درخت حوزه‌های تخصصی

The article investigates in-depth the theoretical provisions for the construction of modern product quality management systems, providing for the optimization and transformation of individual production and economic processes to a higher quality level, as well as the functions integration of enterprise management system and product quality management system; such integration is carried out on the basis of knowledge, information and communications, which are the basis of external integration processes of the enterprise. This provided an opportunity to develop a scientific and methodological approach to the formation of an adaptive quality system capable of producing high quality products over a long period of time. The generalization of practical aspects of the research provided an opportunity to build an algorithm and determine the tools for the formation of an adaptive product quality management system using modern computer technology for effective information support.

As an emerging technology, blockchain can be used in all areas of e-commerce, one of which is advertising. The present study has been designed to identify a dynamic model of trust in blockchain-based advertising and acceptance of this type of advertising. In order to achieve the purpose of the research, the required information was collected by literature, background review and experts opinions. In the next step, the research information was analyzed using systems dynamics methodology (Sterman approach) and a dynamic research model was presented. After model validation and examining the causal relationships, simulations and scenario analysis were performed using Vensim software. The results show the positive effect of the audience, advertiser and advertising characteristics on trust in blockchain-based advertising and, consequently, the improvement of the audience loyalty coefficient. Also, the results of scenarios indicate an ascending growth (Pessimistic: about %60, Optimistic: about %80) in audience loyalty. Finally, practical suggestions were presented in accordance with the research results

In recent times amount of data is increasing rapidly and analysis of data is a must to come up with business decisions, predictions etc. It’s not just text are numbers which has to be stored properly. Data Types these days varies from images, video, social media data, surveys to blogs etc. When this is the case its mandatory to deal with Bigdata and to safeguard those Bigdata. Technologies available in Bigdata and Bitcoin helps us in achieving this. Bigdata technologies helps in storing the unstructured data effectively and processing of such voluminous data is taken care of. Hashing algorithms are used in Blockchain to keep the data safe. Hashing algorithms like SHA 256 are used to make it more secure. Its almost not possible to break the data available in Blockchain. Let’s try to secure the data even more using encryption algorithms to make sure that proper data is used for analysis etc.

At this time in history that the world is becoming smaller every day due to globalization; it is important to achieve an acceptable degree of trust and confidence in any field; and blockchain can put the world on this path. Since there is no central authority governing blockchain, its decentralized nature can turn it into an appropriate platform for project management. Implementing blockchain eliminates project management bureaucracies and allows a single, unchangeable, controllable, and encrypted source to be accessible to everybody and extensible throughout the project life cycle. This approach is the key to achieve an integrated information system and is one of the important reasons for blockchain applicability as an ideal platform for project management and construction. This issue that using blockchain technology in project management will bring about capacities and capabilities has not yet been seriously addressed. Changing the management of construction projects by blockchain is just an idea for now. This study aims to identify the capacity and platforms for developing blockchain technology in project management and construction using systematic and scoping literature reviews. Moreover, it provides some suggestions about the development and improvement areas of this technology. The review results showed that in all areas of project management and construction, there are applications relevant to blockchain technology that confirm the gradual understanding of the significance and positive impact of this technology on enhancing project management and construction performance. According to the results, a major part of blockchain studies was focused on “program and project management”, and two fields of human resources and behavioural areas and research in project management and schools of thought with a share of less than 2% of existing studies were neglected.

The article explores approaches to assessing the quality of health care sites. The main components of evaluation are identified. The author's toolkit for health site evaluation is offered. It is based on the three branches of resource competitiveness (content, technical and marketing components). Twenty parameters of site quality calculation are defined, which have the scale and are supported by determining the weights of components. The scaling of the resulting indicator is proposed. It allows designation of the level of development of the particular site in the field of health. The practical approbation of the proposed method was carried out on the example of Ukrainian sites on the topic of rehabilitation and disease prevention. The diagram of the grouping of websites on a level of development of separate components is constructed to define the further directions of work with these sites to strengthen their competitive positions in the "Health" category. The author's typology of health sites depending on the calculated numerical combinations of the quality assessment level of components is given. It also describes the state of the "ideal" mix of components of the health site, which is formed based on the balance of individual elements.

In recent years, Blockchain technology has been recognized as one of the most important new technologies affecting businesses and society, with a very bright future ahead. This technology is known as one of the most critical technologies of industry 4. Given that Blockchain technology is a new concept, before implementing it, the organization must understand it and be prepared to accept it; otherwise, the organization will incur irreparable damages. The main purpose of this study is to provide a framework within which organizations can evaluate their readiness to implement Blockchain technology. For this purpose, after a library study and systematic literature review, this study provided the initial research framework using the meta-synthesis method. Finally, the Lawshe model was applied to validate the proposed framework for the organization's readiness to implement Blockchain technology. Based on the findings, the final established framework included seven main dimensions, including Blockchain-based business strategy, operations of a distributed system, culture, people in Blockchain decentralized network, decentralized governance, technology, and Blockchain-based product traceability. Besides, 37 main criteria affected these dimensions, which can help companies increase the level of Blockchain technology readiness. Finally, this framework was used to measure the readiness of an airline company. According to the results of the analysis, Blockchain-based business strategy, culture, and Blockchain-based product traceability showed a medium level of readiness. Moreover, none of the main dimensions of Blockchain technology readiness were sufficiently prepared in this case study. This paper suggests practical recommendations to improve the level of Blockchain technology readiness for the managers

Heart disease is stated as the world's biggest killer. The risk factors of this deadly disease are due to some bad habits such as being overweight, bad eating diet, smoking, assumption of alcohol, etc. Nevertheless, patients can live a healthy lifestyle if they have the proper guidance of persuasive-emotional featured technologies. In line with this, this study focuses on developing an emotional-persuasive habit-change support mobile application called BeHabit to improve heart disease patients’ lifestyles. Persuasive-emotional features are two different features that are integrated with BeHabit to distinguish this application from the existing ones. The proposed system is designed, implemented, tested, and evaluated by 10 users. In conclusion, the users are satisfied to used BeHabit to change their bad habits. Emotional and persuasive features that are integrated into BeHabit are the key to help patients to change their bad habits. BeHabit and the integrated feature can be used as a guideline for healthcare developers and providers for the improvement of mHealth services.

Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.

Scientific sources demonstrate different attitudes of researchers to cryptocurrencies because they treat them as a category of currency, virtual money, commodity, etc. Accordingly, the relation to the valuation and risk of cryptocurrency as an investment object is different. The purpose of the article is to identify cryptocurrency value formation factors and determine the risks of investing in cryptocurrency. Cryptocurrency is simultaneously considered a currency, an asset with uncertain income, and a specific product, the price of which is determined by the energy costs for mining new cryptocurrency blocks. Thus, the paper examines the risks of investing in cryptocurrency from several positions. First, the study identifies the factors of formation of the value and risk of cryptocurrency as ordinary money based on comparing cryptocurrency with traditional money. Unlike traditional money, cryptocurrency is not tied to the economic performance of a particular country; also, central banks do not control or regulate their mining. Instead, the cryptocurrency emissions depend on the computational capacity of the equipment used for their mining. As a financial asset, cryptocurrency can be a “financial bubble” because their value increasing often exceeds the cost of mining. On the other hand, given the emergence of cryptocurrency as a phenomenon of the information economy, the paper analyses the impact of specific technical features (cryptographic hashing algorithm, the complexity of creating new blocks, the technology of verification of mining operations, etc.) on the risk of investing in cryptocurrency assets.

Detecting occluded faces is a non-trivial problem for face detection in computer vision. This challenge becomes more difficult when the occlusion covers majority of the face. Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. In this paper, a deep-learning-face-detection model Niqab-Face-Detector is proposed along with context-based labeling technique for detecting unconstrained veiled faces such as faces covered with niqab. An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms

Healthcare service institutions (HIS) seeking to motivate patient loyalty have identified Hospital Information Systems (HIS) as a potential solution to gather, measure, and analyze the healthcare data necessary for this goal. The purpose of this systematic review of the literature is to reveal how prevalent the use of HIS with respect to motivating patient loyalty, and to investigate the efficacy of HIS in doing so. To generate data, published empirical studies and conference papers from the past five years were compiled from the following online databases: Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Emerald Insight. The search results indicate that, while the use of HIS in motivating patient loyalty is rare relative to other topics within the general field of HIS, HIS use have a significant positive impact on patient satisfaction, which is understood in the literature to be directly related to patient loyalty. There remains a gap in empirical studies on the direct application of HIS with the purpose of increasing patient loyalty. Future research may be required on the development of an HIS focused on motivating patient loyalty, which can be empirically tested in a real-world HSI setting.

Healthcare service institutions (HSIs) have sought ways to motivate patient loyalty in response to surging rates of medical tourism. Previous research indicates that Hospital Information System (HIS) is essential for HSIs to gather, measure, and analyze the massive amounts of data required to generate patient loyalty. There is currently no consensus on the factors that comprise HIS specifically geared towards motivating patient loyalty (HISPL). Furthermore, HIS requires full adoption by HSI staff to be effective. Thus, to reduce wastage of HSI resources, it is necessary to predict whether a given HIS specifically geared towards motivating patient loyalty is likely to be adopted. The purpose of this study is to reveal the factors that comprise HISPL and to modify the Unified Theory of Acceptance and Use of Technology (UTAUT) model to help predict the likelihood of an HISPL to be fully adopted by HSI staff. The results revealed that pertinent HISPL factors are capability, configurability, ease of use/help desk availability and competence (EU), and accessibility/shareability (AS). Using these factors, the UTAUT model was modified to fit the specific needs of HISPL. The modifications are theoretical and will have to be validated in future empirical studies.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly