Reliable Decision Research Articles

Measurement properties of the Body Awareness Scale Movement Quality (BAS MQ) in persons on the autism spectrum: A preliminary Rasch analysis

BackgroundPersons on the autism spectrum exhibit poorer body awareness than neurotypical persons. Since movement quality may be regarded as an expression of body awareness, assessment of movement quality is important. Sound assessments of measurement properties are essential if reliable decisions about body awareness interventions for persons on the autism spectrum are to be made, but there is insufficient research. ObjectiveTo assess measurement properties of the Body Awareness Scale Movement Quality (BAS MQ) in an autism and a neurotypical reference group. MethodsPersons on the autism spectrum (n=108) and neurotypical references (n=32) were included. All were assessed with BAS MQ. Data were analyzed according to the Rasch model. ResultsBAS MQ was found to have acceptable unidimensionality, supported by the fit statistics. The hierarchical ordering showed that coordination ability was the most difficult, followed by stability and relating. Response category functioning worked as intended for 19 out of 23 items. There were few difficult items, which decreased targeting. Reliability measures were good. BAS MQ discriminated between the autism and the reference groups, with the autism group exhibiting poorer movement quality, reflecting clinical observations and previous research. ConclusionsBAS MQ was found to have acceptable measurement properties, though suffering from problems with targeting item difficulty to person ability for persons on the autism spectrum. The BAS MQ may, along with experienced movement quality, contribute to clinically relevant information of persons on the autism spectrum, although we encourage refinements and further analyses to improve its measurement properties.

Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems

In recent years, significant advancements in deep learning technology have facilitated the development of intelligent health monitoring approaches for energy systems. However, when dealing with safety-critical energy systems, such as nuclear energy systems, conventional deep learning models with point estimation fail to account for the inherent uncertainty in the predictions. This limitation poses challenges for providing reliable and trustworthy decision support for critical operations. To overcome this challenge, this study proposes a novel intelligent monitoring approach that integrates uncertainty-aware deep neural networks. Firstly, a spatio-temporal state matrix-based signal preprocessing method is proposed to enhance feature extraction capabilities, enabling the effective integration of diverse multi-source data. Secondly, a probabilistic distribution is developed to generate predictive uncertainty for all network parameters, enabling the assessment of the confidence of the model’s outputs not only for known operation scenarios but also for unknown scenarios. Finally, the experiments are conducted using an established advanced nuclear energy research platform and a public nuclear accident simulation platform, ensuring the effectiveness and applicability of the proposed approach in practical settings. Overall, the proposed approach significantly enhances the reliability and trustworthiness of the monitoring outputs while mitigating the risks associated with the decision-making process in safety-critical energy systems.

ENGINE-An EHS Project for Future Guidelines.

Clinical guidelines are evidence-based recommendations developed by healthcare organizations or expert panels to assist healthcare providers and patients in making appropriate and reliable decisions regarding specific health conditions, aiming to enhance the quality of healthcare by promoting best practices, reducing variations in care, and at the same time, allowing tailored clinical decision-making. European Hernia Society (EHS) guidelines aim to provide surgeons a reliable set of answers to their pertinent clinical questions and a tool to base their activity as experts in the management of abdominal wall defects. The traditional approach to guideline production is based on gathering key opinion leader in a particular field, to address a number of key questions, appraising papers, presenting evidence and produce final recommendations based on the literature and consensus. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) method offers a transparent and structured process for developing and presenting evidence summaries and for carrying out the steps involved in developing recommendations. Its main strength lies in guiding complex judgments that balance the need for simplicity with the requirement for complete and transparent consideration of all important issues. EHS guidelines are of overall good quality but the application of GRADE method, began with EHS guidelines on open abdomen, and the increasing adherence to the process, has greatly improved the reliability of our guidelines. Currently, the need to application of this methodology and the creation of stable and dedicated group of researchers interested in following GRADE in the production of guidelines has been outlined in the literature. Considering that the production of clinical guidelines is a complex process, this paper aim to highlights the primary features of guideline production, GRADE methodology, the challenges associated with their adoption in the field of hernia surgery and the project of the EHS to establish a stable guidelines committee to provide technical and methodological support in update of previously published guideline or the creation of new ones.

Automatic Gear Tooth Alignment in Vision Based Preventive Maintenance

Thorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network.

The Bumpy Road to Becoming a Data-Driven Enterprise

Nowadays organizations are often inspired to become data-driven enterprises that continuously and systematically use data (from internal and external sources) to inform strategic decision-making and actions to improve operational efficiencies, products, services, customer experience, and competitiveness. Such use of data implies that organizational actors across different organizational functions are making decisions based on information derived from analytical insights. Yet, many companies find the journey to becoming a data-driven enterprise a bumpy ride. A key issue faced by most firms is that their data management approach is not ready for data and analytics-based decision-making. In this teaching case, we follow the journey of an industrial enterprise, PETRONAS, attempting to transform into a data-driven enterprise. We report on four key data-driven ‘blockers’ encountered by PETRONAS Downstream and provide insights into steps taken to address these data challenges. This case study is suitable for courses that explore digital transformation and data management in an organizational context, from strategic and operational perspectives. For example, instructors teaching a Digital Transformation course might use this case for a session dedicated to learning about data-readiness for digital transformation. It is intended for postgraduate students (Master and MBA levels) as well as late-stage undergraduate students. Specific angles that could be explored in depth are: identifying and capturing data-related requirements and issues from the perspectives of different organizational actors; examining interdependencies between functional and technical aspects of data management and use (e.g., requirements, availability, sustainability of data supply) for fast and reliable decision-making; sourcing of data: evaluating in-house and outsourced approaches for data sourcing and management; implementation of data-related projects and change management.

ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.

Background: Multi-source evidence fusion aims to process and combine evidence from different sources to support rational and reliable decision-making. The evidential reasoning (ER) approach is a helpful method to deal with information from multiple sources with uncertainty. It has been widely used in business analytics, healthcare management, and other fields for optimal decision-making. However, computerized implementation of the ER approach usually requires much expertise and effort. At present, some ER-based computerized tools, such as the intelligent decision system (IDS), have been developed by professionals to provide decision support. Nevertheless, IDS is not open source, and the user interfaces are a bit complicated for non-professional users. The lack of a free-to-access and easy-to-use computerized tool limits the application of ER. Methods: We designed and developed a Python package that could efficiently implement the ER approach for multi-source evidence fusion. Further, based on it, we built an online web-based system, providing not only real-time evidence fusion but also visualized illustrations of combined results. Finally, a comparison study between the Python package and IDS was conducted. Results: A Python package, ERTool, was developed to implement the ER approach automatically and efficiently. The online version of the ERTool provides a more convenient way to handle evidence fusion tasks. Conclusions: ERTool, compatible with Python 3 and can be installed through the Python Package Index at https://pypi.org/project/ERTool/, was developed to implement the ER approach. The ERTool has advantages in easy accessibility, clean interfaces, and high computing efficiency, making it a key tool for researchers and practitioners in multiple evidence-based decision-making. It helps bridge the gap between the algorithmic ER and its practical application and facilitates its widespread adoption in general decision-making contexts.

The influence of supply chain integration on firm performance through lean manufacturing, green supply chain management and risk management

The rapid development of technology has enabled companies to integrate internal and external partners working together in the supply chain network. Supply chain integration allows fast information to facilitate real-time and reliable decision-making. This study investigates the role of supply chain integration on firm performance through adopting lean manufacturing, green supply chain management, and risk management. The study surveyed manufacturing companies implementing ISO 14000 to represent green supply chain management and integrated information technology as a form of integration. The questionnaires were distributed using a Google form, and 93 valid responses were obtained. Data analysis employed a partial least square approach with SmartPLS software 4.1 version. The data processing results found that supply chain integration increased lean manufacturing by 0.684, green supply chain management by 0.451, and supply chain risk management by 0.333. Lean manufacturing companies using a continuous process control system and process improvements significantly improve green supply chain management by a path coefficient of 0.477, supply chain risk management by 0.206, and firm performance by 0.370. Green supply chain management significantly impacts supply chain risk management by a coefficient of 0.416 and firm performance by 0.189. Supply chain risk management with a system for detecting operational process risks and emergency procedures in overcoming changes in customer orders affects the increase in firm performance by 0.354. The practical contribution of research provides insight for practitioners to invest in information technology and adopt ISO 14000 implementation. Theoretical contributions in developing resources-based view theory in adopting green supply chain management and lean manufacturing.

ВНЕДРЕНИЕ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ УПРАВЛЕНИЯ ДЛЯ АВТОМАТИЗИРОВАННЫХ ПОДСТАНЦИЙ

The article discusses the possibilities of implementing intelligent control systems at automated substations. The key advantages of such systems are emphasized, including increased reliability, resource optimization, and real-time decision support. Examples of successful application of intelligent systems are given, prospects for their further development are analyzed, and challenges associated with their integration into the existing infrastructure are considered.

Evaluation and Power Grid Investment Efficiency under High Quality Development

In the context of the reform of transmission and distribution prices, how to improve the investment efficiency of power networks is a noteworthy issue. This article constructs a transmission and distribution price system under the electricity market price system from the perspective of efficiency, and evaluates the effectiveness of each indicator based on the upper limit, benchmark, and profit criteria of transmission and distribution prices. It establishes evaluation methods for investment efficiency of power grids in various regions and provides the optimal solution based on gross profit margin. Through example analysis, the relationship between transmission coefficient, asset return rate, and investment and energy utilization rate was obtained, which is an important basis for making safe and reliable investment decisions. The experiment shows that the accuracy of the three stage DEA method based on the dataset is higher than 89%, which is higher than other algorithms.

A Reliable Clinical Decision Support System for Post Traumatic Stress Disorder using functional Magnetic Resonance Imaging data

A Reliable Clinical Decision Support System for Post Traumatic Stress Disorder using functional Magnetic Resonance Imaging data

The Web-based Open-access Reliable Decision on Synonyms (WORDS) English Vocabulary Test.

A quick and reliable test of vocabulary knowledge is a vital component of many studies looking at a range of language processing skills. Recent proliferation of online (web-based) research has generated a growing need for reliable open-access vocabulary tests that can be administered online. This data report presents the newly developed 30-item Web-based Open-access Reliable Decision on Synonyms (WORDS) English Vocabulary Test. In Experiment 1, we tested 109 participants (age range: 18-69 years) on an initial set of 51 potential multiple-choice test items in which participants read a target word (e.g., ubiquitous) and selected a (near-)synonym (e.g., omnipresent) from among three semantically unrelated foils (e.g., interpersonal, catatonic, voluminous). We conducted an item response theory analysis of participants' accuracy data to select an optimal subset of 30 items to include in the final version of the test. In Experiment 2, we verified the reliability of this 30-item version in a different sample (N = 121; 18-79 years); reliability (internal consistency) was good (Cronbach's α = 0.82). We confirmed that, consistent with numerous previous studies, (1) responses were more accurate and quicker for more frequent compared to less frequent words, and (2) older adults showed greater vocabulary knowledge compared to younger adults. The WORDS test takes on average 4 minutes (5 minutes 40 seconds including consent/debrief) to complete. It can be freely accessed via Gorilla Open Materials (https://app.gorilla.sc/openmaterials/694887), allowing ease of use and for useful comparisons between data collected by different researchers.

A REVIEW ON GLYCEROL-BASED DRILLING FLUIDS AND GLYCERINE AS A DRILLING FLUID ADDITIVE

A significant increase of energy demands all over the world and production decline from available oil and gas reservoirs have led the industry to invest in major offshore petroleum resources. However, drilling operations in offshore environments are usually restricted by environmental constraints. Therefore, recent studies are devoted to the development of environmentally compatible fluids with adequate technical properties. Glycerine is a non-toxic, lubricating, colorless, odorless substance with a higher density than water. Due to the properties of glycerine, it can be used as the base of drilling fluid to formulate synthetic-based fluids. This research aimed to review the studies on the applications of glycerine in the composition of drilling fluid. Based on the results, glycerine-based fluids can be considered as an environmentally compatible fluid with sufficient technical properties to replace other drilling fluids. However, there is a lack of experimental studies on the glycerine fluid properties for a reliable decision. For the application of glycerine fluids, an economic feasibility study is mandatory for both pure and crude glycerine. Also, the thermal stability of glycerine fluids is an important aspect, which should be covered in future research studies.

Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring

Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models that address this problem using ocean remote sensing satellite images, there is still a lack of effective sun glint correction solutions for high-resolution RGB imagery obtained by consumer-grade UAVs in marine ecological monitoring, particularly at the centimeter level. In this study, an innovative sun glint correction pipeline combining a Foreground Attention-based Semantic Segmentation Network (FANet) with optical flow-based pixel propagation has been proposed, aiming to achieve accurate sun glint detection and restoration of real texture of coral reefs. To further understand the inner mode of sun glint segmentation FANet model, we conduct explainable analysis to ensure that it learns the informative sun glint features and makes reliable decisions. According to the comprehensive experimental evaluation using three datasets, our FANet model exhibits accurate and effective sun glint detection, achieving an IoU range of 70.86%–83.96%. Moreover, the sun-glint free images processed by our sun glint correction method have been employed for large-scale image stitching and shallow-water coral reef habitat semantic mapping. The results demonstrate that our sun glint correction approach successfully restores the texture of coral reefs and prepares a reliable foundation for subsequent downstream applications. We believe that our method as an open-source sun glint correction solution in high-resolution marine UAV RGB images is able to enhance the accuracy and efficiency of various marine ecological remote sensing tasks at a low-cost. The relevant code can be found in https://github.com/jyqinnn/Sun-glint-correction.

A DE Novo multi criteria heterogeneous group decision making approach for green performance assessment of CNC machine tools

In the contemporaneous sustainable manufacturing scenario and fourth industrial revolutions, requirements of most cutting-edge CNC machine tools are indispensable for finished products with high accuracy, precision and green complaints in particular. Such requirements have impelled the advanced manufacturing industries to evaluate and choose the proper CNC machine tools for best customized performances. In the face of proper and effective green evaluation, this paper incorporates a heterogeneous expert group based decision framework considering multiple significant technical and green criteria by assessing relative importance of diverse conflicting criteria having substantial contribution in performance analysis of CNC machine tools. As a demonstration of the suggested mathematical model, three real life decision making problems related to 3 axes-CNC machine tools based on the collected quantitative and linguistic data from catalogues, manufacturer’s portals, questionnaires, customer reviews etc. are established. The calculated findings are close to those obtained by previous researchers as well as are verified by well-established techniques. Besides, sensitivity and statistical analysis are performed to examine the robustness and stability of the ranking orders of the alternatives as well as to investigate the efficacy and consistency of the proposed method. Hence thus proposed formulated MCDM approach proves to be a highly effective and reliable decision making tool for choosing the most suitable CNC machine tools.

A Survey on Sun Protection and Skin Cancer Understanding of Women at a Shelter in Long Island

As a medical student, I want to take action to make people understand the significance of skin health and protection from the sun. The United States faces a skin cancer epidemic, with cancer cases on the rise, emphasizing the importance of preventative education. However, not all people have the same opportunity to acquire this vital data. This is the reason why I am suggesting a skin education and sun protection advocacy project aimed at the high-risk communities in New York City. I aim to enlighten vulnerable groups to enable them to make reliable decisions that safeguard their health. I will start by doing workshops and practical training at women's shelters in the neighbourhood. Despite being one of the most preventable forms of cancer. Melanoma continues to claim lives due to lack of understanding and awareness regarding the skin health.

Design of Subway Investment Management Platform Under the Background of Informatization

Abstract This article aims to design a subway investment management platform based on DL (Deep Learning) to improve subway projects' investment efficiency and risk management levels. The article adopted the DL algorithm to comprehensively analyze historical subway investment data and constructed prediction and risk assessment models to achieve this goal. The application effect of the DL algorithm in subway investment management and the practicality of the platform were verified through simulation experiments. This article first collected a large amount of historical subway investment data and processed and learned these data using the DL algorithm. By constructing an NN (Neural Network) model, accurate prediction of investment return rate and market demand for subway projects has been successfully achieved. Furthermore, a risk assessment model was also constructed to quantify and evaluate potential risks in subway projects. The experimental results indicate that the DL algorithm has significant prediction and risk assessment advantages. Compared with traditional statistical learning methods and machine learning algorithms, the DL algorithm can more accurately predict investment return and market demand, providing users with more reliable decision support. In addition, the risk assessment model effectively helps users understand the risk status of the project and formulate corresponding risk management measures.

Deep Tensor Evidence Fusion Network for Sentiment Classification

Recently, a multimodal sentiment analysis of social media has attracted increasing attention, and its core idea is to discovery heuristic fusion strategy to analyze the sentiment orientations over heterogeneous multimodal source from a learned compact multimodal representation. The existing multimodal fusion techniques not only struggle to achieve full heterogeneous data interaction, but also they are unable to dynamically assess the quality of various modal data to determine predictability. In this article, we present a novel deep tensor evidence fusion (DTEF) network for multimodal sentiment classification. First, we propose a common view evaluation network that uses a long short-term memory (LSTM) network and a tensor-based neural network to extract rich intermodal and intramodal information. Then, we propose a unique time cue evaluation network that takes advantage of the temporal granularity associated with numerous pattern sequences. To make reliable decisions, we finally incorporate uncertainty through the trusted fusion layer, which improves the accuracy and robustness of sentimental classification. Our model is validated using the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) and CMU Multimodal Corpus of Sentiment Intensity (CMU-MOSI) datasets, and the experimental findings demonstrate the superior performance of the proposed network in terms of accuracy compared with the state-of-the-art methods.

An automatic glaucoma grading method based on attention mechanism and EfficientNet-B3 network.

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients is expected to exceed 110 million by 2040. Early identification and detection of glaucoma is particularly important as it can easily lead to irreversible vision damage or even blindness if not treated with intervention in the early stages. Deep learning has attracted much attention in the field of computer vision and has been widely studied especially in the recognition and diagnosis of ophthalmic diseases. It is challenging to efficiently extract effective features for accurate grading of glaucoma in a limited dataset. Currently, in glaucoma recognition algorithms, 2D fundus images are mainly used to automatically identify the disease or not, but do not distinguish between early or late stages; however, in clinical practice, the treatment of early and late glaucoma is not the same, so it is more important to proceed to achieve accurate grading of glaucoma. This study uses a private dataset containing modal data, 2D fundus images, and 3D-OCT scanner images, to extract the effective features therein to achieve an accurate triple classification (normal, early, and moderately advanced) for optimal performance on various measures. In view of this, this paper proposes an automatic glaucoma classification method based on the attention mechanism and EfficientNetB3 network. The EfficientNetB3 network and ResNet34 network are built to extract and fuse 2D fundus images and 3D-OCT scanner images, respectively, to achieve accurate classification. The proposed auto-classification method minimizes feature redundancy while improving classification accuracy, and incorporates an attention mechanism in the two-branch model, which enables the convolutional neural network to focus its attention on the main features of the eye and discard the meaningless black background region in the image to improve the performance of the model. The auto-classification method combined with the cross-entropy function achieves the highest accuracy up to 97.83%. Since the proposed automatic grading method is effective and ensures reliable decision-making for glaucoma screening, it can be used as a second opinion tool by doctors, which can greatly reduce missed diagnosis and misdiagnosis by doctors, and buy more time for patient's treatment.

Corporate Governance and Financial Distress: A Comprehensive Analysis of Indonesian Manufacturing Companies

Aims: This study aims to find empirical evidence and analyze the effect of institutional ownership, managerial ownership, independent commissioner, board of directors, audit committe on financial distress. In addition, this research can also be used as a reference for further researchers as well as a reference for stakeholders (investors, creditors, and the government) in making relevant and reliable decisions.
 Study Design: The method used is quantitative research with secondary data taken from the company's financial statements with data collection techniques using purposive sampling.
 Place and Duration of Study: The study was conducted on 19 manufacturing companies listed on the Indonesia Stock Exchange for the period 2010 – 2021.
 Methodology: The method used in this research is explanatory with a quantitative approach and the sampling used is purposive sampling. Explanatory analysis is used to explain the relationship between variables Institutional ownership, Managerial Ownership, Independent Commissioners, Board of Directors, Audit Committee.
 Results: The results of this study indicate that: (1) Institutional Ownership have a negative effect on Financial Distress. (2) Managerial Ownership have a negative effect on effect on Financial Distress. (3) Independent Commissioners have a negative effect on effect on Financial Distress. (4) Board of Directors has a negative and significant effect on Financial Distress. (5) Audit Committee have no effect on Financial Distress.
 Conclusion: Based on the conclusion of the effect of good corporate governance on financial distress, it can be seen that GCG implementation can improve company performance, especially financial performance and reduce the risk of financial distress and in general GCG implementation can increase investor confidence. Conversely, the implementation of low Corporate Governance will reduce investor confidence and can be a factor that prolongs the economic crisis.

Recent advances in multi-criteria decision analysis: A comprehensive review of applications and trends

In recent years, multi-criteria decision support methods have become widely used research tools by both scientists and practitioners. Theoretical works involving new multi-criteria methods and developments of existing methods immediately find applications in areas of business practice. This paper attempts to sort out the areas of application of MCDA methods. For this purpose, the relevant literature from 2018–2023 was reviewed. The inclusion selection criteria were defined as addressing multi-criteria practical issues, open accessibility, comprehensive research methodology and findings, and the use of an MCDA method in the evaluation process. Detailed research identified that the main areas of use of MCDA methods are healthcare, energy management, supplier selection, and transportation. Due to the timeliness and importance of the problem, a detailed study of the use of MCDA methods in sustainability issues was separated. The paper contributes contributions to both theory and economic practice. The article provides a series of recommendations both on the methodological side of the problems to be solved and shows the practical prism of individual decision models, offering ready-to-use decision models, and providing a comprehensive review of recent MCDA advancements, fostering informed and reliable decision-making. Additionally, this review sheds light on the latest developments in MCDA, emphasizing trends in Decision Support Systems (DSS) and prominent application areas in contemporary research.