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As the EU and the US successively proposed the carbon border adjustment mechanism (CBAM) and the Clean Competition Act (CCA), importers are now required to declare the carbon emissions of their products. Particularly, wheel companies face increasing time pressure with the gradual development of implementation paths for net-zero emissions and implementing carbon pricing policies in various countries. This research uses the production data of Taiwan wheel companies to establish a mathematical model for the activity-based cost optimisation decision-making process. It explores the impact of different carbon emission costs on a company's product mix with pricing decisions. Further, the research proposes five mathematical programmes for single-period cost optimisation decision-making under carbon pricing. These models provide some detailed view, such as changes in profits and product mix caused by high carbon taxes or other changes for each resource. Furthermore, the production cost optimal model proposed in this research can be used by enterprises to fulfill their social responsibilities while ensuring profitability and serve as a reference for making net-zero transformation decisions. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Libcint is a library designed for the evaluation of analytical integrals for Gaussian type orbitals. It prioritizes simplicity, ease of use, and efficiency for the development of quantum chemistry programs. In the release of version 6.0, Libcint supports the computation of integrals for various operators, such as overlap, Coulomb, Gaunt, Breit, attenuated Coulomb, Slater-type geminals, and Yukawa potential, as well as arbitrary orders of derivatives for these operators. To enhance the usability of the library, Libcint provides a uniform function signature for all integral functions. A code generator is included to automate the implementation of new integrals. To achieve better performance on modern central processing unit architectures, the library employs explicit single instruction multiple data parallelization in the code implementation. [ABSTRACT FROM AUTHOR]

This work describes optimizations of N-electron system wave functions by means of the simulated annealing technique within the doubly occupied configuration interaction framework. Using that technique, we minimize the energy variance of a Hamiltonian, providing determinations of wave functions corresponding to ground or excited states in an identical manner. The procedure that allows us to determine electronic spectra can be performed using treatments of restricted or unrestricted types. The results found in selected systems, described in terms of energy, spin, and wave function, are analyzed, showing the performance of each method. We also compare these results with those arising from more traditional approaches that minimize the energy, in both restricted and unrestricted versions, and with those obtained from the full configuration interaction treatment. [ABSTRACT FROM AUTHOR]

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment. [ABSTRACT FROM AUTHOR]

This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

Precision systems have extensively employed compliant mechanisms. Nevertheless, its utilization in vibration-assisted polishing has not garnered much interest. Therefore, this study presents a novel framework for a mechanism that adheres to two degrees of freedom. The mechanism that has been constructed exhibits a symmetrical design, enabling it to extract comparable performances in two directions. The proposed construction incorporates a novel combination of a three-lever amplification mechanism and a parallelogram to improve both the stroke and resonant frequency. The output amplification ratio is calculated by the graphical scheme method. The pseudo-rigid-body model, the free-body diagram, and the Lagrange method are all used together in this study to look into kinetostatics and dynamics. The water cycle algorithm is used to conduct structural optimization based on mathematical models. The prototype is fabricated by CNC milling, and physical tests are conducted. According to the findings for the resonant frequency, the analytical results well match the finite element analysis (FEA) results with an error of 8.44%. Besides, the measured resonant frequency was determined approximately 486 Hz, while the FEA yielded a result of 460.4 Hz. The discrepancy between the FEA and experimental results is 5.26%. The utilization of the design mechanism holds promise for the application of vibration-assisted polishing. [ABSTRACT FROM AUTHOR]

Considering the extensive applications of turning and facing operations in mechanical engineering manufacturing, the energy consumption of machining equipment has emerged as a significant concern in the manufacturing industries, such as aerospace and automotive. This article focuses on establishing a predictive model and optimizing energy consumption for facing and turning operations on two types of steel, namely, AISI 1038 and AISI 4142, using two different cutting tools. The experiments were planned using Taguchi's L16 orthogonal array, and the coefficients of the predictive model were determined through a linear regression approach. Additionally, a genetic algorithm (GA) with two distinct selection techniques was employed to optimize the three main variables: cutting depth, cutting speed, and feed rate, all aimed at reducing energy consumption. The results of this study show that the selection method used in GA significantly affects convergence toward the optimal solution, while the choice of cutting tool has a considerable impact on energy usage. Moreover, the variation in the effect of cutting parameters on machining energy consumption is analyzed to determine which parameters contribute most to energy savings. The results underscore the importance for manufacturers of using advanced predictive and optimization tools, offering them a competitive edge by achieving economic, environmental, and performance-related benefits. [ABSTRACT FROM AUTHOR]

This paper investigates the robust stabilization problem of linear delay systems with parametric uncertainty. First, by means of the argument principle, a delay‐dependent sufficient condition is derived for designing a feedback controller, with which the closed‐loop system can achieve robust stability. Then, based on the fundamental matrix of the linear delay systems, a constrained optimization problem is formulated for solving the feedback gain matrices. The effectiveness of the proposed method is verified by some simulation results. [ABSTRACT FROM AUTHOR]

Machine learning techniques are potent tools to predict structural failures and forecast future failure patterns. They can provide insights that are not immediately recognizable to human operators, and hence, enhance the maintenance decision-making process, leading to better safety and longevity of structures. This research employed a genetic algorithm based on an identification technique to estimate the location and depth of cracks in circular arches. The arches were made of homogeneous material and had a rectangular cross-section. The study adopted two different procedures for detecting cracks in the circular arches. The first method used natural frequency contours represented in a three-dimensional plot to identify the location and depth of cracks by intersecting a set of natural frequency contours. The second procedure utilized the regression method and an optimization technique to minimize the cost function objective function and determine the crack parameters. The influences of crack location and depth on the vibrational behavior of cracked arches were presented and discussed. Similarly, the results indicate a high level of consistency between the two techniques in predicting both crack size and location. [ABSTRACT FROM AUTHOR]

In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self‐designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA‐based systems. [ABSTRACT FROM AUTHOR]

Food is important for long-term national peace and stability. Land, water, and energy are essential resources that support food production. Conflicts among economic, social, and environmental objectives are inevitable in agricultural systems. On the basis of the concept of the water‒energy‒food (WEF) nexus, this study presents an interval multiobjective nonlinear optimization model to balance the conflicts of agricultural systems. This optimization model is then applied to Jiangsu Province to verify its applicability. Using the model, the optimal allocation of land, water, and energy and the corresponding system benefits under ten scenarios are obtained. The results show that wheat has the largest planting area when the decision is biased toward economic objectives. Planting more rice can improve the environment, and planting less rice can strengthen social fairness. The water and energy allocated to crops gradually decrease as decision-making preferences shift from the economy to society and the environment. The results also indicate that the comprehensive scores under scenario S4 (society → environment → economy) reached the ideal state among the seven multiobjective scenarios. Compared with the control year (2020), the optimal total land allocation in scenario S4 is reduced by 17.49%-17.84% to ensure food security. Notably, economic profits decrease by 6.68%-38.6%, social fairness significantly improves by 45.39%–55.62%, and total carbon emissions decrease by 8.88%–25.78%. This study can help decision-makers obtain a management scheme for maximizing comprehensive interests in the agricultural WEF nexus system. [ABSTRACT FROM AUTHOR]

With the increased construction reservoirs, hydropower systems are becoming larger and more complex, which brings challenges of optimal operation of large-scale reservoirs to improve the power generation. To address this efficiently, we propose an aggregation-decomposition method based on cascade reservoir drawdown rule. Based on a two-stage method, we analyze the monotonicity of power generation increment of cascade reservoirs and propose the drawdown rule, which we used to guide the drawdown order of cascade reservoirs. On this basis, we propose an aggregation-decomposition coupling drawdown rule and progressive optimal algorithm (ADDR-POA) method of large-scale reservoirs. To confirm the viability of the proposed approach, we selected 29 series–parallel-mixed reservoirs in the upper Yangtze River Basin in China as the study subjects and optimized them with the goal of maximizing the total power generation. Results show that compared to conventional mathematical optimization method and heuristic algorithm, ADDR-POA can effectively express the compensation effect between reservoirs and has a good performance in improving the total power generation of the basin and reducing iteration times, which presents a novel approach for solving the problem of drawdown operation of large-scale reservoirs. [ABSTRACT FROM AUTHOR]

This paper proposes novel multi-energy inertia support for simultaneous frequency and voltage control of an isolated hybrid power system (IHPS). Multi-energy storage (gas inertia – hydrogen storage, thermal inertia – solar thermal storage, hydro inertia – gravity hydro storage, chemical inertia – battery energy storage) supported by demand side management (DSM) for simultaneous voltage and frequency regulation and backed by biodiesel generators, are the essential elements of IHPS. A novel control strategy of concurrent virtual droop control, virtual damping control, virtual inertia control, and virtual negative inertia control is proposed to utilise multiple inertia sources and to improve LFC and AVR performance effectively. The effective coordination of inertia sources in eradicating oscillations in IHPS, is aided by a developed cascaded proportional integral-tilt-integral-sliding mode (PI-TISMC) controller. The performance of PI-TISMC is compared with PID, PI-PID, and PI-SMC controllers. A maiden attempt has been done by training five diverse classes of optimization techniques to optimize the parameters of controllers in the present work. The results are evaluated in MATLAB and it is evident from the results that the performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). The performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). Similarly, the performance of voltage control is improved by 6.7%, 4.8% and 2.3 s (over shoot, undershoot, and settling time) by employing developed PI-TISMC controller and proposed concurrent inertia control. The combination exhibits superior performance in minimizing oscillations in IHPS due to variations in loading and solar insolation. [ABSTRACT FROM AUTHOR]

To meet the strict requirement on whole chain of vaccine production, storage, transportation, and distribution, most researches have been done on cascade refrigeration systems to achieve high operation performance, while a research gap on the performance exploration of eco‐friendly refrigeration systems still exists. In this paper, a comprehensive thermodynamic model was established to analyze the operation performance of a pre‐cooled cascade refrigeration system with eco‐friendly refrigerants propane (R290) and ethane (R170). Based on the present thermodynamic model, the performance optimization on the R290‐R170 cascade refrigerator was made with considerations of degree of subcooling, degree of superheat, evaporation temperature, condensation temperature, cascade condensation temperature, and cascade temperature difference. Variations of the coefficient of performance, exergy destruction, and total exergy efficiency of the refrigeration cycle were analyzed. Two mathematical correlations yielding the optimal cascade condensation temperature and maximized coefficient of performance were developed by multilinear regression analysis. When the evaporation temperature is − 60°C, the maximized coefficient of performance and total exergy efficiency are 1.276 and 49.51%. This paper demonstrates the potential for improving the R290‐R170 cascade refrigeration system and furnishes the basis for further exploration on ultra‐low‐temperature refrigerators. [ABSTRACT FROM AUTHOR]

This work presents a knowledge-based decision-making robust antenna design (KDRAD) method that employs a heuristic-optimisation technique for antenna design. The method is used to create and scale a single-element antenna to operate at multiple frequencies within the 5 G range. In this study, a low-profile planar four-element microstrip antenna of 14.8 mm × 14.8 mm × 0.508 mm gives simulated and measured impedance bandwidths of about 1.18 GHz (26.43–27.61 GHz) and 1.08 GHz (26.42–27.50 GHz), respectively, with a centre frequency of 27 GHz. The mm-wave planar four-port antenna has a maximum gain of 7.2 dBi; isolation of 33.2 dB; and multiple antenna matrices with minimal ECC (0.000000109), high diversity gain (10 dB) and channel capacity loss of 0.00110 bits/s/Hz. [ABSTRACT FROM AUTHOR]

The inconsistency of conformal array elements poses a huge challenge to pattern synthesis. Based on convex optimization theory, this paper proposes a novel directivity pattern synthesis method. It can maximize the directivity of dual‐polarized conformal arrays with a low dynamic range ratio (DRR). Firstly, the directivity function is presented under a dual‐polarized conformal array. Then the pattern synthesis based on maximizing directivity is summarized as a multiconstraint optimization problem. In addition, the DRR constraint is introduced to control the weight range. By using Taylor expansion, the problem is transformed into a convex optimization problem and solved iteratively. Finally, simulation results demonstrate that the proposed method can effectively achieve a good directivity pattern with low sidelobe level (SLL) and low DRR. [ABSTRACT FROM AUTHOR]

Background: Social acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition's sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers' needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects. Results: We identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population's exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs. Conclusions: Disamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFC) possess features like high specific power density, low operating temperature, and low operating pressure and thus are most widely used. The performance of PEMFC highly depends on its output voltage which further affects its efficiency. This research paper aims to improve this output voltage by minimizing the losses through parameter estimation technique for three well-known commercial PEMFC stacks, namely, 250W, BCS 500W, and Ballard Mark V. The multiobjective function framed for optimization serves two goals. One is to extract the unknown parameters of FC, and second is to achieve the minimum sum of squared errors (SSE) that occur due to the difference between experimental voltage and estimated voltage. Chaotic teaching–learning-based optimization (CTLBO) algorithm is used for optimization process. SSE values obtained for three commercial PEMFC stacks 250W, BCS 500W, and Ballard Mark V are 7.02267, 4.00150, and 0.8100, respectively. The applicability of this technique is further checked for different operating conditions of each model. The I-P (current–power) curve and I-V (current–voltage) curve obtained using estimated data closely matched the experimental data that showed the practical relevance and efficacy of the algorithm. A comparison is done among the SSE results obtained using CTLBO to other competitive algorithms mentioned in the literature. CTLBO showed its superiority over other algorithms in estimating the unknown parameters of PEMFC stack parameters. [ABSTRACT FROM AUTHOR]

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community. [ABSTRACT FROM AUTHOR]

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution times for complex problems. In this work, we propose a novel method for solving linear systems. Our approach leverages binary optimization, making it particularly well-suited for problems with large condition numbers. We transform the linear system into a binary optimization problem, drawing inspiration from the geometry of the original problem and resembling the conjugate gradient method. This approach employs conjugate directions that significantly accelerate the algorithm's convergence rate. Furthermore, we demonstrate that by leveraging partial knowledge of the problem's intrinsic geometry, we can decompose the original problem into smaller, independent sub-problems. These sub-problems can be efficiently tackled using either quantum or classical solvers. Although determining the problem's geometry introduces some additional computational cost, this investment is outweighed by the substantial performance gains compared to existing methods. [ABSTRACT FROM AUTHOR]

In the current work, an innovative nondominated sorting colliding bodies optimization (NSCBO) technique is introduced to tackle multiobjective optimal power flow (MOOPF) challenges within electrical power networks. This method offers a means to generate a diverse array of nondominated solutions in a single iteration by including the nondominated (ND) sorting process and the concept of crowding distance. Additionally, it utilizes a spread indicator to archive the latest nondominated solutions. In the NSCBO method, the mass of each colliding body is determined by its nondominated rank rather than relying on objective function information. Moreover, a fuzzy decision-making strategy is employed to identify a suitable solution from the set of ND solutions. To showcase the scalability and viability of the NSCBO method, experiments are conducted on IEEE 30-bus, considering both bi- and tri-objective models. Comparative analysis with existing methods from recent literature demonstrates the efficacy of the NSCBO technique in handling constraints and deriving nondominated solutions for MOOPF problems. [ABSTRACT FROM AUTHOR]

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets. [ABSTRACT FROM AUTHOR]

The reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy. [ABSTRACT FROM AUTHOR]

In the context of competitive sports, the evaluation of compensation incentive effectiveness is key to the optimization of compensation systems for athletes. This study creates a model of the athlete compensation incentive effect from single and multinomial grouping perspectives, combining empirical research (SEM) with fuzzyset qualitative comparative analysis (fsQCA) based on a sample of 352 validated data. The results lead to two findings. First, athletes' direct economic compensation, direct non-economic compensation, and indirect non-economic compensation have a significant positive effect on the incentive effect of compensation. Second, that the incentive effect of high compensation has two configurations, namely "economic value" and "economic environmental value." It seems that the effective combination of compensation factors can enhance the motivation effect in a "different way." [ABSTRACT FROM AUTHOR]

Paclitaxel (PTX), a naturally occurring diterpenoid isolated from Taxus brevifolia, is a first-line drug for the treatment of glioblastoma; however, it suffers from the disadvantages of poor water solubility and nonspecific biodistribution, which cause serious side effects in the human body. The marketed formulation suffers from serious side effects, such as allergic reactions, neutropenia, and neuropathy, which require safe and effective formulations of PTX. In the present study, PTX was entrapped in a solid–liquid lipid mixture with the aid of a surfactant using a modified solvent evaporation technique. Higher entrapment of the impressive stability of the formulation was achieved by employing quality design-based strategies. Optimized levels by employing a numerical optimization technique for each factor, that is, surfactant concentration (X1), lipid concentration (X2), and amount of organic solvent (X3) were 0.3%, 0.76% & 8.3 ml respectively. The resultant formulation exhibited a particle size of 121.44 nm, entrapment efficiency of 94.27%, and zeta potential of −20.21 mV with unimodal size distribution. A reduction in the % crystalline index from 48 to 3.4% ensured the amorphous form of the entrapped drug inside the formulation, which precludes the fear of leakage and instability of the formulation. Cell line studies conducted on U87MG Cell lines also suggested that the NLC of paclitaxel are more effective than those of pure PTX. In summary, PTXNLC seem to be a superior alternative carrier system for the formulation industry to obtain higher entrapment with excellent stability. [ABSTRACT FROM AUTHOR]

We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex-constrained multi-agent optimization problems. The algorithm enjoys performance guarantees on the last primal iterate, as opposed to those derived for ergodic means for standard DDSG algorithms. Our work improves the recently published convergence rate of O (log T / T) with decaying step-sizes to O (1 / T) with constant step-size on a metric that combines sub-optimality and constraint violation. We then numerically evaluate the algorithm on three grid optimization problems. Namely, these are tie-line scheduling in multi-area power systems, coordination of distributed energy resources in radial distribution networks, and joint dispatch of transmission and distribution assets. The DDSG algorithm applies to each problem with various relaxations and linearizations of the power flow equations. The numerical experiments illustrate various properties of the DDSG algorithm–comparison with standard DDSG, impact of the number of agents, and why Nesterov-style acceleration can fail in DDSG settings. [ABSTRACT FROM AUTHOR]

The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability. This paper introduces an advanced online optimization technique for decomposing and identifying power quality disturbances (PQDs). Leveraging an improved variational mode decomposition method and an optimized support vector machine algorithm, the framework enables the detection of multiple PQDs even with minimal training data. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed constrained optimization in a multi-agent system where some agents may deviate from the prescribed update rules due to failures or malicious adversarial attacks. The objective is to minimize the collective cost of the unattacked agents while respecting the constraint limitations. To tackle this, we propose a resilient distributed projected gradient descent algorithm for online optimization that achieves sublinear individual regret, defined as the difference between the online and offline solutions. Additionally, we extend the cost function from convex combinations to more general distributed optimization scenarios. The proposed algorithm demonstrates resilience under adversarial conditions, allowing it to handle an unknown number of adversarial nodes while maintaining performance. Compared to existing methods, this approach offers a robust solution to adversarial attacks in constrained distributed optimization problems. [ABSTRACT FROM AUTHOR]

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical. [ABSTRACT FROM AUTHOR]

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system's topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations. [ABSTRACT FROM AUTHOR]

The snow-shaped distribution network is developed and upgraded from the traditional single-loop network and double-loop network structure. It is composed of three or four groups of single-loop networks in pairs that form a feeder cluster in the form of a ring network. Based on this topology, the coordinated optimization operation problem is studied. First, the game elements are analyzed, and then a cooperative game-based, coordinated optimization operation model is established by taking the minimum network loss and voltage deviation of the system as the utility function. Then, the alternating direction multiplier method (ADMM) is employed in Cplex solver to solve the established optimization model. Finally, a numerical case is given to verify the effectiveness of the proposed model and method. The results show that, by using the complementary difference relationship of source–charge characteristics between different regions in the snow-shaped distribution networks, the cooperative game model can realize the energy interconnection among different regions to improve the operation level. [ABSTRACT FROM AUTHOR]

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system. [ABSTRACT FROM AUTHOR]

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance (d) and Residual Energy ( R egy ). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency. [ABSTRACT FROM AUTHOR]

The airline decision-making process is intended to solve complex operations planning problems sequentially. As computational power increases so do integrated approaches to solving major airline optimisation problems. In general, network carriers encounter more complex integrated problems of fleet planning, aircraft routing and increasing connectivity in the airline network. Bank optimisation problem addresses creating competitive advantage for airline network carriers, by providing better connectivity to passengers via improving their flight schedules in the airline bank structure. The aim of this study is to determine the optimal fleet to cover all flights in a flight schedule which is designed to improve connectivity in the network. The objective function takes account of the overall fleet allocation profitability on the flight schedule, the total worth of passenger connections, route profits, and the penalty cost for using excess slot. We propose a mixed integer linear programming model for the integrated bank optimisation-fleet assignment problem which can be defined as a specific flight scheduling model. To test the model, we have utilised a dataset from a Turkish carrier. The results from the integrated model show that small changes in the bank structure create more efficient schedule and fleet assignment could increase profitability at the same time. The findings imply that network and schedule planners are able to solve two major problems, simultaneously. Advances in the optimisation techniques will increase the application of the integrated models in the other parts of airline planning world, such as operations, crew planning, revenue management. [ABSTRACT FROM AUTHOR]

Mathematical modeling of combined-cycle units has produced a variety of valuable results, primarily significant advances in their energy efficiency and education of power plant operators. These positive results motivate the need for more complex modeling and simulation techniques with the goal of greater accuracy and accessibility. This study presents a simplified and enhanced version of a gas turbine model for dynamic performance simulation. The theoretical improvements have been proved through modeling philosophy and simulation accuracy attained by metaheuristic optimization techniques. The model has been constructed using thermodynamic and mathematical concepts, with parameter optimization and calibration performed using metaheuristic optimizers. The suggested model's resilience has been demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a numerical approach to solve the estimator-based filtering problem of a linear system with multiple and distributed delays. We consider the problem of finding a filter or estimator that suppresses the $ \mathcal {H}_2 $ H 2 filtering cost on the transfer matrix from a given noise input to the filtering error. By solving an optimisation problem associated with the $ \mathcal {H}_2 $ H 2 performance, the gain matrices of the filter are obtained. The effectiveness of the presented approach is illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Moving vehicle-based sensors (MVSs) have been increasingly used for real-time sensing and anomaly detection in various applications such as the detection of wildfires and oil spills. In this article, we propose data-driven sampling strategies using MVSs to quickly identify abrupt changes in an area of interest in real time considering their pathwise movement constraints. To tackle challenges due to variability and partial observability of online observations, we integrate instruments of statistical process control and mathematical optimization to monitor the global status of the area of interest and adaptively adjust paths of MVSs to sample from suspicious locations based on real-time data. We provide theoretical investigations and conduct simulations to validate the superior performance of the proposed methods. In a numerical study based on real-world wildfire data, we illustrate that our proposed strategies are able to detect wildfires much earlier than benchmark methods and can significantly reduce wildfire-related costs. [ABSTRACT FROM AUTHOR]

Operational water management is a critical global challenge, and decision making can be improved by using mathematical optimization. This paper provides an overview of optimization techniques, both exact and heuristic, used in water management. It focuses on the use of optimization techniques in the short term: operational planning in reservoir management, control of open channels, hydropower scheduling, and operation of polder drainage pumps. Principles of model predictive control, methods for optimization under forecast uncertainty, and approaches for conflict resolution are explained with the help of educational examples and practical cases. Challenges and research questions to be addressed in the future are presented as an outlook. This article is categorized under:Engineering Water > MethodsScience of Water > Water and Environmental ChangeWater and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

Purpose: This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search. Design/methodology/approach: A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed. Findings: The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency. Originality/value: This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA. [ABSTRACT FROM AUTHOR]

Objective: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images. Materials and methods: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥ 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Characteristic (FROC) curve, and the structural similarity (SSIM) were compared as metrics for diagnostic and visual quality, respectively. DLDetect was validated with a reader concordance analysis. Statistical analysis included Wilcoxon, permutation, and Cohen's kappa tests for visual quality, diagnostic performance, and reader concordance. Results: DLRecon improved visual quality at 4- and 8-fold (R4, R8) subsampling rates, with SSIM (range: −1 to 1) improved to 0.78 ± 0.02 (p < 0.001) and 0.67 ± 0.03 (p < 0.001) from 0.68 ± 0.03 and 0.51 ± 0.03, respectively. However, diagnostic performance at R4 showed a pAUC FROC of 1.33 (CI 1.28–1.39) for DL and 1.29 (CI 1.23–1.35) for naive reconstructions, both significantly lower than fully sampled pAUC of 1.58 (DL: p = 0.024, naïve: p = 0.02). Similar trends were noted for R8. Conclusion: DL reconstruction produces visually appealing images but may reduce diagnostic accuracy. Incorporating diagnostic AI into the assessment framework offers a clinically relevant metric essential for adopting reconstruction models into clinical practice. Clinical relevance statement: In clinical settings, caution is warranted when using DL reconstruction for MRI scans. While it recovered visual quality, it failed to match the prostate cancer detection rates observed in scans not subjected to acceleration and DL reconstruction. Key Points: Increasing demand for prostate MRI requires ongoing optimisation of MR techniques to maximise efficiency while maintaining accuracy. Deep learning reconstruction models may reduce diagnostic performance at 4- and 8-fold subsampling rates. Automated quantifiable metrics focused on diagnostic quality are recommended when evaluating DL reconstruction methods in MRI. [ABSTRACT FROM AUTHOR]

The propulsion system of mine boring machines often experiences severe vibration during mining. To suppress the vibration of the main beam system of roadheader and prevent the excessive fatigue of propulsion system, a magnetic nonlinear energy sink (MNES) is designed to suppress vibration. In this paper, firstly, the structure and design principle of MNES are introduced, and the dynamic equations of the main beam-MNES system are established. On this basis, the genetic algorithm is applied to optimize the vibration suppression of MNES, and the optimal parameters are determined. In results, the accumulative energy dissipation rate of optimal MNES in transient vibration can reach 90.2% and the vibration reduction effect of the optimal MNES in steady-state vibration is about 82.2%. [ABSTRACT FROM AUTHOR]

In this study, a grey wolf optimizer is proposed to find the optimum stacking sequence to maximize the fundamental frequency of shells. The vibration analysis of the shells is done using first-order shear deformation theory and a nine-node isoparametric element with five degrees of freedom per node. A mass lumping scheme with rotary inertia is considered in the finite element formulation to include the rotary inertia effect. The design variable considered is the fiber angle orientation of the shells. The present approaches are compared with previously published results using layerwise optimization theory, the Ritz method, and classical lamination theory. A comparison study is also performed, comparing the GWO-based finite element model against six different metaheuristic approaches, and it demonstrates that the Grey Wolf Optimizer consistently outperforms these competing algorithms. The optimum fiber angle orientation of shells carrying distributed mass for various boundary conditions and shell types is analyzed for the first time, to the best of the author's knowledge. This novel discovery will benefit future studies. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) are moderately defeating more roads and replacing pollutants of classical vehicles. Due to rising EV, it is imperative to provide charging stations. However, unscheduled EVs are left because of the unavailability of adequate energy or charging slots. This paper develops an optimisation aware technique for charge scheduling in EV. The first step is the simulation of EV in the Vehicular Ad-hoc Network (VANET) model. Here, the discovery of the changing request from EVs and accessible charging stations is performed. Then, the charge scheduling algorithm is called for scheduling the EV, wherein the charge scheduling algorithm is newly modelled using Jaya-based Multi-Verse Optimizer (JMVO). The proposed JMVO is devised by combining Multi-Verse Optimiser (MVO) and Jaya optimisation algorithm. Here, a multiobjective fitness function is newly devised with certain parameters, including charging cost, user preference, remaining power and distance parameter. As per the scheduling method, the EVs are allocated to the charging station. Then, the EV charging scheduling model attributes are updated to expose the method's efficiency. The proposed JMVO outperformed with the smallest charging cost of 62.721, smallest fitness of 0.010, highest power of 2.605J and highest user convenience of 0.769. durgamahato379@gmail.com [ABSTRACT FROM AUTHOR]

The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search process and gradually improve performance. A comparative analysis is presented exploring supervised learning performance and interpretability of AUTO models. Results indicate AUTO outperforms genetic programming and rivals multilayer perceptrons. AUTO automatically builds models to closely match datasets without the limited search space of traditional supervised learning. With enough time and computational resources, AUTO can generate any function. [ABSTRACT FROM AUTHOR]

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model's performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K. HIGHLIGHTS: A framework for optimizing the land-use dataset construction process is proposed. Filtering and pre-labeling improved the quality and efficiency of data labeling. The performance of land-use classification model is enhanced by dataset optimization. Preconceived results have a subjective impact on the data labelers. The first study to introduce DCAI for land-use classification is launched. [ABSTRACT FROM AUTHOR]