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In order to study the cutting performance of the straight edge hob and reduce the cutting power consumption model of the straight edge hob, this paper takes the cutting power of the straight edge hob as the minimum goal, and establishes the mathematical model of the optimization design of the straight edge hob based on the composite optimization method. The mathematical model is solved by MATLAB software. At the same time,the mowing characteristics of a roller blade were studied by investigating the relationship between the hob and the coordination of variables such as rotational speed and roller diameter with the mowing parameters. The parameter analysis of straight edge hob before and after structural parameter optimization is generated, and a design method is proposed based on this. After defining the objective function and constraint conditions, the influence of structural parameters on the power consumption and efficiency of the hob was determined by optimising the complex method; this could significantly adjust the hob parameters to lower its power consumption. The energy consumption of the optimized design is reduced by 11.1 % compared with the original scheme, and the optimization effect is remarkable. The results show that the best working parameters of the hob are cutting speed of 1000 r/min, sliding cutting angle and grinding edge angle of 25~30°. Moreover, practical tests demonstrated the feasibility of using the proposed method to design the straight edge hob to improve mowing performance and hob stability. This study can provide parameter foundation and an optimization method for lowering chopping power consumption of the roller mower blade. [ABSTRACT FROM AUTHOR]

The numerical algorithm by Cristofari et al. modifies the augmented Lagrangian method ALGENCAN proposed by Andreani and his collaborators by incorporating certain second-order information into the augmented Lagrangian framework. Professor Franco Giannessi, University of Pisa, is an outstanding mathematician whose contributions to optimization theory and its applications and to the world optimization community are difficult to overstate. The paper by Izmailov and Solodov introduces and develops a novel perturbed augmented Lagrangian method framework for constrained optimization problems. [Extracted from the article]

The MMOR Best Paper Award is given annually to an outstanding article published in the Mathematical Methods of Operations Research journal. The 2023 award was presented to Zhuoyi Xu, Linbin Li, and Yong Xia for their paper on a partial ellipsoidal approximation scheme for nonconvex homogeneous quadratic optimization with quadratic constraints. The authors propose an efficient method to find an approximation solution for this type of optimization problem. Linbin Li is currently pursuing a Ph.D. in applied mathematics, Yong Xia is a full professor and deputy dean at Beihang University, and Zhuoyi Xu is a lecturer at Capital University of Economics and Business. Congratulations to the authors on behalf of the MMOR editorial board. [Extracted from the article]

Thus, (3) treats the constraints HT ht and HT ht implicitly, and works solely with the auxiliary support variables I y i . [Extracted from the article]

The potential of laser ablation in conservation of paper artifacts of cultural interest is under investigation since the second half of nineties. However, the works carried out on the topic are relatively a few as compared with those dedicated to stone, metal and painted artworks. Furthermore, in the latter cases, widespread applications have been successfully carried out, whereas laser cleaning of aged paper is still far from the conservation practice. There are serious risks to produce short- and/or long-term chemical alterations and/or mechanical damages at relatively low fluences. Here, we report a systematic investigation on laser removal of mold growths from prepared and naturally aged paper samples. Fundamental wavelength and second harmonic of QS Nd:YAG laser are tested and compared through optical and spectroscopic characterizations. This allowed defining optimized irradiation conditions and foreseeing further improvements from pulse duration optimization. [ABSTRACT FROM AUTHOR]

In this work, an improved moth-flame optimization algorithm is proposed to alleviate the problems of premature convergence and convergence to local minima. From the perspective of diversity, an inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm's exploitation and global search abilities. Furthermore, a small probability mutation after the position update stage is added to improve the optimization performance. The performance of the proposed algorithm is extensively evaluated on a suite of CEC'2014 series benchmark functions and four constrained engineering optimization problems. The results of the proposed algorithm are compared with the ones of other improved algorithms presented in literatures. It is observed that the proposed method has a superior performance to improve the convergence ability of the algorithm. In addition, the proposed algorithm assists in escaping the local minima. [ABSTRACT FROM AUTHOR]

With this note, we want to open a discussion on the duality theory for Interval Optimization. We begin by showing that some duality results Wu ( J Optim Theory Appl 144:615-628, ) are useless. [ABSTRACT FROM AUTHOR]

In the context of integrated energy systems, the synergy between generalised energy storage systems and integrated energy systems has significant benefits in dealing with multi-energy coupling and improving the flexibility of energy market transactions, and the characteristics of the multi-principal game in the integrated energy market are becoming more and more obvious, but it is difficult to improve the flexibility of the transactions between "source-load-storage" in a one-way master–slave game structure, and the problem of how to establish an optimisation strategy for the coordination of integrated energy systems and energy storage systems is an urgent issue. How to establish a coordinated optimisation strategy of integrated energy system and energy storage system is an urgent problem. Therefore, this paper proposes a generalised shared energy storage and integrated energy system transaction optimisation method based on a two-stage game model, which improves the flexibility of the system transaction by constructing a two-stage game energy transaction model in which the subject acts as a leader and a gamer. Compared with the current one-way game model that does not consider the game on the energy storage side, the coordinated optimisation method proposed in this paper enables the energy storage side to participate more actively in the scheduling, which improves its revenue by 20.6%, the revenue on the energy-using side by 6.3%, and the overall revenue of the system by 5.4%, and at the same time, the load demand response regulation effect is more obvious, so the energy scheduling strategy proposed in this paper is able to weigh the interests of each subject and increase the overall economic benefits of the system. [ABSTRACT FROM AUTHOR]

In this note, it is shown that there is error in the existing technique (Khan, I.U., Ahmad, T., Maan, N: A simplified novel technique for solving fully fuzzy linear programming problems. J. Optim. Theory Appl. 159:536-546, ). [ABSTRACT FROM AUTHOR]

In the recent years, numerous studies have demonstrated the importance and efficiency of reservoir computing (RC) approaches. The choice of parameters and architecture in reservoir computing, on the other hand, frequently leads to an optimization task. This paper attempts to present an overview of the related work on echo state network (ESN) and deep echo state network (DeepESN) optimization and to collect research papers through a systematic literature review (SLR). This review covers 129 items published from 2004 to 2022 that are concerned with the issue of our focus. The collected papers are selected, analysed and discussed. The results indicate that there are two techniques of parameters optimization (bio-inspired and non-bio-inspired methods) have been extensively used for various reasons. But Different models employ bio-inspired methods for optimizing in a variety of fields. The potential use of particle swarm optimization (PSO) has also been noted. A significant portion of the research done in this field focuses on the study of reservoirs and how they behave in relation to their unique qualities. In order to test reservoirs with varied parameters, topologies, or training techniques, NARMA, the Mackey glass, and Lorenz time-series prediction dataset are the most commonly employed in the literature. This review debate diverse point of view about ESN's hyper-parameter optimization, metrics, time series benchmarks, real word applications, evaluation measures, and bio-inspired and non-bio-inspired techniques, this paper identifies and explores a number of research gaps. [ABSTRACT FROM AUTHOR]

The article focuses on the paper "A primal-dual aggregation algorihtm for minimizing conditional value-at-risk in linear programs," by Daniel Espinoza and Eduardo Moreno which won the Computational Optimization and Applications (COAP) Best Paper Award. Topics discussed include the approximation method for stochastic optimization problems, the conditional value at risk (CvaR), and production scheduling problems.

An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuations and noise signals due to complexity, stochasticity, non-stationarity, and nonlinearity with uncertain and time-varying nature. Aside from being highly nonlinear, solar radiation is highly influenced by the environment and environmental parameters such as air temperature, cloud cover, surface reflectivity, and aerosols. In addition, the spatial measurements of these variables are not readily available. To tackle these challenges, it is necessary to consider data preprocessing techniques and to develop and test precise solar radiation predicting models at different forecast horizons. There is, however, controversy regarding the performance of such models in various studies. Comparisons are not conducted systematically among the different studies. Using a critical literature review, the authors hope to answer these questions and believe that further investigation of solar radiation can benefit researchers and practitioners alike. This study presents a comprehensive evaluation of solar radiation modeling using artificial intelligence in the last 15 years and provides a novel detailed analysis of the available models. The studies conducted in different climates of the world that were published in distinguished journals were considered (i.e., 90 papers in total) for this purpose. Newly discovered procedures for optimizing forecasts, data cleaning, feature selection, classification methods, and stand-alone or hybrid data-driven models for solar radiation prediction and modeling were evaluated. The results strikingly showed that the most used artificial intelligence methods were artificial neural network, adaptive neuro-fuzzy inference system, and decision tree family of models. In addition, the extreme learning machine, support vector machine, and particle swarm optimization were the most used optimization techniques in solar radiation modeling. In terms of forecast horizons, the most common forecast horizon found in papers was on the daily scale (51% of studies), followed by the hourly scale (26%), and the least common was the monthly scale (18%). Based on the regional studies, the highest number of solar radiation papers originated from Asia, with Europe in second place and African countries in third place. An increasing trend in the number of papers from 2011 to 2015 was noted, and the second peak started from 2018 till the present. Under each section, a summary of findings is provided. The paper concludes with future thoughts and directions on solar radiation modeling. [ABSTRACT FROM AUTHOR]

This paper introduces three novel approaches to size geothermal energy piles in a MILP, offering fresh perspectives and potential solutions. The research overlooks MILP models that incorporate the sizing of a geothermal borefield. Therefore, this paper presents a new model utilizing a g-function model to regulate the power limits. Geothermal energy is an essential renewable source, particularly for heating and cooling. Complex energy systems, with their diverse sources of heating and cooling and intricate interactions, are crucial for a climate-neutral energy system. This work significantly contributes to the integration of geothermal energy as a vital energy source into the modelling of such complex systems. Borehole heat exchangers help generate heat in low-temperature energy systems. However, optimizing these exchangers using mixed-integer-linear programming (MILP), which only allows for linear equations, is complex. The current research only uses R-C, reservoir, or g-function models for pre-sized borefields. As a result, borehole heat exchangers are often represented by linear factors such as 50 W/m for extraction or injection limits. A breakthrough in the accuracy of borehole heat exchanger sizing has been achieved with the development of a new model, which has been rigorously compared to two simpler models. The geothermal system was configured for three energy systems with varying ground and bore field parameters. The results were then compared with existing geothermal system tools. The new model provides more accurate depth sizing with an error of less than 5 % compared to simpler models with an error higher than 50 %, although it requires more calculation time. The new model can lead to more accurate borefield sizing in MILP applications to optimize energy systems. This new model is especially beneficial for large-scale projects that are highly dependent on borefield size. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

This review paper explores the use of optimization techniques in steganography, the art of hiding secret information within other data. Steganography is a field that has gained significant attention in recent years due to the increasing need for secure communication and data protection. Optimization techniques have been shown to be effective in improving the performance of steganography systems, particularly in terms of maximizing the amount of secret data that can be hidden while minimizing the impact on the cover data. This paper provides a comprehensive overview of the state-of-the-art optimization techniques in steganography, including genetic algorithms, particle swarm optimization, ant colony optimization and fruit fly optimization algorithm. This paper also discusses the advantages and limitations of these techniques and their potential for further development in the future. Also the state-of-the-art methods were compared in terms of PSNR (Peak Signal to Noise Ratio), payload capacity, SSIM (Structured Similarity Index) etc. The review concludes by highlighting the importance of optimization techniques in steganography and their potential impact on the development of more efficient and secure steganography systems. [ABSTRACT FROM AUTHOR]

The author replies to a note on the paper 'Duality Theory for Optimization Problems with Interval-Valued Objective Function' by saying that the note is invalid. [ABSTRACT FROM AUTHOR]

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

The popularity of digital cameras and social networks has greatly enriched people's spiritual life, and we can easily obtain massive amounts of digital photos. However, due to the lack of professional guidance and differences in aesthetic appreciation, the photos taken many photographers lack aesthetics. This article is dedicated to the research of image aesthetics, using computers to simulate human perception, and realize the evaluation or beautification of images in line with human aesthetics. In terms of image classification, this article examines the unique perception of human vision on images and proposes new aesthetic features. Combining visual features and semantic features, the SVM algorithm is utilized to build an aesthetic classifier. In the aspect of image optimization, this paper uses the detection of the main image area and the division line of the area and adjusts the main body size and position of the image according to common aesthetic rules, so as to realize the optimization adjustment of the composition of the social image. The experimental results show that the accuracy of social image classification is 97.7%, and the optimized and adjusted images are more aesthetic. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including gradient descent, random initialization, and nonconvex optimization.

Bitcoin exchange security is crucial because of MEC's widespread use. Cryptojacking has compromised MEC app security and bitcoin exchange ecosystem functionality. This paper propose a cutting-edge neural network and AdaHessian optimization technique for cryptojacking prediction and defense. We provide a cutting-edge deep neural network (DNN) cryptojacking attack prediction approach employing pruning, post-training quantization, and AdaHessian optimization. To solve these problems, this paper apply pruning, post-training quantization, and AdaHessian optimization. A new framework for quick DNN training utilizing AdaHessian optimization can detect cryptojacking attempts with reduced computational cost. Pruning and post-training quantization improve the model for low-CPU on-edge devices. The proposed approach drastically decreases model parameters without affecting Cryptojacking attack prediction. The model has Recall 98.72%, Precision 98.91%, F1-Score 99.09%, MSE 0.0140, RMSE 0.0137, and MAE 0.0139. Our solution beats state-of-the-art approaches in precision, computational efficiency, and resource consumption, allowing more realistic, trustworthy, and cost-effective machine learning models. We address increasing cybersecurity issues holistically by completing the DNN optimization-security loop. Securing Crypto Exchange Operations delivers scalable and efficient Cryptojacking protection, improving machine learning, cybersecurity, and network management. [ABSTRACT FROM AUTHOR]

This study seeks to plan and evaluate the cost of the logistics in manufacturing tetra duplex board using prime grade and recycled materials. The real-world data for this study is obtained from one of the largest paper and board industries in Asia. The bi-objective problem is formulated by developing a mixed integer linear programming (MILP) model considering the constraints related to raw material supplies, processing, and storage. The metaheuristic optimization techniques are applied based on the concept of epsilon dominance to balance the conflicting objectives to counter the complex problem in the real world of transportation for the ease of the decision-makers to make the best-informed decisions in the selection of raw material. The investigation results indicate that the cost of prime-grade material in the tetra duplex board supply chain is 71 percent higher than recycled fiber. Furthermore, this study can be extended by evaluating the environmental aspects of prime and recycled-grade transportation. Moreover, the logistics of the prime grade can further be narrowed down by investigating it in various modes of transportation such as highways, waterways, rail, and air. [ABSTRACT FROM AUTHOR]

Learning label-specific features is an effective strategy for multi-label classification. Existing multi-label classification methods for learning label-specific features face two challenges. One is the incompleteness of the training label data, and the other is that existing techniques generate label-specific features and build multi-label classifiers independently, ignoring the decoupled nature of the two phases. In this paper, we propose an embedding packing method that combines the generation of label-specific features and subsequent model induction to deal with the multi-label classification problem of missing labels. The proposed method first recovers the missing labels using higher-order label correlation while completing the generation of label-specific features in the embedded feature space and finally completes the construction of a multi-label classifier by combining empirical loss minimization and learned label correlation. Moreover, this paper utilizes the kernel expansion technique to ensure that the model can handle linearly inseparable data. We use alternating optimization techniques to solve the potential optimization problem effectively. Comparative analysis experiments are conducted on ten benchmark multi-label datasets to verify that the proposed method is as competitive as other state-of-the-art methods in dealing with missing-label data. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid genetic-particle swarm optimization (GPSO) algorithm for solving multi-constrained optimization problems. This algorithm is different from the traditional GPSO algorithm, which adopts genetic algorithm (GA) and particle swarm optimization (PSO) in series, and it combines PSO and GA through parallel architecture, so as to make full use of the high efficiency of PSO and the global optimization ability of GA. The algorithm takes PSO as the main body and runs PSO at the initial stage of optimization, while GA does not participate in operation. When the global best value (gbest) does not change for successive generations, it is assumed that it falls into local optimum. At this time, GA is used to replace PSO for particle selection, crossover and mutation operations to update particles and help particles jump out of local optimum. In addition, the GPSO adopts adaptive inertia weight, adaptive mutation parameters and multi-point crossover operation between particles and personal best value (pbest) to improve the optimization ability of the algorithm. Finally, this paper uses a nonlinear constraint problem (Himmelblau's nonlinear optimization problem) and three structural optimization problems (pressure vessel design problem, the welded beam design problem and the gear train design problem) as test functions and compares the proposed GPSO with the traditional GPSO, dingo optimization algorithm, whale optimization algorithm and grey wolf optimizer. The performance evaluation of the proposed algorithm is carried out by using the evaluation indexes such as best value, mean value, median value, worst value, standard deviation, operation time and convergence speed. The comparison results show that the proposed GPSO has obvious advantages in finding the optimal value, convergence speed and time overhead. [ABSTRACT FROM AUTHOR]

This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications. [ABSTRACT FROM AUTHOR]

Rain is a common weather phenomenon, and the challenge of removing rain streaks from a single image is crucial due to its detrimental impact on image quality and the extraction of valuable background information. Existing methods commonly rely on specific assumptions regarding rain models, which restricts their ability to accommodate a wide range of real-world scenarios. To overcome this limitation, these methods often require complex optimization techniques or stepwise refinement strategies. In this paper, we propose a novel wide rectangular regional block and dual attention complementary enhancement deraining kernel prediction subnet to meet the challenge. The network called WRRDANet consists of a kernel prediction subnet and pixel-wise dilation filtering. In the kernel prediction subnet, we capture more specific contextual background information and complex pixel-wise kernels. Afterward, the learned pixel-wise multi-scale kernels from the kernel prediction subnet are used to perform dilation filtering on the original rainy image, effectively restoring richer background details by expanding the scope of deraining to a larger extent. We conducted a comprehensive evaluation using synthetic and real rainfall datasets to demonstrate the effectiveness of our approach. The results, both qualitatively and quantitatively, indicate that our approach outperforms other popular rain removal methods. [ABSTRACT FROM AUTHOR]

As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]

We present an improved analysis of the running time of a dynamic program for the routing flow shop problem with two machines on an asymmetric network. Our analysis is based on structural properties of optimal schedules and significantly improves the bound on the running time of the algorithm obtained in the conference version of our paper (Chernykh, Kononov, and Sevastyanov, in: Kononov, A. et al. (eds) Mathematical optimization theory and operations research. MOTOR 2020. Lecture notes in computer science, Springer, Switzerland, 2020). To the best of our knowledge, our polynomial-time algorithm (under the assumption that the number of network nodes is bounded by any constant) is the first positive result on the complexity of the routing flow shop problem with an arbitrary structure of the transportation network, even in the case of a symmetric network. This result contrasts with the complexity of the two-machine routing open shop problem, which is NP-hard even on the two-node network. [ABSTRACT FROM AUTHOR]

Satellite networks have attracted a lot of attention due to their unique advantages such as wide coverage and high data rate. In particular, the use of inter-satellite links (ISLs) allows multiple satellites to collaborate to provide a wider range of services. However, it is more difficult for the design of ISLs and resource allocation with the increasing number of satellites, which further leads to high network cost. Therefore, this paper aims to design an efficient ISLs establishment scheme and power allocation method to reduce the network cost. First, the network cost optimization problem is formulated as a mixed integer linear programming. Then, with equal power allocation scheme, a Lagrange relaxation method is used to decompose the optimization problem into two subproblems, i.e., routing problem and ISLs establishment problem. Specifically, the optimal routing scheme is equivalent to solving a min-cost max-flow problem of time expanded graph. The ISLs design problem can be directly solved by using branch and bound method in parallel. Moreover, with given routing and ISL scheme, the optimal power allocation is given based on convex optimization theory. Finally, the simulation results have be provided for verifying the effectiveness of proposed algorithm. [ABSTRACT FROM AUTHOR]

Tennis is an elegant sport characterized by confrontation, controllability, leisure, and entertainment. With the continuous improvement of Chinese women's tennis in the world, men's tennis also appears in the world arena, and the popularity of Chinese sports also appears tennis hot. Since the 2008 Beijing Olympic Games, tennis courts have been built vigorously all over China. Due to the facilities, operational capabilities, and services vary in tennis courts, how to book a proper tennis court for clubs or citizens becomes the research content of this study. In the context of cloud computing, a lot of resources and information enter the cloud. This study puts tennis court resources in the cloud and allocates tennis court resources based on game theory. According to the situation that multi-users successively request cloud tennis court resources, this study designs a method of resource allocation for cloud tennis courts based on a dynamic game model. Aiming at the problems existing in traditional resource allocation, this method uses dynamic game theory to formally describe and analyze the resource allocation process and establish a game model, realize a game equilibrium solution based on the quantitative calculation of income, and realize fair resource allocation. The experimental results show that the proposed resource allocation of the tennis courts method has a good performance in success rate and task completion time. It also supports clubs or citizens in the global resource allocation of tennis courts. [ABSTRACT FROM AUTHOR]

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research. [ABSTRACT FROM AUTHOR]

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research. [ABSTRACT FROM AUTHOR]

In this paper, an efficient methodology that could be implemented within any analysis software is developed to automate the design process of post-tensioned slabs. Optimization techniques are used to compute the necessary number of strands. The present methodology is based on computing an innovative influence matrix that contains the effect of each strand on stresses at a set of control points (points of peak stresses). This matrix will be used in the optimization avoiding reanalysis in each optimization iteration. The sum of the squares of the difference between the allowable stress and the computed stresses at all control points is considered an objective function. The optimization is then carried out under the constraints that the stresses at each control point should not exceed the allowable limit and to ensure that the number of strands is an integer value. Alternatively, the proposed methodology could be used as a value-engineering tool. Practical examples are presented to demonstrate the efficiency of the developed methodology. Highlights: • Automated design tool of practical PT slabs independent of designer experience. • Ability to perform value engineering tasks to obtain optimum number of cables in practical PT slabs. • Practical use of optimization methods in structural design automation tool. [ABSTRACT FROM AUTHOR]

In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems. [ABSTRACT FROM AUTHOR]

The need for reconstruction of the distribution of physical properties like dielectric permittivity and electrical conductivity of shallow subsurface sedimentary architecture leads to the development of an optimum strategy of GPR data inversion. In this paper, we present finite difference frequency domain (FDFD) full waveform inversion (FWI) method to get high-resolution subsurface model using GPR data. FWI is an optimization technique which involves in search of the minima between recorded and predicted data. The inversion process includes the quasi-Newton method and simultaneous frequency sampling strategy of irregular sampling. The Hessian term in quasi-Newton algorithm is approximated using preconditioned-LBFGS consideration and the search directions are also optimized after following the Wolfe conditions. At the end of each iteration during inversion, permittivity and conductivity models were updated and became ready to be the initial model for the next iteration. The goals of this research were to develop a robust framework for sedimentary-GPR data inversion and to evaluate the efficacy of the novel grid strategy introduced by Layek and Sengupta (2021) proposed for FWI. This paper presents a comparative analysis between conventional and newly proposed technique from Layek and Sengupta (2021), supported by numerical experiments conducted through our own MATLAB programming. Numerical tests conducted on a benchmark from previously published article, established the fact that new grid formulation produces a faster converging rate and required less computation time. This approach demonstrates remarkable efficacy when applied to a comprehensive sedimentary model comprising a lossy medium. [ABSTRACT FROM AUTHOR]

Homomorphic evaluation of hash functions offers a solution to the challenge of data integrity authentication in the context of homomorphic encryption. The earliest attempt to achieve homomorphic evaluation of SHA-256 hash function was proposed by Mella and Susella (in: Cryptography and coding—14th IMA international conference, IMACC 2013. Lecture notes in computer science, vol 8308. Springer, Heidelberg, pp 28–44, 2013. https://doi.org/10.1007/978-3-642-45239-0%5f3.) based on the BGV scheme. Unfortunately, their implementation faced significant limitations due to the exceedingly high multiplicative depth, rendering it impractical. Recently, a homomorphic implementation of SHA-256 based on the TFHE scheme (Homomorphic evaluation of SHA-256. https://github.com/zama-ai/tfhe-rs/tree/main/tfhe/examples/sha256%5fbool) brings it from theory to reality, however, its current efficiency remains insufficient. In this paper, we revisit the homomorphic evaluation of the SHA-256 hash function in the context of TFHE, further reducing the reliance on gate bootstrapping and enhancing evaluation latency. Specifically, we primarily utilize ternary gates to reduce the number of gate bootstrappings required for logic functions in message expansion and addition of modulo 2 32 in iterative compression. Furthermore, we demonstrate that our optimization techniques are applicable to the Chinese commercial cryptographic hash SM3. Finally, we give specific comparative implementations based on the TFHE-rs library. Experiments demonstrate that our optimization techniques lead to an improvement of approximately 35–50% compared with the state-of-the-art result under different cores. [ABSTRACT FROM AUTHOR]

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. [ABSTRACT FROM AUTHOR]

This article is part of the Topical Collection: Special Issue on Networked Cyber-Physical Systems Guest Editors: Heng Zhang, Mohammed Chadli, Zhiguo Shi, Yanzheng Zhu, and Zhaojian Li Cyber-Physical Systems (CPSs) represent a bold new generation of systems that integrate computation and communication capabilities with the dynamics of physical and engineering systems. The thirteenth paper, titled "Cyber-physical battlefield perception systems based on machine learning technology for data delivery" by J. Zhao, C. Han, et al., provides an air-ground coordination communication transmission network. [Extracted from the article]

As is well known that the global optimization ability of the Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect. [ABSTRACT FROM AUTHOR]

Clustering is a technique that segregates a provided dataset into homogenous groups in accordance with the provided features. It aims to determine a structure in a group of unlabelled data. Cluster analysis is an unsupervised learning technology that determines the interesting patterns in data objects without class labels. K mode clustering algorithm seems to be effective in clustering categorical data due to its easy implementation and capability to handle the massive amount of data. But because of its random selectivity of initial centroids, it gives the local optimum solution. The main contribution of the paper is to evaluate the performance of clustering on the various dataset with the proposed system. The proposed method utilizes a genetic-based Metaheuristic encircle algorithm to select enriched features and novel dynamic K modes clustering based on Dimensionality Reduced PSO for clustering process with better computational time. The encircling Prey concept has been incorporated to choose the fitness function and overcome the genetic algorithm limitations in feature selection. This paper integrated the k-modes algorithm with particle swarm optimization algorithm to obtain a global optimum solution and update the initial centroid. Several dataset utilized for the evaluation of the proposed work has been found to achieve low accuracy in the previous work. But the proposed approach's effectiveness has been proved to be better by performing a comparative analysis with the state of art methods in terms of performance metrics such as F1 score, accuracy, NMI. [ABSTRACT FROM AUTHOR]

This is the Preface to the Special Issue of JOTA dedicated to the 60th birthday of Professor Aram V. Arutyunov [ABSTRACT FROM AUTHOR]

Background: MathWorks has provided an invaluable tool for designing and implementing FPGAs. MATLAB HDL coder serves a dual purpose, providing a quick proof of concept on the one hand and providing the g an easy-to-use platform for testing and verification on the other. It has main drawbacks over these advantages; it generates a code that is not optimized for both area and frequency. Results: In this paper, we provide a technique for optimizing both area and frequency without losing the main advantages. The most affecting problem we found is loops. This paper classifies loop writing purposes into two types. The first one is preferable and introduces ease of writing a few lines instead of repeating the code. The second type is the problem that we intended to solve. Type II loop is appearing when the algorithm should perform these lines for several clock cycles. Writing it traditionally, force the synthesizer to implement all the repetitive clock cycles as repetitive hardware to be done in one clock cycle. This clock cycle is wide in time and is slow in frequency. This paper introduces an optimization technique for this problem. We compare before and after the implementation of our proposed technique. Conclusions: We used Xilinx Spartan 6 XC6SLX4-2CPG196 FPGA. Our proposed technique improves the number of slice LUTs (Look Up Tables) requirement from 366 to 72%. The frequency improved from: 26.574 to 185.355 MHz. Based on that, we now recommend using MATLAB HDL coder in FPGA Design. [ABSTRACT FROM AUTHOR]

As an emerging computing technology, approximate computing enables computing systems to utilize hardware resources efficiently. Recently, approximate arithmetic units have received extensive attention and have been employed as hardware modules to build approximate circuit systems, such as approximate accelerators. In order to make the approximate circuit system meet the application requirements, it is imperative to quickly estimate the error quality caused by the approximate unit, especially in the high-level synthesis of the approximate circuit. However, there are few studies in the literature on how to efficiently evaluate the errors in the approximate circuit system. Hence, this paper focuses on error evaluation techniques for circuit systems consisting of approximate adders and approximate multipliers, which are the key hardware components in fault-tolerant applications. In this paper, the characteristics of probability mass function (PMF) based estimation are analyzed initially, and then an optimization technique for PMF-based estimation is proposed with consideration of these features. Finally, experiments prove that the optimization technology can reduce the time required for PMF-based estimation and improve the estimation quality. [ABSTRACT FROM AUTHOR]

As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes "external dispersion method" to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes "free-particle swarm optimization" to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage. [ABSTRACT FROM AUTHOR]

In this paper, a Gravitational Search-based neural weight optimization technique for faster neural synchronization is proposed. To share the key over a public channel, two neural networks are coordinated by mutual learning. The primary problem of neural synchronization in the absence of a weight vector from another party is deciding how to determine the synchronization of two communication parties. The existing synchronization evaluation strategies also have a latency problem that affects the protection and privacy of neural synchronization. A significant technique for evaluating synchronization is introduced in this paper to evaluate the complete coordination of two neural networks more effectively and rapidly. The frequency of the two networks having the same output in previous iterations is used to measure the degree of synchronization. When a certain threshold is reached, the hash is used to decide if both networks are fully synchronized. This proposed methodology uses Gravitational Search optimized weight vectors to achieve total synchronization between two communicating parties. Unlike existing approaches, the proposed solution helps two communication parties to detect complete synchronization faster. The successful geometric probability is decreased as a consequence. As a result, the proposed method strengthens the security of the neural key exchange protocol. Different parametric experiments have been performed on the proposed methodology. In terms of the cited findings in the study, simulations of the procedure indicate efficacy. [ABSTRACT FROM AUTHOR]

Context: The recent research trend has highlighted that multiple stakeholders are involved during requirement gathering in agile software development. Hence, leading to an increased number of duplicate user stories in agile product backlog during requirement gathering. Objective: The objective of this paper is to evaluate the existing techniques employed in identifying and eliminating the duplicate user stories from agile product backlog and to overcome the existing gaps with the help of a newly proposed clustering algorithm. Method: An agile user story is expressed as a function of input and output parameters. That said multiple user stories having similar set of input parameters are most likely to be duplicate causing a redundancy. The newly proposed algorithm is used for clustering user stories having similar set of input parameters through various iterations and then removing the identified duplicate user stories from agile product backlog. This paper also introduces the concept of mass clustering which means clustering a number of user stories in single run. Results: Experimental results prove the proposed model is capable of handling small and large releases ranging between 100 to 1000 user stories with similar efficiency. The proposed clustering algorithm outperformed the clustering algorithms and resulted in 37% decrease in agile product backlog by eliminating duplicate user stories causing redundancy. The experimental results are obtained from the logs of the MATLAB tool. However, the provided algorithm is generic in nature and can be implemented using R, Python or SAS programming tools. The provided algorithms employs proven matrix operations. Conclusion: The proposed clustering algorithm overcomes the limitation of existing user story management methods and clearly out performs when compared with other clustering algorithms. Finally, this paper gives recommendations about the usage of the provided clustering algorithm during agile release planning for eliminating duplicate user stories from agile product backlog. [ABSTRACT FROM AUTHOR]