The distribution of different intensity demanding scenarios in elite rink hockey players using an electronic performance tracking system.

Despite the traditional use of average values for determining physical demands, the intermittent and fluctuating nature of team sports may lead to underestimation of the most demanding scenarios. All the most demanding scenario-related investigations to date only report one maximal scenario per game, the greatest. However, the latest research on this subject has shown additional scenarios of equal or similar magnitude that most researchers have not considered. This repetition concept started a new way of describing competition and training loads; then the study aims were: first, to quantify and assess differences between playing positions in terms of the most demanding scenarios in official matches; and second, to quantify and assess the differences between playing positions in the repetition of different intensity scenarios relative to the most demanding individual scenario. We monitored nine professional rink hockey players (7 exterior and 2 interior players) in 18 competitive matches using an electronic performance tracking system. The interior players are closest to the opponent's goal, while the exterior players are farthest from it. Peak physical demands variables included total distance (m), distance covered at >18 km·h-1 (m), the number of accelerations (≥2 m∙s-2, count) and decelerations (≤-2 m∙s-2, count) in 30 s. An average from the top three individual most demanding scenarios was used to define a reference value to quantify the distribution scenario repetition during matches. The results showed that peak demands in rink hockey are position-dependent, with more distance covered by exterior players and more accelerations performed by interior players. In addition, rink hockey matches include multiple scenario exposures that are close to the peak physical demands of a match. Using the results of this study, coaches can prepare tailored training plans for each position, focusing on distances covered or accelerations for exterior players.

The effects of nearby trees on the positional accuracy of GNSS receivers in a forest environment.

Global Navigational Satellite System (GNSS) technologies are actively being developed to address the demand for enhanced positional accuracy. Smartphones are the most prevalent GNSS receiver today and have garnered attention thanks to improved positional accuracy and usability that can be accessed at an affordable price. In a forested environment, multipath error can deteriorate the positional accuracy, depending on the state of nearby vegetation. Therefore, this study was conducted to investigate the impacts of the size and location of vegetation on positional accuracy of GNSS receivers to determine whether the errors observed are systematic. Twenty-six control points within the Whitehall Forest GPS Test site in Athens, Georgia were used to evaluate positional accuracy of three different GNSS receivers (two traditional handheld GNSS receivers (including Garmin and Trimble receivers) and a smartphone). Thirty-five forest variables were developed from information around each control point to conduct a correlation analysis with observed horizontal position error in the positions determined by each device. In this study, we confirmed that the positional error of the smartphone was significantly lower than the Garmin receiver, and similar, but significantly different than the positional error observed by the Trimble receiver. It was confirmed that correlations between forest variables and horizontal position error regardless of the GNSS receiver employed were significant, yet trends were not consistent. The effect of the size of nearby trees on horizontal position error could not be generalized; however, the location of nearby trees on horizontal position error could.

Delineation of groundwater potential zones of the transboundary aquifers within the semiarid Bulal catchment, Southern Ethiopia.

In the semiarid Bulal transboundary catchment of southern Ethiopia, groundwater is the only reliable drought-resilient water source. The central and southern parts of the catchment are dominantly overlain by the transboundary aquifers of the Bulal basalts, while the basement rocks outcrop in the eastern part. This study uses an integrated geographic information system (GIS), remote sensing (RS), and analytical hierarchal process (AHP) to identify and delineate the groundwater potential zones of the semiarid Bulal catchment within the Ethiopian territory. Based on their relative importance to groundwater occurrence and movement, ten input parameters were chosen. According to Saaty's AHP approach, the input themes and each of their distinct features were given normalized weights. A composite groundwater potential zone index (GWPZI) map was generated by integrating all the input layers employing the GIS-overlay analysis technique. The map was validated using the yield of wells from the catchment. The GWPZI map depicts four groundwater potential zones: high (representing 27% of the total area), moderate (20%), low (28%), and very low (25%). The geological feature has the greatest influence on the distribution of groundwater potential. Areas with high potential are mainly overlain by the Bulal basaltic flow, while low groundwater potential zones are in the regolith over the basement rocks. Unlike conventional methods, our novel approach is effective in identifying relatively shallow GWPZs throughout the catchment, and it can be applied in similar semiarid regions. The GWPZI map serves as a quick guide for effectively planning, managing, and developing the catchment's groundwater resources.

Source apportionment and quantitative risk assessment of heavy metals at an abandoned zinc smelting site based on GIS and PMF models.

The abandoned smelters have caused serious hazards to the surrounding environment and residents. Taking an abandoned zinc smelter in southern China as an example, a total of 245 soil samples were collected to study spatial heterogeneity, source apportionment, and source-derived risk assessment of heavy metal(loid)s (HMs) in the region. The results showed that the mean values of all HMs concentrations were higher than the local background values, with Zn, Cd, Pb, and As contamination being the most serious and their plume penetrating to the bottom layer. Four sources were identified by principal component analysis and positive matrix factorization, with their contributions to the HMs contents ranked as: surface runoff (F2, 63.2%) > surface solid waste (F1, 22.2%) > atmospheric deposition (F3, 8.5%) > parent material (F4, 6.1%). Among these, F1 was a determinant source of human health risk with a contribution rate of 60%. Therefore, F1 was considered to be the priority control factor, but it only accounted for 22.2% of HMs contents contribution. Hg dominated the ecological risk with a contribution of 91.1%. Pb (25.7%) and As (32.9%) accounted for the non-carcinogenic risk, while As (95%) dominated the carcinogenic effect. The spatial characteristics of human health risk values derived from F1 indicated that high-risk areas were mainly distributed in the casting finished products area, electrolysis area, leaching-concentration area, and fluidization roasting area. The findings highlight the significance of priority control factors (including HMs, pollution sources and functional areas) for consideration in the integrated management of this region, thus saving costs for effective soil remediation.

Geographic information system protocol for mapping areas targeted for mosquito control in North Carolina.

Geographic information systems (GIS) can be used to map mosquito larval and adult habitats and human populations at risk for mosquito exposure and possible arbovirus transmission. Along with traditional methods of surveillance-based targeted mosquito control, GIS can help simplify and target efforts during routine surveillance and post-disaster (e.g., hurricane-related flooding) to protect emergency workers and public health. A practical method for prioritizing areas for emergency mosquito control has been developed and is described here. North Carolina (NC) One Map was used to identify state-level data layers of interest based on human population distribution and mosquito habitat in Brunswick, Columbus, Onslow, and Robeson Counties in eastern NC. Relevant data layers were included to create mosquito control treatment areas for targeted control and an 18-step protocol for map development is discussed. This protocol is expected to help state, territorial, tribal, and/or local public health officials and associated mosquito control programs efficiently create treatment area maps to improve strategic planning in advance of a disaster. This protocol may be applied to any NC county and beyond, thereby increasing local disaster preparedness.

Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study.

BACKGROUND: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. OBJECTIVE: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. METHODS: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. RESULTS: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI -50.08 to -31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI -19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. CONCLUSIONS: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period.

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation.

BACKGROUND: Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE). METHODS: Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic. RESULTS: We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results. DISCUSSION: The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Are Coastal Hotels Ready for Climate Change? The Case of Alexandria, Egypt.

Climate change casts a shadow on the tourism industry in Egypt in general, and on coastal hotels in particular, as the coastal areas of Egypt have been classified as the most vulnerable to climate change in the Middle East. As such, mitigating the negative impacts and threats of climate change requires an assessment of the vulnerability of coastal hotels and the extent to which adaptation measures are applied. Accordingly, this study applied a hybrid methodology to achieve three main objectives. First, to evaluate Alexandria's vulnerability to future climate change (at the destination level) by analyzing the recent climatic trends and expected scenarios. Second, to assess the vulnerability of Alexandria's coastal hotels to climate change (sector level) using satellite images, aerial mapping, remote sensing, and geographic information systems (GIS). Third, to explore how coastal hotels are adapting to the risks of climate change using the four business-focused adaptation measures (i.e., technical, managerial, policies, and awareness-raising). The findings of the study revealed and confirmed that the hotel sector in Alexandria is threatened by sea level rise (SLR). Four hotels are at risk of inundation, and the extent of hotels at risk will increase with future scenarios of SLR. On the other hand, the results of examining the adaptation measures of 36 hotels indicated that the scope of the adaptation measures differed significantly between hotels due to factors such as hotel category, size, duration of operation, and EMS status, but overall, the scope of application was more comprehensive and varied than expected. Technical adaptation measures were the most common and applied by the majority of hotels in Alexandria. The results of this study should help figure out what adaptation measures coastal hotels should take and show policymakers where they should focus their adaptation efforts.

Fuzzy-based models' performance on qualitative and quantitative land suitability evaluation for cotton cultivation in Sarayan County, South Khorasan Province, Iran.

Using appropriate models in the land use planning process will help increase the accuracy and precision of decisions made by designers. The aim of this study was to investigate and compare fuzzy-based models (fuzzy set theory, fuzzy-AHP, and fuzzy-ANP) to evaluate the suitability of cotton cultivation in Sarayan region (located in eastern Iran). Twenty-eight land units were selected. Weighted arithmetic means of characteristics were performed in representative soil profiles of each unit. Landform-related characteristics were directly entered into the land suitability evaluation modeling. The land index was calculated using three selective qualitative land suitability model guidelines. Qualitative and quantitative land suitability was estimated. The validity of models was determined by r2, RMSE, GMER, and MAPE indicators between predicted and actual production. Soil texture, pH, calcium carbonate equivalent, drainage, organic matter, salinity and sodicity, slope, and gypsum are the most important, respectively. Also, the fuzzy-ANP method is more efficient than other models due to its higher r2 (0.98) and lower RMSE (4.31) and MAPE (0.56) and GMER (0.99) closer to 1. The value of cotton production using fuzzy, fuzzy-AHP, and fuzzy-ANP methods was calculated as 1085 to 4235, 1235 to 4318, and 1391 to 4452 tons per hectare, respectively. The high efficiency of the fuzzy-ANP model is due to the characteristics of the lands used in the evaluation process that are not independent of each other and this model considers them. Examining these models in different weather conditions and combining with the other computational intelligence methods in future experiments are recommended.

GIS-based slope-adjusted curve number methods for runoff estimation.

Accurate estimation of surface runoff and determination of susceptible lands to runoff generation in ungauged watersheds were the problems for hydrologic engineering which could be predicted through a simple model such as Soil Conservation Service Curve Number (SCS-CN). Due to the slope effects on this method, slope adjustment for curve number was developed to improve its precision. So, the main objectives of this study were to apply GIS-based slope SCS-CN approaches for surface runoff estimation and compare the accuracy of three slope-adjusted models including: (a) model with three empirical parameters, (b) model with two parameters slope function, and (c) model with one parameter in the region located in the central part of Iran. For this purpose, soil texture, hydrologic soil group, land use, slope, and daily rainfall volume maps were utilized. In order to provide the curve number map of the study area, land use and hydrologic soil group layers built in Arc-GIS were intersected and the curve number was determined. Then, three slope adjustment equations were used to modify curve numbers of AMC-II by employing slope map. Finally, recorded runoff data of the hydrometric station were applied to assess the performance of the models through four statistical indicators of the root mean square error (RMSE), the Nash-Sutcliffe efficiency (E), the coefficient of determination [Formula: see text], and percent bias (PB). Land use map analysis showed that rangeland was the dominant land use, whereas the soil texture map specified the greatest and smallest area belonging to loam and sandy loam textures, respectively. Although the runoff results showed the overestimation of large rainfall values and underestimation for rainfall with less than 40 mm volume in both models, the values of E (0.78), RMSE (2), PB (16), and [Formula: see text] (0.88) revealed that eq. (a) with three empirical parameters was the most accurate equation. The maximum percent of runoff generated by rainfall for eqs. (a), (b), and (c) were 68.43, 67.28, and 51.57% which showed that bareland located in south part with the slope of more than 5% was susceptible to runoff generation and should be paid attention to watershed management.

GIS-based approach and multivariate statistical analysis for identifying sources of heavy metals in marine sediments from the coast of Hong Kong.

Hong Kong is an urbanized coastal city which experiences substantially different metal loads from anthropogenic activities. This study was aimed at analyzing the spatial distribution and pollution evaluation of ten selected heavy metals (As, Cd, Cr, Cu, Pb, Hg, Ni, Zn, Fe, V) in the coastal sediments of Hong Kong. The distribution of heavy metal pollution in sediments has been analyzed using the geographic information system (GIS) technique, and their pollution degrees, corresponding potential ecological risks and source identifications, have been studied by applying the enrichment factor (EF) analysis, contamination factor (CF) analysis, potential ecological risk index (PEI), and integrated multivariate statistical methods, respectively. Firstly, the GIS technique was used to access the spatial distribution of the heavy metals; the result revealed that pollution trend of these metals was decreased from the inner to the outer coast sites of the studied area. Secondly, combining the EF analysis and CF analysis, we found that the pollution degree of heavy metals followed the order of Cu > Cr > Cd > Zn > Pb > Hg > Ni > Fe > As > V. Thirdly, the PERI calculations showed that Cd, Hg, and Cu were the most potential ecological risk factors compared to other metals. Finally, cluster analysis combined with principal component analysis showed that Cr, Cu, Hg, and Ni might originate from the industrial discharges and shipping activities. V, As, and Fe were mainly derived from the natural origin, whereas Cd, Pb, and Zn were identified from the municipal discharges and industrial wastewater. In conclusion, this work should be helpful in the establishment of strategies for contamination control and optimization of industrial structures in Hong Kong.

Mapping Eastern (EEE) and Venezuelan Equine Encephalitides (VEE) among Equines Using Geographical Information Systems, Colombia, 2008-2019.

INTRODUCTION: Eastern equine encephalitis virus (EEEV) and Venezuelan equine encephalitis virus (VEEV) viruses are zoonotic pathogens affecting humans, particularly equines. These neuroarboviruses compromise the central nervous system and can be fatal in different hosts. Both have significantly influenced Colombia; however, few studies analyse its behaviour, and none develop maps using geographic information systems to characterise it. OBJECTIVE: To describe the temporal-spatial distribution of those viruses in Colombia between 2008 and 2019. METHODS: Retrospective cross-sectional descriptive study, based on weekly reports by municipalities of the ICA, of the surveillance of both arboviruses in equines, in Colombia, from 2008 to 2019. The data were converted into databases in Microsoft Access 365®, and multiple epidemiological maps were generated with the Kosmo RC1®3.0 software coupled to shape files of all municipalities in the country. RESULTS: In the study period, 96 cases of EEE and 70 of VEE were reported, with 58% of EEE cases occurring in 2016 and 20% of EEV cases in 2013. The most affected municipalities for EEE corresponded to the department of Casanare: Yopal (20), Aguazul (16), and Tauramena (10). In total, 40 municipalities in the country reported ≥1 case of EEE. CONCLUSIONS: The maps allow a quick appreciation of groups of neighbouring municipalities in different departments (1° political division) and regions of the country affected by those viruses, which helps consider the expansion of the disease associated with mobility and transport of equines between other municipalities, also including international borders, such as is the case with Venezuela. In that country, especially for EEV, municipalities in the department of Cesar are bordering and at risk for that arboviral infection. there is a high risk of equine encephalitis outbreaks, especially for VEE. This poses a risk also, for municipalities in the department of Cesar, bordering with Venezuela.

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study.

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

Characteristics of external loads of Hockey5s associated with the new version of U16 youth field hockey competition.

External workloads associated Hockey 5 s, the new version of youth field hockey, were evaluated in 31 elite U16 male field players (15.4 ± 0.7 years) from three national teams. Mixed-longitudinal observations for the 31 players provided complete data for 33 forwards and 43 defenders. Activities of the players during games were monitored with the GPSports SPI Elite System with a sampling frequency of 10 Hz and were analysed with GPSports Team AMS (version R1 2015.14, Australia). Observed variables did not differ between forwards and defenders, and the three periods of play were differentiated only by maximal speed in the second and third periods. The greatest distances covered were in speed zone 3 (10.0-15.9 km h-1; 35.5-38.2%) and the smallest in speed zones 4 (16.0-22.9 km h-1; 14.8-15.6%) and 5 (> 23 km h-1; 0.4-1.4%). The trends indicated high intensity levels for the entire match and by position and periods. Active time of forwards and defenders accounted for about one-half of a game's duration (~ 15.7 of 30 min). Overall, the Hockey 5s format was highly demanding of players and included relatively short intervals for recovery. The results emphasize the need for preparation that includes specific mixed anaerobic and aerobic training and also the importance of recovery during breaks.

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China.

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

Pollution Vulnerability of the Ghiss Nekkor Alluvial Aquifer in Al-Hoceima (Morocco), Using GIS-Based DRASTIC Model.

Groundwater resources of the alluvial aquifer Ghiss Nekkor, which covers an area of 100 km2, are the main source of domestic and agricultural freshwater supply in the region of Al Hoceima in Morocco. Due to human activities (overexploitation, increase in agricultural activity), this alluvial aquifer has become very sensitive to chemical pollution. The principal objective of this current study is to develop and implement a calibration method to assess, map, and estimate the vulnerability of the Ghiss Nekkor alluvial aquifer to pollution risk. In this work, the GIS-based DRASTIC model was used to estimate the inherent vulnerability to contamination of the Ghiss Nekkor alluvial aquifer with seven standard hydrogeological parameters. Nitrate (NO3) and electrical conductivity (EC) data were used to validate the DRASTIC map. The results of the vulnerability map analysis show that the vulnerability to contaminants varies from non-existent in the southwestern part of the plain (7.3% of the total area), to very high (14.5%). The vulnerability is moderate in the central and northeastern areas (26.9%), while it is high in the other areas (17.5%). Furthermore, the most sensitive areas are mainly concentrated near the coastal strip and the central plain on both sides of the Nekkor River. In these areas, the NO3 and EC values are above the maximum allowable limit of the World Health Organization. The results suggest that the DRASTIC model can be an effective tool for decision-makers concerned about managing groundwater sustainability.

Sleep duration during the COVID-19 pandemic in Bangladesh: A GIS-based large sample survey study.

Although several studies have been conducted in Bangladesh regarding sleep problems during the COVID-19 pandemic, none have utilized a large nationwide sample or presented their findings based on nationwide geographical distribution. Therefore, the aim of the present study was to explore the total sleep duration, night-time sleep, and daily naptime and their associated factors as well as geographic information system (GIS) distribution. A cross-sectional survey was carried out among 9730 people in April 2020, including questions relating to socio-demographic variables, behavioral and health factors, lockdown, depression, suicidal ideation, night sleep duration, and naptime duration. Descriptive and inferential statistics, both linear and multivariate regression, and spatial distribution were performed using Microsoft Excel, SPSS, Stata, and ArcGIS software. The results indicated that 64.7% reported sleeping 7-9 h a night, while 29.6% slept less than 7 h nightly, and 5.7% slept more than 9 h nightly. 43.7% reported 30-60 min of daily nap duration, whereas 20.9% napped for more than 1 h daily. Significant predictors of total daily sleep duration were being aged 18-25 years, being unemployed, being married, self-isolating 4 days or more, economic hardship, and depression. For nap duration, being aged 18-25 years, retired, a smoker, and a social media user were at relatively higher risk. The GIS distribution showed that regional division areas with high COVID-19 exposure had higher rates of non-normal sleep duration. Sleep duration showed a regional heterogeneity across the regional divisions of the country that exhibited significant associations with a multitude of socioeconomic and health factors.

Peak Running Speeds in Professional Male Football: Influence of Division and Playing Position.

ABSTRACT: Fahey, JT, Aldred, K, Greig, M, and Rhodes, D. Peak running speeds in professional male football: Influence of division and playing position. J Strength Cond Res 37(3): 636-640, 2023-Well-established physical demands of competitive professional football facilitate prescription and monitoring of training. However, many factors influence these physical demands with implications for efficacious practice. Match-play data were analyzed over 2 seasons using global positioning systems technology, differentiating English Championship (33 matches) and League One (27 matches) demands. Playing position categorized wide and central defenders and midfielders and forwards. Peak running speeds defined the outcome measure, assessing the influence of the competition level and playing position across 1, 5, and 10-minute rolling average durations using a linear mixed model. Significant effects were detected for the competition level (F1,324.5 = 5.44, p = 0.02) and playing position (F4,328.3 = 89.90, p < 0.001). League One matches demonstrated greater peak running speeds than Championship matches (mean difference = 2.72 m·min-1 [95% confidence intervals: 0.4, 5.0]). No difference was observed between central and wide midfielders (mean difference = 0.62 m·min-1 [95% confidence intervals: -3.1, 4.3]). Wide midfielders presented faster peak running speeds than forwards (mean difference = 18 m·min-1 [95% confidence intervals:14.1, 22.1], p < 0.05), central defenders (mean difference = 25 m·min-1 [95% confidence intervals: 21.7, 29.8], p < 0.05), and wide defenders (mean difference = 12 m·min-1 [95% confidence intervals: 8.2, 16.5], p < 0.05). Interaction effects were found for division*position (F4,328.3 = 2.57, p = 0.038) demonstrating greater running speeds in League One, except for central defenders. Wide midfielders presented greater peak 1-minute running speeds, whereas 5 and 10-minute peak running speeds were greatest in central midfielders. The sensitivity of peak running speeds to competition level and playing position has implications for training prescription, monitoring particularly when transitioning between competition levels, determining and monitoring positional training intensities, and objective targets for progressive overload during rehabilitation.

Development of Health Digital GIS Map for Tuberculosis Disease Distribution Analysis in Sudan.

Health digital GIS map provides a great solution for medical geographical distribution to efficiently explore diseases and health services. In Sudan, tuberculosis disease is expanding in different areas, which requires a digital GIS map to collect information about the patients and support medical institutions by geographical distribution based on health services, drug supply, and consumption. This paper developed a health digital GIS map to provide a fair geographical distribution of tuberculosis health centers and control the drug supply according to medical reports. The proposed approach extracts the unfair distribution of medicine, as some centers receive medicine but do not receive patients, while others receive a large number of patients but limited amounts of medicine. The analysis results show that there is a defect in some states representing the distribution of tuberculosis centers. In the Northern State, there are 15 tuberculosis centers distributed over all localities, serving about 84 tuberculosis-infected patients only.