The Indonesian government provides incentives to facilitate community development through various funding programs to improve the economy and restore the national economy. However, there were many ...obstacles in determining the proper target beneficiaries. This study aims to assist decision-makers in determining targeted and accountable beneficiary candidates. In this study, a hybrid Analytical Hierarchy Process (AHP) method with Simple Additive Weighting (SAW) was used and integrated with machine learning modeling using Logistic Regression (LR). The AHP approach is used to determine the weight of each criterion, and the SAW method is used to sort out each available alternative with the help of an expert team's assessment. Instead, the LR method is used for the predictive analysis and classification of the resulting data.
The COVID-19 has created significant impacts on the economy and individual life around the world. Various countries and cities have adopted corresponding control measures to reduce transport ...activities and maintain social distance to combat the spread of COVID-19. In the circumstances, residents only maintained essential travel to ensure a normal and fundamental life. In order to explore the impacts of the epidemic and control measures on individually essential travel, we have collected 513 questionnaires between February and March 2020 in China to investigate the various characteristics of essential travel. Using a multivariate logistic regression model, we examine the major factors that potentially impact the mode choices of essential travel. Results show that various socioeconomic, transport supply, health concern and travel purpose have significantly influenced travel mode choices of essential travel. The concept of essential travel will, in the era of port-pandemic, have profound implications on transportation policy making, especially on how to improve the fundamental welfare of the disadvantaged population.
•A new method is proposed for evaluating the suitability of land for urban development.•The method avoids effects of subjectivity on evaluation process and highly accurate.•Logistic regression, PCA, ...K-means, kriging interpolation, and Geodetector were combined.•The results provided a scientific reference to support land allocation decisions.
Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.
Background Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who ...are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. Objective The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. Methods The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model’s validity was assessed through calibration curves and decision curve analysis (DCA). Results A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model’s reliability, and DCA highlighted its clinical utility. The model’s sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. Conclusion This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model’s high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.
•SVM and logistic regression models are built to predict crashes in an urban highway.•We used highly accurate disaggregate traffic data from free-flow toll gates.•68% of crashes are predicted in a ...real online environment with 21% false alarm rate.•Prediction was done on real unbalanced data rather than on artificially balanced data.
We develop accident prediction models for a stretch of the urban expressway Autopista Central in Santiago, Chile, using disaggregate data captured by free-flow toll gates with Automatic Vehicle Identification (AVI) which, besides their low failure rate, have the advantage of providing disaggregated data per type of vehicle. The process includes a random forest procedure to identify the strongest precursors of accidents, and the calibration/estimation of two classification models, namely, Support Vector Machine and Logistic regression. We find that, for this stretch of the highway, vehicle composition does not play a first-order role. Our best model accurately predicts 67.89% of the accidents with a low false positive rate of 20.94%. These results are among the best in the literature even though, and as opposed to previous efforts, (i) we do not use only one partition of the data set for calibration and validation but conduct 300 repetitions of randomly selected partitions; (ii) our models are validated on the original unbalanced data set (where accidents are quite rare events), rather than on artificially balanced data.
Bat species show global ecological importance, yet their numbers are declining worldwide. Understanding bat-habitat interactions is crucial in terms of developing effective conservation plans. In an ...effort to model bat habitat suitability in the Cassadaga Creek watershed, long-term bioacoustic bat data (spanning 2009–2020) was compiled, georeferenced and statistically analyzed using logistic regression techniques. In total, 1600 bat occurrence records from five species of bat (559 Eptesicus fuscus, 560 Lasionycteris noctivagans, 143 Lasiurus borealis, 260 Lasiurus cinereus, and 78 Myotis lucifugus) were paired with pseudo-absence points to study the relationship between bat calling behavior and land cover. All bats but Myotis lucifugus had a statistically significant relationship with forested land cover, and all bats had negative interactions with agricultural habitats. Geospatial data was coupled with the statistical output to create maps of habitat suitability and echolocation calling density. This work provides a model that can be employed worldwide to evaluate bat habitat needs or patterns in echolocation behavior. Future research will incorporate a more recently collected dataset that is of greater geographic diversity with a larger number of environmental variables in the species distribution model.
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•Statistically significant bat-habitat interactions identified for multiple species of bat•Catchment-scale habitat suitability maps produced for 5 species of bat in the Cassadaga Creek watershed•Species-specific patterns of bat echolocation calling density for 5 species of bat in the Cassadaga Creek watershed•Opportunity to scale up and refine this analysis to incorporate additional environmental variables at the County level
Using a passive method to catch fish, the set-net fishery is an environmentally friendly fishery posing little risk to the marine ecosystem. To date, no studies have been conducted by industry, ...government, or academia to formulate a set of physical selection criteria for the installation of set nets. In this study, meteorological and sea condition data were collected around existing set-net fishing grounds in Taiwan, and binary logistic regression analysis and sequential logistic regression methods were used to determine the factors that affect the installation of set nets to confirm the future development of this fishery. According to the results of binary logistic analysis and comparison of physical factors in the waters near Xiaoliuqiu, Pingtung County, Nanliao City, Hsinchu City, Qixingtan City, and Hualien City, where there are set-net fishing grounds, ocean temperature (OCT) and sea surface factors, namely, maximum wind speed (OSWm), wave (WV), air temperature (TEMP) and air pressure (PRES), are the primary factors affecting the installation of set-nets. The results of sequential logistic regression and odds ratio analysis were also used to verify the degree of influence of the determinants of these three sea areas on the nearby set-net fishing grounds. The fish catch data from the fishing grounds studied fitted very well to the examined physical factors in different regions.
Background Surgical site infection (SSI) is a common complication in HIV-positive fracture patients undergoing surgery, leading to increased morbidity, mortality, and healthcare costs. Accurate ...prediction of SSI risk can help guide clinical decision-making and improve patient outcomes. However, there is a lack of user-friendly, Web-based calculator for predicting SSI risk in this patient population. Objective This study aimed to develop and validate a novel web-based risk calculator for predicting SSI in HIV-positive fracture patients undergoing surgery in China. Method A multicenter retrospective cohort study was conducted using data from HIV-positive fracture patients who underwent surgery in three tertiary hospitals in China between May 2011 and September 2023. We used patients from Beijing Ditan Hospital as the training cohort and patients from Chengdu Public Health and Changsha First Hospital as the external validation cohort. Univariate, multivariate logistic regression analyses and SVM-RFE were performed to identify independent risk factors for SSIs. A web-based calculator was developed using the identified risk factors and validated using an external validation cohort. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (AUC) curves, calibration plots, and decision curve analysis (DCA). Results A total of 338 HIV-positive patients were included in the study, with 216 patients in the training cohort and 122 patients in the validation cohort. The overall SSI incidence was 10.7%. The web-based risk calculator ( https://sydtliubo.shinyapps.io/DynNom_for_SSI/ ) incorporated six risk factors: HBV/HCV co-infection, HIV RNA load, CD4+ T-cell count, Neu and Lym level. The nomogram demonstrated good discrimination, with an AUC of 0.890 in the training cohort and 0.853 in the validation cohort. The calibration plot showed good agreement between predicted and observed SSI probabilities. The DCA indicated that the nomogram had clinical utility across a wide range of threshold probabilities. Conclusion Our study developed and validated a novel web-based risk calculator for predicting SSI risk in HIV-positive fracture patients undergoing surgery in China. The nomogram demonstrated good discrimination, calibration, and clinical utility, and can serve as a valuable tool for risk stratification and clinical decision-making in this patient population. Future studies should focus on integrating this nomogram into hospital information systems for real-time risk assessment and management.
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