Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Spatial and temporal-trend assessment of desertification-sensitive land using the desertification sensitivity index in the provincial Ninh Thuan, Vietnam.

Desertification is a specific land-degrading process, reducing soil productivity and potentially threatening global food security. Therefore, spatially and temporally identifying and mapping desertification-sensitive areas is essential for better management. The current study aimed to (1) assess spatial areas sensitive to desertification and (2) examine the changing tendency of the desertification-sensitive areas over the past 25 years in the provincial Ninh Thuan. The desertification sensitivity index (DSI) was computed based on the Medalus model using 10 quantitative parameters, grouped into the soil, climate, and vegetation quality indexes, computed for the years 1996, 2005, 2010, and 2016. GIS was used to map desertification-sensitive areas associated with five DSI classes. Results showed that classes II and III had the highest area percentage, followed by classes IV and V, and class I. The classes most sensitive to desertification (classes IV and V) covered around 13 to 17%, and classes II and III were 25 to 32% of the total study area, respectively. The coastal areas located in the southeastern parts were more sensitive to desertification than the other parts. Over the four examined periods, the areas of classes IV and V increased while those of classes II and I decreased. These indicated that the study province tended to increase in its desertification sensitivity with a severe increase in the coastal areas over the past 25 years. The key factors involved in these changes could be related the human activities and climate variation, which could be more serious in southeastern areas than in the other areas.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

Comparative evaluation of the web-based contiguous cartogram generation tool go-cart.io.

Area cartograms are map-based data visualizations in which the area of each map region is proportional to the data value it represents. Long utilized in print media, area cartograms have also become increasingly popular online, often accompanying news articles and blog posts. Despite their popularity, there is a dearth of cartogram generation tools accessible to non-technical users unfamiliar with Geographic Information Systems software. Few tools support the generation of contiguous cartograms (i.e., area cartograms that faithfully represent the spatial adjacency of neighboring regions). We thus reviewed existing contiguous cartogram software and compared two web-based cartogram tools: fBlog and go-cart.io. We experimentally evaluated their usability through a user study comprising cartogram generation and analysis tasks. The System Usability Scale was adopted to quantify how participants perceived the usability of both tools. We also collected written feedback from participants to determine the main challenges faced while using the software. Participants generally rated go-cart.io as being more usable than fBlog. Compared to fBlog, go-cart.io offers a greater variety of built-in maps and allows importing data values by file upload. Still, our results suggest that even go-cart.io suffers from poor usability because the graphical user interface is complex and data can only be imported as a comma-separated-values file. We also propose changes to go-cart.io and make general recommendations for web-based cartogram tools to address these concerns.

Geographical distribution of nitrate pollution and its risk assessment using GIS and Monte Carlo simulation in drinking water in urban areas of Fars province-Iran during 2017-2021.

Pollution of water resources with nitrate is currently one of the major challenges at the global level. In order to make macro-policy decisions in water safety plans, it is necessary to carry out nitrate risk assessment in underground water, which has not been done in Fars province for all urban areas. In the current study, 9494 drinking water samples were collected in four seasons in 32 urban areas of Fars province in Iran, between 2017 and 2021 to investigate the non-carcinogenic health risk assessment. Geographical distribution maps of hazard quotient were drawn using geographical information system software. The results showed that the maximum amount of nitrate in water samples in 4% of the samples in 2021, 2.5% of the samples in 2020 and 3% of the samples in 2019 were more than the standard declared by World Health Organization guidelines (50 mg/L). In these cases, the maximum amount of nitrate was reported between 82 and 123 mg/L. The HQ values for infants did not exceed 1 in any year, but for children (44% ± 10.8), teenagers (10.8% ± 8.4), and adults (3.2% ± 1.7) exceeded 1 in cities, years, and seasons, indicating that three age groups in the studied area are at noticeably significant non-carcinogenic risk. The results of the Monte Carlo simulation showed that the average value of non-carcinogenic risk was less than 1 for all age groups. Moreover, the maximum HQ values (95%) were higher than 1 for both children and teenager, indicating a significant non-carcinogenic risk for the two age groups.

Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and accelerometer devices.

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70 years. In 2020, the mangrove forest cover was estimated to be 201.73 ha, equivalent to only 61.99% of the 325.43 ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29 ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51 ha, averaging 6.46 ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84 ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

Geographic information system-based determination of priority monitoring areas for hazardous air pollutants in an industrial city.

Industrial cities are hotspots for many hazardous air pollutants (HAPs), which are detrimental to human health. We devised an identification method to determine priority HAP monitoring areas using a comprehensive approach involving monitoring, modeling, and demographics. The methodology to identify the priority HAP monitoring area consists of two parts: (1) mapping the spatial distribution of selected categories relevant to the target pollutant and (2) integrating the distribution maps of various categories and subsequent scoring. The identification method was applied in Ulsan, the largest industrial city in South Korea, to identify priority HAP monitoring areas. Four categories related to HAPs were used in the method: (1) concentrations of HAPs, (2) amount of HAP emissions, (3) the contribution of industrial activities, and (4) population density in the city. This method can be used to select priority HAP monitoring areas for intensive monitoring campaigns, cohort studies, and epidemiological studies.

Hazard estimation in urban home garden soils in an industrial area using microarthropods, soil properties and GIS modelling: an integrated approach.

The use of soil microarthropods as indicators of soil pollution in home gardens of an industrial area has been covered in this study. Soil samples were collected from 25 home gardens in three zones in Eloor during summer and North East monsoon from 2014 to 2018, for the study of soil microarthropods, soil properties, soil nutrients, and trace elements. The relationships among QBS-ar, microarthropod abundance, soil properties, and soil nutrients, were used to estimate the pollution hazard of the industrial area. The microarthropods present in the study area were Coleoptera, Hymenoptera, Diplopoda, and Araneae. A prominent study area feature was the absence of Collembola and Acari. The QBS-ar index score in these regions showed that the home gardens located adjacent to the industrial area showed low soil quality, with soil quality class values ranging from 1 to 2 throughout the study period. Discriminant analysis of soil nutrients with soil properties and microarthropod abundance showed that in Zone 1 and Zone 2, the data in 2018 was very well discriminated compared to other years. The hazard assessment in the Eloor region showed various levels of hazard zonation: Zone 1 with high-hazard and medium-hazard areas, Zone 2 with medium-hazard areas, and Zone 3 with low- and medium-hazard areas. The study is one of the first kinds that have used QBS-ar scores and soil properties along with soil nutrients and trace elements for estimating the level of hazard in home garden agroecosystems and thus points to an easy, simple, and practical approach in the monitoring and management of soil ecosystems.

Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight.

Community food environments (CFEs) have a strong impact on child health and nutrition and this impact is currently negative in many areas. In the Republic of Argentina, there is a lack of research evaluating CFEs regionally and comprehensively by tools based on geographic information systems (GIS). This study aimed to characterize the spatial patterns of CFEs, through variables associated with its three dimensions (political, individual and environmental), and their association with the spatial distribution in urban localities in Argentina. CFEs were assessed in 657 localities with ≥5,000 inhabitants. Data on births and CFEs were obtained from nationally available open-source data and through remote sensing. The spatial distribution and presence of clusters were assessed using hotspot analysis, purely spatial analysis (SaTScan), Moran's Index, semivariograms and spatially restrained multivariate clustering. Clusters of low risk for LBW, macrosomia, and preterm births were observed in the central-east part of the country, while high-risk clusters identified in the North, Centre and South. In the central-eastern region, low-risk clusters were found coinciding with hotspots of public policy coverage, high night-time light, social security coverage and complete secondary education of the household head in areas with low risk for negative outcomes of the birth variables studied, with the opposite with regard to households with unsatisfied basic needs and predominant land use classes in peri-urban areas of crops and herbaceous cover. These results show that the exploration of spatial patterns of CFEs is a necessary preliminary step before developing explanatory models and generating novel findings valuable for decision-making.

Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API's in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

Mapping sedentary behaviour (MAPS-B) in winter and spring using wearable sensors, indoor positioning systems, and diaries in older adults who are pre-frail and frail: A feasibility longitudinal study.

Older adults who are frail are likely to be sedentary. Prior interventions to reduce sedentary time in older adults have not been effective as there is little research about the context of sedentary behaviour (posture, location, purpose, social environment). Moreover, there is limited evidence on feasible measures to assess context of sedentary behaviour in older adults. The aim of our study was to determine the feasibility of measuring context of sedentary behaviour in older adults with pre-frailty or frailty using a combination of objective and self-report measures. We defined "feasibility process" using recruitment (20 participants within two-months), retention (85%), and refusal (20%) rates and "feasibility resource" if the measures capture context and can be linked (e.g., sitting-kitchen-eating-alone) and are all participants willing to use the measures. Context was assessed using a wearable sensor to assess posture, a smart home monitoring system for location, and an electronic or hard-copy diary for purpose and social context over three days in winter and spring. We approached 80 potential individuals, and 58 expressed interest; of the 58 individuals, 37 did not enroll due to lack of interest or medical mistrust (64% refusal). We recruited 21 older adults (72±7.3 years, 13 females, 13 frail) within two months and experienced two dropouts due to medical mistrust or worsening health (90% retention). The wearable sensor, indoor positioning system, and electronic diary accurately captured one domain of context, but the hard copy was often not completed with enough detail, so it was challenging to link it to the other devices. Although not all participants were willing to use the wearable sensor, indoor positioning system, or electronic diary, we were able to triage the measures of those who did. The use of wearable sensors and electronic diaries may be a feasible method to assess context of sedentary behaviour, but more research is needed with device-based measures in diverse groups.

Investigating young children's physical activity through time and place.

BACKGROUND: Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children's physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children's daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place. METHODS: We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately. RESULTS: Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA. CONCLUSIONS: We identified several social contexts relevant for children's daily MVPA. Schools have the potential to significantly contribute to young children's PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines.

Proximity to Hospital-Based Obstetric Care in a Maternity Desert in the Deep South.

Objectives. To examine the accessibility of hospital facilities with maternity care services in 1 rural county in Alabama in preparation for the initiation of prenatal care services at a federally qualified health center. Methods. We analyzed driving distance (in miles) from maternal city of residence in Conecuh County, Alabama to hospital of delivery, using 2019-2021 vital statistics data and geographic information system (GIS) software. Results. A total of 370 births to mothers who have home addresses in Conecuh County were reported, and 368 of those were in hospital facilities. The majority of deliveries were less than 30 miles (median = 23 miles) from the maternal city of residence. Some women traveled more than 70 miles for obstetrical care. Conclusions. Pregnant patients in Conecuh County experience significant geographic barriers related to perinatal care access. Using GIS for this analysis is a promising approach to better understand the unique challenges of pregnant individuals in this rural population. Public health policy efforts need to be geographically tailored to address these disparities. (Am J Public Health. 2024;114(S4):S330-S333. https://doi.org/10.2105/AJPH.2024.307692).

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

BACKGROUND: In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. RESULTS: We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. CONCLUSIONS: Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Longitudinal study of changes in greenness exposure, physical activity and sedentary behavior in the ORISCAV-LUX cohort study.

BACKGROUND: Greenness exposure has been associated with many health benefits, for example through the pathway of providing opportunities for physical activity (PA). Beside the limited body of longitudinal research, most studies overlook to what extent different types of greenness exposures may be associated with varying levels of PA and sedentary behavior (SB). In this study, we investigated associations of greenness characterized by density, diversity and vegetation type with self-reported PA and SB over a 9-year period, using data from the ORISCAV-LUX study (2007-2017, n = 628). METHODS: The International Physical Activity Questionnaire (IPAQ) short form was used to collect PA and SB outcomes. PA was expressed as MET-minutes/week and log-transformed, and SB was expressed as sitting time in minutes/day. Geographic Information Systems (ArcGIS Pro, ArcMap) were used to collect the following exposure variables: Tree Cover Density (TCD), Soil-adjusted Vegetation Index (SAVI), and Green Land Use Mix (GLUM). The exposure variables were derived from publicly available sources using remote sensing and cartographic resources. Greenness exposure was calculated within 1000m street network buffers around participants' exact residential address. RESULTS: Using Random Effects Within-Between (REWB) models, we found evidence of negative within-individual associations of TCD with PA (ß = - 2.60, 95% CI - 4.75; - 0.44), and negative between-individual associations of GLUM and PA (ß = - 2.02, 95% CI - 3.73; - 0.32). There was no evidence for significant associations between greenness exposure and SB. Significant interaction effects by sex were present for the associations between TCD and both PA and SB. Neighborhood socioeconomic status (NSES) did not modify the effect of greenness exposure on PA and SB in the 1000 m buffer. DISCUSSION: Our results showed that the relationship between greenness exposure and PA depended on the type of greenness measure used, which stresses the need for the use of more diverse and complementary greenness measures in future research. Tree vegetation and greenness diversity, and changes therein, appeared to relate to PA, with distinct effects among men and women. Replication studies are needed to confirm the relevance of using different greenness measures to understand its' different associations with PA and SB.

Predicting black ice-related accidents with probabilistic modeling using GIS-based Monte Carlo simulation.

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents.

Analysis of peak locomotor demands in women's football-the influence of different epoch lengths.

The quantification of peak locomotor demands has been gathering researchers' attention in the past years. Regardless of the different methodological approaches used, the most selected epochs are between 1-, 3-, 5- and 15-minutes time windows. However, the selection of these time frames is frequently arbitrary. The aim of this study was to analyse the peak locomotor demands of short time epochs (15, 30, 45, and 60 seconds) in women's football, with special emphasis over the high-speed metrics. During two seasons, the match physical performance of 100 female football players was collected with Global Positioning System units (STATSports Apex). Peak locomotor demands for the selected variables were calculated by using a 1-second moving average approach. For statistical procedures, linear mixed modelling was used, with total distance, high-speed running distance (>16 km∙h-1), sprint distance (>20 km∙h-1), and acceleration and deceleration distance (±2.26 m∙s-2) considered as the dependent variables and the epoch lengths (15, 30, 45, and 60 seconds) considered as the independent variables. A novel finding was the high ratio observed in the 15 seconds epochs of high-speed running distance and sprint distance (77.6% and 91.3%, respectively). The results show that most peak high-speed demands within 60 seconds are completed within just 15 seconds. Thus, intensity-related variables, such as high-speed metrics, would be better contextualised and adapted into training practices if analysed in shorter epoch lengths (15-30 seconds), while longer periods might be used for volume-related metrics (i.e., total distance), depending on the purpose of the analysis.

A strategy to balance location privacy and positioning accuracy.

In privacy protection methods based on location services, constructing anonymous areas using location information shared by collaborative users is the main method. However, this collaborative process not only increases the risk of mobile users' location privacy being leaked, but also reduces positioning accuracy. In response to this problem, we propose a balancing strategy, which transforms the problem of protecting mobile users' location privacy and improving positioning accuracy into a balance issue between location privacy and positioning accuracy. The cooperation of mobile users with different collaborating users is then modeled as an objective optimization problem, and location privacy and positioning accuracy are evaluated separately to make different selection strategies. Finally, an optimization function is constructed to select the optimal selection strategies. Experimental results show that our proposed strategy can effectively achieve the balance between location privacy and positioning accuracy.