Journal of Ecohumanism

The Effect of Organic Fertilizer on Nitrogen Mineralization, Nitrogen Uptake, and Protein Content of Mung Bean (Vigna radiata) in the Sandy Coastal Soil of Parangtritis, Yogyakarta, Indonesia

2025-04-03T00:10:46-05:00

This research aims to examine the impact of organic fertilizer on nitrogen mineralization in the soil, nitrogen absorption, and the protein content of green bean seeds in sandy coastal areas. The study was carried out in the sandy coastal region of Parangtritis, Kretek, Bantul, Yogyakarta, Indonesia, at an altitude of approximately 10 meters above sea level, with a soil pH ranging from 5.6 to 6.0 and air humidity between 65% and 85%. The soil type is sandy. The experiment was based on a single-factor design, focusing on organic fertilizer, which included seven treatments: no organic fertilizer, cow manure, goat manure, chicken manure, compost, vermicompost, and guanophosphate. Data analysis was conducted using variance analysis at a 5% significance level, while differences between treatments were evaluated using the Duncan Multiple Range Test at the same significance level. The application of organic fertilizer enhances nitrogen mineralization, except in the case of guanophosphate fertilizer. Among the treatments, vermicompost fertilizer resulted in the highest nitrogen mineralization (NNH4) at 43.89 ppm. The application of nitrogen fertilizer also boosts nitrogen absorption and the protein content of green beans. The highest nitrogen absorption was observed with chicken manure at 2.13%, while the highest protein content in green beans was achieved with compost treatment at 22.33%. Additionally, organic fertilizer application contributes to an increase in green bean yield. The highest yields were obtained with compost fertilizer at 1.6 tons per hectare, followed by vermicompost at 1.541 tons per hectare, and guanophosphate at 1.554 tons per hectare.

Geometric communication in organizations. A review from the administrative sciences

2025-04-03T07:56:09-05:00

The aim of this paper is to interpret geometric communication in business organizations from the perspective of administrative sciences. Among the main findings, it stands out that geometric communication within modern educational organizations is a complex whole that links institutional purposes and individual interests, giving a nuance to the information that is communicated not only by the personality of the informants, but also by the objects and people that surround them. without neglecting feelings, emotions and moods. To this end, geometric communication is understood as a process where self-regulation must be included as a dimension that articulates all the others (contour, environment and surroundings). Likewise, it is concluded that geometric communication is a guarantor of the good management of the educational leader, since it articulates the elements of authority and power, with those of interpersonal relationships and affectivity.

DYNAMICS OF SOCIAL INEQUALITIES IN THE FACE OF MACROECONOMIC SHOCKS IN MOROCCO: AN ECONOMETRIC ANALYSIS USING THE MODEL ARDL

2025-04-05T07:59:23-05:00

This article evaluates the relationship between macroeconomic shocks and social inequalities in Morocco through the lens of the ARDL model. It uses annual information on GDP, unemployment, and inflation and social spending in order to analyzed social inequalities manifested by the Gini index.Empirical studies suggest that the economic growth has ambiguous consequences on inequality, as its resource mobilization tendency on one hand leads to a long term decrease in income disparity. Furthermore, unemployment and inflation are negative determinants of inequalities, specifically targeting the lower most household. On the other hand, social transfers do help mitigate inequality, even though their effect is virtually nonexistent in the absence of any form of redistribution.The econometric findings indicate a long run co-integration of the variables which in effect means that there are persistent inequalities over time which tend to be influenced by the macro shock incidence. This strengthens the urgency for comprehensive economic strategies that incorporate active labor market policies and social services to counter inequalities.In conclusion, this study appeals for increased attention towards redistributive policies alongside implementable economic strategies that can soften the impact of macroeconomic shocks on social inequalities in Morocco.

URBAN AGRICULTURE CROPS AND ITS POTENTIALITIES

2025-04-06T23:32:46-05:00

This paper is based on urban agriculture supporting to reduce household poverty, as well as this types of agricultural production and management of crops and livestock/poultry directly help to household economy of urban or periphery area, especially to meet local needs. This paper aims to explore the urban agriculture (UA) in terms of urban food security and employment in the Kirtipur, Kathmandu. Primary data collection was done in February- April, 2023 by snowball sampling technique because of the farm owner couldn’t meet at farm areas and only found the workers then collected the phone number (all respondents) and fixed the interview time then collect the data, considering the households with more than 4 Ropany (0.2 ha) of farmland as sampling units in three urban pockets- one road accessible pocket (Taudaha), second one less accessible pocket with Bhatkepati settlement, and the last one isolated pocket (Jankha), followed by observation and face to face interviews which includes total 64/64 household surveys (20 in Taudaha, 24 in Bhatkepati and 20 in Jankha) using structured questionnaire for interview schedule.

The analysis of the development functions shows that most of the people in the urban area emphasized to better employment and education as well as drinking water, road, trade/business and agricultural integration for overall development this places but lack of irrigation facilities was the most crucial problem for the development of agriculture. Other major problems include lack of modern inputs and technologies, decreased interest of youth in agricultural occupation, inadequate road network and financial problems. The organizations supporting agricultural activities and the potentials of integrated development of agriculture in Kirtipur has been assessed and analyzed with suggestions and recommendations for reducing urban poverty and food insecurity. The major suggestions include promoting irrigation facilities; market oriented organic vegetable production; in-depth study for fruit growing and beekeeping promotion; and strengthening agricultural extension services with proper sustainable urban farming policies for promoting urban food security and employment.

Unveiling the Digital Revolution: Assessing the Dynamic Impact of E-Banking Adoption among Generations Y and Z in Malaysia

2025-04-08T00:35:36-05:00

This study investigates the influence of e- banking based on the perspective of Generations Y and Z, with a specific emphasis on their adeptness with technology. The research conducted a quantitative study with 384 Malaysian participants, which revealed that these generations exhibit a high level of familiarity with digital technology and enthusiastically use e-banking due to its convenient and time-saving advantages. Nevertheless, the implementation of the technology is impeded by issues over security, privacy, and trust. The research also evaluates the impact of age, education, and money on their viewpoints. The findings indicate that resolving these issues might improve the adoption of e-banking, which would be advantageous for banks, regulators, and academics. The study proposes the elimination of obstacles to enhance e-banking and promote financial inclusion among Generations Y and Z, providing valuable guidance for policymakers to formulate regulations that safeguard consumers. Subsequent studies may broaden the assessment of electronic banking among diverse client segments. Furthermore, the data collected from participants was analyzed using the Statistical Package for Social Sciences (SPSS). The researcher used descriptive statistics, Pearson's correlation coefficient, and multiple regression analysis to examine the hypothesis. The study found that perceived usefulness, perceived ease of use, perceived data security and privacy, and perceived technology and innovation all have a significant impact on the usage of e-banking among Generation Y and Z. Among these factors, perceived technology and innovation was found to be the most influential in driving the adoption of e-banking among Generation Y and Z. In future research, the researchers might use the suggested novel conceptual framework to conduct the investigation or incorporate other variables for examination.

SDG#4, 8 and 9 - Trends of Halal Audit Training with Blockchain Technology for Sustainable Development with Decent Job Creation

2025-04-09T06:31:26-05:00

This paper outlines a comprehensive strategy to harness the power of integrated skills in technology, artificial intelligence (AI), blockchain, and halal concepts to rethink the design, delivery and assessment of the impact of halal training related courses under the green econmy. Aligning with the HKSAR policy address 2025, specifically paragraph 107, which emphasizes the enhancement of the General Employment Policy and the introduction of new mechanisms to attract top-tier talent, this project aims to position Hong Kong as a global/ virtual/ physical hub for halal training certificates in blockchain technology for revitalization. It seeks to address the acute manpower shortages - halal audit and halal executive competency in technology related training programmes in Hong Kong to promote SDG#9 innovations in the halal economy.

How To Achieve Good Financial Governance by Reconstruction of Management Accounting Information System and Asset Management for Better Village

2025-04-10T01:02:01-05:00

The purpose of this study is to reconstruct the organizational structure along with the main tasks and functions of village officials in terms of management accounting information systems for village asset management in order to create good village governance. The method used in this study is inductive. The type of research used is qualitative with a phenomenological approach. The researcher conducted field observations with a focus on digging up information on how village assets have been managed so far. The results of the study stated that there must be an additional special official who has the field of village asset management so that village assets and potential can be managed properly. To improve asset management, the village should introduce a Technical Implementer position, specifically a Head of Asset Management, with the following responsibilitie likes Administration of village assets, Inventory management, Cash flow management for asset operations, and Maintenance and preservation of village assets. Researcher hopes that there will be writing that focuses on researching villages all over the world but with different or the same topics so that there is additional insight for readers.

Sustainability, Stability, and Growth: A Time Series Analysis of Tourism Drivers in Thailand

2025-04-10T22:28:35-05:00

This study examines the dynamic effects of renewable energy consumption, economic growth (GDP), and geopolitical risk (GPR) on tourism in Thailand during the period 1995-2023. Utilizing rigorous time series econometric techniques, including cointegration analysis and error correction modeling, the study examines both the short-run and long-run relationships between the selected macroeconomic variables and tourism development. The findings confirm that GDP and renewable energy both exert positive and statistically significant influences on tourism in the short and long term, mirroring economic strength and sustainability in driving tourism performance. The geopolitical risk, however, significantly negatively affects tourism in the long term, inferring that political instability and conflict in the region in the long term can ruin Thailand's image as a destination for tourists. These results emphasize the importance of sustainable energy investment, macroeconomic stability, and effective geopolitical risk management in supporting the growth and resilience of Thailand's tourism sector. The study offers valuable policy recommendations to create a secure, green, and economically prosperous tourism sector in Thailand.

Eco-Design for Sustainability: A Mediating Role in Achieving Sustainable Manufacturing through Lean Production

2025-04-10T22:53:57-05:00

The increasing awareness of environmental issues and sustainability has driven the manufacturing industry to adopt a more holistic approach. Sustainable Manufacturing (SM) has emerged as a comprehensive concept that integrates environmental, social, and economic aspects into the production process. This study aims to examine the role of Eco-Design for Sustainability as a mediating factor in achieving SM through the implementation of Lean Production Practices in the Fast-Moving Consumer Goods (FMCG) industry. The research uses a quantitative approach, with data collected through questionnaires and analyzed using Structural Equation Modeling (SEM). The results show that Eco-Design for Sustainability acts as a significant mediating variable in the relationship between Lean Production Practices and SM. This implies that the implementation of Lean Production Practices, although contributing to efficiency, does not necessarily guarantee the achievement of SM without being integrated with Eco-Design. The study also found that Eco-Design, with its focus on minimizing environmental impacts throughout the product life cycle, strengthens and directs the Lean production system to be more effective in achieving SM goals. Based on these findings, the study proposes strategies to drive FMCG companies towards SM practices, including optimizing the effectiveness of Total Productive Maintenance (TPM) to improve efficiency and reduce waste, developing an Operational Excellence program, Life Cycle Optimization, digital transformation, improving employee capabilities, and developing environmentally friendly product designs. The research underscores the importance of Eco-Design for Sustainability as a key strategy in achieving SM in the FMCG industry and provides a deeper understanding of how Lean Production Practices can be effectively implemented to optimize environmentally conscious product designs.

MENA SMEs: Assessing Barriers and Enablers of Technological Adoption

2025-04-11T21:43:46-05:00

Small and Medium-Sized Enterprises (SMEs) in the Middle East and North Africa (MENA) are critical drivers of economic diversification and employment. However, these enterprises often face significant challenges in adopting digital technologies, hindering their growth and competitiveness. This study employs a mixed-methods approach, integrating quantitative surveys of 400 SMEs across five MENA countries—Egypt, Jordan, Morocco, Saudi Arabia, and the United Arab Emirates—and qualitative interviews with 35 stakeholders, including SME owners, policymakers, and technology providers. The research identifies key barriers such as limited access to finance, inadequate digital infrastructure, regulatory fragmentation, and cultural resistance. Conversely, enablers include government incentives, managerial digital literacy, capacity-building programs, and cross-sector collaborations exemplified by initiatives like Abbott’s (2023) telehealth partnerships. Comparative analysis of Southeast Asia and Sub-Saharan Africa highlights the best practices and contextual nuances. The findings offer actionable policy recommendations aimed at enhancing financing mechanisms, harmonizing regulatory frameworks, upgrading digital infrastructure, and fostering managerial competencies. By addressing these factors, MENA SMEs can effectively integrate digital solutions, thereby promoting sustainable economic development and enhancing regional competitiveness.

Understanding Social Media Behavior in the USA: AI-Driven Insights for Predicting Digital Trends and User Engagement

2025-04-12T23:52:44-05:00

The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The data used in this analysis are posts aggregated from two leading online platforms, X-Twitter and Reddit, and consist of user-generated material covering a multifaceted set of topics and opinions. X-Twitter posts are current and real-time and give insight into what is currently being discussed and the opinions that are making headlines, whereas Reddit content provides extensive commentary and user engagement on numerous subreddits. To make the dataset more comprehensive and richer in information, extensive engagement metrics like likes, shares, and comments are used to extend its reach and provide insight into the extent to which users engage with the material presented to them. For this research, we used the multi-model approach so that there would be an exhaustive study and strong predictions, by implementing models such as Logistic Regression, Random Forest, and XGB-Classifier. To assess the models properly, we made use of several performance metrics like Accuracy, Precision, Recall, and F1-score. Logistic Regression only manages to achieve a below-average accuracy, signaling an average level of predictive quality. The Random Forest model fares slightly better with a slightly better accuracy rate, which implies that its ensemble method increases its predictive power to classify instances more effectively. In turn, the XG Boost model took the top spot in the comparison with an accuracy rate, projecting its ability to identify complex patterns in the data and showcasing the highest predictive level among the three models. The use of model outputs can greatly maximize real-time content strategy for brands and organizations seeking to maximize engagement in the USA. Based on user behavior patterns and engagement metrics, models can give insight into what should be posted and when to maximize attention. In campaign optimization, predictive modeling assists U.S. brands in making strategic decisions regarding ad spend allocation. Based on an examination of past performance and engagement metrics, brands can see which content is driving the greatest impact and engagement levels and make strategic investments in high-performance content that genuinely resonates with desired audiences. For public communication and policy, predictive models are particularly helpful in projecting how the U.S. public will react to news announcements, policy initiatives, or campaigns. Boosting the effectiveness of predictive models by incorporating transformer-based NLP models like BERT is one direction to explore in the future.

Efficiency of traditional and green cryptocurrencies: A comparative analysis

2025-04-14T22:39:23-05:00

The main objective of this research is to evaluate the efficiency, in its weak form, of digital currencies classified as "dirty", such as Bitcoin (BTC) and Ethereum (ETH), and the green ones, namely Lisk (LISK), Metaverse (METAVERSE), Quantum (QTUM), Litecoin (LTC), Augur (REP), Cardano (ADA), Dash (DASH), EOS (EOS), Quantum (QTUM), Litecoin (LTC), Ripple (XRP), Augur (REP), Cardano (ADA), Dash (DASH), EOS (EOS), IOTA (IOTA), Monero (XMR), Neo (NEO), Omisego (OMG), Stellar (XLM) and Zcash (ZEC), for the period from 1 January 2018 to 23 November 2023. The results show that Bitcoin (BTC), Metaverse, Litecoin (LTC) and Cardano (ADA) have persistent behaviour with a long memory, which favours long-term strategies. Long memories indicate that markets are less efficient, where trends tend to continue, making long-term strategies more effective. On the other hand, cryptocurrencies such as Lisk, Quantum, Ethereum (ETH), Ripple (XRP), Augur, Dash, EOS, IOTA, Monero, Neo, Omisego, Stellar and Zcash show anti-persistent behaviour, with rapid correction of deviations, suggesting more efficient markets, but with less predictability. This favours short-term strategies such as arbitrage and scalping. The analysis reveals that cryptocurrencies with long memory, such as BTC, LTC and ADA, are more predictable in the long term, while most others, such as ETH and XRP, are more suitable for short-term trading, reflecting structural differences in the market.

The Influence of the International Oil Price on the EUR/USD Exchange Rate

2025-04-14T23:04:47-05:00

The study analyses the influence of the international oil price, focusing on the CBOE Crude Oil Volatility Index and Brent crude oil, on the main exchange rates: EUR/GBP, EUR/USD, GBP/USD, USD/CHF and USD/JPY. The analysis covers the period from 3 January 2022 to 8 December 2024 to examine the relationship between fluctuations in the oil market and the behaviour of exchange rates. The results show that the CBOE index directly impacts the EUR/GBP currency pair, reflecting the influence of global volatility on financial markets. The EUR/USD pair is influenced by the behaviour of the USD/JPY currency pair, showing the interconnection between the major currencies. Brent futures significantly affect the USD/CHF and USD/JPY currency pairs due to the importance of oil as a strategic commodity and the sensitivity of currencies such as the Yen and Swiss franc to fluctuations in energy prices. These relationships show the strong interconnectedness of global markets, highlighting the importance of informed risk management and decision-making strategies in interdependent environments.

Climate Variations and Implementation of Climate Planning in The Municipalities of Zio1 (Togo) And Zou (Benin)

2025-04-15T23:18:32-05:00

Climate variations negatively impact municipalities in West Africa. In the municipalities of Zio1 and Zou, climate variations have consequences on the local economy. However, these municipalities are unable to implement climate actions defined at the local level. This research is based on the following hypothesis : in Zio1 as in Zou, the same difficulties in implementing climate planning are explained by choices of carrying out socio-economic development projects to the detriment of implementing climate actions. This article aims to examine the explanatory factors for the difficulties in implementing climate planning in the municipalities of Zio1 and Zou. In this research, climate variables from gridded observed data for the years 1981 to 2022 were used to analyze current climate conditions. For the analysis of future climate conditions, namely from 2021 to 2050, nine general circulation models were used. For socio-demographic data, a survey was conducted among 449 people aged 18 and over, spread across the territories of Zio1 and Zou. The results show that the implementation of climate actions is not a priority in the municipalities of Zoi1 and Zou. Several projects carried out at the local level are not sized to take into account climate variations.

Qualitative Study of Reasons for Effective Human Resource Information Systems in the Health Sector of a Southern African Country

2025-04-16T22:29:48-05:00

Human Resource Information Systems (HRIS), as mechanisms for human resource management, are recognised to facilitate transformative change within organisations. In the context of the health sector, effective human resource performance is essential for organisational development and to achieve the United Nations (UN) Sustainable Development Goals (SDGs). It is noteworthy that, despite a focus on research and development regarding HRIS within the health sector, the precise mechanisms through which such systems can enhance sustainability in Southern African countries, such as South Africa, remain ambiguous. This research underscores the importance of implementing effective HRIS in the health sector. The research employed qualitative methodologies, utilising interviews as the primary data collection tool. Multiple hospitals expressed their interest in participation, and the Department of Health of the Southern African country granted the necessary approvals. A purposive sampling approach was employed to select the participants from the hospitals. The research adopted an inductive approach within an interpretive paradigm. To ensure the validity of the research instrument, psychometric property testing was conducted. The findings indicate that the implementation of HRIS within the health sector is partially comprehended. Furthermore, several reasons that hinder the effectiveness of HRIS were identified, including lack of familiarity, resistance, perception, guidance and issues related to security. The research provides recommendations for overcoming these barriers to enhance the efficacy of HRIS in the health sector, thereby fostering employee commitment and future research.

Enhancing Mathematics Education with Modern Teaching Equipment in Junior Secondary Schools in Nigeria

2025-04-18T02:07:23-05:00

Mathematics is mainly considered a difficult subject for students, but when the teaching is simplified, assimilation becomes effortless as well. This study examined the role of modern teaching equipment in achieving mathematics education objectives in schools. Four research questions were raised to guide the study. This study aimed to see how modern technological equipment can ease the teaching and learning of Mathematics in junior secondary schools. The study employed a descriptive survey research design, focusing on Nigeria's junior secondary school mathematics teachers. A purposive sampling technique was used to select 100 teachers to participate in the study. Data were analyzed using descriptive statistics, including frequency counts and simple percentages. The findings indicated that modern teaching equipment enhances teaching by making it more engaging, improves the efficiency and effectiveness of mathematics education instructors, and highlights important aspects of learning, thus reducing rote memorization. It also facilitates school management’s access to real-time pupil and school data, strengthens the professional image of mathematics teachers, and helps maximize students’ attentiveness. However, many teachers reported challenges such as insufficient modern teaching equipment, frequent power interruptions, high costs of equipment, limited skills among teachers in using these technologies, lack of curriculum experts, and inadequate funding for teaching equipment. Despite these challenges, the use of modern teaching equipment in mathematics education is generally on the rise. Based on these findings, the study recommends that schools prioritize providing adequate ICT resources, maintain equipment properly, and employ well-qualified ICT teachers to foster the effective use of modern teaching equipment in mathematics education.

Gold-Backed Cryptocurrencies as Diversifiers and Hedging Instruments for NFTs, DeFi, and Traditional Cryptocurrencies: Insights from Dynamic GARCH-Copula Analysis

2025-04-18T22:12:37-05:00

This study explores the role of gold-backed cryptocurrencies (PAXG and XAUT) as effective diversifiers, hedges, and safe havens for NFTs and DeFi assets, particularly during market crises such as the COVID-19 pandemic and the 2022 cryptocurrency crash. By employing a dynamic GARCH-copula approach, the research analyzes the interconnectedness and volatility spillovers between these digital asset classes, providing insights into their behavior during times of heightened uncertainty. We also compute the optimal hedge ratio for each gold-backed cryptocurrencies/stabelcoins-NFT/DeFi/Traditional cryptocurrencies pair and evaluate their dynamic hedging effectiveness. The findings reveal that gold-backed cryptocurrencies offer superior hedging capabilities compared to stablecoins (USDT and BUSD), enhancing portfolio diversification and risk management. The results underscore the importance of incorporating gold-backed assets into digital portfolios to improve resilience and achieve better risk-adjusted returns during periods of market turmoil.

AI-Driven Sentiment Analysis for Bitcoin Market Trends: A Predictive Approach to Crypto Volatility

2025-04-20T08:41:46-05:00

Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, and discussion forum postings. The central aim of this study was to establish a strong model that integrates sentiment analysis and machine learning methods to forecast the price movements of Bitcoin. The dataset used included multi-source sentiment data and cryptocurrency market indicators, which allow for in-depth analysis of public emotion on cryptocurrency volatility. Sentiment was sourced from Twitter (tweet text with Bitcoin hashtags and keyword mentions), Reddit (r/Bitcoin and r/Crypto Currency subreddits), and financial headlines (Bloomberg, CoinDesk, Reuters), covering the timeframe of 2019–2024 to ensure the inclusion of various market cycles. Textual data was pre-cleaned to remove noise signals (bots, spam, non-English text) and annotated for sentiment polarity (positive, negative, neutral) using both VADER (Valence Aware Dictionary for sEntiment Reasoner) and fine-tuned BERT models for contextual relevance. In analyzing how sentiment affects the volatility of the Bitcoin market, we used various modeling methods such as Logistic Regression, Random Forest Classifier, and Support Vector Machines. Support Vector Machines stands slightly ahead in terms of accuracy, implying that it might be the strongest among the three for this particular task. Logistic Regression and Random Forest both show similar levels of accuracy, which means that both of them are also strong, though less optimal compared to the Random Forest model. The use of sentiment analysis in financial markets, especially in the cryptocurrency market, provides U.S.-based investors and traders with a valuable means of risk protection. Through the use of sentiment-aware forecasts, investors can make predictions of market trends and probable price movements based on public sentiment. Crypto-fintech platforms can leverage sentiment analysis to build real-time alert systems that update users on important market movements. Through social media and news channels, the platforms can issue alerts on impending price volatility or impending trends, allowing the user to react quickly to market forces. The capability to bring in real-time social media APIs for live predictions marks a critical leap for sentiment analysis in the cryptocurrency market. Through APIs like Twitter, Reddit, and other social media platforms, investors can get instant readings on public sentiment, which in turn will allow them to make real-time and better-informed trading decisions.

Factors Influencing the Distribution of Community Libraries in China: A Systematic Literature Review

2025-04-20T23:34:40-05:00

Community libraries, as an integral part of community culture, play a key role in collecting and providing knowledge and information for residents. Their distribution directly affects the equity and convenience of services. However, the rational distribution of community libraries is influenced by a combination of factors, but there is a small body of literature on the impact of the distribution of community libraries, and the literature covers an incomplete range of influencing factors. This study systematically collects and analyses relevant literature to identify the key factors affecting the distribution of community libraries, encompassing five main aspects: population, transportation, economy, environment, and policy. The research aims to determine the factors that influence the distribution of community libraries in China. The research methodology applied in this study is a systematic literature review comprising 15 indexed journals. The integration of the existing literature is expected to provide theoretical support for the next research.

Assessing Urban-Rural Income Disparities in the USA: A Data-Driven Approach Using Predictive Analytics

2025-04-21T23:01:55-05:00

Income inequality in the US is a major quagmire that requires a multifaceted understanding of the difference between urban and rural regions. This study seeks to utilize predictive analytics and machine learning methodologies to help analyze these disparities in a thorough manner. The principal objective of this research was to design machine learning models that classify and analyze the urban-rural gap in income by employing a blend of demographic, geographic, and economic variables. The data for measuring urban-rural income inequality in the USA has been carefully pieced together from a range of trusted sources for broad coverage and reliability. The U.S. Census Bureau provides critical demographic and economic data through the American Community Survey (ACS), which offers nuanced detail on income, education, and work by geographic location. For the analysis of socioeconomic data in this study, three different models were chosen based on their individual strengths and appropriateness for the task of classification. To thoroughly analyze the performance of each of these models, a set of evaluation metrics was used that includes accuracy, precision, recall, F1-score, and ROC-AUC. XG-Boost has the highest accuracy, followed by Logistic Regression. The SVM model has a slightly lower accuracy. From the comparison, one sees that both Logistic Regression and XG-Boost perform significantly better than SVM in classifying the dataset, while SVM, although the least accurate, still has a robust performance. Combining machine learning with social science holds tremendous promise for creating evidence-based policy suggestions that target socioeconomic inequalities in the USA. Using the predictive strength of machine learning algorithms, researchers have the ability to study large sets of datasets and reveal patterns and insights that may escape other methodologies.

Sovereign Credit Risk Interdependencies and Shock Transmission Across Countries and Maturities in BRICS

2025-04-24T21:20:16-05:00

This study examines the interconnectedness of sovereign credit risk across BRICS countries and different maturities, focusing on the transmission of shocks within the Credit Default Swap (CDS) market. Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) methodology, we analyze daily sovereign CDS spreads for maturities of 6 months, 5 years, and 10 years from June 2018 to April 2022. The results reveal significant co-movements among BRICS CDS spreads, with distinct patterns across countries and maturities. Brazil and China emerge as key transmitters of shocks over medium and long terms, while South Africa shows rapid responses to short-term changes. Conversely, India and China act as net receivers of shocks, highlighting varying sensitivities across time horizons. This research provides fresh insights into the dynamic interplay of sovereign credit risk within BRICS, emphasizing both country-specific and maturity-related dimensions. The findings have practical implications for investors and policymakers, suggesting the need for enhanced economic policy coordination, including central bank collaboration and the creation of monitoring and research mechanisms to bolster economic resilience and mitigate systemic risks.

Kazakh-Polish Literary Relations: A Study of Mutual Influence And Cultural Exchange

2025-04-25T02:11:30-05:00

Kazakh-Polish literary relations are a unique phenomenon formed due to historical, cultural and social interactions between the two countries. This article analyzes the main stages of these relations, starting from the period of Polish emigration to Kazakhstan in the 19th century and ending with modern forms of cooperation. The study includes an analysis of translations, joint literary projects and themes touched upon in the works of authors from both countries. Particular attention is paid to the role of translation as a mediator of cultural dialogue, as well as the influence of historical events on literary narratives. The work is based on textual analysis, comparative methodology and archival data. The conclusions of the article emphasize the importance of such relations for strengthening mutual understanding and cultural exchange.

Exploring The Role of Effective Use of AI In Improving Strategic Performance Under the Digital Transformation of Higher Education Institutions

2025-04-26T00:04:36-05:00

This study aims to diagnose the role of the effective use of Artificial Intelligence (EFUS) in enhancing strategic performance (STPE) within the context of digital transformation in higher education institutions. Grounded in the Theory of Effective Use, the research conceptualizes EFUS through key behavioral and system-based dimensions, while STPE is assessed via a balanced set of organizational performance indicators. A quantitative approach was employed using a structured electronic questionnaire administered to a random sample of 243 faculty members at the University of Kufa. Data were analyzed using Structural Equation Modeling to examine the hypothesized relationships between EFUS and STPE. The findings reveal a strong and positive association, emphasizing the strategic importance of moving from basic AI adoption to purposeful and effective integration. The study contributes to the theoretical advancement of effective AI utilization in educational contexts and provides practical guidance for institutional leaders aiming to align technological innovation with long-term strategic objectives. Ethical and governance considerations are also highlighted as critical for the responsible use of AI in higher education.

Transforming Thai University Demonstration Schools into Learning Communities: A User Experience and Design-Based Research Approach

2025-02-06T04:25:49-06:00

This study aimed to develop a model for the administration of demonstration schools in public universities based on the concept of "School as a Learning Community" (SLC). The research utilized user experience and design-based research in three phases. Phase 1 involved analyzing the current administrative practices of school administrators under the SLC framework with a sample of 5 administrators. Phase 2 focused on developing a model for promoting SLC-based school administration, targeting 63 participants, including administrators and teachers from Ramkhamhaeng University Demonstration School, Bangna Campus. Phase 3 evaluated the feasibility and suitability of the developed model using a sample of 657 stakeholders, including seven administrators, 52 teachers, and 598 parents of students in grades 1–5. Research tools included interviews and questionnaires, with data analyzed through content analysis, percentages, mean scores (M), and standard deviations (SD). The findings revealed: (1) user experiences highlighted key practices such as inclusive decision-making, shared learning spaces, and stakeholder collaboration, (2) eighteen prototype models for SLC-based administration were developed, integrating stakeholder-driven vision, excellence-focused philosophy, and professional learning community activities, (3) stakeholders most preferred Model 17, selected by 19.33%, emphasizing a shared vision (A2), the philosophy of excellence (B3), and collaborative activity systems (C2), and (4) expert evaluations showed high feasibility (97.40%) and suitability (96.10%) of the proposed model. This study contributes to the literature by providing an actionable framework for transforming demonstration schools into thriving learning communities and fostering collaboration among diverse stakeholders to enhance educational quality and administration efficiency.

The Role of Job Satisfaction in Increasing Productivity trainers at Vocational and Business Skills Training Centers in Khartoum State (Karari Center Model)

2025-04-28T23:18:33-05:00

The study applied to know the criteria of Incentive finance for the trainers in vocational training Centre and business skills in Khartoum state.Also to fiquer out the difference between productivity and incentive finance for trainers in Vocational training center and business skills in Khartoum state.And to know the incentive finance difference between trainers in Vocational training center,according to variability of salary and the incentive finance (wages).Beside, to know the impact of directions for trainers in Vocational training center.The study applied discriptive analytical method for trainers at Vocational training center Khartoum-state.The researcher usr Questionnair and the sample of study was taken randomly from the population.The researcher distributed (200) Questionnair for the study population and it was accepted from all the total numbers no exception was done for the Questionnair validity which it led the total number (200).The study finding and results as follows:There was unacceptably of salary for the trainers at the Vocational training center Khartoum- state and had resulted in workers' performance.Also there was an obvious weakeness in salary payment that offer for trainers,and it indicted the unacceptability of salary system in training center.There was a consistency shows that incentive finance and good salary encourage effective working.The study recommendation: improving salary payment and the incentive finance system to achieve accebtailty in work which will result in performance and productivity.Laying basic and specific critira for offering salary payment which will achieve justice between workers .Improve work environment. Designing safety occupational procedures perfectly.Give trainers a participations to make decisions and offer workers trust to excuse work better.

Exploring Interconnection of Rural Female Learners' Psychological Resilience and Academic Achievement Amidst Climate Change in Zimbabwe

2025-04-29T02:48:24-05:00

This qualitative study explored the intricate relationship between the psychological resilience of rural female learners and their academic achievement in the context of climate change in Zimbabwe. Focusing on two secondary schools in the Masvingo North region, the research employed a case study design to examine the lived experiences of 13 participants, including learners, educators, and community members. Despite the growing body of literature on climate change and education, a specific gap existed regarding the interplay between psychological resilience and academic performance among rural female learners in Zimbabwe. This study aimed to fill that gap by providing nuanced insights into how environmental challenges impacted learners' psychological well-being and educational outcomes. Through focus group discussions, the research captured authentic narratives, revealing that enhanced resilience led to improved academic performance among participants. Thematic analysis of the data highlighted critical patterns. One finding was that enhanced psychological resilience positively influenced academic performance among rural female learners facing climate change challenges. Therefore, it was recommended that stakeholders implement targeted resilience-building programs in rural schools to support female learners' academic achievements.

Predicting the Adoption of Clean Energy Vehicles: A Machine Learning-Based Market Analysis

2025-04-29T05:37:13-05:00

Switching towards clean energy vehicles (CEVs) is a key measure in curbing greenhouse gas emissions and fighting climate change in the USA. Yet, despite mounting environmental consciousness and policy stimulus, the uptake of CEVs is still quite low. The main aim of this research is the creation of a market analysis framework based on machine learning for the prediction of CEV adoption. Utilizing supervised learning algorithms—Random Forest, Logistic Regression, and Decision Tree—the research compares their performance in segmenting prospective CEV adopters in terms of infrastructural, environmental, and socio-economic variables. The dataset included an extensive list of variables designed to capture the various factors that drive clean energy vehicle (CEV) adoption. It includes demographic variables like age, income, educational level, and geographical region, as well as economic variables like vehicle price, purchase incentives, and cost of ownership. In addition, it covers environmental attitudes, captured in terms of questionnaire responses on climate change concerns as well as sustainability values. We initiated this research using a range of machine learning models for the prediction of clean energy vehicle adoption, each of which was used for its particular strengths. To assess the performance of our predictive models, we utilized an extensive range of evaluation metrics: Accuracy, Precision, Recall, F1 Score, and ROC-AUC. Perfect scores on all metrics were recorded for the Decision Tree model, with 100% accuracy, precision, recall, and F1-score. Meanwhile, slightly lower overall performance values were reported for both Logistic Regression and Random Forest models. Sophisticated CEV adoption models' granular outputs can be directly applied in designing and implementing clean vehicle incentive structures at local, state, and federal levels. Knowing the particular socioeconomic, demographic, and geospatial drivers or impediments of adoption in specific regions allows policymakers to craft optimally effective incentive structures. Sophisticated insights derived from patterns of CEV adoption provide irreplaceable value for automotive companies and clean technology firms working in the US market. Future demand for CEVs is important for successful infrastructure planning, especially for the siting of electric charging stations. Monitoring CEV adoption rates is critical for measuring progress towards emissions reduction targets and facilitating broader sustainability planning activities.

Towards An Intelligent Arabic-Speaking Tool For Dyslexia Screening Using Naturallanguage Processing

2025-04-29T08:14:18-05:00

Reading is a fundamental skill for children and is crucial to their educational development. However, this development is not always smooth and simple. Some children exhibit specific difficulties known as reading disorders. These disorders vary from case to case; they can range from severe conditions such as dyslexia to less severe forms of learning difficulties, affecting a child's ability to read fluently and comprehend text. In this paper, we proposed anArabic-speaking Tool for Dyslexia Screening based on Natural Language Processing (NLP) and Artificial Intelligence (AI) for problems detecting of Dyslexia in children and for accurate test to measure the student's reading performance.

Unleashing Employee Engagement: Exploring the Influence of Human Resource Practices in a Local Municipality in South Africa

2025-04-30T04:34:13-05:00

This research investigates the potential of human resource policies to increase employee engagement in a South African local government municipality. The research employed qualitative naturalistic interpretive methods with semi-structured interviews as data collections tools. Data was collected from ten participants code-named P1 to P10. The study was guided by three core questions: i). What are the current HR practices within the studied Municipality, and how do employees perceive them? To what extent do these HR practices influence employee engagement levels? iii). What recommendations can be made to enhance HR practices to improve employee engagement? Findings revealed among others that recruitment and selection, training and development, performance management systems, and employee recognition influenced employee engagement in the municipality. The research suggests that the municipality should support professional development alongside transparent hiring practices and fair performance management as well as reasonable employee appreciation. It also recommends establishing positive workplace culture together with work-life balance programmes to increase employee engagement. Additionally, it was recommended that municipal managers should establish HR strategies which support all employees and fulfill their needs to build a positive workplace environment. By incorporating these elements into their HR strategy, the municipality can enhance employee engagement, leading to better performance and service delivery. This research demonstrates how HR strategies in local governments enable organisations to enhance employee morale and workplace engagement.

Black Experience in Two Worlds: Minority Marginalization in Al-Mugri's Blak Taste, Black Smell and Zakes Mda's The Zulus of New York

2025-05-01T05:37:30-05:00

This study explores the Black experience through a comparative analysis of Al-Mugri's marginalized Akhdam community in Yemen in his novel Black Taste, Black Smell and Zakes Mda's Zulus performers in The Zulus of New York. Examining these groups across historical, emotional, and social contexts, the research aims to achieve a twofold objective. Firstly, it seeks to identify universal themes within the Black experience, highlighting shared struggles faced by black communities despite geographical and cultural differences. Secondly, the study recognizes the distinct challenges faced by each group within their specific societal contexts. Through this comparative approach, the research offers a nuanced understanding of the complexities of the black, acknowledging both the unifying threads of marginalization and the unique realities shaped by each group's historical and social background. Contribution: This study intervenes in Black experience scholarship by examining the intricate marginalization faced by Black characters navigating dual cultural contexts.

Management of the Production Process of the Colombian Caribbean Coastal Cheese Supply Chain using Lean Manufacturing and Simulation in ProModel

2025-05-01T23:32:28-05:00

Resource and production yield management in the Colombian Caribbean Coastal Cheese supply chain was carried out through the structuring of a lean manufacturing model and process simulation in Promodel. Production input variables such as raw milk, rennet, salt, equipment, times, processes, and allocations were evaluated in corresponding scenarios. Significant variations in production by department and cheese type were obtained. Scenario 4 stood out due to the increase in production achieved, as well as the improvement in production capacity and demand satisfaction. This model optimized resources and established minimum lead times, improving the efficiency and quality of the final product, and identifying bottlenecks such as milk availability. It was also important to manage its supply to avoid interruptions, providing a comprehensive view for decision-making and effective strategies in the value chain.

Rapid Communication: The Accreditation Council of Business Schools and Programs 2023 Conference (ACBSP’23), “Celebrating Collaborative Alliances: Stronger Together”

2025-05-02T00:26:00-05:00

The business accreditation conference, ACBSP 23, held in Chicago, June 29 through July 2, 2023, had about 750 participants, with nearly 100 sessions and 100 speakers. This conference note is an oﬃcer's reﬂection on the event from the perspective of one of the officers, the Treasurer of ACBSP Region 8, the International Council of Business Schools, and Programs. ACBSP Region 8, the International Council of Business Schools and Programs is celebrated for it’s history via the ACBSP Region 8 Journal leading up through the first three ACBSP International Conferences, and onto Dubai 2016, where the editorship passed to me, dr. Alfred Howard Miller.

Assessing the mental health impacts of COVID-19 on Healthcare Workers in South Africa: A Systematic Review

2025-05-06T00:50:25-05:00

This paper looks at the impact of COVID-19 on the mental health of healthcare workers (HCWs) in public health facilities in South Africa. The pandemic's massive caseload forced HCWs to work longer hours and multiple shifts without any breaks, frequently with fewer resources, and in questionable facilities. Methodology: The qualitative study used evidence-based bibliometrics to explore how the COVID-19 pandemic affected the mental health of HCWs in public health facilities in South Africa. A world-curated search was conducted on Google Scholar and Scopus to obtain a relevant sample of peer-reviewed journal articles from March 2020 to August 2022. Through content analysis, the research presented in this paper highlights the lived experiences of HCWs at the forefront of the COVID-19 pandemic. Study findings and conclusions: The study found five themes that emerged from the contents: 1) Perceptions of COVID-19. The HCWs did not have much knowledge of COVID-19, which brought about a lot of fear and anxiety. 2) Working experience during the COVID-19 pandemic, reports of HCWs from the studies analysed showed that the HCWs' had to work longer hours and sometimes without the required PPE to protect them from contracting the virus. 3) Mental health support from health facilities: while mental health support services were offered, not all HCWs sought assistance for various reasons. 4) How mental health support can be improved: more support was suggested in the form of fostering a culture of health and wellness because burned-out and distressed HCWs typically do not seek help. 5) Mental health impact. This study, based on the papers analysed, concludes that COVID-19 had a negative impact on the mental health of HCWs in public health facilities in South Africa. The content analysis revealed that many HCWs developed PTSD while others developed anxiety and depression.

Industry-Specific Factors and Retail Stores' Financial Health: Evidence from JSE Listed Retail Stores in SADC

2025-05-06T08:01:26-05:00

This study investigates the influence of industry-specific factors on the financial health of JSE-listed retail stores in the SADC region, focusing on inventory management, operational efficiency, market share, interest rates, firm size, and leverage. Stock turnover (ST) is employed as a proxy for financial health, reflecting the ability of firms to manage resources and generate sales efficiently. Using Feasible Generalized Least Squares (FGLS) to analyse a panel dataset spanning 1994 to 2022, the findings reveal that inventory management, market share, and leverage positively and significantly impact stock turnover. Conversely, interest rates exhibit a significant negative relationship, underscoring the sensitivity of retail firms to macroeconomic conditions. The study also demonstrates the relevance of trade-off theory in explaining the relationship between leverage and financial health, emphasising the need for retail firms to balance the benefits of debt financing against its associated risks. The results provide critical insights for managers in optimising inventory management practices and capital structures to enhance financial performance. Policymakers are advised to consider the implications of interest rate fluctuations on the retail sector's stability. Overall, the study contributes to the literature on financial health in the retail industry, offering theoretical and practical perspectives on managing industry-specific challenges.

COVID-19, Stock Market Crisis and Investor Sentiment: French and USA Case

2025-05-07T03:00:16-05:00

Purpose: This work is part of the behavioral literature, whose objective is to evaluate the capacity of investor sentiment to predict the Covid-19 crisis on the French and American stock markets. Design/methodology/approach: Our study is based on all companies listed in the CAC40 index for France and the Dow Jones index for the US during the first half of 2020, when the Covid19 pandemic started. The statistical methods "Time Series" model used to test our hypothesis on the contribution of investor sentiment, the interest rate and the inflation rate to development of stock market crises. Findings: The results show that the Covid-19 crisis is positively and significantly explained by investor sentiment. While,the interest rate and inflation negatively influence the probability of a stock market crisis. The results also show that the inclusion of psychological factors improves the explanatory power of our alert model and proves to be effective in predicting stock market crises. Originality: This work can be considered as the first one to evaluate the cointegration between the Covid-19 crisis and the investor's sentiment.

Entrepreneurship Education in Tertiary Institutions as Tool for Reducing Unemployment

2025-05-07T03:26:01-05:00

This study explored the role of entrepreneurship education in alleviating unemployment. The research was guided by four key questions, and relevant literature was reviewed to provide context. A descriptive survey research design was employed, focusing on the undergraduate student population at Adekunle Ajasin University, Akungba-Akoko. The sample consisted of 300 randomly selected students from six faculties within the university, chosen using a simple random sampling technique. Data were collected via a self-designed questionnaire and analyzed using frequency counts and simple percentages. The study's findings revealed that skills such as design, food catering, photography, laundry/dry cleaning, barbering/hairdressing, web design, bead making, music, makeup artistry, and event planning can empower Nigerian graduates to become entrepreneurs and help reduce unemployment. Furthermore, the benefits of entrepreneurship education for Nigerian graduates were found to include self-employment, job creation in rural areas, opportunities to harness personal potential, and enhanced ability to adapt to societal changes. Challenges identified include inadequate funding from both government and non-governmental organizations, a shortage of qualified instructors, insufficient teaching materials, equipment, and infrastructure, lack of capital for starting businesses, and a poor business environment. Suggested solutions to these challenges include creating a supportive business environment, ensuring the availability of qualified teachers, establishing funding sources for entrepreneurs, and providing necessary teaching resources and infrastructure. Based on these findings, the study recommends that Nigerian graduate entrepreneurs acquire skills such as those listed above to help reduce the unemployment rate. It also calls on the government to improve the infrastructure for entrepreneurial activities, provide adequate teaching resources, increase funding for entrepreneurial programs, and create diverse funding opportunities for Nigerian graduate entrepreneurs.

Intelligent Streetlight Control System Using Machine Learning Algorithms for Enhanced Energy Optimization in Smart Cities

2025-05-08T01:33:05-05:00

In the USA, where large-scale city infrastructure uses huge amounts of energy, street lighting collectively makes up a large percentage of city-wide electricity consumption. As cities become smarter and greener, there is an immediate demand to update the management of public lighting networks. This research's prime objective was to create an adaptive machine learning system for streetlight control that can react automatically to the environment and human activity patterns in real time. The development of our intelligent streetlight control system led us to build a complete dataset that contains the necessary elements for context-based lighting decisions. The dataset contains contemporary, along with historical readings of ambient light intensity expressed in lux units, which delivers an essential understanding of natural illumination and lighting needs. The primary performance metric is accuracy, which indicates the number of accurately predicted instances against the number of overall predictions. Furthermore, a Confusion Matrix is utilized to present an in-depth breakdown of the outcomes of classification, illustrating the number of examples that were accurately or inaccurately classified into each class. The application of an intelligent streetlight system using machine learning is directly in line with the strategic policies of the U.S. Department of Energy (DOE), specifically its requirements regarding the modernization of the smart grid, energy efficiency, and carbon reduction. By providing real-time data-driven control of street lighting in response to environmental and usage conditions, the system makes full integration of municipal infrastructure into the smart grid possible. At the policy level, the system is an effective and pragmatic tool for municipalities looking to achieve federal and local climate action targets. Greenhouse gas (GHG) mitigation is achieved through the reduction of electricity consumption through adaptive lighting, and the machine learning function minimizes human interaction, maximizing operational autonomy and cost savings. One of the strongest ramifications of using the intelligent streetlight system is the possibility of huge cost reductions on city utility budgets.