Search Results (9,814)

In this paper, the problem of pitching attitude finite-horizon optimization for aircraft is posed with system uncertainties, external disturbances, and input constraints. First, a neural network (NN) and a nonlinear disturbance observer (NDO) are employed to estimate the value of system uncertainties and external disturbances. Taking input constraints into account, an auxiliary system is designed to compensate for the constrained input. Subsequently, the backstepping control containing NN and NDO is used to ensure the stability of systems and suppress the adverse effects caused by the system uncertainties and external disturbances. In order to avoid the derivation operation in the process of backstepping, a dynamic surface control (DSC) technique is utilized. Simultaneously, the estimations of the NN and NDO are applied to derive the backstepping control law. For the purpose of achieving finite-horizon optimization for pitching attitude control, an adaptive method termed adaptive dynamic programming (ADP) with a single NN-termed critic is applied to obtain the optimal control. Time-varying feature functions are applied to construct the critic NN in order to approximate the value function in the Hamilton–Jacobi–Bellman (HJB) equation. Furthermore, a supplementary term is added to the weight update law to minimize the terminal constraint. Lyapunov stability theory is used to prove that the signals in the control system are uniformly ultimately bounded (UUB). Finally, simulation results illustrate the effectiveness of the proposed finite-horizon optimal attitude control method. Full article

Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics. Full article

The internal combustion process in a boiler in power plants has a direct impact on boiler efficiency and NOx generation. The objective of this study is to propose an intelligent deep learning modeling and multi-objective optimization approach that considers NOx emission concentration and boiler thermal efficiency simultaneously for boiler combustion in power plants. Firstly, a hybrid deep learning model, namely, convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU), is employed to predict the concentration of NOx emissions and the boiler thermal efficiency. Then, based on the hybrid deep prediction model, variables such as primary and secondary airflow rates are considered as controllable variables. A single-objective optimization model based on an improved flow direction algorithm (IFDA) and a multi-objective optimization model based on NSGA-II are developed. For multi-objective optimization using NSGA-II, the average NOx emission concentration is reduced by 5.01%, and the average thermal efficiency is increased by 0.32%. The objective functions are to minimize the boiler thermal efficiency and the concentration of NOx emissions. Comparative analysis of the experiments shows that the NSGA-II algorithm can provide a Pareto optimal front based on the requirements, resulting in better results than single-objective optimization. The effectiveness of the NSGA-II algorithm is demonstrated, and the obtained results provide reference values for the low-carbon and environmentally friendly operation of coal-fired boilers in power plants. Full article

To address energy shortages and achieve carbon peaking/neutrality, this study develops a distributed renewable-based integrated energy system (IES) for rural active zero-energy buildings (ZEBs). Energy consumption patterns of typical rural houses are analyzed, guiding the design of a resource-tailored IES that balances economy and sustainability. Key equipment capacities are optimized to achieve net-zero/zero energy consumption targets. For typical daily cooling/heating/power loads, equipment output is scheduled using a dual-objective optimization model minimizing operating costs and CO₂ emissions. Results demonstrate that: (1) Net-zero-energy IES outperforms separated production (SP) and full electrification systems (FES) in economic-environmental benefits; (2) Zero-energy IES significantly reduces rural building carbon emissions. The proposed system offers substantial practical value for China’s rural energy transition. Full article

Background: The ROSA Knee System (Zimmer Biomet, Warsaw, IN, USA) is a robotic system aiming to increase bone resections and component alignment accuracy during TKA. While much is known about its performance in the coronal plane, its accuracy in the sagittal plane remains debated. The present investigation evaluated the system’s accuracy in achieving planned mechanical axis alignment and specific knee angles in both planes. Methods: A retrospective analysis was performed on 55 consecutive patients who underwent robotic-assisted TKA using the ROSA Knee System. Data on the medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), hip–knee–ankle angle (HKA), tibial slope (TS), and distal femoral flexion (DFF) were collected pre- and post-operatively using the ROSA software. Planned and achieved angles were compared, with deviations greater than 2° and 3° defined as outliers. Results: The mean differences between planned and achieved angles for LDFA and MPTA were 0.5° ± 1.00° and 0.3° ± 1.3°, respectively, with less than 10% outliers. The hip–knee angle recorded only a minimal deviation from planned values. In contrast, the TS angle showed a statistically significant difference between planned and achieved values, while no significant difference was found for the DFF angle. The surgeon’s experience did not impact alignment accuracy. Conclusions: The ROSA Knee System demonstrates high accuracy in achieving planned alignment in the coronal plane during robotic-assisted TKA, with minimal outliers and reliable predictions for both femoral and tibial angles. However, the ROSA Knee System showed less accuracy in the sagittal plane, particularly for the tibial slope, which did not adversely affect the implant’s stability. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. Time-triggered communication standards such as time-sensitive networking (TSN) and TTEthernet effectively enable deterministic and reliable communication across distributed systems, including MPSoC-based platforms connected via Ethernet. However, their dependence on static resource allocation limits adaptability under dynamic operating conditions. To address this challenge, we propose an offline metascheduling framework that generates multiple precomputed schedules corresponding to different context events. The proposed algorithm introduces a selective communication strategy that synchronizes context information exchange with key decision points, thereby minimizing unnecessary communication while maintaining global consistency and system determinism. By leveraging knowledge of context event patterns, our method facilitates coordinated schedule transitions and significantly reduces communication overhead. Experimental results show that our approach outperforms conventional scheduling techniques, achieving a communication overhead reduction ranging from 9.89 to 32.98 times compared to a two-time-unit periodic sampling strategy. This work provides a practical and certifiable solution for introducing adaptability into Ethernet-based time-triggered MPSoC systems without compromising the predictability essential for safety certification. Full article

Double-suction centrifugal pumps are extensively employed in industrial applications owing to their high efficiency, low vibration, superior cavitation resistance, and operational durability. This study analyzes how impeller oblique cutting angles (0°, 6°, 9°, 12°) affect a double-suction pump at a fixed 4% trimming ratio and constant average post-trim diameter. Numerical simulations and tests reveal that under low-flow (0.7Q_d) and design-flow conditions, the flat-cut (0°) minimizes reflux ratio and maximizes efficiency by aligning blade outlet flow with the mainstream. Increasing oblique cutting angles disrupts this alignment, elevating reflux and reducing efficiency. Conversely, at high flow (1.3Q_d), the 12° bevel optimizes outlet flow, achieving peak efficiency. Pressure pulsation at the volute tongue (P11) peaks at the blade-passing frequency, with amplitudes significantly higher for 9°/12° bevels than for 0°/6°. The flat-cut suppresses wake vortices and static–rotor interaction, but oblique cutting angle choice critically influences shaft-frequency pulsation. Entropy analysis identifies the volute as the primary loss source. Larger oblique cutting angles intensify wall effects, increasing total entropy; pump chamber losses rise most sharply due to worsened outlet velocity non-uniformity and turbulent dissipation. The flat-cut yields minimal entropy at Q_d. These findings provide a basis for tailoring impeller trimming to specific operational requirements. Furthermore, the systematic analysis provides critical guidance for impeller trimming strategies in other double-suction pumps and pumps as turbines in micro hydropower plants. Full article

This research presents the development and management principles of mobile resonant electrical systems designed for high-voltage generation, intended for non-destructive diagnostics of insulation in high-power electrical equipment. The core of the system is a series inductive–capacitive (LC) circuit characterized by a high quality (Q) factor and operating at high frequencies, typically in the range of 40–50 kHz or higher. Practical implementations of the LC circuit with Q-factors exceeding 200 have been achieved using advanced materials and configurations. Specifically, ceramic capacitors with a capacitance of approximately 3.5 nF and Q-factors over 1000, in conjunction with custom-made coils possessing Q-factors above 280, have been employed. These coils are constructed using multi-core, insulated, and twisted copper wires of the Litzendraht type to minimize losses at high frequencies. Voltage amplification within the system is effectively controlled by adjusting the current frequency, thereby maximizing voltage across the load without increasing the system’s size or complexity. This frequency-tuning mechanism enables significant reductions in the weight and dimensional characteristics of the electrical system, facilitating the development of compact, mobile installations. These systems are particularly suitable for on-site testing and diagnostics of high-voltage insulation in power cables, large rotating machines such as turbogenerators, and other critical infrastructure components. Beyond insulation diagnostics, the proposed system architecture offers potential for broader applications, including the charging of capacitive energy storage units used in high-voltage pulse systems. Such applications extend to the synthesis of micro- and nanopowders with tailored properties and the electrohydropulse processing of materials and fluids. Overall, this research demonstrates a versatile, efficient, and portable solution for advanced electrical diagnostics and energy applications in the high-voltage domain. Full article

Liquid biopsy (LB) has gained attention as a valuable approach for cancer diagnostics, providing a minimally invasive option compared to conventional tissue biopsies and helping to overcome issues related to patient discomfort and procedural invasiveness. Recent advances in biosensor technologies, particularly photonic sensors, have improved the accuracy, speed, and real-time capabilities for detecting circulating biomarkers in biological fluids. Incorporating these tools into clinical practice facilitates more informed therapeutic choices and contributes to tailoring treatments to individual patient profiles. This review highlights the clinical potential of LB, examines technological limitations, and outlines future research directions. Departing from traditional biosensor focused reviews, it adopts a reverse-mapping approach grounded in clinically relevant tumor biomarkers. Specifically, biomarkers associated with prevalent cancers, such as breast, prostate, and lung cancers, serve as the starting point for identifying the most suitable photonic sensing platforms. The analysis underscores the need to align sensor design with the physicochemical properties of each biomarker and the operational requirements of the application. No photonic platform is universally optimal; rather, each exhibits specific strengths depending on performance metrics such as sensitivity, limit of detection, and easy system integration. Within this framework, the review provides a comprehensive assessment of emerging photonic biosensors and outlines key priorities to support their effective clinical translation in cancer diagnostics. Full article

This study focuses on the long-standing issue of θ-assignment in the generative enterprise literature. Despite the asymmetry of θ-assignment regarding structural positions (Head–Complement/Specifier–Head) being sanctioned by the Duality of Semantics, I argue that it is possible to eliminate the asymmetry in full accordance with third-factor principles by proposing two independent frameworks. In the first framework, I propose that θ-assignment is executed by applying Minimal Search to locate the assigner and the assignee, where both the external argument and the internal argument receive the θ-role in the same way. In the second framework, which does not hinge on the assumptions or results of the first one, I propose that θ-assignment is a postsyntactical operation; thus, the Duality of Semantics, as well as concepts like θ-assignment in the syntax or θ-position, may be disregarded. For a proper θ-interpretation to be possible, the assigner and the assignee must be in the same transfer domain. Nonetheless, the empirical coverage of the Duality of Semantics is largely retained, suggesting merge can and must be simplest with respect to θ. Full article

A green scheduling problem is proposed in this work, where both constraints on intermediate storage capacity and job transportation requirements are simultaneously considered. An improved discrete pathfinder algorithm (IDPFA) with multi-neighborhood local search is proposed to minimize the maximum completion time and total energy consumption. The algorithm addresses the green flow shop scheduling problem with limited buffers and automated guided vehicle (GFSSP_LBAGV). Firstly, based on the machine speed constraints, the transportation time for moving jobs by the automated guided vehicle (AGV) is incorporated to establish a mathematical model. Secondly, the core idea of the pathfinder algorithm (PFA) is applied to the evolutionary process of the discrete PFA, where three different crossover operations are used to replace the exploration process of the pathfinder, the influence of the pathfinder on the followers, and the mutual learning among the followers. Then, a multi-neighborhood local search is employed to conduct a detailed exploration of high-quality solution spaces. Finally, extensive standard test sets are used to verify the effectiveness of the proposed IDPFA in solving GFSSP_LBAGV. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

Phosphoinositide-binding pleckstrin homology (PH) domains interact with both phospholipids and proteins, often complicating their use as specific lipid biosensors. In this study, we introduced specific mutations into the phosphatidylinositol 3,4,5-trisphosphate (PIP₃)-specific PH domains of protein kinase B (Akt) and general receptor for phosphoinositides 1 (GRP1) that disrupt protein-mediated interactions while preserving lipid binding, in order to enhance biosensor specificity for PIP₃, and evaluated their impact on plasma membrane (PM) localization and lipid-tracking ability. Using bioluminescence resonance energy transfer (BRET) and confocal microscopy, we assessed the localization of PH domains in HEK293A cells under different conditions. While Akt-PH mutants showed minimal deviations from the wild type, GRP1-PH mutants exhibited significantly reduced PM localization both at baseline and after stimulation with epidermal growth factor (EGF), insulin, or vanadate. We further developed tandem mutant GRP1-PH domain constructs to enhance PM PIP₃ avidity. Additionally, our investigation into the influence of ADP ribosylation factor 6 (Arf6) activity on GRP1-PH-based biosensors revealed that while the wild-type sensors were Arf6- dependent, the mutants operated independently of Arf6 activity level. These optimized GRP1-PH constructs provide a refined biosensor system for accurate and selective detection of dynamic PIP₃ signaling, expanding the toolkit for dissecting phosphoinositide-mediated pathways. Full article