How Socioeconomic, Health Seeking Behaviours, and Educational Factors Are Affecting the Knowledge and Use of Antibiotics in Four Different Cities in Asia

Antibiotic resistance is occurring widely throughout the world and is affecting people of all ages. Socioeconomic factors, education, use of antibiotics, knowledge of antibiotics, and antibiotic resistance were assessed in four cities in Asia, namely Hong Kong, Shanghai, Hangzhou, and Bangkok. A survey using cluster sampling was used in 2021 to collect data on 642 subjects. Hongkongers used less antibiotics and were knowledgeable about using antibiotics to treat diseases, while Shanghainese were knowledgeable about antibiotic resistance. The multi-linear regression model reported that respondents who lived in Hong Kong (β = 0.744 (95% CI: 0.36–1.128), Shanghai (β = 1.65 (95% CI: 1.267–2.032), and Hangzhou (β = 1.393 (95% CI: 0.011–1.775) (reference group: Bangkok), who had higher scores on antibiotics knowledge (β = 0.161 (95% CI: 0.112–0.21)), higher educational attainment (β = 0.46 (95% CI: 0.296–0.625)), and who were more likely to consult a doctor on using antibiotics (β = 1.102 (95% CI: 0.606–1.598)), were more likely to give correct answers about antibiotic resistance, p < 0.001. Older respondents were less likely to answer the items correctly (β = −0.194 (95% CI: −0.333–−0.055), p < 0.01. When educating the public on the proper use of antibiotics and antibiotic resistance, multiple strategies could be considered for people from all walks of life, as well as target different age groups.

IMPACT OF INDUSTRIAL FREIGHT FLOWS ON THE OVERALL TRAFFIC IN SURAT CITY, INDIA

Surat is one of the major textile manufacturing hubs in India, having 40% of the synthetic fabric produced in the country. The textile industry in the city has witnessed tremendous growth in the last decade, leading to many transportation-related changes within it. Textile manufacturing has different phases like weaving, processing, value addition and trading or distribution. These phases are located as clusters or pockets in different parts of the city. The scattered nature of the industry generates numerous freight trips. This study focuses on characterizing and estimating textile freight trips in the city. Establishment survey data was collected from production units located in various clusters. A multi-linear regression model for freight trips generated using the quantity of cloth produced was developed for the estimation of the total textile freight trips. Thus, this study will help the planner identify the strategic location of the textile and its allied industries as well as for freight infrastructure in the city. More so, it would help in understanding the impacts of textile freight movement on the city’s overall traffic.

Data-based estimates of interannual sea–air CO₂ flux variations 1957–2020 and their relation to environmental drivers

This study considers year-to-year and decadal variations as well as secular trends of the sea–air CO2 flux over the 1957–2020 period, as constrained by the pCO2 measurements from the SOCAT data base. In a first step, we relate interannual anomalies in ocean-internal carbon sources and sinks to local interannual anomalies in sea surface temperature (SST), the temporal changes of SST (dSST/dt), and squared wind speed (u2), employing a multi-linear regression. In the tropical Pacific, we find interannual variability to be dominated by dSST/dt, as arising from variations in the upwelling of colder and more carbon-rich waters into the mixed layer. In the eastern upwelling zones as well as in circumpolar bands in the high latitudes of both hemispheres, we find sensitivity to wind speed, compatible with the entrainment of carbon-rich water during wind-driven deepening of the mixed layer and wind-driven upwelling. In the Southern Ocean, the secular increase in wind speed leads to a secular increase in the carbon source into the mixed layer, with an estimated reduction of the sink trend in the range 17 to 42 %. In a second step, we combined the result of the multi-linear regression and an explicitly interannual pCO2-based additive correction into a “hybrid” estimate of the sea–air CO2 flux over the period 1957–2020. As a pCO2 mapping method, it combines (a) the ability of a regression to bridge data gaps and extrapolate into the early decades almost void of pCO2 data based on process-related observables and (b) the ability of an autoregressive interpolation to follow signals even if not represented in the chosen set of explanatory variables. The “hybrid” estimate can be applied as ocean flux prior for atmospheric CO2 inversions covering the whole period of atmospheric CO2 data since 1957.

Peran Ambiguity Confusion dalam Word of Mouth pada Platform Marketplace

This study is an case study that aims to analyze the effect of ambiguity confusion on word of mouth. The type of data used in this study is primary data from platfom marketplace user. The number of samples in this study were 150 user that selected by purposive sampling method. The independent variable used is ambiguity confusion, while dependent variable is word of mouth. The analysis model used in this research is multi linear regression analysis using SPSS 25. The result show that the ambiguity has positive effect on word of mouth.

Machine Learning Model for Predicting Number of COVID19 Cases in Countries with Low Number of Tests

The COVID-19 pandemic has presented a series of new challenges to governments and health care systems. Testing is one important method for monitoring and therefore controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries not every country is able to employ widespread testing. Here we developed machine learning models for predicting the number of COVID-19 cases in a country based on multilinear regression and neural networks models. The models are trained on data from US states and tested against the reported infections in the European countries. The model is based on four features: Number of tests Population Percentage Urban Population and Gini index. The population and number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi linear regression and 0.91 for the neural network model. The model predicts that the actual number of infections in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers.

The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.

Firm Ownership Characteristics and Long-run Return on Equity Issued: A Case of the Nairobi Securities Exchange

This investigation looked at the link between firm ownership characteristics and long-run return on firms that issued equity at the Nairobi Securities Exchange (NSE) in Kenya. The study covered 12 firms that issued shares in the NSE market from 2006-2008. Ownership characteristics included (state ownership, institutional Ownership, foreign Ownership, big five shareholders, market capitalization, age of the firm and Leverage of the firm) in relation to the average return. The study tested whether each of the firm ownership characteristics influenced long-run performance. Annual return for these companies was based on market return for five years after the firm’s equity shares were issued. The long-run performance was compared with three benchmarks, namely, NSE index, CAPM and Matching firms. Seven hypotheses were developed for the study. Simple-liner and multi-linear regression analyses based on panel data were carried out to relate the extended run return on shares issued. The result of the survey showed that issuing firms performed better than non-issuing firms. These issuing firms also performed better in comparison to CAPM. However, the issuing firms performed worse than NSEI. In conclusion, the long-run performance of equity issued at the NSE does not necessarily underperform relative to non-issuing establishments.