Processing Cost Research Articles

Automatic categorization of medical documents in Afaan Oromo using ensemble machine learning techniques

Automatic medical document classification using machine learning techniques can enhance the productivity of healthcare services by reducing processing time and cost. This work proposes an ensemble learning approach to develop a model that classifies electronic medical documents in Afaan Oromo. The main tasks in this work are preparing the corpus, pre-processing, training the models, and the classification process. We used the term frequency-inverse document frequency (TF-IDF) and bag of words (BOW) feature extraction methods. An ensemble technique in this work is that it creates multiple individual classifier predictions from naïve Bayes, random forest, SVM, and logistic regression and then combines them to advance a reliable and more accurate classifier. Evaluation measures were employed using accuracy, F1-score, recall, and precision for performance comparison. The efficiency of the proposed method is compared with the two existing boosting approaches, namely gradient boosting and adaboost. The experimental result shows the efficiency of BOW feature extraction over TF-IDF in this work on our dataset. These results also illustrated the effectiveness of the proposed model by scoring 94.81% accuracy and 94.84% F1-score. This work significantly contributes to the technological enhancement of service delivery, managing documents through classification methods, and advancing the data processing systems in healthcare sectors.

Segmentation of MR images for brain tumor detection using autoencoder neural network

Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process.

Research on time-division multiplexing for error correction and privacy amplification in post-processing of quantum key distribution

The post-processing of quantum key distribution mainly includes error correction and privacy amplification. The error correction algorithms and privacy amplification methods used in the existing quantum key distribution are completely unrelated. Based on the principle of correspondence between error-correcting codes and hash function families, we proposed the idea of time-division multiplexing for error correction and privacy amplification for the first time. That is to say, through the common error correction algorithms and their corresponding hash function families or the common hash function families and their corresponding error-correcting codes, error correction and privacy amplification can be realized by time-division multiplexing with the same set of devices. In addition, we tested the idea from the perspective of error correction and privacy amplification, respectively. The analysis results show that the existing error correction algorithms and their corresponding hash function families or the common privacy amplification methods and their corresponding error-correcting codes cannot realize time-division multiplexing for error correction and privacy amplification temporarily. However, according to the principle of correspondence between error-correcting codes and hash function families, the idea of time-division multiplexing is possible. Moreover, the research on time-division multiplexing for error correction and privacy amplification has some practical significance. Once the idea of time-division multiplexing is realized, it will further reduce the calculation and storage cost of the post-processing process, reduce the deployment cost of quantum key distribution, and help to remote the practical engineering of quantum key distribution.

Techno-economic assessment of modified Fischer-Tropsch synthesis process for direct CO2 conversion into jet fuel

Direct use of CO2 through modified Fischer-Tropsch synthesis (FTS) process presents a viable approach for the production of carbon–neutral jet fuel. However, achieving high performance for the CO2-FTS process remains a significant challenge. Current technology can only achieve low CO2 conversion and low jet fuel yield using tailored catalysts, which hinders the commercial deployment of direct CO2-FTS process. This paper presents a techno-economic assessment (TEA) of jet fuel production via CO2-FTS process at commercial-scale. Two ex-situ water removal configurations: (a) multi-stage CO2-FTS process and (b) CO2-FTS with tail gas recycle process were conducted to assess their potential for performance improvement. Process performance was evaluated using a model developed in Aspen Plus® linking with Aspen Custom Modeller® (ACM), while economic evaluation was carried out in Aspen Process Economic Analyzer®. The CO2-FTS model was based on first principles and modified Anderson-Schulz-Flory (ASF) distribution. Results indicated that both ex-situ water removal configurations can significantly improve the process performance. CO2-FTS process with 90% tail gas recycle has the best performance for both technical and economic analysis. The process achieved 85.8% CO2 conversion and 35.6% jet fuel yield with the lowest energy demand of 4.57 MWh per tonne of produced jet fuel. Moreover, it boasts the lowest minimum selling price (MSP) of jet fuel at US$2.6/kg despite incurring the second-highest capital expenditure cost of M$ 451.3. A comparison of the two ex-situ water removal configurations at 61.9% and 76.5% CO2 conversion, suggests that both processes exhibit comparable technical performances, yet the recycling of tail gas holds the potential to reduce economic costs. Consequently, this study will inspire researchers on process improvement and cost reduction of the commercial-scale CO2-FTS jet fuel production.

Cu-TiO2/Zeolite/PMMA Tablets for Efficient Dye Removal: A Study of Photocatalytic Water Purification

In this study, Cu-doped TiO2 combined with natural zeolite (ZT) was synthesized and applied as a fixed powder layer on poly(methyl methacrylate) (PMMA) tablets. The material’s morphology, structural, and chemical properties were characterized using high-resolution scanning electron microscopy, Raman spectroscopy, and Brunauer–Emmett–Teller analysis. The antioxidant capacity was evaluated by assessing the neutralization of hydroxyl radicals and iron (III) ions. For the first time, tablets with Cu-TiO2 and ZT deposited on PMMA as the carrier were investigated for removing two dyes, methyl orange (MO) and methylene blue (MB), from water under simulated solar (SS) and UVC irradiation. Under SS irradiation, the Cu-TiO2/PMMA and Cu-TiO2/ZT/PMMA tablets achieved about 21% degradation of MB after 240 min. This result is particularly noteworthy because SS radiation provides lower energy compared with UVC, making the process more economically efficient. Furthermore, the photocatalysts are immobilized on a stable carrier, which enhances the method’s cost-effectiveness by reducing material loss and simplifying recovery. In the presence of ZT/PMMA tablets, 69% of MB was removed by adsorption after 240 min. Additionally, we explored the mechanism of degradation, revealing that the enhanced generation of hydroxyl radicals plays a pivotal role in the effective degradation of MB. At the same time, photogenerated holes contribute to the removal of MO. The overall results suggest that the tablets obtained are a promising solution for water purification due to their effectiveness, simplicity, and low processing cost.

A Business Management Model in the Circular Economy: Technological Solutions for Plastic Recycling

Objectives: this article develops a model for managing business processes in a circular economy using technologies for plastic recycling. Theoretical Framework: The theoretical idea and basis of the article consists in the development of project management methodology, process approach in the closed-loop economy, development of methods and economic tools aimed at effective resource management taking into account waste reduction and increased reuse of plastic fractions in Municipal Solid Waste (MSW). The application of digital technologies and solutions in the context of business process management in the field of plastics recycling will be substantiated, which will allow to identify new opportunities and reserves for optimising business processes in this field, increasing the efficiency of resource use, improving the environment and reducing costs. Method: the work uses a project approach, a process approach, digital modeling methods, and the “polluter pays” method. To implement the process approach, it is proposed to use the concept of Business Process Management (BPM). Competent business process management allows the company to react quickly to changing market conditions and customer orientation. Digital modelling in this paper means the representation of actions in the form of business processes in the organisation of solid waste management, the 'polluter pays' method means the economic assessment and accounting of environmental damage in waste management in the costs of business processes. Results and Discussion: The results of the study can be used to introduce the process approach to plastics recycling in individual regions and countries for the development of a green economy. According to the estimates of the authors, the optimisation and cost reduction of business processes, taking into account the environmental factor and the increase of the revenue part of the project from the reuse and inclusion in the economic turnover of plastic waste recycling products on the scale of the considered city (population of 1800 thousand people, area of 2856 ha, 18.14 tonnes of plastic waste accumulated per day) will reduce the costs of business process management by more than 200 thousand euros. Research Implications: the results of the study can be used to justify projects for the processing and recycling of solid municipal waste, including plastic waste, to introduce environmentally friendly production, to reduce the negative impact on the environment, to develop a process approach to the organisation of all activities for the collection of plastic waste in the city, district. In addition, the application of the proposed approach allows to obtain economic benefits for companies through the sale of secondary resources for the processing of plastic waste, as well as to reduce the negative impact on the environment. Originality/ Value: The originality of the research and its value lies in a new scientific formulation of the problem of reducing environmental pollution from plastic waste through the use of business processes in the field of plastic recycling and digital solutions, as well as the possibility of practical application of the results obtained in the field of green economy.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

Reconfiguration of acid gas removal process matching the integration of coal chemical industry with green hydrogen

The focus of the acid gas removal process has shifted from decarbonization and desulfurization to desulfurization in the context of integrating coal chemical technology with green H2 without a water gas shift unit. The conventional Rectisol process, Selexol process, organic amine process, and sulfone-amine process have been reconfigured to form the R-Rectisol process, R-Selexol process, R-MDEA process, and R-Sulfolane-MDEA process to adapt to the shift of acid gas removal target. The removal efficiency of H2S and CO2, process energy consumption, and process cost are investigated to evaluate the technical and economic performance of the above four reconfigured acid gas removal processes. The results indicate that for the R-Recitsol process and R-Selexol process, both high pressure and low temperature increase the relative selectivity of H2S to CO2, thereby enhancing the selective removal of H2S. In contrast, for the R-MDEA process and R-Sulfolane-MDEA process, low pressure and low temperature improve the relative selectivity of H2S to CO2, thereby facilitating the selective removal of H2S. Under optimal operating conditions, the value of relative selectivity of H2S to CO2 of the R-Sulfolane-MDEA process is the largest, and the relative selectivity of H2S to CO2 of the R-Rectisol process, R-Selexol process and R-MDEA process are only 7.64 %, 12.85 %, and 68.40 % of the relative selectivity of H2S to CO2 of the R-Sulfolane-MDEA process, respectively. Compared to the other three reconfigured acid gas removal processes, the R-Sulfolane-MDEA process demonstrates superior advantages in both energy consumption and economic feasibility, provided that the H2S levels remain below 0.1 ppm in purified syngas. This study aims to enhance the decision-making process for gas purification methods in the coal chemical industry by considering integrated green H2 as an alternative to the water gas shift unit.

Investigation and review of FGM functional gradient materials

With the advancement of modern industries and processes, numerous structures are now required to function in thermal environments, leading to a new class of composite materials known as functional gradient materials (FGMs). Initially developed as thermal barrier materials for structural applications in aerospace and fusion reactors, FGMs have gained global recognition as vital composite materials. The overall properties of FMG are unique and different from any of the individual material that forms it. There is a wide range of applications for FGM and it is expected to increase as the cost of material processing and fabrication processes are reduced by improving these processes. Their application is increasingly recognized as a highly effective method for achieving sustainable development across various industries. In this study, an an overview of resents a detailed examination of the development, features, and essential manufacturing processes of FGMs is presented, based on a review of existing literature.

Rational parameters of drilling and blasting works providing the least costs of ore mining and processing

Отгрузка разрушенной горной массы требуемого качества и количества на обогатительную фабрику является одной из основных задач подготовительных работ при добыче полезных ископаемых. Основным способом регулирования этого процесса является оптимизация параметров и технологий ведения буровзрывных работ. На Актогайском месторождении возникла проблема с увеличением крепости добываемой руды по мере углубления карьера, что сильно влияет на производительность обогатительной фабрики. В статье приведен опыт проведения мероприятий по оптимизации БВР для улучшения работы обогатительной фабрики, а точнее стабилизации процесса первичного дробления руды. В статье приведены методы расчета рациональных параметров и результатов БВР. Проведен расчет рациональных параметров БВР, а также расчет гранулометрического состава взорванных пород при рациональных параметрах и при предложенных для экспериментальных взрывов Қажетті сападағы және көлемдегі бұзылған тау жынысын байыту фабрикасына жөнелту пайдалы қазбаларды өндіруге дайындық жұмыстарындағы негізгі міндеттердің бірі болып табылады. Бұл процесті реттеудің негізгі әдісі бұрғылау-жару жұмыстарының параметрлері мен технологияларын оңтайландыру болып табылады. Ақтоғай кен орнында карьердің тереңдеуімен өндірілетін кеннің беріктігінің артуы проблемасы туындады, бұл байыту фабрикасының өнімділігіне айтарлықтай әсер етеді. Мақалада байыту фабрикасының жұмысын жақсарту, дәлірек айтқанда, кенді бастапқы ұсақтау процесін тұрақтандыру мақсатында бұрғылау-жару жұмыстарын оңтайландыру шараларын өткізу тәжірибесі баяндалған. Мақалада бұрғылау-жару жұмыстарының ұтымды параметрлерін есептеу әдістері және нәтижелері келтірілген. Бұдан басқа, ұтымды параметрлерге және эксперименттік жарылыстар үшін ұсынылған параметрлерге сәйкес жарылған жыныстардың гранулометриялық құрамын есептеу жүргізілді One of the main tasks of preparatory work in mineral extraction is the delivery of the required quality and quantity of broken rock mass to the processing plant. The primary method of regulating this process is optimizing the parameters and technologies of drilling and blasting operations. At the Aktogay deposit, a problem has arisen with the increasing hardness of the mined ore as the pit deepens, significantly affecting the performance of the processing plant. The article presents the experience of implementing measures to optimize drilling and blasting operations to improve the performance of the processing plant, specifically to stabilize the primary ore crushing process. The article provides methods for calculating rational parameters and results of drilling and blasting operations. Calculations were made for the rational parameters of drilling and blasting operations, as well as for the granulometric composition of blasted rock under rational parameters and those proposed for experimental blasts

Evaluation of Elevated Temperature Oxidation of Higher Manganese Ni-Resist Alloy

Nowadays Nickel was used mostly in battery, elevated temperature application and also corrosive environment. The boost of electric and hybrid vehicle had maximized the usage of Nickel as main composition in alloying industry. As such the Nickel price was at peak and become volatile to interest manufacturer. Among affected compound is Ni-Resist alloy. In this study, an alloy known as ductile Ni-resist alloy was modified to minimize its processing cost by decreasing its Nickel weight percentage. The modification was investigated in order to reduce nickel usage in the alloying composition so the alloying process cost can be minimized. More than 12 wt % chromium and manganese were alloyed with 10 wt % nickel during melting stage. Then, the evaluation of properties, oxidation behavior and its microstructure were examined. The results indicated more carbide was formed which later decreased tensile strength at elevated temperature. Oxidation resistance increased with addition of Mn/wt%. After oxidation at 765oC for 25 hours, there are oxide layers visible on the changed alloy.

Fertigungsprozessanalyse eines mittels Innenhochdruck-Umformung hergestellten Bauteils

Abstract Series manufacturers in the field of hydroforming do not always have the necessary database for predictive maintenance, especially for the production of complex hydroformed components. Small and medium-sized companies in particular often lack the resources to acquire, process and profitably utilize these data sets under production conditions. The IHU processes that occur in practice are usually highly complex, both in terms of geometry and against the background of additional process steps (punching, plunging). This results in large amounts of data and complex data analyses, which are reflected in the data processing costs. A data analysis is to be carried out on the basis of a complex hydroforming process and its additional benefit for quality management and predictive maintenance is to be explained.

Assessment of agroclimatic resources in agricultural landscapes of the Turkestan region of the Republic of Kazakhstan Askha

The article presents the scientific results of a study assessing agroclimatic resources in agricultural landscapes located in various natural zones of the Turkestan region of the RK (Republic of Kazakhstan). The research methods included classical and modern methods of mathematical statistics using digital technology and time series graphs to develop a mathematical model for climatic and hydrological indicators. Assessment of changes in indicators of agroclimatic resources in agricultural landscapes for 1941–2020 showed that the sum of air temperature, evaporation from the water surface and radiation balance of the daytime surface, characterising the energy resources of landscapes, increased by 10–15%, which contributes to increase in the total water consumption of agricultural land by 10–12%. Meanwhile, the decreasing tendency of the amount of precipitation by 5–10% in all natural climatic zones of the region has become one of the factors leading to a decrease in the natural moisture supply of the soil and vegetation cover of landscapes by 10–15%, acting as important environment-forming and ecological functions. The combined impact of these environment-forming factors has become the key reason for the increase in the deficit of agricultural land water consumption by 15–20%, the reduction of solar energy costs for the soil-forming process by 10–15% and the increase of the climate aridisation, and has become a signal for the need in the safety of agricultural activities, requiring the development of a set of adaptive measures to mitigate this process in the region.

BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis.

Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, namely titer, rate, and yield (TRY), which respectively reflect the downstream processing, reactor size, and raw material costs, are not well captured in bioinformatics databases. In this paper, we present BioTRY, an intuitive and user-friendly tool that contains >5,000 biochemicals and >3,800 strains, along with over 52,000 corresponding TRY entries with original references. It is freely available at http://www.synbiohealth.cn/biotry. To our knowledge, BioTRY is the first available database on biosynthesis TRY data from original research. We anticipate that BioTRY will become a useful tool that aids researchers and decision-makers in understanding the current development state of biosynthesis and allows them to foresee potential prospects and applications for biosynthesis.

MIDAS: Multi-layered attack detection architecture with decision optimisation

The proliferation of cyber attacks has led to the use of data-driven detection countermeasures, in an effort to mitigate this threat. Machine learning techniques, such as the use of neural networks, have become mainstream and proven effective in attack detection. However, these data-driven solutions are limited by: a) high computational overhead associated with data pre-processing and inference cost, b) inability to scale beyond a centralised deployment to cope with environmental variances, and c) requirement to use multiple bespoke detection models for effective attack detection coverage across the cyber kill chain. In this context, this paper introduces MIDAS, a cost-effective framework for attack detection, which introduces a dynamic decision boundary that is used in a multi-layered detection architecture. This is achieved by modelling the decision confidence of the participating detection models and judging its benefits using a novel reward policy. Specifically, a reward is assigned to a set of available actions, corresponding to a decision boundary, based on its cost-to-performance, where an overall cost-saving is prioritised. We evaluate our approach on two widely used datasets representing two of the most common threats today, i.e., phishing and malware. MIDAS shows that it effectively reduces the expenditure on detection inference and processing costs by controlling the frequency of expensive detection operations. This is achieved without significant sacrifice of attack detection performance.

Evolution of solid residue composition during inert and oxidative biomass torrefaction

Biomass torrefaction is a thermochemical pretreatment that can be used to improve biomass properties, contributing to the energy densification of biomass or increasing its grindability, burning and gasification characteristics, porous structure, etc. While it is typically carried out under inert conditions, oxygen-lean torrefaction of biomass can reduce the process cost and complexity. This work proposes a novel extended 2-step kinetics mechanism capable of accurately predicting the evolution of the chemical composition of torrefied biomass. The model parameters are obtained from data collected from an extensive experimental campaign in which the chemical composition of torrefied biomass was measured at different temperatures, residence times, and oxygen fractions. The carbon mass fraction of the torrefied products was found to increase by up to 50 % under severe inert torrefaction conditions, i.e., high temperatures and long residence times, whereas the hydrogen fraction decreased. In the presence of oxygen during torrefaction, the carbon and hydrogen contents in the solid residue decrease compared with those produced under inert torrefaction. The extended 2-step model can also be used to determine the high heating value and energy densification of the torrefied biomass, with the latter ranging from 1 to 1.4 for the operating conditions tested.

Two factors facilitate the cost-effective and harmless cementation of rare earth slags through MICP technology: Carbonic anhydrase bacteria and endogenous calcium ions

Microbially induced carbonate precipitation (MICP), a solidification/stabilization technique that doesn't interfere with recycling and can reduce the migration of pollutants, has been employed to cement rare earth slags (RES). Currently, the MICP process of the urease/carbonic anhydrase pathway has attracted considerable attention. However, the MICP process of the urease pathway may produce high concentrations of ammonia, which can cause secondary contamination; the addition of a calcium source (calcium chloride) may increase the cost of the treatment process. In this study, two factors, carbonic anhydrase bacteria and endogenous calcium ions (Ca²⁺), facilitated the MICP technology to cost-effectively and harmlessly cement RES and stabilize contaminants. The findings indicated that strain Z1 could utilize endogenous Ca²⁺ to cement the RES, thereby enhancing its unconfined compressive strength, reducing the disintegration rate, radiation dose, and wind erosion intensity, and facilitating the stabilization of heavy metals and radionuclides. In comparison to the CK group, the leaching concentrations of Cu2+, Pb2+, Zn2+, Cd2+, Th(Ⅳ), and U(Ⅵ) in the C1 group were reduced by 42.37 %, 20.20 %, 18.77 %, 22.53 %, 12.32 %, and 23.66 %, respectively. The treatment effect can be further enhanced by adding exogenous Ca2+. This study's findings can help reduce the potential environmental risks in the long-term sequestration of RES, while also providing technical support for the sustainable development of the rare earth industry.

Design Guidelines for Building and Infrastructure Integrated Photovoltaic Modules

The demand for renewable energy is increasing as efforts to decarbonize energy sources continue. Photovoltaic (PV) generation systems are the main contributor to the growth of renewable energy, but limited land availability in countries such as Belgium and Netherlands poses a challenge to their deployment. Integrated PV (IPV) can be a promising solution, but requires special consideration regarding electrical and fire safety, efficiency, durability, cost, and environmental impact in the design process. This study seeks to assist designers of IPV products by guiding the selection of materials, technologies, mechanical designs, and production methods for PV semifabricates (SF). It provides a comprehensive list of general design criteria, each offering various options in terms of bill of materials and production technologies. These options come with their own set of advantages and disadvantages, which are enumerated and quantified wherever feasible. The general design guidelines are validated based on the building‐integrated PV and infrastructure‐integrated PV demonstrators (in this case a noise barrier) being developed in the Solar Energy Made Regional (SolarEMR) project.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Algorithm for solving multi-objective hybrid flow shop scheduling problem

Aiming at the multi-objective unrelated parallel machine hybrid flow shop scheduling problem, a multi-objective mathematical model is established with the goal of minimizing the maximum completion time, total machine energy consumption and machine processing cost. An improved multi-objective evolution algorithm based on decomposition (IMOEAD) is proposed. The uniform design table is used to generate the initial weight vector to improve the population diversity. Normal distribution crossover and adaptive Gaussian mutation are used to improve the global and local search capabilities of the algorithm. Individuals are selected in the neighborhood of the weight vector to generate new solutions. The non-dominated rank and crowding distance are used to update the external archive. The inverse generation distance, generation distance and number of non-dominated solutions are used as performance indicators. Through a large number of case simulations, the algorithm is compared with the non-dominated sorting genetic algorithm II and the multi-objective evolution algorithm based on decomposition. The results verify the effectiveness of the algorithm.