An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

Optimizing Resource Allocation Framework for Multi-Cloud Environment

Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to resources.Resource allocation is a complex challenge due to the versatile service providers and resource providers.The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service.The objective of a coherent and rational resource allocation is to attain the quality of service.It also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment.The three main parameters of the proposed framework are data accessibility,optimization,and collaboration.Using an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service quality.The CloudSim simulator has been used to validate the suggested framework.Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS.The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century,most industries are moving towards automation,where human intervention is dramatically reduced.This revolution leads to industrial revolution 4.0,which uses the Internet of Things(IoT)and wireless sensor networks(WSN).With its associated applications,this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance.In general,WSNs,the gateways are a long distance from the base station(BS)and are communicated through the gateways nearer to the BS.At the gateway,which is closer to the BS,energy drains faster because of the heavy load,which leads to energy issues around the BS.Since the sensors are battery-operated,either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas.In that situation,energy plays a vital role in sensor survival.Concerning reducing the network energy consumption and increasing the network lifetime,this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set(ISSRS)and routing path selection to reduce the network load using the Improved Grey wolf optimization(IGWO)approach.(i)Using ISSRS,the initial clusters are formed with the local nodes,and the cluster head is chosen.(ii)Load balancing through routing path selection using IGWO.The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency,packet delivery ratio,network throughput,and packet loss percentage.

Analyzing the Effect of the Intra-Pixel Position of Small PSFs for Optimizing the PL of Optical Subpixel Localization

Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement fields.With unavoidable imaging noise,there is a precision limit(PL)when estimating the target positions on image sensors,which depends on the detected photon count,noise,point spread function(PSF)radius,and PSF’s intra-pixel position.Previous studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position information.Here,we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small PSFs.To accurately estimate the PL in practical applications,we provide effective PSF(e PSF)modeling approaches and apply the Cramér–Rao lower bound.Based on the characteristics of small PSFs,we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF;we then verify these equations on real PSFs.Next,we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible,indicating that the optimum is ultimately limited by light diffraction.Finally,we apply the maximum likelihood method.Its combination with e PSF modeling allows us to successfully reach the PL in experiments,making the above theoretical analysis effective.This work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory,thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization.

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things(IoT) services and solutions requires extensive communication and computingresources to place and process IoT applications in real time, which consumesa lot of energy and increases operational costs. Usually, IoT applications areplaced in the cloud to provide high-quality services and scalable resources.However, the existing cloud-based approach should consider the above constraintsto efficiently place and process IoT applications. In this paper, anefficient optimization approach for placing IoT applications in a multi-layerfog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into accountIoT application requirements, available resource capacities, and geographicallocations of servers, which would help optimize IoT application placementdecisions, considering multiple objectives such as data transmission, powerconsumption, and cost. Simulation experiments were conducted with variousIoT applications (e.g., augmented reality, infotainment, healthcare, andcompute-intensive) to simulate realistic scenarios. The results showed thatthe proposed approach outperformed the existing cloud-based approach interms of reducing data transmission by 64% and the associated processingand networking power consumption costs by up to 78%. Finally, a heuristicapproach was developed to validate and imitate the presented approach. Itshowed comparable outcomes to the proposed model, with the gap betweenthem reach to a maximum of 5.4% of the total power consumption.

Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual task.The main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images.To this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)algorithm.This algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial positions.It leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search space.Testing on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization techniques.The proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within ODIR.These results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation tasks.It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.

Fusing Satellite Images Using ABC Optimizing Algorithm

Fusing satellite(remote sensing)images is an interesting topic in processing satellite images.The result image is achieved through fusing information from spectral and panchromatic images for sharpening.In this paper,a new algorithm based on based the Artificial bee colony(ABC)algorithm with peak signalto-noise ratio(PSNR)index optimization is proposed to fusing remote sensing images in this paper.Firstly,Wavelet transform is used to split the input images into components over the high and low frequency domains.Then,two fusing rules are used for obtaining the fused images.The first rule is“the high frequency components are fused by using the average values”.The second rule is“the low frequency components are fused by using the combining rule with parameter”.The parameter for fusing the low frequency components is defined by using ABC algorithm,an algorithm based on PSNR index optimization.The experimental results on different input images show that the proposed algorithm is better than some recent methods.

Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks.We first model the WVC problem as a general game on weighted networks.Under the framework of a game,we newly define several cover states to describe the WVC problem.Moreover,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the game.Then,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time.Subsequently,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the GAA.Finally,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

Optimizing N fertilizer use for sesame under rain fed and irrigation conditions in Northern Ethiopia

Inappropriate use of fertilizers is one of the major production constraints in sesame. Studies on N fertilizer optimization on sesame were conducted at Humera Agricultural Research Center(Hu ARC) under rain fed and irrigation conditions. Thirteen(13) N doses were evaluated in a Randomized Complete Block Design(RCBD)during 2016–2018 for rainfed conditions and 2017 to 2019 for irrigation conditions. The study was conducted with objective to optimize N fertilizer use for sesame. In the rainfed condition, the results demonstrated a prolonged duration to reach 50% flowering with higher nitrogen(N) application rates. The application of 52.5–110kg N ha^(-1) resulted in significantly higher seed yield, while lower(18 kg N ha^(-1)) and higher(156 kg N ha^(-1)) doses of N led to reduced seed yield. Under irrigation conditions, superior seed weights and maximum seed yield were observed at 64 and 75 kg N ha^(-1), whereas lower N doses resulted in diminished seed yield. The agronomic efficiency of N fertilizer(N-AE) was found to be highest at the rate of 64 kg N ha^(-1) under both growing conditions.The partial budget analysis revealed that applying 64 kg N ha^(-1) for rainfed cultivation and between 64 and 75 kg N ha^(-1) for irrigated sesame production yielded greater net profit, MRR, and residual ranking. Therefore, it is recommended to apply a rate of 64 kg N ha^(-1) for rainfed sesame cultivation and between 64 up to 75 kg N ha^(-1) for the irrigated sesame inorder to increase the productivity of this crop.

Optimizing Factory Performance for Unit Cost in Semiconductor Manufacturing

The integrated circuit (IC) manufacturing process is capital intensive and complex. The production process of unit product (or die, as it is commonly referred to) takes several weeks. Semiconductor factories (fabs) continuously attempt to improve their productivity, as measured in output and cycle time (or mean flow time). The conflicting objective of producing maximum units at minimal production cycle time and at the highest quality, as measured by die yield, is discussed in this paper. The inter-related effects are characterized, and a model is proposed to address this multi-objective function. We then show that, with this model, die cost can be optimized for any given operating conditions of a fab. A numerical example is provided to illustrate the practicality of the model and the proposed optimization method.

The Study of Optimizing Reservoir Model Using Experimental Design in Stochastic Models

In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual situation of the oil field,the uncertain variables were determined that affect the geological reserves of the model and their possible range of variation,and experimental design was used to determine the modeling plan.Then,multiple geological models were established and reserves were calculated,and multiple regression was performed between uncertain variables and the corresponding geological reserves of the model.Finally,Monte Carlo simulation technology was applied to determine the parameters of the P10,P50,and P90 models for probabilistic reserves,and P10,P50,and P90 models were established.This method is not only more objective and time-saving in the application process,but also can determine the main geological variables that affect geological reserves,providing a new idea for evaluating the uncertainty of geological reserves.

Optimizing Designed Variable Speed Control Moment Gyroscopes with Variable Skew Angle

Large torque can be output by the single gimbal control momentum gyroscope (SGCMG) based on the principle of the gyroscopic precession. However, the singularity is a major obstacle to successfully implement the task of the attitude control. The singularity can be avoided by the additional variable flywheel speed of variable speed control moment gyroscopes (VSCMG). Unfortunately, some kind of singularity cannot be effectively avoided. Consequently, the output toque can be only supported by the reaction torque of the flywheel when the singularity is encountered, and the consume power that is determined by the flywheel speed and reaction torque can be greatly increased when the flywheel spin rate over one thousand revolutions per minute. In this paper, the pyramid configuration with variable skew angle of the VSCMG is considered. A new steering law for the VSCMG with variable skew angle is proposed. The singularity that cannot be avoided by the varying flywheel speed can be effectively avoided with assisting of varying the skew angle. Consequently, the requirement of flywheel torque can be reduced. At last, the optimizing VSCMG with variable skew angle can be cast as a multi-objective function with multi-constraints. The particle swarm optimization method is used to solve the optimizing problem. In summary, the VSCMG with variable skew angle can be redesigned with considering of the singularity avoidance and minimizing system power.

Optimizing Social Impact in Organizations:Concepts,Levers and Barriers

With the socio-economic change that has taken place over the last years,in addition to an increase in sustainability regulation,stakeholders have gained importance and organizations are more active in relation to generating social impact,but society demands more and better social impact from organizations.The objectives of this paper are to clarify the concepts of impact and social impact optimization,and to detect levers and barriers to help organizations optimize the social impact that they generate.A qualitative approach based on interviews with social impact leaders from organizations with different forms(big companies,small and medium-sized enterprises,corporate foundations,b-corps,community foundations,public and private foundations,associations and investing firms)is applied,together with focus groups with stakeholders from those organizations that are best practices.

OPTIMIZING DESIGN OF MECHANICAL SELF-CENTERING DEVICE FOR SUSPENSION HEIGHT

Firstly, in view of the respective defects of existing self-centering devices for vehicle suspension height, the design scheme of the proposed mechanical self-centering device for suspension height is described. Taking the rear suspension of a certain light bus as a research example, the structures and parameters of the novel device are designed and ascertained. Then, the road excitation models, the performance evaluation indexes and the half-vehicle model are built, the simulation outputs of time and frequency domain are obtained with the road excitations of random and pulse by using MATLAB/Simulink software. So the main characteristics of the self-centering suspension are presented preliminarily. Finally, a multi-objective parameter design optimization model for the self-centering device is built by weighted sum approach, and optimal solution is obtained by adopting complex approach. The relevant choosing-type parameters for self-centering device components are deduced by using discrete variable optimal method, and the optimal results are verified and analyzed. So the performance potentials of the self-centering device are exerted fully in condition of ensuring overall suspension performances.

An optimizing selection model in preparation of powdery emulsion explosives

In order to get cheap and excellent PEE (Powdery Emulsion Explosives), themodel of optimizing selection on preparation of PEE was established by the Neural Net Theory (NNT).On the basis of some data in the study of PEE, the training, prediction and optimizing selection ofthe Neural Net (NN) model were finished by compiling procedures. The results indicate that the modelis helpful to the preparation of PEE and worthy to extend and apply broadly.

Optimization Algorithms for Fully Automatic Optimizing Cross-cut Saw

The optimization of boards by grades plays an important role in the production for cross cutting boards, and the outturn rate and utilization of boards are directly affected by the optimization results of boards by grades. At present, the OptiCut series fully automatic optimizing cross-cut saw（FAOCCS） from Germany Weinig Group occupies the main markets in the world, but no report about the relative theories on the optimization technology and its algorithms is available. There exist some disadvantages in woodworking machinery and equipment used for cross cutting boards in China, for example, low sawing precision, outturn rate of boards and productivity, and difficulty in making statistics on the sawing results. Three optimization modes are presented for the optimization algorithms for FAOCCS, namely, optimization of fixed length, optimization of finger-jointed lumber and mixed optimization. Mathematical models are then established for these three optimization modes, and the corresponding software for realizing the optimization is prepared. Finally, Synthetic evaluation on the established mathematical models is presented through three practical examples. The results of synthetic evaluation indicate that FAOCCS using the optimization modes may raise the outturn rate of boards approximately 8% and the productivity obviously, and allows accurate statistics on the cross cut products of boards. The mathematical models of above three optimization modes are useful for increasing the outturn rate and utilization ratio of boards.