Search Results (4)

Key populations are particularly vulnerable to human immunodeficiency virus (HIV) infection. Nearly half of Tajikistan’s gross domestic product (GDP) originates from labor migrant transfers. While not officially designated as a key population, over 300,000 migrants return to Tajikistan every year at increased risk for HIV due to absence or interruption of treatment, change in risky behaviors, and other factors. We analyzed cross-sectional data from the national registry system operated by the Tajikistan Ministry of Health and Social Protection of individuals (n = 10,700) who had been diagnosed with HIV from 1 January 2010 to 30 May 2023. Individual HIV cases resided in five regions: Districts of Republican Subordination (DRS), Dushanbe (Tajikistan’s capital city), Gorno-Badakhshan Autonomous Oblast (GBAO), Khatlon, and Sughd. We developed logistic regression models to investigate the relationships between key population status and demographic characteristics. GBAO has the largest proportion of labor migrants (49.59%), which is much larger than that of the other regions (<32%). In contrast to other key populations, there was a larger proportion of HIV cases in rural areas that were labor migrants (23.25%) in comparison to urban areas (16.05%). In multivariable analysis, the odds of being a labor migrant were 6.248 (95% CI: 4.811, 8.113), 2.691 (95% CI: 2.275, 3.184), and 1.388 (95% CI: 1.155, 1.668) times larger if a case was residing in GBAO, Sughd, or DRS, compared to Dushanbe, respectively. Our research contributes to the field by proposing to expand the definition of key population to include labor migrants in Central Asia who should be emphasized as a vulnerable population at high risk of HIV. We encourage policy action to provide designated HIV funding for labor migrants, increase international attention, and promote potential modifications of national regulations and/or laws regarding prevention and treatment of HIV among non-citizen populations. Full article

Solar energy is an unlimited and sustainable energy source that holds great importance during the global shift towards environmentally friendly energy production. However, integrating solar power into electrical grids is challenging due to significant fluctuations in its generation. This research aims to develop a model for predicting solar radiation levels using a hybrid power system in the Gorno-Badakhshan Autonomous Oblast of Tajikistan. This study determined the optimal hyperparameters of a multilayer perceptron neural network to enhance the accuracy of solar radiation forecasting. These hyperparameters included the number of neurons, learning algorithm, learning rate, and activation functions. Since there are numerous combinations of hyperparameters, the neural network training process needed to be repeated multiple times. Therefore, a control algorithm of the learning process was proposed to identify stagnation or the emergence of erroneous correlations during model training. The results reveal that different seasons require different hyperparameter values, emphasizing the need for the meticulous tuning of machine learning models and the creation of multiple models for varying conditions. The absolute percentage error of the achieved mean for one-hour-ahead forecasting ranges from 0.6% to 1.7%, indicating a high accuracy compared to the current state-of-the-art practices in this field. The error for one-day-ahead forecasting is between 2.6% and 7.2%. Full article

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply. Full article

This paper proposes a bottom–up “nexus medium” perspective to examine and understand social organization and how socio-ecological challenges in remote rural regions are dealt with in communities that receive only limited external support. While “nexus mediums” constitute substances, matter, or objects that combine manifold vital meanings and can be seen as socially constructed and materialized arenas of social interaction, autonomous resource management is seen as a means of local social organization. Taking water as the nexus medium of choice allows us to generate locally informed insights about the role of this scarce resource for the everyday life and social organization of communities inhabiting arid rural areas. This reasoning will be exemplified by three local case studies conducted during empirical research in the Pamirs of Tajikistan utilizing a mix of qualitative methods. The findings reveal how many fundamental everyday-life-related aspects and activities of the studied communities are related to water, and how these communities are organized around common water use and management arrangements that are based on joint decision-making, shared benefits and responsibilities, and collaborative action. The “nexus medium” concept appears to be an appropriate approach for research that seeks to understand from a local perspective how communal living is organized and how socio-ecological challenges are addressed. Full article