In the contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

Bagasse fiber from sugarcane waste is used with epoxy resin to make natural composites. The raw fibers are treated chemically to improve compatibility and adherence with the epoxy polymer. It’s anticipated that epoxy resin matrix composites reinforced with bagasse particles would work as a trustworthy replacement for conventional materials utilized in the building and automobile sectors. The amount and distribution of reinforcing particles inside the matrix are two factors that impact the composite’s strength. Furthermore, the precise proportion of reinforcing elements—roughly 20–30 weight percent—into the matrix plays a critical role in providing a noticeable boost in improving the properties of the composites. This research investigates the impact of reinforcing alkali-treated bagasse and untreated bagasse powder into an epoxy matrix on aspects of mechanical and morphological characteristics. The hand layup technique is used to create alkali-treated bagasse and untreated bagasse powder-reinforced epoxy composites. Composites are designed with six levels of reinforcement weight percentages (5%, 10%, 15%, 20%, 25%, and 30%). Microstructural analysis was performed using SEM and optical microscopes to assess the cohesion and dispersion of the reinforcing particles throughout the hybrid composites’ matrix phase. With reinforcement loading up to 20 wt%, the tensile strength, impact strength, and toughness of epoxy-alkali-treated bagasse and untreated bagasse powder-reinforced composites increased. In contrast, treated bagasse epoxy composites were superior to untreated epoxy composites in terms of efficacy. The results indicate that 20 wt% alkali bagasse powder provides better mechanical properties than other combinations.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

A reservoir of vegetation, wildlife, and medicinal plant abundance is represented by the Haridwar forest divisions. This study deals with the results of ethnobotanical survey of medicinal plants conducted in the Haridwar forest division during the period of December 2016 and March 2019. The information on folk medicinal use of plants were gathered by interviewing with local healers and Vaidya’s who have long been advising the folk medicines for medication of various disorders. The important folk medicinal data of 33 medicinal plants species belonging to 22 families and 33 genera practiced by tribal and local people of the study area has been recorded by the survey team of the Institute. Fabaceae followed by the Lamiacea and Asteraceae were the dominant families. The species diversity showed maximum exploration of Trees, Herbs followed by Shrubs and Climbers. Leaves, seed and root were the most prevalently used part in study followed by the stem bark, fruit, flower, stem and fruit pulp. During the study it was observed that the traditional practices of Gujjars of Uttarakhand have close relation with forests and have strong dependency on the same for food, medicine, timber and fodder etc. The information recorded for the treatment in different ailments has been presented in the paper in the pie charts and tabular form. In the recorded information most of the plants along with Plant name, Family name, Voucher Specimen No., Local Name/Unani name, Part Used, Diseases/Condition and Habitat/ICBN status so as to enrich the existing knowledge on ethnopharmacology. Many of the medications used today have their roots in traditional knowledge of medicinal plants and indigenous uses of plant material, and there are still a plethora of potentially useful pharmaceutical chemicals to be found. In this regard, more in-depth field research could aid in the discovery of novel plant species utilized in indigenous medical systems to improve patient needs. With this aim this study was conducted to explore and trace the ethnobotanical potential of flora of the Haridwar forest division so that it could prove to be immensely advantageous for both the development of new medications to treat dreadful and catastrophic illnesses as well as for the study and preservation of cultural and social variety.

Diabetic retinopathy (DR) is a major cause of blindness globally. Effective screening programs are essential to mitigate this burden. This review outlines key principles and practices in implementing DR screening programs, emphasizing the roles of technology, patient education, and healthcare system integration. Our analysis highlights key principles for establishing successful screening initiatives, including the importance of regular screenings, optimal intervals, recommended technologies, and necessary infrastructure. We emphasize the roles of healthcare providers, patients, and policymakers in ensuring the effectiveness of these programs. Our recommendations aim to support the creation of robust policies that mitigate the impact of DR, ultimately improving public health outcomes and reducing the incidence of blindness due to diabetic retinopathy.