Population increase and the adoption of new power appliances have significantly increased electrical demands. As a result, the utility (electricity supplier) faces difficulties in maintaining the ...balance between supply and demand. Further, such supply and demand imbalance leads to frequent load-shedding and a drop in power quality (PQ), predominantly in the developing world. Increasing consumer awareness of energy consumption and implementing efficient demand-side management (DSM) algorithms in a metering instrument can be utilized to avoid such issues. Nowadays, access to the internet in developing nations is increasing rapidly. Hence to solve the existing limitations, the internet of things (IoT)-based smart meter (SM) is proposed and its practical application demonstrated in households in Bangladesh as a case study. The proposed SM primarily serves local and online monitoring, bidirectional data transmission, and DSM at the consumer side by maintaining PQ and peak-clipping. The MySql cloud database is used here for data storage and bidirectional data transmission between consumers and the utility. Web applications are developed for real-time data visualization, enabling consumers to track their hourly, daily, and monthly energy consumption by accessing the web page. The SM data shows that the over-voltage varied (from a nominal 220 V) within 15.45–16.36%, and the under-voltage varied between 10.45% and 11.82% from 220 V. The frequency fluctuations are found to be 2.2% under and 2.4% over the nominal value of 50 Hz (standard is nominal value ± 1%). The experimental result showed that the proposed IoT-based SM could ensure the smooth operation of electrical home appliances by maintaining PQ-related parameters (voltage and frequency) within a standard limit. Additionally, the proposed SM also helps to maintain the maximum pre-defined demand of a household during peak times through an appropriate load-clipping algorithm. The utility company can remotely define peak time hours and maximum peak demands when necessary. The real-life demonstration of the SM's operation advocated that this type of IoT-based SM could be easily adapted to maintain the balance between supply and demand through DSM application, and increase consumers' awareness of energy consumption in developing countries.
•A low-cost smart meter (SM) is proposed for developing countries.•Suitable for the nations where a smart grid is yet to be deployed.•The smart meter is capable of ensuring power quality.•Demand-side management (i.e., direct load control) can be achieved through the SM.
Classified as a pandemic by the World Health Organization, the novel Coronavirus Disease (COVID-19) has spread to Bangladesh since early March of 2020, and people are getting daily updates from the ...social and electronic media. We aimed at assessing the prevalence of anxiety among Bangladeshi people during the pandemic in connection with social media exposure (SME) and electronic media exposure (EME). For this cross-sectional study, data were collected from 880 participants by a self-administered online-based questionnaire relating personal characteristics, self-rate health (SRH), SME, and EME with anxiety. Findings show that around half of the surveyed population experienced a spike of anxiety (49.1%) during the pandemic, ten times higher than the national anxiety rate in 2019. The participants with an increased SME of over four hours per day experienced a higher level of anxiety than individuals with < = 2 hours exposure to social media. Similarly, the anxiety was higher among people with fair/bad SRH compared to individuals with excellent SRH. It is highly recommended to develop active surveillance and effective monitoring systems to reduce the spread of misinformation from both social and electronic media to improve the state of mental health conditions during the pandemic.
Antenatal care (ANC) contacts have long been considered a critical component of the continuum of care for a pregnant mother along with the newborn baby. The latest maternal mortality survey in ...Bangladesh suggests that progress in reducing maternal mortality has stalled as only 37% of pregnant women have attended at least four ANC contacts. This paper aims to determine what factors are associated with ANC contacts for women in Bangladesh. We analysed the data, provided by Bangladesh demographic and health survey 2014, covering a nationally representative sample of 17,863 ever married women aged 15-49 years. A two-stage stratified cluster sampling was used to collect the data. Data derived from 4,475 mothers who gave birth in the three years preceding the survey. Descriptive, inferential, and multivariate statistical techniques were used to analyse the data. An overall 78.4% of women had ANC contacts, but the WHO recommended ≥8 ANC contacts and ANC contacts by qualified doctors were only 8% for each. The logistic regression analysis revealed that division, maternal age, women's education, husband's education, wealth index and media exposure were associated with the ANC contacts. Likewise, place of residence, women's education, religion, and wealth index were also found to be associated with the WHO recommended ANC contacts. Furthermore, the husband's education, division, religion and husband's employment showed significant associations with ANC contacts by qualified doctors. However, Bangladeshi women in general revealed an unsatisfactory level of ANC contacts, the WHO recommended as well as ANC contacts by qualified doctors. In order to improve the situation, it is necessary to follow the most recent ANC contacts recommended by the WHO and to contact the qualified doctors. Moreover, an improvement in education as well as access to information along with an increase of transports, care centres and reduction of service costs would see an improvement of ANC contacts in Bangladesh.
In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) ...based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using low-frequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a data-acquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web applications are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoT-based information exchange systems ensure the proposed system's applicability for efficient energy management.
Biofilm development significantly enhances the virulence of methicillin-resistant Staphylococcus aureus (MRSA), leading to severe infections and decreased susceptibility to antibiotics, especially in ...strains associated with hospital environments. This study examined the occurrence of MRSA, their ability to form biofilms, agr typing, and the antibiotic resistance profiles of biofilm-forming MRSA strains isolated from environmental surfaces at Mymensingh Medical College Hospital (MMCH). From 120 swab samples, 86 (71.67%) tested positive for S . aureus . MRSA was identified in 86 isolates using the disk diffusion technique, and by polymerase chain reaction (PCR), 56 (65.1%) isolates were confirmed to carry the mecA gene. The Crystal Violet Microtiter Plate (CVMP) test revealed that 80.35% (45 isolates) were biofilm-forming and 19.6% (11 isolates) were non-biofilm-forming. Out of 45 biofilm producer isolates 37.5% and 42.9% isolates exhibited strong and intermediate biofilm-forming characteristics, respectively. Molecular analysis revealed that 17.78% of MRSA isolates carried at least one gene related to biofilm formation, specifically icaA , icaB , and icaD genes were discovered in 13.33%, 8.89%, 6.67% of the MRSA isolates, respectively. In agr typing, the most prevalent group was agr I (71.11%), followed by group III (17.78%) and group II (11.11%). Group IV was not detected. The distribution of agr gene groups showed a significant difference among biofilm-forming isolates ( p < 0.05). In agr group I, 18.75% of isolates carried the icaA gene, 12.5% carried the icaB gene, and 9.37% carried the icaD gene. Biofilm-forming genes were not detected in any of the isolates from agr groups II or III. There are no statistically significant differences between agr groups and the presence of these genes ( p > 0.05). Antibiotic resistance varied significantly among agr groups, with agr group I displaying the highest resistance, agr group II, and agr group III exhibiting the least resistance ( p < 0.05). Seventy-three (73.3%) of the isolates were multi-drug resistant, with agr group I displaying nineteen MDR patterns. The occurrence of MRSA in hospital environments and their capacity to form biofilm raises concerns for public health. These findings support the importance of further research focused on agr quorum sensing systems as a basis for developing novel antibacterial agents.
The outbreak of new coronavirus disease (COVID-19) has triggered a global panic, affecting the mental well-being of people of all ages, including students. The aim of this study was to explore the ...relationship between self-reported mental health concerns and subjective sleep quality of the Bangladeshi university students during the COVID-19 pandemic. A web-based cross-sectional study was conducted to maintain the social distancing recommended by the World Health Organization. There were 1,317 student responses from 49 universities across Bangladesh. Data was analyzed by executing both bi-variate and multivariate analysis. Findings indicate that 27.1%, 51.0%, 45.9%, and 86.0% of students had poor subjective sleep quality, anxiety, depression, and fear of COVID-19, respectively. Anxiety (AOR = 1.09, 95% CI: 1.06–1.12, p < 0.001) was a risk factor for increasing the poor subjective sleep quality of university students. In contrast, the odds of poor subjective sleep quality were lower with increasing the score of depression (AOR = 0.88, 95% CI: 0.86–0.90, p < 0.001) and fear of COVID-19 (AOR = 0.97, 95% CI: 0.94–0.99, p < 0.05). Compared to public university students, private university students were more likely to report poor subjective sleep quality since the pandemic began. Therefore, it is strongly recommended that psychiatric conditions of university students should be monitored during the COVID-19 epidemic, and necessary strategies, such as allocation of resources, implementation of awareness programs, establishment of psychological counselling unit, should carefully be devised.
COVID-19, Anxiety, Depression, Subjective sleep quality, Fear of COVID-19; University students, Bangladesh.
Due to unemployment, the prolonged lockdown during the COVID-19 pandemic caused panic and deepened poverty, especially among lower-class and marginal people. The related financial crises led to ...harmful practices such as the early marriage of adolescent girls, which deteriorated these girl's mental state.
This study attempted to assess the prevalence of mental health problems among early married girls and determine the associated predictors of the growing mental health burden.
This cross-sectional survey was conducted during the third wave of the COVID-19 pandemic in Dumuria Upazila in the Khulna district of Bangladesh. Data were collected purposively from 304 girls who were married off during the COVID-19 pandemic, this was carried out between 22 July and 31 August 2022 by administering a semi-structured interview schedule, with mental health measured by the depression, anxiety, and stress scale 21 (DASS 21). The data were analyzed using IBM SPSS Statistics (version 25), and multiple linear regression was executed in order to predict mental health problems among early married girls.
The findings show that the overall prevalence of depression, anxiety, and stress among early married girls during the COVID-19 pandemic in Bangladesh was 60.9% (95% CI: 0.554-0.663), 74.7% (95% CI: 0.698-0.796), and 23.7% (95% CI: 0.189-0.285). The prevalence was relatively higher among girls from the
(Hindu) religion and younger girls than among Muslim and older girls, respectively. The multiple linear regressions indicate that age, age at marriage, duration of the marriage, spousal occupation, intimate partner violence (IPV), and subjective happiness were the critical predictors of mental health problems among early married girls.
Early marriage, along with various adverse outcomes, i.e., IPV, maladjustment, and poor subjective happiness, has resulted in heightened mental health problems for young girls. Policymakers should implement coercive measures to prevent early marriage, especially during social, economic, political, and health crises; in addition, more research is recommended in order to explore the mechanisms that make early married girls psychologically vulnerable and thus formulate protective and preventive programs for addressing such vulnerabilities.
Globally, internet use has increased significantly during the COVID-19 pandemic, and internet addiction (IA) has become a severe public health issue. Therefore, this study aimed to assess IA ...prevalence among adults and identify its determinants during the COVID-19 pandemic in Bangladesh.
Using a cross-sectional design, this study recruited 608 participants through a self-administered online-based e-questionnaire. Young’s internet addiction test (YIAT) of 20 items was used to assess the prevalence of IA among adults in Bangladesh. Bivariate and binary logistic regression analyses explored the factors influencing IA.
The overall prevalence of IA was 29.4% among adults during the COVID-19 pandemic. However, the addiction rate was 34.7% among participants under 20 years old. Tobacco smoking (AOR = 1.88, 95% CI 1.15–3.07) and spending more time on the internet during the COVID-19 pandemic (AOR = 2.06, 95% CI 1.08–3.94) were likely the reasons for IA among Bangladeshi adults. Participants aged over 24 years (AOR = 0.39, 95% CI 0.17–0.91), living in rural areas (AOR = 0.51, 95% CI 0.32–0.81), living away from family (AOR = 0.45, 95% CI 0.26–0.79), attached to physical activity (AOR = 0.35, 95% CI 0.24–0.52), and sleeping less than or equal 6 hours (AOR = 0.63, 95% CI 0.42–0.93) had a lower chance of IA during the COVID-19 pandemic.
This study has shown that the prevalence of IA was comparatively higher among younger participants during the COVID-19 pandemic. Smoking, long-time use of the internet, physical activity status, and sleeping duration were the most significant determinants of IA. Thus, raising awareness among the younger generation is the most important strategy to reduce IA. The findings of this study can be used to support health and educational organizations to design their programs, which will help prevent IA in Bangladesh during the COVID-19 pandemic.
COVID-19, Internet addiction, Prevalence, Adult, Bangladesh.
ObjectivesThis study was designed to identify the patterns, prevalence and risk factors of intimate partner violence (IPV) against female adolescents and its association with mental health ...problems.DesignCross-sectional survey.SettingsDumuria Upazila (subdistrict) under the Khulna district of Bangladesh.ParticipantsA total of 304 participants were selected purposively based on some specifications: they must be female adolescents, residents of Dumuria Upazila and married during the COVID-19 pandemic when under 18 years of age.Outcome measuresBy administering a semi-structured interview schedule, data were collected regarding IPV using 12 five-point Likert scale items; a higher score from the summation reflects frequent violence.ResultsThe findings suggest that the prevalence of physical, sexual and emotional IPV among the 304 participants, who had an average age of 17.1 years (SD=1.42), was 89.5%, 87.8% and 93.7%, respectively, whereas 12.2% of the participants experienced severe physical IPV, 9.9% experienced severe sexual IPV and 10.5% experienced severe emotional IPV. Stepwise regression models identified age at marriage (p=0.001), number of miscarriages (p=0.005), education of spouse (p=0.001), income of spouse (p=0.016), age gap between spouses (p=0.008), marital adjustment (p<0.001) and subjective happiness (p<0.001) as significant risk factors. Hierarchical regression, however, indicated that age at marriage (p<0.001), age gap between spouses (p<0.001), marital adjustment (p<0.001) and subjective happiness (p<0.001) had negative associations with IPV, while the number of miscarriages (p<0.001) had a positive relationship. Pearson’s correlation showed that IPV was significantly associated with depression, anxiety and stress.ConclusionDuring the COVID-19 pandemic, an increase in IPV and mental health problems among early married adolescents was documented. To reduce physical and mental harm and to assure their well-being, preventive and rehabilitative measures should be devised.
Real-time Driver Drowsiness Detection using Deep Learning Md. Tanvir Ahammed Dipu; Hossain, Syeda Sumbul; Arafat, Yeasir ...
International journal of advanced computer science & applications,
2021, Volume:
12, Issue:
7
Journal Article
Open access
Every year thousands of lives pass away worldwide due to vehicle accidents, and the main reason behind this is the drowsiness in drivers. A drowsiness detection system will help to reduce this ...accident and save many lives around the world. To defend this problem, we propose a methodology based on Convolutional Neural Networks (CNN) that illustrates drowsiness detection as a task to detect an object. It will detect and localize whether the eyes are open or close based on the real-time video stream of drivers. The MobileNet CNN Architecture with Single Shot Multibox Detector is the technology used for this object detection task. A separate algorithm is used based on the output given by the SSD_MobileNet_v1 architecture. A dataset that consists of around 4500 images was labeled with the object’s face yawn, no-yawn, open eye, and closed eye to train the SSD_MobileNet_v1 Network. Around 600 randomly selected images are used to test the trained model using the PASCAL VOC metric. The proposed approach is to ensure better accuracy and computational efficiency. It is also affordable as it can process incoming video streams in real-time and does not need any expensive hardware support. There only needs a standalone camera to be implemented using cheap devices in cars using Raspberry Pi 3 or other IP cameras.