Search Results (21)

In the single-depot multi-traveling salesman problem, traditional depot location methods often overlook task balance among traveling salesmen, leading to excessive load on certain units and compromising overall operational efficiency. To address this issue, this paper proposes an optimized depot location method based on clustering and multi-task balancing. The core contribution lies in the design of a multi-weight adaptive depot optimization method. This approach clusters city nodes into multiple groups through cluster analysis and dynamically synthesizes direction vectors using information such as the number of samples within each cluster and the convex perimeter. It iteratively optimizes depot locations, minimizing the total path length while enhancing workload balance across all traveling salesman routes. Additionally, a “divide-and-conquer” strategy decomposes the complex MTSP into multiple parallel TSP subproblems, which are then efficiently solved using Or-Tools. A comprehensive evaluation framework is introduced, incorporating Total-Sum distance, Min-Max distance, Workload Balance, Cluster separability, Robustness, and Running time. Experimental results on the TSPLIB standard dataset demonstrate that the proposed method exhibits significant advantages over various traditional clustering algorithms in both route optimization and route balancing, validating its effectiveness and practicality. The method’s robust performance provides a reliable solution for real-world applications such as logistics distribution, further highlighting its practical value. Experimental results show that the proposed method reduces the total travel distance and improves workload balance on multiple TSPLIB instances compared with conventional depot selection baselines. Full article

Human motivation is governed by a long-memory cognitive process in which the depth of temporal integration—how far into the past the system draws upon accumulated experience—is not fixed, but dynamically compressed under cognitive stress. Despite extensive empirical evidence that acute stress impairs working memory and narrows temporal integration in decision-making, no existing mathematical framework has formally coupled the memory depth of the governing operator to a physiologically grounded stress indicator. To address this gap, we propose a stress-adaptive variable-order fractional model for motivational intensity $M\left(t\right)$, in which the Caputo fractional order $\alpha \left(t\right)$ varies inversely with an aggregated stress indicator $\sigma \left(t\right)$ through the Hill-type coupling $\alpha \left(t\right)={\alpha }_{min}+({\alpha }_{max}-{\alpha }_{min})C/(C+\sigma \left(t\right))$, thereby encoding the empirically documented shift from deep integrative to shallow heuristic processing as cognitive load increases. Rather than deriving the model by algebraic manipulation of a differential equation, we formulate it directly as a causally consistent type-III Volterra integral equation, in which the memory kernel is evaluated at the history time s, ensuring that the weight assigned to each past state reflects the memory depth that was physiologically active when that state was experienced. Well-posedness is established rigorously via the Banach fixed-point theorem with explicit contraction constants, uniform boundedness and non-negativity of solutions are derived through the fractional Gronwall inequality, and numerical solutions are computed using an Adams–Bashforth–Moulton predictor–corrector scheme adapted to the variable-order kernel. Five numerical experiments demonstrate that stress-induced variation in $\alpha \left(t\right)$ produces qualitatively richer dynamics compared with the tested constant-order baselines: the proposed model achieves a steeper peak decline rate (0.48 versus 0.19–0.45), a larger burnout gap (3.15 versus 1.92–2.81), and faster recovery to ninety percent of peak motivation (4.2 versus 3.9–7.3 time units), while the empirically observed numerical convergence approaches $O\left(h^2\right)$ for sufficiently small step sizes. The framework offers a principled phenomenological substrate for memory-adaptive cognitive modelling, with direct implications for stress-aware intelligent tutoring systems that are capable of inferring $\alpha \left(t\right)$ in real time from biometric signals such as heart rate variability or galvanic skin response, and adjusting instructional complexity accordingly. Empirical calibration against learning-analytics and psychophysiological datasets, together with stochastic extensions for probabilistic burnout-risk prediction, are identified as immediate priorities for future research. Full article

To precisely evaluate the fatigue hot-spot stress concentration factor (SCF) of welded tubular joints and verify the accuracy of existing methods, this research selects Y-type tubular joints as the research subject. The dihedral angle formula is re-derived, and the dihedral angles corresponding to each polar angle along the intersection line are calculated using MATLAB R2018a (MathWorks Inc., Natick, MA, USA). After determining the geometric parameters of the weld profile in accordance with AWS specifications, finite element models named “AWS-max” and “AWS-min” are established in ANSYS 2022 R1 (ANSYS Inc., Canonsburg, PA, USA). These models meet the maximum and minimum allowable weld sizes respectively, and a novel modeling approach is proposed. Tests on tubular joints under axial tension loading are conducted, and the SCF is obtained through the surface stress interpolation method. Comparative analyses are carried out among the SCF from the established “AWS-max” and “AWS-min” weld models, the non-weld model, and the test results of the tubular joints. The results indicate that the weld geometric size has a significant impact on SCF: a larger weld cross-section results in a lower SCF. For the AWS maximum weld model, the SCF of the chord ranges from 4.21 to 5.42, and that of the brace ranges from 1.71 to 5.33; for the AWS minimum weld model, the chord SCF is 4.41–5.73, and the brace SCF is 2.11–5.79. The numerical results are in good accordance with the experimental data, while the non-weld model produces obviously conservative results with inconsistent distribution laws. The calculated dihedral angles obtained by the proposed method are highly consistent with the AWS standard. The modeling method is characterized by reliable accuracy and strong engineering applicability, and can be extended to the SCF calculation and fatigue evaluation of various tubular joints. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance. Full article

Under cyclic loading, fatigue limits Δσ_FL and fatigue crack growth (FCG) thresholds ΔΚ_th are usually examined using the S-N (or ε-N) and FCG da/dN-ΔK approaches, respectively. Historically, these two approaches are treated as a separate domain. This separation was due to the recognition that the nonuniform local stress field ahead of a crack differs significantly from the uniform stress field in a smooth specimen under axial fatigue loading. At present, there are no reliable approaches to analyzing these two regions in a unified way. In this paper, we first attempt to relate the experimental results of a cracked sample in the near-threshold region to the S-N fatigue limit of a smooth pull-push specimen. Then establish analytically the local stress intensity factor range ΔK at the process/damage zone ahead of the crack utilizing the local stress equal to Δσ_FL in a smooth specimen. Doing such an analysis, we can account the variations between the applied and the local stress ratios R (=min stress/max stress) for both cracked and smooth samples. The proposed relationship between ΔK_th and Δσ_FL would enable the development of a unified framework for fatigue analysis methods to predict damage evolution under low-stress in-service loading conditions. Full article

In this paper, the low-cycle reciprocating load test was carried out on four-limb concrete-filled steel tubular (CFST) lattice columns with different slenderness ratios and axial compression ratios, and the seismic performance was studied. Two performance indicators, namely damage and hysteretic energy dissipation, were defined as the objective functions, and the axial compression ratio was used as an optimization variable to perform the multi-objective optimization analysis of four-limb CFST lattice columns. Optimization using the max–min problem approach aims to optimize the axial compression ratio to minimize damage and maximize the dissipation of hysteresis energy. The seismic performances before and after optimization were determined using a restoring force model and were evaluated by the finite element method under different axial compression ratios. The results show that, under low-cycle reciprocating loads, the load–displacement hysteresis curve is a bow shape (Members 1 and 2), inverse S-shape (Member 3), and approximate shuttle shape (Member 4). Through multi-objective optimization, the optimized axial compression ratio is 0.25 and the finite element analysis indicates that the optimal seismic performance is at an axial compression ratio of 0.25. Through the optimized design, the maximum horizontal load of lattice columns, the elastic stiffness, the dissipation capacity, and the seismic performance are all improved, under the premise of satisfying the structural safety. Full article

Background: Assessments of muscle strength help prescribe and monitor training loads in cyclists (e.g., triathletes). Some methods include repetition maximum, joint isokinetic tests, and indirect estimates. However, their specificity for cycling’s dynamic force application and competitive cadences is lacking. This study aims to determine the influence of the cycling isokinetic peak force (cIPF) at different cadences on aerobic performance-related variables in trained triathletes. Methods: Eleven trained male athletes (33 ± 9.8 years, 173.1 ± 5.0 cm height, 73.9 ± 6.8 kg body mass, and ≥5 years of triathlon experience) were recruited. Maximal oxygen consumption (VO₂ max), ventilatory thresholds (i.e., VT1 and VT2), and cIPF were assessed. cIPF testing involved 10 s sprints at varied cadences with 4 min rest intervals. Pedaling cadences were set at low (60 rpm), moderate (80 and 100 rpm), and high (120 and 140 rpm) cadences. A regression model approach identified cIPF related to aerobic performance. Results: IPF at 80 and 120 rpm explained 49% of the variability in power output at VT1, 55% of the variability in power output at VT2, 65% of the variability in power output at maximal aerobic power (MAP), and 39% of the variability in VO₂ max. The cycling economy was not explained by cIPF. Conclusions: This study highlights the significance of cIPF, particularly at moderate to high cadences, as a determinant of aerobic-related variables in trained triathletes. Cycling cIPF should be tested to understand an athlete’s profile during crank cycling, informing better practice for training specificity and ultimately supporting athletes in achieving optimal performance outcomes in competitive cycling events. Full article

In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited. Full article

Maintaining the peak temperature of a battery within limits is a mandate for the safer operation of electric vehicles. In two-wheeler electric vehicles, the options available for the battery thermal management system are minuscule due to the restrictions imposed by factors like weight, cost, availability, performance, and load. In this study, a multi-strategical cooling approach of forced convection and mist cooling over a single-cell 21,700 lithium-ion battery working under the condition of 4C is proposed. The chosen levels for air velocities (10, 15, 20 and 25 m/s) imitate real-world riding conditions, and for mist cooling implementation, injection pressure with three levels (3, 7 and 14 bar) is considered. The ANSYS fluent simulation is carried out using the volume of fluid in the discrete phase modelling transition using water mist as a working fluid. Initial breakup is considered for more accurate calculations. The battery’s state of health (SOH) is determined using PYTHON by adopting the Newton–Raphson estimation. The maximum temperature reduction potential by employing an airflow improviser (AFI) and additives (Tween 80, 1-heptanol, APG0810, Tween 20 and FS3100) is also explored. The simulation results revealed that an additional reduction of about 11% was possible by incorporating additives and AFI in the multi-strategical approach. The corresponding SOH improvement was about 2%. When the electric two-wheeler operated under 4C, the optimal condition (Max. SOH and Min. peak cell temp.) was achieved at an air velocity of 25 m/s, injection pressure of 7 bar with AFI and 3% (by wt.) Tween 80 and a 0.1% deformer. Full article

This paper presents a numerical approach for investigating fatigue delamination propagation in composite stiffened panels loaded in compression in the post-buckling field. These components are widely utilized in aerospace structures due to their lightweight and high-strength properties. However, fatigue-induced damage, particularly delamination at the skin–stringer interface, poses a significant challenge. The proposed numerical approach, called the “Min–Max Load Approach”, allows for the calculation of the local stress ratio in a single finite element analysis. It represents the ratio between the minimum and maximum values of the stress along the delamination front, enabling accurate evaluation of the crack growth rate. The methodology is applied here in conjunction with the cohesive zone model technique to evaluate the post-buckling fatigue behavior of a composite single-stringer specimen with an initial delamination. Comparisons with experimental data validate the predictive capabilities of the proposed approach. Full article

Topology optimization (TO) is currently a focal point for researchers in the field of structural optimization, with most studies concentrating on single-loading conditions. However, real engineering structures often have to work under various loading conditions. Approaches addressing multiple-loading conditions often necessitate subjective input in order to determine the importance of each loading condition, aiming for a compromise between them. This paper proposes a so-called bisection constraint method (BCM), offering a unique, user-preference-independent solution for TO problems amidst multiple-loading conditions. It is well-known that minimizing the system’s compliance is commonly used in TO as the objective. Generally, compliance is not as sufficient as stress to be used as a response to evaluate the performance of structures. However, formulations focusing on minimizing stress levels usually pose significant difficulties and instabilities. On the other hand, the compliance approach is generally simpler and more capable of providing relatively sturdy designs. Hence, the formulation of min–max compliance is used as the target problem formulation of the proposed method. This method attempts to minimize compliance under only one loading condition while compliances under the remaining loading conditions are constrained. During the optimization process, the optimization problem is automatically reformulated with a new objective function and a new set of constraint functions. The role of compliance under different loading conditions, i.e., whether it is to be treated as an objective or constraint function, might be changed throughout the optimization process until convergence. Several examples based on the solid isotropic material with penalization (SIMP) approach were conducted to illustrate the validity of the proposed method. Furthermore, the general effectiveness of the compliance approach in terms of stress levels is also discussed. The calculation results demonstrated that while the compliance approach is effective in several cases, it proves ineffective in certain scenarios. Full article

Subthreshold design provides the promising advantage of low power consumption at the cost of performance variation and even circuit failure. An accurate and efficient statistical timing model is crucial for timing analysis and performance optimization guidance. Prior works lack the consideration of the impact of slew time or the transitional region for input slew due to process variation and efficient approaches considering the impact of load capacitance and multiple process variations in complex gates, resulting in accuracy loss. In this work, an accurate end efficient gate delay variation model is analytically derived for various input slews and load capacitances. The transitional region between fast and slow input slew is efficiently partitioned with an adaptive error tolerance method so as to characterize timing variation by linear interpolation based on that for fast and slow input slew. In order to consider the impact of load capacitance, the relation between the sensitivity of step delay and the dominant threshold voltage variation is analytically derived. For complex gates, the multiple process variations for both parallel and stacking structures are equivalently expressed by threshold voltage variation from each transistor. The proposed model has been validated under advanced TSMC (Taiwan Semiconductor Manufacturing Company) 12 nm technology at subthreshold region and achieves excellent agreement with Monte Carlo SPICE (Simulation Program with Integrated Circuit Emphasis) simulation results with the max error less than 6.49% for standard deviation of gate delay and 4.63%/6.40% for max/min delay, demonstrating over 4 times precision improvement compared with competitive analytical models. Full article

Load variations in any power system result in loss escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in existing methods that, ideally, the allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and the allocation of DGs must be done in accordance with the variations of the connected load. Therefore, the current paper addresses the aforementioned problem by the distinctive optimal allocation of DGs for each variability of 24 h load horizon. However, a single exclusive solution is considered among all allocations of 24 h. The min-max regret concept has been utilized in order to deal with such a methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royale optimization (BRO), accelerated particle swarm optimization (APSO), and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises active and reactive power losses and voltage deviation. Hence, in order to find the robustness of the proposed technique, Conseil international des grands reseaux electriques’ (CIGRE) MV benchmark model incorporating 14 buses has been used considerably as a test network. In the end, the results of three proposed algorithms have been compared. Full article