Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

The prevalence of human immunodeficiency virus (AIDS) and hepatitis B virus among heavy truck drivers and their assistants has been well documented globally in correlation with their behavioral characteristics. The present study aimed to screen for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and behavioral characteristics among heavy truck drivers in Port Sudan. A cross-sectional study was conducted on 274 heavy truck drivers and their assistants who used the highway Port Sudan-Khartoum in Port Sudan city during 2019-2021. Data on behavioral characteristics and substance use habits were collected using a structured questionnaire, and an ELISA test was used to screen for HIV and HBV infections in the study participants. The chi-square test, odds ratio, and confidence intervals were used to find the association between behavioral characteristics and seropositive HIV/HBV. Of the 274 enrolled participants, the seroprevalence rates of HIV were 2.7% and HBV was 23.7%. Ninety-four (34.3%) of them had a history of high-risk sexual behavior outside of marriage;only two (0.7%) used condoms;14.2% of participants reported alcohol use;and 1.1% reported drug use. Univariate analysis revealed that having a sex history outside of marriage with ≥1 sex partner and never using a condom with a spouse or casual partner were significant risk factors for HIV and HBV among drivers. Fortunately, we found that most of the drivers reported low alcohol and drug use. Concerning this study, the seroprevalence of HIV and HBV is highly associated with a history of having sex outside of marriage and sexual behavior among truck drivers and assistances. Additional studies are needed to further investigate other STIs and behavioral characteristics associated with factors in truck drivers/assistance in different truck stop regions in Sudan.

A Framework for Driver DrowsinessMonitoring Using a Convolutional Neural Network and the Internet of Things

One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.

Leading Pattern of Spring Drought Variability over East Asia and Associated Drivers

Drought events have become more frequent and intense over East Asia in recent decades,leading to huge socioeconomic impacts.Although the droughts have been studied extensively by cases or for individual regions,their leading variability and associated causes remain unclear.Based on the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis product from 1979 to 2020,this study evealuates the severity of spring droughts in East Asia and investigates their variations and associated drivers.The results indicate that North China and Mongolia have experienced remarkable trends toward dryness during spring in recent decades,while southwestern China has witnessed an opposite trend toward wetness.The first Empirical Orthogonal Function mode of SPEI variability reveals a similar seesawing pattern,with more severe dryness in northwestern China,Mongolia,North China,South Korea,and Japan but increased wetness in Southwestern China and southeast Asia.Further investigation reveals that the anomalously dry(wet)surface in North(Southwestern)China is significantly associated with anomalously high(low)temperature,less(more)precipitation,and reduced(increased)soil moisture during the previous winter and early spring,regulated by an anomalous anticyclone(cyclone)and thus reduced(increased)water vapor convergence.The spring dry-wet pattern in East Asia is also linked to cold sea surface temperature anomalies in the central-eastern Pacific.The findings of this study have important implications for improving the prediction of spring drought events in East Asia.

OSMR is a potential driver of inflammation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disease,and the molecular mechanism underlying its pathology remains poorly understood.However,inflammation is known to play an important role in the development of this condition.To identify driver genes that affect the inflammatory response in amyotrophic lateral sclerosis,as well as potential treatment targets,it is crucial to analyze brain tissue samples from patients with both sporadic amyotrophic lateral sclerosis and C9orf72-related amyotrophic lateral sclerosis.Therefore,in this study we used a network-driven gene analysis tool,NetBID2.0,which is based on SJARACNe,a scalable algorithm for the reconstruction of accurate cellular networks,to experimentally analyze sequencing data from patients with sporadic amyotrophic lateral sclerosis.The results showed that the OSMR gene is pathogenic in amyotrophic lateral sclerosis and participates in the progression of amyotrophic lateral sclerosis by mediating the neuroinflammatory response.Furthermore,there were differences in OSMR activity and expression between patients with sporadic amyotrophic lateral sclerosis and those with C9orf72-related amyotrophic lateral sclerosis.These findings suggest that OSMR may be a diagnostic and prognostic marker for amyotrophic lateral sclerosis.

Statistical Analysis of Accident Proneness of Drivers

A mathematical model describing the risks of road accidents has been built on the basis of statistical data of drivers’ accident rate. It has been revealed that drivers can be divided by the degree of their accident proneness into four categories with sharply differing probabilities of road accidents. It has been shown that there is a possibility of classification of drivers in accordance with specified categories.

Sonam Wangta:A Driver on the Worlds Highest Train Line

In midsummer,the banks of the Yarlung Zangbo River were covered in lush green.Bathed in the bright plateau sunshine,the Fuxing bullet train ran through the mountains and valleys like a long green dragon.In the cab,Sonam Wangdrak was holding the brake handle,his eyes fixed on the horizon ahead and his handsome face full of confidence.

Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

Higher Plasma Potassium Level Reduces 10-Year Cardiovascular Disease Risk Predicted by the Framingham Risk Score among Taxi-Motorbike Drivers Residing and Working in Cotonou, Benin

Hypertension, obesity, smoking, dyslipidemia, and type 2 diabetes (T2D) are the major risk factors for developing cardiovascular diseases (CVD). Recent studies revealed that taxi-motorbike drivers (TMDs) in Cotonou had higher rates of CVD risk factors, but their impacts on cardiovascular events have rarely been studied. The Framingham risk score (FRS) is an algorithm that considers CVD risk factors and estimates the risk of developing CVD in the next 10 years. Our objectives were to assess the 10-year CVD risk predicted by the FRS, and to examine the relationships of 10-year CVD risk with plasma iron and potassium levels among TMDs. We included 134 TMDs (22 - 59 years old) who had no prior diagnosis of CVD or T2D, and not taking medications affecting iron and potassium homeostasis. Conventional cardiovascular risk factors were used to calculate the 10-year CVD risk, which was categorized as low (20%). FRS > 2%, which corresponded to the 75th percentile of FRS distribution in our study population, was used as a cut-off value to classify participants into two groups. Plasma iron and potassium levels were segregated into tertiles and their associations with 10-year CVD risk were quantified by multivariate-adjusted logistic regression to calculate the odd ratios (ORs) to being above the 75th percentile of 10-year CVD risk with the corresponding 95% confidence intervals (CIs). We found that 62.0% of participants had at least one of cardiovascular risk factors. Approximately 97.8% of TMDs had 10-year CVD risk 4.8 mmol/L led to an 83% risk reduction of having 10-year CVD risk > 2% (OR = 0.17, 95% CI: 0.04 - 0.82, P = 0.027). In conclusion, our findings showed that high plasma potassium levels associate with reduced 10-year CVD risk among TMDs. Interventions focused on monitoring of plasma potassium, particularly in those with existing cardiovascular risk factors, may help prevent CVD.

Analysis of rear-end risk for driver using vehicle trajectory data

To explore the relationship between rear-end crash risk and its influencing factors, on-road experiments were conducted for measuring the individual vehicle trajectory data associated with novice and experienced drivers. The rear-end crash potential probability based on the time to collision was proposed to represent the interpretation of rear-end crash risk.One-way analysis of variance was applied to compare the rearend crash risks for novice and experienced drivers. The rearend crash risk models for novice and experienced drivers were respectively developed to identify the effects of contributing factors on the driver rear-end crash risk. Also, the cumulative residual method was used to examine the goodness-of-fit of models. The results show that there is a significant difference in rear-end risk between the novice and experienced drivers.For the novice drivers, three risk factors including the traffic volume, the number of lanes and gender are found to significantly impact on the rear-end crash risk, while significant impact factors for experienced drivers are the vehicle speed and traffic volume. The rear-end crash risk models perform well based on the existing limited data samples.

A 64-Step Gray Scale Driver Chip for a 132×64-Pixel Passive Matrix OLED

A mixed-signal driver chip for a 132 × 64-pixel passive matrix OLED panel is presented. The chip has a 64-step gray scale control using the PWM method and two-step voltage pre-charge technology to pre-charge the OLED pixels. It consists of a digital controller,SRAM for display data memory,a DC-DC voltage converter,reference current generators,a pre-charge voltage generator,64 common drivers, and 132 segment drivers. The single chip is a typical current-drive circuit. It has been implemented in a Chartered 0.35/μm 18V HV （DDD） CMOS process with a die area of 10mm× 2mm. Test results show that the power consumption of the whole chip and all pixels with a constant driving current of 100μA while displaying the highest gray scale is 294mW with a 12V high voltage supply and a 3V low voltage supply.

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Driver identification in intelligent transport systems has immense demand,considering the safety and convenience of traveling in a vehicle.The rapid growth of driver assistance systems(DAS)and driver identification system propels the need for understanding the root causes of automobile accidents.Also,in the case of insurance,it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing.It is observed that drivers with frequent records of paying“fines”are compelled to pay higher insurance payments than drivers without any penalty records.Thus driver identification act as an important information source for the intelligent transport system.This study focuses on a similar objective to implement a machine learning-based approach for driver identification.Raw data is collected from in-vehicle sensors using the controller area network(CAN)and then converted to binary form using a one-hot encoding technique.Then,the transformed data is dimensionally reduced using the Principal Component Analysis(PCA)technique,and further optimal parameters from the dataset are selected using Whale Optimization Algorithm(WOA).The most relevant features are selected and then fed into a Convolutional Neural Network(CNN)model.The proposed model is evaluated against four different use cases of driver behavior.The results show that the best prediction accuracy is achieved in the case of drivers without glasses.The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors(KNN)and Support Vector Machines(SVM)models with and without using dimensionality reduction approaches.

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.

Research on selection methods of cost driver

In the implementation of activity-based costing, cost driver is the true reason to determine the occurrence of costs and the consumption of resources, and correctly selecting the cost driver is the key to calculating the cost of the product accurately. Beginning with the meaning of cost driver and its characteristics, this article analyses its classification, and then, focuses on its selection method.

Prevalence and Factors Associated with Psychoactive Substance Misuse among Heavy Goods Vehicle Drivers in Cotonou, Benin

Introduction: In Benin, Heavy Goods Vehicle (HGV) drivers play an essential role in the logistics chain, facilitating the transportation of goods within the country or between other countries in the sub-region. HGV drivers are professionals who experience adverse working conditions, exposing them to risky behaviours, including Psychoactive Substance (PAS) misuse, leading to particularly severe road accidents. This study aimed to determine the prevalence of PAS misuse among HGV drivers and identify associated factors in Cotonou, Benin. Materials and Methods: We conducted a cross-sectional survey involving HGV drivers at parking areas in Cotonou from 26 March to 10 April 2023. The dependent variable was the PAS misuse by HGV drivers, and the independent variables were related to their socio-demographic characteristics, health status and lifestyle habits, and professional characteristics. Data analysis involved determining the prevalence of PAS misuse with a 95% Confidence Interval (95% CI). Subsequently, we identified factors associated with PAS misuse among the participants using multivariate logistic regression. We presented the final regression results as adjusted odds ratios (aOR) with 95% CI. Results: The study included 425 HGV drivers. Among them, 53 (12.47%, 95% CI = 9.64 - 15.98) were misusing PAS. Compared to drivers aged 35 and older, those aged under 25 (aOR = 10.67, 95% CI = 3.56 - 32.03) and those aged 25 to 34 (aOR = 3.47, 95% CI = 1.37 - 8.82) had higher odds of PAS misuse. Drivers with a primary education were less likely (aOR = 0.43, 95% CI = 0.19 - 0.97) to misuse PAS than those with no formal education. Drivers suffering from cardiovascular diseases were also three times more likely (aOR = 3.08, 95% CI = 1.08 - 8.81) to misuse PAS than others. The odds of PAS misuse were also higher among drivers who reported taking breaks than those claiming not to (aOR = 3.11, 95% CI = 1.57 - 6.18). Conclusion: Driving under the influence of PAS is a risk factor for road accidents, associated with other factors highlighted in this study. Addressing these factors in prevention strategies through integrated approaches could lead to more effective results.

Impact of Manipulative Treatment on Professional Drivers

The experimental research, presented in the study, focuses on track tests with the aim of highlighting changes in lap times after manipulative treatment of drainage of the glymphatic system and stimulation of the sympathetic nervous system. Introduction: The experimental research, presented in this study, focuses on analyzing the potential effects of a manipulative treatment on the performance of a professional driver. The main objective is to evaluate the change in lap times after the application of the treatment, trying to understand whether it can actually positively influence the driver’s performance. The study stands an important opportunity to extend knowledge, regarding the use of manipulative therapies in the context of optimized driving skills. The results obtained could provide useful insights and contribute to improving the performance of professional drivers by offering new perspectives and strategies to improve their performance. Leveraging a rigorous scientific approach and a sample of highly skilled drivers, the research aims to provide concrete evidence on the effectiveness of manipulative treatment in driving skills. Monitoring lap times before and after the intervention also capture any temporary or long-term effects of the treatment, ensuring a thorough and reliable analysis of the results. Materials and methods: 15 professional drivers, aged 18 to 36 years, with at least 10 years of experience as drivers, participated in this study. The test consisted of analyzing lap times before and after treatment.

Exploring the drivers of urban expansion in a medium-class urban agglomeration in India using the remote sensing techniques and geographically weighted models

Rapid urbanization urges the immediate attention of policymakers to ensure sustainable city development.Under-standing the urban growth drivers is essential to address effective strategies for urbanization-related challenges.This work aims to study Raiganj’s urban development and the factors associated with this expansion.This study employed global logistic regression(LR)and geographical weighted logistic regression(GWLR)to explore the role of different factors.The results showed that the role of the central business district(covariate>-1),commercial market(covariate>-3),and police station(covariate>-4)were significant to the development of new built-up areas.In the second period,major roads(covariate>-2)and new infrastructures(covariate>-4)became more relevant,particularly in the eastern and southern areas.GWLR was more accurate in assessing the different fac-tors’impact than LR.The results obtained are essential to understanding urban expansion in India’s medium-class cities,which is critical to effective policies for sustainable urbanization.

Robot driver for vehicle durability emission test on chassis dynamometer

A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simulation strategy and the tele-monitoring systembased on Browser/Client structure are described. Furthermore, the construction of the robot driver,vehicle performance self-learning algorithm, multi-mode vehicle control model and vehicle speedtracking strategy based on fuzzy logic arealso discussed. Besides, the capability of controlparameters self-compensation on-line makes it possible to compensate the wear of vehicle componentsand the variety of clutch true bite point during the long term test. Experimental results show thattherobot driver can be applicable to a wide variety of vehicles and the obtained results stay withina tolerance band of ± 2 km/h. Moreover the robot driver is able to control tested vehicles withgood repeatability and consistency; therefore, this methodpresents a solution to eliminate theuncertainty of emission test results by human drivers and to ensure the accuracy and reliability ofemission test results.

Two-Stage Driving Circuit for One-Chip TFT-LCD Driver IC

A two-stage driving circuit of a one-chip TFT-LCD driver IC for portable electronic devices is proposed. The driving buffers of the new circuit are built in the γ-correction circuit rather than in the source driver. The power consumption,die area, and driving capability of the driving circuit are discussed in detail. For a two-stage driving circuit with 13 driving buffers, the settling time of the driving voltage within 0.2% error is about 19.2μs when 396 pixel-loads are driven by the same grayscale voltage. The quiescent current of the whole driving circuit is 518μ~A,and the power consumption can be reduced by 77%. The proposed driving circuit is successfully applied in a 132RGB × 176-dot,260k color one-chip driver IC developed by us for the TFT-LCD of mobile phone, and it can also be used in other portable electronic devices, such as PDAs and digital cameras.

Environmental complaint insights through text mining based on the driver,pressure,state,impact,and response(DPSIR)framework:Evidence from an Italian environmental agency

Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.