Decision Support Information Research Articles

Integrating a novel algorithm in assessing the impact of floods on the genetic diversity of a high commercial value fish (Cyprinidae: Spinibarbus sp.) in Lang Son province of Vietnam.

Floods, which occur when the amount of precipitation surpasses the capacity of an area to drain it adequately, have detrimental consequences on the survival and future generations of fishes. However, few works have reported the prediction of this natural phenomenon in a relation to certain fish species, especially in fast-flowing rivers. In the specific context of the northern mountainous provinces of Vietnam, where the Spinibarbus sp. fish species resides, it has been observed through the current study that the fish population in Lang Son exhibits the lowest genetic diversity and genetic distance. Consequently, the population of Spinibarbus sp. in Lang Son shows a heightened susceptibility to floods, resulting in reduction in population size and compromised population resilience. In order to provide decision support information for managers, conservationists, and researchers, we have employed a genetic algorithm-support vector machine regression (GA-SVR) predictive model to map flood vulnerability using thirteen dependent variables. The study findings have unveiled a significant negative correlation between flood-sensitive regions and genetic diversity. These discoveries emphasize the significance of considering the impact of floods on the genetic diversity of Spinibarbus sp. in Lang Son through flood vulnerability mapping. This underscores the value of establishing a comprehensive framework based on the GA-SVR algorithm for early flood detection, thereby facilitating the implementation of effective measures to minimize damages and conserve this commercial fish species.

Carbonic Anhydrase IX Targeted Polyaspartamide fluorescent Probes for Tumor imaging.

Precise intraoperative tumor delineation is essential for successful surgical outcomes. However, conventional methods are often incompetent to provide intraoperative guidance due to lack specificity and sensitivity. Recently fluorescence-guided surgery for tumors to delineate between cancerous and healthy tissues has attracted widespread attention. The contrast-enhanced fluorescent imaging has been applied for non-invasive diagnosis of cancers using tumor-targeting fluorescent probes. The carbonic anhydrase IX targeted polyaspartamide fluorescent compounds (SD-PHEA-NI) were synthesized by incorporating a tumor-targeting group of sulfadiazine (SD) and N-butyl-4-ethyldiamino-1,8-naphthalimide (NI) into water-soluble carriersof poly-α,β-[N-(2-hydroxyethyl)-L-aspartamide] (PHEA). These derivatives were also characterized by Fourier transform infrared spectroscopy, gel permeation chromatography, ultraviolet-visible spectroscopy, nuclear magnetic resonance spectroscopy and fluorescence assays. The cellular uptake, cytotoxicity, and fluorescence imaging ability were evaluated. Experiment results indicated that SD-PHEA-NI has low cytotoxic to Henrietta Lacks (HeLa) cells. Moreover, B16F10 melanoma cells can take up SD-PHEA-NI and show good green fluorescent images. However, SD-PHEA-NI displayed a low-intensity green fluorescence signal in healthy human embryonic kidney (293T) cells. SD-PHEA-NI can be considered a potential fluorescent probe for the detection of tumors. This study has the potential to enhance tumor diagnosis and image-guided surgical interventions by providing real-time information and robust decision support, thereby reducing recurrence and complication rates and ultimately improving patient outcomes.

Leveraging artificial intelligence (AI) for information dissemination and decision support in livestock husbandry

Leveraging artificial intelligence (AI) for information dissemination and decision support in livestock husbandry

Erb palsy due to birth injury (a draft of the clinical recommendations)

The article presents a draft of clinical recommendations for the diagnosis and treatment of Erb palsy in birth injury. These recommendations are intended for physicians of maternity hospitals and are aimed at providing information support for decision-making by a doctor in the event of a birth injury, as well as improving the quality of medical care to a patient. The recommendations correspond to the latest scientific data on the topic, and also contain information that is of an applied nature for practical activities. These practical recommendations are offered for public discussion and are posted in full on the website of the Ministry of Health of the Russian Federation. The recommendations contain an information note on Erb palsy in birth injury, including the definition, etiology and pathogenesis of the disease. Three types of classification of generic traumatic lesions of the brachial plexus are presented: by prevalence, by severity of nerve damage and by severity of paresis. The clinical features of the disease are considered in detail. To diagnose the disease, an algorithm of physical examination is proposed, and the use of instrumental research methods: MRI, ultrasonic, X-ray and electromyographic studies. Recommendations for the conservative treatment of pathology and the use of physiotherapy and rehabilitation techniques are given. An algorithm of the doctor’s actions and an information certificate about the disease are proposed to inform the patient’s parents. The recommendations are given taking into account the level of credibility of the recommendations and the level of reliability of the evidence.

Информационная система поддержки принятия решений при экоинвестиционном подходе к восстановлению природных экосистем, нарушенных в ходе освоения георесурсов

A conceptual approach to the restoration of natural ecosystems is to use the idea of adaptive systematic organization of ecosystems, since the driving force of evolution is the purposeful adaptation of ecosystems based on feedback, oriented towards increasing their viability and sustainability. The Mining Institute of the KSC of RAS has justified a methodology and developed a technology for restoring natural ecosystems disrupted during geo-resources development, in accordance with the principle of their self-organization, the creation of a biologically active environment. Formation of a biologically active environment as a result of establishing a seeded cereal phytocenosis without applying a fertile layer is the essence of an eco-investment approach, which consists in increasing the energy potential of the system-forming function of biota to increase the rate of restoration of natural ecosystems. A decision support information system has been developed for an eco-investment approach to restoring natural ecosystems disrupted during the development of geo-resources based on the principle of information unity of natural and technical systems, which allows us to consider natural ecosystems as information-driven, and environmental conditions as the controlling factors. The information system integrates a set of software data processing modules to monitor the dynamics of the natural ecosystems restoration at all the stages of the soil biological organization and implements the interaction of functional modules (databases, algorithms and data processing software, geoinformation components), linking them with unified information flows, which ensures the efficiency and quality of management decisions taken based on the ecoinvestment approach to restoration of the natural ecosystems disrupted during the development of geo-resources.

Nationwide Assessment of Solar PV Power Potential Using Geospatial Data and Collaborative Research with Higher Education Institutions: Case of the Philippines

Abstract. Geospatial technologies present a significant opportunity to assess solar photovoltaic (PV) power potential through remote sensing techniques and geospatial analysis. This aligns with the goal of ensuring access to affordable, reliable, and sustainable energy, one of the Sustainable Development Goals (SDG) that the Philippine National Economic and Development Authority (NEDA) aims to achieve. In support of the said SDG, a research project, called Project SINAG, has developed a method for estimating solar PV power resources utilizing high-temporal remotely-sensed data. The primary objectives of this study are twofold: first, to develop a webGIS platform for solar energy resource information access and decision support; second, to conduct information dissemination and capacitybuilding activities for various stakeholders in the solar energy sector. The localized assessment of solar PV power potential incorporates meteorological and geomorphological factors such as high temperature, dust deposition, and slope. Collaboration with academic institutions, government agencies, and the solar energy industry has been instrumental in providing capacity-building through training workshops, internships, and on-the-job training. Moreover, the project has produced information, education, and communication (IEC) materials, launched a webGIS portal, and supported data requests and student research. The webGIS platform serves as the primary information dissemination tool for the project, enabling rapid sharing of research outputs with end-users. To enhance the platform, future developments may include additional functionalities such as community forums, client feedback mechanisms, mobile application development, and the integration of real-time data for map layers and forecasting tools.

Perspectives on the quality of climate information for adaptation decision support

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making.

A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions

Abstract Background Paediatric cardiology presents unique challenges with its diverse and complex cases, limited evidence base, and the necessity for multi-expert involvement in decision-making processes. In this context, the introduction of generative pre-trained transformer (GPT) based large language models (LLMs) offers a potential avenue for the provision of complex information and clinical decision support. Purpose This study evaluates the quality of three different GPT LLMs in answering complex medical questions, including a state-of-the-art preview model that incorporates the German paediatric cardiology guidelines. Methods Seven paediatric cardiologists and paediatric cardiac surgeons generated 72 questions, including complex questions and medical cases with associated questions. The questions were categorized by difficulty and required knowledge (factual and experience-based or mostly experience-based). We prompted the questions to three LLMs: GPT 3.5, GPT 4 and a GPT 4 turbo preview. The GPT 4 turbo preview was customized by incorporating all guidelines from the German Society for Paediatric Cardiology by a retrieval function. Employing one complex instruction for all questions, we prompted the LLMs to provide precise and detailed expert-level responses. The responses from each model were evaluated by experts based on relevance, factual accuracy, severity of possible harm, completeness, superfluous content, and age-related appropriateness from 0 (very bad) to 7 (very good). Differences were calculated using the Kruskal-Wallis-test in SPSS Version 28. Results Our findings indicated a good performance of all models regarding the dimensions tested. The figures show the average ratings (Figure 1, Figure 2A) and highlight significant differences after Bonferroni correction in bold (Figure 2B). The GPT 4 turbo preview, including the retrieval of guidelines, provided significantly more relevant (average rating [AR] 5.94, meaning mostly relevant), accurate (AR 5.6, meaning between somewhat and mostly accurate) and complete (AR 5, meaning fairly complete) answers compared to GPT 3.5 and GPT 4. In terms of difficulty levels or the type of questions, there was no significant difference in rating. Relevance ratings were slightly better in factual questions (AR 5.7) than in those requiring more experience-based knowledge (AR 5,3). Although GPT4 had higher average scores compared to GPT 3.5 in all dimensions except superfluous content, the differences in rating were not statistically significant. All models had relevant difficulties considering the age-related aspects of the questions (AR 4.06-4.45, p=0.455). Conclusion This study highlights the potential and limitations of AI language models in addressing complex medical questions in fields characterized by highly individualized decision-making scenarios. The findings advocate for the development of more specialized AI tools in medicine, tailored to specific medical fields and patient age groups.Fig 1:Average ratings of LLMsFig 2:Rating differences between LLMs

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control.

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

Development of a Decision Support Information System for the Regulation of Consumer Goods in the Agricultural Sector

Development of a Decision Support Information System for the Regulation of Consumer Goods in the Agricultural Sector

Web search results processing in an information support system for decision-making processes

Objectives. The problem of the web search results processing in an information support system for decision[1]making is solved in order to create and correct a meaningful description of a problem situation. An approach to solving this problem is proposed based on the use of thematic text corpora (collections of texts on a specific topic) as knowledge about the subject area, as well as a knowledge representation model based on verbal associations. When solving problems of the web search results processing in a decision-making information support system, five main goals are pursued: the formation of an extended description of a problem situation, the synthesis of a search prescription, an Internet search for information about decisions made, the synthesis of a retelling of the information found, and an assessment of the quality of the found analogues of the decisions made.Methods. Methods of set theory, graph theory and mathematical linguistics are used.Results. A mathematical model has been developed for the web search results processing in an information support system for decision making. The concepts of verbal association of words and texts, as well as pragmatically complete lexical structure, are formalized. The proven properties of such structures provide algorithmization of information processes in the model under consideration.Conclusion. The approach to modeling is based on the formalization of the concepts of the informativeness of words, sentences, texts and the informativeness of verbal associations between them. As an implementation of the model proposed in the article, algorithms have been developed for creating a dictionary of pragmatically complete lexical structures, creating structural-lexical templates for sentences, texts and subject areas, synthesizing a brief retelling of the information found, and assessing the quality of the found analogues of the decisions made.

Dynamic viewshed analysis based on the digital elevation model: dynamic range proximity-direction-elevation reference line

Viewshed analyses using Digital Elevation Models (DEMs) can provide decision support information in a variety of areas. Traditional viewshed analyses are mainly based on a circular visual range centred on an observation point, and lack the ability to dynamically adjust the visual range by view angle. In addition, these algorithms face challenges in achieving a balance between computational efficiency and accuracy. To solve the above problems, this study proposes a field-of-view (FOV) angular range based partitioning strategy to realise dynamic range proximity-direction-elevation reference lines (DPDERL), which improves the adaptability to complex terrains and the flexibility of viewpoint application environments. After partitioning, the algorithm is optimised for grid point search and reference line update. Experimental results show that the algorithm takes less than half the time of the Xdraw algorithm for a variety of terrain regions, has an error of no more than 5% compared to the R3 algorithm, and has less aggregation of error points than the PDERL and the XPDERL. The DPDERL approach provides an effective solution for large-scale dynamic range viewshed analysis by balancing speed and accuracy.

Применение теории множеств для разработки ключевых параметров эффективности трудового сектора пенитенциарной системы

The article considers the development of information management systems, decision support and modelling for production processes in penitentiary institutions. The work outlines the problem of insufficient study of production and labour sector data both from a scientific viewpoint and from the standpoint of developing new software and practical application. It is found that the first steps to solve the above-mentioned problems will be proper data structuring and identifying key performance parameters. Presenting data as sets and studying the interaction between them can be a good solution to the problems posed. Thus, the work formulates the task of determining the key performance parameters of the labour sector of the penitentiary system, and considers the principles of set theory as tools for solving this problem. In addition, the authors use previously created developments such as an indexing system and a software package for collecting statistical data to settle this issue. As a result of the study, rules are determined, as well as relationships that can be applied to detect the key performance parameters and develop an information system, as well as to create databases with improved structuring that are more practically oriented.

Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.

Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA-related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes.

Contemporary Management Accounting System Practices and Managerial Performance of Nepalese Commercial Banks

Contemporary management accounting systems provide managers with the necessary organizational performance in commercial banks. The study examines the adoption and influence of contemporary management accounting system practices (CMASPs) on managerial performance within commercial banks in Kathmandu Valley, Nepal. The study treats costing, budgeting, strategic analysis, decision-support information, and performance measurement as independent variables, with managerial performance as the dependent variable. Employing descriptive and causal-comparative research designs alongside convenience sampling, the study gathered primary data from selected twenty Nepalese commercial bank branch managers and deputy managers using structured survey questionnaires rated on a 5-point Likert scale. Analysis encompassed descriptive statistics, and inferential statistics including mean, standard deviation, skewness, kurtosis, and structural equation modeling (SEM) via Path Analysis. The study reveals that CMASPs function effectively, positively affecting managerial performance. These findings highlight the significance of CMASPs in achieving superior managerial performance within Nepal's commercial banking industry, offering actionable insights for bank managers and policymakers aiming to optimize performance through effective CMASPs. Future research directions could explore these management accounting system practices across different sectors and investigate advanced MAS practices and technological integration.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

Suicide Death Prediction Using the Maryland Suicide Data Warehouse: A Sensitivity Analysis

Objective Nearly 50,000 Americans die each year from suicide, despite suicide death being a rare event in the context of health risk assessment and modeling. Prior research has underscored the need for contextualizing suicide risk models in terms of their potential uses and generalizability. This sensitivity analysis makes use of the Maryland Suicide Data Warehouse (MSDW) and illustrates how results inform clinical decision support. Method A cohort of 1 million living control patients were extracted from the MSDW in addition to 1,667 patients who had died by suicide between the years 2016 and 2019 according to the Maryland Office of the Medical Examiner (OCME). Data were extracted and aggregated as part of a 4-year retrospective design. Binary logistic and two penalized regression models were deployed in a repeated fivefold cross-validation. Model performances were evaluated using sensitivity, positive predictive value (PPV), and F1, and model coefficients were ranked according to coefficient size. Results Several features were significantly associated with patients having died by suicide, including male sex, depressive and anxiety disorder diagnoses, social needs, and prior suicidal ideation and suicide attempt. Cross-validated binary logistic regression outperformed either ridge or LASSO (least absolute shrinkage and selection operator) models but generally achieved low-to-moderate PPV and sensitivity across most thresholds and a peak F1 of 0.323. Conclusions Suicide death prediction is constrained by the context of use, which determines the best balance of precision and recall. Predictive models must be evaluated close to the level of intervention. They may not hold up to different needs at different levels of care.

Implementasi Metode Multi Attribute Utility Theory pada Sistem Pendukung Keputusan dalam Pemilihan Siswa Unggulan

Vocational School of Al Furqon is a private school that annually holds an election of excellent students. Vocational school Al Furqon selects excellent students manually so that the results of the assessment of superior students are less effective. The purpose of our research is to produce a decision-support information system for the selection of Excellent Students at Al-Furqon Batu Bara Vocational School by building a decision-support system using the Multi-Attribute Utility Theory (MAUT) method. The type of research used is quantitative. The research method used to develop this system is the waterfall method which starts from analysis, planning, implementation, and testing. In our research stages are interviews, observations, and literature studies. There are 70 alternatives but only 9 alternatives fall into the criteria, so only 9 alternatives are processed in the calculation. The subject and object of our research is the excellent student of Al Furqon Vocational School. The results of our research are presented in the form of a Decision Support System, the selection for superior students has been successfully implemented in the form of a website that can be accessed universally and anytime using the Internet with the results of the MAUT method calculation, Tantri Agustin Damanik got the highest score, namely with a total score of 1000 and was successfully named as an excellent student according to the calculation of the MAUT method and according to predetermined criteria.

Information Support for Decision-Making in the Management of Regional Investment and Construction Projects: A Systematic Approach

Information Support for Decision-Making in the Management of Regional Investment and Construction Projects: A Systematic Approach

V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.