Information System Research Articles

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Роль и место медицинской информационной системы при оказании дерматологической помощи в частной медицинской организации

Dermatological diseases retain statistically significant positions in the structure of modern somatic pathology and are often characterized by severe medical and social consequences. The latter include an unreasonably high number of unsatisfactory treatment results, high level of expenses for diagnostics and treatment, low level of patient satisfaction. Taken together, this allows us to consider the comprehensive quality of dermatological care as a whole insufficient. Medical organizations in the private healthcare segment are no exception in this regard. One of the modern effective directions in improving the quality of a wide variety of types of medical care for the population is the widespread use of the so-called medical information systems (MIS). However, at the level of dermatological organizations in the private healthcare segment, this direction has not been practically worked out to date. Purpose of the study: improving the quality of dermatological care for the population in a private medical organization based on the development and implementation of a medical information system. Materials. The study included 2000 dermatological patients of a private medical organization. The control group (1000 people) consisted of patients in the period before the implementation, the main group (1000 people) – patients in the period after the implementation of the developed system. The duration of the compared periods was 2,5 years. Мethods. To obtain the results, the methods of objective examination, specialized patient questionnaires, simple and extended dermatoscopy, content analysis of medical documentation and databases of the medical organization, pharmacoeconomic analysis were used. The system of integrated assessment of the quality of medical care was applied. The methods of variation statistics were used to analyze the results. The exact level of statistical significance of differences in the results before and after the implementation of the developed MIS was calculated.. Subject. The study is at the intersection of two subject areas: «Organization of dermatological care for the population» and «Medical information systems». Results. As part of this study, a MIS was developed aimed at improving the organization of dermatological care for the population. The system was implemented in a medical organization in the private healthcare segment. The main results of such implementation were: an increase in the number of successful treatment results, the level of patient satisfaction with the quality of dermatological care, the level of its standardization, a decrease in direct economic costs for diagnostics and treatment. The integrated and comprehensive indicators of the quality of dermatological care have been increased.. Conclusion. Improving the quality of dermatological care for the population in a medical organization of the private healthcare segment based on the development and implementation of a medical information system has been achieved. The results obtained have a high level of statistical and medical-social significance.

BG-LoA: A Benchmarking Framework for BIM/GIS Data Integration Based on Meta-Modelling Theory

Abstract. There are various studies on the integration of building information modelling (BIM) and geographic information system (GIS) that convert BIM data into GIS data. These studies used different technologies, data standards, data models, and data formats such as RDF (Resource Description Framework), graph, CityGML, and Shapefile to address the data incompatibility problem between BIM and GIS. This makes it difficult to compare these studies for the purpose of assessing research innovation, and sometimes, improper comparisons were carried out between studies. To solve this problem, this study adopted the metamodeling theory used in model-driven software engineering (MDSE) and extended its 4-layer framework of level of abstraction. The new benchmarking framework, referred to as level of abstraction for BIM/GIS data integration (BG-LoA), has 5 levels, including meta metamodel (M3), metamodel (M2), model (M1), reality (data) (M0), and reality (real world) (M00). The common types of IFC (Industry Foundation Classes) conversion were analysed using the new framework, including IFC-to-CityGML, IFC-to-CityJSON, IFC-to-Shapefile, IFC-to-GML, IFC-to-3DTiles, IFC-to-RDF, IFC-to-IfcGraph, and IFC-to-UML. The result shows that the new framework can show the difference between those conversion types, which makes it easier for future studies to benchmark their work and assess level of contribution to the body of knowledge.

Decision regret and long-term success rates after ventral buccal mucosa graft urethroplasty.

To characterise the long-term success rate of ventral onlay buccal mucosa graft urethroplasty (vBMG) in the management of bulbar urethral stricture disease (USD), assess patient-reported postoperative satisfaction and decision regret, and delineate clinical factors impacting patient-reported metrics. Patients with prior vBMG for bulbar USD, performed at Cleveland Clinic between 2003 and 2022, were contacted and brief structured interviews were performed. Stricture recurrence and need for secondary procedures, baseline demographics, and patient-reported outcome surveys were collected. The surveys included the Decision Regret Scale (DRS), the Urethral Stricture Symptom Impact Measure (USSIM) and the 10-item Patient-Reported Outcomes Measurement Information System Short Form, version 1.2 (PROMIS-10). Descriptive, univariate and multivariable analyses were performed for clinical outcomes and survey responses. A total of 104 patients recorded responses. The median patient age was 49 years and the median follow-up was 7.4 years at time of survey. The median graft length was 5 cm and 38% of patients underwent partial thickness augmented anastomotic urethroplasty. At time of follow-up, 10 patients underwent a secondary procedure. Moderate to severe regret on the DRS was found in 12% of patients, and greater regret was associated with recurrence. The mean physical and mental health PROMIS-10 Global Health T-scores were 52 and 53. The mean total USSIM score was 56. A significant correlation was found between USSIM and DRS scores, with higher DRS score and recurrence negatively impacting USSIM score. USSIM scoring across all domains was significantly worse in the moderate to severe DRS group. This study showed that vBMG for bulbar USD confers both high success rates and patient-reported satisfaction at extended follow-up, based on emerging and validated patient-reported outcome measures.

A Systematic Review on Fluoride Contamination in Water Resources of Iran from 2016 to 2023: Spatial Distribution and Probabilistic Risk Assessment (Monte Carlo Simulation).

Because of significant variations and existence of high fluoride (F-) in some waters, Iran has been considered for various fluoride studies in recent decades. Nevertheless, there is a no updated review on fluoride levels especially including F- risk assessment. Thus, this systematic review is mainly aimed to evaluate the spatial distribution of F- content in water sources of Iran using geographic information system (GIS) and conduct the health risk assessment. Besides, the Monte-Carlo Simulation technique with 10,000 iterations was applied for determination of the non-carcinogenic effects of F- in different exposed groups (infant, children, teenagers, and adults). The results indicated that the maximum and minimum concentrations of F- content were related to Jazmourian (Roudbar plain) (4.8mg/L) and Sahneh (0.1mg/L) provinces, respectively, and F- content of more than ≅ 19% of the samples exceeds the Iranian standard value (1.5mg/L). The results showed that the HQ of F- in all groups were higher than 1 with the order of children > infants > teenager > adults in which children were the vulnerable group to F- consumption in study area. Therefore, it is necessary to monitor and continuously measure water supplies for fluoride content and control measures, including removal steps, be taken for human risk reduction.

Assessing the measurement properties of PROMIS Computer Adaptive Tests, short forms and legacy patient reported outcome measures in patients undergoing total hip arthroplasty.

The commonly used ('legacy') PROMs evaluating outcomes of total hip arthroplasty (THA), have several limitations regarding their measurement properties and interpretation of scores. One innovation in PROMs is the use of Computerized Adaptive Testing (CAT). The Patient-Reported Outcomes Measurement Information System (PROMIS®) is a validated system of CATs. The aim of this study was to assess the measurement properties of PROMIS and legacy instruments in patients undergoing THA. Patients in this multicenter study filled out a questionnaire twice, including Dutch-Flemish PROMIS v1.2 Physical Function (PROMIS-PF) and v1.1 Pain Interference (PROMIS-PI) CATs and short forms, PROMIS v1.0 Pain Intensity, and legacy PROMs (Hip disability and Osteoarthritis Outcome Score (HOOS), HOOS-Physical function Shortform (HOOS-PS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Hip Score (OHS), and two numeric rating scales measuring pain). The reliability, measurement precision (Standard Error of Measurement (SEM)), smallest detectable change (SDC), and burden of PROMIS instruments were presented head-to-head to legacy PROMs. Furthermore, construct validity was assessed. 208 patients were included. All instruments had a sufficient test-retest reliability (range ICC: 0.83-0.96). The SEM of PROMIS CATs and short forms ranged from 1.8 to 2.2 T-score points, the SEM of legacy instruments 2.6-11.1. The SDC of PROMIS instruments ranged from 2.1 to 7.3 T-score points, the SDC of legacy instruments 7.2-30.9. The construct validity of PROMIS CAT and short forms were found sufficient, except for the PROMIS-PI short form. The burden of PROMIS CATs was smaller than PROMIS short forms (range 4.8-5.2 versus 8-20 items, respectively). The burden of legacy instruments measuring physical functioning ranged from 5 to 40 items. The PROMIS-PF is less burdensome, with high measurement precision, and almost no minimal or maximal scores, and an equal reliability compared to legacy instruments measuring physical functioning in patients undergoing THA. The PROMIS Pain Intensity 1a is comparable to the legacy pain instruments in terms of burden, reliability and SDC. Measuring the construct Pain Interference may not have additional value in this population because of its high correlation with instruments measuring physical functioning. The SDC values presented in this study can be used for individual patient monitoring.

Comparison of Urban Environment Factors for Solar-Powered Vehicles

Abstract. Urban areas face significant environmental challenges, including high CO2 emissions largely attributed to the transportation sector. Introducing solar-powered vehicles (SPVs) presents a promising solution to reduce urban carbon footprints. However, in dense urban settings, the effectiveness of SPVs is often compromised by shading from buildings, which significantly diminishes solar power generation. This study explores the impact of shading on urban roads, aiming to identify roads with substantial shadow variations that affect solar energy harvesting. Using Geographic Information Systems (GIS) and hillshade analysis, the study examines how nearby buildings and road orientation influence shadow variability. The findings reveal considerable variability in shadow throughout the year, with significant fluctuations during transitional months like February, August, October, and November. These results highlight the importance of considering shadow patterns in urban planning and vehicle routing to optimise the use of solar energy in urban settings.

Intrinsic data quality dimensions: expanding on Wand and Wang’s data quality model

PurposeStudies show that data quality (DQ) issues are extremely costly for companies. To address such issues, as a starting point, there is a need to understand what DQ is. In his context, the 1996 paper “Anchoring data quality dimensions ontological foundations” by Wand and Wang has been highly influential on the understanding of DQ. However, the present study demonstrates that some of the assumptions made in their paper can be challenged. On this basis, this study seeks to develop clearer definitions.Design/methodology/approachThe assumptions behind Wand and Wang’s DQ classification are discussed, on which basis three counter-propositions are formulated. These are investigated through a representation theoretical approach involving analyses of deficient mappings between real-world and information system states. On this basis, an intrinsic DQ classification is derived. A case study is conducted to investigate the value of the developed DQ classification.FindingsThe representation theoretical analysis and the case study support the three propositions. These give rise to the development of a DQ classification that includes three primary intrinsic DQ dimensions (accuracy, completeness and conciseness), which are associated with six primary value-level DQ deficiencies (inaccuracy, incorrectness, meaninglessness, incompleteness, absence and redundancy). The case study supports the value of extending Wand and Wang’s DQ classification with the three additional data deficiencies.Research limitations/implicationsBy improving the conceptual clarity of DQ, this study provides future research with an improved basis for studies and discussions of DQ.Originality/valueThe study advances the understanding of DQ by providing additional clarity.

Усовершенствование первичной медико-санитарной помощи с применением цифровых технологий: методология разработки инструментария оценки «цифровой зрелости» и научно обоснованные рекомендации

The digitalization of the economy, especially within the framework of the concept of Industry 4.0, has a significant impact on the medical field. In healthcare, digital technologies such as telemedicine technologies, decision support systems and artificial intelligence increase the availability and quality of medical services. The purpose of the study is to develop scientifically based recommendations for improving primary health care using the developed tools for assessing the «digital maturity» of primary care organizations. Materials and methods. To develop recommendations, a methodology was used to assess the «digital maturity» of medical organizations providing primary health care. Experts assessed the indicators of «digital maturity», and their significance was determined through a weighted average. The results of the assessment made it possible to identify the main, additional and excluded indicators, which became the basis for further development of recommendations. Results. Based on the developed methodology, classifications of the levels of «digital maturity» of medical organizations providing primary health care, ranging from low to high levels, were created. These classifications made it possible to identify key aspects that need improvement, including digital infrastructure, information system functionality, the use of artificial intelligence, as well as digital literacy of medical personnel. Conclusions. The digital transformation of primary health care contributes to improving the quality and accessibility of medical services, especially in remote regions. The developed methodology makes it possible to objectively assess the level of digitalization and formulate recommendations for individual digital transformation strategies in the regions of Russia. It is important to continue monitoring and research to assess the long-term effects of digitalization.

Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

Model For Improving The Performance Of Hospital Management Information System Services Through Transformational Leadership

The role of Transformational Leadership in hospital organizations is very important, where hospitals are organizations that have a complex environment, there is a lot of unclear information. With the increase in health services to the community along with changes in demographics, changes in health service technology, and the adoption of new information technology. So performance measurement is important in order to improve the quality of health services. The aim of this research is to find out how transformational leadership influences the success of hospitals management information systems (HMIS). The method used was a quantitative descriptive and verification survey with 181 respondents from 15 hospital in the city of Bandung. The sampling technique uses proportionate stratified random sampling. Data analysis using structural equation modeling (SEM) with partial least squares (PLS). The findings of the proposed hypothesis are that the quality of technology, transformational leadership, and users have a significant effect on service performance. Users can mediate technology quality, transformational leadership and service performance. Conclusion of the study is the novelty of the development of the information system success model by adding the context of transformational leadership in evaluating the success of the information system, where transformational leadership has a positive effect on improving the performance of hospital management information system services through users. Implications of the results of this study contribute to the development of an information system success model that can be used to evaluate the success of information systems in general.

The Impact of Accounting Information Systems on Ensuring the Accuracy and Reliability of Financial

The study aims to explore the effect of accounting information systems including (internal control, software, devices and equipment, information systems security) on ensuring the accuracy and reliability of financial statements in Al-Rafidain Bank in Iraq. The descriptive and analytical approach was employed to achieve the purposes of the study. A questionnaire was designed to collect data and distributed to a random sample of (150) employees of the bank's head office at different job levels, and then the data from the questionnaire was analyzed using the spss program. The results demonstrate a strong support by the sample members for the impact of accounting information systems in ensuring the accuracy and reliability of financial statements in Al-Rafidain Bank, as this indicates the importance of these systems in the context of banking, and the study also showed a statistically significant positive impact of accounting information systems represented in (internal control, software, hardware and equipment, information systems security) in ensuring the accuracy and reliability of financial statements in Al-Rafidain Bank in Iraq, which is a positive indication of the importance of these systems in improving Data quality and enhanced efficiency in banking.

Development of an Information System for eTax Return Reporting PPh 21 Employees at PT. FBR Mitra Sejati

This research aims to develop an information system that supports collecting Income Tax (PPh) for employees in a tax consulting company. This research analyzes the suitability of the tax collection procedures applied by the company and the applicable tax provisions. The method used in this research is descriptive analysis based on data obtained through interviews, observations, and literature review. The results show that the principle of income tax collection in tax consulting companies is in line with the general tax withholding method, with estimated net income as the basis for calculation. The tax rate variations applied include the provisions in Article 17 of the Income Tax Law and related government regulations. The information system developed aims to improve efficiency and accuracy in the reporting of Income Tax Return 21 and support better tax compliance.

Evaluating Higher Education Performance via Machine Learning During Disruptive Times: A Case of Applied Education in Türkiye

ABSTRACTIn response to the COVID‐19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacks in knowledge is how effective the anti‐COVID measures were in maintaining quality education. Using machine learning to analyse student grades as a proxy for educational standards, this study investigates and demonstrates the evaluative potential of machine learning (vs. traditional statistics) with respect to not only crisis responses in education but also applied studies such as Information Systems and Tourism. Main implication of this study is the analytical utility of machine learning even when educational data are irregular and small. However, incorporating accurate and meaningful data points into the existing online educational systems is crucial to leverage this utility of machine learning.

Выбор аналитической модели и количественных показателей для геоинформационного анализа устойчивого развития территориальных образований

Currently, there are no generally accepted algorithms for assessing, analyzing and forecasting the sustainable development of administrative-territorial units. The authors introduce the definition of this term, which implies the successful functioning of a human community over time carrying out economic activities without threats to its environment. Also, an analytical model for representation of its regional integrated indicator in the form of a vector with differential components, which characterize economic growth, environmental and social metrics, in a three-dimensional affine space, is proposed. Furthermore, we present a multiplicative criterion for assessing partial criteria of sustainable development efficiency and the basic expression for its parameters computation based on the set of economic, environmental and social ones, including such as the availability and quality of natural resources, regional labour resources, eco-factors, etc. The proposed vector model is based on the geoinformation approach and can be deployed within the framework of GIS of sustainable development for geo-analysis, modeling and mapping of natural resource potential and ecological state of intensively advancing territories, considering the dynamics of this process. Important properties of the geoinformation approach to assessing territorial entities’ forward growth, spatial and semantic scalability, are noted. In addition, the functional requirements and preliminary architecture of the core, modules, clients and layers of the related thematic geographic information system for the management, planning and forecasting the said indicator within the framework of digital economy are described

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

Related Factors and Risk Prediction of Chronic Pain after Knee Replacement.

This study aimed to explore potential risk factors associated with chronic pain after total knee arthroplasty (TKA) and to establish the risk prediction model of chronic postoperative pain (CPSP). This study retrospectively analyzed the clinical data of 160 patients who underwent TKA in our hospital between January 2021 and January 2024. Relevant data such as the baseline characteristics, past medical history, CPSP condition, and pain numerical rating scale (NRS) were retrieved from the medical information system. Logistic regression analysis was performed on the risk factors affecting the postoperative CPSP of the patients. The identified risk factors were incorporated to develop a risk-prediction model. Among the 160 patients, 67 (41.88%) had CPSP at or around the operation incision. The NRS pain score was significantly higher in the CPSP group than in the non-CPSP group during exercise preoperative and 3 months post-operation. Furthermore, the CPSP group had a higher NRS score than the non-CPSP group at rest 3 months after the procedure (p < 0.05). We observed that the preoperative NRS score, preoperative hospital for special surgery (HSS) score, postoperative functional training, and postoperative adverse events were the independent factors influencing the occurrence of CPSP after TKA (p < 0.05). Additionally, there was a significant positive correlation between preoperative NRS score, postoperative adverse events, and CPSP pain severity, and a significant negative correlation between preoperative HSS score, postoperative functional training, and CPSP pain severity (p < 0.05). The receiver operating characteristic (ROC) curve had excellent calibration and prediction capabilities for the predictive model of CPSP after TKA, with the area under the curve (AUC) of 0.868 (95% CI: 0.811-0.925). In this study, the predictive model of CPSP risk for patients after TKA surgery was initially constructed, which can help medical staff predict the risk of CPSP in patients after surgery individually, thereby preventing the occurrence of CPSP.

Identifying Points of Rainwater Recharge in Rural Areas using Remote Sensing (RS), Geographic Information Systems (GIS), and Field Surveys in Debari, Udaipur, Rajasthan

Identifying Points of Rainwater Recharge in Rural Areas using Remote Sensing (RS), Geographic Information Systems (GIS), and Field Surveys in Debari, Udaipur, Rajasthan

THE APPLICATION OF RSA CRYPTOGRAPHY TO PREVENT COUNTERFEITING IN THE BOOK PUBLISHING INDUSTRY

This study aims to develop a book publishing information system that can assist lecturers in publishing their academic works more efficiently and securely. The system implements the Rivest-Shamir-Adleman (RSA) cryptographic algorithm to protect book data from unauthorized access, as well as to prevent counterfeiting and copyright infringement. With the encryption applied, only authorized parties can access sensitive information. The implementation of this system has proven to increase efficiency in the book publishing process, reduce administrative obstacles, and enhance authors' confidence in the security of the system. It is expected that this system will boost academic productivity and maintain the integrity of lecturers' intellectual works.