International Journal of System Assurance Engineering and Management最新文献

{"title":"Study of mediating effect of emotional coping ability in the relationship between emotional intelligence and wellness of nursing professionals","authors":"Sumit Kumar Debnath, Puja Khatri, Shweta Nanda","doi":"10.1007/s13198-024-02459-9","DOIUrl":"https://doi.org/10.1007/s13198-024-02459-9","url":null,"abstract":"<p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic model analysis of a noodles manufacturing system","authors":"Alka Chaudhary, Nidhi Sharma, Suman Jaiswal","doi":"10.1007/s13198-024-02421-9","DOIUrl":"https://doi.org/10.1007/s13198-024-02421-9","url":null,"abstract":"<p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel ALM based security framework for block chain in healthcare platform","authors":"D. Praveena Anjelin, S. Ganesh Kumar","doi":"10.1007/s13198-024-02443-3","DOIUrl":"https://doi.org/10.1007/s13198-024-02443-3","url":null,"abstract":"<p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of industry 4.0 in construction industry: a review","authors":"Ankur Tayal, Saurabh Agrawal, Rajan Yadav","doi":"10.1007/s13198-024-02432-6","DOIUrl":"https://doi.org/10.1007/s13198-024-02432-6","url":null,"abstract":"<p>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.</p><p>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.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pythagorean fuzzy approach to consecutive k-out-of-r-from-n system reliability modelling","authors":"Aayushi Chachra, Mangey Ram, Akshay Kumar","doi":"10.1007/s13198-024-02435-3","DOIUrl":"https://doi.org/10.1007/s13198-024-02435-3","url":null,"abstract":"<p>The linear consecutive (LC) <i>k</i>-out-of-<i>r</i>-from-<i>n</i> system is an incredibly important configuration used in various engineering systems. Such a system will break down if at least <i>k</i> out of <i>r</i> consecutive elements become inoperable in a system consisting of <i>n</i> ordered components. For any system, the critical necessity is that it should be reliable and remain in a properly functioning state for a stipulated period of time, thus, making it necessary to evaluate the reliability of such systems as well. However, the conventional reliability evaluation methods fail to consider the fuzziness or prospect of errors while computing the reliability, which can be resolved by incorporating fuzzy theory. This particular work presents a novel method for the computation of fuzzy reliability and its sensitivity for an LC <i>k</i>-out-of-<i>r</i>-from-<i>n</i> system, where its inherent fuzziness is addressed with the help of Pythagorean fuzzy sets (PFS), by representing the fuzzy variables as a trapezoidal Pythagorean fuzzy number (TrPFN), due to its ability to consider both membership and non-membership values, unlike the traditional fuzzy sets. Moreover, the universal generating function (UGF) technique is used to obtain the reliability function. Further, two different distributions are considered to represent the failure rates, namely, the Weibull and Pareto distributions and it was established that the Pareto distribution yields better results than the Weibull distribution. The obtained results are then compared with the help of both tabular and graphical illustrations.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On solving the 2L-CVRP using an adaptive chemical reaction algorithm: postal transportation real-case","authors":"Nadia Dahmani, Ines Sbai, Takwa Tlili, Saoussen Krichen","doi":"10.1007/s13198-024-02452-2","DOIUrl":"https://doi.org/10.1007/s13198-024-02452-2","url":null,"abstract":"<p>The postal sector plays a crucial role in enhancing and advancing services for businesses and citizens through its diverse services. Hence, optimizing the routing system collecting and transporting letters and parcels is a vital element within a well-rounded delivery management system. We model the problem as a Capacitated vehicle routing problem (CVRP) with two-dimensional loading constraints (2L-CVRP). This involves designing a set of routes that start and end at a central depot. Moreover, items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraints. The main objective is to optimize the total transportation costs using a homogeneous vehicle fleet. Due to the NP-hardness of the 2L-CVRP, we proposed an adaptive chemical reaction optimization (ACRO) metaheuristic to generate potential solutions. The algorithm adjusts its parameters and is intelligent search strategies during the optimization process based on the characteristics of the problem. Consequently, the algorithm can exploit and explore new regions of the search space. We compared our results with state-of-the-art meta-heuristics using 2L-CVRP benchmark instances from the literature. The results showed competitive solutions regarding the optimal ones. The empirical results, derived from benchmark datasets comprising a total of 180 instancesrove the high competitiveness of the proposed ACRO. It achieves a 67% success rate out of 36 instances for class 1 and a 59% success rate out of 144 instances for class 2–5 in terms of obtained solutions. In addition to benchmarking, we considered a real-world case study from the Tunisian Post Office. The ACRO results outperform the scenario adopted by the post office.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering climate change impacts on resource extraction supply chain: a systematic review","authors":"Ali Nouri Qarahasanlou, A. H. S. Garmabaki, Ahmad Kasraei, Javad Barabady","doi":"10.1007/s13198-024-02398-5","DOIUrl":"https://doi.org/10.1007/s13198-024-02398-5","url":null,"abstract":"<p>Mining is becoming increasingly vulnerable to the effects of climate change (CC). The vulnerability stems from changing weather patterns, leading to extreme weather events that can cause damage to equipment, infrastructure, and mining facilities and disrupt operations. The new demand from governments and international agreements has placed additional pressure on mining industries to update their policies in order to reduce greenhouse gas emissions and adapt to CC. This includes implementing carbon pricing systems, utilizing renewable energy, and focusing on sustainable development. Most mining and exploration industries prioritize reducing mining’s impact on climate change rather than adapting to extreme weather events. Therefore, it is important to study and investigate the impacts of climate change on the mining sector. This paper aims to investigate the challenges and strategies for adapting to and mitigating the impacts of climate change on mining through a systematic literature review. The results indicate that the majority of proposed models and strategies in the mining field are still in the conceptual phase, with fewer practical implementations. It has been identified that there is a requirement for long-term planning, improved risk management plans, and increased awareness and education within the industry. Practical strategies such as integrating renewable energy, enhancing operational safety, and improving water and tailings management have been recognized as crucial for effective climate change adaptation and mitigation.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alzheimer’s disease diagnosis using deep learning techniques: datasets, challenges, research gaps and future directions","authors":"Asifa Nazir, Assif Assad, Ahsan Hussain, Mandeep Singh","doi":"10.1007/s13198-024-02441-5","DOIUrl":"https://doi.org/10.1007/s13198-024-02441-5","url":null,"abstract":"<p>Alzheimer’s disease (AD) is a condition characterized by the degeneration of brain cells, leading to the development of dementia. Symptoms of dementia include memory loss, communication difficulties, impaired reasoning, and personality changes, often deteriorating as the disease advances. As per the statistics, around 6.9 million individuals in the United States are diagnosed with AD. Approximately two-thirds of Americans with Alzheimer’s are female. Of the total population affected, 4.2 million are women, while 2.7 million are men aged 65 and older in the U.S., constituting 11% of women and 9% of men within this age group. While treatment options for AD are available, they primarily aim to address symptoms rather than providing a cure or slowing down the progression of the disease. Several neural network scans play crucial roles in medical diagnostics, including “Magnetic Resonance Imaging (MRI)” and “Positron Emission Tomography (PET)”. However, these techniques often involve manual examination, resulting in drawbacks such as slow processing and the risk of human error. This study aims to demonstrate how Artificial Intelligence (AI) techniques, including computer vision, Machine Learning (ML), and Deep Learning (DL), can precisely diagnose the early stages of AD, potentially delaying or preventing disease progression. DL algorithms, known for their ability to handle vast amounts of data and extract relevant features, allow the detection of treatable symptoms of the disease before it reaches irreversible stages. The study begins with an overview of AD and the prevailing methodologies utilized for its early detection. It delves into examining diverse DL techniques in scrutinizing clinical data to identify the disease in its early stages. Further, the study explores various publicly accessible datasets, addressing associated challenges and proposing potential future research directions. A significant contribution of this research lies in introducing holography microscopic medical imaging as a novel approach to AD diagnosis, an area previously unexplored by researchers. The discussion section thoroughly explores different interpretations and implications arising from the conducted study. The second last section addresses ongoing research obstacles and looks at potential avenues for future studies. Ultimately, the study concludes by presenting its findings and considering their implications.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TabNet unveils predictive insights: a deep learning approach for Parkinson’s disease prognosis","authors":"Tapan Kumar, R. L. Ujjwal","doi":"10.1007/s13198-024-02450-4","DOIUrl":"https://doi.org/10.1007/s13198-024-02450-4","url":null,"abstract":"<p>Parkinson’s disease (PD) is a neurodegenerative disorder affecting movement, speech, and coordination. Early diagnosis and intervention are crucial for improving the quality of life for PD patients. This study aims to enhance early PD diagnosis and improve patient outcomes using a novel approach. We proposed a TabNet model to classify patients with PD based on voice recordings and other features. TabNet is a neural network architecture designed specifically for tabular data. We compared its performance with support vector machines (SVMs), random forests (RFs), and decision trees (DTs). The TabNet model outperformed these methods, achieving an F1 Score of 83.03%. This demonstrates the model’s potential for more accurate PD diagnosis, which could lead to better patient management and treatment strategies.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cellular automata-based simulation study to optimize supply chain operations during sudden-onset disruption","authors":"Ravi Suryawanshi, R P Deore","doi":"10.1007/s13198-024-02428-2","DOIUrl":"https://doi.org/10.1007/s13198-024-02428-2","url":null,"abstract":"<p>There are noticeable cases today that affect supply chain (SC) planning due to disasters. Such events, which occur without prior information, affect the overall decision-making in SC operations. The nature of such events can be mild and severe depending on the intensity of their characteristics. Moreover, recovering in such trying times becomes a primary objective in any business situation. The study proposes a simulation approach based on cellular automata that suggests an effective recovery strategy to minimize the impact of disruptions. The simulation tool analyzes the performance of firms that cooperate in a serial SC structure and exchange the items depending on ordering frequency. We consider two key performance indicators to gauge the overall sensitivity of the network against the disruption, namely, network strength and resource levels of the SC agents. Two disruption scenarios, namely, mild and severe, are considered, and the analysis highlights a gap of 10.94% in the network performance comparing the two situations simultaneously. A conceptual framework with algorithmic flowchart is presented in the paper to provide over-arching view of the study. The study observes the effectiveness of collaboration among the firms to overcome the disaster situation and identify the best recovery approach. The study quantifies the relationship between resource investment during such a difficult time versus the recovery phase. Though the simulation solution does not account for the implied uncertainty due to exogenous variables such as demand, the analysis provides substantial insights that are suitable to mitigate real-world SC decision-making problem due to disruptions.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}