Effectiveness Of Decision-making Process Research Articles

A Digital One Health framework to integrate data for public health decision-making

The current implementation of One Health (OH) primarily focuses on multi-sectoral collaboration but often overlooks opportunities to integrate contextual and pathogen-related data into a unified data resource. This lack of integration hampers effective, data-driven decision-making in OH activities. In this perspective, we examine the existing strategies for data sharing and identify gaps and barriers to integration. To overcome these challenges, we propose the Digital OH (DOH) framework for data integration, which consolidates data-sharing principles within five pillars for the OH community of practice: (a) Harmonization of standards to establish trust, (b) Automation of data capture to enhance quality and efficiency, (c) Integration of data at point of capture to limit bureaucracy, (d) Onboard data analysis to articulate utility, and (e) Archiving and governance to safeguard the OH data resource. We discuss an upcoming pilot program as a use case focusing on antimicrobial resistance surveillance to illustrate the application of this framework. Our ambition is to leverage technology to create data as a shared resource using DOH not only to overcome current structural barriers but also to address prevailing ethical and legal concerns. By doing so, we can enhance the efficiency and effectiveness of decision-making processes in the OH community of practice, at a national, regional, and international level.

Generalized Ordered Intuitionistic Fuzzy C-Means Clustering Algorithm Based on PROMETHEE and Intuitionistic Fuzzy C-Means

The problem of ordered clustering in the context of decision-making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real-world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered-clustering outcomes. However, the intuitionistic fuzzy c-means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision-making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm, called the generalized ordered intuitionistic fuzzy c-means (G-OIFCM), based on PROMETHEE and the IFCM clustering algorithm. Different from the classical IFCM clustering algorithm, we use positive flow ( φ + s i ∈ 0 , 1 ) and negative flow ( φ − s i ∈ 0 , 1 ) of PROMETHEE to generate ordered clusters within the intuitionistic environment. We define a new objective function based on the positive and negative flow of the PROMETHEE and IFCM clustering algorithm, whose properties are mathematically justified in terms of convergence and optimization. The performance of the proposed algorithm is evaluated using two different real-world datasets to assess both the ordered clustering and the quality of partitioning. To demonstrate the effectiveness of G-OIFCM, a comparison is conducted with three other algorithms: fuzzy c-means (FCM), ordered fuzzy c-means (OFCM), and an adaptive generalized intuitionistic fuzzy c-means (G-IFCM). The results demonstrate the effectiveness of G-OIFCM in enhancing optimal ordered clustering and utility when dealing with uncertainty in datasets.

Deep Learning and Neural Networks: Decision-Making Implications

Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. The integration of these techniques underscores the significance of interdisciplinary research. In particular, decisions often rely on the output’s projected value or probability from neural networks, considering different values of the relevant output factor. This interdisciplinary review examines the impact of deep learning on decision-making systems, analyzing 25 relevant papers published between 2017 and 2022. The review highlights improved accuracy but emphasizes the need for addressing issues like interpretability, generalizability, and integration to build reliable decision support systems. Future research directions include transparency, explainability, and real-world validation, underscoring the importance of interdisciplinary collaboration for successful implementation.

Risk Identification in Sports Organization

As the COVID-19 pandemic enters its third year and the economy continues to recover, it is clear that today's dynamic, unpredictable risk environment will remain. Due to the influence of other unexpected and changing sources of risks from the external and internal environment, situations arise that affect the fulfillment of set goals and the effectiveness of decision-making processes. This uncertainty underlines the need for prevention and the importance of risk management in sports organizations so that organizations can better prepare for potential crises. While in some countries (e.g., the USA, Australia, and others), it is common for the management of a sports organization to be aware of its serious (key) risks, this is not the case in Slovakia. The contribution aims to present the basic concept of risk identification for sports organizations to support sustainability resulting from the current requirements of practice in Slovakia and respect for world trends. The proposed concept's essence is identifying risks in connection with the application and significance of selected methods and techniques of risk identification. The proposed concept should focus on strategic management and establish responsibility for identifying risks to minimize the risks' negative impact. The benefit of the contribution is the strengthening of support for the application of risk management in sports organizations not only in Slovakia but also in neighboring countries. With a proper understanding and effective management of risks within processes and functions, sports organizations can anticipate emerging risks and increase the probability of achieving the goals of profit and sports success.

Diagnostic Informatics-The Role of Digital Health in Diagnostic Stewardship and the Achievement of Excellence, Safety, and Value.

Diagnostic investigations (pathology laboratory and medical imaging) aim to: increase certainty of the presence or absence of disease by supporting the process of differential diagnosis; support clinical management; and monitor a patient's trajectory (e. g., disease progression or response to treatment). Digital health can be defined as the collection, storage, retrieval, transmission, and utilization of data, information, and knowledge to support healthcare. Digital health has become an essential component of the diagnostic process, helping to facilitate the accuracy and timeliness of information transfer and enhance the effectiveness of decision-making processes. Digital health is also important to diagnostic stewardship, which involves coordinated guidance and interventions to ensure the appropriate utilization of diagnostic tests for therapeutic decision-making. Diagnostic stewardship and informatics are thus important in efforts to establish shared decision-making. This is because they contribute to the establishment of shared information platforms (enabling patients to read, comment on, and share in decisions about their care) based on timely and meaningful communication. This paper will outline key diagnostic informatics and stewardship initiatives across three interrelated fields: (1) diagnostic error and the establishment of outcomes-based diagnostic research; (2) the safety and effectiveness of test result management and follow-up; and (3) digitally enhanced decision support systems.

RESEARCH GAPS IN MULTI-CRITERIA DECSION MAKING METHODS FOR RESEARCHERS AS AN AREAS OF INTEREST

The increase in Multi criteria decision-making studies is reflecting its importance as an interested area for research. Even though a high number of revealed studies mainly in last decade, still this field have challenges that require attentions from researchers in future. Most of MCDM related studies did not consider determining DM weights or using subjective methods, and there is an absence of implementing sensitivity analysis to DM weights as well. In addition to the need to develop new methods that utilizing the web or mobile technologies to deal with complexity and uncertainty adhered to MCDM problems. This paper represents a trial to list current challenges in this field as areas of interest for researchers in future studies that aims to enhance the quality and effectiveness of decision-making process.

Intelligent health monitoring system modeling based on machine learning and agent technology

The use of machine learning approach in the field of health informatics improves the quality and effectiveness of decision-making process. Its particular integration into the Remote Health Monitoring Systems (RHMS) allows: collecting, analyzing and learning from real-time data, automatically gainin g knowledge and making predictions on the patients state. The RHMS represent an effective solution to control and monitor a growing number of dependent or elderly patients. They showed impressive results in healthcare applications. These systems are built around the sensors installed on the body of the patient and others embedded in their environments. In this context, we are confronted with the challenge of temporal aspect of data. To meet this challenge, we propose that RHMS combine machine learning for generating intelligent valuable information, and visual analytics for gaining insight the collected real-time data. The success of these applications is based on the quality of their design and development. For this reason, we propose to design a RHMS using multi-agent technology. The developed prototype was evaluated to verify our proposal feasibility.

How uncertainty influences decision-making effectiveness in conceptual ship design processes

BACKGROUND: Understanding how and why the development of conceptual ship designs sometimes become ineffective is essential for ship design firms. Our proposition is that in many projects, uncertainty influences negatively the effectiveness of the decision-making process. OBJECTIVE: The objective of this article is to quantify the perception of uncertainty in conceptual ship design processes. METHODS: In this article, we propose a research model to study such a phenomenon. The research model is tested using multivariate regression analysis, building on a survey conducted among 23 shipping companies. RESULTS: Our model suggests that 14% ( R 2 ) of the variability in the effectiveness of decision-making processes in ship design can be explained by changes in the perception of uncertainty. We can extract three interesting insights from this research work for the ship design practitioners as to how to improve the effectiveness of their design processes: (i) put more effort into the contextual factors affecting the ship design process, (ii) improve the communication with vessel owners and other stakeholders, and (iii) improve the agility of the design process. CONCLUSIONS: This study contributes to research on uncertainty in ship design processes by: (a) proposing an investigative model, (b) developing and testing a survey instrument and (c) running a multivariate regression analysis to study the effect of perceived uncertainty on the effectiveness of decision-making processes in conceptual ship design.

An integrated flood management system based on linking environmental models and disaster-related data

The flood disaster management system (FDMS) is a platform developed to provide ongoing disaster reduction capabilities that cover the entire process of flood management. The ontology-based approach links environmental models with disaster-related data to support FDMS-constructed workflows with suitable models and recommend appropriate datasets as model input automatically. This automated activity for model selection and data binding reduces the time-consuming and unreliable operations involved in traditional management techniques, which rely on manual retrieval through simple metadata indices—typically when flood management personnel are overwhelmed with large quantities of observed data. The OpenMI-based modular design used in the system unifies interfaces and data exchange to provide flexible and extensible architecture. Subsequent 3D visualization improves the interpretability of disaster data and the effectiveness of decision-making processes. This paper presents an overview of the design and capabilities of FDMS that provides one-stop management for flood disasters.

Prototyping the visualization of geographic and sensor data for agriculture

The effectiveness of decision-making processes in the agricultural domain can be improved by integrating current local environmental and agromonitoring with the Geographic Information System (GIS) and wireless sensor networks (WSNs). The presented paper describes conceptual approaches to context-based cartographic visualization methods for agricultural and metrological data acquired by WSN and a portal prototype for integrated visualization.Each sensor used for agricultural applications has a location and can be placed within a broader spatial context. In our study, sensor characteristics (soil temperature and moisture, atmospheric temperature and moisture) were automatically monitored at frequent intervals and these readings were aggregated with geospatial data from both local and remote (Web Map Service) sources. An experimental portal for the integration and visualization of sensor data and geospatial data was designed and prototyped on the basis of an open source interoperable platform. Conceptual approaches were successfully implemented on four experimental small-plot fields planted with different crop species and operated using different soil tillage practices. Experimental fields were situated in the southeast of the Czech Republic. Very Long range Identification Tag (VLIT) technology was used for wireless communication. Sensor observations verified differences in agrometeorological variables for the conventional tillage of soil and the no-tillage variant. Contextual cartographic visualization was successfully deployed for map view, dynamic cartographic symbology, and the dynamic measurement chart.

The Effects of Decision-Making Processes and Population Turnover on the Formation of Social Networks

The researchs on the structure and formation mechanism of social networks lead to several models with differences in the attachment patterns of new links (edges). In fact, the driving factor behind the addition of new links is just as important as the attachment patterns, while very little attention has been devoted so far to this exploration. We present an agent-based model which could successfully reproduce large-scale social networks. We find that the structure of social networks is a consequence of continuous individuals’ decision-making processes based on self-evaluations and the turnover of the population. The individuals’ self-evaluation processes are key motivating factors for the addition of new links, while the attachment patterns and the turnover of the population should be responsible for the topology of social networks. The resulting networks of our model display dynamics between order and randomness, which is greatly consistent with current observations and research achievements of social networks. We also find that some plausible properties of empirical data are actually artifacts due to the boundedness of sampling. Our research has revealed the driving factors behind the evolution of social networks as well as the underlying evolving patterns. These findings will lead to a better understanding of social structures.

A simulation metamodelling based neural networks for lot-sizing problem in MTO sector

Simulation is essentially a trial-and-error approach, and is therefore, time-consuming and does not provide a method for optimization. Metamodelling techniques have been recently pursued in order to tackle these drawbacks. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper proposes an application of simulation metamodelling through artificial neural networks (ANNs). The building of the appropriate ANN model over second-order linear regression model and the reverse simulation metamodelling as simulation-optimization are assisted by the Neuro ® Software. To validate the proposed approach, a case study which is adopted from literature, deals with a lot sizing problem in make-to-order supply chain. The optimal solution is to determine the fixed lot size for each manufacturing product type that will ensure order mean flow time target. The comparative results with others metamodels techniques; illustrate the efficiency and effectiveness of the proposed approach.

Simulation metamodeling through artificial neural networks

Simulation metamodeling has been a major research field during the last decade. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper discusses the importance of simulation metamodeling through artificial neural networks (ANNs), and provides general guidelines for the development of ANN-based simulation metamodels. Such guidelines were successfully applied in the development of two ANNs trained to estimate the manufacturing lead times (MLT) for orders simultaneously processed in a four-machine job shop. The design of intelligent systems such as ANNs may help to avoid some of the drawbacks of traditional computer simulation. Metamodels offer significant advantages regarding time consumption and simplicity to evaluate multi-criteria situations. Their operation is notoriously fast compared to the time required to operate conventional simulation packages.

Evoked Brain Potentials as Indicators of Decision-Making

The effects of decision-making processes on evoked brain potentials recorded at the vertex were studied in human subjects. Significantly different visual evoked potentials to the same physical stimulus were obtained in trails that resulted in different behavioral decisions. The results suggest that certain characteristics of evoked potentials may perhaps be used as indicators of specific behavioral outcomes.