Background and aims We sought to measure the effect of lockdown, implemented to contain COVID-19 infection, on routine living and health of patients with chronic diseases and challenges faced by them. Methods A semi-structured online questionnaire was generated using “Google forms” and sent to the patients with chronic diseases using WhatsApp. Data were retrieved and analyzed using SPSS. Results Out of 181 participants, 98% reported effect of lockdown on their routine living while 45% reported an effect on their health. The key challenges due to lockdown were to do daily exercise, missed routine checkup/lab testing and daily health care. Conclusion It is important to strategize the plan for patients with chronic diseases during pandemic or lockdown.

The amount of CO 2 in the atmosphere is rising due to the combustion of fossil fuels to fulfill the energy demand. The need to build cleaner and more efficient energy systems is motivated by the introduction of chemical looping combustion (CLC) as an alternative to conventional combustion by transferring oxygen. The transfer of oxygen from the air to fuel is carried out by a metal oxide known as an oxygen carrier (OC). For high fuel conversion and oxygen transport capacity, many efforts have been made for the preparation of an OC with minimal material cost. This review aims to summarize the recent advances and development of various types of OCs; particularly those developed within the previous five years are critically discussed in this paper. The main criteria for the selection of the OCs for CLC include their oxygen vacancies, oxygen transport capacities, costs, and tendencies over coke deposition, agglomeration, and attrition. OCs for CLC can be generally divided into single oxides, mixed oxides, natural mineral, spinel from mixed metal oxides, and perovskite. These have been critically discussed with their significance in CLC. The performances, advantages, and limitations of the OCs are presented and compared in detail.

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This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte Carlo simulations. To evaluate the performance of these estimators, we assess the Mean Square Errors (MSE) and the Mean Absolute Percentage Errors (MAPE). The simulation results clearly illustrate the benefit of the methods of estimating these types of shrinkage parameters in finite samples. Finally, we illustrate the empirical relevance of our newly proposed methods using an empirically relevant application. Thus, in summary, via simulations of empirically relevant parameter values, and by a standard empirical application, it is clearly demonstrated that our technique exhibits more precise estimators, compared to traditional techniques-at least when multicollinearity exist among the regressors.

Abstract:The aim of this work was to study the combustion, performance, and emission characteristics of a 5.5 kW four-stroke single-cylinder water-cooled direct-injection diesel engine operated with blends of biodiesel-like fuel (BLF15, BLF20 & BLF25) obtained from a 50:50 mixture of transesterified waste transformer oil (TWTO) and waste canola oil methyl esters (WCOME) with petroleum diesel. The mixture of the waste oils was named as biodiesel-like fuel (BLF). The engine fuelled with BLF blends was evaluated in terms of combustion, performance, and emission characteristics. FTIR analysis was carried out to know the functional groups in the BLF fuel. The experimental results revealed the shorter ignition delay and marginally higher brake specific fuel consumption (BSFC), brake thermal efficiency (BTE) and exhaust gas temperature (EGT) values for BLF blends as compared to diesel. The hydrocarbon (HC) and carbon monoxide (CO) emissions were decreased by 10.92-31.17% and 3.80-6.32%, respectively, as compared to those of diesel fuel. Smoke opacity was significantly reduced. FTIR analysis has confirmed the presence of saturated alkanes and halide groups in BLF fuel. In comparison to BLF20 and BLF25, the blend BLF15 has shown higher brake thermal efficiency and lower fuel consumption values. The HC, CO, and smoke emissions of BLF15 were found lower than those of petroleum diesel. The fuel blend BLF15 is suggested to be used as an alternative fuel for diesel engines without any engine modification.

Coronavirus disease (Covid-19) has been spreading all over the world and its diagnosis is attracting more research every moment. It is need of the hour to develop automated methods, which could detect this disease at its early stage, in a non-invasive way and within lesser time. Currently, medical specialists are analyzing Computed Tomography (CT), X-Ray, and Ultrasound (US) images or conducting Polymerase Chain Reaction (PCR) for its confirmation on manual basis. In Pakistan, CT scanners are available in most hospitals at district level, while X-Ray machines are available in all tehsil (large urban towns) level hospitals. Being widely used imaging modalities to analyze chest related diseases, produce large volume of medical data each moment clinical environments. Since automatic, time efficient and reliable methods for Covid-19 detection are required as alternate methods, therefore an automatic method of Covid-19 detection using Convolutional Neural Networks (CNN) has been proposed. Three publically available and a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur (BVHB), Pakistan have been used. The proposed method achieved on average accuracy (96.68%), specificity (95.65%), and sensitivity (96.24%). Proposed model is trained on a large dataset and is being used at the Radiology Department, (BVHB), Pakistan.

The classic statistical method for modelling the rates and proportions is the beta regression model (BRM). The standard maximum likelihood estimator (MLE) is used to estimate the coefficients of the BRM. However, this MLE is very sensitive when the regressors are linearly correlated. Therefore, this study introduces a new beta ridge regression (BRR) estimator as a remedy to the problem of instability of the MLE. We study the mean squared error properties of the BRR estimator analytically and then based on the derived MSE, we suggest some new estimators of the shrinkage parameter. We also suggest a median squared error (SE) performance criterion, which can be used to achieve strong evidence in favour of the proposed method for the Monte Carlo simulation study. The performance of BRR and MLE is appraised through Monte Carlo simulation. Finally, an empirical application is used to show the advantages of the proposed estimator.

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