Reduce Time Cost Research Articles

High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation

Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the existing scientific error-bounded lossy compressors, some of them (such as SPERR and FAZ) can reach fairly high compression ratios and some others (such as SZx, SZ, and ZFP) feature high compression speeds, but they rarely exhibit both high ratio and high speed meanwhile. In this paper, we propose HPEZ with newly-designed interpolations and quality-metric-driven auto-tuning, which features significantly improved compression quality upon the existing high-performance compressors, meanwhile being exceedingly faster than high-ratio compressors. The key contributions lie as follows: (1) We develop a series of advanced techniques such as interpolation re-ordering, multi-dimensional interpolation, and natural cubic splines to significantly improve compression qualities with interpolation-based data prediction. (2) The auto-tuning module in HPEZ has been carefully designed with novel strategies, including but not limited to block-wise interpolation tuning, dynamic dimension freezing, and Lorenzo tuning. (3) We thoroughly evaluate HPEZ compared with many other compressors on six real-world scientific datasets. Experiments show that HPEZ outperforms other high-performance error-bounded lossy compressors in compression ratio by up to 140% under the same error bound, and by up to 360% under the same PSNR. In parallel data transfer experiments on the distributed database, HPEZ achieves a significant performance gain with up to 40% time cost reduction over the second-best compressor.

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

A Hybrid QOGWO-GPR Algorithm for Antenna Optimization

Optimization of antenna performance is a non-linear, multi-dimensional and complex issue, which entails a significant investment of time and labor. In this paper, a hybrid algorithm of quasi-opposition grey wolf optimization (QOGWO) and Gaussian process regression (GPR) model is proposed for antenna optimization. The QOGWO is prone to global optimality, high precision for complex problems, and fast convergence rate at the later stage. The GPR model can reduce time cost of antenna samples generation. After being optimized by the proposed approach, a stepped ultrawideband monopole antenna and a dual-band MIMO antenna for WLAN can achieve wider bandwidth and higher gain or isolation at low time cost, compared to other intelligent algorithms and published literatures.

Tradeoff Between Time and Energy Costs for Controlling Stochastic Coupled Neural Networks

Time cost (TC) and energy cost (EC) are two fundamental indicators for evaluating the designed protocols for controlling networked systems. Yet, the relationships of which as well as their dependence on the network topology are far from clear. In this note, we explore this problem with the stochastic synchronization of coupled neural networks. A novel controller is articulated, which switches between the linear feedback control and finite-time feedback control relying on the size of system error. Sufficient criteria for achieving stochastic synchronization are derived, and the analytical estimates of TC and EC for synchronization are further obtained. Especially, we show that a reduction in TC will lead to an increase in EC, i.e., there is a tradeoff between TC and EC. Afterward, we further report that different optimal values of the steepness exponent and control strength should be taken to minimize the total costs of time and energy with the different performance indices.

Pre-matching study of the natural gas engine turbocharging system based on the coupling of experiments and numerical simulation

In this study, a pre-matching method was developed based on measured performance parameters and theoretical calculations of turbochargers. First, the turbocharger of a natural gas engine was subjected to a comprehensive performance experiment. According to the experimental results, the maximum efficiencies of the turbine and compressor are 70% and 75%, respectively, and the efficiency of the turbine drops sharply from 70% to 56.6% as the pressure ratio increases from 1.25 to 2.4. In this thesis, a specific turbocharger pre-matching software has been developed in conjunction with a database. Three turbines and three compressors were selected from the self-developed database for matching and comparative study using this method. The simulation results showed that the maximum efficiency of turbine #1, #2 and #3 is 71.3%, 72.2% and 72.7%, respectively, and the efficiency of these three turbines is concentrated between 65% and 72.5%. Obviously, the maximum efficiency of the turbine has increased by 1.3–2.7% and the overall efficiency has improved after the pre-matching. Therefore, this developed pre-matching method can reduce time cost, improve work efficiency and engine performance, and is important for the design and development of turbochargers.

PYTHON: ИСПОЛЬЗОВАНИЕ PYTHON ДЛЯ АВТОМАТИЗАЦИИ ПОВСЕДНЕВНЫХ ЗАДАЧ

The article discusses the possibilities of using the Python programming language to automate everyday tasks in various fields. The main automation scenarios are presented, such as data processing and analysis, file and document management, interaction with web APIs and cloud services. The advantages of using Python for automation are described, including increased productivity, reduced time costs and reduced likelihood of errors. The limitations and challenges that user face when imple-menting automation are discussed, as well as prospects for further development in this area.

A scalable blockchain-enabled federated learning architecture for edge computing.

Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.

Business Intelligence System for A Weaving Industry

The practical and precise management of company transactions and accounting is paramount for achieving organizational success in the dynamic market characterized by intense competition within local production sectors. Manually maintaining transactions has become increasingly challenging due to the continuous expansion of data. The primary objective of this project is to implement an automated management system to replace the existing manual method. Implementing this solution will yield benefits in terms of time efficiency, cost reduction, and space optimization while safeguarding sensitive information. Additionally, the used technology will enhance the manageability and searchability of data and documents. Furthermore, using Business Intelligence will enhance strategic business decision-making processes and facilitate the generation of comprehensive reports.

Organization of the Process of Complex BIM-Design of Technological Facilities

The article deals with the issues of optimization of complex BIM-design processes of capital construction objects on the example of design of technological objects, namely buildings and facilities of water treatment complex. The model of organization of BIM-designing processes (construction of information model and obtaining documentation of various stages of design) with the help of interactive interaction means is presented, which allows to reduce time costs for information exchange and coordination of design decisions. A brief algorithm of this organizational model is given. Further possibilities of refinement and construction of new algorithms of work with the use of software complexes and programming elements are revealed. The results obtained in the systematization of individual stages of information modeling are demonstrated.

Predicting Mechanical Properties of Boron Nitride Nanosheets Obtained from Molecular Dynamics Simulation: A Machine Learning Method

Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young’s modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs’ chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young’s modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors.

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

AbstractThis paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

Energy performance prediction of pump as turbine (PAT) based on PIWOA-BP neural network

The greenhouse effect and the depletion of fossil energy sources have emerged as significant challenges impacting development of society. The development of micro hydropower stations is crucial in reducing carbon emissions and achieving carbon neutrality. Among them, pump as turbine (PAT) technology is considered one of the most promising alternatives in small and micro hydropower. However, pump manufacturers don't provide performance curves in turbine conditions, leading to issues such as difficulty in selecting a model and inaccuracy in calculating the turbine's high-efficiency zone when employing PAT in practice. This study proposes a new solution to these problems by introducing a new PIWOA-BPNN model. The model integrates the enhanced WOA algorithm with the parameter-optimized multilayer BPNN and employs the innovative spiral guidance vector p to direct the random search within the WOA algorithm, significantly enhancing the prediction capability of the neural network while reducing time cost. Additionally, the internal parameters of the turbine are analyzed, and a network model incorporating the geometric parameters of the impeller as auxiliary input is proposed. Several experimental tests demonstrate that the method offers greater accuracy and reliability compared to current mainstream algorithms. The prediction accuracy of the proposed model is approximately 4 %, meeting engineering requirements.

Control costs of long-range interacting multi-agent systems with noise perturbation

As an important stage of collective dynamics, consensus is widespread in real-world moving groups, where one of the long-term challenges is to optimize the control costs of reaching consensus. However, current research on the consensus of multi-agents systems (MASs) usually only considers direct interactions between agents and ignores long-range interactions (LRIs), which may influence or even determine the collective behavior of MASs. In our paper, a basic framework is developed for analyzing the control costs for reaching consensus of MASs with LRIs and noise perturbation. The theoretical estimate of the control cost from a time and energy perspective will depend on the network diameter, control parameters, noise strength, and network size, and LRIs can promote MASs to reach consensus. We provide some numerical simulations to test our theory. Both theoretical and numerical analyses reveal that there is a trade-off between time and energy costs regardless of whether MASs have LRIs or not, i.e., adjusting control parameters to reduce time cost inevitably leads to an increase in energy cost. Our results provide a new option for controlling MASs: designing control protocols based on LRIs which in some cases have superior performance to typical controllers.

Autoimmune Pancreatitis Surveillance: A Simplified MRI Protocol Versus a Comprehensive Pancreatic MRI Protocol

Magnetic resonance imaging (MRI) has good diagnostic performance and causes no radiation damage, making it an ideal tool for the autoimmune pancreatitis (AIP) surveillance. However, its time cost is high. This study aimed to evaluate (1) whether a simplified protocol (SP) of MRI for AIP surveillance provides information equivalent to the comprehensive protocol (CP) and (2) the time cost reductions associated with using an SP. This retrospective single-institutional study included 40 patients with AIP with at least two contrast-enhanced MRI/magnetic resonance cholangiopancreatography studies. Two radiologists evaluated two imaging sets (CP/SP) per patient, independently. Intra- and inter-observer agreement in the evaluation of the pancreas and extrapancreatic organs involvement using the SP/CP in addition to the time cost differences between the SP and CP were assessed. Intra- and inter-rater reliability were assessed using Cohen's kappa test, intraclass correlations, or the weighted kappa test. The differences in time costs between the CP and SP were compared using the Mann-Whitney U test or Wilcoxon signed-rank test. The SP had strong intra- and inter-observer agreement with the CP in evaluating MRI parameters (κ＞0.60, moderate to excellent) and disease activity status (κ＞0.80, all excellent). The overall image acquisition time cost for the SP was 49.2% of the CP. For the two radiologists, the image interpretation time cost of the SP was reduced by approximately 35% and 27% compared to the CP. For AIP surveillance, SP MRI provides information consistent with the CP and is less time-consuming.

Reuse-based online joint routing and scheduling optimization mechanism in deterministic networks

Deterministic networks plan the entire network traffic and calculate the scheduling time to meet the critical traffic requirements of specific domains, enabling real-time and deterministic interaction of massive data. However, in dynamic industrial automation scenarios where devices undergo changes, existing mechanisms face challenges in quickly responding to dynamic transmission demand changes caused by rapid traffic migration. To address this issue, this paper proposes a reuse-based online scheduling mechanism that utilizes dynamic path planning of flows and coordinated scheduling of time slots to achieve deterministic transmission of dynamic flows. In the offline phase, the mechanism proposes a backbone link selection and a scalable intelligent routing strategy, constructs a set of routing and scheduling co-design constraints, and generates an offline scheduling table using an iterative scheduling algorithm. In the online scheduling phase, a reuse-based online scheduling algorithm is proposed to achieve rapid scheduling and deterministic transmission of dynamic real-time flows. It utilizes the offline scheduling results and the period offset of migrated flows. The reuse of offline scheduling results reduces computation time and expands the solution space. Experimental results demonstrate that the proposed mechanism achieves a maximum increase in scheduling success rate of 37.3% and reduces time costs by up to 66.6% compared to existing online scheduling algorithms.

Integrated optimization and engineering application for disassembly line balancing problem with preventive maintenance

The current research on the disassembly line balancing (DLB) problem has not yet considered the preventive maintenance of workstation equipment. The lack of preventive maintenance can easily lead to sudden failure of the disassembly line equipment, which will affect the disassembly efficiency. Preventive maintenance is planned maintenance, which can effectively reduce the probability of equipment failure in actual disassembly line engineering applications. This study integrates preventive maintenance into the DLB. The objectives focus on optimizing both cycle times under the regular DLB scenario and the preventive maintenance DLB scenario, and the number of task adjustments between the two scenarios. A mixed integer linear programming model integrating preventive maintenance is established. Then an improved genetic simulated annealing (IGSA) algorithm based on the disassembly characteristics is designed. The exact solver and the proposed IGSA are used to solve a small-scale case simultaneously to verify the correctness of the model and algorithm. The performance of the proposed IGSA is verified by comparing the solution results of 21 benchmarks. Finally, the proposed model and algorithm are applied to a microwave disassembly line as the engineering application case. The results show that the integrated optimization can ensure the disassembly efficiency of the regular disassembly scenario, and can fully utilize workstations of unperformed maintenance during the preventive maintenance scenario. This can also effectively improve workstation utilization and reduce time costs.

Optimization of food-grade bioactive compound extraction from plant sources by response surface methodology approach: A comprehensive review

Abstract This comprehensive review delves into the extensive application of response surface methodology (RSM), a prominent mathematical and statistical technique, for modeling and optimizing the extraction of food-grade bioactive compounds from plant sources. The paper elucidates the optimization approach, covering experimental design, empirical models for response prediction, and the utilization of the desirability function for multiple response optimization. RSM provides a contemporary means to concurrently analyze and optimize various factors, presenting mathematical models for enhancing extraction processes efficiently. The review showcases RSM applications in traditional extraction techniques such as classical solvent extraction, Soxhlet extraction, and hydrodistillation, with a focus on factors like extraction time, temperature, ratio of plant material to solvent, and solvent concentration. The economic feasibility of RSM-optimized extraction processes is discussed, encompassing considerations of processing time, solvent consumption, and overall cost reduction. Critical aspects and challenges related to RSM implementation in extraction optimization are addressed, underscoring the significance of appropriate experimental design, model accuracy, and the incorporation of multiple responses for comprehensive optimization. The review concludes by emphasizing the pivotal role of RSM in guiding rational and efficient extraction processes for various valuable natural compounds from plant materials.

Comparative Evaluation of the Digital Workflow and Conventional Method in Manufacturing Complete Removal Prostheses.

The aging population in developed countries has increased the number of edentulous patients and, therefore, the need for prosthetic rehabilitation to improve their quality of life. Complete dentures are the main treatment option in these cases. The use of CAD/CAM (Computer Aided Design/Computer Aided Manufacturing) in dentistry has improved clinical protocols and outcomes, achieving a reduction in work time and economic costs for the patients. The main objective of this review was to compare the characteristics of conventional and digital dentures, attempting to determine whether the use of new technologies represents an improvement in the properties of removable complete dentures. A bibliographic review was carried out in the PubMed/MEDLINE, Cochrane Library, Scielo, and Embase databases. With the initial search, 157 articles were obtained. After applying the inclusion and exclusion criteria, 64 publications were selected for this bibliographic review. The different conclusions of the studies consulted were compared regarding fit and retention, fracture resistance, surface roughness, biocompatibility, and aesthetics, taking into account the different methods of prostheses fabrication. In general, digital prostheses have shown better mechanical properties and, consequently, better biocompatibility and aesthetics than conventional prostheses. However, the obtained results were very heterogeneous, preventing a supported conclusion.

Нечеткая динамическая онтологическая модель для поддержки принятия решений по управлению энергоемкими системами на основе прецедентов

The article discusses the features of applying the precedent approach when managing complex energy-intensive systems in the context of the need to take into account various energy, technical, environmental and operational indicators, as well as the uncertainty of many internal and external factors influence. This leads to the presence of a large amount of semi-structured information that can be presented using various scales, which determines the prospects of using the precedent approach. The proposed fuzzy ontological model for supporting decision support based on precedents is described, characterized by the use of dynamic concepts, as well as concepts in the form of different scale numerical and linguistic variables. An algorithm for assessing the proximity of precedents based on an ontological model is proposed, which differs by taking into account the dynamic aspects of changes in the state of controlled systems. The developed algorithms for fuzzy inference for decision support based on precedents are presented, which allow the use of both linguistic and numerical variables as input characteristics of the fuzzy production model, as well as using various logical connections between the rules pre-requisites. The software that implements the developed model and algorithms is described. Particular attention is paid to the modified fuzzy inference component, implemented using Python 3.8.7 language tools. To implement the user interface of the specified component, the cross-platform graphic library Tkinter was used. The results of computational experiments using real data obtained during the operation of an energy-intensive system for processing fine ore raw materials, including a conveyor-type roasting machine, are presented. Minimization of specific total costs for thermal and electrical energy was considered as a criterion for the effectiveness of management decisions. The outcome obtained showed that the proposed model and software make it possible to obtain a result comparable to the one of using complex analytical dependencies, while ensuring a reduction in time and financial costs.