Search Results (415)

Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m³/hm²) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article

Innovative Credit Scoring (ICS) holds promise for reshaping financial inclusion in Indonesia, offering a potent alternative to conventional credit assessments that often exclude underserved populations. By leveraging alternative data—from telco records to e-commerce and social media footprints—and AI/ML technologies, ICS can deliver more accurate, inclusive, and responsive credit evaluations. However, its potential is constrained by structural inefficiencies and weak regulatory frameworks. This study employs a qualitative, exploratory design based on eight focus group discussions with 36 stakeholders, including regulators, financial institutions, data providers, and academics. Thematic analysis reveals three core barriers: fragmented regulation, limited data interoperability, and algorithmic opacity. To address these challenges, the paper recommends four policy priorities: (1) enforce and expand POJK 29/2024; (2) establish interoperable, integrated MSME data systems; (3) mandate algorithm audits to reduce bias and opacity; and (4) invest in digital infrastructure to close regional access gaps. Without these systemic shifts, ICS may fall short of its inclusive promise. Full article

Investigating the coevolution between ecosystem services (ES) and human well-being (HWB) holds significant implications for achieving the sustainable operation of human–environment systems. However, limited research has focused on ES-HWB interactions in ecotourism-dominated counties. To address this gap, this study takes Chun’an County in Zhejiang Province, China, as a case study, with the research objective of exploring the processes, patterns, and mechanisms of the coevolution between ecosystem services (ES) and human well-being (HWB) in ecotourism-dominated counties. By integrating multi-source heterogeneous data, including land use data, the normalized difference vegetation index (NDVI), and statistical records, and employing methods such as the dynamic equivalent factor method, the PLUS model, the coupling coordination degree model, and comprehensive evaluation, we analyzed the synergistic evolution of ES-HWB in Chun’an County from 2000 to 2020. The results indicate that (1) the ecosystem service value (ESV) fluctuated between 30.15 and 36.85 billion CNY, exhibiting a spatial aggregation pattern centered on the Qiandao Lake waterbody, with distance–decay characteristics. The PLUS model confirms ecological conservation policies optimize ES patterns. (2) The HWB index surged from 0.16 to 0.8, driven by tourism-led economic growth, infrastructure investment, and institutional innovation, facilitating a paradigm shift from low to high well-being at the county level. (3) The ES-HWB interaction evolved through three phases—disordered, antagonism, and coordination—revealing tourism as a key mediator driving coupled human–environment system sustainability via a pressure–adaptation–synergy transmission mechanism. This study not only advances the understanding of ES-HWB coevolution in ecotourism-dominated counties, but also provides a transferable methodological framework for sustainable development in similar regions. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

This article analyzes the work of Fr. Elias Friedman, whose legacy of theological work on Jewish identity and Jewish conversion to Catholicism serves as the foundation of the Association of Hebrew Catholics, of which he is the founder. Friedman frames his work as a sensitive approach to Jewish identity and Catholic faith, but as this paper demonstrates, his work reveals a reiteration of some of the most entrenched and historically devastating tropes of Christian anti-Judaism, as well as racial antisemitism. This article presents three main arguments. First, it demonstrates that Friedman’s work evidences a theological anti-Judaism characteristic of Catholicism prior to the Second Vatican Council, which he maintained firmly even after the theological revision of Vatican II rejected such views; and furthermore, that his work also expresses an antisemitism that reflects the modern racial antisemitism adopted by the Nazi regime. Second, this article examines the positive reception of Friedman’s work, as evidenced not only in the revered position he holds within the Association for Hebrew Catholics, but also by the nihil obstat and imprimatur on both of Friedman’s monographs, that is, the official stamp of ecclesiastical approval within the Catholic Church, which declares that the work is “free of doctrinal and moral error.” It proposes that these factors evidence the uncritical reception of his work not only within the Association of Hebrew Catholics, but also on behalf of the institutional Catholic Church. Third, it raises the question of the extent to which Friedman’s identity as a Jewish convert to Catholicism is relevant in the analysis and reception of his work. It argues that his Jewish identity makes his concoction of religious anti-Judaism and racial antisemitism particularly potent, rendering anodyne even the most virulently antisemitic of his statements. Full article

Whilst there has been an increasing trend of women holding academic positions in European Higher Tertiary Institutions (HTIs), leadership positions are held predominantly by men. The study draws on radical feminism theory with which its methodology is aligned by investigating the perceptions of both genders. To that end, the study categorizes the impediments holding women back from breaking the glass ceiling into endogenous and exogenous factors. By doing so, the authors are in a better position to recommend the implementation of policies and procedures to address this inequality and navigate towards achieving sustainable gender equality. The research was conducted using an online survey questionnaire administered among all academic and administrative staff of universities in the Republic of Cyprus, the country with the highest glass ceiling in the EU. The authors found that the binary genders differ in their perceptions of what keeps women from breaking the glass ceiling and that this is attributable to exogenous factors, namely, (a) the walls created by male leaders, reinforcing a feeling of marginalization and mansplaining; and (b) family obligations enhancing women’s experiencing a lack of time and burnout. Furthermore, the exogenous factors and the extremely gendered higher echelons of HTIs underpin the endogenous factor of self-sabotage, making women feel they would rather avoid the toxic leadership environment with its lack of professional credit, a view supported by radical feminism theory. The authors suggest practical policy implications to rectify the gender imbalance in leadership in HTIs and suggest directions for future research. Full article

China’s internal control framework follows the Committee of Sponsoring Organizations (COSO) framework, emphasizing enterprise risk management and encompassing financial reporting, operations, compliance, and strategies. The authors review research that uses the COSO-based Internal Control Index to assess internal control quality among all publicly listed firms in China. Unlike the binary classification of internal control weaknesses under the Sarbanes-Oxley Act Section 404, this continuous index captures more nuanced variations in internal control effectiveness and provides two key advantages over traditional assessment of internal control over financial reporting (ICFR). First, while financial reporting can enhance a firm’s monitoring and decision-support systems, the underlying information is determined by operations. Thus, internal control over operations has a greater impact on a firm’s performance than ICFR. While U.S.-based research argues that the effects of ICFR extend to operations, the COSO-based index includes operational controls, allowing for a more direct study of internal control effects. Second, many U.S. corporations fail to report internal control weaknesses, particularly during misstatement years. In contrast, the COSO-based index, compiled by independent scholars, avoids managerial incentives to withhold negative internal control information. Covering institutional background and research evidence from China, the authors survey a wide range of internal control studies related to various aspects of enterprise risk management, such as earnings quality, crash risk, stock liquidity, resource extraction, cash holdings, mergers and acquisitions, corporate innovation, receivable management, operational efficiency, tax avoidance, and diversification strategy. Full article

The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. Full article

Background/Objectives: Artificial intelligence (AI) holds great potential to reshape the academic paradigm. They can process large volumes of information, assist in academic literature reviews, and augment the overall quality of scientific discourse. This narrative review examines the application of various AI tools in surgical research, its present capabilities, future directions, and potential challenges. Methods: A search was performed by two independent authors for relevant studies on PubMed, Cochrane Library, Web of Science, and EMBASE databases from January 1901 until March 2025. Studies were included if they were written in English and discussed the use of AI tools in surgical research. They were excluded if they were not in English and discussed the use of AI tools in medical research. Results: Forty-two articles were included in this review. The findings underscore a range of AI tools such as writing enhancers, LLMs, search engine optimizers, image interpreters and generators, content organization and search systems, and audio analysis tools, along with their influence on medical research. Despite the multitude of benefits presented by AI tools, risks such as data security, inherent biases, accuracy, and ethical dilemmas are of concern and warrant attention. Conclusions: AI could offer significant contributions to medical research in the form of superior data analysis, predictive abilities, personalized treatment strategies, enhanced diagnostic accuracy, amplified research, educational, and publication processes. However, to unlock the full potential of AI in surgical research, we must institute robust frameworks and ethical guidelines. Full article

Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation. Full article

This study investigates the convergence of blockchain, artificial intelligence (AI), near-field communication (NFC), and mobile technologies in electronic payment (e-payment) systems, proposing an innovative integrative framework to deconstruct the systemic innovations and transformative impacts driven by such technological synergy. Unlike prior research, which often focuses on single-technology adoption, this study uniquely adopts a cross-technology convergence perspective. To our knowledge, this is the first study to empirically map the multi-technology convergence landscape in e-payment using scientometric techniques. By employing bibliometric and thematic network analysis methods, the research maps the intellectual evolution and key research themes of technology convergence in e-payment systems. Findings reveal that while the integration of these technologies holds significant promise, improving transparency, scalability, and responsiveness, it also presents challenges, including interoperability barriers, privacy concerns, and regulatory complexity. Furthermore, this study highlights the potential for convergent technologies to unintentionally deepen the digital divide if not inclusively designed. The novelty of this study is threefold: (1) theoretical contribution—this study expands existing frameworks of technology adoption and digital governance by introducing an integrated perspective on cross-technology adoption and regulatory responsiveness; (2) practical relevance—it offers actionable, stakeholder-specific recommendations for policymakers, financial institutions, developers, and end-users; (3) methodological innovation—it leverages scientometric and topic modeling techniques to capture the macro-level trajectory of technology convergence, complementing traditional qualitative insights. In conclusion, this study advances the theoretical foundations of digital finance and provides forward-looking policy and managerial implications, paving the way for a more secure, inclusive, and innovation-driven digital payment ecosystem. Full article

Every minute, vast amounts of video and image data are uploaded worldwide to the internet and social media platforms, creating a rich visual archive of human experiences—from weddings and family gatherings to significant historical events such as war crimes and humanitarian crises. When properly analyzed, this multimodal data holds immense potential for reconstructing important events and verifying information. However, challenges arise when images and videos lack complete annotations, making manual examination inefficient and time-consuming. To address this, we propose a novel event-based focal visual content text attention (EFVCTA) framework for automated past event retrieval using visual question answering (VQA) techniques. Our approach integrates a Long Short-Term Memory (LSTM) model with convolutional non-linearity and an adaptive attention mechanism to efficiently identify and retrieve relevant visual evidence alongside precise answers. The model is designed with robust weight initialization, regularization, and optimization strategies and is evaluated on the Common Objects in Context (COCO) dataset. The results demonstrate that EFVCTA achieves the highest performance across all metrics (88.7% accuracy, 86.5% F1-score, 84.9% mAP), outperforming state-of-the-art baselines. The EFVCTA framework demonstrates promising results for retrieving information about past events captured in images and videos and can be effectively applied to scenarios such as documenting training programs, workshops, conferences, and social gatherings in academic institutions Full article

The E-7 nations face significant challenges in harmonizing artificial intelligence (AI) adoption with sustainable economic and environmental goals. While AI holds transformative potential to revolutionize energy structures, modernize infrastructure, broaden financial inclusion, and reduce carbon emissions, its effective integration is frequently hindered by policy inertia, economic limitations, and long-standing institutional barriers. Using the multi-level perspective (MLP), this study employs the method of moments quantile regression (MMQREG) on panel data from 2004 to 2024 to investigate the determinants of artificial intelligence (AI) adoption, focusing on the roles of energy structure (ES), infrastructure (INFRA), financial inclusion (FI), economic growth (GDP), patent activity (Tpatent), population (TP), and carbon emissions (CE) across E-7 nations. The study findings reveal that economic growth and energy structure play a significant role in driving AI adoption, while inadequacies in infrastructure and limited financial inclusion significantly hinder AI progress. Additionally, the analysis reveals a positive relationship between AI adoption and CO₂ emissions, where early stages of technology uptake lead to increased emissions, but sustained integration eventually results in efficiency gains that help to reduce them. These findings underscore the need for E-7 nations to adopt targeted policies that modernize digital and physical infrastructure, broaden financial access, and expedite the transition to sustainable energy systems. This study offers actionable insights for policymakers to align digital innovation with sustainable development goals. Full article

Across the globe, many animals with disabilities live in zoos and aquariums, yet these institutions often face difficult questions about how to share those stories with the public in a way that raises awareness and minimizes concern about the animals’ well-being. This study explored whether anthropomorphic narrative signage could help visitors form meaningful emotional connections with these animals and, in doing so, encourage greater understanding and support for people with disabilities. Drawing on ideas from contact and transfer theories, we conducted observational research in zoo settings, comparing visitor reactions to no signage, detailed signage, and simple signage. We found that simple signage resulted in fewer negative comments, fewer questions about the animal’s health, and less frustration directed at the organization. In a related survey experiment, participants who saw a photo and brief story about an animal with a disability were more likely to express empathy and hold more positive views toward both animals and people with disabilities. These findings highlight the power of accessible messaging in informal learning spaces to reduce stigma and promote inclusion. The project was led by a diverse, interdisciplinary team of zoo professionals and disability scholars, most of whom bring personal experience with physical or cognitive disabilities to the work. Full article

This paper explores the link between the results of the UK’s Research Evaluation Exercises (REEs) and university decisions on which Units of Assessment (UOA) to submit to in future REEs. How the raw data from REEs can be converted into two novel measurements of research performance—an internal and an external measurement—is explained. Data on two UOAs, Business and Management Studies (BMS) and Economics and Econometrics (E&E), from five consecutive REEs undertaken in the United Kingdom (UK) between 1992 and 2014, was then used to assess whether and how the results of one REE were related to UOA submissions in the next. The findings reveal that both the internal and external assessments of performance were associated with changes in the probability of resubmission to the same UOA in the next REE, with the external comparisons being particularly important. It also appears that while one instance of poor performance might be tolerated by a university, repeated poor performance was associated with a heightened risk of withdrawal from both the BMS and E&E UOAs in the next REE. In addition, holding research performance constant, universities were significantly more likely to withdraw from the E&E UOA than the BMS UOA. New (post-1992) universities were also more likely to continue to submit to a UOA in the next REE than pre-1992 institutions. There is also some evidence that the quality of submissions to the BMS UOA is catching up with that of submissions to the E&E UOA. The somewhat worrying implications of these findings for the health of the Economics discipline in UK universities are assessed. Full article