Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Detection of Campylobacter spp. in farmed sheep in Mymensingh division of Bangladesh: Prevalence, risk factors, and antimicrobial susceptibility status.

Background and Aim: Campylobacter infections in sheep may be asymptomatic or cause enteritis, ileitis, infertility, and abortion. Thus, this study aimed to estimate the prevalence of Campylobacter spp. in farming sheep and to detect risk factors, molecular patterns, and antimicrobial susceptibility status of these pathogens. Materials and Methods: Four hundred and eight fecal samples were collected from 12 flocks in the Mymensingh and Sherpur districts. Samples were tested by both basic (culture and biochemical tests) and molecular (initially 16S rRNA and later hipO gene-based polymerase chain reaction). Furthermore, the antimicrobial susceptibility status of Campylobacter jejuni was confirmed using disk diffusion. Flock- and animal-level data were captured using semi-structured interviews with farm owners under bivariate and multivariate logistic regression analyses to confirm the risk factors for Campylobacter-positive status. Results: The prevalence of C. jejuni staining at the animal and flock levels was 8.82% (36/408) and 66.70% (8/12), respectively. The age of sheep was identified as an important risk factor. Up to 1 year of age, sheep were 3.78 times more likely to be infected with C. jejuni (95% confidence interval: 1.0736-13.3146, p = 0.038). Of the 36 isolates of C. jejuni, all were found to be fully susceptible (100%) to gentamicin and ciprofloxacin. In this study, three antimicrobial agents, oxytetracycline, azithromycin, and ceftriaxone, were fully resistant (100%). The majority of isolates were resistant to a combination of 4-6 antimicrobial agents. Conclusion: The present study highlights the predominant maintenance of zoonotic Campylobacter species in sheep, and their burden on human health is enormous. Therefore, environmental, animal, and human health needs to be focused under a One Health lens to mitigate the occurrence of Campylobacter in farm settings and to prevent further introduction to animals and humans.

Assessing sustainable waste management practices in Rajshahi City Corporation: an analysis for local government enhancement using IoT, AI, and Android technology.

This study addresses the pressing challenge of inefficient waste management practices within the Rajshahi City Corporation (RCC), Bangladesh. Despite rapid urbanization and escalating waste generation rates, RCC struggles with diverse waste disposal practices, limited supervision, irregular waste collection schedules, and inadequate disposal infrastructure. In this context, this study examines the possible improvements that could be made by combining the Internet of Things (IoT), artificial intelligence (AI), and Android application to improve waste management methods in the RCC. The study's foundation is a vast amount of information gathered from residents, with particular attention paid to waste disposal methods, the role of the local government, the frequency of waste collection, and public attitudes toward waste management. The results point to a complicated waste management environment with a range of waste disposal practices, little supervision, irregular waste collection, and insufficient disposal methods. The importance of RCC in waste management is emphasized, highlighting the need for proactive measures including effective monitoring, constant waste collection, and routine drain cleaning. Additionally, it is suggested that combining IoT, AI, and Android technology is a possible way to improve waste management procedures. These technologies have the potential to increase productivity, lessen their negative effects on the environment, and produce cleaner, more sustainable urban environments.

OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Development of DynamicMC for PHITS Monte Carlo package.

Previously, we have developed DynamicMC for modeling relative movement of Oak Ridge National Laboratory phantom in a radiation field for the Monte Carlo N-Particle package (Health Physics. 2023,124(4):301-309). Using this software, three-dimensional dose distributions in a phantom irradiated by a certain mono-energetic (Mono E) source can be deduced through its graphical user interface. In this study, we extended DynamicMC to be used in combination with the Particle and Heavy Ion Transport code System (PHITS) by providing it with a higher flexibility for dynamic movement for an anthropomorphic phantom. For this purpose, we implemented four new functions into the software, which are (1) to generate not only Mono E sources but also those having an energy spectrum of an arbitrary radioisotope (2) to calculate the absorbed doses for several radiologically important organs (3) to automatically average the calculated absorbed doses along the path of the phantom and (4) to generate user-defined slab shielding materials. The first and third items utilize the PHITS-specific modalities named radioisotope-source and sumtally functions, respectively. The computational cost and complexity can be dramatically reduced with these features. We anticipate that the present work and the developed open-source tools will be in the interest of nuclear radiation physics community for research and teaching purposes.

Hypoalbuminaemia as a Marker of Severity of Patients of Community Acquired Pneumonia.

Community-acquired pneumonia (CAP) is a common presentation with an acute infection of the pulmonary parenchyma occurring in the community level. Despite the availability of potent antibiotics, it remains as a serious illness with significant morbidity and mortality in both developed and developing countries. This study was undertaken to determine the relation between serum Albumin and severity of CAP. This was a cross sectional descriptive study which was carried out in the Department of Medicine of Mymensingh Medical College Hospital (MMCH), Bangladesh from July 2019 to December 2019. The sample size was 67. Purposive sampling technique was employed. Patients of community acquired pneumonia (CAP), aged ≥14 years of both sex with recently developed radiological pulmonary shadowing with compatible clinical symptoms and signs were included. Patients who were chronically immunosuppressed, with chronic starvation, advanced liver disease or chronic kidney disease with or without receiving haemodialysis were excluded. Data analysis was done by SPSS software for Windows (version 23.0). The mean age 65.7±15.3 years, majority 13(19.4%) patients had chronic lung disease, 12(17.9%) had diabetes mellitus, 9(13.4%) had heart failure, 6(9.0%) had cerebrovascular disease, 6(9.0%) had neoplastic disease and 5(7.5%) had chronic renal failure. Majority 22(32.8%) patients had CURB-65 score 3, out of which 12(54.5%) had albumin level <20g/l, 9(40.9%) had albumin level 20.0-24.9g/l and 1(4.5%) had albumin level 25-29g/l. 17(25.4%) had score 4-5 out of which 10(58.8%) had albumin level <20g/l and 7(41.2%) had albumin level 20.0-24.9g/l, 15(22.4%) had score 2 and 13(19.4%) had score 0-1. Negative significant correction (r=-0.782; p=0.001) was found between CURB-65 score and albumin level. Significant number of patients with severe CAP show low serum albumin level at admission which is statistically significant when compared with CURB-65 score. Thus hypoalbuminaemia may be a good marker of severity of patients with CAP.

LDSSNV: A Linkage Disequilibrium-Based Method for the Detection of Somatic Single-Nucleotide Variants.

Single nucleotide variants (SNVs) are very common in human genome and pose a significant effect on cellular proliferation and tumorigenesis in various cancers. Somatic variant and germline variant are the two forms of SNVs. They are the major drivers of inherited diseases and acquired tumors respectively. A reasonable analysis of the next generation sequencing data profiles from cancer genomes could provide crucial information for cancer diagnosis and treatment. Accurate detection of SNVs and distinguishing the two forms are still considered challenging tasks in cancer analysis. Herein, we propose a new approach, LDSSNV, to detect somatic SNVs without matched normal samples. LDSSNV predicts SNVs by training the XGboost classifier on a concise combination of features and distinguishes the two forms based on linkage disequilibrium which is a trait between germline mutations. LDSSNV provides two modes to distinguish the somatic variants from germline variants, the single-mode and multiple-mode by respectively using a single tumor sample and multiple tumor samples. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that the LDSSNV method outperforms competing methods and can become a robust and reliable tool for analyzing tumor genome variation.

Scattering of e± by C2H6 Molecule over a Wide Range of Energy: A Theoretical Investigation.

The present work reports the theoretical investigation of the scattering of electrons and positrons by the ethane (C2H6) molecule over the energy range 1 eV-1 MeV. The investigation was carried out by taking into account the screening correction arising from a semiclassical analysis of the atomic geometrical overlapping of the scattering observables calculated in the independent atom approximation. The study is presented through the calculations of a broad spectrum of observable quantities, namely differential, integrated elastic, momentum transfer, viscosity, inelastic, grand total, and total ionization cross-sections and the Sherman functions. A comparative study was carried out between scattering observables for electron impact with those for positron impact to exhibit the similarity and dissimilarity arising out of the difference of the collisions of impinging projectiles with the target. Partial-wave decomposition of the scattering states within the Dirac relativistic framework employing a free-atom complex optical model potential was used to calculate the corresponding observable quantities of the constituent atoms. The results, calculated using our recipe, were compared with the experimental and theoretical works available in the literature. The Sherman function for a e±-C2H6 scattering system is presented for the first time in the literature. The addition of the screening correction to the independent atom approximation method was found to substantially reduce the scattering cross-sections, particularly at forward angles for lower incident energies.

Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.

DynamicMC: An Open-source GUI Program Coupled with MCNP for Modeling Relative Dynamic Movement of Radioactive Source and ORNL Phantom in a 3-dimensional Radiation Field.

ABSTRACT: The present work introduces an open-source graphical user interface (GUI) computer program called DynamicMC. The present program has the ability to generate ORNL phantom input script for the Monte Carlo N-Particle (MCNP) package. The relative dynamic movement of the radiation source with respect to the ORNL phantom can be modeled, which essentially resembles the dynamic movement of source-to-target (i.e., human phantom) distance in a 3-dimensional radiation field. The present program makes the organ-based dosimetry of the human body much easier, as users are not required to write lengthy scripts or deal with any programming that many may find tedious, time consuming, and error prone. In this paper, we have demonstrated that the present program can successfully model simple and complex relative dynamic movements (i.e., those involving rotation of source and human phantom in a 3-dimensional field). The present program would be useful for organ-based dosimetry and could also be used as a tool for teaching nuclear radiation physics and its interaction with the human body.

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review.

Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapist-like assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues.

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems' progress.

The impact of mobile money on long-term poverty: evidence from Bangladesh.

Mobile money has become a lifeline for millions of poor people who have limited access to a formal banking system. It encompasses a wide range of benefits such as women's empowerment, risk sharing, improved labor market outcomes and reductions in poverty. In this paper, we ask whether mobile money can help lift people out of poverty. Previous studies have addressed this question by using microanalyses of field experiments or longitudinal data on rural households, whereas we use district-level data to reevaluate the mobile money-poverty nexus. In particular, we study the impact of mobile money on district-level poverty in Bangladesh over the period 2010-2016. Our study finds that every 1 billion Taka (approximately US$ 11.76 million) increase in mobile money transactions via the bKash system in 2015 is associated with 0.71% point reduction in the poverty rate in Bangladesh. The marginal impact ranges from 0.12 to 1.15% points across the districts categorized in five groups as per 2010 poverty rates. The findings suggest that mobile money has been successful in fostering various poverty reduction initiatives and that targeted policy prescriptions can be devised to lift up poorer societies that are still outside the purview of mobile financial services. To further increase mobile money use, the government could use its own infrastructure to enhance mobile agent density in the poorest sectors of society.

A dual-antigen self-amplifying RNA SARS-CoV-2 vaccine induces potent humoral and cellular immune responses and protects against SARS-CoV-2 variants through T cell-mediated immunity.

Self-amplifying RNA vaccines may induce equivalent or more potent immune responses at lower doses compared to non-replicating mRNA vaccines via amplified antigen expression. In this paper, we demonstrate that 1 µg of an LNP-formulated dual-antigen self-amplifying RNA vaccine (ZIP1642), encoding both the S-RBD and N antigen, elicits considerably higher neutralizing antibody titers against Wuhan-like Beta B.1.351 and Delta B.1.617.2 SARS-CoV-2 variants compared to those of convalescent patients. In addition, ZIP1642 vaccination in mice expanded both S- and N-specific CD3+CD4+ and CD3+CD8+ T cells and caused a Th1 shifted cytokine response. We demonstrate that the induction of such dual antigen-targeted cell-mediated immune response may provide better protection against variants displaying highly mutated Spike proteins, as infectious viral loads of both Wuhan-like and Beta variants were decreased after challenge of ZIP1642 vaccinated hamsters. Supported by these results, we encourage redirecting focus toward the induction of multiple antigen-targeted cell-mediated immunity in addition to neutralizing antibody responses to bypass waning antibody responses and attenuate infectious breakthrough and disease severity of future SARS-CoV-2 variants.

svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network.

Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network- (CNN-) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.

Current pathogens infecting open fracture tibia and their antibiotic susceptibility at a tertiary care teaching hospital in South East Asia.

BACKGROUND: Tibia fractures represent the most prevalent open long-bone injuries. Indiscriminate, extensive, and unnecessary use of antibiotics has led to the emergence of infections caused by multidrug resistant organisms that increase morbidity and mortality. This study evaluated the spectrum of current organisms infecting the open tibia fractures and their antibiotic susceptibility pattern. This research did not alter the exiting practice of the institute to evaluate the current status. METHODS: This was a cross-sectional study on 628 patients presenting with open fractures of the tibia from July 2018 to July 2020. Sampling for three successive culture (and sensitivity) tests were carried out, 1st on specimens taken in the emergency room (upon patient presentation), 2nd in the emergency theatre after initial debridement, and 3rd in the ward between 12 to 14 days post operatively. RESULTS: The average age of the patients was 36.2± 15.4 years, with motor vehicle accidents being the predominant aetiology (72.2%). Results of specimen culture demonstrated that debridement could reduce microbial contamination significantly (P<.05) from 38.5 % to 26.4%. But from the ward sample, the infection rate was 45.1%, while contamination at entering the ward was only 26.4%. The bacteriological study found predominant multidrug-resistant Gram-negative organisms, namely Pseudomonas spp., Escherichia coli, Klebsiella spp., Acinetobacter spp., Enterobacter spp. and Proteus spp. Though Gram-positive Staphylococcus aureus was found significantly in the initial culture, they contributed minimally (1.4%) to infect the fracture site. CONCLUSION: The current study found a predominant shift in the trend toward multidrug-resistant Gram-negative organisms in orthopaedic infection, which was accompanied by a worrying pattern of hospital-acquired infection. These results will help to inform future research and policies within our institution.

Investigation of Clostridium perfringens in small-scale commercial broiler flocks in Mymensingh district of Bangladesh.

BACKGROUND AND AIM: Necrotic enteritis (NE) is one of the most prevalent diseases in broiler poultry caused by Clostridium perfringens connected with significant economic losses. A cross-sectional survey was conducted in Mymensingh district of Bangladesh to assess the prevalence of C. perfringens through toxinotyping molecular assay and confirm the risk factors for NE, including antimicrobial-resistant (AMR) status of the isolates. MATERIALS AND METHODS: We included 40 small-scale commercial broiler farms randomly selected from two subdistricts of Mymensingh district of Bangladesh. As an individual sample, 240 cloacal swabs, and as a pooled sample, 40 drinking water, 40 workers' hand washing, 40 litter swab, and 40 feed samples were collected and evaluated by culture, biochemical, and molecular assays. A pretested semi-structured interview questionnaire was employed to capture flock-level data on risk factors from the farm owners. The flock-level data on risk factors were assessed through univariable and multivariable logistic regression analyses with p<0.05 was considered statistically significant. RESULTS: Overall flock-level prevalence of C. perfringens was estimated to be 10.3% (95% confidence interval [CI] 7.5-13.6%). Litter swab (pooled) was found to be highly contaminated with C. perfringens (25.0%, 95% CI: 12.7-41.2%) followed by the cloacal swab (10.4%, 95% CI: 6.9-15.0%) and feed sample (5.0%, 95% CI: 0.6-16.9%). History of coccidia infection (Adjusted odds ratio =33.01, 95% CI: 2.14-507.59, p=0.01) was significantly associated with flock-level C. perfringens infection status. In this study, 78.1% isolates were found as multidrug-resistant as they demonstrated resistance to 3-5 antimicrobial agents. CONCLUSION: Evidence-based control options need to be taken through the uses of prebiotics and probiotics, biosecurity, and hygienic measurement, including control of coccidia infection, is needed to lessen the NE infection and AMR related to this pathogen in small-scale commercial broiler poultry.

In-plane directionality control of strongly localized resonant modes of light in disordered arrays of dielectric scatterers.

In this work we propose and analyze techniques of in-plane directionality control of strongly localized resonant modes of light in random arrays of dielectric scatterers. Based on reported diameters and areal densities of epitaxially grown self-organized nanowires, two-dimensional (2D) arrays of dielectric scatterers have been analyzed where randomness is gradually increased along a preferred direction of directionality enhancement. In view of the multiple-scattering mediated wave dynamics and directionality enhancement of light in such arrays, a more conveniently realizable, practical structure is proposed where a 2D periodic array is juxtaposed with a uniform, random scattering medium. Far- and near-field emission characteristics of such arrays show that in spite of the utter lack of periodicity in the disordered regime of the structure, directionality of the high-Q resonant modes is modified such that on average more than 70% of the output power is emitted along the pre-defined direction of preference. Such directionality enhancement and strong localization are nonexistent when the 2D periodic array is replaced with a one-dimensional Bragg reflector, thereby confirming the governing role of in-plane multiple scattering in the process. The techniques presented herein offer novel means of realizing not only directionality tunable edge-emitting random lasers but also numerous other disordered media based photonic structures and systems with higher degrees of control and tunability.

Public policy lessons from the Covid-19 outbreak: How to deal with it in the post-pandemic world?

Using cumulative confirmed cases of Covid-19 covering 163 countries, this paper tests several hypotheses that have received extensive attention in the popular media and academic research during the ongoing coronavirus pandemic. Our goal is to identify lessons for designing better public health policies in the post-pandemic era based on the past 6 months' experiences of these 163 countries. Based on 2SLS regression, we derive the following lessons. First, providing universal health care is a significant public health strategy for countries to help deal with similar outbreaks in the future. Second, tackling air pollution is a win-win solution, not only for better preparedness against Covid-19 or other airborne diseases, but also for the environment and climate change. Third, lockdowns may help to reduce community spread, but its impact on reducing Covid-19 incidence is not statistically significant. Similarly, antimalarial drugs have no significant effect on reducing the spread of the disease. Fourth, countries should encourage home-based work as much as possible until some treatment or cure is found for the virus. Fifth, the lessons of past SARS experience helped contain the spread of the infection in East Asian countries; other countries must adjust their social and cultural life to the new normal: wearing masks, washing hands, and keeping a distance from others in public places.