BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers' perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature's existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.

Effects of physical property changes of expelled respiratory liquid on atomization morphology

A better understanding of the fluid dynamics of disease transmission by disintegrated respiratory droplets has been the focus of great attention since the recent outbreak of COVID-19. In particular, human respiratory activities such as coughing, sneezing and even talking and eating expel a large amount of pathogen-laden droplets. Particularly, during eating or drinking, the physical properties of saliva can be changed. In this study, we investigate the atomization morphology of expelled artificial saliva mixtures from the perspective of varying fluid physical properties, specifically surface tension and dynamic viscosity. Using high-speed shadowgraph experiments on artificial saliva, we visualize and analyse the disintegration of saliva liquid sheets into ligaments and droplets. We find that the viscosity and surface tension affect the droplet size formed from expelled saliva and follow scaling laws that have been previously observed and predicted for constant shear viscosity. We conclude that the changes in physical properties of saliva induced by eating and drinking tend to favour the formation of smaller droplets during sneezing or coughing, which could drive the airborne transmission pathway of pathogens. Furthermore, we derive a theoretical model based on scaling arguments that shows the breakup time of ligaments produced from the artificial saliva mixtures is dependent on the capillary number.

Tolerance limits for mixture-of-normal distributions with application to COVID-19 data

Tolerance intervals (TIs) are widely used in various applications including manufacturing engineers, clinical research, and pharmaceutical industries. TIs can be used to construct limits of control charts for monitoring quality characteristics. For manufacturing processes where multiple factors may contribute to defects or multiple-stream processes, a mixture distribution of several suitable probabilistic models may be a better choice than a simple distribution for modeling the data. TIs for the normal mixture distribution have been studied in the literature. This article reviews the TIs of the normal mixture distribution, the applications of the mixture distribution, and the control charts of the mixture distribution. A rule for constructing modified two-sided TIs of the normal mixture distribution is summarized, and this rule may be extended to construct modified two-sided TIs for general mixture distributions. The feasibility of using TIs to build control charts for mixture distributions is also discussed. A real data example of coronavirus disease 2019 is used to illustrate the method by linking the TI to control charts. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification. © 2023 Wiley Periodicals LLC.

MODELING AND CLASSIFICATION OF DEATHS DUE TO COVID-19 BASED ON MACHINE LEARNING TECHNIQUE

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building mod-els that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The con-vergence in the algorithm of expectation-maximization is satisfied after 167 itera-tions. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020. © 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Seasonal variation and ecological risk assessment of Pharmaceuticals and Personal Care Products (PPCPs) in a typical semi-enclosed bay — The Bohai Bay in northern China

The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay. © 2022 Elsevier B.V.

Optimization of gasification process parameters for COVID-19 medical masks using response surface methodology

Due to the COVID-19 pandemic, large amounts of medical wastes have been produced and their disposal has resulted in environmental and human health problems. This medical waste may include face masks, gloves, face shields, goggles, coverall suits, and other related wastes, such as hand sanitizer and disinfectant containers. To address this issue, the effect was investigated of gasification process parameters (type of COVID-19 medical mask based on the polypropylene ratio, pressure, steam ratio, and temperature) on hydrogen syngas and cold gas efficiency. The gasification model was developed using process modeling based on the Aspen Plus software. Response surface methodology with a 3k statistical factorial design was used to optimize the process aiming for the highest hydrogen yield and cold gas efficiency. Analysis of variance showed that both the steam ratio and temperature were significant parameters regarding the hydrogen yield and cold gas efficiency. Proposed models were constructed with very high accuracy based on their coefficient of determination (R2) values being greater than 0.97. The optimum conditions were: 65 % polypropylene in the mixture, a pressure of 1 bar, a steam ratio of 0.38, and a temperature of 900 °C, producing a maximum hydrogen yield of 40.61 % and cold gas efficiency of 81.43 %. These results supported the efficacy of the primary design for steam gasification using a mixture of plastic wastes as feedstock. The hydrogen could be utilized in chemical applications, whereas the efficiency could be used as a basis for further development of the process. © 2022 THE AUTHORS

Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations.

Counterfeit or substandard drugs are pharmaceutical formulations in which the active pharmaceutical ingredients (APIs) have been replaced or ingredients do not comply with the drug leaflet. With the outbreak of the COVID-19 pandemic, fraud associated with the preparation of substandard or counterfeit drugs is expected to grow, undermining health systems already weakened by the state of emergency. Analytical chemistry plays a key role in tackling this problem, and in implementing strategies that permit the recognition of uncompliant drugs. In light of this, the present work represents a feasibility study for the development of a NIR-based tool for the quantification of dexamethasone in mixtures of excipients (starch and lactose). Two different regression strategies were tested. The first, based on the coupling of NIR spectra and Partial Least Squares (PLS) provided good results (root mean square error in prediction (RMSEP) of 720 mg/kg), but the most accurate was the second, a strategy exploiting sequential preprocessing through orthogonalization (SPORT), which led (on the external set of mixtures) to an R2pred of 0.9044, and an RMSEP of 450 mg/kg. Eventually, Variable Importance in Projection (VIP) was applied to interpret the obtained results and determine which spectral regions contribute most to the SPORT model.

Analysis of Factors Influencing COVID-19 Mortality among Vaccinated People in Peru

We are currently facing the global pandemic caused by COVID-19, in the case of Peru, this disease has caused the death of approximately 200,000 people (September 2021), being one of the countries with the most deaths per thousand people. Due to this, progress is being made in the vaccination process, of which it has been possible to immunize more than 72% of the population with two doses. However, according to data collected by the Peruvian government, the deaths of people who would have been inoculated with at least one dose have been recorded. The present work proposes to apply machine learning models (Machine Learning), where the factors that influence the death of people are analyzed despite having been vaccinated with at least one dose, to achieve this goal, unsupervised learning techniques such as Kmeans, Spectral Clustering, Gaussian Mixture, Hierarchical Clustering, as well as data visualization techniques were applied. The results obtained reveal that the main factors that led to death are elderly people, mostly men, and that their health centers are also far from their homes, in addition to not having had access to hospitalization for adequate treatment. © 2022 WCSE. All Rights Reserved.

Skewed Auto-Regressive Process with Exogenous Input Variables: An Application in the Administered Vaccine Doses on Covid-19 Spread

This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP-SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.

Lutein and astaxanthin as dietary support of therapy

Lutein and astaxanthin belong to carotenoids which have wide applications in food, nutraceutical and pharmaceutical industries. The total chemical syntheses of lutein and astaxanthin produces a mixture of stereoisomers. Both carotenoids are extracted from the plant material as the mixture of mono- and diesters. Lutein is extracted from marigold and tagetes flowers. Lutein supplementation increases its concentration in blood serum and in the macula of the eye. There is increasing evidence that lutein is important in the prevention of age-related macular degeneration (AMD) and may improve eye health. Astaxanthin is extracted from the Haematococcus pluvialis algae. Due to its strong antioxidant, anti-inflammatory, and immunomodulatory properties, they can be used to support the treatment of neurodegenerative and cardiovascular diseases, diabetes, eye diseases, and to supplement the diet of COVID-19 patients.

Effect of Al2O3 Nanoparticles on Performance and Emission Characteristics of Diesel Engine Fuelled with Diesel–Neem Biodiesel Blends

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines.

Is it correct to name DESs deep eutectic solvents?

Recent years of research and development have brought frequently used terms for new types of green solvents to the lexicon of scientists. This can lead to terminological inaccuracies. In particular, different names are being used for the same types of solvents: Deep Eutectic Solvents (DES);Natural Deep Eutectic Solvents;Low-Transition Temperature Mixtures;Low-Melting Mixtures. It would, therefore, be appropriate to eliminate certain inaccuracies and to use simplification, which means using the general term “Low-Temperature Transition Mixtures” or introducing the term “DES-like mixtures”.

The Relaxation Effect of Music and Natural Sounds

With the spread of the coronavirus infection in Japan, the suicide rate has increased. So, we thought that we want to take care of people's mental health by using music. In previous studies, they showed that music included 1/f frequency influence the state of mind relaxing. On the other hand, there are reports that the natural sounds induce the state of relaxing. In this study, we investigated whether the reminiscence of scenes by natural sounds can work on the central nervous system and bring about a relaxing effect, and whether a synergistic effect can be observed by working together with the autonomic nervous system. Headphones and a questionnaire were also used. Twelve young male and female were recruited as subjects. The results showed that most of the subjects had a good impression of music, nature sounds and their mixtures. The results for music and nature sounds were mixed, but most of the subjects had a good impression of the blended sounds. The effect on relaxation, as measured by the questionnaire, was better after listening to the music than before in all conditions. In all conditions, there was a greater improvement in relaxation after listening to the music than before, and the degree of improvement was as follows: mixture > nature sounds > music. Although there was no statistical superiority, it was possible to show this trend. This suggests that there may be a synergistic effect of mixing music, which affects the autonomic nervous system, with nature sounds, which affect the central nervous system. © 2022 IEEE.

Interpretable Hierarchical Bayesian Modeling of Cell-Type Distributions in COVID-19 Disease

High-throughput sequencing of ribonucleic acid molecules is used increasingly to understand gene expression in organs, tissues, and therapies, at a single-cell level. To facilitate the discovery of the heterogeneity and cell-specific factors of the COVID-19 disease, we use an interpretable computational approach that derives cell mixtures from peripheral blood mononuclear cells of healthy donors, and influenza, asymptomatic, mild and severe COVID-19 patients. Cell mixtures are generated using hierarchical Bayesian modeling and are subsequently used as features in the gradient boosting tree classifier. Balanced accuracy of five-fold cross-validation was 68%, significantly higher than expected by random chance. Moreover, 11 out of 19 donors' samples were classified accurately. The main advantage of the mixture-based approach compared to the traditional feature-based classification, is its ability to capture associations between genes as well as between cells. © 2022 IEEE.

The Diffusion Behavior of CO2 Adsorption from a CO2/N2 Gas Mixture on Zeolite 5A in a Fixed-Bed Column

The objective of this research was to investigate the behavior and conditions for CO2 adsorption using a mixture of CO2/N2 over a fixed-bed column of zeolite 5A. The study was performed with a variation in gas composition of CO2/N2 as a 20/80, 50/50, and 80/20 volume %, the adsorption temperatures as 298, 333, and 373 K and the total feed flow rates as 1, 2, and 4 L/h under 100 kPa pressure. The Bohart–Adams, Yoon–Nelson, and Thomas models were used to predict the breakthrough behavior of CO2 adsorption in a fixed column. Furthermore, the adsorption mechanism has been investigated using the kinetics adsorption of pseudo-first-order, pseudo-second-order, Boyd model, and intraparticle model. Increasing the CO2 composition of a gas mixture resulted in a high CO2 adsorption capacity because of the high partial pressure of CO2. The capacity of CO2 adsorption was decreased with increasing temperature because of physical adsorption with an exothermic reaction. The CO2 adsorption capacity was also decreased with increasing feed flow rates with inadequate time for CO2 adsorbates diffusion into the pores of the adsorbent before exiting the packed bed. The CO2 adsorption by zeolite 5A confirmed that the physical adsorption with intraparticle diffusion was the rate-controlling step of the whole process.

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany;Kuopio, Finland;Leipzig, Germany;and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) COVID-19 campaign in May 2020. The reanalysis (fully quality-assured) lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (±0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch-dominant pollen periods, whereas for Hohenpeißenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods;the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

Analyzing the Interactions among the Barriers for Safe and Effective Medical Waste Management

Because of the growing number of hospitals in the country like the United Arab Emirates, huge medical wastes are generated in the hospitals, and managing this medical waste is considered a big challenge. In recent days, COVID 19 pandemic has paved the way for the generation of relatively huge amounts of infectious and hazardous waste in healthcare hospitals, and proper disposal of this heterogeneous mixture of medical waste is the biggest challenge. Improper waste management developed in health care units causes a direct impact on the workers, waste handlers, patients, caregivers, and the community. Also, it is important to manage the medical waste properly so that the environment will not get affected. In order to overcome this problem, both the manufacturer and the medical practitioner should take utmost care in managing the medical waste properly in all stages, starting from collection to the final disposal. The main aim of this research is to understand the different types of medical waste in the hospital and identify the barriers that impede the effective management of medical waste. For analyzing the interactions among the barriers, Interpretive Structural Modelling (ISM) approach is proposed as a solution methodology in this research work. By analysing the interaction among the barriers using the ISM model, we may extract the most influencing barrier that challenges both hospital management and government in managing medical waste safely and effectively. © 2021 IEEE.

Microplastic Extraction from the Sediment Using Potassium Formate Water Solution (H2O/KCOOH)

Microplastics (MPs) are considered an important stratigraphic indicator, or ‘technofossils’, of the Anthropocene. Research on MP abundance in the environment has gained much attention but the lack of a standardized procedure has hindered the comparability of the results. The development of an effective and efficient method of MP extraction from the matrix is crucial for the proper identification and quantifying analysis of MPs in environmental samples. The procedures of density separation used currently have various limitations: high cost of reagents, limited solution density range, hazardous reagents, or a combination of the above. In this research, a procedure based on density separation with the use of potassium formate water solution (H2O/KCOOH) in controlled conditions was performed. Experimental sediment mixtures, spiked with polyethylene (PE), polystyrene (PS), polyurethane (PUR) and polyethylene terephthalate (PET) particles were prepared and an extraction procedure was tested in the context of a weight-based quantitative analysis of MPs. This article discusses the effectiveness and safety of the method. It additionally provides new information on the interactions between MP particles and the mineral matter of the sediment. Results were acquired with the use of instrumental methods, namely thermogravimetry (TG), Fourier Transform Infrared (FTIR) spectroscopy, Field Emission Scanning Electron microscopy and Energy Dispersive spectrometry (SEM/EDS), as well as X-ray fluorescence (XRF) analysis.

Thermodynamic Analysis of a Regenerative Brayton Cycle Using H2, CH4 and H2/CH4 Blends as Fuel

Considering a simple regenerative Brayton cycle, the impact of using different fuel blends containing a variable volumetric percentage of hydrogen in methane was analysed. Due to the potential of hydrogen combustion in gas turbines to reduce the overall CO2 emissions and the dependency on natural gas, further research is needed to understand the impact on the overall thermodynamic cycle. For that purpose, a qualitative thermodynamic analysis was carried out to assess the exergetic and energetic efficiencies of the cycle as well as the irreversibilities associated to a subsystem. A single step reaction was considered in the hypothesis of complete combustion of a generic H2/CH4 mixture, where the volumetric H2 percentage was represented by fH2, which was varied from 0 to 1, defining the amount of hydrogen in the fuel mixture. Energy and entropy balances were solved through the Engineering Equation Solver (EES) code. Results showed that global exergetic and energetic efficiencies increased by 5% and 2%, respectively, varying fH2 from 0 to 1. Higher hydrogen percentages resulted in lower exergy destruction in the chamber despite the higher air-excess levels. It was also observed that higher values of fH2 led to lower fuel mass flow rates in the chamber, showing that hydrogen can still be competitive even though its cost per unit mass is twice that of natural gas.

Therapeutic Treatment Plan Optimization during the COVID-19 Pandemic: A Comprehensive Physicochemical Compatibility Study of Intensive Care Units Selected Drugs.

BACKGROUND: The SARS-CoV-2 pandemic has resulted in a dramatic rise of the demand for medical devices and drugs. In this context, an important shortage of programmable syringe pumps, used to administrate different drugs in intensive care units, was seen. The opportunity of administrating combinations of five intensive care units selected drugs (Sufentanil, Clonidine, Loxapine, Midazolam, and Ketamine) was considered. METHODS: The drug mixtures were studied in a pure form or diluted in NaCl 0.9% or G5%. Twenty-six possible combinations of the five drugs were produced in glass vials or polypropylene syringes and stored at 25 °C for 14 days. The LC method was implemented to study drugs combinations in the presence of the degradation products. The clearness and pH were also monitored. RESULTS: All the 26 possible combinations displayed adequate physicochemical stability at 25 °C: at least 3 days and 7 days, respectively, for the dilution in 0.9% NaCl or glucose 5%, and the pure drug products mixtures. CONCLUSIONS: The study provided sufficient stability results, covering the medication administration period of at least three days. The combination of more than two drugs offers the advantage of minimizing the individual doses and reduces unwanted side-effects. Hence, this study opens up the possibility of combining the five drugs in one single syringe, which is useful especially under the current circumstances associated with an important shortage of programmable syringe pumps and pharmaceuticals.