Dhofar University

The acceleration of green technological innovation is an important strategy to sustain economic and human development. Prior studies focus on the financial inclusion-human capital linkage but overlook the role of green technological innovation within the financial inclusion-human capital framework. This study bridges this gap by examining the influence of financial inclusion on human capital by considering the role of green technological innovation. We use panel data from seven emerging (E7) countries, namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey, from 2004 to 2021. We selected these nations due to their rapidly evolving financial ecosystems and urgent sustainability challenges. We adopted the Method of Moments Quantile Regression (MMQR) approach, which captures heterogeneous effects across quantiles to analyze the heterogeneous relationship among the selected variables. The outcomes state that financial inclusion promotes human capital across all quantiles. Likewise, the outcomes show that green technological innovation positively and significantly affects human capital across low, medium, and high quantiles. Additionally, the robustness check study is supported by parametric methods, including FMOLS and CCR regression. Furthermore, using the Dumitrescu–Hurlin technique, the study finds a bidirectional causality between technological innovation and human capital and a bidirectional causality between technological innovation and financial inclusion. Based on these findings, the study suggests helpful lessons for top emerging economies in stimulating human capital via green technological innovation and financial inclusion channels. Key policy recommendations include investing in green technology education and skills training; and promoting green bonds and green technology entrepreneurship by providing support and resources, which can all strengthen human capital. • Financial inclusion promotes human capital development across economic levels. • Green technological innovation enhances human capital in emerging economies. • Non-parametric technique quantifies the impact of financial inclusion and innovation. • Bidirectional causality found between technological innovation and human capital. • E7 governments should prioritize green tech investments through subsidies and incentives.

Background/Objectives: REM Sleep Behavior Disorder (RBD) is a parasomnia that manifests as the occurrence of abnormal motor movement in the REM sleep and is becoming known as a prominent early indicator of neurodegenerative diseases. Over the last couple of years, due to the increasing need and desire to find efficient and objective means of diagnostics, there has been much research within the area of automated RBD detection. This paper will attempt to give a deep introduction to the contemporary methodologies and technological developments in the automated detection of RBD with the help of physiological signals. Methods: Current literature was also reviewed in a systematic manner, and the emphasis was laid on studies that employ polysomnography data such as electroencephalography (EEG) and electromyography (EMG) to detect RBD. The focus was made on signal processing, feature extraction algorithms, especially spectral and time-frequency features, as well as machine learning and deep learning models application. Unimodal and multimodal approaches were also compared to each other. Results: Available studies reveal that spectral characteristics, particularly the ones obtained using the higher frequencies bands, are significant in discriminating between RBD victims and healthy participants. EMG-based features have high discrimen-native ability because of their capability to obtain an abnormal muscle activity whereas the EEG-based features offer the supplementary information in neural dynamics. Multimodal frameworks that combine EEG and EMG signals have also been shown to enhance accuracy of detection. The use of new models of computation, such as convolutional neural networks and hybrid classifiers, has made additional improvements to the per-performance, yet issues of data variability and generalization persist. Conclusions: Signal processing and smart algorithms have also led to significant development in automated RBD detection. The results of this work are promising but more efforts are needed to work on standardized, reliable and clinically relevant systems. Future studies need to work on increasing the model interpretability, whereas feature selection should be optimized, and need validation on a wide range of datasets to facilitate the implementation in the real world.Overall, automated RBD detection is one of the rapidly developing areas that can help diagnose the disorder earlier, conduct mass screening, and gain a deeper insight into sleep-related disorders.

In an era of rapid change, businesses must undergo constant evolution to maintain competitiveness while mitigating the potential for exacerbating social inequality and environmental harm. Failure to implement and transparently communicate corporate social responsibility (CSR) initiatives can jeopardize not only a company's reputation and market position, but also its legal standing and long-term viability. Consequently, this investigation aims to inspect the concurrent and partial effects of firm size, gender diversity, and profitability on the CSR disclosure level in food and beverage sub-sector businesses registered on the Indonesia Stock Exchange (IDX) from 2014 to 2023. This investigation employs quantitative, descriptive, and analytical techniques. This investigation utilized panel data regression as the analytical method. The purposive sampling technique yielded 8 data points annually on food and beverage sub-sector companies registered on the IDX between 2014 and 2023. The investigation's findings indicate that gender diversity, business size, and profitability simultaneously impact CSR disclosure, while the firm size variable solely influences CSR disclosure, not gender diversity or profitability. The practical implications of prioritizing CSR implementation in the food and beverage sub-sector are significant. Firms must ensure that CSR practices are effectively disclosed to stakeholders and integrate CSR considerations into their overall business strategy. This study provides new insights into the drivers of sustainable agrifood systems by identifying corporate and institutional drivers of CSR disclosure in Indonesia's food and beverage industry, a key sector of the agricultural economy.

State losses from corruption cases in Indonesia are commonly interpreted solely as financial losses, while broader economic losses are largely overlooked. Corruption generates impacts that extend beyond fiscal depletion, affecting economic stability, social welfare, ecological integrity, and other systemic dimensions. This study therefore aims, first, to identify and examine the challenges arising from the ambiguous definition of state loss in Indonesia’s legal framework, particularly in price-related corruption cases; second, to analyse Singapore’s conduct-based model of corruption enforcement, which does not treat state loss as an element of the offence; and third, to formulate an ideal normative framework that can strengthen Indonesia’s anti-corruption regime. Using a normative legal research method with statutory, comparative, and conceptual approaches, this study finds that the ambiguity of the state-loss concept has made it difficult to establish economic losses in court, leading judges to focus exclusively on financial loss and leaving broader economic harm unaddressed in judicial decisions. In contrast, Singapore’s approach, by excluding state loss as an element of corruption, allows enforcement to centre on gratification and corrupt intent, resulting in a more coherent and efficient process than the Indonesian model. Consequently, a reframing of the state-loss concept is required, including more precise parameters of economic loss within anti-corruption law, standardised methodologies for its calculation, and broader asset-recovery mechanisms to enhance legal certainty and improve the overall effectiveness of corruption enforcement.

This study aimed to assess the psychological well-being (PWB) of early childhood teachers and their classroom management skills (CMS), as well as to examine the relationship between these variables using a descriptive correlational approach. A random sample of ninety-two teachers in Saudi Arabia was selected, and data were collected using scales for PWB and CMS. The findings indicated that early childhood teachers demonstrated a high level of PWB, while their CMS were at a moderate level. The results also revealed statistically significant differences between public and private school teachers on both scales, attributed to the type of kindergarten they teach in. Additionally, significant differences were found in CMS based on teaching experience, favoring teachers with higher levels of experience. However, no significant differences were observed in PWB between teachers with varying levels of experience. The study concluded that there is a strong positive correlation between PWB and CMS among early childhood teachers. It recommended continued psychological support and well-being initiatives for these teachers, alongside ongoing training programs to enhance classroom management strategies and improve communication with children. Moreover, creating a positive work environment that promotes collaboration and work-life balance is essential. The study also emphasized the importance of periodic assessments to identify teachers’ needs and enhance their PWB and CMS through professional development workshops.