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Abstract In this work, Density Functional Theory (DFT) on Gaussian 09 W software was utilized to investigate the phenylephrine (PE) molecule (C9H13NO2). Firstly, the optimized structure of the PE molecule was obtained using B3LYP/6-311 + G (d, p) and CAM-B3LYP/6-311 + G (d, p) basis sets. The electron charge density is shown in Mulliken atomic charge as a bar chart and also as a color-filled map in Molecular Electrostatic Potential (MEP). Using these properties, the possibility of different charge transfers occurring within the molecule was evaluated. The calculated values of the energy gap from HOMO-LUMO mapping, illustrated in Frontier Molecular Orbitals (FMO) and Density of State (DOS), were found to be similar for both the neutral and anion states in the gaseous and water solvent phases. Both the global and local reactivity were studied to understand the reactivity of the PE molecule. Using the thermodynamic parameters, the thermochemical property of the title molecule was understood. Non-covalent interaction was studied to understand the Van der Waals interactions, hydrogen bonds, and steric repulsion in the title molecule. Natural Bond Orbital (NBO) Analysis was performed to understand the strongest stabilization interaction. In the vibrational analysis, Total Electron Density (TED) assignments were done in the intense region where the frequency of the title molecule was shifted distinctly. For vibrational spectroscopy, FT-IR and Raman spectra in the neutral and anion states were plotted and compared. Using the TD-DFT technique, the UV-Vis spectra along with Tauc’s plot were studied. Finally, topological analysis, electron localized function (ELF), and localized orbital locator (LOL) were performed in the PE molecule.

Abstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach.

Abstract The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.

Abstract The increasing number of private cars, public transportation vehicles, and pedestrians, as well as the absence of adequate space for these ground amenities, are one of the primary causes of traffic congestion and accidents in the Kathmandu Valley. Investigations have indicated that the Kathmandu Valley has the greatest traffic accidents despite the heavy presence of the government and its agencies there. Most teens and young adults suffer injuries while using motor vehicles. The study's primary objective is to foresee and prevent such complications by planning for sufficient subsurface infrastructure (a cut‐and‐cover rectangular tunnel) for the Kathmandu Valley's transportation network. The overlying pressure, lateral earth pressure, live load, uplift pressure, and live surcharge are some of the forces acting on the tunnel, creating unique stress and moment zones. The tunnel meets the following geometric requirements: (a) Each of the tunnel's two cells has a clear span of 10 m and a clear height of 5.5 m. The side walls, inner walls, top slab, and bottom slab are all 700 mm thick. Soil has built up to a height of 4 m over the tunnel's roof. The analytical method is used in the tunnel segment's analysis. Furthermore, the designed tunnel has been evaluated for stability, considering the deflection and shear resistance. The analysis indicates that the tunnel meets the stability requirements. This implies that the structure is capable of withstanding the applied forces without excessive deflection. Non‐linear dynamic time history analyses of the El Centro earthquake and the Gorkha earthquake were computed. From the El Centro earthquake, the maximum displacement was 23.63 mm at 10.59 s, and from the Gorkha earthquake, the maximum displacement was 16 mm at 0.19 s for the modeled structures.

This research aims to reveal in-depth and give meaning to the planning, control, and improvement of religion-based elementary school education quality. This research method uses a qualitative approach through a multicase study plan with a constant comparative method. The research locations were SD Onekore 2 Santa Ursula Ende, SDK Santo Antonius Ende 2, and MIN Ende. The informants in this research were the Principal and Teachers. Data collection techniques use in-depth interviews, participant observation, and documentation studies. A structured set of questions designed to elicit information from stakeholders such as teachers, administrators, parents, and students about their perceptions, experiences, and suggestions regarding quality management in primary education with a religious foundation. The research results show that the Religious Primary School Quality Management Process has a strong commitment, which the founders inherited, managers, and previous education staff, and is preserved, continued, and strengthened by the management and education staff in the future. The meaning of quality by the management and educational staff of religious-based elementary schools is more holistic; the students' personalities are intact, and the quality management of religious-based elementary schools consists of school quality planning, quality control, and quality improvement. Through the quality management process of religious-based elementary schools, the school has a strong commitment, inherited by the founder, managers, and previous education staff, and preserved, continued, and strengthened by managers and education personnel.

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 ( R 2 = 0 . 88 ) between the reduction in the number of vehicles and the reduction in emissions like Carbon Dioxide ( CO 2 ), 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.

Recent research conducted by Nadler presented the concept of multi-valued contractive signals. There can be only one fixed point for the contraction signal of a complete metric space F into itself, according to the Banach contractions signal concept. Nadler presented a theorem that is very similar to this one for multi-valued contraction signals. Fixed points do occur, therefore multiple-valued contraction signals on two-dimensional metric spaces are studied in this paper. Although we acquired some new conclusions from our investigation, the results obtained are inferior to those obtained by Iseki’s theorems. We investigate the equivalence of the existence of fixed points of single-valued and multi-valued signals for particular classes of signals by proving some equivalence theorems for the completeness of 2-metric spaces. These theorems show that the existence of fixed points for single-valued signals is equivalent to the existence of fixed points for multi-valued signals.

Abstract 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 (β = -0.174, p

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.

The growth of informal settlements in the city of Cuidad Juarez have been part of the historical development of urbanization of the city. The sporadic rise in the disturbing trend was later grounded to halt by the agencies of government across the 3 tiers, namely, municipal, state, and federal. Outstandingly, the growth of the city of Cuidad Juarez and the existence of slums was largely pronounced by location and the neoliberalist ideology of the commodification of human needs. The rise of an informal settlement in recent times in the city, namely Estralla Sol del Poniente was basically due to the population growth in irregular settlements otherwise known as colonia which have prompted those living in precarious conditions to re-establish a habitat of their own. The living condition of characterizing a slum are poor public services such as water supply, sanitation, lack of electricity, among others. Through the methodology of qualitative research, the ethnography findings suggest that lack of trust in government agencies which also have reinforced social exclusion have further justified the construction of informal settlements. There is the need for the municipal authority of the border city to understand that the ripple effects of the immediate past have raised its ugly repercussions. The need to subjectively understand policies the effectiveness of land policies and all-inclusive urban housing, skill acquisitionand firm legislative and executive policy implementation would further control the rise of slums.

The prime goal of this research is to investigate if the cyber-crime has risen during coronavirus. People are using more internet and electronic devices in this pandemic time in the purpose of to perform their works those are possible through internet, as they are unable to go out due to coronavirus. For collection of data, it has been generated a questionnaire comprising of six questions. In these restrictions’ times, data cannot be gathered interacting with people physically so social media has been used for gathering data. The questionnaire was sent to people through social media 1088 people were approached and 400 out of them responded on the questionnaire. The data is presented in different charts for analysis. This research is significant in knowing if people are being attacked or their data is being stolen more than ever before during coronavirus. The outcomes of this study show that people are literally suffering through a surge in cyber-crime and most of the people said their data have been stolen and they have also been attacked by hackers.

Issues and topics concerning spirituality is not new in the social sphere. Many great thinkers in the fields of philosophy and theology have tried to excavate the richness of the topic. Though not new a topic, there are still various avenues in the sphere of spirituality that need to be examined and discovered through intellectual abstraction and practical observation; it is a topic that is ancient yet new, an idea suggesting a paradox of time and permanence. The relevance of spirituality cannot be contained in a specific era nor time frame or even in established social structures because it deals with people and their disposition toward life and a certain desire in their very being for transcendence. In other words, spirituality is all about a person’s attitude towards life and a quest for an existential meaning behind every experience that might be of unequalled value or significance. Hence, spirituality is not static but dynamic in its very being, for as long as man desires to go beyond his present state of being and moves into another manner of existence, spirituality is evident; it manifests itself in that affinity for self-transcendence. The term youth, alternatively, also speaks of a dynamic progressive or regressive movement of the self, outside of its present state of being. This study, therefore, is an attempt to phenomenologically interpret and appropriate the concept of spirituality as an unfolding of existence on the concept of youthfulness not just an ordinary process in the life of a person wherein one becomes open for self-improvement or self-transcendence

INTRODUCTION: The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system has seen a significant increase. In such a dataset, images and videos must be retrieved, explored, as well as inspected. OBJECTIVES: Text extraction is crucial for locating critical as well as important data. Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations. An image can be contaminated by a variety of noise-type disturbances. A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars. The particulars in the complicated corrupted images have been considered important for individuals seeing the entire issue. However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated.METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements. RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used. The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score. It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases.CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text. The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate. Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural images. As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.

Coronavirus disease (COVID-19) has been increasing slowly and steadily in all the districts of Jammu and Kashmir, India. It is essential for the government and health management system to monitor the districts affected due to COVID-19. The main objective of this study is to ascertain and categorize the COVID-19 affected districts into real clusters based on similarities within a cluster and differences among clusters in order to imply standard operating procedures (SOPs) policies, decisions, medical facilities, etc. could be improved for reducing the risk of infection and death and optimize the deployment of resources for preventing subsequent outbreaks. DOI: http://dx.doi.org/10.5281/zenodo.4677470

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.