A Study on Environmental Impact of Slow Moving Electric Vehicles Using Microsimulation on Lucknow Urban Road With an On-Ramp.

Background: The adoption of electric vehicles for mobility is seen as a major step towards the conservation of the environment. In India, slow-moving Electric 3-Wheelers (E3Ws) have been adopted for last-mile connectivity. The present study investigated the impact of slow-moving electric 3-wheelers on the environment in terms of emissions and traffic performance in mixed conditions. Methods: Field traffic data from a section of road in the city of Lucknow was collected and used for the calibration of the traffic model. A total of 6 scenarios were tested using traffic modelling in the open-source microsimulation software SUMO. Krauss model was used to model mixed traffic and HBEFA 4 was used to calculate the emissions of fuel-driven vehicles. In each scenario, the volume of fuel-driven vehicles was kept constant and the volume of E3Ws was varied. For the last 2 scenarios, E3Ws were replaced with modified Electric 3-wheelers (ME3Ws) and Electric Buses. Results: Initial findings showed that the average emission decreased as the number of slowly moving electric vehicles increased, but the average flow and harmonic mean speed decreased by 49.8% and 28.8%, respectively, despite keeping the original composition of fuel-driven vehicles the same in every scenario. Further analysis of scenarios revealed a strong correlation (R2=0.88) between the reduction in the number of vehicles and the reduction in emissions like Carbon Dioxide (CO2), which is responsible for global warming. Scenarios in which faster electric vehicles and electric buses replace slow-moving E3Ws also demonstrate emission reduction without noticeably affecting traffic performance parameters. Conclusion: The study shows that the environmental benefits of E3Ws in a limited section of Lucknow road are offset by their low-speed capability. Hypothetical scenarios wherein Modified E3Ws and Electric Buses were introduced reported benefits both in terms of emissions and traffic performance.

An integrated deep-learning model for smart waste classification.

Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution - a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.

Application of Parametric Shared Frailty Models to Analyze Time-to-Death of Gastric Cancer Patients.

BACKGROUND: Despite its declining incidence, gastric cancer (GC) is one of the world's leading malignancies and a major global health concern due to its high prevalence and fatality rate. Furthermore, it is the world's fourth most common cancer and the second leading cause of cancer death. Studying the determinants of time to death of gastric cancer patients will give clinicians more information to develop specific treatment plans, forecast prognosis, and track the progress of death cases. The application of the frailty model can help account for random variation in survival that may exist due to unobserved factors, as well as show the impact of latent factors on death risk. As a result, the purpose of this study was to assess the determinants of time to death of GC patients' by applying the parametric shared frailty models. METHODS: The data for this study were obtained from gastric cancer patients admitted to the Tikur Anbesa Specialized Hospital, Addis Ababa, from January 1, 2015, to February 29, 2020. With the aim of coming up with an appropriate survival model that determines factors that affect the time to death of gastric cancer patients, various parametric shared frailty models were compared. In all of the frailty models, patient regions were used as a clustering variable. The current study implemented exponential, Weibull, log-logistic, and lognormal distributions for baseline hazard functions with gamma and inverse Gaussian's frailty distributions. The performance of all models was compared using the AIC and BIC criteria. R statistical software was used to conduct the analysis. RESULTS: A retrospective study was undertaken on a total of 407 gastric cancer patients under follow-up at Tikur Anbesa Specialized Hospital. Of all 407 GC patients, 56.3% died while the remaining 43.7% were censored. The patients' median time to death was 21.9 months, with a maximum survival time of 49.6 months. In the current study, the clustering effect was significant in modeling the time to death from gastric cancer. The Weibull model with inverse Gaussian frailty has the minimum AIC and BIC value among the candidate models compared. The dependency within the clusters for the Weibull-inverse Gaussian frailty model was [Formula: see text] (13.4%). According to the results of our best model (Weibull-inverse Gaussian), the sex of the patient, the smoking status, the tumor size, the treatment taken, the vascular invasion, and the disease stage was found to be statistically significant at an alpha = 0.05 significance level. CONCLUSION: Time to death of GC patient's data set was well described by the Weibull-inverse Gaussian shared frailty. Furthermore, Weibull baseline distribution best fits the GC data set as it enables proportional hazard and accelerated failure time model, for time to failure data. There is unobserved heterogeneity between clusters (patient regions), indicating the need to account for this clustering effect. In this study, survival time to death among GC patients was discovered to be small. Covariates like older age, being male, having higher (advanced) stage of GC disease (stage three and stage four), advanced tumor size, being smoker, infected by Helicobacter pylori, and existence of vascular invasion significantly accelerate the time to death of GC patients. In contrast, talking combination of more treatments prolongs the time to death of patients. To improve the health of patients, interventions should be taken based on significant prognostic factors, with special attention dedicated to patients with such factors to prevent GC death.

Squatter settlement and informal urbanization: causes and consequences.

Squatter settlement and informal urbanization trends in Addis Ababa were a great concern with highest population living in informal settlements. Squatter settlements were illegally built due to urbanization in an open space, farmland, steep slope, and along river buffer which do not have legal title deed particularly at expansion of cities. Formal land value in the city through time has been increasing. This study focused on the causes and consequences of squatter settlement and informal urbanization. In this study, household income, family size, occupation, and the characteristics of the squatter settlements regarding its physical, social, and economic dimensions were studied using physical observation, questionnaires, interviews, and focus group discussion. Systematic sampling technique was employed for questionnaires to investigate the actual causes and consequences. Among 10 districts in Wereda 03, districts 4, 7, 8, and 9 were purposely selected since there were recorded large numbers (964 households) of new squatter and informal settlements by Wereda 03 administration in one year (2018). Out of 964 newly settled squatter households, 49%, 31%, 11%, and 5% were site-related pulling factors, sloppy topography, migration, and urban policy, respectively. Major causes were migration of persons from rural to urban areas, too expensive formal land price, availability of sloppy land, condominium housing shortage, and could not afford 10/90 government-led condominium housing payment. In addition to this, land speculation and establishment of religious institutions have attracted informal settlement. Consequently, unplanned housing construction and squatter settlement in Addis Ababa has required high cost of infrastructure and unintegrated land use plan, and also it has been difficult to provide infrastructure in this settlement. The affordable housing policy was based on pro-poor development assisting the low-income group in minimum of 10/90 housing scheme, but the policy still did not answer the demand of low income rather than the high-income groups because the poor under poverty line are marginalized due to lack of income source to down payment. Hence, investing more on affordable housing targeting the urban poor to minimize informal settlement as home is the human right to live.

Understanding COVID-19 response by twitter users: A text analysis approach.

COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include "cases", "home", "people" and "help". We identified "30" such topics and categorized them into "three" themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people.

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

Prevalence and Determinants of Contraceptive Utilization among Women in the Reproductive Age Group in Ethiopia.

INTRODUCTION: Despite the huge advantages of family planning programs, contraception use in Ethiopia remains low. Determining the magnitude and associated factors of contraceptive utilization helps to take action for further improvement. Therefore, this study aimed to assess the prevalence and identify determinants of using contraceptives among women of reproductive age in Ethiopia. METHODS: The 2019 Ethiopian Mini Demographic and Health Survey 2019 (EMDHS 2019) dataset was utilized in this population-based investigation. In the current study, 8885 reproductive-age women were included. Binary logistic regression analysis was employed to examine significant factors associated with the utilization of contraceptive methods. The analysis was done using SPSS software version 20. RESULTS: The prevalence of contraceptive utilization amongst women's reproductive age in Ethiopia was 37.6%. Of all contraceptive users, a large number of women, 57.0%, used injectable kinds of contraceptives followed by implants (24.3%). Participants aged 20-29 (AOR = 2.32, 95%CI: 1.79-3.01) and aged 30-39 years (AOR = 3.12, 95%CI: 2.58-3.78); from Addis Ababa (AOR = 3.27, 95%CI:2.42-4.43), Dire Dawa (AOR = 2.96, 95%CI:2.28-3.84), and urban residence (AOR = 2.49, 95%CI:2.13-2.91); who had secondary education level 1.391(AOR = 1.14-1.70), diploma and above (AOR = 1.39, 95%CI:1.12-1.72); being in rich wealth index (AOR = 1.260, 95%CI:1.06-1.50); having five or more children (AOR = 1.37, 95%CI:1.17-1.61); and who had knowledge about contraceptives (AOR = 1.88, 95%CI:1.42-2.48) and being married (AOR = 5.82, 95%CI: 4.60-7.36) had higher odds of utilizing contraceptives, while women aged 40-49 years (AOR = 0.93, 95%CI: 0.89-0.96) and from residential region of Oromia (AOR = 0.516, 95%CI: 0.40-0.67), Somalia (AOR = 0.48, 95%CI: 0.38-0.62) and Benishangul (AOR = 0.53, 95%CI: 0.40-0.70) had lower odds of using contraception. CONCLUSIONS: The study concluded that the use of contraceptives remained very low (found below the national target) in Ethiopia. Factors like age, educational level, number of children, and region of the women, religion, wealth index, and marital status are determinant factors associated with contraceptive use among reproductive-age women in Ethiopia. For a more successful intervention approach that encourages the use of contraceptive methods, these variables should be considered.

A Case Study on Modeling the Time to Recurrence of Gastric Cancer Patients.

BACKGROUND: Gastric cancer is a malignant tumor of the stomach and it is one of the leading causes of death worldwide. The study aimed to model the time to first recurrence of gastric cancer patients at the Tikur Anbesa specialized hospital. METHODS: The data for this study were gastric cancer patients followed up from January 1, 2013 to February 29, 2020 at Tikur Anbesa Specialized Hospital, Oncology Center, Addis Ababa. We used Weibull, log-logistic and lognormal as baseline hazard functions with the gamma and the inverse Gaussian frailty distributions. Data analyzed with the statistical software R. RESULTS: The median recurrence time of the patients was about 23.96 months with a maximum recurrence time of 60.81 months, of which about 61.2% had first recurrences of gastric cancer. The clustering effect is significant in modeling the time to recurrence of gastric cancer. According to the result of the log-logistic inverse Gaussian frailty model, the sex of the patient, the tumor size, smoking habit, the treatment carried out, the vascular invasion, the stage of the disease, the helicobacter pylori infection and the histological type were the significant prognostic factors  at 5% level of significance. CONCLUSION: Inverse Gaussian frailty model is the model that best describes the time to recurrence of the gastric cancer data set. Gender of the patients, tumor size, treatment taken, vascular invasion, disease stage, helicobacter pylori infection and histological type were the determining prognostic factors. This requires measures to improve patient health and prevent relapse based on significant risk factors, and particular attention should be paid to patients with such factors.

Prevalence of depression, anxiety and stress during the COVID-19 pandemic: a cross-sectional study among Palestinian students (10-18 years).

BACKGROUND: The COVID-19 pandemic considers a threat to students' well-being and mental health. The current descriptive cross-sectional study aims to identify psychological distress among school students during the lockdown period. METHODS: This study was carried out in a sample of 420 primary and secondary school students from June 10 to July 13, 2020, in the Gaza Strip in Palestine. Data was collected using an online questionnaire that included informed consent, socio-demographic questions, and a psychometric scale (DASS-21). RESULTS: The results revealed that most students experienced moderate to severe levels of anxiety (89.1%) and depression (72.1%), whereas less than half of them (35.7%) experienced moderate to severe stress. Stress, anxiety and depression scores were significantly different across gender, age groups, family size, and family's economic status. The results showed that gender (ß = -0.174, p < 0.001), age (ß = -0.155, p = 0.001) and economic level of family (ß = -0.147, p = 0.002) were negative predictors correlated with stress. Family size (ß = 0.156, p = 0.001) played a positive role in stress. It was found that gender (ß = -0.105, p = 0.031), age (ß = -0.135, p = 0.006) and economic level of family (ß = -0.136, p = 0.005) were negative predictors correlated with anxiety, whereas family size (ß = 0.139, p = 0.004) played a positive role in anxiety. For depression, gender (ß = -0.162, p = 0.001), age (ß = -0.160, p = 0.001) and economic level of family (ß = -0.131, p = 0.007) were negative predictors correlated with depression, whereas family size (ß = -0.133, p = 0.006) was found to be a positive predictor. Concerns about the influence of COVID-19 on economic, education, and daily life were positively correlated to the levels of depression, anxiety and stress, whereas the availability of social support was negatively correlated. CONCLUSION: The development of a health protocol for influenced students is urgently needed to maintain them remain resilient during dangerous times.

Corrigendum to "The prevalence of general anxiety disorder and its associated factors among women's attending at the perinatal service of Dilla University referral hospital, Dilla town, Ethiopia, April, 2020 in Covid pandemic" [Heliyon 6 (11) (November 2020) Article e05593].

[This corrects the article DOI: 10.1016/j.heliyon.2020.e05593.].

An efficient route design for solid waste collection using graph theory and the algorithm of the traveling agent in dynamic programming.

In Santo Domingo de los Tsa'chilas province, Ecuador, the population grows proportionally to the territorial extension in urban and rural parishes; therefore, the conception of domestic solid waste has increased exponentially. In this context, in recent years, the distribution of routes for waste collection has not been dealt with or technically explored. The research objective is to apply the theory of graphs to the sector and use the exact method of the Travel Agent Problem (TSP) in dynamic programming to generate optimal routes by sectors. In addition to measuring the variables longitudinally, we test the researcher's hypothesis using parametric techniques for independent samples in the variable's travel time and distance between the usual route and the new route in the Río Verde parish of Santo Domingo Canton.

Combating the Spread of COVID-19 Through Community Participation.

India has a vast population with a weak public health system, which is vulnerable to the COVID-19 pandemic. Economically and physically, India is in a state of considerable risk of the COVID-19 pandemic. Community participation through various measures is the only way to limit the spread of the virus. The present study investigates the possibility of social intervention and involvement in controlling the pandemics and its cascading effect. The study identifies 5 'S', namely, segregation, sensitization, social fencing, solidarity, and social services, to control the disease through people's participation that could throw insights into controlling the virus and minimizing the aftershock of the pandemic.

The Current Mental Health Crisis of COVID-19 Pandemic Among Communities Living in Gedeo Zone Dilla, SNNP, Ethiopia, April 2020.

The current COVID-19 pandemic is not still controlled around the world and affects all humans' domains of day to day life. Peoples have killed themselves due to the fear of stigma by their community. This study aimed to assess the current mental health crisis of the COVID-19 pandemic among communities living in, Gedeo zone, Dilla, Ethiopia. The study was community based cross sectional design conducted from March 10-Apr 10, 2020, using a multi stage sampling techniques. Structured interview, Depression, Anxiety and Stress Scale (DASS-21), and logistic regression analysis (95% CI, p value < 0.05) was used. This study included 420 respondents who were living in the Gedeo zone. In total, 44.4% of respondents had a psychological problem (21.4% mild and 23% moderate level of the mental crisis). Gender female, monthly income below 500 ETB, and more than three family size were variables associated with the outcome variable (p < 0.05). Nearly half of the respondents had mild to the moderate mental crisis in response to the pandemic. It is better to give mental health support for the peoples living in the zone to enhance their mental resilience.

The prevalence of general anxiety disorder and its associated factors among women's attending at the perinatal service of Dilla University referral hospital, Dilla town, Ethiopia, April, 2020 in Covid pandemic.

BACKGROUND: Coronavirus is rapidly increasing in Ethiopia, and the number of perinatal service users at the hospital decreased due to the fear of contracting the virus. The mental health of a pregnant mother is vital for preventing pregnancy and birth-related complications. This study aimed to determine the magnitude and associated factors of General anxiety disorder among perinatal service users in Dilla University referral hospital, Dilla, Ethiopia. METHODS: A hospital-based cross-sectional study was conducted among 178 respondents from April 6 - May 6, 2020. The sampling technique of this study was Consecutive sampling. Data were collected using a structured interview. General anxiety disorder (GAD-7) was used to determine the outcome variable. Logistic regression analysis and adjusted odd ratio at 95% CI and p < 0.05 was used to determine the statistically significant association between general anxiety disorder and its predictors. RESULT: A total of 178 respondents with a 100% response rate were enrolled in the study. The mean income of respondents was 1500 (±700) Ethiopian birr. The overall prevalence of general anxiety disorder (GAD) was 32.2%. Living in Rural area [AOR = 0.48; 95% CI: (0.25-0.9) P = 0.02∗], Primary level of education [AOR = 0.41; 95%CI:(0.21-0.75), P = 0.03∗], poor social support [AOR = 4.3995%CI:(2.29-12.53), P = 0.001∗∗] and primigravida [AOR = 3.05; 95% CI: (1.53-6.08), P = 0.001∗∗] were variables significantly associated with general anxiety disorder at 95% confidence interval, p < 0.05. CONCLUSIONS: This study found that nearly one-third of the respondents had general anxiety disorder. Therefore, working on the mental health impact of the pandemic among perinatal service users is an urgent solution to promote their physical, mental, and psychological health of a mother and her baby.

Extraction and purification of purple membrane for photochromic thin film development: application in photoelectrochemical investigation.

Purple membrane (PM) has been extracted and purified from archaebacteria for thin film development. The purified purple membrane is isolated in 1 % polyvinyl alcohol solution for making thin film within gelatin and organically modified silicate matrices. For thin film within gelatin matrix, homogenized purple membrane suspension is mixed with 8 % gelatin and poured into a specially designed block with desired thickness of spacer having hydrophobicity followed by gelatinization of the same over home-made thermostatic control unit at 38 °C. The gelatinized matrix is then allowed to dry under controlled conditions of humidity and temperature. The films of varying thicknesses ranging between 40, 50, and 60 µ are used for photo-electrochemical measurements. The results on photo-electrochemistry of non-oriented purple membrane film provides valuable information on the generation of forward (light on) and backward (light off) photocurrent as a function of: (a) applied potential and (b) film thickness. An increase in applied negative potential increases the amplitude of photocurrent whereas decrease in film thickness facilitates the reversibility of photocurrent response.