OBJECTIVE: The aim of this study was to assess perceptions of CPs regarding their role, awareness, collaboration, facilitators and barriers towards effective AMS practices in Pakistan.
METHOD: ology: A descriptive, cross-sectional study design was adopted, where convenience and snowball sampling methods were applied to enroll respondents (pharmacists working at these community pharmacies in different cities of Pakistan) of the study. After sample size determination, n = 386 CPs were enrolled. A pre-validated questionnaire was used regarding CPs roles and perceptions in association with AMS. Statistical analysis was performed using SPSS v. 21.
RESULTS: The results of the study reported that 57.3% (n = 221) of CPs had strong familiarity with term AMS. 52.1% (n = 201) of CPs agreed that they require adequate training to undertake activities in AMS programmes in their setting. The results of the study showed that 92.7% (n = 358) of the pharmacists thought real time feedback would be helpful. Significant association was observed in AMS awareness, approach, collaboration and barriers with regards to the respondents' gender, age groups and levels of experience in a community pharmacy.
CONCLUSIONS: The study concluded that CPs were aware of AMS programmes, their relevance and necessity of AMS in their everyday practice, but had inadequate training and resources to implement it in Pakistan.
METHODS: A validated instrument was used to interview 399 randomly selected respondents from the public (n = 202, 50.6%) and scientists (n = 197, 49.4%) in the Klang Valley region of Malaysia. The data were analysed using PLS-SEM involving Smart-PLS software.
RESULTS: The results confirmed that stakeholder attitudes toward fogging should be viewed in terms of a multi-dimensional association. The stakeholders surveyed were highly positive with regard to the application of fogging to control dengue but professed moderate concerns as to associated risks. The PLS-SEM analyses demonstrated that the perceived benefit was the most important factor influencing attitudes, followed by trust in key players.
CONCLUSIONS: This result provides a good insight from the perspective of education and unravels the underlying fundamentals of stakeholders' attitudes toward the fogging technique. The findings also provide a positive indicator to the responsible parties involved to continue the usage of this technique in conjunction with improvements with regard to its safety aspects, and possibly in combination with other environmental-friendly alternatives in order to achieve a healthy environment without dengue in Malaysia.
METHODS: This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions.
RESULTS: In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.