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We consider the problem of assigning ship assembly blocks to storage locations at a ship yard while allowing one-way movement of the blocks. The objective is to minimize the number of blocks obstructing other blocks’ movements. We show that the integer program in a previous study contains errors in identifying obstructive blocks and therefore, suggest another integer program without the error. Also, we suggest construction heuristics and a tabu search algorithm using properties characterizing optimal solutions of subproblems. Computational experiment results indicate that our heuristics outperform the existing algorithm in terms of solution quality.

While there is ample discussion in management studies and organizational behavior textbooks about the factors that impact organizational outcomes, such as employee retention, this research is focused on exploring the previously unexplored question of how procedural justice, job characteristics and meaningful work influence employees' intentions to leave their organizations. As such, this study aims to investigate the impact of procedural justice on employees' intentions to leave, both independently and in conjunction with job characteristics and meaningful work as mediators.

This study uses partial least squares structural equation modeling (PLS-SEM) to develop the research model and for hypothesis testing. The path model is assessed using critical model fit indices and measures of goodness of fit.

The results reveal a negative relationship between procedural justice and employees’ intentions to leave. This negative relationship persists and is strengthened when both job characteristics and meaningful work act as mediators. Although job characteristics only exerted a significant effect through indirect effects, meaningful work demonstrated a significant negative impact on the intentions to leave through both direct and indirect effects.

This study presents a new perspective on employee retention by proposing an original mediation-based path model. Through the testing of eleven hypotheses, the study reveals the intricate relationships between the four constructs examined. The findings provide valuable insights that can serve as a basis for future research in management studies and organizational behavior.

Many problems occur when assigning tasks to work centres, especially in determining the required number of workstations for line balancing which requires a minimum theoretical number of workstations. The most common problem is bottleneck. In this paper, a method is proposed to solve floating tasks problem in single-model line when the actual required number of workstations exceeds the minimum theoretical number, and the standard time of the floating task (work center) exceeds the cycle time. The floating task will represent a critical bottleneck activity in line. The proposed method depends on minimizing the standard time of critical bottleneck and non-critical activities by a minimum free-floating time depends on the average of slack times of the non-critical activities, and it will increase the line efficiency from (77%) to (88%), and balance delay is minimized from (23%) to (12%).

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.

Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.

This study aims to identify European positioning on the use of remote customer onboarding solutions in combating financial crime.

This study is a desktop research that examines European Banking Authority (EBA) policy statements relating to the use of innovative solutions in combating financial crime.

Technological advancements in biometric data and software tools provide a unique opportunity to address potential paper customer onboarding process deficiencies. Electronic remote customer onboarding solutions equip credit, financial institutions and investment firms with an alternative FTE cost-saving solution, in their pursuit of revenue generation. Whilst the EBA and Financial Action Task Force have provided approval for the utilisation of innovative solutions and AML technologies in combatting financial crime. Hesitancy remains on the ability of credit and financial institutions to use technological solutions as a “magic solution” in preventing the materialisation of money laundering/terrorist financing related risks. Analysis of policy suggests a gravitation towards the increased use of the aforementioned technologies in the interim.

Capitalisation of European banking authority.