Changes of bone turnover markers and testosterone in Japanese male endurance runners: a pilot study.

INTRODUCTION: The bone metabolism of male endurance runners during a competitive season has been little studied. Furthermore, Japanese runners have a special competitive season. This study aimed to investigate the change of bone turnover markers (BTMs) and other hormones in Japanese male endurance runners. MATERIALS AND METHODS: We investigated biochemical markers, bone formation and resorption markers and sex hormones, body composition using dual-energy X-ray absorptiometry (DXA), and training volume during a competitive season. 11 (age: 24.0 ± 4.3 years) Japanese male endurance runners were analyzed in this study. They participated in all measurements three times a year: the baseline (BL), the post-track period (PT), and the road race period (RR). RESULTS: Fat mass (FM) and percentage of body fat (% body fat) at RR (p = 0.009, p = 0.009) were lower than at BL. Specific bone alkaline phosphatase (BAP) decreased at PT (p = 0.004) and RR (p = 0.004) compared with BL. However, the bone resorption marker did not change. Free testosterone at PT (p = 0.032) was lower than BL. There was no correlation with blood variables and body composition or training volume. CONCLUSION: BAP and testosterone in Japanese male endurance runners decreased during the competitive season. However, there was no correlation between blood variables and training volume.

A comprehensive study on emission of volatile organic compounds for light duty gasoline passenger vehicles in China: Illustration of impact factors and renewal emissions of major compounds.

BACKGROUND: Many volatile organic compounds (VOCs) are efficient precursors for both ozone and secondary organic aerosol (SOA) which are problematic to environmental controls in many global cities. Vehicle emission is one of the most important anthropogenic sources for VOCs. In between, light duty gasoline passenger vehicles (LDGPVs) contributed more than half of the on-road vehicles in China, demonstrating unique emission characteristics and also significant contributions in emission inventory. OBJECTIVES: To illustrate the variation of VOCs emissions from LDGPVs under different conditions, evaluate the sensitivity of the potential influencing factors, such as emission standards, displacement, cumulative mileage, and driving mode, on the VOCs emissions profiles, and to update the emission inventory by taking into account of the most influential factor. METHODS: In this study, seventy-four in-used LDGPVs were examined by the chassis dynamometer. A total of 25 VOCs in the emissions was quantified using an online time of flight mass spectrometer (TOF-MS). Fuel-based emission factors (EF) were calculated. RESULTS: The results showed that the maximum ΣEF (260.4 ± 241.1 mg/L) was seen for LDGPVs at a high acceleration rate (0.9 m/s2). From the technical control emission standard aspect, the total emission factor of VOCs (expressed as ∑EF) was declined for China IV LDGPVs relative to China III vehicles, with the decreasing extent determined as 24.8%. Among LDGPVs with engine capacity of <1.5 L, 1.5-2.5 L and >2.5 L, it is for 1.5-2.5 L that the lowest ∑EF (101.2 ± 70.1 mg/L) was determined. Along with the increasing of cumulative mileage, the EF values of VOCs were uplift, and the ∑VOCs increased almost linearity, while the increasing trend became steady for ∑EF when the cumulative mileage more than 8 × 104 km. As for different driving conditions, the sequence of EFs from high to low are presented as acceleration (239.3 ± 203.5 mg/L) >idle (226.1 ± 195.6 mg/L) >deceleration (218.5 ± 193.1 mg/L)>uniform (218.2 ± 182.5 mg/L)>slide (176.1 ± 165.2 mg/L). While the cruising speeds increased between 15 and 50 km/h, the linearity between ΣEF declined; however, an opposite trend was seen when the acceleration rates increased. In the chemical speciation, the reactive aromatics contributed the largest fraction of the VOCs (>40%). The ratio of benzene to toluene (B/T) was loaded in high interval (0.94-1.33) in this paper. Larger fluctuation of coefficient of variation (CV) was found among different cumulative mileage and displacement. By taking into account the deterioration effect related to cumulative mileage, emissions of butene, pentene, benzene and toluene were 42.9 Gg, 49.7 Gg, 109.6 Gg and 51.9 Gg in respect for LDGPVs that composed of China III and China IV. CONCLUSIONS: Our results demonstrated the necessaries in upgrading the emission control technology. Cumulative mileages and displacements are the two most dominated factors that impacted on the EFs and chemical profiles of VOCs. The higher emission of benzene estimated demonstrated the possible elevation of VOCs in comparison with previous studies.

Age and training volume influence joint kinetics during running.

Recently, we proposed the hypothesis that weekly running volume and preferred running pace may play a role in preserving ankle joint kinetics in middle-age runners as ankle joint kinetics were generally similar in young and middle-aged runners with similar running volume and preferred pace. To further address this hypothesis, we compared lower extremity joint kinetics between high and low training volume runners in both young and middle-aged groups. Joint kinetics calculated from 3D kinematic and ground reaction force data during over-ground running at 2.7 m·s-1 from young and middle-aged runners who ran low or high weekly volume were analyzed. A two-factor analysis of variance was used to compare joint kinetics between age and running volume groups. Positive hip work was greater in middle-aged compared to young runners (P = .005). Plantarflexor torque (P = .009) and positive ankle work (P = .042) were greater in young compared to middle-aged runners. Positive ankle work was also greater in the high compared to the low volume group (P = .021). Finally, age by volume interactions were found for knee extensor torque (P = .024), negative knee work (P = .018), and positive knee work (P = .019) but not for ankle and hip joint kinetics. These findings suggest less distal-to-proximal difference in positive joint work with high running volume in both young and middle-aged runners as a result of greater power generation at the ankle. Given the age main effects, our findings are also the first to suggest the age-related distal-to-proximal shift in joint kinetics appears in middle-aged runners.

Driving propensity and vehicle lifetime mileage: A quantile regression approach.

This study aims to determine the relationship between motor vehicle lifetime and lifetime mileage, while considering drivers' propensity to drive and its effects on vehicle CO2 emissions. To do this, we analyze the relationship between lifetime mileage and vehicle lifetime for two vehicle types; a hybrid and a gasoline vehicle. We also employ a quantile regression approach to estimate the effects of drivers' propensity to drive on lifetime mileage. By estimating the CO2 emissions based on driver's propensity to drive, we analyze the effects of propensity to drive on vehicle CO2 emissions. Our results show that, for drivers who drive longer distances, the rate of decrease in average mileage grows as the vehicle age increases. Further, the results of our analysis, which considers this decrease in mileage, show that the cumulative CO2 emissions calculated under the assumption of uniform average annual mileage have been overestimated. The actual lifecycle CO2 emissions for the Prius are therefore smaller than previously reported by the previous studies, leading us to conclude that the hybrid is a more environmentally friendly vehicle than previously thought. Those of the Premio as a conventional gasoline vehicle, however, is approximately twice that. We suggest that vehicle lifecycle assessments should take into account the annual decrease in mileage demonstrated in this study.

The Use of Telematics Devices to Improve Automobile Insurance Rates.

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay-as-you-drive sample of insureds. We include distance traveled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

Risk-Exposure Density and Mileage Bias in Crash Risk for Older Drivers.

Crash rates per mile indicate a high risk of vehicle crash in older drivers. A reliance on mileage alone may underestimate the risk exposure of older drivers because they tend to avoid highways and travel more on nonfreeways (e.g., urban roads), which present greater hazards. We introduce risk-exposure density as an index of exposure that incorporates mileage, frequency of travel, and travel duration. Population-wide driver fatalities in the United States during 2002-2012 were assessed according to driver age range (in years: 16-20, 21-29, 30-39, 40-49, 50-59, 60-69, ≥70) and sex. Mileage, frequency, and duration of travel per person were used to assess risk exposure. Mileage-based fatal crash risk increased greatly among male (relative risk (RR) = 1.73; 95% CI: 1.62, 1.83) and female (RR = 2.08; 95% CI: 1.97, 2.19) drivers from ages 60-69 years to ages ≥70 years. Adjusting for their density of risk exposure, fatal crash risk increased only slightly from ages 60-69 years to ages ≥70 years among male (RR = 1.09; 95% CI: 1.03, 1.15) and female (RR = 1.22; 95% CI: 1.16, 1.29) drivers. While ubiquitous in epidemiologic research, mileage-based assessments can produce misleading accounts of driver risk. Risk-exposure density incorporates multiple components of travel and reduces bias caused by any single indicator of risk exposure.

Size-segregated emissions and metal content of vehicle-emitted particles as a function of mileage: Implications to population exposure.

The study aims at investigating the characteristics (size distribution, active surface and metal content) of particles emitted by cars as a function of mileage using a novel methodology for characterizing particulate emissions captured by Exhaust Gas Suspension (EGS). EGS was obtained by passing the exhaust gases through a container of deionized water. EGS analysis was performed using laser granulometry, electron scanning microscopy, and high resolution mass spectrometry. Implications of the differences in key features of the emitted particles on population exposure were investigated using numerical simulation for estimating size-segregated PM deposition across human respiratory tract (HRT). It was found that vehicle mileage, age and the respective emissions class have almost no effect on the size distribution of the exhaust gas particulate released into the environment; about half of the examined vehicles with low mileage were found to release particles of aerodynamic diameter above 10 µm. The exhaust gas particulate detected in the EGS of all cars can be classified into three major size classes: (1) 0.1-5 µm - soot and ash particles, metals (Au, Pt, Pd, Ir); (2) 10-30 µm - metal (Cr, Fe, Cu, Zr, Ni) and ash particles; (3) 400-1,000 µm - metal (Fe, Cr, Pb) and ash particles. Newer vehicles with low mileage are substantial sources of soot and metal particles with median diameter of 200 nm with a higher surface area (up to 89,871.16 cm(2)/cm(3)). These tend to deposit in the lower part of the human respiratory tract.

Experimental Dust Absorption Study in Automotive Engine Inlet Air Filter Materials.

The purpose of this study was to empirically evaluate the performance of fibrous materials that meet the criteria for inlet air filtration in internal combustion engines. The characteristics of filtration efficiency and accuracy, as well as the characteristics of flow resistance, were determined based on the mass of dust accumulated in the filter bed during the filtration process. Single-layer filter materials tested included cellulose, polyester, and glass microfiber. Multilayer filter media such as cellulose-polyester-nanofibers and cellulose-polyester were also examined. A new composite filter bed-consisting of polyester, glass microfiber, and cellulose-and its filtration characteristics were evaluated. Utilizing specific air filtration quality factors, it was demonstrated that the composite is characterized by high pre-filtration efficiency (99.98%), a short pre-filtration period (qs = 4.21%), high accuracy (dpmax = 1.5-3 µm) for the entire lifespan of the filter, and a 60-250% higher dust absorption coefficient compared to the other tested materials. A filtration composite bed constructed from a group of materials with different filtration parameters can be, due to its high filtration efficiency, accuracy, and dust absorption, an excellent filter material for engine intake air. The composite's filtration parameters will depend on the type of filter layers and their order relative to the aerosol flow. This paper presents a methodology for the selection and testing of various filter materials.

Potential assessment of CO2 source/sink and its matching research during CCS process of deep unworkable seam.

It is of great significance for the engineering popularization of CO2-ECBM technology to evaluate the potential of CCUS source and sink and study the matching of pipeline network of deep unworkable seam. In this study, the deep unworkable seam was taken as the research object. Firstly, the evaluation method of CO2 storage potential in deep unworkable seam was discussed. Secondly, the CO2 storage potential was analyzed. Then, the matching research of CO2 source and sink was carried out, and the pipe network design was optimized. Finally, suggestions for the design of pipe network are put forward from the perspective of time and space scale. The results show that the average annual CO2 emissions of coal-fired power plants vary greatly, and the total emissions are 58.76 million tons. The CO2 storage potential in deep unworkable seam is huge with a total amount of 762 million tons, which can store CO2 for 12.97 years. During the 10-year period, the deep unworkable seam can store 587.6 million tons of CO2, and the cumulative length of pipeline is 251.61 km with requiring a cumulative capital of $ 4.26 × 1010. In the process of CO2 source-sink matching, the cumulative saving mileage of carbon sink is 98.75 km, and the cumulative saving cost is $ 25.669 billion with accounting for 39.25% and 60.26% of the total mileage and cost, respectively. Based on the three-step approach, the whole line of CO2 source and sink in Huainan coalfield can be completed by stages and regions, and all CO2 transportation and storage can be realized. CO2 pipelines include gas collection and distribution branch lines, intra-regional trunk lines, and interregional trunk lines. Based on the reasonable layout of CO2 pipelines, a variety of CCS applications can be simultaneously carried out, intra-regional and inter-regional CO2 transport network demonstrations can be built, and integrated business models of CO2 transport and storage can be simultaneously built on land and sea. The research results can provide reference for the evaluation of CO2 sequestration potential of China's coal bases, and lay a foundation for the deployment of CCUS clusters.

Understanding pre-crash speed sampling by the VAG Event Data Recorder.

European-sold vehicles of the Volkswagen Audi Group (VAG) have an accessible Event Data Recorder (EDR) since 2018. One of the most important data elements for accident reconstruction is "Speed, Vehicle Indicated": a series of 11 samples of the driving speed over a 5 s pre-crash time period. The research identifies the specific source of the data element and its offset. We found that the EDR-recorded values originate from specific CAN transmissions by the instrument panel. Having identified the data source allowed us to efficiently investigate the properties of that data source, without having to invoke activation the Event Data Recorder. In that manner we assessed for our case vehicle - a Volkswagen Golf R of model year 2019 - that the recorded speed relates to the average of the wheel speeds of both front wheels, and has a 5% offset to real speed. Knowing that the EDR uses data transmissions from the instrument panel, we consider manipulation of the instrument panel as a possible source for error. Our case vehicle was fitted with a so-called "mileage blocker" that affects the rate at which the odometer reading increases. We unveiled the operation of the device and concluded that it doesn't affect the EDR-recorded speed values.

Driver mileage and accident involvement: A synthesis of evidence.

The relationship between driver mileage and accident involvement has been a controversial topic for at least 20 years. The key issue is whether driver accident involvement rate increases in proportion to miles driven or has a non-linear relationship to miles driven. This paper presents a synthesis of evidence from studies of how the number of accidents per driver per unit of time relates to distance driven in the same period. Most studies of this relationship are methodologically weak and their results highly inconsistent and potentially misleading. Unreliable data and poor control for confounding factors characterise most studies. Only a few studies based on multivariate statistical models control for at least some of the confounding factors that may influence the relationship between distance driven and accident involvement. These studies consistently show that the number of accidents per driver per year increases less than in proportion to distance driven. A good approximation is that the number of accidents per driver per unit of time is proportional to the square root of distance driven. Potential methodological and substantive explanations of this finding are discussed.

A probabilistic bias analysis on the magnitude of unmeasured confounding: The impact of driving mileage on road traffic crashes.

INTRODUCTION: Unmeasured confounding can lead to biased interpretations of empirical findings. This paper aimed to assess the magnitude of suspected unmeasured confounding due to driving mileage and simulate the statistical power required to detect a discrepancy in the effect of polypharmacy on road traffic crashes (RTCs) among older adults. METHODS: Based on Monte Carlo Simulation (MCS) approach, we estimated 1) the magnitude of confounding of driving mileage on the association of polypharmacy and RTCs and 2) the statistical power of to detect a discrepancy from no adjusted effect. A total of 1000 studies, each of 500000 observations, were simulated. RESULTS: Under the assumption of a modest adjusted exposure-outcome odds ratio of 1.35, the magnitude of confounding bias by driving mileage was estimated to be 16% higher with a statistical power of 50%. Only an adjusted odds ratio of at least 1.60 would be associated with a statistical power of about 80% CONCLUSION: This applied probabilistic bias analysis showed that not adjusting for driving mileage as a confounder can lead to an overestimation of the effect of polypharmacy on RTCs in older adults. Even considering a large sample, small to moderate adjusted exposure effects were difficult to be detected.

On-road mileage-based emission factors of gaseous pollutants from bi-fuel taxi fleets in China: The influence of fuel type, vehicle speed, and accumulated mileage.

Urban taxis tend to be high mileage vehicles, and therefore potentially a significant contributor to local air pollution. To investigate the on-road emissions of gaseous air pollutants from urban taxi fleets, the mileage-based emission factors (EFs) for 20 gasoline/CNG bi-fuel taxis in China were measured using the in situ monitoring system. It was found that shifting the taxis' fuel from gasoline to CNG could cause significant emissions reduction in CO, slight drop in HC and small increase in NOx. With the fuel shift from China-VI gasoline to GB 18047-2017 CNG, approximately 82% reduction in CO, 14% reduction in HC and about 14% more NOx was observed. With fuel shift from China-III gasoline to GB 18047-2000 CNG, the reductions were 65%, 6% and 11%, respectively. When the China-VI gasoline-fueled taxis travel at higher speed (60 km/h or higher), the vehicles emit approxiamtly ten times more CO than that at lower speed (20-50 km/h). The mileage-based NOxEFs was strongly and positively related to the vehicles' speed for the taxis fueled by China-VI gasoline and GB 18047-2017 CNG. The ratio of NOx and HC emissions from gasoline-fueled taxis is lower than that from CNG-fueled taxis. Considerable reductions of the mileage-based EFs of CO, HC, and NOx from China-VI gasoline- and GB 18047-2017 CNG-fueled bi-fuel taxis were recorded. The taxi vehicles with higher accumulated mileage (greater than 200,000 km) were found with higher CO and HC emissions, due to the deterioration of vehicle engine performance and the exhaust catalyst system.

Effective emissions reduction of high-mileage fleets through a catalytic converter and oxygen sensor replacement program.

High-mileage vehicles such as taxis make disproportionately large contributions to urban air pollution due to their accelerated engine deterioration rates and high operation intensities despite their small proportions of the total fleet. Controlling emissions from these high-mileage fleets is thus important for improving urban air quality. This study evaluates the effectiveness of a pilot repair program in reducing emissions from taxis in Hong Kong which account for about 2 % of the total licensed vehicles. The emission factors of a large sample of 684 in-service taxis (including 121 for an emission survey program and 563 for a pilot repair program) were measured on transient chassis dynamometers. The results showed that 63 % of the sampled taxis failed the driving cycle test before the pilot repair program. Most of failed taxis were NO related and 91 % of failed taxis exceeded the emission limits of at least two regulated pollutants simultaneously. After the pilot repair program by replacing catalytic converters and oxygen sensors, the failure rate was significantly reduced to only 7 %. In addition, the fleet average NO, HC and CO emission factors were reduced by 85 %, 82 % and 56 %, respectively. In addition, on-road remote sensing measurements confirmed the real-world emission reductions from the taxis that participated in the pilot repair program. These findings led to the implementation of a large-scale replacement program for all taxis in Hong Kong during 2013-2014, which was estimated to have reduced the total HC, CO and NO emissions by about 420, 2570 and 1000 t per year, respectively (equivalent to 5-8 % emission reductions from the whole road transport sector). Therefore, reducing emissions from the small high-mileage fleets is a highly cost-effective measure to improve urban air quality.

Exploring the efficacy of traditional and alternative funding mechanisms to provide transportation revenue during the COVID-19 pandemic.

Steep reduction in motor vehicle travel during the COVID-19 pandemic has plummeted the fuel sales affecting the revenue streams of state Departments of Transportation (DOTs) across the US. The objective of this study is to investigate the effectiveness of a number of user-based and general revenue generation mechanisms in reducing the transportation revenue shortfall or providing more stable revenue during a pandemic. State policies and pilot programs as well as public perception studies are reviewed to develop reasonable scenarios of tax and fee schemes, and price elasticity estimates are used to account for the effect of higher travel cost on demand for travel. We specifically focus on the adverse impacts of the COVID-19 pandemic on the transportation revenue in North Carolina (NC) using data from January to October 2020. The results indicate that monthly transportation revenue in NC could not have been sustained by increasing the state motor fuels tax up to 50% or motor vehicle fees up to 100%. On the other hand, increasing the highway use tax (state vehicle sales tax) from 3% to 8% would have eliminated the monthly shortfall in the state transportation revenue. Replacing the state fuels tax by mileage-based user fees could not bridge the gap between the monthly collected and projected state transportation revenue, even for high per-mile charges for passenger vehicles and trucks. Promising results are found for instituting an additional 0.75% state sales tax dedicated to general transportation use which could have provided adequate funding to eliminate the monthly shortfall in transportation revenue in NC during the COVID-19 pandemic. Dependence on state sale and use tax for transportation revenue is preferred and would lead to a lower shortfall compared to the motor fuels tax in a pandemic.

An in situ exploratory analysis of diesel cars' emission: way forward on policy evaluation.

Keeping in view the significant number of diesel-driven passenger cars in the existing light motor vehicle fleet in Delhi, India, a case study on smoke emission measurement from 460 number of such cars was conducted. Smoke exhaust data was collected from the diesel cars while the vehicles presented themselves for periodic renewal of pollution under control (PUC) certification at authorized emission testing centers across Delhi, India. Along with the smoke emission, various vehicle- and engine-related aspects, supposed to affect tailpipe smoke emission, were also recorded aiming at data analysis for two datasets, namely whole and top 5 makes. The smoke density under no-loading condition in the free acceleration test mode was measured. The study reported a strong correlation between vehicle parameters, such as age, mileage, maintenance category, emission norm, and engine aspiration; and the smoke emission (R2 values for vehicle age and mileage vs. smoke emission for whole dataset = 0.872 and 0.873, respectively). Top 5 make-wise correlations fared even better (R2 for age and mileage vs. emission in the range of 0.85-0.92 and 0.86-0.93, respectively). Further, the predictive emission equations using best-fit trendlines were also developed for both datasets. Such equations may be used by the car manufacturers to adopt a suitable strategy for tuning of engine or vehicle as such, to retain their cars in the longer state of compliance to the extant emission norms. Further, the study recommends to include vehicle mileage as an important factor in upgrading the existing inspection and maintenance programs, especially in the developing countries.

Bias-corrected estimators for proportion of true null hypotheses: application of adaptive FDR-controlling in segmented failure data.

Two recently introduced model-based bias-corrected estimators for proportion of true null hypotheses ( π 0 ) under multiple hypotheses testing scenario have been restructured for random observations under a suitable failure model, available for each of the common hypotheses. Based on stochastic ordering, a new motivation behind formulation of some related estimators for π 0 is given. The reduction of bias for the model-based estimators are theoretically justified and algorithms for computing the estimators are also presented. The estimators are also used to formulate a popular adaptive multiple testing procedure. Extensive numerical study supports superiority of the bias-corrected estimators. The necessity of the proper distributional assumption for the failure data in the context of the model-based bias-corrected method has been highlighted. A case-study is done with a real-life dataset in connection with reliability and warranty studies to demonstrate the applicability of the procedure, under a non-Gaussian setup. The results obtained are in line with the intuition and experience of the subject expert. An intriguing discussion has been attempted to conclude the article that also indicates the future scope of study.

Long-term effects of shoe mileage on knee and ankle joints muscle co-contraction during walking in females with genu varus.

BACKGROUND: Shoe mileage may influence the risk of sustaining injuries during walking. RESEARCH QUESTION: What are the effects of shoe mileage on knee and ankle muscle co-contraction during walking in females with genu varus? METHODS: Fifteen healthy and 15 women diagnosed with genu varus received a new pair of running shoes. They were asked to wear these shoes over 6 months. Pre and post intervention, muscle activities of the dominant limb were recorded during a walking test at preferred gait speed. Two dependent variables were assessed to examine muscle co-contraction: (1) directed co-contraction ratios of agonists and antagonists, and (2) general joint muscle co-contraction. FINDINGS: Results demonstrated significant main effects of the "shoe" factor for general ankle co-contraction during the push-off phase (p = 0.013, d = 1.503). Irrespective of experimental group, paired comparisons revealed significantly lower general ankle co-contraction during the push-off phase after the intervention. A significant main effects of "shoe" for general knee co-contraction during loading phase (p = 0.025, d = 0.895) was also observed. In both groups, paired comparison revealed significantly lower general knee co-contraction during the push-off phase in the post condition. We did not find any significant main effect of group nor group-by-shoe interaction for general ankle co-contraction during the stance phase. Likewise, we did not observe any significant main effect of "shoe", "group" and "group-by-shoe" interaction for mediolateral directed knee co-contraction during stance phase of walking (p > 0.05). SIGNIFICANCE: Our findings showed that the shoe mileage but not the genu varus condition affects the general and directed co-contraction of the muscles stabilizing the knee and ankle joints. Together with the observed findings on ankle and knee muscle co-contraction, it is essential to change running shoes after a long wearing time in both healthy and genu varus females.

TRODO: A public vehicle odometers dataset for computer vision.

In the field of transportation and logistics, smart vision systems have been employed successfully to automate various tasks such as number-plate recognition and vehicle identity recognition. The development of such automated systems is possible with the availability of large image datasets having proper annotations. The TRODO dataset is a rich-annotated collection of odometer displays that can enable automatic mileage reading from raw images. Initially, the dataset consisted of 2613 frames captured in different conditions in terms of resolution, quality, illumination and vehicle type. After data pre-processing and cleaning, the number of images was reduced to 2389. The images were annotated using the CVAT image annotation tool. The dataset provides the following information for each frame: the type of odometer (analog or digital), the mileage value displayed on the odometer, the bounding boxes of the odometer, and the digits and characters displayed on the screen. Combined with machine learning and artificial intelligence, the TRODO dataset can be used to train odometer classifiers, digit recognition and number reading models from odometers and similar types of displays.

Distribution Network for the Last Mile of Cross-Border E-business in a Smart City at Emerging Market in Response to COVID-19: A Key Node Analysis Based on a Vision of Fourth Party Logistics.

This research studies the development of distribution networks for the last mile distribution for cross-border E-business based on a vision of fourth party logistics (4PL) in smart cities in emerging markets in response to COVID-19. This research analyzes the distribution centers of distribution companies in Beijing city using fuzzy cluster analysis as a case study of smart cities. The location decision for distribution centers to serve cross-border E-business is further analyzed by considering the local conditions of the distribution centers. The solutions to the location decisions for distribution centers in different cases are further visualized by 2-mode networks. The key nodes in the distribution network of the last mile for cross-border E-business are further studied based on fourth-party logistics by a immune algorithm. Cross-border E-business value creation based on the development of distribution networks using fourth-party logistics is further discussed. The location distribution of key nodes can spread from the downtown district to suburban areas as the coverage of the distribution network is expanded. This research can help managers and decision makers address the last mile distribution for cross-border E-business in smart cities in emerging markets based on a vision of fourth-party logistics in response to COVID-19.