Intolerance of uncertainty across stress, anxiety, and depression among university students in Pakistan: A descriptive cross-sectional study.

Background: The mental health issues due to COVID-19, such as intolerance of uncertainty (IOU), anxiety, stress, and depression, have attracted extensive attention from researchers. The challenges for Pakistani university students could be worse than developed countries due to the lack of online courses/programs and online mental health support provided by academic institutions. Therefore, the current study aims to assess the intolerance of uncertainty, depression, anxiety, and stress of Pakistani university students after the second wave of COVID-19 and the relationship among these constructs. Methods: A convenience cross-sectional sampling method was used to collect data from university students in Pakistan between January 2021 and April 2022 via a structured online questionnaire. The Descriptive analysis focused on frequencies, percentages, mean, and standard deviation (SD) were calculated on IOU-12 and DASS-21. Covariance for the research model and confirmatory factor analyses fit indices for the IOU-12 and DASS-21 were analyzed by AMOS statistical packages. Results: As expected, anxiety, depression, and stress persist among Pakistani university students. On average, they report mild to moderate mental health problems regarding anxiety, depression, stress, and intolerance of uncertainty. Our results indicate a strong positive relationship among the three emotional distress components - anxiety, depression, and stress. However, our results suggest no significant relationship between IOU and the three subcomponents of emotional distress (anxiety, depression, and stress). Limitations: First, the cross-sectional survey design means we cannot conclude on the causal relations. Second, the self-report questionnaire embeds subjectivity issues. Last, the generalizability of the sample to the whole student population in Pakistan is limited, considering the sampling method. Conclusion: This study expanded the current knowledge in the psychological health domain (intolerance of uncertainty, anxiety, depression, and stress) due to the COVID-19 pandemic. In practice, higher education institutions should further mitigate university students' mental health issues. For researchers, our findings inspire future studies to delve into the relationship between IOU and mental health issues due to COVID-19 since our findings display contrary evidence for various reasons.

Prediction of the COVID-19 transmission: a case study of Pakistan.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We've used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

Clinicopathological and Radiological Features of Cats Presented with Infectious Respiratory Disease Signs: A Focus on Rhodococcus equi and Klebsiella pneumoniae.

The objective of this study was to evaluate the prevalence of involvement of common viral organisms R. equi and K. pneumoniae and their clinicopathological and radiological features in respiratory disease of Malaysian domestic cats. A total of 34 feline cases with acute/chronic infectious respiratory disease signs were followed prospectively to investigate respiratory disease due to R. equi and K. pneumoniae and their relationship with concurrent viral infections in disease manifestation. All sampled cats (n = 27) were positive for FCoV antibodies and negative for FeLV. A significantly high antibody titer for FCV in n = 26 cases was also noticed. A single sample of pyothorax from a 3-months-old, non-vaccinated kitten was positive for R. equi. Bronchopneumonia with severe infiltration of the polymorphs and mononuclear inflammatory cells were prominent features of lungs histopathology from the kitten positive for R. equi. K. pneumoniae subsp. pneumoniae was confirmed from tracheal swabs of two cats. Histologically, the tracheal tissues of the two cats positive for K. pneumoniae were normal. In diagnostic imaging, epicenter of the infectious URT disease was nasal conchae rostrally and nasal turbinates caudally, however for infectious LRT disease was bronchial tree. Conclusively, infectious respiratory disease is a complex illness in cats, predominantly for unvaccinated kittens and young adult cats, especially those kept in multi-cat household or shelter environments because of the involvement of multiple bacterial and viral organisms as primary or secondary invaders. Clinicians should not preclude feline rhodococcosis from differentials, especially in kittens with pyothorax and less than one year of age. Unlike R. equi, K. pneumoniae has the potential to colonize URT of cats which might be disseminating further to cause LRT disease.

Identification of RdRp inhibitors against SARS-CoV-2 through E-pharmacophore-based virtual screening, molecular docking and MD simulations approaches.

The outbreak of novel Coronavirus, an enduring pandemic declared by WHO, has consequences to an alarming ongoing public health menace which has already claimed several million human lives. In addition to numerous vaccinations and medications for mild to moderate COVID-19 infection, lack of promising medication or therapeutic pharmaceuticals remains a serious concern to counter the ongoing coronavirus infections and to hinder its dreadful spread. Global health emergencies have called for urgency for potential drug discovery and time is the biggest constraint apart from the financial and human resources required for the high throughput drug screening. However, computational screening or in-silico approaches appeared to be an effective and faster approach to discover potential molecules without sacrificing the model animals. Accumulated shreds of evidence on computational studies against viral diseases have revealed significance of in-silico drug discovery approaches especially in the time of urgency. The central role of RdRp in SARS-CoV-2 replication makes it promising drug target to curtain on going infection and its spread. The present study aimed to employ E-pharmacophore-based virtual screening to reveal potent inhibitors of RdRp as potential leads to block the viral replication. An energy-optimised pharmacophore model was generated to screen the Enamine REAL DataBase (RDB). Then, ADME/T profiles were determined to validate the pharmacokinetics and pharmacodynamics properties of the hit compounds. Moreover, High Throughput Virtual Screening (HTVS) and molecular docking (SP & XP) were employed to screen the top hits from pharmacophore-based virtual screening and ADME/T screen. The binding free energies of the top hits were calculated by conducting MM-GBSA analysis followed by MD simulations to determine the stability of molecular interactions between top hits and RdRp protein. These virtual investigations revealed six compounds having binding free energies of -57.498, -45.776, -46.248, -35.67, -25.15 and -24.90 kcal/mol respectively as calculated by the MM-GBSA method. The MD simulation studies confirmed the stability of protein ligand complexes, hence, indicating as potent RdRp inhibitors and are promising candidate drugs to be further validated and translated into clinics in future.

Land subsidence analysis using synthetic aperture radar data.

Land subsidence is considered a threat to developing cities and is triggered by several natural (geological and seismic) and human (mining, groundwater withdrawal, oil and gas extraction, constructions) factors. This research has gathered datasets consisting of 80 Sentinel-1A ascending and descending SLC images from July 2017 to July 2019. This dataset, concerning InSAR and PS-InSAR, is processed with SARPROZ software to determine the land subsidence in Gwadar City, Balochistan, Pakistan. Later, the maps were created with ArcGIS 10.8. Due to InSAR's limitations in measuring millimeter-scale surface deformation, Multi-Temporal InSAR techniques, like PS-InSAR, are introduced to provide better accuracy, consistency, and fewer errors of deformation analysis. This remote-based SAR technique is helpful in the Gwadar area; for researchers, city mobility is constrained and has become more restricted post-Covid-19. This technique requires multiple images acquired of the same place at different times for estimating surface deformation per year, along with surface uplifting and subsidence. The InSAR results showed maximum deformation in the Koh-i-Mehdi Mountain from 2017 to 2019. The PS-InSAR results showed subsidence up to -92 mm/year in ascending track and -66 mm/year in descending track in the area of Koh-i-Mehdi Mountain, and up to -48 mm/year in ascending track and -32 mm/year in descending track in the area of the deep seaport. From our experimental results, a high subsidence rate has been found in the newly evolving Gwadar City. This city is very beneficial to the country's economic development because of its deep-sea port, developed by the China-Pakistan Economic Corridor (CPEC). The research is associated with a detailed analysis of Gwadar City, identifying the areas with significant subsidence, and enlisting the possible causes that are needed to be resolved before further developments. Our findings are helpful to urban development and disaster monitoring as the city is being promoted as the next significant deep seaport with the start of CPEC.

Pharmacotherapeutic Potential of Natural Products to Target the SARS-CoV-2 PLpro Using Molecular Screening and Simulation Approaches.

Because of the essential role of PLpro in the regulation of replication and dysregulation of the host immune sensing, it is considered a therapeutic target for novel drug development. To reduce the risk of immune evasion and vaccine effectiveness, small molecular therapeutics are the best complementary approach. Hence, we used a structure-based drug-designing approach to identify potential small molecular inhibitors for PLpro of SARS-CoV-2. Initial scoring and re-scoring of the best hits revealed that three compounds NPC320891 (2,2-Dihydroxyindene-1,3-Dione), NPC474594 (Isonarciclasine), and NPC474595 (7-Deoxyisonarciclasine) exhibit higher docking scores than the control GRL0617. Investigation of the binding modes revealed that alongside the essential contacts, i.e., Asp164, Glu167, Tyr264, and Gln269, these molecules also target Lys157 and Tyr268 residues in the active site. Moreover, molecular simulation demonstrated that the reported top hits also possess stable dynamics and structural packing. Furthermore, the residues' flexibility revealed that all the complexes demonstrated higher flexibility in the regions 120-140, 160-180, and 205-215. The 120-140 and 160-180 lie in the finger region of PLpro, which may open/close during the simulation to cover the active site and push the ligand inside. In addition, the total binding free energy was reported to be - 32.65 ± 0.17 kcal/mol for the GRL0617-PLpro, for the NPC320891-PLpro complex, the TBE was - 35.58 ± 0.14 kcal/mol, for the NPC474594-PLpro, the TBE was - 43.72 ± 0.22 kcal/mol, while for NPC474595-PLpro complex, the TBE was calculated to be - 41.61 ± 0.20 kcal/mol, respectively. Clustering of the protein's motion and FEL further revealed that in NPC474594 and NPC474595 complexes, the drug was seen to have moved inside the binding cavity along with the loop in the palm region harboring the catalytic triad, thus justifying the higher binding of these two molecules particularly. In conclusion, the overall results reflect favorable binding of the identified hits strongly than the control drug, thus demanding in vitro and in vivo validation for clinical purposes.

Impact of barriers to SARS-Cov-2 vaccination uptake in HIV-infected patients.

HIV-positive (PLWH) persons should take the SARS-CoV-2 vaccination first due to the increased risk of developing a deadly strain of COVID-19. That's why it's important to monitor population vaccination rates and identify PLWH who aren't immunized. PLWH were examined for SARS-CoV-2 immunization and non-vaccination. It was a cross-sectional study, conducted in the Tehsil Headquarters Hospital Sohawa from May-October 2021. Ninety five HIV-positive patients of both genders were presented. 14-60 years were the age of patients. HIV, demographics and vaccination status were collected after written informed consent. Clinically adverse outcomes were assessed among vaccinated and non-vaccinated HIV infected patients. There were 56 (58.9%) males and 39 (41.1%) females. Frequency of homosexual transmission group was higher among 48 (50.2%) cases, followed by heterosexual group 25 (26.3%), 15 (15.8%) patients with injected drugs and 7 (7.4%) patients were HIV-infected with other reason. We found that 54 (56.8%) patients were vaccinated and 41 (43.2%) patients were unvaccinated. Frequency of ICU stay and mortality was significantly higher among non-vaccinated patients with p value <0.005. Non-vaccinated patients cited no-safety, medical facility distrust and covid-19 as a transient illness. This study found HIV-unvaccinated people had increased odds of unfavorable outcomes.

New Insights into the Basic and Translational Aspects of AMPK Signaling.

5'-adenosine monophosphate (AMP)-activated protein kinase (AMPK) is an enzyme regulating numerous cellular processes involved in cell survival as well as health- and lifespan [...].

Immunotherapeutic and immunomodulatory potentials of Antigen-Antibody complex vaccines

Immune system battles with deadly pathogens that mostly deteriorate health and cause morbidity and mortality. Antibodies are considered great players in the elimination of pathogens and hence, provide a shield against the future onset of various diseases. The immune complex, also known as an antigen-antibody (Ag-Ab) complex, exhibits the immunomodulatory potential leading to enhanced vaccine efficacy. A deeper understanding of Fc receptors (FcRs) and the interaction of the Fc part of an antibody with the different immune cells lead to in-depth knowledge of using this strategy for the development of immune complex vaccines. The concept of the Ag-Ab complex has been used in the prevention and therapy of various viral and bacterial diseases. Ag-Ab complex is gaining attention in COVID-19 vaccine development too, due to their greater immunoregulatory potential. The present literature highlights the importance of the Ag-Ab complex, the role of Fc receptors in immunomodulation, and their success as vaccines in viral and bacterial diseases of human and animal origin. Moreover, potential areas and lapses are explored to make better use of this prospect as a vaccine candidate. Studies revealed that the immunogenic and immunomodulatory potential of Ag-Ab complex can lead to greater protection. However, there is a dire need of establishing a link between laboratory and clinical findings to make it effective and safe tool for in vivo treatments.

Correlation Between Plasma Vitamin C Concentration and COVID-19 Outcomes among Patients Seen at a Major Hospital in the United Arab Emirates.

Background and Objective: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a newly emerged coronavirus that causes coronavirus disease-2019 (COVID-19) with varying degrees of disease manifestations. Vitamin C is an essential water-soluble vitamin with anti-inflammatory, antioxidant, antiviral, and immunomodulatory functions. The study aimed to investigate the association between serum vitamin C concentration and outcomes of COVID-19 among adults in the United Arab Emirates (UAE). Methods: This retrospective observational study included 67 COVID-19 patients aged 30-59 years old. Measurement of vitamin C levels was performed at the National Reference Laboratory, UAE using liquid chromatography-tandem mass spectrometry (LC/MS-MS). The cut-off value was 0.4 mg/dl; plasma levels that ranged from 0.4 to 2 mg/dl were defined as sufficient. Values above 2 mg/dl were recognized as high and values less than 0.4 mg/dl are considered low or deficient. Results: Among the included patients, 58.2% suffered from vitamin C deficiency. We found a statistically significant correlation between the concentration of serum vitamin C and age (p=0.03), the presence of hypertension (p=0.013), diabetes (p=0.01), and the development of pneumonia (p=0.012). There was no significant correlation between the concentration of serum vitamin C and the need for mechanical ventilation, Intensive Care Unit (ICU) admission, COVID-19 severity, or mortality. The risk of COVID-19 severity decreased in patients with sufficient vitamin C levels by 52% compared to patients with vitamin C deficiency (p=0.177). There was a statistically significant correlation between vitamin C sufficiency and low lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen. However, the odds of vitamin C sufficiency in COVID-19 patients were significantly associated with lowering CRP levels (OR=0.99, 95% CI (0.98-1.00), p=0.024). Conclusion and Global Health Implications: Low serum vitamin C concentrations were associated with several demographic characteristics of patients, the presence of pneumonia, and inflammation. Furthermore, improving our social determinants, such as how we live, eat, drink, and vitamin C supplementation could positively impact the future health of the individual, community, and population.

Decreased incidence of febrile neutropenia in Michigan following masking and social distancing orders for the COVID-19 pandemic: A population based cohort study.

BACKGROUND: It has been theorized that 75%-80% of febrile neutropenia (FN) is caused by endogenous pathogens, while up to 20% of cases are thought to be caused by a viral infection. It is unknown if precautions such as masking and social distancing reduce the risk of FN in susceptible populations. AIM: To determine whether coronavirus disease 2019 (COVID-19) infection mitigation efforts, namely masking and social distancing, were associated with a reduction in the incidence of FN. METHODS: This was a retrospective population based cohort study comparing the incidence of FN in the 13 mo prior to (Year 0) and 13 mo following (Year 1) the public health executive orders (PHEO) in Michigan. Data was queried for all emergency department (ED) visits from April 1, 2019 to March 31, 2021 from the National Syndromic Surveillance Program, a program which collects data that is voluntarily submitted by approximately 89% of Michigan EDs. The primary study outcome was the incidence of FN as a proportion of ED visits in the 13-mo before and 13-mo after COVID-19 mitigations efforts, namely masking and social distancing. We hypothesized that there would be a significant decrease in the incidence of FN in the period following the PHEO aimed at reducing the spread of the severe acute respiratory syndrome coronavirus 2 virus. RESULTS: There was a total of 8979221 total ED visits captured during the study period. In Year 0 there were 5073081 recorded ED visits and 3906140 in Year 1. There was a significant reduction in the proportion of total ED visits with a diagnosis of FN, decreasing 13.3% across periods (0.15% vs 0.13%, P = 0.036). In patients with a hematologic malignancy a more impressive reduction in the incidence of FN was evident following PHEO (22% vs 17%, P = 0.02). CONCLUSION: We found a significant association between social distancing and mask guidelines implemented on a large public scale with decreased rates of FN, particularly in those with a hematologic malignancy. These findings may be useful in the design of future research and recommendations regarding the prevention of FN.

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.

Sampling Inspection Plan to Test Daily COVID-19 Cases Using Gamma Distribution under Indeterminacy Based on Multiple Dependent Scheme.

The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.

Design of a new Z-test for the uncertainty of Covid-19 events under Neutrosophic statistics.

BACKGROUND: The existing Z-test for uncertainty events does not give information about the measure of indeterminacy/uncertainty associated with the test. METHODS: This paper introduces the Z-test for uncertainty events under neutrosophic statistics. The test statistic of the existing test is modified under the philosophy of the Neutrosophy. The testing process is introduced and applied to the Covid-19 data. RESULTS: Based on the information, the proposed test is interpreted as the probability that there is no reduction in uncertainty of Covid-19 is accepted with a probability of 0.95, committing a type-I error is 0.05 with the measure of an indeterminacy 0.10. Based on the analysis, it is concluded that the proposed test is informative than the existing test. The proposed test is also better than the Z-test for uncertainty under fuzzy-logic as the test using fuzz-logic gives the value of the statistic from 2.20 to 2.42 without any information about the measure of indeterminacy. The test under interval statistic only considers the values within the interval rather than the crisp value. CONCLUSIONS: From the Covid-19 data analysis, it is found that the proposed Z-test for uncertainty events under the neutrosophic statistics is efficient than the existing tests under classical statistics, fuzzy approach, and interval statistics in terms of information, flexibility, power of the test, and adequacy.

A mini-review: positive impact of COVID-19 on Arial health and ecology.

COVID-19 can cause global pandemics; however, no specific vaccine has been recommended for COVID-19. Nearly 216 countries are trying to stop the spread of the disease, recover from it, and improve its mobility. In a way that people have not experienced in recent years, the COVID-19 pandemic affected humans through the year 2020. To stop the spread of the disease, many governments declared a complete lockdown.The nationwide lockdown had some positive effects on the environment even though it led to a decline in global economic growth. Air pollution levels reduced dramatically as a result of this lockdown on pollution. Most of Europe's populated cities saw a reduction in NO2 concentration of 45-54%. COVID-19 and air, water, and ecology are connected via two pathways, one occurring before the spread of the disease and the other following after. As a result of industrial activity, transportation, and high human density, pollutants were high in many areas before the disease spread. There was a reduction in population movements as well as a decline in human activities which resulted in a reduction in carbon dioxide emissions, an improvement of the ozone layer, as well as improvements in the Earth's weather and environment. As a result of a COVID-19 pandemic, human activities are negatively impacted, and the environment is positively affected. Our objective is to provide an assessment of the impact of human activities on the environment and ecology. During times of lockdown, there is a correlation between atmospheric changes and the behavior of natural creatures. Several significant findings are presented, including air pollution reduction, air quality improvement, ozone healing, and ecological sustainability. COVID-19 is beneficial for aerial health, aquatic health, and ecology in this paper.

Deviated nasal septum and nasal polyps in COVID-19 patients: A case control study in DHQ Teaching Hospital, Mirpur, Azad Jammu and Kashmir Pakistan

Objective: To correlate the prevalence of COVID-19 and the rhinopathological conditions simultaneously in the population visiting the hospital of Mirpur, Azad Jammu and Kashmir. Study Design: Case-control study. Setting: District Head Quarter (DHQ) Teaching Hospital Located in Mirpur, Azad Jammu and Kashmir. Period: September 2020 to February 2021. Material & Methods: Patients visiting the DHQ teaching hospital, Mirpur, Azad Jammu and Kashmir for their COVID-19 related symptoms were included in the study. Demographic data was obtained. Prevalence of DNS and nasal polyps was recorded for further statistical analysis of the symptoms in the population. The obtained patient data was evaluated for the rhinopathological conditions that exists with the prevailing COVID-19 or may even aggravate the situation. Although nasal polyps and DNS are not among the typical symptoms of the COVID-19 but they may add to the results of PCR testing following swab testing. Conclusion: Despite being a significant relationship, there lies a chance of misleading results of swab testing as these rhinopathies obstruct the airway hindering the path. Therefore, while carrying out a confirmation test for the infection it is important to consider the chances of rhinopathies in patients beforehand. [ FROM AUTHOR] Copyright of Professional Medical Journal is the property of Professional Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Modeling and forecasting the total number of cases and deaths due to pandemic.

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.

On Neutrosophic Extension of the Maxwell Model: Properties and Applications

This work presents the neutrosophic Maxwell distribution (NMD) as a novel probability distribution. The proposed model represents a generalized design of Maxwell distribution that provides more analytical flexibility for data, including all imprecise observations or some degree of vagueness within the dataset. Important reliability characteristics and distributional properties of NMD are developed under the notion of neutrosophy. The neutrosophic forms of some commonly used functions in applied statistics such as mean, variance, moment generating function, and shape coefficients are explored. In view of uncertainties involved in the processing data and indeterminacy in the defined parameters, an estimation framework using the maximum likelihood approach is established. Additionally, the quantile function is developed to validate the distributional properties of NMD. The efficiency of the neutrosophic estimate has been studied through a Monte Carlo simulation. Finally, real data on the incubation period of COVID-19 are considered for numerical illustration, and further extensions of the NMD for future research works are discussed.

Biofortification of Diverse Basmati Rice Cultivars with Iodine, Selenium, and Zinc by Individual and Cocktail Spray of Micronutrients

Given that an effective combined foliar application of iodine (I), selenium (Se), and zinc (Zn) would be farmer friendly, compared to a separate spray of each micronutrient, for the simultaneous biofortification of grain crops, we compared effectiveness of foliar-applied potassium iodate (KIO3, 0.05%), sodium selenate (Na2SeO4, 0.0024%), and zinc sulfate (ZnSO4∙7H2O, 0.5%), separately and in their combination (as cocktail) for the micronutrient biofortification of four Basmati cultivars of rice (Oryza sativa L.). Foliar-applied, each micronutrient or their cocktail did not affect rice grain yield, but grain yield varied significantly among rice cultivars. Irrespective of foliar treatments, the brown rice of cv. Super Basmati and cv. Kisan Basmati had substantially higher concentration of micronutrients than cv. Basmati-515 and cv. Chenab Basmati. With foliar-applied KIO3, alone or in cocktail, the I concentration in brown rice increased from 12 to 186 µg kg−1. The average I concentration in brown rice with foliar-applied KIO3 or cocktail was 126 μg kg−1 in cv. Basmati-515, 160 μg kg−1 in cv. Chenab Basmati, 153 μg kg−1 in cv. Kisan Basmati, and 306 μg kg−1 in cv. Super Basmati. Selenium concentration in brown rice increased from 54 to 760 µg kg−1, with foliar-applied Na2SeO4 individually and in cocktail, respectively. The inherent Zn concentration in rice cultivars ranged between 14 and 19 mg kg−1 and increased by 5–6 mg Zn per kg grains by foliar application of ZnSO4∙7H2O and cocktail. The results also showed the existence of genotypic variation in response to foliar spray of micronutrients and demonstrated that a foliar-applied cocktail of I, Se, and Zn could be an effective strategy for the simultaneous biofortification of rice grains with these micronutrients to address the hidden hunger problem in human populations.