional coupling coordination degree (CCD) evaluation methods fail to simultaneously ensure the accuracy and reliability of evaluation results. To overcome this limitation, a novel evaluation method that integrates the logical multiplication of connection numbers with stochastic simulations (ECCD-LMS) is developed to assess the CCD of regional water resources, social economy, and ecological environment (WSE) composite systems. The method adopts three-element connection numbers to quantify the comprehensive evaluation level of each system. Overall partial connection numbers and triangular fuzzy numbers then define dynamic value intervals for the connection number components, and the Monte Carlo method is integrated to simulate stochastic variation in each component. The simulated values are substituted into the logical multiplication of connection numbers to generate evaluation results that include both mean CCD estimates and 95% uncertainty intervals. The empirical application of ECCD-LMS in China’s Jing River Basin indicated that the coupling coordination level of the regional WSE composite system exhibited an overall increasing tendency with fluctuations from 2012 to 2023. These fluctuations were closely associated with variations in the comprehensive evaluation level of the water resources system. Spatially, the disparities in evaluation grades among subregions gradually narrowed during the study period. Compared with traditional CCD evaluation methods, ECCD-LMS effectively corrects systematic overestimation while maintaining objectivity and produces more dispersed CCD estimates that reflect differences among evaluation samples with greater clarity. Furthermore, by outputting mean CCD estimates and 95% uncertainty intervals, ECCD-LMS outperforms traditional methods that only provide static point estimates. It thus enables robust and credible evaluation of the coupling coordination level of WSE composite systems under multiple sources of uncertainty, suggesting potential applicability across diverse regional contexts. Full article

The cleaning system of combine harvesters is an important component whose efficiency directly depends on the uniform distribution of the air velocity profile on the sieve surface. However, the experimental evaluation of the air flow rate is often limited by the high cost and the impossibility of simultaneous multi-point acquisition using commercial anemometers. This paper presents the design, development, and calibration of a low-cost anemometric sensor, based on an NTC thermistor, intended for aerodynamic optimization in the design and laboratory testing phase of cleaning systems. The proposed system replaces the need to use Wheatstone bridges by combining a constant current source, a 16-bit ADC converter, and an RC filter, allowing it to resolve fine voltage variations at low velocity. The algorithm integrates temperature compensation in the 3rd degree polynomial equation by simultaneously reading the environmental temperature using a digital sensor with an accuracy of ±0.1 °C. Experimental validation on the bench against the reference anemometer testo 405i showed excellent agreement (R² = 0.998, mean bias = 0.017 m/s, and a maximum error of ±0.08 m/s), falling within the tolerance of the reference anemometer. By the ability to use multiple sensors and parallel acquisition in real time, the proposed solution offers an alternative for 2D/3D aerodynamic mapping of sieves under controlled laboratory conditions for the design of combine harvester cleaning systems. Full article

Foods can be contaminated by a wide range of microorganisms, originating from multiple sources within domestic kitchens. However, the role of flies and ants as carriers of bacteria in these environments remains poorly understood, and data on the antimicrobial susceptibility profiles of insect-associated bacteria remain scarce. Therefore, this study aimed to identify Enterobacterales and Staphylococcus spp. recovered from flies and ants collected in domestic kitchens and to characterize the antimicrobial susceptibility profiles of the isolates. Additionally, selected virulence genes were investigated in Staphylococcus aureus and Escherichia coli, two recognized foodborne pathogens. A total of 240 insects (113 flies and 127 ants) were collected from 58 domestic kitchens. Twenty-six Enterobacterales isolates belonging to nine species were recovered from 17 kitchens, including a single multidrug-resistant (MDR) isolate identified as Enterobacter hormaechei. In addition, 45 staphylococcal isolates representing nine species were recovered from 25 kitchens, originating from 36 flies and 9 ants. Among these, 13 isolates exhibited an MDR phenotype, including S. aureus and several non-aureus Staphylococcus species. None of the E. coli isolates carried the eae or stx1/stx2 genes, whereas none of the S. aureus isolates harbored the sea, seb, sec, see, or mecA genes. To the best of our knowledge, this is the first study to report the simultaneous recovery of MDR bacteria from flies and ants collected in domestic kitchens. These findings provide new evidence that household insects may act as carriers of antimicrobial-resistant bacteria, highlighting their potential role in the dissemination of clinically relevant bacterial species, including MDR strains, within domestic environments. Effective insect control and good household food hygiene practices may help reduce the spread of antimicrobial-resistant bacteria and mitigate the risk of difficult-to-treat foodborne infections. Full article

Accurate localization and mapping are essential for autonomous mobile robots operating in unknown environments. This study investigates the impact of Extended Kalman Filter (EKF)-based sensor fusion on the performance of three widely used two-dimensional (2D) LiDAR Simultaneous Localization and Mapping (SLAM) algorithms: GMapping, Karto SLAM, and SLAM Toolbox. Wheel encoder longitudinal velocity and inertial measurement unit (IMU) yaw angular velocity were fused using an EKF and compared with raw wheel odometry using the MIT Stata Center dataset. Localization performance was evaluated both before and after SLAM using translational and rotational Absolute Pose Error (APE) across multiple trajectory segments. Five repeated executions were performed for each SLAM configuration to characterize run-to-run variability. Prior to SLAM, EKF-filtered odometry reduced translational APE root mean square error (RMSE) by approximately 61–75% and rotational APE RMSE by approximately 65–77% relative to raw odometry. After SLAM, translational differences between the two odometry sources were substantially smaller and varied according to the evaluated algorithm and trajectory, while rotational performance exhibited larger and less consistent changes. These results demonstrate that substantial improvements in upstream odometry accuracy do not necessarily produce proportional improvements in final SLAM localization and that the influence of sensor fusion varied across the evaluated SLAM algorithm and trajectory segments, providing practical guidance for selecting localization strategies in autonomous mobile robots. Full article

Unlike conventional models that typically rely on a single memristive synapse, this study uniquely proposes a novel 5D fractional-order memristive Hopfield neural network (FOMHNN) modulated by dual memristors to simultaneously emulate internal synaptic plasticity and external electromagnetic radiation effects in brain-like computing. Analytically, the FOMHNN features multiple parallel lines of equilibria with double-zero eigenvalues, rigorously proving the generation of hidden attractors. The continuous dynamical behaviors are systematically evaluated using the Adomian Decomposition Method (ADM), revealing rich phenomena including transient chaos, grid multi-scroll hidden attractors, and frequency-controllable extreme multistability with fractal-like basin boundaries. The theoretical model is physically validated on a Field Programmable Gate Array (FPGA) platform, demonstrating high precision and ultra-low power consumption. To bridge theoretical dynamics with cryptographic applications, a novel pseudo-random number generator is designed. By incorporating a chaotic derivative extractor, the generated sequences significantly reduce topological periodicity, successfully passing all rigorous NIST SP 800-22 statistical tests. Furthermore, an adaptive color image encryption scheme is developed, utilizing bidirectional feedback diffusion and least significant bit (LSB) key embedding. Security analyses confirm that the cipher, under the fractional order $q=0.95$, achieves near-ideal information entropy, optimal resistance against differential attacks, with NPCR and UACI values reaching 99.6114% and 33.4910%, both extremely close to their theoretical ideals (99.6094% and 33.4635%), and robust resilience against noise. Ultimately, the FOMHNN provides a highly secure and physically realizable chaotic source for advanced secure communications. Full article

The Greater Mekong Subregion (GMS) has emerged as a strategic logistics hub due to rapid economic integration and the expansion of cross-border road transportation. However, international road logistics networks in the region must address several competing objectives, including minimizing transportation costs, reducing delivery times, and balancing transport distances among trading partners. To overcome these challenges, this study proposes an innovative hybrid computational framework that integrates the classical Transportation Problem with the Pythagorean methodology (TPPM). The proposed approach consolidates multiple transportation objectives into a unified performance metric based on the Pythagorean concept, thereby enabling simultaneous optimization under practical constraints. In addition, geographic inputs derived from Google Maps and Google Earth via web platforms, which are reliable open-source GIS tools, are incorporated into the transportation model to improve spatial accuracy. A simulated dataset comprising 35 suppliers and 42 customers, representing major logistics nodes in the GMS, is developed to evaluate the proposed method. The computational results indicate that the TPPM approach outperforms the conventional single-objective Classical Transportation Problem (CTP) by producing higher solution quality and more balanced performance. Overall, the findings demonstrate that the proposed hybrid method is a robust decision-support tool for sustainably enhancing the resilience of international logistics planning in emerging economic regions. Full article

Wind power, photovoltaic (PV), hydropower, and energy storage, along with other multi-type clean energy sources, transmitted via islanded voltage source converter based high voltage direct current (VSC-HVDC) systems, will become an important form of delivery for renewable energy bases. However, due to the volatility and uncertainty of renewable energy, its high-proportion integration significantly exacerbates system frequency fluctuations and voltage violation risks, posing severe challenges to the stable operation of the system. To strike a balance between maximizing clean energy integration and maintaining the stability of the islanded system, this paper presents a capacity optimization approach for multiple types of clean energy within an islanded VSC-HVDC transmission system. First, typical wind power and PV output scenarios are obtained via Monte Carlo simulation, and a virtual slack bus is introduced to establish a power flow calculation model for the islanded VSC-HVDC transmission system. Second, the active power is regulated through fast VSC-HVDC support and droop control mechanisms, while a quadratic programming model for voltage is established based on the relationship between reactive power and voltage, aiming to drive the virtual slack bus power to zero, thereby improving system frequency and voltage stability. Finally, a genetic algorithm (GA) is employed to achieve optimal capacity configuration for maximizing renewable energy integration, and the corresponding optimal energy storage capacity is determined accordingly. Simulation results demonstrate that, while satisfying operational constraints, the proposed method identifies the maximum installable capacities of wind power and PV while simultaneously reducing the required energy storage capacity. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. Full article

Active surface shape control is a key engineering technology enabling high-frequency operation and high-performance observations in modern large-aperture radio telescopes. By determining the achievable controllable accuracy of the primary reflector, its performance further constrains the aperture efficiency and long-term stability of telescope sensitivity. As millimeter- and submillimeter-wave astronomy advances toward higher operating frequencies and larger survey scales, key astrophysical questions increasingly demand the simultaneous achievement of high angular resolution, high surface-brightness sensitivity, and high imaging efficiency over wide fields of view. Limited by field-of-view coverage, sensitivity, or spatial-scale uniformity, traditional single-dish or interferometric array systems struggle to simultaneously satisfy these observational requirements. Consequently, large-aperture, wide-field millimeter/submillimeter single-dish telescopes are regarded as an important technological pathway for achieving multi-scale, high-fidelity observational capability. Their performance critically depends on effective control of primary reflector accuracy and system stability under multiple disturbance sources, such as gravity and thermal effects. From a system-level perspective, this paper provides an overview of the overall architecture of active surface control technologies for large-aperture millimeter- and submillimeter-wave single-dish radio telescopes. Focusing on three core components—surface measurement, actuator execution, and surface control strategies—it systematically reviews the underlying technical principles, representative engineering practices, technological evolution, and recent research progress. The characteristics of different technical approaches are summarized and analyzed, providing a reference for the design and further study of active surface control systems for large-aperture radio telescopes. Full article

In the new-type power system, the traditional generation planning paradigm has shifted to a new paradigm of source–storage collaborative planning. However, source–storage coordinated planning is facing deep-seated structural challenges of unbalanced multi-objective coordination and insufficient adaptability to market mechanisms. This paper first designs a source–storage coordinated planning framework with a two-layer structure of planning decision-making and operation verification, which takes into account multiple low-carbon, economic, and security planning objectives, and considers the dual market driving of energy storage participating in active-power and reactive-power regulations. Secondly, a two-layer optimal planning model is constructed: the upper-layer aims at minimizing the investment cost of new source–storage and minimizing annual carbon emissions, while the lower-layer aims at minimizing the comprehensive operation cost and maximizing the revenue of the energy storage market. The feature of this model is that it can simultaneously consider the coupling effect of the active-power market and the reactive-power market. Thirdly, a two-layer closed-loop iterative solution method based on Non-dominated Sorting Genetic Algorithm II is adopted to generate the source–storage coordinated planning scheme. Finally, simulation calculations are performed on the modified New England 39-bus system. The results show that, when considering the market driving of energy storage in both active-power and reactive-power regulations, the installed capacity of new energy reaches 465 MW, which is 55% higher than that in the no-market scenario, while the renewable energy curtailment rate is only 1.7%. The correctness and effectiveness of the proposed two-layer source–storage coordinated planning method in this paper are verified. Full article

Accurate identification of customer transformer relationships in distribution sub-zones is a fundamental prerequisite for the refined management of low-voltage distribution networks and the integration of distributed generation sources. Addressing current issues such as missing records, non-standard wiring and unclear boundaries between multiple sub-zones, this paper proposes an identification method based on the Power Admittance Regression Algorithm (PARA). Based on the fundamental laws of electrical circuits, this method constructs a regressible model of the linear relationship between the total admittance at the transformer end and the admittances at each consumer end. By utilising electrical data collected simultaneously from smart metres and distribution transformer terminals, it formulates the identification of consumer transformer relationships as a problem of minimising regression residuals. For three typical operating conditions—pure residential load, mixed residential and commercial load, and photovoltaic connection at the feeder terminus—constrained least-squares regression models and binary regression models incorporating PV variables were established respectively; ridge regression regularisation was introduced to suppress multicollinearity and enhance model robustness. Simulation tests were conducted using a dataset comprising 150 consecutive time sections and 70 test nodes (of which 60 were customers within the local substation area and 10 were interference nodes from other substation areas) for validation. The results indicate that, under the three conditions described above, in engineering simulations accounting for three-phase imbalance, random perturbations in line parameters and measurement noise, the average accuracy of this method, as determined by 100 Monte Carlo simulations, was 86.2 percent, 92.8 percent and 93.1 percent respectively, with standard deviations ranging from 1.6% to 1.9%, thereby validating its effectiveness and superiority in scenarios involving complex load structures and the integration of renewable energy. As this work is based on simulation data, further online validation using actual feeder data from electricity consumption data acquisition systems is required. Full article