International Journal of System Assurance Engineering and Management最新文献

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.

The linear consecutive (LC) k-out-of-r-from-n system is an incredibly important configuration used in various engineering systems. Such a system will break down if at least k out of r consecutive elements become inoperable in a system consisting of n 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 k-out-of-r-from-n 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.

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.

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.

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.

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.

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.

Life cycle cost (LCC) analysis is an important tool for effective infrastructure management. It is an essential decision support methodology for selection, design, development, construction, maintenance and renewal of railway infrastructure system. Effective implementation of LCC analysis will assure cost-effective operation of railways from both investment and life-cycle perspectives. A major setback in the successful implementation of LCC analysis by infrastructure managers is the availability of relevant, reliable, and structured data. Different cost estimation methods and prediction models have been developed to deal with this challenge. However, there is a need to include condition degradation models as an integral part of LCC model to account for possible changes in the model variables. This article presents an approach for integrating degradation models with LCC model to study the impact of change in design speed on key decision criteria such as track possession time, service life of track system, and LCC. The methodology is applied to an ongoing railway investment project in Sweden to investigate and quantify the impact of design speed change from 250 to 320 km/h. The results of the studied degradation models show that the intended change in speed corresponds to correction factor values between 0.79 and 0.96. Using this correction factor to compensate for changes in design speed, the service life of ballasted track system is estimated to decrease by an average of 15%. Further, the expected value of LCC for the route under consideration will increase by 30%. The outcome of this study will be used to support the design and requirement specification of railway track system for the project under consideration. 

The present paper addresses the reliability modeling of a three-unit soft biscuit-making system. The system under consideration consists of three units, namely the mixer, depositor, and oven. Depositor and oven are connected through the same conveyor belt, so if there is a failure in either of them then another will be in a down state. On the other hand, the mixer works as a separate unit that provides feed to the depositor. However, the mixer can also be in a down state if the failures of either depositor or oven are not repaired within the stipulated time. Two repair personnel are appointed to handle the failures associated with the units. The system is assessed by employing the semi-Markov process and regenerative point technique. Additionally, relevant measures of system effectiveness are derived, accompanied by a comprehensive sensitivity analysis to assess the impact of various parameters on the system’s performance. Graphical representations are employed to visually analyze the influence of these parameters on the system’s overall efficiency.

Oral Cavity Squamous Cell Carcinoma (OCSCC) represents a common form of head and neck cancer originating from the mucosal lining of the oral cavity, often detected in advanced stages. Traditional detection methods rely on analyzing hematoxylin and eosin (H&E)-stained histopathological whole-slide images, which are time-consuming and require expert pathology skills. Hence, automated analysis is urgently needed to expedite diagnosis and improve patient outcomes. Deep learning, through automated feature extraction, offers a promising avenue for capturing high-level abstract features with greater accuracy than traditional methods. However, the imbalance in class distribution within datasets significantly affects the performance of deep learning models during training, necessitating specialized approaches. To address the issue, various methods have been proposed at both data and algorithmic levels. This study investigates strategies to mitigate class imbalance by employing a publicly available OCSCC imbalance dataset. We evaluated undersampling methods (Near Miss, Edited Nearest Neighbors) and oversampling techniques (SMOTE, Deep SMOTE, ADASYN) integrated with transfer learning across different imbalance ratios (0.1, 0.15, 0.20, 0.30). Our findings demonstrate the effectiveness of SMOTE in improving test performance, highlighting the efficacy of strategic oversampling combined with transfer learning in classifying imbalanced medical datasets. This enhances OCSCC diagnostic accuracy, streamlines clinical decisions, and reduces reliance on costly histopathological tests.