Decision Support Technology Research Articles

Are conductivity sensors useless for irrigators? Exploring measurement consistency around soil moisture thresholds relevant to different applications

AbstractThe cheap conductivity‐based sensors favoured by farmers for soil moisture monitoring remain largely ignored by researchers because of their low accuracy, which results in high uncertainty regarding their utility for irrigators. In this work, conductivity measurements were compared with dielectric permittivity measurements in moisture ranges relevant to different applications. The results showed that the permittivity measurements captured moisture variability well (R2 > 0.8) throughout the full range tested (0%–35%), which is consistent with the literature. Conductivity measurements consistently distinguished dry from wet conditions (p < 0.0001) and reflected moisture variability in lower ranges (R2 > 0.5) but not in higher ranges (>20%). This is problematic because important monitoring thresholds such as field capacity and saturation are in the upper moisture ranges. Conductivity measurements were found to lack any meaningful utility in most applications except those relevant to distinguishing dry from wet conditions and indicating lower‐range moisture patterns, such as monitoring in arid environments. This gives some merit to conductivity sensors considering their very low cost if corrosion is minimised. The described evaluation approach is suggested as an example for developers, labs and extension services to better communicate potential sensor utilities and restrictions to practitioners to improve their accessibility to decision support technologies.

Consumer Attitudes towards AI-based Financial Advice: Insights for Decision Support Systems (DSS) and Technology Integration

As financial technology (fintech) rapidly transforms the financial services landscape, the integration of artificial intelligence (AI) in providing financial advice becomes a focal point. This study delves into the intricate dynamics of AI-based financial advice adoption, focusing on technology integration, decision support systems, and the mediating role of perceived utility. The primary purpose of this research is to unravel the complex relationships between technology integration, Decision Support System (DSS), perceived utility, and their collective influence on consumer attitudes toward AI-based financial advice and technology adoption. Adopting a cross-sectional design, we target the Chinese population and utilize an online questionnaire for data collection. A sample size of 259 participants is determined using the rule of thumb technique, with random sampling ensuring representativeness. Data analysis will be conducted using the AMOS software, allowing for an in-depth examination of the relationships between variables. The study findings demonstrate how integrating technology enhances customers' perceptions and acceptance of it, especially when it comes to data security and compatibility. The study also demonstrates how customer attitudes and the uptake of AI-driven financial aid are significantly impacted by DSS aspects like decision transparency and predictive analytics precision. The study is unique because of its cross-cultural approach and perceived value as a mediating component. While this study focuses on the Chinese population, the researchers acknowledge the importance of cultural differences in shaping user perceptions and behaviors. While the findings may offer valuable insights into the Chinese context, caution is advised in generalizing the results to other cultural contexts.

Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes

Multi-Criteria Decision Analysis (MCDA) methods are essential for addressing real-world challenges in the dynamic field of decision support technology. As technology advances, the expanding variety of multi-criteria evaluation methods highlights the critical importance of method selection for reliable results. These methods can be applied to scenarios involving both precise data and uncertainties. Expert knowledge is relevant in navigating real-world decision problems, particularly in uncertain environments where judgment inaccuracies can create challenges. Determining criteria weights is a pivotal step, as it significantly influences final outcomes and provides a nuanced understanding of each factor's impact on the evaluation.This paper addresses the pressing need for methods that account for uncertainty and effectively reduce the impact of inaccuracies in expert judgment. To this end, we introduce the Fuzzy RANking COMparison (RANCOM) method, a novel fuzzy subjective weighting approach that extends the advantages of the original RANCOM method to manage uncertainty. Using Triangular Fuzzy Numbers (TFNs), the proposed extension maintains simplicity, rapid execution, and flexibility in establishing criteria relationships. Key contributions of this paper include the development of the Fuzzy RANCOM method and a comparative analysis with the Fuzzy Analytical Hierarchy Process (AHP) method. Through multiple simulations, the study examines the operational dynamics of these methods, offering a valuable tool for decision-makers, especially in contexts where expert judgments may be prone to inaccuracies. The development of the Fuzzy RANCOM method provides a robust solution, equipping decision-makers with an effective tool to determine criteria weights while minimizing the impact of potential judgment errors.

Artificial Intelligence in the Organization of Nursing Care: A Scoping Review.

The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest-Dissertations and Theses to search gray literature. Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Patient-centered clinical decision support challenges and opportunities identified from workflow execution models.

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows. To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee. We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices. Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation. This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4years of age using ML algorithms. A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked. The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration. An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

Large language models present new questions for decision support

Large language models (LLMs) have proven capable of assisting with many aspects of organizational decision making, such as helping to collect information from databases and helping to brainstorm possible courses of action ahead of making a choice. We propose that broad adoption of these technologies introduces new questions in the study of decision support systems, which assist people with complex and open-ended choices in business. Where traditional study of decision support has focused on bespoke tools to solve narrow problems in specific domains, LLMs offer a general-purpose decision support technology which can be applied in many contexts. To organize the wealth of new questions which result from this shift, we turn to a classic framework from Herbert Simon, which proposes that decision making requires collecting evidence, considering alternatives, and finally making a choice. Working from Simon’s framework, we describe how LLMs introduce new questions at each stage of this decision-making process. We then group new questions into three overarching themes for future research, centered on how LLMs will change individual decision making, how LLMs will change organizational decision making, and how to design new decision support technologies which make use of the new capabilities of LLMs.

Decision process for industry 5.0

ABSTRACT Industry 5.0 is an emerging paradigm that enhances the established Industry 4.0. Automation, digitalisation, big data, and artificial intelligence are becoming complemented by human-centricity, active cooperation between people and machines, and decision-making based on human expertise. This research introduces a generic decision-making process for Industry 5.0. It defines the underlying principles to incorporate multi-criteria decision models, decision procedures, and decision support technology into business and production processes for Industry 5.0. We justify the motivation to aggregate holistic objectivedecisions, which are inferred automatically from big data, with subjective judgements and knowledge of human decision-makers to guarantee unbiased actions. We draw parallels with the aggregation-disaggregation paradigm and constructive learning to address the human-centric specification of preferences in cooperation with technology-driven entities. We assess the relevance and efficiency of decision support for Industry 5.0 with a SWOT analysis. Finally, we apply the introduced generic process to the use case of the flexible electricity supply market.

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

Harnessing Automation and IIoT in Air Traffic Control for Advanced Efficiency and Safety

The combination of automation and Industrial Internet of Things (IIoT) technology has transformed air traffic control (ATC), improving efficiency and safety in aviation. This article investigates the varied influence of automation and IIoT on ATC operations, emphasizing their importance in maximizing airspace use, boosting situational awareness, and limiting operational hazards. The advent of automatic flight data processing, radar surveillance, and automated decision-support technologies has reduced controller workload and increased operational efficiency in ATC. IIoT sensors and devices enable ATC systems to integrate real-time data on aircraft positions, weather, and runway status, allowing controllers to make more informed decisions and manage air traffic flow. Finally, automation and IIoT have evolved into critical tools for upgrading air traffic control, assisting controllers in managing airspace congestion, optimizing operational performance and safely and quick, efficient traffic movement. This study examines how automation and IIoT alter ATC operations, resulting in a more efficient, robust, and sustainable aviation environment. Keywords— Industrial Internet of Things (IIoT), Air Traffic Control (ATC), Automation, Operational Hazards, Radar surveillance, IoT sensors, Aviation environment.

Big Data-driven Decision Support: Enhancing Information Integration and User Experience with Mobile Integrated Technology

This study examines how big data-driven decision support and mobile technology interact to improve information integration and user experience. The research studies big data for digital decision-making and provides theoretical and practical suggestions to assist organizations in overcoming its challenges. This study used mixed method analysis to find the relationship between big data-driven user experience and mobile-integrated technology. Businesses require sophisticated decision support tools to navigate the digital landscape of massive data. Big data-driven decision support is examined to determine how information integration and user experience affect mobile-integrated technologies. A rigorous quantitative technique examines data volume and decision precision. Although big data volumes may have diminishing returns, decision-making generally improves. The study emphasizes the delicate balance between data volume, quality, velocity, diversity, and governance. Beyond quantitative analysis, the study examines complex decision-making. Information integration methods and user experience affect decision-making time, with more data offering strategic options. Agile integration and user-centric design boost efficiency and decision-making. The research highlights the change in mobile integrated technology. The title fits the research since mobile technology increases information integration and user experience. According to the study, mobile technology's user-friendly gadgets, quick internet connectivity, security safeguards, and app functionality boost user contentment, productivity, and decision-making accuracy. The report also emphasizes big data governance in decision quality. Decision support systems need big data governance for data access, accuracy, security, and compliance. Finally, this study provides theoretical insights into big data-driven decision support and practical suggestions for organizations navigating it. The study uses data, technology, user experience, and governance to improve business decision-making. This provides them with digital-era precision, agility, and strategic edge.

Understanding, experience and attitudes towards artificial intelligence technologies for clinical decision support in hearing health: a mixed-methods survey of healthcare professionals in the UK.

Clinician acceptance influences technology adoption, but UK health professionals' attitudes towards artificial intelligence (AI) in hearing healthcare are unclear. This study aimed to address this knowledge gap. An online survey, based on the Checklist for Reporting Results of Internet E-Surveys, was distributed to audiologists, ENT specialists and general practitioners. The survey collected quantitative and qualitative data on demographics and attitudes to AI in hearing healthcare. Ninety-three participants (mean age 39 years, 56 per cent female) from three professional groups (21 audiologists, 24 ENT specialists and 48 general practitioners) responded. They acknowledged AI's benefits, emphasised the importance of the clinician-patient relationship, and stressed the need for proper training and ethical considerations to ensure successful AI integration in hearing healthcare. This study provides valuable insights into UK healthcare professionals' attitudes towards AI in hearing health and highlights the need for further research to address specific concerns and uncertainties surrounding AI integration in hearing healthcare.

AiWATERS: an artificial intelligence framework for the water sector

The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation.

Application of integrative information technology in the evaluation processes of research institutions

The article proposes a solution to the scientific-applied problem of automating decision support regarding the review of criteria and indicators used in the evaluation of state-supported scientific institutions. This topic is highly relevant for scientific and technical activities both in the conditions of martial law and during the restoration of the country in peacetime. To address this issue, an information technology for decision support by experts is proposed based on an integrative methodology that involves data structuring, support for expert judgments, and visualization of decision-making processes using methods such as approval voting, hierarchy analysis, analytical networks, computer ontologies, and elements of graph theory. A concise description of the integrative methodology and the cognitive process of solving unstructured multi-criteria problems supported by this methodology is provided. The functionality of the developed software toolkit is discussed using an illustrative example related to determining the criteria base for the evaluation of scientific institutions. Elements of the ontological evaluation framework, a hierarchical model for the decision-making task of selecting quantitative assessment alternatives, and the results of experts determining criteria weights for evaluating alternatives and conducting pairwise comparisons of alternatives are presented. The conducted research indicates that the developed methodology and software tools, which have previously been tested in law enforcement agencies, can also be applied in other structures of government administration. The obtained results, aimed at enhancing the efficiency of evaluating scientific institutions, can be utilized to support decision-making in various aspects of managing scientific and technical activities.

Analysis of Multimodal Data in Project Management: Prospects for Using Machine Learning

The modern project environment is characterized by high complexity, uncertainty, speed and depth of changes that affect the project during its life cycle. However, the project’s change management processes do not take into account the need to implement analytical procedures for dynamic processing of multimodal data arrays. The purpose of the study is to determine the content of analytical procedures for project management and substantiate the use of machine learning technologies for their effective implementation. The methodological basis was project management methods, theory of change, concepts of artificial intelligence and machine learning, as well as analytical approaches. Methods of descriptive modeling of the project management process and expert assessments of the prospects for using machine learning technologies were also used in the work. The information base was made up of scientific materials on the topic under consideration, as well as expert assessments. The results of the study allowed us to conclude that for the analysis of multimodal data, natural language processing and intellectual decision support technologies are most in demand, which can serve as the basis for new technological solutions in the field of project management.

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Is your city planned for all citizens as they age? Selecting the indicators to measure neighbourhoods’ age-friendliness in the urban planning field

ABSTRACT In many countries, urban population ageing trends are a recognized policy issue that requires a multidisciplinary approach. Although some fields, such as urban planning, encounter challenges in incorporating age-friendliness, they are crucial in enhancing the quality of life and well-being of all urban inhabitants. Additionally, they should provide solutions on how cities can cater to the needs of a population that is living longer than ever before. To accomplish this, older people’s needs can be translated into multidomain indicators to be adopted when planning the cities. Using the World Health Organization’s age-friendly cities indicators framework as a basis, the objective of this research is to establish a new indicators framework for urban planners and policymakers. With this aim, within the H2020 URBANAGE project, various cities have followed a process to adapt the WHO´s general framework to their specific needs and interests, through research, iteration with the cities and co-creation methodologies with older people and civil servants. This process has resulted in the definition of an indicators framework, which aims to evaluate the age-friendliness of various neighbourhoods within a city. It also intends to inform the development of decision-support technologies to achieve age-friendly cities in the different cities involved.

Leveraging Implementation Science With Using Decision Support Technology to Drive Meaningful Change for Nurses and Nursing Leadership

Technology, such as clinical decision support, can play a role in supporting nurses’ decision-making, but understanding the complexity and current challenges in nurse decision-making is needed to guide the implementation of technology interventions focused on supporting effective decision-making in practice and leadership. The purpose of this article is to discuss decision-making and information needs among nurses across roles and explain how technology and implementation science approaches can complement effective decision-making interventions.