This study used quantitative methods to examine the correlation between adaptive learning technology and cognitive flexibility in kids receiving special education. The study included a cohort of 120 kids, ages 8–12, who were diagnosed with particular learning difficulties, ADHD, or autism spectrum disorder. Cognitive flexibility was evaluated using the Wisconsin Card Sorting Test (WCST), while the utilization of adaptive learning technologies was quantified using self–report questionnaires. The data was analyzed using several statistical methods, such as independent samples t-tests, regression, Pearson correlation coefficients, ANOVA, and ANCOVA. The findings revealed a noteworthy and favorable correlation between the utilization of adaptive technology and the scores of cognitive flexibilities. This correlation remained significant even after accounting for demographic characteristics. Moreover, it was shown that the diagnostic status had a moderating effect on the correlation between the utilization of adaptive technology and cognitive flexibility. The results emphasize the capacity of adaptive learning technologies to improve cognitive flexibility abilities in kids with special needs, offering significant knowledge for educators, legislators, and technology developers.

Language is fundamental to human communication, allowing individuals to express and exchange ideas, thoughts, and emotions. In early childhood, some children experience communication disorders that impede their ability to articulate words correctly, posing significant challenges to their learning and development. This issue is exacerbated in developing countries, where limited resources and a lack of technological tools hinder access to effective speech therapy. Traditional speech therapy remains vital, but the latest technological advancements have introduced robotic assistants to enhance therapy for communication disorders. Despite their potential, these technologies are often inaccessible in developing regions due to high production costs and a lack of sustainable manufacturing models. For these reasons, this paper presents “FONA,” a robotic assistant that employs rule-based expert systems to provide tactile, auditory, and visual stimuli. FONA supports children aged 3 to 6 in speech therapy by delivering exercises such as syllable production, word formation, and pictographic storytelling of various phonemes. Notably, FONA was successfully tested on children with cochlear implants, reducing the number of sessions required to produce isolated phonemes. The paper also introduces an innovative analysis of the Make To Order (MTO) manufacturing system for producing FONA in developing countries. This analysis explores two key perspectives: collaborative networks and entrepreneurship, offering a sustainable production model. In a pilot experiment, FONA significantly improved children’s attention spans, increasing the period by 17 min. Furthermore, the economic analysis demonstrates that producing FONA through collaborative networks can significantly reduce costs, making it more accessible to institutions in developing countries. The findings suggest that the project is viable for a five-year period, providing a sustainable and effective solution for addressing communication disorders in children.

The article aims to evaluate the participation of below-poverty-line local community in tourism-related business activity in Himalayan state of Uttarakhand. Further, this article addressed for those who work in the tourism sector. The study employs a mix of methods, including survey data from 500 respondents with a random sampling approach, using Analysis of variance (ANOVA) statistical tools for analysis, other methods were interviews and observations at six tourism sites in Garhwal and four sites in Kumaun. Our findings showed that there has declined in community participation in tourism development, due to the lack of economic benefits obtained in the tourism sector, many believe that the tourism sector does not provide much income growth for them and does not make a significant contribution to the development of their region. Moreover, lack of understanding is considered the basis for community’s inability to play an active role, and lack of stakeholders’ involvement in encouraging them to improve their economy and culture through the tourism sector. Ultimately, this research also underlines the existence of some efforts by tourism travel to encourage public trust, which can help reduce poverty and increase community trust in tourism development in their region.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

This study explores the impact of technology effectiveness, social development, and opportunities on higher education accessibility in Myanmar, focusing on private higher education institutions. Utilizing a sample of 199 respondents, with an average age of X (SD = Y), the research employs standardized questionnaires and descriptive statistics, correlation analysis, and multiple regression analysis to examine the relationships between these variables. The findings indicate that technology effectiveness significantly enhances higher education accessibility, with strong positive correlations (r = 0.752, p < 0.001) and substantial impacts on educational outcomes (β = 0.334, p = 0.001). Social development also plays a crucial role, demonstrating that supportive social norms and community engagement significantly improve accessibility (β = 0.405, p < 0.001). Opportunities provided by technological advancements further contribute to enhanced accessibility (β = 0.356, p < 0.001), although socio-political and economic challenges pose significant barriers. The study highlights the interconnectedness of these factors and their collective influence on educational accessibility. Practical implications include the need for strategic investments in technological infrastructure, promotion of supportive social environments, and innovative solutions to leverage opportunities. Future research directions suggest longitudinal studies, broader demographic scopes, and in-depth analyses of specific technological and infrastructural challenges. By addressing these areas, stakeholders can develop effective strategies to improve higher education accessibility, ultimately contributing to the socio-economic development of Myanmar.

This study aims to evaluate the relationship between financial resilience, exchange rate, inflation, and economic growth from 1996 to 2022 using secondary data from the World Bank. The analysis method uses vector autoregressive to understand the causality dynamics between these variables. The results show that past economic growth positively impacts current economic conditions, but an increase in the exchange rate can hinder economic growth. The exchange rate also tends to be influenced by previous values, but high economic growth does not always increase the exchange rate. Previous conditions significantly affect financial resilience and can be strengthened by a strong currency. Meanwhile, inflation has an inverse relationship with economic growth, where past inflation seems to suppress current inflation, which price stabilization policies can cause. From an institutional economics perspective, this study provides an understanding of the interaction between various economic factors in the structural framework and policies that regulate economic activities. The impulse response function (IRF) shows that economic growth can react strongly to sudden changes, although this reaction may not last long. The exchange rate fluctuates with economic changes, reflecting market optimism and uncertainty. Financial resilience may be strong initially but may weaken over time, indicating the need for policies to strengthen the financial system to ensure economic stability. Furthermore, the role of social capital in economic resilience is highlighted as it can amplify the positive effects of a robust institutional framework by fostering trust and collaboration among economic actors. Inflation reacts differently to economic changes, challenging policymakers to balance growth and price stability. Overall, the IRF provides insights into how economic variables interact with each other and react to sudden changes, albeit with some uncertainty in the estimates. The forecast error decomposition variance (FEVD) analysis in this study reveals that internal factors initially influence economic growth, but over time, external factors such as the exchange rate, financial resilience, and inflation come into play. The exchange rate, which was initially volatile due to internal factors, becomes increasingly influenced by economic growth, indicating a close relationship between the economy and the foreign exchange market. From an institutional economics perspective, financial resilience, which was initially stable due to internal factors, becomes increasingly dependent on global economic conditions, suggesting the importance of a solid institutional framework for maintaining economic stability. In addition, inflation, which was initially explained by economic growth and exchange rates, has gradually become more influenced by financial resilience, indicating the importance of effective monetary policy in controlling inflation. This study highlights the importance of understanding how economic variables influence each other for effective economic governance. Integrating institutional economics and social capital perspectives provides a comprehensive framework for enhancing financial resilience and promoting sustainable economic development in Indonesia.

Since 2007, Peru has implemented results-based budgeting in order to ensure the quality of public spending in State entities and that the population receives goods and services in a timely manner; However, the demands of the current legal and regulatory context require a progressive application to budget processes such as that of the National Penitentiary Institute, which is basically focused on the allocation of resources by the central government, the collections it receives for penitentiary work. and the TUPA; Likewise, it requires strategic programming based on results, refining the procedures for programming, formulation, execution and evaluation of the budget. The objective of this research work is to describe the relationship between results-based budget management and the quality of spending in the Altiplano-Puno Regional Directorate of the National Penitentiary Institute in the period 2019. To achieve the objective, the descriptive explanatory method was used; in addition, the questionnaire and documentary analysis were used as a data collection instrument to determine the relationship between the study variables. Finally, it is concluded that the results-based budget is significantly related to the quality of spending, which means that the entity managed to apply the results-based budgeting methodology efficiently, obtaining an improvement in the quality of spending, consequently focusing on the optimization of the use of financial resources to achieve the strategic objectives of the penitentiary administration in this region. This approach seeks not only to guarantee the correct execution of spending, but also to maximize its positive impact on the management and conditions of penitentiary centers. In this way, a results-based budget approach must be implemented and ensuring the quality of public spending will allow the Office Regional Altiplano Puno of the INPE use its resources more effectively, achieving the objectives of prison security and rehabilitation and improving conditions in penitentiary centers. The adoption of efficient and transparent management practices will contribute significantly to a more responsible and results-oriented public administration.

E-cigarettes pose a significant public health concern, particularly for youth and young adults. Policymaking in this area is complicated by changing consumption patterns, diverse user demographics, and dynamic online and offline communities. This study uses social network analytics to examine the social dynamics and communication patterns related to e-cigarette use. We analyzed data from various social media platforms, forums, and online communities, which included both advocacy for e-cigarettes as a safer smoking alternative and opposition due to health risks. Our findings inform targeted healthcare policy interventions, such as educational campaigns tailored to specific network clusters, regulations based on user interaction and influence patterns, and collaborations with key influencers to spread accurate health information.