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Background: Self-medication is one of the major health concerns worldwide. Most people are not aware of the serious consequences faced by them if they take medications specially the antibiotics on their own. It is very evident that self-medication is increasing in the developing countries like India. Hence, this study is designed to study the self-medication practices in the metro city Bangalore, India. Materials and Methods: A cross sectional study was conducted, and data was collected by using structured questionnaire. Data was collected from 350 persons and analyzed using SPSS. Results: The prevalence of self-medication was 94%. The study revealed that self-medication was practiced more among young than old age group (P = 0.004). Unmarried people practiced self-medication more than the married people (P = 0.011). 80% preferred allopathic medicines. The common ailments for which self-medication was practiced were Common cold (59%), Headache (57%) and fever (55%). The type of drugs commonly used were Antipyretics (51%) and analgesics (43%). Conclusion: The study revealed that prevalence of self-medication was high. 91% of the respondents were belonging to urban area and hence it attributes to higher educational background and socioeconomic status. More awareness should be created on the side effects of practicing self-medication. Key words: Self-medication, Urban population, Rural population, Prevalence .

Renewable energy sources are not widely adopted due to their variability. Solar Power prediction is one such technique that can predict the solar power to be generated in the near future. Artificial Intelligence-based techniques are also used widely for power prediction. Short-term prediction is of much interest nowadays as it can help in scheduling activities. In this paper, a solar power prediction is done using Genetic Algorithm with ANN to help better understand the various concepts involved in power prediction. Artificial Neural Networks has shown much progress in the field of prediction. Power prediction proves to be beneficial for users who want to reduce their dependence on grid power. Keywords: Genetic Algorithm, Solar Power, Backpropagation, Solar Power Prediction, ANN

Introduction: Organizations are dynamic places; health care organizations are service organizations with a complex social system of employees. Organizational culture is commonly defined as the shared values, beliefs, and assumptions that guide employees' behavior and work style, thus affecting performance and job satisfaction. This study examines organisational culture and determines the association between organizational culture and job satisfaction. Methodology: It was a descriptive cross-sectional survey study where the study population was categorized into clinical and non-clinical categories. A total of 196 employees from 18 to 60years of age were included using stratified random sampling. Organizational Culture is measured using factors: Innovation and risk-taking, Openness, Identity, Result Orientation and Management Support. Job satisfaction is measured using variables: Job nature, Job security, Talent used in the job, working hours, relationship with co-workers and supervisors, and Job recognition. Participants’ responses were recorded on the Likert scale. ANOVA, T-test, and chi-square test were used to analyze the data Results: The study finds that "Identity" and Openness" are dominant cultural factors. 90.5% of the employees who said Organisation culture is good rated their satisfaction level as good.100% of the clinical staff said they take pride in working for the organisation and study and culture and Job satisfaction association P-value is

The current kinetic models for polystyrene (PS) pyrolysis contain many simplifications to reduce their size and the corresponding simulation time. Moreover, they are often based on rate coefficients determined using non -ideal experimental data featuring ambiguous process conditions with respect to mixing and temperature uni-formity. The practical interest of PS pyrolysis is the production of styrene monomer to be reused as a feedstock in the polymerization of styrene. In the present work, a lab-scale tree-based kinetic Monte Carlo (kMC) model is presented that differentiates between 18 reaction families and 26 end-group pairs to study the product yield variations for thermal degradation of PS. Model parameters follow from Bayesian optimization to experimental data recorded with an in-house micro-pyrolysis unit coupled with comprehensive two-dimensional gas chro-matography. Low chain length (CL) anionic-made PS is specifically considered to gain an understanding of the role of specific end-groups. The experimental yields of the major products (monomer: 74.7-80.8 wt%, dimer: 5.1-5.5 wt%, trimer: 1.6-7.7 wt%) are well-predicted with the fine-tuned parameters. The main reaction pathway in the formation of styrene monomer is end-chain beta-scission, while mid-chain beta-scission is primarily involved in the formation of the styrene dimer and trimer. Our model shows that the pyrolysis of low CL anionic-made PS leads to better rate coefficients than those obtained from state-of-the-art pyrolysis of long CL PS, in which end-groups play a much smaller role.

Sulfolane alters the mechanism and energetics of dissociation of H2O2, thereby controlling the active oxygen species in the Fe-ZSM-5 catalyst.

A novel stability-indicating isocratic reverse phase high-performance liquid chromatographic method was developed for the quantitative determination of perampanel in the presence of degradation products and its process-related impurities. Good resolution was achieved between the peaks corresponding to process-related impurities and degradation products from the analyte using Shim-pack GIST C18 column with mobile phase, potassium dihydrogen phosphate (pH 2.5; 30 mM)-acetonitrile (45: 55, v/v) at a wavelength of 294 nm. Perampanel and its impurities were well-separated within a run time of 7 min. To show the stability-indicating power of the method, forced degradation studies were performed on bulk samples of perampanel as per ICH prescribed stress condition using acid, base, hydrolytic, oxidative and photolytic degradation conditions. The degradation products were resolved from main peak and its impurities. The developed method was validated as per ICH guidelines with respect to specificity, linearity, limit of detection, limit of quantification, accuracy, precision and robustness. This method was also applied for assay and related substances determination of perampanel in bulk and pharmaceutical dosage form.

SARS-CoV-2 has caused over 100,000,000 cases and almost 2,500,000 deaths globally. Comprehensive assessment of the multifaceted antiviral Ab response is critical for diagnosis, differentiation of severity, and characterization of long-term immunity, especially as COVID-19 vaccines become available. Severe disease is associated with early, massive plasmablast responses. We developed a multiplex immunoassay from serum/plasma of acutely infected and convalescent COVID-19 patients and prepandemic and postpandemic healthy adults. We measured IgA, IgG, and/or IgM against SARS-CoV-2 nucleocapsid (N), spike domain 1 (S1), S1–receptor binding domain (RBD) and S1–N-terminal domain. For diagnosis, the combined [IgA + IgG + IgM] or IgG levels measured for N, S1, and S1-RBD yielded area under the curve values ≥0.90. Virus-specific Ig levels were higher in patients with severe/critical compared with mild/moderate infections. A strong prozone effect was observed in sera from severe/critical patients—a possible source of underestimated Ab concentrations in previous studies. Mild/moderate patients displayed a slower rise and lower peak in anti-N and anti-S1 IgG levels compared with severe/critical patients, but anti-RBD IgG and neutralization responses reached similar levels at 2–4 mo after symptom onset. Measurement of the Ab responses in sera from 18 COVID-19–vaccinated patients revealed specific responses for the S1-RBD Ag and none against the N protein. This highly sensitive, SARS-CoV-2–specific, multiplex immunoassay measures the magnitude, complexity, and kinetics of the Ab response and can distinguish serum Ab responses from natural SARS-CoV-2 infections (mild or severe) and mRNA COVID-19 vaccines.

ispartof: Progress In Energy And Combustion Science vol:96 status: published

The effect of hydrocarbon addition on tip opening of lean and stoichiometric hydrogen-air flames is studied computationally by performing two-dimensional numerical simulations. The numerical study reveals that the flame tip of the H2-air burner stabilized flame is open at lean and stoichiometric mixture conditions. The flame tip closes upon hydrocarbon addition. The tip closing is mainly affected by preferential diffusion of the multi-component mixture and the stretch effects. In the addition of light hydrocarbon (CH4), the tip closing starts at a higher percentage of hydrocarbon addition in H2-air flames. Whereas, upon the addition of heavy hydrocarbons such as propane and butane in H2-air flames, tip closing starts with a lesser amount of hydrocarbon addition. Temperature, OH mole fraction and heat release rate have been investigated, focusing on the flame structure at the tip. The tip opening regime diagram for H2–CH4-air, H2–C3H8-air and H2–C4H10-air mixtures are presented.

An experimental and numerical study on the combined effect of N2/CO2 dilution and hydrogen addition on the laminar burning velocity (LBV) of methane-oxygen mixtures was conducted. The experiments were performed at atmospheric conditions using the heat flux method for effective equivalence ratios (ϕF) varying from 0.7 to 1.3. The results show that the hydrogen addition causes an increase in LBV for all the mixture conditions. The variation in LBV based on hydrogen addition parameter (RH) for all N2 dilution conditions were following a linearly increasing trend. The strong effect of hydrogen addition on LBV is observed at lean and rich mixtures compared to that at near stoichiometric mixture conditions. The experimental results show that the percentage variation in LBV with RH at rich mixture is more substantial at 75% N2 dilution compared to that at 65% N2 dilution.

Soft wearable robots are a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints like the shoulder, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multi-DoF shoulder exosuit capable of sensing the kinematics of the shoulder joint. The proposed tendon-based sensing system is inspired by the concept of muscle synergies, the body's sense of proprioception, and finds its basis in the organization of the muscles responsible for shoulder movements. A motion-capture-based evaluation of the developed sensing system showed conformance to the behaviour exhibited by the muscles that inspired its routing and validates the hypothesis of the tendon-routing to be extended to the actuation framework of the exosuit in the future. The mapping from multi-sensor space to joint space is a multivariate multiple regression problem and was derived using an Artificial Neural Network (ANN). The sensing framework was tested with a motion-tracking system and achieved performance with root mean square error (RMSE) of approximately 5.43 degrees and 3.65 degrees for the azimuth and elevation joint angles, respectively, measured over 29000 frames (4+ minutes) of motion-capture data., 8 pages, 7 figures, 1 table

The aim of this work is to develop insights into adsorption of hydrogen and carbon dioxide in a zeolitic imidazolate framework, ZIF-8, using high-pressure adsorption studies, adsorption isotherm model fitting, and DFT investigation of preferential adsorption sites and binding energies. The robustness of ZIF series metal–organic frameworks has drawn interest towards its utility in large scale applications in gas storage and separation. We use room temperature synthesis of ZIF-8 using DMF as a solvent, and benchmarked it against typical solvothermal synthesis. The resulting material is characterized using XRD, SEM, TG–DSC and N2 adsorption isotherm. High-pressure volumetric adsorption of the activated materials is conducted to analyze the hydrogen and carbon dioxide storage capacities up to 50 and 40 bar, respectively. ZIF-8 shows maximum H2 storage capacity of 3.13 wt% at 50 bar and 77 K, and CO2 storage capacity of 46 wt% at 40 bar and 300 K. The parameters of Unilan adsorption isotherm are estimated from the equilibrium adsorption data and isosteric heats of adsorption for H2 and CO2 on ZIF-8 are computed. DFT calculations are used to obtain preferential adsorption sites of H2 and CO2. Adsorption enthalpy values were calculated from DFT as − 7.08 and − 25.98 kJ/mol, respectively for H2 and CO2 at the most preferred sites. We found a close agreement between isosteric heat of adsorption of hydrogen (− 4.68 kJ/mol) and the enthalpy of hydrogen adsorption from DFT (− 6.04 kJ/mol) at 77 K.

Pyrolysis is one of the promising routes for the chemical recycling of polystyrene. However, the challenge lies in developing optimal contacting patterns to enhance the yield of liquid products (styrene). The present work demonstrates the applicability of a vortex reactor to effectively pyrolyze polystyrene. Three experiments were conducted at temperatures of 450, 500 and 550 oC, wherein the polystyrene was fed at a rate of 1kg/h. Prior experiments had indicated that such a flow rate ensures a smooth and uniform injection of PS in the reactor. The liquid products obtained were condensed in a system of two condensers. Subsequently, the products were analyzed offline in GC x GC- FID. The analysis showed the significant formation of styrene.