Facebook use and its predictive factors among students: Evidence from a lower- and middle-income country, Bangladesh

Background:Facebook is a popular social networking site in the modern world. It has an adverse effect such as impairing daily health and psychological health and also interpersonal relationships when the use becomes problematic.AimsTo examine problematic Facebook use (PFU) and its predictors among Bangladeshi students during the COVID-19 pandemic.MethodA cross-sectional online survey was conducted among 601 Bangladeshi students and collected data related to socio-demographic information, behavioral health, internet use behavior, depression, anxiety and problematic Facebook use [assessed using the Bergen Facebook Addiction Scale (BFAS)]. The data were analyzed using descriptive (frequencies and percentages) and inferential statistics (independent sample t-tests, one-way ANOVAs, correlations, and multivariable linear regression).ResultsThe results indicated that 29.1% of participants were problematic Facebook users (using cutoff ≥18 out of 30). Medical college students had higher mean score on PFU than other students (p < 0.001). In addition, the mean score of PFU was significantly higher among the students who were in a relationship (p = 0.001), did not engage in physical activity (p < 0.001), used the internet more than 5 h per day (p < 0.001), used social media (p < 0.001), and had depression or anxiety symptoms (p < 0.001). PFU was significantly associated with depression and anxiety among the whole sample. Predictive factors for PFU included relationship status, daily internet use time, gaming, social media use, depression, and anxiety. The model predicted almost 33.2% variance for PFU.ConclusionsFindings suggest interventions should be implemented for students with a special focus on medical students who had higher score of PFU than other types of students

Embedded system for motion control of an omnidirectional mobile robot

In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-Tuned proportional-integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots. 2018 IEEE.Scopu

Lane marking detection using simple encode decode deep learning technique: SegNet

In recent times, many innocent people are suffering from sudden death for the sake of unwanted road accidents, which also riveting a lot of financial properties. The researchers have deployed advanced driver assistance systems (ADAS) in which a large number of automated features have been incorporated in the modern vehicles to overcome human mortality as well as financial loss, and lane markings detection is one of them. Many computer vision techniques and intricate image processing approaches have been used for detecting the lane markings by utilizing the handcrafted with highly specialized features. However, the systems have become more challenging due to the computational complexity, overfitting, less accuracy, and incapability to cope up with the intricate environmental conditions. Therefore, this research paper proposed a simple encode-decode deep learning model to detect lane markings under the distinct environmental condition with lower computational complexity. The model is based on SegNet architecture for improving the performance of the existing researches, which is trained by the lane marking dataset containing different complex environment conditions like rain, cloud, low light, curve roads. The model has successfully achieved 96.38% accuracy, 0.0311 false positive, 0.0201 false negative, 0.960 F1 score with a loss of only 1.45%, less overfitting and 428 ms per step that outstripped some of the existing researches. It is expected that this research will bring a significant contribution to the field lane marking detection

Cognitive features of self-stigmatization among juvenile delinquents

The present study investigates cognitive features of stigmatization phenomenon among juvenile delinquents in Kyrgyzstan. It attempts to describe certain peculiarities of juvenile delinquents’ self-schemas and self-stigmatization. The study, additionally, tackles the issue of currently existing stigmas regarding juvenile delinquency in the country. One hundred and fifty four university students were asked to complete a questionnaire that would measure the level of external stigmatization towards juvenile delinquents and those who were once placed into correctional institution. Students showed presence of stigmatization towards people with a criminal record. Fifteen juveniles from the detention school who attended a socio-psychological training as well as eighteen delinquent juveniles who attended the same detention school, but did not have any training, were asked to complete semantic differential self-questionnaires that measured the level of internal stigmatization. It was found that delinquent juveniles in Kyrgyzstan self stigmatize, when compared to the control group of fifty four non-delinquent juveniles, who attended a regular school. However, there was a trend towards positive effect of the socio-psychological training that was intended to develop delinquents’ social skills. Since self-stigmatization was shown to influence the process of rehabilitation and social adaptation, it might become one of the major obstacles for successful rehabilitation and re-integration of juvenile delinquents into society after graduation from the detention school. Our study, therefore, argues for the high need of specialized socio-psychological training program for juvenile delinquents

Perception of muslim consumers towards tax deduction through Zakat in malaysia: an empirical investigation on muslims in Malaysia

The aim of this study is to explore the factors which are affecting Muslim consumers‟ perception towards tax deduction through zakat in Malaysia. A conceptual framework was drawn based on the literature. Six factors were extracted through principal component analysis and SEM was run to test the hypotheses. This research found that halal-haram aspect of Islamic Shariah has a very positive influence on Muslim consumers‟ perception towards the tax rebate system. In addition, legal consciousness and knowledge about tax and zakat have positive significant impact on Muslim consumers‟ perceptions towards this system. Due to the limited literature available on this subject matter, this study offers unique findings that may help in capitalizing the practices in Muslim countries and to understand their consumers‟ perception regarding the tax deduction system. In conclusion zakat institutions in Malaysia will also be better benefitted through this research finding. Keywords: Muslim Consumer, Perception, Tax deduction through Zaka

Urban runoff quantity and quality control – Malaysian perspective.

continued to increase at the urban areas in Malaysia. Such deteriorating trend was linked to increased land conversion activities, rapid disposal concept of drainage systems, main target on the control of point pollution sources (municipal and industrial wastewater) only, illicit connections and discharge of untreated sullage (grey-water) to the drainage systems. Realizing the limitations of the past efforts, various initiatives are taken in the recent pasts to improve the flood mitigation measures and river water quality throughout the country. Quantity and quality control of urban runoff is one of the most significant initiatives taken by the government of Malaysia. The significance of urban runoff quantity and quality control is gaining recognition throughout the country since the endorsement of Urban Stormwater Management Manual for Malaysia (USMMM), which was mandated in June 2000 by the Ministry Cabinet. It is now being applied for urban land development approval. The Manual consists of detailed engineering procedures and guidelines for runoff quantity control and treatment of non-point (diffuse) source pollutants. Receiving response from various stake-holders during the last 10 years, the government has taken another initiative to further improve the USMMM and prepare Standard Books for the legal enforcement of the runoff quantity and quality control. Such initiative by the government is highly expected to assist regulatory authorities and practitioners to reduce urban runoff related problems (flash flood and diffuse pollution) from the municipalities and help achieve the target of improved in river water quality nationwide. Various types of structural and non-structural best management practices (BMPs) are proposed in the manual. All stakeholders are working together to adopt the BMPs recommended in the USMMM. Lack of nationwide data on runoff quality from various landuses and local performance data of the structural best management practices (BMPs), are the main constraints the authorities are focusing on. The initiatives taken by the government of Malaysia can be a model for other developing nations in controlling runoff quantity and quality from urban areas. This paper briefly overviews the background of the urban runoff (both quantity and quality) management practices highlighting the issues regarding its implementation and improvement

An efficient encode-decode deep learning network for lane markings instant segmentation

Nowadays, advanced driver assistance systems (ADAS) has been incorporated with a distinct type of progressive and essential features. One of the most preliminary and significant features of the ADAS is lane marking detection, which permits the vehicle to keep in a particular road lane itself. It has been detected by utilizing high-specialized, handcrafted features and distinct post-processing approaches lead to less accurate, less efficient, and high computational framework under different environmental conditions. Hence, this research proposed a simple encode-decode deep learning approach under distinguishing environmental effects like different daytime, multiple lanes, different traffic condition, good and medium weather conditions for detecting the lane markings more accurately and efficiently. The proposed model is emphasized on the simple encode-decode Seg-Net framework incorporated with VGG16 architecture that has been trained by using the inequity and cross-entropy losses to obtain more accurate instant segmentation result of lane markings. The framework has been trained and tested on a vast public dataset named Tusimple, which includes around 3.6K training and 2.7 k testing image frames of different environmental conditions. The model has noted the highest accuracy, 96.61%, F1 score 96.34%, precision 98.91%, and recall 93.89%. Also, it has also obtained the lowest 3.125% false positive and 1.259% false-negative value, which transcended some of the previous researches. It is expected to assist significantly in the field of lane markings detection applying deep neural networks

Educational Life in the Interregnum: Race, Dis/ability, and Special Education

This article undertakes a comparative analysis of special education policy through the juxtaposition of two recent Supreme Court actions: Allston v. Lower Merion School District (2015) and Endrew F. v. Douglas County School District (2017). This comparison reveals an ordering of special education policy around questions of race. Specifically, this article argues that special education policy is governed by a racecraft of disability labeling that defines students of color as variously disabled and through a biopolitics of special education that expands disability services for individual students who are within the truth demarcated by scientific-juridical mediations of life. Against such negative inflections of life, this article concludes by turning to John Dewey’s educational and democratic thinking to posit an affirmation of educational life that counters the morbid symptoms that presently define education’s interregnum