Search

One of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy‐based algorithms in last studies. Greedy‐based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta‐heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA‐based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy‐based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets. [ABSTRACT FROM AUTHOR]

Tricubic interpolation, originally introduced by Lekien and Marsden (Int J Numer Methods Eng. 2005; 63(3): 455–471), has been a cornerstone in the field of interpolation, providing C1$$ {C}^1 $$ continuous interpolations within three‐dimensional spaces. However, real‐world applications often demand higher levels of smoothness within M$$ M $$‐dimensional spaces. This paper introduces LocalPoly interpolation, a novel generalization of tricubic interpolation that extends to Cn$$ {C}^n $$ continuity and M$$ M $$ dimensions. A key property is the use of solely local data for interpolation, allowing for on‐demand computation of interpolation polynomials, which is particularly advantageous in scenarios where a minor subset of the space is of interest. We rigorously prove the Cn$$ {C}^n $$ continuity achieved by the LocalPoly interpolation method; the proof features a numerically exact method for computing polynomial coefficients. The enhanced continuity is of great relevance in optimization algorithms, where efficient convergence often relies on the availability of C2$$ {C}^2 $$ information. The paper explores the use of LocalPoly interpolation applied to a squared distance field in ℝ3$$ {\mathbb{R}}^3 $$, offering insights into computational efficiency and practical implications. It also discusses future research directions to address the method's limitations in terms of dimensionality, making it a valuable addition to the toolbox of interpolation methods for various scientific and engineering applications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy penetrated in power systems further increases, the optimal operation faces a large obstacle to satisfying various conflict interests simultaneously. This paper first formulates a many‐objective optimal operation problem of the new power system considering a comprehensive set of economic, environmental, and reliable objectives. To figure out this problem efficiently, this paper proposes a probability confidence correlation analysis method (PCCA) utilizing a t‐distributed stochastic neighbor embedding (T‐SNE) standard to greatly preserve the distribution information and separability of the objectives. Consequently, the objectives can be aggregated and the objective dimension is reduced based on the important sorting and correlation of the objectives. Based on the outcome of the T‐SNE, the adopted optimal operation problem with low dimensional objectives is solved by a multiple producer group search optimizer (GSOMP) with less computation burden, to obtain the Pareto‐optimal solution set. Simulation studies are conducted on IEEE 30‐bus, 39‐bus and 57‐bus systems to investigate the performance of the proposed PCCA in terms of efficiency and accuracy, making comparisons of the existing many‐objective optimization algorithms. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

An adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models. [ABSTRACT FROM AUTHOR]

In the realm of evolutionary game theory, the majority of scenarios involve players with incomplete knowledge, specially regarding their opponents' actions and payoffs compounded by the ever‐shifting landscape of players' interactions. These dynamics present formidable challenges in both the analysis and optimization of game evolution. To address this, a novel model named the networked evolutionary game (NEG) is proposed based on incomplete information with switched topologies. This model captures situations where players possess limited insight into their opponents' benefits, yet make decisions based on their own payoffs while adapting to different networks and new players. To bridge the gap between incomplete and complete information games, R. Selten's transformation method is leveraged, a renowned approach that converts an incomplete information game into an interim agent game, thereby establishing the equivalence of pure Nash equilibria (NE) in both scenarios. Employing the semi‐tensor product (STP) of matrices, a powerful tool in logistic system, the evolution of the model is articulated through algebraic relationships. This enables to unravel the patterns of game evolution and identify the corresponding pure Nash equilibria. By introducing control players, strategically positioned within the game, optimized control is facilitated over the evolutionary trajectory, ultimately leading to convergence towards an optimal outcome. Finally, these concepts are illustrated with a practical example within the paper. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

In the paper, the maiden application of Tilted Integral Fractional Derivative with Filter plus Fractional Derivative controller (TIDμ1NDμ2$TI{D^{{\mu _1}}}N{D^{{\mu _2}}}$), called TIFDNFD controller, is proposed to achieve load frequency control (LFC) of two area‐multi source power system. In the study, both areas include thermal power, hydropower, and gas turbine power systems. In this study, generation rate constraint and governor dead‐band are integrated into the power system to evaluate the performance of the controller in a non‐linear environment. The wild horse optimizer (WHO) is used for controller parameter tuning in the LFC problem for the first time. The performance of the proposed controller is demonstrated by comparing it with the recently published studies including different controllers. In this study, to obtain more realistic results, solar photovoltaic (PV) power, wind power, and load demand with stochastic form have been added to the power system. In addition, the dynamic effect of the hydrogen storage system, consisting of an electrolyser and a fuel cell, is considered a grid supporter. The superiority of the WHO algorithm in optimization has been verified by comparing it with three optimization algorithms recently proposed. Finally, the resilience and robustness of the power system are evaluated in perturbed system parameters. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Air‐cored axial‐flux permanent‐magnet synchronous generators (AFPMSGs) are potential candidates for gearless direct‐coupled wind turbines (DCWTs) owing to providing high efficiency and power density. The design of a DCWT generator is so complicated since the generator cost, dimension, and weight affected by gear elimination. Therefore, it is essential to find an optimal AFPMSG design at rated conditions. In this paper, an accurate procedure for the optimal design of an air‐cored AFPMSG applicable for DCWTs is proposed. The genetic algorithm (GA) is used for multi‐objective design optimization to reach the optimal configuration as well as system dimension in order to decrease the weight, increase the power density and enhance the effectiveness of the generator. To validate the efficiency of the suggested optimization proceducer, a 30 kW AFPMSG has been considered as a case study. The results of optimization have been investigated by finite element analysis (FEA). A prototype generator is also fabricated, and the test results are offered and compared with the numerical study. The outcomes show that there exists an acceptable agreement between FEA and experimental outcomes with the error percentage about of 1.35%. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers' voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs' chargers. Here, a model for encouraging industrial loads to dynamically invest in implementing the conservation voltage reduction (CVR) strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to the fact that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. [ABSTRACT FROM AUTHOR]

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm "LWaOA," making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

Polypropylene is a useful and widely consumed thermoplastic with very good properties that can be mixed with a combination of rubber and nanoparticles in order to eliminate its shortcomings. In this paper, the simultaneous combination of both the toughening effect of styrene–butadiene rubber (SBR) and the reinforcing effect of silicon carbide (SiC) nanoparticles were investigated in polypropylene matrix. Mechanical properties (tensile and impact strength [TS and IS]) of compounds were analyzed using the central composite design approach in Design‐Expert software. With two factors for input variables of SBR and SiC weight percentages, and their five levels (5, 10, 15, 20, and 25) and (0, 1.5, 3, 4.5, and 6), respectively, the central composite design approach employs 11 experiments for creation of models and the analysis of variance investigations with two output responses of TS and IS. Statistical results revealed that SBR and SiC contents had a significant effect on TS, IS, and the microstructure of compounds, as confirmed by scanning electron microscopy and energy‐dispersive spectroscopy images. To maximize all mechanical properties simultaneously with desirability functions, TS and IS of the optimum sample were computed to be 21.98 MPa and 8.66 kJ/m2 in amounts of 13.45 and 2.80 weight percentage (wt%) of SBR and SiC, respectively. Highlights: By increasing silicon carbide (SiC) in polypropylene/styrene–butadiene rubber (SBR), tensile strength decreases after a peak due to agglomeration effect.By increasing SiC in polypropylene/SBR, impact strength decreases after a peak due to agglomeration effect.By increasing SiC, the rubber particles' dimensions decrease according to scanning electron microscopy analysis.Simultaneous maximum of tensile strength and impact strength are 21.98 MPa and 8.66 KJ/m2, respectively.Optimum value of SBR and SiC in above maximums are 13.45 and 2.8 wt%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a second‐order modifier adaptation optimization based on the transfer model, which aims to improve the optimization efficiency. Although the traditional modifier adaptation strategy adds a bias to the model to meet the necessary condition of optimality (NCO), approximating only to the first‐order may neglect some key higher‐order information. Making use of as much higher‐order information as possible is an effective way to improve the efficiency of optimization. For this issue, by introducing the second‐order information into the modifier adaptation method to compensate the mismatch of higher‐order information during the optimization, the compensation of the mismatch between model and plant can get accelerated, thus improving the optimization efficiency. To overcome the difficulty of insufficient data for new process modelling, a process transfer model is also used to fit the relationship between manipulated variables and final product quality. The simulation study of the cobalt oxalate synthesis process shows that this strategy has better optimization efficiency. [ABSTRACT FROM AUTHOR]

E‐glass fibers are widely preferred due to ease of processing and its low cost, which has substantial scope in the fields of electronics and electrical insulation applications. Because of its low strength and corrosion resistance, use of E‐glass fibers is limited in aerospace and automotive applications. There is a need for enhancing the properties of the composite to overcome such limitations. Therefore, an attempt is made to introduce multi‐walled carbon nanotubes (MWCNT) as fillers into E‐glass fibers to meet the industry needs. In the current study, woven glass fiber of 5 layers and multi‐walled nano carbon fillers of 2, 4 and 6 by wt%, LY556 epoxy resin, and HY951 hardener were used to prepare 4 different type of composites along with the neat epoxy glass fiber reinforced polymer composites (GFRP). The hand‐layup route was used in the composite preparation due to its low cost, technological feasibility, and simple process setup. The developed samples were characterized for mechanical properties via tensile, flexural and impact tests. Tribological characteristics were performed by air jet erosion test. Chauvenet's criterion is applied for identifying the outliers (if any) from the data of repeated test properties. Taguchi's L9OA (orthogonal array) is selected for obtaining optimal hybrid composite, which yield better mechanical properties. Empirical relations are developed for the material properties in terms of process variables. The sample (4 wt% MWCNT) exhibited enhancement of 17.27% in tensile strength, 6% of impact strength and 7.3% of flexural strength when compared with neat epoxy GFRP. This hybrid composite is considered for thermal aging and observed at 60°C, 8% increase in tensile, 7% increase of impact and 15% in flexural strength due to the precipitation on carbon nano tubes along the gain boundaries. The present study recommends 4% MWCNT fillers in developing hybrid glass epoxy polymer composites for use in aerospace, automotive and civil construction industries due to economic and technological feasibility. Highlights: Utilize low‐cost E‐glass fibers in electronics and electrical insulation applications.Improve composite properties for aerospace and automotive industries.Develop hybrid glass epoxy composites with 2 to 6 wt% MWCNT fillers.Examine wear characteristics under air jet erosion and study the impact of thermal aging on mechanical properties.Apply Chauvenet's criterion for outlier identification in measured properties datasets. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered. [ABSTRACT FROM AUTHOR]

Power flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology‐based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability. [ABSTRACT FROM AUTHOR]

Injera is a type of flatbread that is fermented, naturally leavened, and native to Ethiopia. However, injera quality can vary depending on the processing steps used, even if the same variety of teff is used. This research was conducted to optimize the prebaking processing and baking conditions to produce better quality teff injera suitable for industrial and export purposes. Four experiments were conducted to optimize the injera‐making process. The first two phases focused on optimizing the prebaking processing conditions (fermentation temperature and time, absit mixing ratio, absit cooking time, and secondary fermentation time). The best physicochemical qualities were obtained at a primary fermentation temperature of 25°C for 64 h, an 8% mixing ratio of absit with 10 min of cooking, and a secondary fermentation time of 4 h. In the third phase, baking temperature (195 ± 5, 215 ± 5, 235 ± 5, and 255 ± 5°C) and time (1, 2, and 3 min) were evaluated. The results showed that the best response variables were obtained at a temperature of 255 ± 5°C for 2 min or 235 ± 5°C for 3 min. Finally, the optimized conditions were validated on five different varieties [DZ‐Cr‐387, DZ‐Cr‐2124, white (T‐BT), white (T‐GK), and sergegna teff (T‐E)] of teff grain. The results indicated that the optimized conditions could produce better quality and consistent teff injera on a large commercial scale, which would suit both local and export markets. [ABSTRACT FROM AUTHOR]

Similarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods. [ABSTRACT FROM AUTHOR]

The aim of this study was to produce spicy red pepper paste (Awaze) by addition of various sources of antioxidant, mineral, and fiber and optimize better processing methods of the paste. For this purpose, D‐optimal mixture design was used, Awaze considering color value (a*), antioxidant content, mineral content, fiber content, chewiness, and viscosity of the paste as the main parameters. Various properties of the optimized formulation were evaluated. The optimal formulation contained 65.66% red pepper, 10% garlic, 19.08% red onion, and 5.25% ginger. In the optimized formulation, the redness value (a*) increased by more than 3.12 times compared to the control with the average antioxidant activity (44.6%). The optimal formulation had a higher fiber content, chewiness, and viscosity value compared to control which is probably related to the ingredient proportion difference. Due to the higher nutritional and processing quality obtained, this formulation can be suggested for commercial and household producers as a guide to manufacture Awaze. The results obtained indicate that it is possible to production of Awaze with high nutritional value and improved processing quality by utilizing a blend of red pepper, garlic, red onion, and ginger. Therefore, this formulation stands as a viable recommendation for both commercial enterprises and household producers due to its demonstrated ability to yield Awaze with enhanced nutritional content and superior processing quality. [ABSTRACT FROM AUTHOR]

Determining the harmonic contribution of each harmonic source is conducive to improving the harmonic management of the power system. The main problem with harmonic contribution calculation is determining the equivalent system harmonic impedance. The previous harmonic impedance calculation method can only calculate the impedance value when the harmonic fluctuation is significant, and the calculation fails when the harmonic fluctuation is small. Therefore, a new method called difference recurrence estimation is proposed to construct the objective function from impedance parameters. The advantages of the proposed method are low data requirement, high estimation accuracy, and excellent tracking performance. An improved genetic algorithm (GA) is presented by integrating an adaptive crossover operator and population intervention into the standard GA to improve the global searching impedance parameter ability. Based on these parameters, harmonic impedance and harmonic contribution are calculated accurately. The superiority of the proposed model is verified through simulation and field cases. [ABSTRACT FROM AUTHOR]

In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network. Plain Language Summary: This study introduces a method of optimizing the sparse locations where actual tsunami gauges should be installed to obtain information on tsunami wave heights at any given point. By optimizing the locations of the observation points, it is possible to extend observations recorded at only a small number of points to obtain a good approximation to data at other points where the tsunami was not directly observed. First, numerical simulations are performed based on assumed earthquake and tsunami scenarios to generate synthetic time series data of tsunami waves. Then, by applying proper orthogonal decomposition to the obtained synthetic data, the characteristics of the tsunami wave height distribution are extracted. Finally, these characteristics are used to perform optimization by sequentially selecting the best gauges from among a set of candidate points to reconstruct wave height information for the entire target area, thereby determining the placements of a limited number of gauges. In a numerical demonstration example simulating a Nankai Trough earthquake and tsunami, the placement of gauges off the coast of Shikoku is optimized, and it is shown that the wave heights at arbitrary points over the entire area can be reproduced using data from at least 21 optimally placed gauges. Key Points: An optimization method implementing a greedy algorithm is presented to design a cost‐effective observational network of tsunami gaugesHigh‐resolution information about tsunami wave height is constructed from sparse observations based on the concept of super‐resolutionThe optimization process considering the existing observation network locations identifies their sufficiency or deficiency [ABSTRACT FROM AUTHOR]

In this study, we numerically investigated the performance of a printed circuit heat exchanger (PCHE) featuring National Advisory Committee for Aeronautics (NACA) 0020 airfoil overlap fins integrated into its straight flow channels. Numerical simulations were performed by solving Navier–Stokes and energy equations to analyze liquid water at temperatures ranging from 30 to 90°C, which is possible for use with a geothermal heating system, for example. The optimization of the PCHE airfoil fin geometry is performed using nondominated sorting genetic algorithm II (NSGA‐II) with three design features: front, back, and overlap lengths. The effectiveness and pressure drop of the PCHE are evaluated as two objective functions. The present study shows that, when a Reynolds number falls between 1088 and 5000, the overlapping airfoils exhibit lower pressure drops and higher convective heat flux relative to the separate airfoils, and an increase in overlap length always decreases the pressure drop positively. It is also found that using overlapping airfoils of front length of 0.1 mm, back length of 0.119 mm, and overlap length of 0.203 mm leads to almost the same effectiveness and a 22% decrease in pressure drop (which is generally believed to preferably maintain the high heat transfer performance and minimize pressure drop, as a favorable design), compared to a base design of the airfoils with front and back lengths of 0.2 mm and an overlap length of 0.1 mm. [ABSTRACT FROM AUTHOR]

Inula helenium roots are consumed as natural flavor components and raw or cooked as food, and their extracts are rich in sesquiterpene lactones such as alantolactone (AL) and isoalantolactone (IAL), which have recently attracted great attention due to their pharmacological properties. The industrial utilization of these compounds requires the development of green, efficient, cost‐effective, and sustainable extraction protocols. Therefore, this study focused on the optimization of microwave‐assisted extraction (MAE) process variables using Face‐Centered Central Composite Design (FC‐CCD). Then, maceration was applied as a conventional technique, and these techniques were compared in terms of extraction efficiency, morphological changes, antimicrobial activities, carbon emissions, and energy consumption. As a result, optimal MAE conditions, i.e., EtOH: water ratio (X1) = 100:0, liquid/sample ratio (X2) = 30:1 mL/g, microwave power (X3) = 300 W, and irradiation time (X4) = 5 min, were obtained with AL and IAL yields of 54.99 ± 0.11 (mg/g) and 48.40 ± 0.19 (mg/g), respectively. The extract obtained by MAE had similar or better activity than positive controls in most cases and formed the largest inhibition zones against E. coli (29.5 ± 0.71 mm) and A. niger (34.75 ± 1.06 mm). Morphological changes of I. helenium roots after extraction were observed by scanning electron microscopy. Additionally, MAE was 43.4 times faster than maceration, resulting in 228.6 times less energy consumption and carbon emissions. Based on these findings, it is recommended to use MAE as an industrial green technique for the extraction of sesquiterpene lactones with potential applications in nutraceuticals and food products in terms of sustainable economy and environmental protection. [ABSTRACT FROM AUTHOR]

Radiation schemes are critical components of Earth system models that need to be both efficient and accurate. Despite the use of approximations such as 1D radiative transfer, radiation can account for a large share of the runtime of expensive climate simulations. Here we seek a new state‐of‐the‐art in speed and accuracy by combining code optimization with improved algorithms. To fully benefit from new spectrally reduced gas optics schemes, we restructure code to avoid short vectorized loops where possible by collapsing the spectral and vertical dimensions. Our main focus is the ecRad radiation scheme, where this requires batching of adjacent cloudy layers, trading some simplicity for improved vectorization and instruction‐level parallelism. When combined with common optimization techniques for serial code and porting widely used two‐stream kernels fully to single precision, we find that ecRad with the TripleClouds solver becomes 12 times faster than the operational radiation scheme in ECMWF's Integrated Forecast System (IFS) cycle 47r3, which uses a less accurate gas optics model (RRMTG) and a more noisy solver (McICA). After applying the spectral reduction and extensive optimizations to the more sophisticated SPARTACUS solver, we find that it's 2.5 times faster than IFS cy47r3 radiation, making cloud 3D radiative effects affordable to compute in large‐scale models. The code optimization itself gave a threefold speedup for both solvers. While SPARTACUS is still under development, preliminary experiments show slightly improved medium‐range forecasts of 2‐m temperature in the tropics, and in year‐long coupled atmosphere‐ocean simulations the 3D effects warm the surface substantially. Plain Language Summary: A crucial step in simulating weather and climate is calculating how atmospheric radiation (shortwave radiation from the sun and terrestrial longwave radiation) interacts with the Earth's atmosphere and surface. The complexity of the underlying physics has necessitated making approximations in how radiative transfer is treated, such as assuming that radiation can only enter or leave a cloud through its top or base, thereby ignoring 3D effects. Even so, radiative transfer has historically been one of the computationally most demanding steps in making weather and climate simulations. Here we show that a state‐of‐the‐art radiation code can be sped up threefold by using code optimization techniques that seek to maximize performance on modern processors. Combining this with a recent innovation that reduces the number of spectral computations required for accurate solutions, an order‐of‐magnitude increase in speed is obtained compared to the existing radiation scheme in a global weather model. Crucially, these improvements also make a radiation scheme that accounts for cloud 3D radiative effects fast enough to be used operationally. When included in global simulations, these 3D effects act to warm the lower atmosphere substantially. Key Points: Restructuring and serial code optimization is performed for the ECMWF radiation schemeWhen combined with spectrally reduced gas optics, a speed‐up of 12 is obtained compared to the operational schemeCloud 3D radiative effects become affordable to compute in dynamical models [ABSTRACT FROM AUTHOR]

Purpose: Dose management systems (DMS) have been introduced in radiological services to facilitate patient radiation dose management and optimization in medical imaging. The purpose of this study was to gather as much information as possible on the technical characteristics of DMS currently available, regarding features that may be considered essential for simply ensuring regulatory compliance or desirable to fully utilize the potential role of DMS in optimization of many aspects of radiological examinations. Methods: A technical survey was carried out and all DMS developers currently available (both commercial and open source) were contacted and were asked to participate. An extensive questionnaire was prepared and uploaded in the IAEA International Research Integration System (IRIS) online platform which was used for data collection process. Most of the questions (93%) required a "Yes/No" answer, to facilitate an objective analysis of the survey results. Some free text questions and comments' slots were also included, to allow participants to give additional information and clarifications where necessary. Depending on the answer, they were considered either as "Yes" or "No." Results: Given the way that the questions were posed, every positive response indicated that a feature was offered. Thus, the percentage of positive responses was used as a measure of adherence. The percentages of positive answers per section (and sub‐section) are presented in graphs and limitations of this type of analysis are discussed in detail. Conclusions: The results of this survey clearly exhibit that large differences exist between the various DMS developers. Consequently, potential end users of a DMS should carefully determine which of the features available are essential for their needs, prioritize desirable features, but also consider their infrastructure, the level of support required and the budget available before selecting a DMS. [ABSTRACT FROM AUTHOR]

Purpose: To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity‐modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)‐based optimization. Methods: This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard‐of‐care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose‐averaged LET (LETd) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LETd, LET‐weighted dose, and equivalent uniform dose between the different optimization approaches. Results: We found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans. Conclusion: We demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients. [ABSTRACT FROM AUTHOR]