Electric vehicles have significantly contributed to the sustainability of world economic growth. It is of critical importance to understand and examine the factors impacting the consumers’ attitude ...toward the adoption of electric vehicles. However, limited study investigated the effect of altruism on electric vehicles adoption from a pro-environmental behaviour perspective. Therefore, the present work aimed at identifying the influencing factors on consumers’ intention to use electric vehicles. To this end, a model has been developed based on two theoretical models called the Norm Activation Model and the Theory of Planned Behaviour. The potential consumers in Malaysia were selected to answer questionnaires. Accordingly, 177 valid questionnaires were collected and the influencing factors on the electric vehicles purchase intention were empirically analyzed using a structural equation model. According to the results, perceived value, attitude, the ascription of responsibility, subjective norms, personal norms, perceived consumer effectiveness, and awareness of consequences affected the consumers’ electric vehicles’ purchase intention significantly and positively. Consumers’ behaviour regarding the adoption of electric vehicles can be understood better through the findings of this study, while electric vehicle development can be promoted as well.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Healthcare waste disposal center location (HCWDCL) impacts the environment and the health of living beings. Different and sometimes contradictory criteria in determining the appropriate site ...location for disposing of healthcare waste (HCW) complicate the decision-making process. This research presents a hybrid multi-criteria decision-making (MCDM) method, named PROMSIS, to determine the appropriate HCWDCL in a real case. The PROMSIS is the combination of two well-known MCDM methods, namely TOPSIS and PROMETHEE. Moreover, fuzzy theory is used to describe the uncertainties of the problem parameters. To provide a reliable decision on selecting the best HCWDCL, a comprehensive list of criteria is identified through a literature review and experts’ opinions obtained from the case study. In total, 40 criteria are identified and classified into five major criteria, namely economic, environmental, social, technical, and geological. The weight of the considered criteria is determined by the Analytical Hierarchy Process (AHP) method. Then, the score of the alternative HCWDCLs in each considered criterion is obtained. Finally, the candidate locations for disposing of HCWs are ranked by the proposed fuzzy PROMSIS method. The results show that the most important criteria in ranking the alternatives in the studied case are economic, environmental, and social, respectively. Moreover, the sub-criteria of operating cost, transportation cost, and pollution are identified as the most important sub-criteria, respectively.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The advent of cloud computing has transformed the role of the Internet in many businesses and organizations. Currently, banks are increasingly adopting cloud technologies to fulfil their varied ...purposes and to create a flexible and agile banking environment that can quickly respond to new business needs. However, past studies tend to focus more on the adoption issues of cloud computing from the organizational perspective with little attention paid on the users’ view of these cloud-based services. Therefore, this paper attempts to investigate the factors influencing cloud computing adoption in the banking sector from the customers’ perspective and to propose an adoption model for this purpose. The model is mainly developed based on the TAM-diffusion theory model (TAM-DTM) with the introduction of three new constructs namely trust, cost, and security and privacy. Questionnaires were randomly distributed to 162 bank customers in Malaysia. Survey data were analyzed using the partial least squares (PLS) method while SmartPLS was used to test the hypotheses and to validate the proposed model. The results suggest that trust, cost, and security and privacy can be successfully integrated within the TAM-TDM. The security and privacy constructs exhibited strong positive influence on perceived ease of use, perceived usefulness, and trust. The study concludes that perceived usefulness, perceived ease of use, cost, attitudes toward cloud and trust significantly influence users’ behavioral intention to adopt cloud computing. Thus, the finding of this study will enable banks to focus more on customer perspectives on cloud-based applications and identify their attitude towards their adoption.
•The impact of social media on traveler’ decision making during COVID-19 outbreak.•A recommendation agent for traveler’ decision making through social media.•Use of text mining approach to develop ...recommendation agent.•Evaluation of method through the data in Netherland forums in TripAdvisor.
The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers’ decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers’ decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers’ decision making. We conclude with the implications of our study for future research and practice.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly ...being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this paper include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.
COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. ...It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Free cholesterol in the diet can cause liver fibrosis by accumulating in Hepatic stellate cells (HSCs). The rate of mortality of this disease is high worldwide and there is no definite remedy for it, ...but might be treated by anti-fibrotic therapies. MSCs-derived exosomes are known as the new mechanism of cell-to-cell communication, showing that exosomes can be used as a new treatment. In this study, we investigated the ability of exosomes of WJ-MSCs as a new remedy to reduce cholesterol-induced liver fibrosis in the LX2 cell line.
MSCs were isolated from Wharton's jelly of the umbilical cord and the exosomes were extracted. The LX2 cell line was cultured in DMEM medium with 10% FBS, then cells were treated with 75 and 100 μM concentrations of cholesterol for 24 hr. The mRNA expression of TGF-β, αSMA, and collagen1α genes, and the level of Smad3 protein were measured to assess liver fibrosis.
Cholesterol increased the expression of TGF-β, αand -SMA, and collagen1α genes by increasing the phosphorylation of the Smad3 protein. Treatment with Exosomes significantly reduced the expression of TGF-β, α-SMA, and collagen1α genes (fibrosis genes). Treatment with exosomes prevented the activation of HSCs by inhibiting the phosphorylation of the Smad3 protein.
The exosomes of WJ-MSCs can inhibit the TGFβ/Smad3 signaling pathway preventing further activation of HSCs and progression of liver fibrosis. So, the exosomes of WJ-MSCs s could be introduced as a treatment for liver failure.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, ...air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Institutional repositories (IRs) have received considerable attention from researchers across disciplines and around the globe. They have potentially increased the public value, ranking, prestige, ...and visibility of researchers, and relevant universities. However, despite the important and rapid growth of research in this area, few efforts have been made to systematically review and integrate the findings from previous research studies or to examine the current state of study regarding IRs. The primary goal of this paper is to provide a better understanding and an in-depth review of the current state of study regarding IRs. This research uses a systematic literature review (SLR) and followed a protocol to properly organize the work related to institutional repositories. The data were collected from primary studies published from 2007 to 2018 from the six major databases (ScienceDirect, IEEE Explorer, Springer, ACM, Taylor and Francis, and Emerald insight). Several papers regarding IRs were reviewed, applying inclusion and exclusion criteria, and a total of 115 studies were included as the main part of this research. The results obtained from these studies indicated that the absence of knowledge of open access IRs among scholars and institutions and inadequate information and communication technology infrastructure were significant challenges behind the development of open access IRs. Meanwhile, enhanced visibility of the academic institution, increased local and global rankings, increased prestige and public value, and improved teaching, learning, and research development by the scholars of the institution were found to be the main benefits of institutional repositories. This paper also highlighted that most of the studies in this research area were focused on the "deployment, implementation, and adoption" and "benefits and challenges" of institutional repositories. The outcomes of this paper can assist future researchers by providing a roadmap of institutional repositories and highlighting guidelines for successful implementation of IRs in higher learning institutions.
In today's digital world the information surges with the widespread use of the internet and global communication systems. Healthcare systems are also facing digital transformations with the ...enhancement in the utilization of healthcare information systems, electronic records in medical, wearable, smart devices, handheld devices, and so on. A bulk of data is produced from these digital transformations. The recent increase in medical big data and the development of computational techniques in the field of cardiology enables researchers and practitioners to extract and visualize medical big data in a new spectrum. The role of medical big data in cardiology becomes a challenging task. Early decision making in cardiac healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Therefore, to facilitate this process a detailed report of the existing literature will be feasible to help the doctors and practitioners in decision making for the purpose of identifying and treating cardiac diseases. This detailed study will summarize results from the existing literature on big data in the field cardiac disease. This research uses the systematic literature protocol as presented by Kitchenham et al. The data was collected from the published materials from 2008 to 2018 as conference or journal publications, books, magazines and other online sources. 190 papers were included relying on the defined inclusion, exclusion, and checking the quality criteria. The current study helped to identify medical big data features, the application of medical big data, and the analytics of the big data in cardiology. The results of the proposed research shows that several studies exist that are associated to medical big data specifically to cardiology. This research summarizes and organizes the existing literature based on the defined keywords and research questions. The analysis will help doctors to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients with heart related diseases.
PDF
