Road Distance Research Articles

Abstract 4132875: Living Address Environmental Biotope Affects the Risk of Coronary Obstruction and Myocardial Ischemia Detected by Coronary Computed Tomography

Background: Several studies reported positive associations between air and noise pollution exposure and coronary artery calcification, but environmental effects on coronary obstruction and flow remain largely unknown. Hypothesis: Distance to road and airport, air pollution, and residential green space are linked to coronary artery obstruction and myocardial ischemia. Low CV-risk patients develop coronary obstruction due to environmental factors, while high-risk patients are more prone to develop myocardial ischemia. Methods: Consecutive patients screened by coronary CT angiography for stable chest pain between 01/01/2019 and 31/12/2020 were included in a registry. Distance to airport and major road, and exposure to PM2.5, NO 2 and normalized difference vegetation index (NDVI) were modelled at patients’ living address. Risk of obstructive coronary artery disease (OCAD, stenosis ≥50%) and abnormal fractional flow reserve (FFR CT <80%) was assessed using propensity score (PS)-adjusted logistic regression. Subgroup analysis was performed according to gender and Framingham CV-risk categories. Results: We enrolled 2620 patients, including 420 (16%) FFR CT analyses. OCAD was reported in 518 (20%) patients and abnormal FFR CT in 276 (11%). People with OCAD lived closer to the airport (10.841 [7.869-19.022] km vs. 12.297 [8.691-22.843] km, p<0.001). Distance to airport decreased OCAD risk (OR 0.983, 95%CI 0.974-0.992, per 1 km) in univariate analysis, with the association present in male, female, and low-risk patients. After PS-adjustment, a trend of association was present between airport distance and abnormal FFR CT risk (OR 0.986, 95%CI 0.969-1.003, per 1 km). Road distance tended to decrease OCAD risk in univariate and PS-adjusted analysis, in the general (OR 0.928, 95%CI 0.808-1.309; and OR 0.928, 95%CI 0.840-1.021, per 1 km) and high-risk population. NO 2 exposure tended to increase OCAD risk in univariate analysis (OR 1.012, 95%CI 0.997-1.027, per 1 µg/m 3 ), with the association only present in female patients, and disappearance after PS-adjustment. PM2.5 exposure showed no associations, while NDVI tended to increase OCAD risk in low-risk patients after PS-adjustment. Conclusion: Residential distance to airport is the major risk component compared to other urban exposome factors studied in this epidemiological observation. Green space tends to exert a cardioprotective effect in low-risk patients, whereas living close to major road worsens the risk in high-risk patients

Association between a new healthcare access index and life expectancy in Japan: A nationwide study

Abstract Background Access to medical institutions is crucial to healthcare systems. Japan has universal health coverage and unrestricted access to medical institutions; patients can visit any medical institution without requiring referral letters. We developed the Japan- Spatial Accessibility Scale for Medical Institutions (J-AMI) to provide integrated spatial accessibility to multiple sized healthcare institutions. We also examined the association between the J-AMI and life expectancy. Methods This study included 220,621 administrative census mesh blocks defined by address; 89,248 clinics and 8,181 hospitals across 47 provinces in Japan, with a total population of approximately 126 million. The road distance between the centre of each block and the nearest clinic was calculated and scored every 1 km. Hospitals were classified as secondary and tertiary hospitals based on the availability of emergency and critical care centres. Secondary and tertiary hospitals were scored every 2 and 4 km, respectively. The scores for clinics and secondary and tertiary hospitals (1-8 points each) were aggregated and scored using a mesh. The population-weighted municipal J-AMI (PWM-J-AMI) scores were calculated for each municipality. Correlations between the PWM-J-AMI scores and life expectancy by municipality were examined. Geographic information was analysed using ArcGIS Pro. Results Median life expectancies for male and female individuals were 81.4 years (interquartile range [IQR]: 80.9-81.9 years) and 87.5 years (IQR: 87.2-88.0 years), respectively. The median PWM-J-AMI score was 9.5 (IQR: 5.4-14.5). The PWM-J-AMI scores were negatively correlated with male and female individual’s life expectancy by municipality (r=-0.29, p < 0.01 and r = 0.18, p < 0.01, respectively). Conclusions The results suggest that the better a region’s access to medical care, the longer its life expectancy. Efficient medical institution allocation and telemedicine promotion may be essential to improving access. Key messages • We developed the Japan- Spatial Accessibility Scale for Medical Institutions (J-AMI) to provide integrated spatial accessibility to multiple sized healthcare institutions, using ArcGIS Pro. • The PWM-J-AMI scores were negatively correlated with male and female individual’s life expectancy by municipality, which suggest that the better a region’s access, the longer its life expectancy.

Assessment of land degradation susceptibility within the Shaqlawa subregion of Northern Iraq-Kurdistan Region via synergistic application of remotely acquired datasets and advanced predictive models.

Land degradation (LD) is the decline in a land's functional capacity and productive potential, which includes various anthropogenic and natural drivers. This study focuses on three primary manifestations of LD including soil erosion, landslides, and rockfalls, which are the most prevalent in the Shaqlawa district. A set of 22 LD conditioning factors, encompassing curvature, lithology, aspect, river density, soil type, lineament density, river distance, elevation, road distance, length slope (LS), land use land cover (LULC), stream power index (SPI), valley depth, profile curvature, slope, solar radiation, road density, lineament distance, rainfall, topographic wetness index (TWI), plan curvature, and normalized difference vegetation index (NDVI), were integrated into the analysis. Variance inflation factors (VIF) and tolerance (TOL) values from linear regression indicate that most LD factors have acceptable levels of multicollinearity. The Information Gain Ratio (IGR) identified key variables TWI, NDVI, and lithology-as pivotal factors for predicting LD. Additionally, the study evaluated degradation factors using various machine learning (ML) algorithms, including random forest (RF), Naive Bayes, logistic regression, rotation forest, forest penalized attributes (FPA), and Fisher's Linear discriminant analysis (FLDA). This facilitated categorizing the study area into five susceptibility categories. The FLDA model categorized the highest area under very high degradation risk at 26.72%, emphasizing the varied insights each algorithm brought to characterizing the degradation risk. Additionally, the receiver operating characteristic curves (ROC) were employed for model validation, identifying RF as the most successful model in the training dataset with an area under the curve (AUC) of 0.882, while FLDA outperformed in the testing dataset with an AUC of 0.883. The identified LD-prone areas will help land-use planners and emergency management officials apply effective mitigation strategies for similar terrains.

Analisis Konsumsi Bahan Bakar Alat Muat dan Angkut Pada Pengupasan Overburden di PT Putra Maga Nanditama Bengkulu Utara

The research was conducted at PT Putra Maga Nanditama site Gunung Selan, PT Putra Maga Nanditama is a company engaged in coal mining. The background to the research is that overburden production did not reach the company's target and fuel consumption for overburden loading and transport equipment exceeded the company's fuel target. The research method used is a quantitative method. Data collection consists of primary and secondary data. Primary data includes tool cycle time and work efficiency, actual fuel consumption as well as road geometry and distance to disposal. Secondary data in the form of regional performance maps, equipment specifications, rainfall data, equipment working hours data, drone maps for August 2023 and contours. Based on the results of research in the field, factors that influence fuel consumption such as road geometry, road conditions and distance to overburden disposal are still not in accordance with established standards. The productivity of the DX800LC Excavator is 279.59 BCM/hour, the Doosan DA45 ADT is 60.47 BCM/hour and the Volvo A40G ADT is 59.37 BCM/hour. For Doosan DX800LC Excavator fuel consumption is 48.21 liters/hour, ADT Doosan DA45 is 26.62 and ADT Volvo A40G is 23.72 liters/hour. The actual fuel comparison for the two haulers is that the ADT Volvo A40G is more efficient than the ADT Doosan DA45 both in terms of fuel consumption and fuel costs.

Closed-loop supply chain network of end-of-life wind turbines mathematical model proposal considering environmental, employment, and cost reduction aspects

The worldwide climate issue makes wind energy an attractive renewable energy source. Wind energy reduces carbon emissions, energy prices, creates jobs, improves power access, and ensures energy security. The mass of employed materials in wind turbines indicates a lot of potential raw materials when the whole mass is evaluated. Additionally, a new wind turbine costs $2–$4 million. Remanufacturing or using recycled raw materials from end-of-life wind turbines is practical due to the decreased cost of new wind turbines. To ensure sustainability, CO2 emissions and social advantages like employment must be considered. Given the typical life expectancy of wind turbines at 20–25 years, this issue must be addressed globally in the mid-term. The objective of the conducted study is to provide a multi-objective mathematical framework that encompasses the environmental, social, and economic aspects of wind turbines at the end of their operational lifespan. Hence, this study presents a methodical framework for achieving sustainability in the wind energy sector through the implementation of a closed-loop supply chain. Furthermore, a lexicographic solution approach is offered for each objective of the mathematical model, with the aim of providing decision makers with a comprehensive overview of solutions. The methodology employed in this work involves the utilization of a mixed-integer linear programming (MILP) approach to address the closed-loop supply chain for end-of-life wind turbines. Additionally, a lexicographic solution approach is provided to effectively solve the problem at hand. The novelty of this article lies in its examination of the closed loop supply chain network for end-of-life wind turbines, encompassing the social, environmental, and economic dimensions. This study represents the first of its kind to explore these aspects comprehensively. The proposed mathematical model has three objectives which cover the remanufacturing and recycling and aims to minimize CO2 emissions, unemployment, and transportation cost. In order to evaluate the proposed model, Türkiye is utilized as case with real-life data and Gurobi 9.1.2 is utilized. The locations of wind turbines could not be collected individually, and Euclidian distance was used instead of road distance due to a lack of data. These are the main limitations of the proposed study. The study revealed that the TEC and TCE lexicographic approach has the best recycling and remanufacturing cost-to-manufacturing cost ratio, 52.54 %. The least desired result is 74.50 % from CTE.

Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Projected urban flood risk assessment under climate change and urbanization based on an optimized multi-scale geographically weighted regression

Previous research has primarily focused on evaluating flood risks influenced by single factors, often neglecting the combined impacts of climate change and urbanization on historical and future flood risks. This study utilized 12 assessment indicators across three criteria layers. It employed the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and an optimized Multi-scale Geographically Weighted Regression (MGWR) to develop a comprehensive assessment framework for historical and projected urban flood risks under combined scenarios of RCPs and SSPs. The risk of future extreme precipitation events increased under 4 RCPs. Compared with 2010, Beijing's urban land area is expected to increase by an average of 53.64 % in 2060 under 5 SSPs, accompanied by rapid socioeconomic development. MGWR based on K-means optimization improves operational speed by reducing the number of patches. The areas classified as high and highest flood risk levels increased by 38.87 % and 60.17 % from the 2010s to the 2060s, indicating an upward trend in future flood risk. Higher flood risk is observed under the high emissions combination scenario. Structural equation modeling (SEM) revealed that road network density (RND), runoff depth (RD), and distance to road (DRO) were the main influencing factors contributing to flood risks in Beijing.

Urban flash flood prediction modelling using probabilistic and statistical approaches

The development of a detailed strategy to mitigate the negative consequences of any natural calamities depends on accurately identifying sensitive zones where natural hazards frequently happen. In the present investigations, three widely utilized probabilistic approaches viz., frequency ratio (FR), statistical index (SI), and weighting factor (WF) have been utilized for prediction of flsh flood susceptibility zones in the Coochbehar urban and peri-urban area (CUPUA) (area = 26.22 km2). Ten flash flood conditioning factors have been used in this assessment based on previous literatures and experts' opinions. In the FR model, 29.40 % area is observed in the high and very high flood zones, whereas 36.27 % and 31.16 % area is identified in SI and WF model, respectively. The FR model demonstrates that five conditioning factors, viz., topographic position index (TPI), land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to drainage (DtD) and rainfall were highly impacted in flash flood prediction (FFP) analysis; in SI model, LULC is the major influencing parameter, and in WF model LULC, rainfall, NDVI, and distance to road (DtR) are the effective parameters. The success rate curve of the FR, SI and WF models manifest SI model has highest training (AUC=0.903) and prediction (AUC=0.925) accuracy, and FR and WF also have very good accuracy as their AUC values are 0.899 and 0.877 (in success rate curve) and 0.900 and 0.881 (in prediction rate curve). Therefore, the application of probabilistic approaches in this active flash flood-prone region is excellently performed, and the results of this study will help hydrologists, engineers, and water management administrators to control the areas that are extremely susceptible to flash floods and reduce possible damages.

Urban oasis? Abundant dice snake (Natrix tessellata) populations along artificial lakeside habitats in urban landscapes

ContextHuman-induced landscape modification, such as urbanization, creates new environments that can have adverse effects on flora and fauna, posing threats to biodiversity. Understanding how reptiles respond to urbanization is crucial, especially in light of their ongoing population declines.ObjectivesWe examined the influence of landscape-scale and local-scale urbanization features on the abundance of an aquatic snake species. Our investigation focused on dice snakes (Natrix tessellata) inhabiting a lake with a heavily urbanized shoreline.MethodsWe conducted visual encounter surveys at 25 study sites during the activity period of dice snakes around Lake Balaton in Hungary. We measured both landscape-scale and local-scale variables, including urban land use cover, vegetation cover, road cover, distance of main roads and city size, emergent vegetation cover and the area of artificial rock and concrete shoreline protection structures. We analysed snake survey data using N-mixture models to estimate abundance and examine relationships with landscape-scale and local-scale variables.ResultsUrban land use cover, road cover, the proximity of main roads and the extent of artificial rock and concrete shoreline protection structures positively affected the abundance of snakes. These findings imply that urban habitats may offer new ecological opportunities for dice snakes.ConclusionsThe findings of this study indicate that both landscape-scale and local-scale human-induced landscape modifications may have a positive impact on the abundance of urban snakes. Taken together, our findings suggest that urbanization is a complex phenomenon, affecting species at different levels and with subtle effects.

An attention temporal convolutional network-based hybrid approach to simulating indoor air pollutants and their determinants in classroom and office spaces

Accurately assessing and source tracing indoor air quality (IAQ) is crucial for implementing targeted interventions to improve IAQ. This study aimed to investigate the concentrations and underlying determinants of multiple indoor air pollutants in classrooms and office spaces of educational buildings and develop a robust model for precise IAQ assessment and simulation. A walkthrough-based inspection with continuous monitoring of indoor and outdoor PM2.5, PM10, NO2, and O3 was conducted in ten educational buildings in Gainesville, Florida. IAQ dynamics and determinants were systematically compared across spaces to discern setting-specific patterns. An Evolutionary Polynomial Regression method-assisted Attention Temporal Convolution Network (EPR-ATCN) model was developed to assess and predict IAQ. The results revealed that the influencing factors of indoor pollutants in classrooms and offices were similar, but the contribution proportion and weight of each factor differed. Street distance, wall damage, indoor relative humidity, and air conditioning vent size were identified as key determinants for most indoor pollutants. The EPR-ATCN models significantly outperformed Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) ANN-based models in simulating indoor environments of both classrooms (RMSE: 1.6, 2.4 and 4.9) and offices (RMSE: 1.3, 2.9 and 5.1). Follow-up studies exploring IAQ in similar architectural and environmental settings may accordingly reference these findings.

Modeling Environmental Vulnerability for 2050 Considering Different Scenarios in the Doce River Basin, Brazil

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 °C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin.

Research on The Spatial Heterogeneity of National Physical and Mental Health in China Based on Numerical Simulation

Studying the spatial interactions between socio-economic and natural (SEN) factors and physical health (PF) can help improve national physical and mental health and develop health policies. In order to deeply analyze the relationship between social and economic environment, sports activities, and national physical and mental health, and improve the level of national physical and mental health, this study used mathematical numerical analysis methods using MATLAB and ArcGIS software to determine the determining factors of PF differences in China. The reported National Fitness Composite Index (NFCI) in China was extracted from the National Physical and Mental Health Fitness Monitoring Bulletin in 2015. The SEN factors were gathered from the China Statistical Yearbook in 2015. The OLS model and GWR model were then utilized to investigate the SEN factors of the NFCI. We found that the NFCI of each province had a strong relationship with the natural environment. The NFCI of the eastern coastal regions was higher but also similar to that of the central and western regions. It was also found that the impact of the natural environment on national physical and mental health fitness was greater than that of social and economic factors. Temperature and hours of sunshine were major environmental factors. Annual hours of sunshine (ANNsun, hours), health care expenditure (HEAexpe, Chinese yuan), annual temperature (ANNtem, °C), road distance (ROAdis, kilometers) and annual rainfall (ANNrai, millimeters)are socioeconomic and natural (SEN) determinants of factors and physical fitness (PF). We should increase physical exercise, change our concepts of health, develop a healthy lifestyle and improve our physical fitness in accordance with the national fitness strategy. The regression coefficients obtained by the GWR model show significant differences, indicating that the degree of influence of social, economic, and natural factors on national physical fitness varies significantly with geographical regions..

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Musculoskeletal loading from road cycling and distance running on bone bending strength and BMD in athletes

The purpose of this study was to determine whether chronic sport training comprising of low or nonimpact musculoskeletal loading in road cycling (CYC) and distance running (RUN) were associated with tibial and ulnar bending strength and calcaneus and wrist BMD in male athletes. Method. Study subjects included 12 road cyclists (CYC), 12 distance runners (RUN) and 12 physically active nonathletic (CON). Tibial and ulnar bending strength (EI, Nm2) were assessed using a mechanical response tissue analyzer (MRTA), and BMD (g/cm2) of the wrist and calcaneus were assessed using a peripheral X-ray absorptiometry. Group means differences among the study groups were determined using ANCOVA. Results. Ulnar bending strength (EI, Nm2) of CYC were higher than CON (62.0 ± 20 vs 44.7 ± 9 Nm2, p< 0.05), but not RUN. Tibial bending strength (EI, Nm2) of CYC and RUN were not higher than CON. Calcaneus BMD of RUN was higher than CON (0.666 ± 0.07 vs 0.579 ± 0.06 g/m2, p<0.05), but not CYC. Wrist BMD of CYC was not higher than (p = 0.07) RUN and CON. Conclusion. Chronic engagement in CYC training induces osteogenic adaptation for increased ulnar bending strength and RUN training induces increased calcaneus BMD.

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.

Landslide susceptibility assessment of South Korea using stacking ensemble machine learning

BackgroundLandslide susceptibility assessment (LSA) is a crucial indicator of landslide hazards, and its accuracy is improving with the development of artificial intelligence (AI) technology. However, the AI algorithms are inconsistent across regions and strongly dependent on input variables. Additionally, LSA must include historical data, which often restricts the assessment to the local scale and single landslide events.MethodsIn this study, we performed an LSA for the entirety of South Korea. A total of 30 input variables were constructed, consisting of 9 variables from past climate model data MK-PRISM, 12 topographical factors, and 9 environmental factors. Sixteen machine learning algorithms were used as basic classifiers, and a stacking ensemble was used on the four algorithms with the highest area under the curve (AUC). Additionally, a separate assessment model was established for areas with a risk of landslides affecting areas larger than 1 ha.ResultsThe highest-performing classifier was CatBoost, with an AUC of ~ 0.89 for both assessments. Among the input variables, distance of road, daily maximum precipitation, digital elevation model, and soil depth were the most influential. In all landslide events, CatBoost, lightGBM, XGBoost, and Random Forest had the highest AUC in descending order; in large landslide events, the order was CatBoost, XGBoost, Extra Tree, and lightGBM. The stacking ensemble enabled the construction of two landslide susceptibility maps.ConclusionsOur findings provide a statistical method for constructing a high-resolution (30 m) landslide susceptibility map on a country scale using diverse natural factors, including past climate data.

Does cycle commuting reduce the risk of mental ill-health? An instrumental variable analysis using distance to nearest cycle path.

Previous studies have linked cycling with improved mental wellbeing but these studies tend to use cross-sectional survey data that have small sample sizes and self-reported health measures, and are potentially susceptible to omitted-variable bias and reverse causation. We use an instrumental variable approach and an objective measure of mental ill-health taken from linked administrative data to ask: 'Does cycle commuting reduce the risk of mental ill-health?' Our study links data on commuting in Edinburgh and Glasgow from the Scottish population census with mental health prescriptions from the National Health Service Prescribing Information System records. We use road distance from home to nearest cycle path as an instrumental variable for cycle commuting. In total, 378 253 people aged 16-74years living and working in the City of Edinburgh and Glasgow City council areas at the 2011 census were included in our study; 1.85% of commuters in Glasgow and 4.8% of commuters in Edinburgh cycled to work. Amongst cyclists, 9% had a prescription for mental health compared with 14% amongst non-cyclists. Using a bivariate probit model, we estimate a mean average reduction in prescriptions for antidepressants and/or anxiolytics in the 5years following the census of -15.1% (95% CI: -15.3% to -15.0%) amongst cycle commuters compared with those who use any other mode to commute. This work suggests that cycle commuting is causally related to reduced mental ill-health and provides further evidence in support of the promotion of active travel to encourage commuters travelling shorter distances to shift to cycle commutes.

Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.

Tax Instruments for the Mining Sector: Profitbased Taxes Versus Production-Based Taxes

The sharing of mining rents is a particular challenge for African countries. To explain the determinants of profit-based and production-based taxes, we use a panel of 22 gold-producing countries in Africa between 2000 and 2020 using the ordinary least squares (OLS) method controlled for time and country fixed effects. Our empirical results show that the road distance between the capital of the country and its relevant port is an important indicator in the choice of rent taxation instruments. The road distance between the capital of a country and its relevant port tends to reduce the average effective tax rate (AETR) through the share of profit-based taxes in AETR. Thus, countries that do not have direct access to the sea should favour production-based taxes over profit-based taxes in the taxation of mining rents.