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The IoT can lead to disruptive healthcare innovation. Research articles on IoT in healthcare and COVID-19 pandemics are thus researched in order to discover the potential of this technology. This literature-based research may help professionals to explore solutions to associated issues and battle the COVID-19 epidemic. Using a process diagram, IoT's significant accomplishments were briefly evaluated. Then seven critical IoT technologies that look useful in healthcare during the COVID-19 Pandemic are identified and illustrated. Finally, in the COVID-19 Pandemic, potential fundamental IoT applications were identified for the medical industry with a short explanation. The present predicament has opened up a fresh avenue to creativity in our everyday lives. The Internet of Things is an up-and-coming technology that enhances and gives better solutions in the medical area, such as appropriate medical record-keeping, sample, device integration, and cause of sickness. IoT's sensor-based technology gives a remarkable ability to lower the danger of intervention in challenging circumstances and is helpful for the pandemic type COVID-19. In the sphere of medicine, IoT's emphasis is on helping to treat diverse COVID-19 situations accurately. It facilitates the work of the surgeon by reducing risks and enhancing overall performance. Using this technology, physicians may readily identify changes in the COVID-19′s vital parameters. These information-based services provide new prospects for healthcare as they advance towards the ideal technique for an information system to adapt world-class outcomes by improving hospital treatment systems. Medical students may now be better taught and led in the future for the identification of sickness. Proper use of IoT may assist handle several medical difficulties such as speed, affordability, and complexity appropriately. It may simply be adapted to track patients' calorific intake and therapy with COVID-19 asthma, diabetes, and arthritis. In COVID-19 pandemic days, this digitally managed health management system may enhance the overall healthcare performance.

Secure and reliable identity is essential for identifying a user accurately and providing services. The traditional centralized identity systems have several security weaknesses and do not support user control. Self-sovereign identity (SSI) has been proposed to provide user control and secure identity model. User record privacy and security is a critical factor for any healthcare information system. Healthcare is also a domain where SSI solutions can counter patient information privacy and security threats. This paper reviews the applicability of blockchain-based SSI solution in healthcare, their advantages and requirements. It further provides a model use case for demonstrating the SSI application in healthcare.

Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

In order to optimise the corporate HR information system, IoT first-off technology is used, together with the system demand phase, with edge control in mind. initially, the hardware and software system are set up, and only then the communication models between the edge layer of the system and other parties are created edge layer, and the business type-driven connection selection method are considered. To ensure communication reliability across various levels of the system, northbound multilink switching algorithms are created and implemented, accordingly. After implementing the functions described above, the edge control system may be implemented. Ensure IoT applications satisfy intelligence, expandability, and security needs. due diligence and due diligence are required The campaign was aimed primarily at the enterprise, with the goal of defining the functional and performance requirements of the product. Ensuring the fundamental logical structure in the system design phase; in the system architecture design phase, the other features of the system architecture Realization of design is accomplished. a set of system module functions exists planned down to the last detail. Pay and benefits management are only two aspects of the whole management function. Employee personnel change is one of the e system's modules. Payroll, benefits, and personnel administration are referred to as department management. Human resource management is a process system modules go through; as far as system implementation is concerned, system coding occurs. In addition, development tool and software development methodology support the creation of pages. The system has finally been realised the system design goals are set up to mimic the business's real needs, which are then put to the test.

The required purchasing skills have been evolving due to supply chain globalization, sustainability, and environmental requirements. Modern purchasing skills are also influenced by the expanding adaptation of complex Information Systems (IS) in enterprises. Complex technology purchasing requires the management of multiple organizations and unique competences, hence the needed IS purchasing skills required studies from a different angle. Compared to previous studies, the purpose of this study was to identify the skills required from a purchaser to manage complex IS purchases and address their risks. In-depth qualitative research reveals that the IS purchaser is required to understand SW methodologies and possess strong interorganizational skills in addition to traditional purchasing skills in order to run successful IS purchases. Based on the findings, the study proposes additional technical and project methodological training for educators and purchasing professionals to succeed in IS purchases. Publishers version

This article explains problems of individualization of education and extracurricular activities’ programs’ development. Main problems that need to be solved at various stages of the organization of extracurricular activities, from its methodological study to organizational and technical planning are analyzed. An overview of the directions of extracurricular activities, the forms in which it can be organized as well as the specifics of its implementation at various levels of education is offered. In order to reduce the labor intensity of time costs taking into account the time allocated for its implementation as well as the ability to generate plans taking into account individual preferences an intelligent information system was developed. Multi-criteria mathematical models from the class of optimization methods for solving the posed problem of decision support, functional and software-architectural developed model of a web service and examples of its work on the stages of planning general activities in the optimization formulation are presented. The proposed approach can be widely used in educational institutions of various levels interested in reducing the cost of organizing extracurricular work, automatic checking for compliance with requirements, as well as generating a work plan taking into account the opinions of various categories of experts.

Fuzzy sets introduced to overthrow the uncertainty of an object. Interval valued intuitionistic fuzzy sets in topological structure space is an extension of fuzzy sets. Interval-valued intuitionistic multi fuzzy set theory is a capable tool for commerce with ambivalence of expertise in information systems. The motive of this paper is to present interval valued intuitionistic multi fuzzy completely generalized semiprecontinuous mappings and provide some of their properties. Also we proved the relation between various types of interval valued intuitionistic multi fuzzy continuous mappings. AMS Subject Classification (2010): 54A40.

This study proposes a novel approach to forecast a transhipment port's competitiveness on the basis of market share. The potential reasons for escalating or diminishing competitiveness are explored by utilising a multi-criteria decision-making (MCDM) methodology. We assess the competitiveness of transhipment ports by seven major components: connectivity, port facility, efficiency, cost factor, policy and management, information systems and green port management, and contribute to the understanding of the interdependence among the components. We investigate the Bangladesh container market due to the uniqueness of its regional context and dimensions of competitiveness. Bangladesh has direct connections to four regional hubs, namely, Singapore, Colombo, Port Klang and Tanjung Pelepas. These regional hubs compete to grasp a significant volume of container movements in line with the rapid growth of Bangladesh's industrialisation and national economy. We find that Singapore outperforms others in terms of competitiveness, but all ports perform below expectations in green port management practices. These findings would benefit port authorities for improving their competitiveness, and liner shipping companies and shippers in making strategic port choice decisions.

The lack of national security standardization bodies can have adverse impact on the adoption of international security standards and best practices. To assure security confidence among various organizations and to promote systematic adoption of standards and best standards, a practical framework that can support comparative measures is needed. . This paper presents GoSafe, a novel practical cybersecurity assessment framework that is tailored to the ISO 2700x standard requirements for the development of Information Security Management System (ISMS). GoSafe can be used for both self-assessment and auditing/scoring tool by national cybersecurity authorities. Using GoSafe, organizations can evaluate their existing information security management systems against local and international standards by utilizing built-in pre-audit tools. As such, GoSafe will help organizations evaluate and enhance their readiness for evolving risks and threats. In GoSafe framework, a novel mathematical model was also designed and implemented for the scoring/rating tool, namely, the national cyber security index (aeNCI). The aeNCI employs multiple parameters to determine the maturity of existing cybersecurity programs at national organizations and generate a classification and comparison reports. The efficacy of GoSafe proposed framework is demonstrated using a practical case study. The results enabled the stakeholder to verify the security configuration of their systems and identify potential attack/risk vectors.

Information fusion refers to derive an overall precise description of data by using certain fusion technique for utilizing the complementary information from multiple sources of data, which can facilitate effective decision-making, prediction and classification, etc. Multi-source homogeneous data, characterizing the data type of variables in the sample in form of one type (i.e., numerical or categorical) in different information sources, which widely exists in many practical applications. This paper concentrates on efficient fusion of multi-source homogeneous data with a data-level fusion model which involves the consolidation of multiple information sources and unsupervised attribute selection of the fused data. A unified description and modeling method of a multi-source homogeneous information system is introduced. The neighborhood rough sets model is used to construct the neighborhood granular structure, which uses the idea of granular computing to build methods of uncertainty measures. Given the uncertainty of fusing multiple information sources, Sup-Inf fusion functions are developed based on the proposed uncertainty measures, which can fuse the multi-source homogeneous information system into a single-source information system. Finally, an unsupervised attribute selection approach is employed to eliminate redundant attribute of the single-source information system. Theoretical analysis and comprehensive experiments on several datasets demonstrate the feasibility and superiority of our method.

In the era of big data, faced with massive and complex information, many mathematical concepts used to make judgments and decisions have emerged. In order to better integrate the multi-level fuzzy information, on the basis of the original covering rough set, we generalize the couple approximate operators defined by L.W. Ma to the information system and propose a new binary model—dyad fuzzy β-covering rough set models. This model can analyze and solve practical problems from multiple angles, so as to make more accurate decisions. In addition, in order to make the model more convenient for large and complex data processing, we use matrix to represent the model and realize it by computer programming. Finally, we illustrate the value of this model by solving a practical problem.

Mass customized and mass personalized production has become facilitated by the fourth industrial revolution. The resulting industrial environments require the development of information systems able to take the specifications of customers and convey them to the production system in such a way as to contribute to the coordination of all the stakeholders and activities required to fulfill the orders of the customers. This is beyond the capabilities of traditional systems based on MRP and ERP, since the information should be managed in a flexible and decentralized way to exploit the Smart Manufacturing facilities of Industry 4.0. Blockchain, instead, is a technology that provides those features constituting a sound information supporting basis for mass customized/personalized production. Consequently, we explore the potential of blockchain as an information technology able to support industries that base their business models on mass customized/personalized production processes. This survey allows us to identify important challenges for further developments, highlighting three issues in the production setting: (i) to deepen the interoperability of systems, (ii) to generate more implementations, and (iii) to develop efficient consensus protocols. As a response to these insights we provide a conceptual design of how blockchain contributes to managing efficiently mass customized production systems. In our design the information of customer specifications can be fused with data from the production process to generate a plan to fulfill the demand. This design arises as a solution approach to three stated problem, which are faced by mass customized production systems.

Background There is little published research on whether public and private dental benefits plans affect the types of oral health care procedures patients receive. This study compares the dental procedure mix by age group (children, working-age adults, older adults), dental benefits type (Medicaid and Children’s Health Insurance Program, private), and level of Medicaid dental benefits by state (emergency only, limited, extensive). Methods The authors extracted public dental benefits claims data from the 2018 Transformed Medicaid Statistical Information System. To compare procedure mix with beneficiaries who had private dental benefits, the authors used claims data from the 2018 IBM MarketScan dental database. The authors categorized dental procedures into specific service categories and calculated the share of procedures performed within each category. They analyzed procedure mix by age, plan type (fee-for-service, managed care), and adult Medicaid benefit level. Results Aside from orthodontic services, the dental procedure mix among children with public and private benefits is similar. Among adults with public benefits, surgical interventions make up a higher share of dental procedures than routine preventive services. Conclusions Children with public benefits have a procedure mix comparable with those with private benefits. There are substantial differences in procedure mix between publicly and privately insured adults. Even in states that provide extensive dental benefits in Medicaid, those programs primarily finance invasive surgical treatment as opposed to preventive treatment. Practical Implications There is a need to assess best practices in publicly funded programs for children and translate those attributes to programs for adults for more equitable benefit design and care delivery across public and private insurers.

The use of genomics in medicine is expanding rapidly, but information systems are lagging in their ability to support genomic workflows both from the laboratory and patient-facing provider perspective. The complexity of genomic data, the lack of needed data standards, and lack of genomic fluency and functionality as well as several other factors have contributed to the gaps between genomic data generation, interoperability, and utilization. These gaps are posing significant challenges to laboratory and pathology professionals, clinicians, and patients in the ability to generate, communicate, consume, and use genomic test results. The Association for Molecular Pathology Electronic Health Record Working Group was convened to assess the challenges and opportunities and to recommend solutions on ways to resolve current problems associated with the display and use of genomic data in electronic health records.

Given that the three-way decision (3WD) theory provides a scientific solution and reasonable semantic interpretation for solving multi-criteria decision-making (MCDM) problems, this paper presents a new 3WD model and applies it to realistic MCDM problems. Since the fuzzy neighborhood operator is effective in handling uncertain data, we first define a pair of probabilistic approximation operators via the constructed α -fuzzy neigh borhood class. Then, a 3WD model is explored in decision information systems to address the related issues in the proposed fuzzy probabilistic rough set model. In light of the above works, we establish a novel approach to classify and rank applicants for enterprise talent recruitment problems. Instead of the rule of tie-breaking, a way is given to separate applicants into three parts by combining decision attributes , and the feasibility of the approach is confirmed as well. Finally, by using a data set in the UCI database, the results of comparative and experimental analyses demonstrate that the constructed MCDM approach owns better performances in terms of effectiveness and stability.

As a complicated cognitive process, multi-attribute decision-making usually focuses on the decision-making issue of seeking the optimal alternative or ranking alternatives under the framework of multiple attributes. The three-way decision approach with the delayed decision can more effectively reduce decision risks than traditional two-way counterparts for multi-attribute decision-making. In this article, we aim to put forward a novel three-way multi-attribute decision-making model in light of a probabilistic dominance relation with intuitionistic fuzzy sets. First, we investigate the three-way multi-attribute decision-making in light of a probabilistic dominance relation in an intuitionistic fuzzy information system. Second, we derive the conditional probability of the intuitionistic fuzzy set. Third, we evaluate the part supplier selection via the constructed model. At last, for the sake of showing the validity and applicability of the constructed three-way intuitionistic fuzzy multi-attribute decision-making model, we further perform extensive comparative analysis along with experimental analysis from diverse perspectives.

As a highly prevalent disease, the liver disease is usually diagnosed by testing with several aspects. Due to different accuracies of tests, the obtained results may be different, which may lead to potential errors in diagnosis results. Meanwhile, the psychological behavior and the psychological preference of a decision maker (DM) have a significant influence on decision-making results. Thus, we aim to propose a three-way decision (TWD) approach based on the regret theory (RT) in a multi-scale decision information system (MSDIS), which is named a TWD-R approach. First, by defining a dominating relation and a dominated relation based on the regret-rejoice value of each alternative under each attribute, a novel conditional probability calculation approach is proposed. Second, an objective approach for calculating relative utility functions is proposed to avoid the influence originated from the subjectivity. Third, the key steps and the algorithm of the TWD-R approach are summed up. Finally, the rationality, validity and superiority of the proposed approach are investigated by two cases and some experimental analyses.

Conflict occurs naturally in all walks of life at any time. When agents face a conflict, they usually have multiple alternatives and hesitate to make a decision. This paper studies conflict analysis in the case of hesitant fuzzy setting, i.e. the attitude of an agent on an issue has multiple choices. We first propose a conflict analysis model based on hesitant fuzzy information systems. Subsequently, we present the concepts of conflict, neutrality and alliance sets on one issue and multiple issues respectively, and introduce a method to compute the three sets via Bayesian decision theory. Moreover, the degree of conflict, neutrality and alliance among agents with respect to an issue are defined respectively. According to the conflict and alliance degrees, the set of issues is divided into three parts: the main reason, secondary reason and irrelevant reason. Finally, two methods of computing coalitions are discussed. One is based on Bayesian decision theory. The other is to find a complete subgraph of the coalition graph.

Real Estate Online Platforms (REOPs) are used for conveying real estate and property-related information to potential users (buyers, renters, or sellers). The information leveraged through REOPs supports these users in reaching conclusive rent or buy decisions. Despite their promised utility, user perception about accepting online information through REOPs is unexplored. Using a comprehensive questionnaire and data collected from 65 users, the current study captures the users’ perception of REOPs. Risk, service, information, system, technology adoption model (RSISTAM) is proposed comprising of seven users’ perceptions: risk (PR), service quality (PSEQ), information quality (PIQ), and system quality (PSYQ) from the information systems success model, and usefulness (PU), ease of use (PEU) and behaviour to accept (BAU) from TAM. The results are analysed using the decision making trial and evaluation laboratory (DEMATEL) approach, which shows that PIQ, PSEQ and PEU are the causes and PR, PSYQ, PU and BAU are the effects. Among the criteria, the order of prominence is PEU > PSEQ > PIQ, and for net effects, the order is PU > BAU > PSYQ > PR. For addressing the causes, the REOP managers must provide more transparent, high quality and voluminous information to the users, focus on the system, services, and information qualities, and add more enjoyable, immersive and easy-to-use content through REOPs. This study contributes to the body of knowledge by exploring user perceptions and proposing methods to improve the quality and reliability of REOPs in line with Real Estate 4.0 and industry 4.0 aims.

Pakistan has been a victim of energy crisis since last few decades. This energy crisis has adversely affected country’s socio-economic development and continues to do so. The continuously increasing demand–supply gap has negatively impacted the economic stability of the country. With the recent awareness and development of alternate energy resources like wind and solar, the current energy crisis can be minimized. However, proper planning is essential for successful execution of these renewable energy projects. This study aims to identify the suitable sites for wind farms in District Gwadar, Balochistan using Geographical Information Systems (GIS) and Web-based Spatial Decision Support System (SDSS). In this study, multi-criteria decision making is applied which assists breaking down the site selection complexity. Multi-Criteria evaluation methods provides different set of procedures that facilitate decision making by analyzing different alternatives. The underlying geospatial and ICT technologies used in this analysis form the core component of the planning process. Gwadar is currently drawing investor’s attention due to its geographical location, deep seaport, and proposed China–Pakistan Economic Corridor (CPEC). This research is useful for stakeholders of Wind Energy to explore the wind potentials using GIS as an interactive decision-making tool during the pre-feasibility stage.Furthermore, this research has considered the environmental, social and economic aspects during the decision-making process of wind farm development. This is the strength of multi-criteria evaluation as differently weighted scenarios provide different output, depending on the factors considered of highest importance. A detailed analysis of the sites in terms of their wind potential and energy generation capacity has also been reported in this study. This long coastline of Balochistan with huge wind energy potential has not been explored yet and therefore this study will assist researchers to further explore this area and can have a positive impact on CPEC. Qatar University Internal Grant No. IRCC- 2021-010.

With the rapid development of information and network technology, hospital information systems (HIS) has also become a hot research area. However, data could be subjected to the threat of security attacks, leakage, tampering, and forgery during medical data transmissions, data storage, and sharing based on public networks and cloud environments. The immutability, decentralization and anonymity of the blockchain provided new ways for solving the aforementioned problems. This paper proposes a complete medical information system model based on blockchain technology, to realize the goal of safe storage and sharing of medical data. A data collection system based on Internet of Things (IoT) was also developed that can simultaneously collect data from different types of non-invasive medical instruments to realize real-time collection of patient health record during surgery (SHR). This system designed an anonymous medical data sharing scheme based on cloud servers and proxy re-encryption algorithm to improve the security of private medical data sharing. The system was implemented based on the permissioned blockchain architecture Hyperledger Fabric, and a dual-channel Fabric deployment architecture and medical chaincode were designed for data management and access control. We also carried out computing overhead test, performance test and safety evaluation on the system, and the test results meet the requirements of actual medical production environment. The research work of this paper provides means for remote diagnosis and treatment, data mining and other practical applications based on the medical data on the blockchain.

Solar photovoltaic (PV) technologies receive investment, support, and incentives despite their apparent high costs. In terms of renewable energy policies and spatial planning, site selection for solar PV farms is a critical issue. A novel comprehensive framework for assessing the site suitability of solar PV farms is proposed, which considers the preservation of natural, ecological conservation, and cultural areas. The framework combines a Geographical Information System (GIS) with layers of satellite-derived data for energy resources as well as locally collected data, and the Multi Criteria Decision Making (MCDM) method based on the Analytic Hierarchy Process (AHP). In the GIS environment, maps belonging to fourteen sub-criteria of the three main criteria (Geography, Climate and Location) for Kahramanmaras, Turkey were obtained, as well as suitability map derived from their weighted sum. The AHP method generates the appropriate weight for each input criteria using a pairwise comparison matrix. GHI, aspect, distance from power line network, land use/cover, annual average temperature, and others were found as the most necessary sub-criteria, respectively. According to the findings, 9.62% of study area very low, 20.15% is low, 22.51% is moderate, 23.98% is high and 23.74% is very high, and 26% is unsuitable for solar PV farms. Based on the results, the northern districts of the study region were determined to be the most suitable for the construction of solar PV farms. When existing five PV farms' location selection decisions were examined, it was observed that the investment outcomes were consistent with the results of the study. The proposed framework is projected to minimize the cost, time, and resources used on the construction of solar PV farms.

The notion of q-rung orthopair fuzzy sets (q-ROFSs) enables decision makers to record diverse uncertain information in a larger scope by easing the limitation of membership and non-membership degrees. The paper introduces q-ROFSs into the framework of multi-granularity three-way decisions (MG-3WD) and explores a novel multi-attribute group decision making (MAGDM) approach in q-rung orthopair fuzzy (q-ROF) information systems. After reviewing some fundamental issues on q-ROFSs and MG-3WD, we first propose different types of multigranulation q-ROF probabilistic models and discuss some of their theoretical properties. Afterwards, a q-ROF MAGDM approach is constructed in light of the proposed multigranulation q-ROF probabilistic models and decision making-based 3WD methods. Then, we provide an example of corporate financial quality matching (CFQM) to show the practicability of the established q-ROF MAGDM approach. At last, corresponding sensitivity analysis, validity tests, comparative analysis and experimental analysis are arranged to reveal the applicability of the established approach from the aspect of MG-3WD.

The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention—but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks. We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models. Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics: a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation. Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.

Decision-theoretic rough sets (DTRSs) as a classic model of three-way decisions have been widely applied in the field of risk decision-making. Considering situations where experts hesitate among several evaluation values, hesitant fuzzy sets, as a new generalization of fuzzy sets, can describe uncertain information flexibly in the decision-making process. In this paper, we propose a decision-theoretic fuzzy rough set (DTFRS) model in hesitant fuzzy information systems and discuss its application in multi-attribute decision-making (MADM). More specifically, we first define a novel fuzzy binary relation between two objects by using the hesitant fuzzy distance function. Then, we study the calculations of the fuzzy similarity class and the conditional probability . At the same time, based on the connection between the loss functions and the attribute values, we develop a data-driven calculation method of the relative loss functions. With these discussions, we construct a DTFRS model in hesitant fuzzy information systems and explore the related decision-making mechanism. Furthermore, a three-way decision method based on the proposed DTFRS model is established to handle MADM problems in the context of a hesitant fuzzy environment. The established method not only takes the decision risk into consideration, but also instructs us how to choose the action for each alternative and gives its corresponding semantic explanation. An illustrative example of the stock investment problem is presented to verify the efficacy of our method. Finally, we take a sensitivity analysis and a comparison analysis to show the established method’s performance and characteristics.

Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system.

European management journal (2020). doi:10.1016/j.emj.2020.10.009, Published by Elsevier Science, Amsterdam [u.a.]

This research explores the interactive role of supplier, customer and technology company in business-to-business (B2B) marketing when they develop and use industrial artificial intelligence (AI). From a value co-creation perspective and following a service-dominant logic, this study aims to identify essential value types that are created collaboratively by B2B professionals (namely suppliers, customers and AI providers), and critical capabilities that contribute to their value co-creation practices. Nineteen in-depth semi-structured interviews were conducted with three groups of B2B stakeholders in six companies that involved in an industrial AI development and usage project. The data was then analysed using a thematic analysis approach. The results of this research contain a categorisation of four value types and three sets of capabilities, together with the interrelationships between them. This study contributes to the literature of value co-creation, information system and B2B marketing by bridging these three disciplines within the context of industrial AI development and usage.

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.

Three-way decision (3WD) provides a new perspective and methodology for solving multi-attribute decision-making (MADM) problem. In this paper, we introduce 3WD into a multi-scale decision information system (MS-DIS), which provides a new idea for solving MADM issues in MS-DISs. By using fuzzy membership functions , a multi-scale evaluation information table is first converted into a numerical evaluation value table. To calculate loss functions at two states generated by the decision class partition, a distance-based cost measurement is then defined. With reference to fuzzy σ -neighborhood classes, conditional probabilities are further calculated and 3WD rules from MS-DISs can be unravelled. As a result, the ranking about all objects in the data is obtained according to expected losses. Finally, a specific numerical example is used to illustrate the effectiveness of the proposed method comparing with other ones. An experimental analysis is also used to describe the superiority of the proposed method.

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts.

Introduction This study evaluated the current information services provided to 10 Teenage Young Adult (TYA) oncology patients within a single radiotherapy department through retrospective patient views. Additionally, the participants provided valuable perspectives on the aptness and merit of implementing a dedicated information evening within the radiotherapy department. Methods Focus group sessions and open-ended questionnaires were used as the primary data collection tools for this study, producing rich and informative data. The transcriptions from the focus groups, along with the questionnaire responses were then analysed using coding techniques, resulting in 6 distinct themes. Results The participants felt they received adequate information about their radiotherapy treatment and its side effects. However, participants did report some gaps within the service provision that need to be addressed to improve treatment pathways in future. Furthermore, the information was always supplied in paper format which was often undesirable to the cohort with a more interactive format preferred. The participants disclosed very strong feelings of being different to “usual cancer patients” and displayed a desire for more opportunities to meet other TYA patients to be provided within their pathway. Conclusion and Implications for practice The findings of this study suggest that the implementation of a dedicated TYA open evening would improve the information support and treatment pathway for 18-24-year-old cancer patients. The evening could offer a wanted opportunity where TYA patients could meet each other and additionally, treatment information and age appropriate support information could be provided in a different and more suitable format than current paper-based literature.

A fuzzy set-valued information system (FSVIS) refers to an information system (IS) whose information values are fuzzy sets. This article investigates information structures (ISts) in a FSVIS based on granular computing (GrC). First, FSVISs and homomorphism between them are introduced. Next, ISts in a FSVIS are described. The dependence and information distance between ISts are discussed, and characterizations of ISts in a FSVIS are acquired. Additionally, as an application of ISts in a FSVIS, the entropy measure of uncertainty for a FSVIS is discussed, and on the basis of this uncertainty measure, the optimal selection of ISts is investigated. The obtained consequence will be conducive to establishing a framework for GrC in a FSVIS. Last, the difference between this paper and the references for ISts in this paper is compared and discussed.

In the crowdsourcing task allocation scheme, there is an emerging and realistic co-opetition phenomenon. To availably solve the crowdsourcing task allocation problem with co-opetition, this paper designs a two-stage co-opetition model by constructing novel three-way decision (TWD), including a competition-optimization model and a negotiation-cooperation model. Unlike the other studies, the two-stage co-opetition model with TWD can not only protect the profits of the task candidates, but also optimize the overall benefits. Specifically speaking, in the competition-optimization model, we construct an optimization model based on the data envelopment analysis (DEA) method in advance, which maximizes the personal benefit. By integrating information system and the loss function matrix, we consider the linkage of evaluation information and risk information and then improve the original TWD to make an initial allocation. In the negotiation-cooperation model, considering that the relationship among the candidates may influence the task performance, the fuzzy measure is introduced to describe a broader partnership. Meanwhile, we also design two different schemes to coordinate and optimize the best task allocations based on the initial allocation. In order to choose the best scheme, the selection strategy between schemes is further investigated under the guidance of the utility and the loss. Finally, we give an example of a medical supply chain crowdsourcing problem to illustrate and verify our proposed approach.

Nowadays, cloud computing is rapid growth in the IT industry, which provides a new way to manage the different information systems. The rapid development of technology makes it essential to use this technology and to benefit from its advantages. With raising awareness and worries about cloud services and information security, understanding and knowledge are growing for the usage of algorithms security into the processes and data systems. The primary purpose of this research work is to undertake a security analysis and performance assessment of a new lightweight cryptographic algorithm for enhancing data security in cloud computing. It focuses on studying the performance and security analysis algorithm architecture base on computational time and key sensitivity, statistical analysis, image histogram, and entropy change analysis in cloud computing environment. It also, provides a performance comparison of general symmetric algorithms used to secure the cloud services: DES, AES, Blowfish, RC4, HIGH, SF, and SIT.

Holistic information systems for climate-smart agriculture demands the seamless integration of various categories of climate, meteorological and weather data. Any actor in the agricultural value chain may harness weather forecasts at the short and medium-range, local weather history, and prevailing climatic conditions, to inform decision-making. Weather is fundamental to many day-to-day operations, especially at farm-level, influencing decision-making at various spatial and temporal scales. Many operational decisions ideally require hyper-localized service provision. In practice, integrating weather information into decision-support services demands a comprehensive understanding of various categories of weather-related data, their genesis, as well as the specific standards and data formats used by the meteorological community. This paper considers the weather as a crucial context for the delivery of farm-level operational services in smart agriculture, highlighting critical issues for reflection by system designers during the service design and implementation phases.

Triangular fuzzy numbers (TFNs) can not only provide the range of fuzzy points, but contain the three most representative fuzzy points, which play an essential role in describing fuzzy information. Combining with conflict situations, the agents' caution in the decision-making process can be well depicted by TFNs. However, little effort has been paid to conflict analysis on triangular fuzzy information systems (TFISs). In this paper, we study three-way conflict analysis based on TFISs. First, we propose the concept of TFISs and then establish a TFIS for conflict analysis. Further, relative area ΔS is defined to describe concrete attitudes of agents about issues. Second, since the importance of the same issue may vary for agents, we attach fuzzy weights to issues according to triangular fuzzy symmetric judgment matrices (TFSJMs) of agents. Third, we determine the total attitude of each agent to all issues. Then we illustrate how to compute thresholds α and β using the theory of triangular fuzzy decision-theoretic rough sets. Finally, a tri-partition of agents is obtained based on the thresholds.

Advanced traveler information systems play essential roles in reducing traffic congestion and improving traffic efficiency. In this paper, a global algorithm for the route guidance strategy is proposed in advanced traveler information systems. Compared to the local algorithm, this global algorithm can improve the capacity and efficiency of traffic networks and also counter traffic congestion. The innovative ideas of this algorithm can be viewed mainly from three aspects. First, rather than providing a fixed path between origin and destination, the new algorithm could provide the real-time optimal route guidance information to travelers at every intersection. Moreover, because the route guidance information is based on global traffic information, the optimum global path could also be provided. Lastly, we can use this new global algorithm and apply it to many route guidance strategies. In our study, we first apply it to the congestion coefficient route guidance strategy. The simulation results demonstrate better performances of the global congestion coefficient route guidance strategy compared to that of the local congestion coefficient route guidance strategy. The advantages can be summarized as follow: (1) Critical vehicle occupancy and average flow of saturation state is doubled. (2) Critical vehicle occupancy of meta-stable state and deadlock state are raised by 2.5 times. (3) Vehicle distribution is more homogeneous. We also apply the global algorithm to the travel time route guidance strategy. Even though it brings a slightly lower average flow and a higher origin-destination travel time compared to the global congestion coefficient route guidance strategy, the global travel time route guidance strategy induces a more homogeneous vehicle distribution. Furthermore, we find that the average flow increases when information collection time period decreases, thus information should be updated in a real-time manner in efficient traffic networks.

Objective How best to provide an increasingly diverse population with health information has become a major concern for health policy makers in Europe and beyond. Our study aims to investigate factors explaining variation in people’s health information seeking behaviour. Our findings can be used to identify target groups for policy interventions that aim to provide health information efficiently. Methods Cross-sectional, paper-based, multilingual survey of a random sample of enrolees of two statutory health insurers in Hamburg, Germany. Data were collected from September to December 2017. Multivariable logistic regression was used to examine sociodemographic and health-related individual characteristics and their associations with participants’ choice of ten sources of health information. Findings Participants’ choice of information sources differed significantly across the following sociodemographic and health-related characteristics: age, gender, immigration status, education, employment status, marital status and general state of health. Immigrants and individuals with low educational attainment were most likely to use emergency departments as sources of health information. Conclusion Policy interventions aiming to manage the use of health information sources should focus on immigrants and individuals with low educational attainment. Providing multilingual, low-threshold counselling and information services could be an efficient way to reduce short-term costs of health information seeking behaviour to health insurers or other payers of care while improving patient empowerment.

Plastic packaging has been used increasingly worldwide in a broad range of application. Plastic packaging has a short lifetime, which generates a large amount of waste. However, robust information on plastic packaging waste flow is generally not available, especially for developing countries such as Brazil. We analyzed and quantified Brazilian post-consumer plastic packaging waste (PPW) flows using material flow analysis (MFA) for the year 2017. The system modeled covered from the manufacturing stage of plastic packaging up to its waste management stage. We used a range of data sources, whose quality we assessed using uncertainty characterization. The results showed that Brazil generated 12 Mt of PPW in 2017, and the management of 63% of that was not monitored. The majority of monitored PPW was disposed of into landfills, but 0.8 Mt of PPW was improperly disposed. Informal collection was 24% greater than formally managed selective collection. Only 4.5% of the PPW generated in Brazil was recycled. The results identified the major national challenges in relation to PPW management as being that information systems needed to be improved, informal waste collectors needed to be socially and productively included in the management systems, and recovery systems needed to be developed towards a circular economy.

In the recent past, various factors have led to an increase in the use of renewable energy sources, among which, the depleting fossil fuel reserves, increasing fuel prices, and rising environmental concerns are the most prominent. With this increasing reliance on renewable energy sources, a proper assessment of the suitable sites becomes necessary for the optimum utilization of these resources. The present study investigates the spatial suitability of the solar and wind farms locations in India based on the technical, economic, and socio-environmental perspectives. The analysis is performed with the coupled use of the Geographical Information System (GIS) and Multi-Criteria Decision Making (MCDM) approaches. Analysis of present research work shows that 4.13% of the study area (133,874 km2) is highly suitable for the deployment of solar plants while 0.91% of the total area (29,457 km2) is highly suitable for the wind farms. The study further concludes that the Rajasthan state in India has the highest suitable land for the installation of solar plants (20,881 km2) as well as wind farms (6323 km2). The proposed model can be used for the development of policies related to renewable energy resources and the assessment of suitability of already sanctioned projects.

Chemotherapy as an important therapy for gynaecological cancer needs nurses’ high performances. Thus, remuneration as a factor influencing nurses’ work motivation needs to be considered. The purpose of this study was to explore the perceptions and expectations of chemotherapy nurses in gynaecology wards about remuneration. This study was a qualitative research with phenomenological design. Data were collected by FGD involving 15 chemotherapy nurses in a hospital in Indonesia. The themes produced were as follows: (1) Nurses perceived themselves as chemotherapy nurses with special knowledge and skills and high-risk job, (2) Chemotherapy nurses perceived that the remuneration system is not transparent, unfair, and demotivate them, (3) Chemotherapy nurses expected that the remuneration system could be implemented fairly and transparently, with support of adequate information system. It is recommended to improve the implementation of the remuneration with an appropriate information system, in order to increase chemotherapy nurses’ work performance in gynaecology services.

The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.

Background/objectives The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions. Methods Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology’s capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. Results Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them. Conclusions In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT’s sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT’s focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT’s proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.

Application optimization of computerized documentation systems is influenced by several factors, including nurse satisfaction. Dissatisfaction among nurses may result in lower documentation quality. Satisfaction was measured by a questionnaire based on the End User Computing Satisfaction (EUCS) model. This study used a cross-sectional approach to determine the relationship of variables under study that involved 98 nurses selected by a convenience sampling method. Univariate test results suggest that 53.1% of nurses were satisfied with the current END system. It was also found that satisfied participants tended to be younger, female, more experienced, Diploma graduates, at PK1 level, and did not attend training. The implementation of an information system that accounts for nurses’ user satisfaction significantly affects system utilization and quality of care. Electronic-based systems require further improvement, especially in component accuracy, timeliness, and usability.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. This raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e., smartphones or embedded systems, has been demonstrated in recent works. In this paper, we consider IoT devices characterized by strong limitations in terms of computing, storage, and power. Specifically, we assess the performance of ABE in typical IoT constrained devices. We evaluate the performance of three representative ABE schemes configured considering the worst-case scenario on two popular IoT platforms, namely ESP32 and RE-Mote. Our results show that, if we assume to employ up to 10 attributes in ciphertexts and to leverage hardware cryptographic acceleration, then ABE can indeed be adopted on devices with very limited memory and computing power, while obtaining a satisfactory battery lifetime. In our experiments, as also performed in other works in the literature, we consider only the worst-case configuration, which, however, might not be completely representative of the real working conditions of sensors employing ABE. For this reason, we complete our evaluation by proposing a novel benchmark method that we used to complement the experiments by evaluating the average performance. We show that by always considering the worst case, the current literature significantly overestimates the processing time and the energy consumption.

The implantation of hospital information systems (HISs) has grown dramatically in recent years. Understanding the success rate of HIS is key in health organizations. In this study, a validated questionnaire for HISs evaluation based on the Information System Success Model (ISSM) has been provided. In addition, the HIS success rate was determined.The current study was conducted at one of the largest teaching hospitals in eastern Iran. The 44-items questionnaire was developed for data gathering. The questionnaire covered ISSM dimensions, which include analysis of quality system, quality of information, quality service, system use, usefulness, satisfaction, and net benefits. Content validity, constructs validity, and reliability of the ISSM questionnaire was measured. HIS success rate has been determined and categorized based on users' perspective as follows: appropriate (75%≤HIS success rate), moderate (50%≤HIS success rate75%), low coverage (25%≤HIS success rate50%), and poor (coverage rate25%).In total, 253 users participated in the study. The ISSM questionnaire was validated by an expert panel with CVI: 85.12% and CVR: 88.22%. The overall Cronbach's alpha value of the instrument was determined as 92.2%. Nine factors with eigenvalues greater than 1.00 were identified, jointly accounting for 66.91% of the total variance. The value of KMO was.866 showed that the sample size was adequate for factor analysis. The highly significant Bartlett's test (p0.000) indicated that variables were correlated and the factor analysis was appropriate. Our results demonstrated that the total mean of HIS success was "moderate" base on the users' point of view.The findings of the current study, provide valuable scientific evidence for key affecting factors on hospital EHR in Iran as a developing country.

Long-term travel time prediction, ahead of making a trip, is vital from the planning perspective of delivery freight, timetable design, vehicle/crew scheduling and further activities. The better the prediction is, the higher the reliability of service that can be offered. This study presents a discrete and continuous combined analysis for attaining improved long-term travel time prediction (LTTP) of commercial vehicles. One main problem of LTTP is that the speed factors are unknown ahead of trips. In light of this, the nonnegative tensor factorization and completion with neural weighted initialization is proposed to extract the potential speed patterns among multiple discrete factors and to complete the sparse tensors. The Gaussian mixture regression is adopted for handling the continuous factors. The proposed methodology with a combined discrete and continuous analysis is able to effectively integrate multiple factors into the computation, including vehicle type, road type, days, time period, weather conditions, driver differences and travel distance. The methodology is able to reduce the long-term travel time prediction error between 14% and 43% compared with the traditional average speed method and other baseline methods, which suggests its effectiveness. It can strategically assist policy-making processes of stakeholders on investment, insurance, planning and management, and can help tactically in predicting long-term travel time ahead of the scheduled trips to improve the reliability of the schedules. Furthermore, operationally, it can also be used to enrich current navigation information systems by separately predicting the commercial vehicles’ travel time based on multiple factors.