Ministry of Economy

The English for Economists Course, developed by the Tutorial Education Program in Economics (PET-Economia) at the Federal University of Acre (UFAC), represents a fundamental extension initiative within the Academic and Professional Development Program in Economics. Offered in 2018, 2019, 2023, and scheduled for 2025, the course was specifically designed to address the urgent need for economics students, researchers, and professionals to enhance their ability to read, interpret, and produce academic and technical texts in English. The course is structured into five modules covering everything from instrumental English fundamentals to practical application and final evaluation, utilizing authentic materials such as academic articles, IMF and World Bank reports. The methodology combines lectures, case studies, and interactive exercises, including oral practice sessions to improve presentation and professional debate capabilities. Results demonstrate significant effectiveness in developing linguistic skills and understanding of economic concepts, as evidenced by student testimonials. Future challenges include ensuring long-term sustainability, adapting content to changes in the global economic landscape, strengthening complex discussion skills, and ensuring inclusivity among participants with different proficiency levels.

Objectives. This study provides a quantitative assessment of the impact of latent variables on regional economic activity and examines the interactions among various economic sectors and their contributions to regional growth. The analysis focuses on identifying latent factors of socio-economic development across Belarusian regions during the period 2016-2024, using factor analysis techniques and structural equation modeling (SEM). Methods. To reduce data dimensionality and identify latent factors, exploratory factor analysis (EFA) was applied using the Principal Component Analysis (PCA) extraction method. A structural equation model was constructed using the SEMOPY library in Python to estimate relationships among the identified factors. To assess the quality of the model, standard fit indices were used: CFI – comparative fit index; TLI – Tucker-Lewis index; RMSEA – root mean square error of approximation. The values of these indices allow evaluating the degree of consistency between the model and the empirical data. The model is based on a system of 23 socio-economic indicators across 128 administrative districts and cities of regional subordination. Results. The resulting SEM demonstrates high internal consistency and statistical reliability (CFI = 0,98; TLI = 0,97; RMSEA = 0,04), revealing significant causal linkages between latent factors. It was established that the financial sector is a key driver of investment activity, while growth in the housing stock directly stimulates consumer demand. Negative relationships were identified between agricultural potential and financial stability, as well as between industrial development and financial sustainability. Conclusion. The developed model is an effective analytical tool for formulating evidence-based regional policy, optimizing resource allocation, and strategic planning. Promising directions for future research include incorporating time lags, adding indicators of innovation and human potential, and applying spatial econometrics methods.

Background: Consumers contribute largely in getting healthcare services in Bangladesh. This formal and informal consumer cost leads a large section of population into poverty. Objective of this study was to capture the self-reported consumer cost of illness as well as satisfaction regarding availed healthcare services among patients admitted in Sir Salimullah Medical College Mitford Hospital (SSMCMH). Methods: This cross-sectional survey was conducted from November 2022 to May 2023 among conveniently selected 636 indoor patients of medicine, surgery, orthopedics, pediatrics, and Gynecology & Obstetrics departments of SSMCMH. Mixed method approach was applied to collect data using a semi-structured questionnaire during the discharge of indoor patients whom were selected by stratified sampling method. Results: Respondents had to pay for any of the services, only one fifth (18.10%) said the payment was at the high end. We found that outside facility outweighed inside facility cost, medicine (69.34%) and investigation contributed the largest share. Non-medical costs were a significant component of health expenditure. Though stake of informal tips (2%) were a smaller portion compared to other cost, apparently it was a norm. Patients paid highest for surgical care (Tk 12789.50) and lowest for orthopedic care (Tk 2936.66). There was significant difference between consumer cost of delivery by NVD and CS (p=0.0008). There was significant difference with variation of age (p<0.05), sex (p<0.05), monthly income (p<0.05), but marital status, and year of education was not significant. Consumer satisfaction on payment was seen to be largely dependent on amount they had to spend out of pocket (p<0.05) along with providers behavior and cleanliness of the premises. Conclusion: The study suggested that although direct medical costs are of special importance and it is necessary to protect patients against such costs, patients usually encounter a variety of costs when receiving inpatient services. Findings of this study could help local authority and policy makers to develop strategies within the health system to reduce the burden and ensure patient compliance. Bangladesh Armed Forces Med J Vol 58 No (1) June 2025, pp 1-6

This study explores the application of Google Search Trends (GST) data as a tool for COVID-19 surveillance in Bangladesh. Using the Vector Autoregression (VAR) approach, it investigates how GST search can predict new COVID-19 cases. The findings show that GST can offer timely insights into public health responses. The results also show that GST search items can predict the outbreak before 1–2 weeks. While VAR analysis confirms the causal relationship between the GST terms and the new COVID-19 cases, this study also acknowledges the limitations of inappropriate regional data at the outset of any new pandemic. It also acknowledges the misinformation from GST can underestimate real time surveillance. Policymakers can use GST for real-time risk communication and informed decision during pandemic. The results also suggest the importance of infodemiology to integrate it into the public health system for disease surveillance as the country’s’ resources are constrained. This study highlights the contribution of infodemiology towards the enhancement of traditional methods and systems of surveillance, which is relevant both nationally and globally. GST offers a great opportunity for advancing digital epidemiology and innovation in public health, nonetheless obstacles rest in the way.

Abstract This research compares the DENR-WQI framework by the Philippine Department of Environment and Natural Resources (DENR) with a new thermodynamic-based Gibbs-WQI method using surface water data collected from Iloilo City, Philippines. The Gibbs-WQI was developed to investigate cases where coastal waters labeled as suitable by the DENR-WQI displayed fecal coliform levels that exceedingly surpassed legal limits. The Gibbs-WQI uses entropy-based calculations to evaluate deviations from ideal reference states which helps detect anomalies dynamically. The analysis of 287 surface water samples included key parameters such as fecal coliform, phosphate, and dissolved oxygen through computational methods implemented in Python. The Gibbs-WQI system tended to place samples initially classified as Class SB or SC by the DENR-WQI into poorer quality categories when fecal coliform concentrations surpassed 5,000 MPN/100 mL. The Chi-square statistical test ( χ = 134.43 and p = 0) demonstrated a highly significant difference between the two indices. The Cohen’s Kappa coefficient value of 0.321 showed that there was only fair agreement between the classification systems. While DENR-WQI meets regulatory standards the study reports that Gibbs-WQI offers improved real-time environmental responsiveness. The study recommends combining both indices to achieve optimal monitoring and management outcomes. Predictive modeling in water quality management requires future research to explore both temporal-spatial analysis and the incorporation of machine learning techniques.

Food adulteration has emerged as a significant global issue, impacting consumer health and fair-trade practices. This study aimed to evaluate the quality and potential adulteration with starch in tomato paste products available in the Lebanese market. A total of 41 local and imported tomato paste samples, without starch declarations, were collected from the Lebanese market and analyzed for starch usage and various quality parameters (total soluble solids, Bostwick consistency, viscosity, titratable acidity, color, and dry matter content), as well as compliance with Libnor and Codex Alimentarius standards. Results revealed that 37% of samples failed to meet starch usage standards, and 27% did not comply with the required total soluble solids (>24%), while all samples complied with acidity standards (<7%). Compliant samples had significantly higher values for total soluble solids, acidity, dry matter, and color compared to non-compliant ones (p < 0.01). A comparison of local and imported tomato paste products showed no significant differences in physicochemical properties, color, shelf life, or price, with parameters being similar across samples. Among local samples, 48% did not comply with the starch usage standard, and 26% failed to meet the required total soluble solids level. In contrast, imported samples adhered to starch usage standards, although 30% did not comply with TSS levels. This study highlights the prevalence of adulteration in local and imported tomato paste products in Lebanon and calls for further enforcement measures to ensure consumer protection and fair trade.