Solution Method Research Articles

Engineering of Pt@Ni,N-doped graphene electrocatalyst based on recycled water bottles waste as an efficient cathode material for PEM fuel cells

Fuel cells (FCs) represent one of the most promising clean power sources. Their cost represents a considerable challenge to their widespread commercialization because of their sensitivity and the expensive cost of the employed platinum electrocatalysts. Accordingly, one feature of a fuel cell device that many interested parties are looking for is the engineering of catalysts with a low Pt content. Herewith, Pt supported on nickel loaded nitrogen-doped graphene (Pt@Ni,N-G) was synthesized through a simple pyrolysis process of mineral water bottles waste with urea and nickel metal at 800 °C followed by loading Pt in different proportions via a simple reduction method in aqueous solution at room temperature. The physicochemical and electrochemical properties of a promising Pt@Ni,N-G cathode electrocatalyst for polymer electrolyte membrane fuel cell (PEMFC) were characterized and evaluated using a number of specialized techniques. The results showed that the high Pt@Ni,N-G content enhanced the catalytic activity of the oxygen reduction reaction (ORR), with a specific activity of 3.13 mA cm−2 and a mass activity of 12.35 mA μg−1Pt at −0.1 V. A maximum power density of 87.8 mW cm−2 was achieved which is closest to that of commercial Pt/C. Designing Pt@Ni,N-G electrocatalysts by recycling waste bottles is not only expected to reduce the cost of fuel cells by ~30 % to raise the industry standard but also protect the environment and humanity from harmful pollutants.

Vapour pressure deficit affects crop water productivity, yield, and quality in tomatoes

A high atmospheric vapour pressure deficit (VPD) hinders calcium absorption in tomatoes (Solanum lycopersicum L.), which severely reduces tomato yield, crop water productivity (WPcrop), and quality. Although reducing the VPD is effective for increasing tomato yield and water productivity, regulating the VPD is costly. Therefore, this study aimed to explore the effects of VPD regulation (high VPD=2.22 kPa, low VPD=0.95 kPa) during different growth stages on tomato WPcrop, yield, and quality to identify a VPD management method that saves costs and enhances the above parameters. The results showed that the yield was significantly positively correlated with fruit calcium absorption. Decreasing the VPD during the fruit expansion stage and increasing it during the flowering stage significantly increased calcium accumulation in the fruits, thereby improving tomato WPcrop, yield, and quality. Additionally, a high VPD during the flowering stage increased stomatal conductance and mesophyll conductance, thereby increasing intercellular and chloroplast CO2 concentrations and enhancing plant photosynthetic capacity, and reduced the stem water potential, leaf water potential, and leaf relative water content, thereby increasing the water potential difference, leaf hydraulic conductance, and calcium absorption. By integrating game theory and the technique for order of preference by similarity to ideal solution method, a comprehensive analysis of tomato appearance, nutrients, and flavour quality revealed that growing plants under a high VPD during the seedling, flowering, and fruit ripening stages and a low VPD during the fruit expansion stage not only enhanced the comprehensive tomato quality value but also maximally reduced the humidification cost. Conclusively, controlling the environmental VPD at 2.22 kPa during the seedling, flowering, and fruit ripening stages and at 0.95 kPa during the fruit expansion stage can effectively increase tomato WPcrop, yield, and quality. This study provides a theoretical basis for the environmental regulation of high-quality, efficient, and water-conserving cultivation of greenhouse tomatoes.

Grid-aware Stochastic Game modeling of residential electric flexibility under incentives

We consider a community of households owning electrical assets that can be operated as flexible loads, such as an electric vehicle, a heat pump with thermal storage and a stationary battery. Automated agents are tasked with the management of flexibility under a variable time-of-use tariff with the goal of maximizing final user utility. A population of homogeneous automated agents with similar interests, managing flexibilities of the same nature, runs the risk of stressing the grid through excessively synchronized actions, and this interaction among the agents has to be taken into account. We model this environment as a Stochastic Game, developing a formulation of the state and action spaces and obtaining the transition probability matrix. The probabilities involved are fitted on real or realistic synthetic data. Leveraging the particular structure of the problem, we propose a two-step solution method similar to policy iteration, that proves capable of producing sensible solutions. Finally, we provide an example of flexibility steering through the modulation of the time-of-use tariff, investigating the effectiveness of the incentive depending on its entity and the model parameters that characterize punishment for overcoordination and user satisfaction, and we illustrate how the tool could be employed in a real Energy Community.

An integrated cost based approach for warehouse performance evaluation: A new multiphase model

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.

Modeling and simulation of three-phase IGBT full-bridge inverter circuit based on FPGA

The field of motor drive makes extensive use of electronic power modeling and simulation of three-phase IGBT full-bridge inverter circuits. The accuracy and computational efficiency of these models have a direct impact on the dependability of the motor control system. The majority of earlier research focused solely on static processes of turning on and off in IGBTs, disregarding the transient proesses that occur when three-phase IGBT full-bridge inverter circuits are switched at high frequencies. This has an impact on the circuits' accuracy in real-time simulation. Therefore, this paper proposes and builds a field-programmable logic gate array (FPGA)-based steady-state and transient dual-phase three-phase IGBT full-bridge inverter circuit model for the static and transient characteristics of the insulated gate bipolar transistor (IGBT) element in the circuit. Depending on whether or not the switching states of the six IGBTs in the three-phase IGBT full-bridge inverter circuit are altered, the simulation process is split into steady state and transient phases. In the steady state phase with large step size, the circuit is discretized using the binary L/C approach. In the transient phase, the transient process is divided into several small-step-long time domains. Real-time simulation waveforms are generated by interleaving and combining the multistage fitting method's solution of the circuit's transient waveforms at tiny step lengths with the steady state phase. Finally, in order to demonstrate the accuracy of the circuit model in this work, the simulation results of the two-stage three-phase IGBT full-bridge inverter circuit model based on FPGA are compared with those of the conventional ideal model for waveform comparison and data analysis.

Exploring the evolution of machine scheduling through a computational approach

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling.

A new approach to solve time-fractional Navier–Stokes equation

This article presents a new technique for the solution of Time Fractional Navier–Stokes equation. The approach is a combination of Adomian decomposition method (ADM) with Kamal integral transform (KIT). The proposed method is implemented on time-fractional Navier–Stokes equation (TFNSEs) to yield the analytical solution. Illustrative examples in TFNSEs are discussed to validate the applicability of the technique. The graphical visualization of the solutions is also presented. Further, the obtained results are compared with the existing solution methods.

Elastic deformation analysis of rotational wood-dowel welding joint system based on the variational method

Abstract This study aimed to investigate the elastic deformation of rotational wood-dowel welding joints by using the variational method, as there was no efficient tool to access the deformation behavior of welding joints perpendicular to axis orientation so far. Based on the variational method, the displacement control equation of axially-loaded dowel-welded wood joints was derived. By incorporating the boundary conditions and using an iterative approach, the parameters required in the control equation were determined. The analytical expression ϕ(r) for the deformation function of the wood substrate under elastic conditions was proposed, thereby determining the displacement and axial force of the single wood-dowel. The elasticity modulus was the primary influence parameter on the function ϕ(r). However, the welding depth had a more critical impact, and reasonable combination of parameters could lead to a better interaction between the wood dowel and substrate. The comparison between calculated results and test trials collected from the previous studies indicated that the elastic solution method could accurately estimate the pull-out ultimate bearing capacity and deformation characteristics of the welding joints. This work can provide unique perspective to understand the technology of rotational wood-dowel welding joints, and a good reference suggestion for the manufacture of multi-dowel welding joints.

Study on the Solutions of Impulsive Integrodifferential Equations of Mixed Type Based on Infectious Disease Dynamical Systems

Since ancient times, infectious diseases have been a major source of harm to human health. Therefore, scientists have established many mathematical models in the history of fighting infectious diseases to study the law of infection and then analyzed the practicability and effectiveness of various prevention and control measures, providing a scientific basis for human prevention and research of infectious diseases. However, due to the great differences in the transmission mechanisms and modes of many diseases, there are many kinds of infectious disease dynamic models, which make the research more and more difficult. With the continuous progress of infectious disease research technology, people have adopted more ways to prevent and interfere with the derivation and spread of infectious disease, which will make the state of infectious disease system change in an instant. The mutation of this state can be described more scientifically and reasonably by the mathematical impulse dynamic system, which makes the research more practical. Based on this, a time-delay differential system model of infectious disease under impulse effect was established by means of impulse differential equation theory. A class of periodic boundary value problems for impulsive integrodifferential equations of mixed type with integral boundary conditions was studied. The existence of periodic solutions of these equations was obtained by using the comparison theorem, upper and lower solution methods, and the monotone iteration technique. Finally, combined with the practical application, the established time-delay differential system model was applied to the prediction of the stability and persistence of the infectious disease dynamic system, and the correctness of the conclusion was further verified. This study provides some reference for the prevention and treatment of infectious diseases.

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work.

A Stable High-Order Perturbation of Surfaces/Asymptotic Waveform Evaluation Method for the Numerical Solution of Grating Scattering Problems

A Stable High-Order Perturbation of Surfaces/Asymptotic Waveform Evaluation Method for the Numerical Solution of Grating Scattering Problems

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.

Three-way decision TOPSIS for food business location under a benefit-opportunity-cost-risk platform

This work holistically evaluates the factors that can aid decision makers in finding the optimal location for food businesses in developing economies. It integrates the analytic hierarchy process (AHP) in determining criteria weights (i.e. benefit, opportunity, cost, risk) and the newly introduced three-way decision – Technique for Order Preference by Similarity to Ideal Solution (3WD-TOPSIS) method in identifying the priority factors. Applying the AHP yields the assignment of more priorities to benefits and opportunities rather than costs and risks, reflecting the benefit – or opportunity-oriented attitudes of food businesses. Meanwhile, the implementation of 3WD-TOPSIS results in the identification of government regulations and restrictions, proximity to consumers, parking capacity, supply chain strategy, and socio-economic status as the most crucial location decision factors. Findings from the comparative analyses show a high agreement between the results and those of other comparable methods. Managerial insights from these findings are outlined in this work.

Fast and precise single-frame phase demodulation interferometry

To achieve real-time phase detection, this paper presents a fast and precise spatial carrier phase-shifting interferometry based on the dynamic mode decomposition strategy. The algorithm initially produces a series of phase-shifted sub-interferograms with the aid of a spatial carrier interferogram. Subsequently, the measured phases are derived with great accuracy from these sub-interferograms through the use of the dynamic mode decomposition strategy, an outstanding non-iterative algorithm. Numerical simulation and experimental comparison show that this method is an efficient and accurate single-frame phase demodulation algorithm. The paper also analyzes the performance of the proposed method based on influencing factors such as random noise level, carrier frequency size, and carrier frequency direction. The results indicate that this method is a fast and accurate phase solution method, offering another effective solution for dynamic real-time phase measurement.

Solution-Processed One-Dimensional Photonic Crystals Based on Hollow Silica Exhibiting High Refractive Index Contrast.

Poor interfacial quality and low refractive index contrast (Δn) are critical challenges for the development of high-performance one-dimensional photonic crystals (1DPhCs) via solution methods that impede their optical efficiency. Herein, we introduce an innovative approach by hybridizing hollow SiO2 with poly(vinyl alcohol), referred to as PHS, followed by alternate assembly with TiO2 via spin-coating, achieving a 1DPhC with Δn = 0.76 at the wavelength of 550 nm. This method circumvents the need for high-temperature treatment and complex curing conditions, resulting in a 1DPhC with superior interfacial and optical characteristics. By adjusting the thickness of the PHS layers, we can finely tune the reflectance spectrum, attaining over 99% reflectance at the photonic band gap. Furthermore, 1DPhC demonstrates excellent adhesion to polycarbonate substrates and retains its optimal optical performance even after rigorous environmental testing, including hygrothermal cycles, exposure to hot water, friction, and solvent sonication. This research paves the way for the facile fabrication of high-performance 1DPhCs under mild conditions, offering new perspectives for photonic material processing.

A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

AbstractTo achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day‐ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two‐stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst‐case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration.

Modeling the Decision and Coordination Mechanism of Power Battery Closed-Loop Supply Chain Using Markov Decision Processes

Abstract: With the rapid growth of the new energy vehicle market, efficient management of the closed-loop supply chain of power batteries has become an important issue. Effective closed-loop supply chain management is very critical, which is related to the efficient utilization of resources, environmental responsibility, and the realization of economic benefits. In this paper, the Markov Decision Process (MDP) is used to model the decision-making and coordination mechanism of the closed-loop supply chain of power batteries in order to cope with the challenges in the management process, such as cost, quality, and technological progress. By constructing the MDP model for different supply chain participants, this paper investigates the optimization strategy of the supply chain and applies two solution methods: dynamic programming and reinforcement learning. The case study results show that the model can effectively identify optimized supply chain decisions, improve the overall efficiency of the supply chain, and coordinate the interests among parties. The contribution of this study is to provide a new modeling framework for power battery recycling and to demonstrate the practicality and effectiveness of the method with empirical data. This study demonstrates that the Markov decision-making process can be a powerful tool for closed-loop supply chain management, promotes a deeper understanding of the complex decision-making environment of the supply chain, and provides a new solution path for decision-making and coordination in the supply chain.

E-Commerce Promotional Products Selection Using SWARA and TOPSIS

This research aims to select products that will be used for promotion on e-commerce platforms. The increasing use of e-commerce has led to a high level of competition in the e-commerce field. The company strives to maintain the quality of its services to increase customer satisfaction, one of which is by providing regular promotions. The process of selecting promotional products is a routine activity carried out every week. However, the current promotional product selection process is not effective enough, and there are no criteria to use as a reference for selection. This research was conducted on two e-commerce companies actively operating in Indonesia. The research began with a literature study and expert survey to select important criteria in selecting promotional products. Weighting of important criteria is carried out using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the best products to promote. The results showed that products from Soundcore, Lenovo, and Xiaomi were the best products with preference values of 0.83, 0.65, and 0.60 respectively.

LOCAL TIME FRACTIONAL REDUCED DIFFERENTIAL TRANSFORM METHOD FOR SOLVING LOCAL TIME FRACTIONAL TELEGRAPH EQUATIONS

In this paper, we seek to find solutions of the local time fractional Telegraph equation (LTFTE) by employing the local time fractional reduced differential transform method (LTFRDTM). This method produces a numerical approximate solution having the form of an infinite series that converges to a closed form solution in many cases. We apply LTFRDTM on four different LTFTEs to examine the efficiency of the proposed method. The yielded results established the effectiveness of LTFRDTM as a reliable and solid approach for obtaining solutions of LTFTEs. The solutions coincided with the exact solution in the ordinary case when [Formula: see text]. It also required minimal amount of computational work and saved a lot of time.

A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems

AbstractSpatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their computational intensity. In addition, these problems often have multiple, instead of singular, optimal solutions that have the same or similar objective function values but exhibit different spatial configurations. Existing solution methods often aim to find single solutions to these problems. In this paper, we discuss a new heuristic method that can be used to find a set of unique optimal or near-optimal solutions to spatial aggregation problems. The algorithm consists of two phases. A multistart phase first generates a pool of random solutions to a problem. The size of the pool is specified by the user and contains the number of solutions desired to be found. Each random solution is then improved using an efficient algorithm called give-and-take. The second phase uses a recombination algorithm to create new solutions based on solutions randomly selected from the pool. The worst solution in the pool will be replaced by the new solution if the latter is better and does not exist in the pool. We test this multistart and recombination algorithm (MSRA) using a variety of problems with different sizes and the results suggest the effectiveness of the algorithm in finding multiple unique optimal or near-optimal solutions.