Sustainable Operations and Computers最新文献

Background and aims

Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.

Methods

We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.

Results

AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.

Conclusions

Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.

In recent years, environmental protection and sustainability have become significant issues and have attracted everyone's attention. And many organizations are now interested in using it as their strategy to gain customer satisfaction and market share and outperform competitors. This article aims to identify and prioritize the main factors that implement green supply chain management (GSCM) in the construction industry. To achieve the goal, the integrated approach combining is fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and fuzzy analysis network Process (FANP) developed. The parameters employed are in this approach identified through an extensive literature review, and validation is criteria introduced through the experts’ opinions to discuss data uncertainty. First, the FDEMATEL method sets up the interrelationships between the criteria, which used for determining are the most important factors in the GSCM approach. Then, the local weight of the criteria calculated using the FANP approach based on cause and effect relationships, and through the FDEMATEL method. The results of this study show that external factors are the most important and influential factors in the GSCM approach, Therefore, the findings of this study can guide managers to make better use of the GSCM approach in the Iranian construction industry.

The continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the original vehicle information data collected by GPS easily. To solve it, this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm. Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location., this method attempts to find a function, which is used to establish the relationship between the lost value and the past value of the vehicle. On this basis, a nonlinear function approximation strategy is used to predict the position of the missing vehicle. Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it. The results show that the average relative error between the complemented vehicle position data and the real data is 0.45% and the maximum absolute relative error is 8.25%. This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm, difference matrix, and moving average data preprocessing. It is suitable for real-time acquisition of vehicle position of VANET and can reduce the complexity of detection time.

Distributed generation (DG) and electric vehicles (EVs) have received much attention in recent years due to increasing energy demand and environmental concerns. Smarter and more flexible utilization of different energy carriers can be achieved by the integrated energy scheduling in the presence of such modern technologies. However, the market price uncertainty makes the energy scheduling a challenging task. In this paper, the impact of electricity price uncertainty on the micro energy grid (MEG) operation is studied and the role of DG units and EVs on dealing with such uncertainty is investigated. In this regard, the paper employs energy hub approach to calculate the optimal energy scheduling and the operating profit of the energy network. The Monte Carlo simulation technique is used to address the price uncertainty and the Coefficient of Variation (CV) criterion is selected for the purpose of quantifying the risk of operating profit variability. The results show that using DG in the MEG, increases profit while reducing the risks. It is also shown that adding EVs to the grid can increase operating profit whereas the risk is almost constant.

The growing COVID-19 virus pandemic outbreak causes an urgent need to produce its vaccine. Pharmaceutical companies would encounter a massive wave of unforeseen demand for the COVID-19 Vaccine after the vaccine production that could lead to a bullwhip effect in the COVID-19 Vaccine Supply Chain (CVSC). The main objective of this study is to design a cognitive map based on the influential factors on the Bullwhip Effect Reduction (BER) of CVSC. Hence, in the first step, the affecting factors on the BER of CVSC are identified and ranked based on their importance from the pharmaceutical experts using the AHP technique. In the second step, 13 out of 18 identified factors are considered for further analyzing and understanding their relationship by Fuzzy Cognitive Mapping (FCM) technique. Furthermore, three different forward scenarios and three backward scenarios are carefully constructed to find the optimal solution for BER on pharmaceutical organizations. The obtained results show that the flexibility factor is the starting point of the backward scenario, which reduces the bullwhip effect in CVSC. Beside, by improving the inventory management and reliability factor, it would be effectively possible to control the lead-time factor and consequently, overcome the bullwhip effect in CVSC.

Patent databases represent an essential source of easily accessible information and technological knowledge. Inherent difficulties in the search for useful knowledge, in particular situations from large volumes of companies’ data, have resulted in little exploration in patent databases. The focus in this work was to evaluate the use of automated mind maps as an auxiliary tool in the discovery of knowledge in patent databases. In this context, the objective was to analyze the patent information in a contributory perspective for the sustainability of water and sewage companies. We performed the study using the participant observation technique in a basic sanitation company and resulted in discovering alternatives to improve crucial processes and producing discussions about new technologies aimed at the sector's sustainability. The company selected for the research is within a context of activities in constant challenge for innovations, since the collection and treatment of sewage is currently a problem of global proportions and of great impact in matters of environmental sustainability. In the Sanitation Sector, certain approaches are not necessarily new and much of what is preached in the Circular Economy has already taken place. For example, making beneficial use of sludge in the soil, production and use of reuse water, use of biogas for generating electricity, and heat and energy or bio-methane. The results pointed to the adding of the use of mind maps as another possibility for the discovery of knowledge in the analysis of patents.

In this study, we proposes a multi-period inventory routing problem with pickup and split delivery for distributing flour among bakeries. This study aims to determine the routes for heterogeneous vehicles with different loading capacity and, at the same time, decrease the cost of the system by enabling the pickup and split delivery at customer nodes. Also, we incorporate the environmental aspects into our modeling framework to reduce the adverse effect of transportation operations. We formulate the problem as a bi-objective multi-period inventory routing problem with pickup and split delivery. The proposed framework incorporates different strategical and operational decisions to optimize the supply chain network. We evaluate the performance of the model with and without the pickup option through an illustrative example. From the algorithm perspective, we implemented a priori method to solve the proposed formulation. We performed numerous experiments on real instances to showcase the application of the proposed framework on real-world problems. Finally, we provided the Pareto solution set for decision-makers to choose the best solutions based on their preference.

Despite the successful development of vaccines, coronavirus disease (COVID-19) continues to present unprecedented challenges. Besides the ongoing vaccination activities, many countries still rely on measures including social distancing, contact tracing, mandatory face masking among others. Several digital technologies such as smart devices, social distancing tools, smart applications have been adopted to enhance public adherence to reduce secondary transmission. Such technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation and quarantine compliance. The use of digital technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which subsequently impact their efficacy and adoption. Also, the aggressive and mandatory use of large-scale digital technologies is not easy to implement, adhere to and subsequently difficult to practice which ultimately lead to imperfect public compliance. To alleviate these impediments, we analysed the available literature and propose an ethical framework for the use of digital technologies centred on ethical practices. The proposed framework highlights the trade-offs, potential roles and coordination of different stakeholders involved in the development and implementation of digital technologies, from various social and political contexts in Zimbabwe. We suggest that transparency, regular engagement and participation of potential users are likely to boost public trust. However, the potential violation of ethical values, poor communication, hasty implementation of digital technologies will likely undermine public trust, and as such, risk their adoption and efficacy.

Potential growth and overall economic development across nations since 1990s till 2018 is functioning as a 'wake-up' call for researchers and policy makers to process for investigation the phenomenon of HDI convergence globally via two framework: σ- and unconditional β- convergence. A proper investigation of such knowledge is of utmost important for regulators of economies, suggesting whether the difference in HDI level across natons has narrowed, including the period of global recession (2007/09). The empirical evidence is analyzed from different methods that act in a complementary way: i) bootstrap quantile regression approach in static panel framework, which allows the assumption of nonlinearities, especially for those groups that have shared a common path in terms of economic development while estimating the convergence results. ii) pooled OLS model, which seek to examine the soundness of the evaluated convergence results and confirmed the validity of our core applied model. All methods revealed strong evidences of unconditional β- convergence for all HDI groups, reflecting all classified HDI groups had potential to increase their tendency toward convergence. This is mainly driven by both the slower forward mobility with the less developed nations and faster backward mobility with the more developed nations. The σ- convergence estimates provides further insights and suggests that the formation and comprising of multiple convergence HDI clusters is a salient feature of reduction in inequality and dispersion across medium and lower nations.

The constancy of efficacy derived from parametric and non-parametric is not significant, this paper provides a scientific assessment and proposes two novel combined parametric and non-parametric operation research models, which will be a new experiment in literature perception. A scientific assessment of banks based on two novel optimizations VRS-CRS-SFA (VCS) and CRS-VRS-SFA (CVS) as the combination of the efficiency measurement method of CCR(Charnes, Cooper and Rhodes model) or CRS(Constant Return to Scale), BCC(Banker, Charnes, and Cooper model) or VRS(Variable Return to Scale) in Data Envelopment Analysis (DEA), as well as Stochastic Frontier Approach (SFA) for 65 banks during Feb to July 2020 are introduced. For analyzing the performance of the parametric and non-parametric approaches, we have considered the linear regression and Unreplicated Linear Functional Relationship (ULFR). Finally, the superior bank and the best performance model are introduced. For more clarification, three different approaches, which are production, intermediation, and profit/revenue in financial institutions, are considered. Among the proposed techniques, the two novel recommended VCS and CVS compared with BCC-CCR, CCR-BCC, and SFA models, in all of the three suggested approaches have a more positive correlation with profit risk and show the higher coefficient of determination values.