Time Cost Research Articles

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Optimization Model of Emergency Evacuation Network Based on Genetic Algorithm

Aggregation and dispersion are the normal states. In economic production and social life, the evacuation of people and materials deserves extensive attention. From the perspective of mathematics and economics, arranging a thoughtful evacuation is essential for the best balance between personal satisfaction and cost. In this paper, evacuation is deeply studied by a genetic algorithm; the balance, cost, and willingness (satisfaction) priority models, which consider both satisfaction and cost, are established. In this paper, based on the field investigation and questionnaire survey conducted by Oriental Sports Center, overall satisfaction is inferred by the satisfaction of each person in the tested sample, and the time cost from the evacuation point to different reception points is inferred through actual investigation. Based on these data, a balance model is established, and the calculation is performed. The ratio of the total willingness value of the evacuated individuals to the total cost is taken as the function of the optimization objective. When the optimization objective function is complex, the genetic algorithm is used to search for the optimal solution by simulating the process of natural evolution. At the same time, the cost priority and willingness priority models are established to compare the advantages and disadvantages of different evacuation schemes and their applicable situations. The evacuation of Oriental Sports Center is studied using a genetic algorithm. The objective function value of equilibrium model optimization is 0.2155 (optimal solution), significantly different from willingness priority model optimization (0.0300). After calculating the optimization objective function value and cost value under the conditions of cost priority and willingness priority, a “People-oriented and cost-balanced” suggestion is put forward for a large crowd evacuation scheme.

The Influence of Competing Social and Symbolic Cues on Observers' Gaze Behaviour.

The effects of social (eye gaze, pointing gestures) and symbolic (arrows) cues on observers' attention are often studied by presenting such cues in isolation and at fixation. Here, we extend this work by embedding cues in natural scenes. Participants were presented with a single cue (Experiment 1) or a combination of cues (Experiment 2) embedded in natural scenes and were asked to 'simply look at the images' while their eye movements were recorded to assess the effects of the cues on (overt) attention. Single-gaze and pointing cues were fixated for longer than arrows but at the cost of shorter dwell times on the cued object. When presented together, gaze and pointing cues were fixated faster and for longer than simultaneously presented arrows. Attention to the cued object depended on the combination of cues and whether both cues were directed towards or away from the target object. Together, the findings confirm earlier observations that people attract attention more strongly than arrows but that arrows more strongly direct attention.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

Purpose Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model. Design/methodology/approach In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided. Findings Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions. Originality/value The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Towards sustainable injection moulding using 3D printed conformal cooling channels: a comparative simulation study

AbstractIn recent years, studies have proven that conformal cooling channels (CCC) in an additively manufactured mould result in a more efficient and effective injection moulding process. This can be achieved since CCCs are designed to follow the contour of the part being moulded so that the surface of the part is equidistant from the channel at all points. However, no studies were found which explored the combined effect of mould material thermal conductivity and varying cooling channel designs on the cooling performance of the mould from a sustainability point of view. Within this context, a study was carried out to explore the effect of the tool material’s thermal conductivity on the performance of various CCC designs in comparison with conventional, straight drilled cooling channels. The performance of the cooling channels was analysed from a sustainability point of view by comparing the channel performances in terms of energy consumption, financial implications, and the resulting quality of the part. The results of this study showed that the higher conductivity alloys were especially effective at reducing the cycle time and improving the energy performance of the process in the conventional cooling channel designs. These materials were also capable of reducing the overall cost of the process which was calculated in terms of material costs and electricity consumption. For the CCC designs, however, the high conductivity alloys were less effective in all aspects of this analysis, namely cooling time, energy efficiency, and overall costs. However, it is worth noting that based on the melt flow simulations, the alternative materials had little to no effect on the resulting quality of the part.

ФОРМУВАННЯ СПОСОБІВ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ ЗАСТОСУВАННЯ 3D-ПРИНТЕРІВ ДЛЯ КОМБІНОВАНОГО FDM-ДРУКУ З МОДИФІКАЦІЄЮ ПОЛІМЕРНИХ МАТЕРІАЛІВ

Current manufacturing trends today include additive technologies. The scope of use of industrial technological equipment for 3D-printing is expanding. Printing methods are being improved. New polymer and composite materials with reinforcement are being used. The quality, accuracy and strength of products improves. To assess the effectiveness of the implementation of technological processes in industrial equipment, the OEE (Overall Equipment Efficiency) criterion is used. For industrial 3D printing installations, the OEE indicator includes three component performance criteria: the criterion for realizing the design potential of the target technology workflow; criterion for technological assurance of product strength and quality; criterion for organizational and logistical exclusion of unproductive costs. The work has created a complete set of ways to increase the efficiency of equipment for combined FDM technologies by reducing the cost of operational time for technological and auxiliary transitions, as well as for preparatory actions for modifying polymer materials. Based on this variety of methods, the design of a multifunctional 3D printer for combined FDM technology has been proposed. The technical specifications for the printer provide for: increasing the productivity of 3D printing; ensuring adhesive strength of the connection between the initial layers of polymer material and the desktop; ensuring cohesive strength between layers of polymer material; expansion of technological printing capabilities using additional composite materials. This problem is solved by installing devices on a 3D printer that provide: modification of polymer materials by a pulsed high-frequency electromagnetic field; execution of executive robotic movements in the work cycle; computer control of all subsystems of a combined printing 3D printer using a single control program. The use of the described methods for increasing the efficiency of using 3D printers for combined FDM printing is possible in the production process of a repair and maintenance enterprise, as a set of all functions that ensure the achievement of the operating goal.

Online RL-based cloud autoscaling for scientific workflows: Evaluation of Q-Learning and SARSA

Q-Learning and SARSA are two well-known reinforcement learning (RL) algorithms that have shown promising results in several application domains. However, their approach to build solutions is quite different. For example, SARSA tends to be more conservative than Q-Learning while exploring the solution space. Motivated by such differences, in this paper, we conducted an evaluation of both algorithms in the context of online workflow autoscaling in pay-per-use Clouds, where the goal is to learn optimal virtual machine scaling policies to optimize metrics such as execution time and monetary costs. To do so, we based our experiments on a state-of-the-art scaling strategy with encouraging results in learning optimal scaling policies for reducing execution time and monetary cost. We conducted experiments on simulated environments with four widespread benchmark workflows and two types of virtual machines. Results show that SARSA outperforms Q-Learning in almost all cases. For two workflows SARSA obtains significant gains of up to 40.8% in the first 100 and 300 episodes respectively and losses less than 6% in all episodes observed. In one workflow SARSA achieves significant gains up to 13.9% and no significant losses were observed. There was only one workflow with no significant gains and one significant loss (16.2%) in 1 of 50 observations. In summary, we found multiple stages where SARSA achieves significant and remarkable gains, and the rest of the time both algorithms had a similar performance. In general terms, we can observe that SARSA performs better for learning scaling policies in the Cloud considering workflow applications commonly used by the community to benchmark Cloud workflow resource allocation techniques. These represent interesting results to further drive the design and selection of RL-based autoscaling strategies to schedule workflow executions in the Cloud.

Role of China-Pak Economic Corridor (CPEC) in Logistic System of Pakistan's and China's

This study will explain the impact of China-Pak Economic Corridor (CPEC) on logistic system of China and Pakistan. This project is estimated investment of US $90 billion, CPEC project is consists of various sub-projects including energy, road, railway and fiber optic cable but major portion will be spent on energy. This project will start from Kashgar port of china to Gwadar port of Pakistan. Transportation is sub-function of logistic that consists of 44% total cost of logistic system and 20% total cost of production of manufacturing and mainly shipping cost and transit/delivery time are critical for logistic system. According to OEC (The Observing Economic Complexity) currently, china is importing crude oil which 13.4% from Persian Gulf. CPEC will china for lead time that will be reduced from 45 days to 10 days and distance from 2500km to 1300km. This new route will help to china for less transit/deliver time and shipping cost in terms of logistic of china. Pakistan’s transportation will also improve through road, railway and fiber optic cabal projects from Karachi-Peshawar it will have speed 160km per hour and with help of pipeline between Gwadar to Nawabshah gas will be transported from Iran. According to (www.cpec.inf.com) Pakistan logistic industry will grow by US $30.77 billion in the end of 2020.

Trust, nuance, and care: Advantages and challenges of repeat qualitative interviews

Most methodological discussions about the pros and cons of repeat interviews fall within qualitative longitudinal literature and are premised on project designs with relatively long intervals between encounters. Less attention has been paid to the practice and ethics of repeat interviewing as a stand-alone method, that does not follow participants long-term, but instead conducts several interviews over a short period of time. This article is based on interviews and research logs from a project in which over 350 incarcerated persons in Latin America were interviewed. We evaluate the advantages and shortcomings of repeat interviewing, in this case, three sessions with each participant with up to a week in between sessions. We find that repeat interviewing increases trust and rapport, contributes to nuanced data, generates reflexivity, and ensures more ethical research by making it easier for researchers to care for participants. Yet the method also has the disadvantages of demanding a significant investment of resources, the risk of losing participants, and on occasion, the emotional challenge of breaking strong bonds when researchers and participants part ways. We argue that the advantages of repeat interviews exceed the shortcomings, but ethical concerns added to the cost in time, energy, and money might at times proscribe the method.

The Impact of E-Government on the New Generation Productive Capacities: Evidence from Cross-Country Data

The New Generation Productive Capacity Index (PCI) and the E-Government Development Index (EGDI) are increasingly used in various economies. Therefore, can e-government contribute to the continuous development of the new generation of productive capacity? Focusing on this important issue, this paper systematically examines the mechanism of the impact of e-government on the new generation of productive capacity and tests it using relevant data from major global economies. Firstly, this paper conducts regression analysis using the panel fixed-effects model, and the results show that e-government has a positive facilitating effect on the new generation of productive capacity. Secondly, the development of e-government will directly reduce the time, procedural, and monetary costs for enterprises and individuals to obtain government services, thereby promoting the development of production capacity. Finally, the specific changes in the structure of social expenditures are that government education expenditures and general expenditures will increase, but total government expenditures and household expenditures will not be affected by the construction of e-government. The research contribution of this paper is mainly reflected in the following three aspects: first, in terms of research perspectives, this paper extends the research on e-government’s ability to improve the efficiency of governments and organizations in handling affairs and its impact on the productive capacity of a society. Second, it contributes to the theory on the topic of e-government. This paper proposes a general equilibrium model to reveal the impact of e-government construction on a government and the public, and to more comprehensively reveal the impact mechanism of e-government construction in the whole economy. Third, this paper contributes empirical evidence. This paper utilizes the data of the major economies around the world to reveal the mutual influence between the construction of e-government and the new generation of productive capacity, and then effectively proves the specific impact of e-government on governments, enterprises, and individuals through the test of cost mechanism and financial mechanism. Different from the existing literature, this paper focuses on the impact of e-government on the new generation of productive capacity, revealing that e-government construction does not increase the financial burden on governments but reduces the burden on firms and individuals, thus providing new insights into the productivity effects of e-government development.

Explainable machine learning: Compressive strength prediction of FRP-confined concrete column

Fibre reinforced polymers (FRP) are widely used for the strengthening of beams, slabs and columns due to their light weight and high strength. Compared to the traditional methods like steel cages or steel jackets, it reduces the time and economic cost significantly. This study focuses on the prediction of the compressive strength of FRP-confined concrete columns, which is influenced by multiple parameters. Initially, through experimental observations and the analysis of parameter mechanisms, the key parameters affecting compressive strength were identified. Six prediction models, namely linear regression, ridge regression, decision tree, random forest, and eXtreme Gradient Boosting, were subsequently constructed and evaluated to determine the optimal machine learning model. A comparison between the machine learning models and mathematical models was conducted. Furthermore, SHapley Additive exPlanation (SHAP) analysis was performed to ascertain the parameter importance and sensitivity of the machine learning model, and the analysis results were compared with existing models. The results indicate that compared to other machine learning and mathematical models, the eXtreme Gradient Boosting model demonstrates superior robustness and accuracy. The thickness and elastic modulus of FRP, as well as the concrete strength, are identified as crucial parameters that positively influence compressive strength. The relationships between the former two parameters and compressive strength follow a logarithmic function, while the relationship between concrete strength and compressive strength is approximately linear. These findings hold implications for the future development of codes and standards.

A Final Cost Estimating Model for Building Renovation Projects

The construction sector in Greece has been developing radically in the field of building renovations. The foremost problem for projects in the building construction industry is producing an accurate and reliable cost estimate at the onset of construction. The artificial neural network (AΝΝ) approach, using data available at the early stages of the project, can help resolve or prevent any kind of difficulty that could make the successful completion of a building less likely. ANNs have been highly efficient in gaining results which could prevent the failure of building constructions projects. The ultimate goal is to highlight the usefulness of the adoption of ANNs models to predict the final cost of a building renovation project. Thus, construction companies could avoid financial failure, provided that the gap between cost prediction and final cost for renovation projects is minimized. This paper presents an artificial neural network (ANN) approach for predicting renovation costs in Greek construction projects. The study, based on a comprehensive literature review and real renovation data from construction companies, employs IBM SPSS Statistics software to build, train, and test the ANN model. The model, which uses initial cost, estimated time, and initial demolition cost as inputs, is based on the radial basis function procedure. The model presents high performance with up to 2% sum of squares error and near zero relative error, demonstrating the ANN’s effectiveness in estimating total renovation costs.

How does stress experienced on instagram differ from threads? Comparing social media fatigue based on platform types

Social media fatigue in individuals has been recognized as a significant consequence and has attracted considerable attention in numerous studies. Prior research on social media fatigue has predominantly focused on investigating the direct relationship between stressors and fatigue. Nevertheless, there has been a conspicuous lack of discussion concerning the psychological variable contributing to fatigue. This study aims to address this gap by proposing that the time cost plays a crucial role in mediating the relationship between stressors and fatigue. Furthermore, we propose potential variables that could alleviate these sources of stress, thereby adopting a more comprehensive methodology compared to previous investigations on fatigue. Additionally, through a multi-group analysis based on types of social media (text-based vs. image-based), we explore the relationships between stressors, time cost, and fatigue. This approach is intended to supplement the literature on social media fatigue, confined mainly to analyzing a single platform. The empirical results validate the mediating role of time cost and demonstrate differences in stress perception between platforms. This study expands upon the theoretical framework of social media fatigue and proposes customized strategies for social media types, enhancing academic and practical comprehension.

Analysis and Benchmarking of feature reduction for classification under computational constraints

Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

Effect of Retrogression with Different Cooling Ways on the Microstructure and Properties of T'/η' Strengthened Al-Zn-Mg-Cu Alloys.

Retrogression and re-aging (RRA) treatment has been proven to effectively overcome the trade-off between strength and corrosion resistance. Current research focuses on the heating rate, temperature, and holding time of retrogression treatment while ignoring the retrogression cooling ways. In this paper, the effects of RRA treatment with different retrogression cooling ways on the microstructure and properties of newly developed T'/η' strengthened Al-Zn-Mg-Cu alloys were investigated by performing tests on mechanical properties, intergranular corrosion (IGC) resistance, and electrochemical corrosion behavior. The results show that the mechanical properties of samples subject to RRA treatment with water-quenching retrogression (ultimate tensile strength, yield strength, and elongation of 419.2 MPa, 370.2 MPa, and 15.9, respectively) are better than those of air-cooled and furnace-cooled samples. The corrosion resistance of water-quenching (IGC depth of 162.2 μm, corrosion current density of 0.833 × 10-5 A/cm2) and furnace-cooled samples (IGC depth of 123.7 μm, corrosion current density of 0.712 × 10-5 A/cm2) is better than that of air-cooled samples. Microstructure characterization reveals that the effect of the retrogression cooling rate on mechanical properties is related to the size of T'/η' precipitates with grains as well as the proportion of T' and η', while the difference in corrosion resistance depends on the continuity of grain boundary precipitates (GBPs). With mechanical properties, corrosion resistance, and time cost taken into consideration, it is appropriate to select water quenching for retrogression. These findings offer valuable insights for further design to achieve superior performance in various applications.

Stem cell factor and erythropoietin-independent production of cultured reticulocytes.

Cultured reticulocytes can supplement transfusion needs and offer promise for drug delivery and immune tolerization. They can be produced from induced pluripotent stem cells (iPSCs), but the 45-day culture time and cytokine costs make large-scale production prohibitive. To overcome these limitations, we have generated IPSCs that express constitutive SCF receptor and jak2 adaptor alleles. We show that iPSC lines carrying these alleles can differentiate into self-renewing erythroblast (SRE) that can proliferate for up to 70 cell-doubling in a cost-effective, chemically-defined, albumin- and cytokine-free medium. These kitjak2 SREs retain the ability to enucleate at a high rate up to senescence. Kitjak2 derived cultured reticulocytes should be safe for transfusion because they can be irradiated to eliminate residual nucleated cells. The kitjak2 cells express blood group 0 and test negative for RhD and other clinically significant RBCs antigens and have sufficient proliferation capacity to meet global RBC needs.

KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost

Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.

Application of AIGC in the design of film and television props

This paper discusses the application, influence and long-term value of AIGC in film and television props design. Through deep learning and intelligent algorithms, AIGC technology can quickly generate high-quality film and television prop design schemes, improve design efficiency and accuracy, and save time and labor costs for film and television production. At the same time, AIGC technology can also promote the innovation of film and television props design, stimulate the creative inspiration of designers, and create more diverse design styles. In addition, AIGC technology can intelligently recommend and optimize the design scheme according to the audience 's preferences and needs, enhance the audience 's viewing experience, and enhance the attractiveness of film and television works. All in all, AIGC has important application value in the design of film and television props, and has injected new impetus into the innovation and development of the film and television industry. With the continuous progress of technology and the continuous improvement of application mode, it is believed that the application of AIGC in the design of film and television props will play a more important role and promote the innovation and development of the industry.

Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization‐based metaheuristic approach to neural network modeling for circular economy and disaster resilience

AbstractThis study aims to predict the final‐age compressive strengths of sustainable concrete series produced using different aggregate types with high accuracy using an artificial intelligence model and to reduce environmental degradation and conserve rapidly decreasing natural resources within the scope of sustainable development and circular economy goals. For this purpose, 45 different sustainable concrete series containing all‐natural, recycled, natural, and recycled concrete aggregates were produced and subjected to compressive strength tests at 7 and 28 days. In addition, this study also considers whether deep neural network models, which have gained popularity in recent years but have high time and resource costs, or shallow models are more suitable for determining the compressive strength values with high accuracy. For this purpose, a method is proposed that can automatically generate the optimal neural network model using a metaheuristic approach that eliminates the human factor. For this purpose, the proposed method was extensively compared with different classical machine learning algorithms. The proposed method predicted the 7 and 28‐day compressive strength with a coefficient of determination of 0.999 and presented better compressive strength predictions than all other algorithms. Considering the number of buildings to be constructed after earthquakes, the widespread use of concrete, and the importance of compressive strength, the proposed method will likely provide significant gains within sustainable development, circular economy, and disaster risk reduction.

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems