International Journal of System Assurance Engineering and Management最新文献

In the contemporary era, with the emergence of distributed computing and storage facilities, there has been an increase in the creation of textual data. The invention of the Internet of Things (IoT) and its use cases also led to the creation of big data in textual corpora. At the same time, there are emerging Artificial Intelligence (AI) techniques for processing data in unstructured format. In this context, an important research question is how Natural Language Processing (NLP) and text mining cope with emerging AI techniques. This paper investigates the hypothesis that “NLP and text mining play an increased role in emerging AI techniques.” The investigation uses a dual approach: a literature review and an empirical study. Different aspects of the study, including data science approaches covering AI techniques, are investigated. NLP and text mining are indispensable for meaningful AI outcomes in solving different real-world problems. This paper sheds light on the investigations made and paves the way for exciting future research into utilizing AI along with NLP and text mining. It has covered the research reflecting the dynamics of natural language processing and text mining under emerging artificial intelligence techniques.

Blockchain has gained the attention of scholars and industry practitioners due to its immutability, transparency, and operational features, which can improve overall supply chain efficiency. However, a holistic review of blockchain-based supply chains through the lens of digital trust has remained elusive, making participants reluctant to share information due to growing attacks on digital systems and fraud. Therefore, this study examines digital trust in using blockchain technology for supply chain operations by following a five-stage review process consisting of a systematic literature review protocol. The study performs a bibliometric and morphological analysis of 123 articles published between 2012 and 2023 to explore the current state and provide future research directions to develop safe, secure, reliable, and transparent blockchain-based supply chains. Further, our analysis reveals five characteristics of digital trust: transparency, cybersecurity, data protection, accountability, reliability and provenance, and regulatory compliance, which are essential to ensuring digital trust for supply chain sustainability, four research pathways, and keyword combinations for future research with other industry 4.0 technologies. Although blockchain applications for secure and trusted environments have been recognized, very little attention has been given to the detailed discussion on digitally trusted blockchain-based supply chains. The present study contributes to the literature by synthesizing the available literature on blockchain-based supply chains from the perspective of digital trust, thereby analyzing the current state and providing future opportunities for researchers and practitioners working in industry sectors and developing blockchain-based supply chains.

Simplified swarm optimization (SSO) and particle swarm optimization (PSO) are two types of modern swarm intelligence techniques that are often used for optimization. In order to identify the most effective system RRAP with a cold-standby strategic plan while aiming to exploit the reliability of the organization, the article discusses a PSSO procedure that combines UM of PSO and Simplified swarm optimization, PSSO is especially impressive in comparison with other recently incorporated algorithms into four popular applications, namely a sequences scheme, a complex organization, a series–parallel system, and an airspeed indicator defense system for a turbine, with extensive experiments conducted on the pretty standard and well-known four benchmarks of reliability-redundancy allocation problems. Finally, the experiment findings show that the particle-based simplified swarm optimization can successfully solution to address the reliability-redundancy allocation (RRAP) issues using the cold-standby method and performs well in terms of organization reliability, even though the best platform consistency is not attained in all four benchmarks and experiment is done using python and Google colab.

Twitter has improved in such a way people acquire knowledge or information by making them share their thoughts and opinions on everyday tweets. However, spammers have discovered Twitter to be desirable for spreading spam as a result of its enormous popularity. Twitter spam, in contrast to other types of spam, has recently become a big concern. The enormous number of users and volume of content or information published on Twitter contribute considerably to the rise of spam. To protect users, Twitter and the research team have developed several spam detection systems that employ various machine-learning techniques. According to a new study, existing machine learning-based detection algorithms are unable to detect spam correctly since the features of spam tweets vary over time. The issue is referred to as “Twitter Spam Drift.” In this paper, a semi-supervised learning approach (SSLA) using the YATSI algorithm has been suggested. YATSI is categorized into two steps. An initial prediction model is the first phase. The genuine predictions for unlabeled cases are identified in the second phase by using ML algorithms. To deal with the drift, the study utilizes a live Twitter stream of data acquired using Twitter API. This proposed method uses pre-processed labelled data to learn the structure of unlabeled data that is live-downloaded to distinguish between genuine and fake users. Experiments were conducted on live twitter data using KNN, SVM and NB machine learning classifiers. Among those classifiers SVM is showing the better results, in-terms of accuracy.

With a notable rise in the number of students opting for overseas education, study abroad has developed into a competitive industry over the past few decades. This study aims to identify the essential factors that affect students’ decisions to study abroad at Internalisation. A 32-item questionnaire spanning a range of topics was developed to address students’ goals and motivations for studying abroad. It was scored on a five-point Likert scale. Next, Structural Equation Modelling (SEM) with AMOS is used to test the factors’ validity and reliability. The study’s conclusions will aid in the understanding of the factors influencing students’ decisions and incentives to study abroad by policymakers and educational advisors.

Rotor-to-stator rubbing in rotating machinery, resulting from tight clearances, introduces complex dynamics that can potentially lead to high vibrations and machine failure. Historically, the rubbing models were addressed using cylinder-to-cylinder contacts; however, recent attention has shifted towards examining blade-tip contact in turbines, which affects the systems dynamics and efficiency. This study investigates the impact of the variations in blade number on bladed rotor systems, emphasizing on the types of motion that occur as function of the operational speed in the sub-critical range. A simplified bladed rotor model has been developed, using a Jeffcott rotor with blades represented as damped elastic pendulums. The equations of motion are derived and numerical simulations are performed to explore the system’s behaviour with varying blade numbers (3, 5, 7, and 10) in order to analyse displacements, contact forces and bifurcation diagrams as function of the rotating speed. Results reveal distinct regions: periodic motion (I and III) and chaotic motion (II and IV) appear alternatively in the bifurcation diagram, with the chaotic regions occurring at specific fractions of the natural frequency and the number of blades. The study concludes that chaotic motions are associated with larger displacements and higher contact forces, and the vibrational behaviour becomes less hazardous as the number of blades increases. In addition, the appearance of periodic and chaotic motions occur in the same regions by scaling the rotating speed with the number of blades and natural frequency of the system. From an operational perspective, this dynamic investigation offers valuable insights into the severity of blade rubbing in industrial systems. It can guide the implementation of mitigation solutions to prevent worst-case failure scenarios and help to perform adjustments to either operational or design parameters.

The present study aimed to investigate the mediating role of emotional coping ability in the relationship between emotional intelligence (EI) and wellness and to propose a model. The study was quantitative and a cross-sectional research design was used. The study was conducted in public and private hospitals in Delhi (India). 766 valid responses from registered nurses were considered for the analysis. Information related to the demographic profile, the correlation coefficient of the constructs, direct and indirect effects, and the path coefficient of the structural model was presented in tabular form. Data were analyzed using Statistical Package for the Social Sciences (SPSS 22) and Smart PLS SEM. Study findings show that the path between EI and wellness was mediated by Emotional Coping Ability and the effect was found to be statistically significant. The coefficient of determination (R²) for the model was found to be 41.5%. The correlation coefficient ranged from 0.527–0.601. Moreover, the variance accounted for (VAF) was found to be 24.9% which shows there exists a partial mediating effect of Emotional Coping between EI and wellness The model suggested in the study was able to contribute to the growing literature on EI and wellness. Using PLS-SEM evaluation criteria, the present study was able to propose a model of wellness, which is of great significance for the psychological intervention of nursing professionals in the future.

The paper deals with the stochastic analysis of noodles manufacturing plant. Various subsystems namely—FMCS (feeder system, mixing, compound process, slitting), steaming, CFC (cutting, frying process, cooling and packaging process) are arranged in a series configuration. For the purpose of achieving more reliability of the system an additional steaming unit is attached in parallel to the main steaming unit as a warm standby. Failure rate of the unit is linearly increasing while the distributions of time to repair are general. A single repairman is always available with the system to make the whole system more reliable and productive. The regenerating point technique is used to derive various measures of system effectiveness. The graphs of profit function and MTSF have also been successfully plotted using particular values of various parameters.

Blockchain technology may be a recent advancement and offers a ground-breaking technique for keeping the knowledge for an extended time and completing transactions like knowledge management, knowledge handling, performing arts functions, associated establishing trust in an open atmosphere. Most of them are considering block chain as a technology innovation significantly for cryptography and cyber security with systems like bitcoin, IoT, sensible Grids and etc., albeit this technology proofs its name and has received ton and ton of growing interests in multiple dimensions, the safety and security of the block chains are still in analysis whereas deploying block chain in versatile domains and environments. This work elaborates a comprehensive summary of the safety and privacy of block chain from knowledge management perspective. Attention platform is employed for implementation and testing. Around 20,000 records are being taken and valid mistreatment the projected algorithmic program. Initially, a block chain is created using distributed information, which tracks an ever-changing list of trading records by organising them into a hierarchic chain of block. Because attention knowledge necessitates greater security, a peer-to-peer overlay network is used to create and maintain the block chain, which is secured through the intelligent and suburbanized use of cryptography with crowd computing. ALM algorithmic program projected has been increased with applicable knowledge possession by providing 2 issue authentications with an accuracy of 95%.The projected system may be a decentralized system and thence guaranteeing higher knowledge transparency and auditability. Security and privacy properties are being ensured by providing fine grained access management on the highest of the ALM encoding. Hence, Security and privacy problems with current scenario are addressed well with a high-level access management rulesets.

The article aims to study the literature on Industry-4.0 technologies and “Triple Bottom Line” (social, economical and environmental) parameters in the construction industry. The study focuses on analyzing the gaps in various researches conducted till now and suggests possible information that can be used to improve business processes. Preferred Reporting Items for Systematic Reviews and Meta-Analysis Method is adopted to select the articles. One hundred fifty-six published articles from 2015 to 2023 are examined to understand various theoretical frameworks. Content-based analysis is used for the categorization of five significant categories: (1) Industry 4.0 Enablers; (2) Barriers in Industry 4.0 Adoption; (3) Challenges in Construction Industry; (4) Opportunities for the principle Industry 4.0 Technology; (5) Impact of “Industry 4.0” Technologies. Based on categorization, rewards or incentives, management involvement, employers training, Building Information Modeling, Big Data, Cloud computing, etc., are major enablers of Industry 4.0 in the construction industry. Implementation cost, lack of knowledge, and poor long-term planning are analyzed as common barriers. Numerous challenges and opportunities related to Industry 4.0 technologies have been identified.

Moreover, the Triple Bottom Line impacts of Industry 4.0 technologies, such as waste management, cost reduction, health and security, and resource planning, are also analyzed. The study also revealed that there are numerous research gaps in the integrated application of technology and sustainability because of information inadequacy and unawareness of the stakeholders. The study’s findings will help uncover detailed information in a systematical manner for developing an integrated sustainable business environment in the construction industry. The study considering the specific period and inclusion/exclusion criteria can possibly develop limitations of missing a few relevant articles and information in this context.