Information System Research Articles

Building Sustainable Supply Chains: Role of Supply Chain Flexibility in Leveraging Information System Flexibility and Supply Chain Capabilities

In today's interconnected and intricate global environment, achieving sustainable supply chain performance is paramount for organizations. Within this context, the flexibility of information systems as dynamic capability, is a driving force behind effective supply chain management. In addition, there are a multitude of compliances that supply chains have to comply with in order to achieve sustainable outcomes. Against this background we examine how information systems flexibility, knowledge capabilities, supply chain capabilities and supply chain flexibility contributes towards sustainable performance of the supply chains. Using the lenses of Dynamic Capability View (DCV), this paper explores relationships among these constructs using Partial least Squares-Structural Equation Modeling (PLS-SEM). 321 tire industry professionals participated in the survey and provided their feedback. The findings underscore the fact that information system flexibility significantly enhances knowledge and supply chain capabilities, which subsequently facilitate flexibility in supply chains. The results of this study also established the significant influence of supply chain flexibility on sustainable supply chain performance, emphasizes the need to have flexibility in supply chain to achieve sustainability goals. This research provides an important input into academic debates on sustainable supply chain management, as it sheds light on the role of information system to improve Supply chain flexibility and sustainability.

Objective-based survival individual enhancement in the chimp optimization algorithm for the profit prediction using financial accounting information system

This paper develops an innovative Objective-based Survival Individual Enhancement approach for the Chimp Optimization Algorithm (OSIE-CHOA) designed to enhance financial accounting profit prediction using information systems. The OSIE-CHOA focuses on improving the search process by simultaneously elevating the fitness of under-performing individuals within a population and strengthening the diversity among the top-performing ones. Within the OSIE-CHOA, we identify the four most promising chimps during each iteration. Subsequently, half of the highest-performing chimps are selected for elimination and repositioning around these fortunate individuals, with an equal probability assigned to each chimp. According to the experimental findings, it is clearly seen that OSIE-CHOA considerably enhances prediction accuracy, allowing a decrease in the root mean square error (RMSE) by 15% and the mean absolute error (MAE) by 18% compared to the traditional CHOA. Moreover, OSIE-CHOA shows a convergence rate that is 20% higher, which makes it a good and efficient tool for financial analysts who require accurate and reliable profit forecasting. By facilitating the optimization of profit prediction models, OSIE-CHOA leads to the improvement of decision-making within the context of financial accounting information systems.

Percursos de pedestres: um índice a partir das inter-relações entre caminhabilidade e conforto ambiental

Abstract The study of environmental comfort in open urban spaces has undergone constant updates in the last decades. In the meantime, the specific analysis of these aspects within the context of walkability stands out as an area with considerable potential to be investigated. In this context, this article aims to explore walkability in pedestrian routes and its relationship with environmental comfort, proposing a more systematized approach for its application in the strategic planning of cities. Therefore, we proposed the construction of a Pedestrian Walkability and Comfort Index (ICCP), composed of three partial indices that consider thermal, acoustic, and ergonomic aspects of pedestrian paths. The methodology is based on street visualization tools and Geographic Information System (GIS) tools to spatialize and systematize the interactions between walkability and environmental comfort. The research concludes that ICCP is a valid tool for measuring walkability at meso and micro scales, and it can be replicated in other urban contexts. Furthermore, the development and application of ICCP also highlight that rethinking walkability with environmental comfort in urban areas requires an integrated approach to the entire mobility system and urban infrastructure of cities.

Site selection for solar power plants using GIS and Fuzzy Analytic Hierarchy Process: Case study of the Western Mediterranean Region of Turkiye

As global warming becomes increasingly evident, the need to use renewable energy sources cannot be overstated. The consumption of fossil fuels in energy production not only exacerbates the effects of global warming but also negatively affects air quality and puts human health at serious risk. The objective of this research is to determine the most suitable locations for solar power plants (SPPs) in the Turkish provinces of Antalya, Burdur, and Isparta, which are situated in the Western Mediterranean Region (WMR). The study employs the Fuzzy Analytic Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) method, in conjunction with Geographic Information Systems (GIS) for the extraction of spatial information. In evaluating SPP site selection, 11 criteria were considered, including climate, economy, topography, and environmental factors. To produce more objective results during the decision-making phase, a thorough analysis of the relationship between solar irradiance and climatic factors such as air temperature, cloud frequency, and water vapor density which are crucial for the power plant's efficiency in SPP projects was conducted using machine learning techniques. The criteria weights were calculated by the FAHP method, considering expert opinions, literature observations, and machine learning results. The results show that approximately 20% of the region is suitable for SPP.

Spatial analysis of environmental sanitation and stunting incidents

Environmental factors such as sanitation and hygiene, drinking water sources, quality of drinking water, and ownership of latrines are indirect factors causing stunting. This study aims to analyze the distribution of stunting and environmental sanitation factors that cause stunting in Dairi District, North Sumatra, Indonesia. The case-control design was carried out with 172 toddlers. The distribution pattern of stunting and the association of patient characteristics and environmental risk factors was carried out using the Geographic Information System. Data on healthy clean-living behavior was collected using a questionnaire, and microbiological laboratory tests measured water quality. The spatial analysis uses an average nearest-neighbor overlay. The association of characteristics and risk factors with the incidence of stunting was analyzed using chi-square and logistic regression. The average nearest neighbor analysis shows that the nearest neighbor index is 0.19 ( 0.05) are unrelated to stunting. The pattern of distribution of cases tended to be in clusters, and no relationship was found between population density and the incidence of stunting. The findings of this study provide a new understanding that health promotion to prevent stunting does not only focus on fulfilling nutrition but also on healthy clean-living behavior and water quality.

Non-Attendance in Randomized Remote Mental Health Clinical Trial During the COVID-19 Pandemic

During the COVID-19 pandemic, a significant increase in psychiatric symptoms was found among essential service professionals (professionals or interns from the health and educational sector) who were exposed to high-risk contamination areas. These symptoms impair functionality, impacting quality of life, work, autonomy, and relationships. Recent data show that brief interventions are effective; however, between 20 and 30% of those individuals often do not attend their scheduled appointments. This study aimed to identify variables related to non-attendance for remote telepsychotherapy among participants with COVID-19-related emotional distress compared to those who initiated treatment. We included 2097 essential service professionals with PROMIS (Patient-Reported Outcomes Measurement Information System) T-scores ≥70 for anxiety, depression, or irritability who scheduled a remote mental health appointment. Participants completed clinical and sociodemographic data, mental health risk and protective factors, and PROMIS subscales. We conducted a binary logistic regression, using attendance vs. non-attendance as the dependent variable and these demographic and clinical characteristics as independent variables. Of the 2097 participants, 230 (11%) did not attend the first session despite three rescheduling attempts. Non-attendance was associated with the presence of irritability and seeking treatment during the second COVID-19 wave. In contrast, anxious symptoms, older age, and coping mechanisms that typically include intellectual and reading activities were associated with attendance. Our findings shed light on non-attendance patterns in mental health services, revealing that personal characteristics and environmental factors significantly contribute to missed appointments. Understanding these dynamics can improve patient engagement and adherence to mental health treatments, particularly in the context of telepsychotherapy during the COVID-19 pandemic.

Short-term, lagged association of airway inflammation, lung function, and asthma symptom score with PM2.5 exposure among schoolchildren within a high air pollution region in South Africa.

Asthma affects millions of people globally, and high levels of air pollution aggravate asthma occurrence. This study aimed to determine the association between short-term lagged PM2.5 exposure and airway inflammation, lung function, and asthma symptom scores among schoolchildren in communities in the Highveld high-pollution region in South Africa. A cross-sectional study was conducted among schoolchildren aged 9-14 years in six communities in the Highveld region in South Africa, between October 2018 and February 2019. A NIOX 200 instrument was used to measure fractional exhaled nitric oxide (FeNO). Lung function indices (forced expiratory volume in one second [FEV1]; forced vital capacity [FVC] and FEV1/FVC) were collected using spirometry and the percent of predicted of these was based on the reference equations from the Global Lung Initiative, without ethnic correction. These values were further analyzed as binary outcomes following relevant thresholds (lower limits of normal for lung function and a cutoff of 35 ppb for FeNO). Asthma symptoms were used to create the asthma symptom score. Daily averages of PM2.5 data for the nearest monitoring station located in each community, were collected from the South African Air Quality Information System and created short-term 5-day lag PM2.5 concentrations. Additional reported environmental exposures were collected using standardized instruments. Of the 706 participating schoolchildren, only 1.13% of the participants had doctor-diagnosed asthma, compared to a prevalence of 6.94% with an asthma symptom score suggestive of asthma. Lag 1 (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 1.00, 1.02, P = 0.039) and 5-day average lagged PM2.5 (OR: 1.02; 95% CI: 0.99, 1.04, P = 0.050) showed increased odds of the FeNO > 35 ppb. Lung function parameters (FEV1 < lower limit of normal [LLN] [OR: 1.02, 95% CI: 1.00, 1.03, P = 0.018], and FEV1/FVC < LLN [OR: 1.01; 95% CI: 1.00, 1.02, P < 0.001]) and asthma symptom score ≥ 2 (OR: 1.02; 95% CI: 1.00, 1.04, P = 0.039) also showed significant associations with lag 2, lag 4 and lag 1 of PM2.5, respectively. Lagged PM2.5 exposure was associated with an increased odds of airway inflammation and an increased odds of lung function parameters below the LLN particularly for the later lags, but a significant dose-response relationship across the entire sample was not consistent.

Application of Z-number based fuzzy MCDM in solar power plant location selection problem in Spatial planning

In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.

Clinical relevance of current patient-reported outcome measures for ankle fracture: surgeons' perspective.

Determine the relevance of the most frequently used patient-reported outcome measures (PROMs) for monitoring patient recovery after ankle fracture, from the clinical perspective of orthopaedic trauma surgeons, given lack of validated PROMs. Prospective cohort. Orthopaedic Trauma Association committee meetings, electronic correspondence. Orthopaedic trauma surgeons. Delphi method for consensus activities. IV. Most clinically relevant PROMs for ankle fracture recovery. Several English-language PROMs were identified based on use in literature and relevance to ankle fractures. 7 were selected by expert consensus. These are the Ankle Fracture Outcome of Rehabilitation Measure (A-FORM), Foot and Ankle Ability Measure (FAAM), American Academy of Orthopaedic Surgeons (AAOS), Foot and Ankle Disability Index (FADI) Score, Lower Extremity Functional Scale (LEFS), Olerud-Molander Ankle Score (OMAS), and Patient-Reported Outcome Measurement Information System Physical Function (PROMIS PF). The most clinically relevant PROM is the A-FORM, followed by the AAOS, LEFS, PROMIS PF, FADI, and OMAS, and the least clinically relevant overall, the FAAM. Understanding which PROM best matches physician expectations for tracking recovery is an important step toward a robust, evidence-based approach to patient care. The A-FORM was identified as the most clinically relevant among the most used PROMs. These results will aid surgeons, clinicians, and scientists to identify a uniformly, clinically relevant PROM for the treatment and study of outcomes and recovery after isolated ankle fracture.

Energy, environmental and economic performance of bi-facial photovoltaic noise barrier applied in city scale

Solar energy in transportation reduces fossil fuel consumption and supports carbon neutrality. Photovoltaic noise barriers (PVNB) are used globally to harness renewable energy along roads. This study's novelty lies in the detailed consideration of road orientation and the application of bifacial photovoltaic technology when assessing the potential for large-scale deployment of PVNB. Bifacial PV technology captures solar radiation from both sides, enhancing energy efficiency. A mathematical model was established and validated to predict PVNB power output at 5° azimuth intervals. Geographic information system (GIS) technology was used to quantify trunk roads, motorways, and RNB in Shenzhen, China. Accordingly, the electricity generation potentials of PVNB, as well as the economic and environmental benefits were calculated. The installed capacity and power generation of PVNB in Shenzhen are 432.29 MW and 313.37 GWh, respectively. The levelized cost of electricity is 0.4781 CNY/kWh. Compared to a 600 MW coal-fired unit, 9119.09 tce of fossil fuel can be saved, with lifecycle carbon reduction of 3.80 Tg and net carbon reduction of 3.05 Tg.

Implementationof a Biosafety Software Pop-up after Two Brucella Laboratory Exposures

IntroductionBrucellosis is rare in non-endemic countries where it mainly occurs as an imported or travel-related disease. In rare cases, Brucella species (spp.) are present in clinical specimens processed by clinical microbiology laboratories. These pathogens pose a risk to laboratory technicians, due to the high virulence, a low-infectious dose and ease of aerosol formation. Due to the low incidence in non-endemic countries, clinical samples are routinely processed on laboratory benches outside laminar flow cabinets. Recently, we have had three unexpected cases in which Brucella spp. were cultured at our clinical microbiology laboratory: one Brucella canis case and two Brucella melitenis cases. The B. canis and the first B. melitenis cases prompted the introduction of a biosafety software pop-up, which is presented in this paper. MethodsHere, we describe the two B. melitensis cases and the introduction of a biosafety pop-up. The software pop-up parameters are a time-to-positivity (TTP) of more than 48 hours, in an aerobic blood culture bottle, and a Gram stain appearance as Gram-negative bacteria. The software pop-up warns the technician through the laboratory information system (LIS) to further process the specimen in the Class 2 biological safety cabinet. To assess the number of false-positive pop-ups we can expect and resulting additional workload, we retrospectively analyzed laboratory data from the last seven years. ResultsThe biosafety pop-up prevented laboratory exposure in the second B. melitensis case. Based on the retrospective analysis of laboratory data, we estimated the resulting additional workload of implementation of the biosafety pop-up to be less than one blood culture bottle per week on average to be processed in a Class 2 biological safety cabinet. ConclusionOur experience demonstrates that implementation of the biosafety software pop-up can reduce the risk of laboratory exposure to Brucella spp. This intervention provides a feasible approach even in a setting where Brucella spp. are normally only encountered every few years.

Hydrological Analysis of Wadi Arab Valley Dam by Integrate Soil Conservation Service and Geographic Information Systems

Hydrological Analysis of Wadi Arab Valley Dam by Integrate Soil Conservation Service and Geographic Information Systems

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Investigation of Clinical Features, EEG Findings, and Brain Imaging in Psychiatric Patients With Epilepsy at Razi Psychiatric Hospital

Objectives: This study aimed to assess the clinical features, Electroencephalography (EEG) findings, and brain imaging results in psychiatric patients diagnosed with epilepsy at Razi Psychiatric Hospital. Methods: This retrospective descriptive-analytical study was carried out on patients with epilepsy and psychiatric disorders admitted to Razi Psychiatric Hospital over two years. A total of 94 patient files seizure and epilepsy comorbidity, recorded in the hospital’s health information system (HIS), were reviewed. Data collection involved a demographic checklist and an epilepsy scale; the latter, developed by DiIorio, Colleen, et al., encompassed personal characteristics, mental disorders, epilepsy, and seizures. The Kruskal-Wallis and Mann-Whitney non-parametric tests were utilized to compare the mean scores of variables, with SPSS software, version 21 facilitating the analysis. Results: Out of 94 patients with seizure and epilepsy, 9.6% had focal seizure, 26.6% had generalized epilepsy, 36.1% had focal-generalized seizure, and 26.8% had unknown seizure. About 12% had a structural etiology, while the remaining 88% had an etiology that remained unidentified. Conclusion: The findings indicate that epilepsy, affecting individuals from adolescence through to old age, can lead to psychiatric disorders. For many patients, the etiology of their condition remains elusive, and EEG findings and brain imaging appear normal in the majority of cases.

Information system architecture for healthcare company based on TOGAF

Information system architecture for healthcare company based on TOGAF

Predicting wind turbine energy production with deep learning methods in GIS: A study on HAWTs and VAWTs

The increasing global demand for renewable energy necessitates accurate forecasting methods to optimize wind energy production, particularly in regions with varying climatic conditions. This study addresses this need by utilizing advanced deep learning techniques and Geographical Information Systems (GIS) to estimate the energy output of wind turbines. Specifically, it focuses on predicting the energy production of both horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) using a combination of Markov and Cellular Automata-Markov (CA-Markov) models, alongside deep learning methods such as long short-term memory (LSTM), LSTM-Wavelet, and Support Vector Regression (SVR). Additionally, the study evaluates the energy output of each turbine type, factoring in their construction costs within the study area. The analysis reveals significant variations in energy output over time, with maximum values increasing from 85,017 Wh in 2000 to 166,050 Wh in 2020 in the northern region, while minimum outputs also rose significantly. Projections for 2030 suggest that approximately 17% of the northern region experience a substantial increase in wind power potential. Among the forecasting methods, the LSTM-Wavelet hybrid model demonstrated superior accuracy, surpassing the 90% threshold, primarily due to its effective handling of data instability and noise reduction. This study underscores the potential of using sophisticated modeling techniques to enhance wind energy forecasting, contributing to more efficient energy management in regions with high energy demand and limited resources.

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Quantitative risk assessment of road exposed to landslide: A novel framework combining numerical modeling and complex network theory

The quantitative analysis of the landslide risk posed to road networks is a challenging task owing to the uncertainty involved both in the potential landslide hazard and the road value. To address this challenge, this paper proposes a novel framework to assess the road risk in quantitative terms. The landslide hazard is assessed using the depth-integrated method with consideration of the landslide size probability and initial conditions. The potential direct losses associated with road disruption are determined by exposure analysis in a geographical information system. The indirect losses of the road caused by the landslide were analyzed through complex network theory with consideration to regional socioeconomic development and the time required for road restoration. The proposed framework was used to assess the road risk posed by the Chunchangba landslide, Xiaojin County, China. The results show that the volume size probability of landslides in the Xiaojin area could be assessed using the function PVL=1/1+3.583×10−5VL2×0.391. The depth-integrated method based hazards assessment results show the maximum impact area of the Chunchangba landslide reached 0.383 km2, and the landslide has a high probability of damaging the lower road and forming a barrier lake. The losses associated with road disruption caused by the landslide were estimated. The maximum direct losses reached 293,100 USD, while indirect losses reached 423,800 USD, which has the same importance as direct losses. The risk curve reveals that the maximum probability of the road risk associated with the Chunchangba landslide is 0.0175 %, 0.0189 %, 0.0190 %, and 0.0191 % for the time interval of 5, 10, 20, and 50 years, respectively, with losses of 0.177 million USD/year. The regional disaster mitigation strategy is analyzed based on quantitative risk analysis. The results show that a new 2.7 km road on the mountain opposite the Chunchangba landslide can reduce indirect losses by approximately 300 times. The findings of this study contribute to sustainable development and landslide risk management in mountainous areas.

Associations between obesity and severity of coronavirus disease 2019 in Brazilian inpatients: A 2021 secondary data analysis.

In the backdrop of the global obesity pandemic, recognized as a notable risk factor for coronavirus disease 2019 (COVID-19) complications, the study aims to explore clinical and epidemiological attributes of hospitalized COVID-19 patients throughout 2021 in Brazil. Focused on four distinct age cohorts, the investigation scrutinizes parameters such as intensive care unit (ICU) admission frequency, invasive mechanical ventilation (IMV) usage, and in-hospital mortality among individuals with and without obesity. Using a comprehensive cross-sectional study methodology, encompassing adult COVID-19 cases, data sourced from the Influenza Epidemiological Surveillance Information System comprises 329 206 hospitalized patients. Of these individuals, 26.3% were affected by obesity. Analysis reveals elevated rates of ICU admissions, increased dependence on IMV, and heightened in-hospital mortality among the individuals with obesity across all age groups (p < .001). Logistic regression, adjusting for confounding variables, underscores a progressively rising odds ratio for mortality in younger age brackets: 1.2 (95%CI 1.1-1.3) for those under 50 years, 1.1 (95%CI 1.0-1.2) for the 50-59 age group, and 1.1 (95%CI 1.0-1.2) for the 60-69 age group. Conversely, no significant mortality difference is observed for patients over 70 years (OR: 0.972, 95%CI 0.9-1.1). In summary, hospitalized COVID-19 patients with obesity, particularly in younger age groups, exhibit elevated rates of ICU admission, IMV requirement, and in-hospital mortality compared with the control group. Notably, the 'obesity paradox' is not evident among hospitalized COVID-19 patients in 2021.

Multi-Criteria Decision Making (MCDM) Approach for Identifying Optimal Solar Farm Locations: A Multi-Technique Comparative Analysis

This study explores the spatial suitability for solar farm placement using Geographic Information System (GIS) techniques (ArcGIS Pro 3.0.2) coupled with Muti-Criteria Decision-Making (MCDM) methods. Nine evaluation criteria significantly impacting solar farm placement in Erbil, Iraq were selected, and the Analytic Hierarchy Process (AHP) was utilized to assign proper weights to these criteria. Suitability maps were derived and classified using three MCDM methods: Weighted Overlay (WO), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting) to establish comparisons. Discrepancies were noticeable between the outcomes of these methods, with distinctions observable across most categories. The validation process illustrated superior performance by SAW model, closely followed by TOPSIS model, while the weighted overlay model showed significant differences. The southeastern region of Erbil governorate was identified as the most suitable area for solar farm construction due to favorable criteria values and potential for future expansion. This approach massively facilitates locating and evaluating ideal areas for solar farm construction, thereby minimizing the time, financial resources, and efforts dedicated towards this endeavor.