Search Results (3,033)

Artificial intelligence has been considered as a transformative element capable of reshaping STEM education into equitable, resource-efficient, and scalable learning environments. However, realizing this potential requires striking a careful balance between technological innovation, pedagogical considerations, and ethical concerns. This study sought to examine the implementation of artificial intelligence (AI) tools by STEM university faculty members in Saudi Arabia to promote Sustainable Development Goal 4 (quality education). While doing so, the study attempted to explore how Saudi STEM university faculty members integrated AI tools in their instructional practices and analyze their perceptions towards these tools. To achieve these goals, the study employed an explanatory sequential mixed-methods design. In the first phase of data collection, a close-ended questionnaire was applied to a random sample of (324) STEM university faculty members. The second phase involved gathering qualitative data using a semi-structured interview administered to 12 purposively selected experts. Key quantitative findings revealed an overall AI integration at a medium level with a mean of (2.71) and standard deviation of (0.36) across three instructional practices, namely planning, implementation, and assessment. The highest integration level was in assessment (M = 2.93, medium) while the lowest was in planning (M = 2.61, medium). The results also revealed that the participants’ perceptions towards integrating AI tools were highly positive (M = 4.00, high), albeit with some concerns regarding the effect of excessive and unguided use of AI tools on students’ higher-order thinking skills, particularly the risk of AI functioning merely as an information delivery mechanism rather than serving its more pedagogically valuable role as a brainstorming scaffold. Furthermore, the study unveiled a number of barriers to integrating AI tools, including the weakness of digital infrastructure, lack of professional development, the limited credibility of AI-generated content, and ethical concerns related to academic integrity and copyrights. The research suggests the establishment of a sustainable digital environment by improving the infrastructure, providing specific training in accordance with the principles of sustainability, and implementing policies that promote equitable, transparent, and responsible integration of AI. These strategies can coordinate the growth of technology with the larger needs of the quality of education, inclusion, and sustainability of STEM education in the long term. Full article

With the rapid proliferation of large language models (LLMs), distinguishing machine-generated text from human-authored content has become increasingly critical for ensuring content authenticity, academic integrity, and trust in information systems. However, detecting text generated by LLMs remains a challenging problem, particularly in zero-shot settings where labeled data and domain-specific tuning are unavailable. To address this challenge, in this paper, we propose a novel Collaborative Multi-Agent Zero-Shot Detection framework (CMA-ZSD). In contrast to existing methods based on watermarking, statistical heuristics, or neural classifiers, our CMA-ZSD employs three functionally heterogeneous agents that perform differentiated perturbations of the input text. By jointly modeling semantic consistency, grammatical normalization, and feature-level reconstruction, our method captures intrinsic asymmetries between human-authored and LLM-generated text. A semantic similarity evaluation mechanism, combined with majority voting, enables robust and interpretable detection decisions that balance individual agent autonomy with collective consensus. Extensive experiments across 11 domains demonstrate the effectiveness of our method, with its zero-shot detection achieving accuracy comparable to domain-finetuned models in specific domains such as Finance and Reddit-dli5. Full article

Mountain orchards in southern China are characterized by fragmented and complex terrain with a wide slope variation range (5~30°), which easily leads to uneven pesticide distribution and pesticide accumulation on gentle slopes. These issues give rise to core technical bottlenecks such as low pesticide utilization rate, poor operational efficiency, and unclear atomization mechanism, hindering the optimization of pesticide application parameters, causing pesticide waste and environmental pollution, and restricting the sustainable development of the mountain fruit industry. To address this problem, this study designed a slope-classified pipeline layout and developed a high-efficiency fixed pipeline system for phytosanitary application in mountain orchards, featuring stable operation, low labor intensity, and easy intelligent transformation. Following the technical route of “theoretical design-atomization mechanism analysis-parameter optimization-laboratory verification-field application”, ruby nozzles with high wear resistance, uniform droplet distribution, and long service life were selected and optimized to meet the demand for long-term fixed pesticide application in mountain orchards. High-speed imaging technology was used to real-time capture the dynamic atomization process of nozzles, providing support for clarifying the atomization mechanism. Advanced methods such as fluorescence tracing were adopted to quantitatively evaluate key indicators including droplet deposition in canopies, and the system performance was verified through laboratory and field tests, laying a scientific foundation for its popularization and application. Field test results showed that the optimal spray pressure should not be less than 8 MPa. The XR9002 nozzle can generate fine droplets to achieve pesticide reduction while forming a stable hollow cone atomization flow. Fluorescence tracing analysis indicated that the droplet deposition on the adaxial leaf surface decreases with increasing altitude (presumably affected by wind speed), while the initial deposition on the abaxial leaf surface is low and shows no significant variation with altitude. Deposition on the adaxial leaf surface decreased with canopy height, while abaxial deposition was much lower (8.9–14.9%). This technology enables high-precision quantitative analysis of droplet deposition. The core innovations of this study are: clarifying the atomization mechanism of ruby high-pressure nozzles under pesticide application conditions in mountain orchards, constructing a slope-classified terrain-adaptive pipeline layout model, and establishing a closed-loop technical system of “atomization mechanism-pipeline layout-parameter optimization-deposition detection”. This study provides theoretical and technical support for green and precision pesticide application in mountain orchards, and has important academic value and broad application prospects for promoting the intelligent upgrading of the fruit industry in southern China. Full article

This study examined the effectiveness of an intensive learning model in core mathematics courses within engineering education. The model restructures the academic semester so that students study one course at a time in concentrated learning blocks, rather than studying several courses in parallel, with the aim of improving academic achievement and student engagement in engineering mathematics courses. The research employed a quantitative, quasi-experimental, longitudinal design and included 66 undergraduate engineering students who completed three mathematics courses: Linear Algebra, Calculus II, and Differential Equations. Academic performance and learning behavior data were analyzed using mixed-design ANOVA, multiple linear regression, and MANOVA analyses. The findings indicate that students who studied under the intensive learning model achieved significantly higher final grades compared with students in the traditional parallel-course structure. Engagement variables emerged as strong predictors of academic success, particularly class attendance and assignment submission. Academic performance remained stable across the three mathematics courses, and prior academic background variables did not significantly predict achievement. Overall, the results suggest that restructuring mathematics instruction into intensive learning blocks may enhance student engagement and academic performance in demanding quantitative courses, thereby supporting student success and persistence in engineering education. Full article

Despite being a biodiversity hotspot, the Republic of Seychelles faces a critical challenge with an estimated 90% of its food imported. This dependency exposes the country to global supply disruptions and climate-related risks, while pressure on protected ecosystems continues to rise. In response, the Erasmus+ Capacity Building Higher Education GreenTraINT project (Green Training INTernational Program for agriculture, livestock farming, and conservation), co-funded by the European Union (2024–2026), aims to strengthen local expertise in sustainable agriculture, livestock farming, and biodiversity conservation. Through a transnational partnership involving European and Seychellois universities and institutions, GreenTraINT is co-designing innovative higher education modules tailored to the island’s priorities in agriculture, livestock, and biodiversity conservation. This paper focuses on a detailed needs analysis conducted in early 2025 across a diverse group of 84 stakeholders, including students, educators, NGOs, and professionals. The findings reveal a strong demand for applied training in sustainable food systems and biodiversity conservation, blended teaching methods, and programs that bridge theory with hands-on skills. Inspired by other Erasmus+ projects such as NETCHEM and SPARKLE, GreenTraINT adopts a multi-stakeholder, needs-driven approach that aligns international academic expertise with local development goals. As a key milestone, a Summer School in 2026 will pilot the newly developed modules. In the long term, GreenTraINT seeks to leave a lasting legacy by integrating its curriculum into national education pathways, thereby contributing to food security and environmental resilience. With less than four years remaining to achieve the 2030 Agenda targets, the project positions higher education reform as a strategic accelerator for SDG implementation in small island developing states (SIDS). By linking curriculum innovation to measurable sustainability priorities, GreenTraINT helps narrow the SDG implementation gap in vulnerable island contexts. The project offers a model for international collaboration in higher education for sustainability in SIDS. Full article

This paper investigates women’s movements in German-speaking Europe that operate at the intersection of academic theology and activism, challenging the assumption that gender parity within theological institutions has been achieved. Despite broader European progress toward gender equality, theological faculties continue to exhibit structural disparities, including women’s underrepresentation in senior positions and persistent obstacles such as the “leaky pipeline,” the “glass ceiling,” and restrictive ecclesial procedures like the Nihil Obstat. These dynamics intensify the vulnerability of women theologians, particularly those advocating for gender justice within Church structures that do not consistently recognize women as full participants. The study also highlights the vulnerability experienced by women theologians who advocate for gender equality within ecclesial institutions that do not consistently recognize women as full participants. Interdisciplinary dialogue between theology and the social sciences is often met with suspicion, as religion is frequently portrayed as a source of division rather than a catalyst for transformation. Moreover, extremist and fundamentalist movements instrumentalize gender issues, polarizing European societies and suppressing interfaith initiatives that promote justice, care, and cooperation. The paper argues for transversal, intersectional, and inclusive approaches that bridge academic and activist networks. By fostering collaboration, critical reflection, and shared praxis, these movements reimagine the role of women in both Church and society, offering transformative models grounded in justice, dignity, and equality. Full article

The shift to renewable energy sources is widely seen as a promising way to reduce carbon emissions and mitigate the impacts of climate change. The abundance of renewable energy resources in Africa has enormous potential to reduce greenhouse gas emissions and promote climate resilience. This study conducted a bibliometric analysis of research trends in the adoption of renewable energy systems for climate change mitigation in Africa from 1993 to the first quarter of 2025. The results showed a steady growth in publications during the 2000s, with a growing annual rate of approximately 12.7%, reaching a peak in 2024, indicating increasing research interest in Africa. The thematic analysis highlights key but underdeveloped and emerging themes, including climate change mitigation, renewable energy sources, greenhouse gas assessment, climate change, energy policy, economic growth, carbon emissions, energy consumption, rural electrification, and energy transformation for further investigation. These findings also revealed regional disparities, highlighting the need to strengthen institutional capacity, develop clear long-term policies, and develop innovative financing mechanisms to expedite the deployment of renewable energy. Additionally, results from network analysis and emerging keyword detection revealed that enhanced regional and international cooperation, grid modernization, and technological innovation, such as energy storage and digital solutions, are vital in the developmental efforts to enhance optimized resource utilization and ensure energy access and security. The study thus provides insights into existing research gaps and future research directions, which will benefit policymakers, academics, and related stakeholders in their efforts to utilize Africa’s renewable energy potential to mitigate climate change, enable sustainable development, and achieve energy security throughout the continent. Full article

In multilingual contexts such as the Basque Autonomous Community, fostering cross-linguistic awareness is essential to support literacy development and overall academic achievement. This study investigates a pedagogical intervention aimed at developing morphological awareness as a foundation for cross-linguistic reflection to enhance reading comprehension. A quasi-experimental design was implemented in a trilingual school with 70 sixth-grade students who were assigned to an experimental group (n = 24) or a control group (n = 46). Over a six-week period, the experimental group received explicit morphological instruction in the curricular languages—Basque, Spanish, and English. Morphological awareness and reading comprehension were assessed in all three languages. Although no statistically significant improvements were observed in reading comprehension, the experimental group demonstrated significantly greater gains in morphological awareness across the three languages. In addition, out-of-school exposure to Basque was positively associated with both morphological awareness and reading comprehension, highlighting the role of linguistic input. A strong association was also found between morphological awareness and reading comprehension, supporting the interdependence of these skills. Overall, the findings underscore the potential of pedagogical translanguaging to foster metalinguistic awareness across languages in multilingual educational contexts. Full article

In the era of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures are gaining significant attention for their ability to ground language generation in reliable knowledge sources. Despite their effectiveness, RAG systems based solely on semantic similarity often fail to ensure factual accuracy in specialized domains, where terminological ambiguity can affect retrieval relevance. This study proposes Entity Linking Enhanced RAG (ELERAG), an enhanced RAG architecture that integrates a factual signal derived from Entity Linking to improve the accuracy of educational question-answering systems in Italian. The system includes a Wikidata-based Entity Linking module and implements a hybrid re-ranking strategy based on Reciprocal Rank Fusion (RRF). To validate our approach, we compared it against standard baselines and state-of-the-art methods, including a Weighted-Score Re-ranking, a standalone Cross-Encoder and a combined RRF + Cross-Encoder pipeline. Experiments were conducted on two benchmarks: a custom academic dataset and the standard SQuAD-it dataset. Results show that, in domain-specific contexts, ELERAG significantly outperforms both the baseline and the Cross-Encoder configurations. Conversely, the Cross-Encoder approaches achieve the best results on the general-domain dataset. These findings provide strong experimental evidence of the domain mismatch effect, highlighting the importance of domain-adapted hybrid strategies to enhance factual precision in educational RAG systems without relying on computationally expensive models trained on disparate data distributions. They also demonstrate the potential of entity-aware RAG systems in educational environments, fostering adaptive and reliable AI-based tutoring tools. Full article

Achieving more sustainable production in emerging biomanufacturing sectors depends not only on technological innovation but also on how production systems are organized, governed, and scaled. Fermentation-derived proteins produced through biomass and precision fermentation offer promising pathways to reduce the environmental impacts of conventional livestock production. However, their sustainability and circularity outcomes depend heavily on access to biomanufacturing infrastructure and coordination along global value chains. Drawing on Global Value Chain (GVC) theory and an integrative review of more than 40 academic and industry sources published between 2017 and 2026, spanning global value chain governance, biomanufacturing scale-up, CDMO functions, and sustainability and bioeconomy transitions, this study develops a conceptual framework that positions Contract Development and Manufacturing Organizations (CDMOs) as key infrastructural intermediaries in fermentation-based protein systems. CDMOs facilitate access to fermentation capacity, technical expertise, and regulatory capabilities, thereby shaping governance arrangements, capability development, and the scaling of innovation. In doing so, they influence how cleaner production principles, such as resource efficiency, circular feedstock integration, and improved environmental performance, are translated into industrial practice. The analysis also highlights risks linked to CDMO-driven scaling, including infrastructure concentration, dependency dynamics, and unequal access across regions. By integrating GVC perspectives with insights from sustainability transitions and the circular bioeconomy, the article advances understanding of how infrastructural intermediaries shape cleaner production outcomes in emerging biomanufacturing value chains. Full article

Background/Objectives: Cognitive dysfunction, or “brain fog”, following COVID-19 viral infection is strongly associated with diminished work capacity which disproportionality affects working-age adults. This study examined an existing method of cognitive rehabilitation training applied to adults struggling with workplace functioning and self-efficacy due to post-COVID-19 brain fog. Methods: Nine adults with post-COVID-19 cognitive dysfunction participated in this single arm pilot trial of a severity-adaptive cognitive training program. The participants completed 45–90 h of clinician-delivered cognitive training exercises delivered remotely in 60- to 90-min sessions, two or three times per week. The primary outcome measure was overall workplace self-efficacy with subskills of perceived workplace functioning, perception of cognitive functioning, and perception of home functioning assessed through pre and post surveys and qualitative interviews. The secondary outcome was cognitive function operationalized by an IQ score administered before and after the intervention. Results: The participants achieved significant improvements in workplace self-efficacy and cognition following cognitive training. The main qualitative themes of self-reported improvements were in executive function, health and energy, daily living activities, productivity, and socioemotional functioning. A cross-case synthesis of pre-intervention struggles, and post-intervention improvements revealed subthemes at work or school in cognitive processing and comprehension, memory, executive function, fatigue, emotional distress, confidence in work or academics, and work/academic performance impairment. As a group, the mean gain in IQ score was 10.5 points. Conclusions: This study adds to the growing body of literature examining the possibility of using cognitive rehabilitation for post-COVID-19 cognitive dysfunction impacting workplace self-efficacy and work functioning. Full article

Background/Objectives: Adult spinal deformity (ASD) correction is a complex surgery to restore spinal alignment and relieve patients’ symptoms. Modern techniques and technologies allow for aggressive surgical correction in tissue-friendly ways that preserve anatomy and may enable faster recovery. Robotic-assisted posterior spinal stabilization may be used as an adjunct to complex ASD reconstruction to facilitate a minimally invasive approach, reduce perioperative morbidity and physiological insult, and allow for the performance of procedures traditionally reserved for large academic centers to be effectively performed by qualified surgeons in optimized patients at smaller hospitals with fewer resources. The objective of this study is to assess realignment, perioperative complications, and patient-reported outcomes of complex, minimally invasive, robotic-assisted adult spinal deformity correction in a surgical specialty hospital. Methods: Demographic, surgical, and perioperative data were collected from the medical record. The Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for pain scores were collected preoperatively and at regular post-op visits. X-rays were captured preoperatively before hospital discharge and at follow-up visits. Results: Fifty consecutive deformity patients were corrected with a two-stage approach (anterior column reconstruction followed by posterior stabilization with robotic-assisted screw placement on the next day) at a 48-bed (eight operating rooms), surgeon-owned, subspecialty hospital. The average patient age was 70 years, and 64% were female. The average estimated blood loss (EBL) values for the first and second stages were 62 mL and 205 mL, respectively. The average operative time was 172 min during the first stage and 210 min for the second stage. Three interbody spacers (first stage) and 16 screws (second stage) were inserted on average in each procedure. The average length of stay (LOS) in the hospital was 5 days, and the average follow-up period was 10.6 months. No patients required a transfer to another facility with intensive care unit (ICU) capabilities, and none required a revision of hardware placement. There was an average reduction in the lumbar coronal scoliotic curve of 14.5° and an increase in lumbar lordosis of 14.8° at the latest follow-up (p < 0.01). The average mismatch between pelvic incidence and lumbar lordosis (PI-LL) preoperatively was 17.6°, which was reduced to 9.6° at the latest postoperative follow-up (p < 0.01). Mean ODI (%) and NRS scores were significantly improved by 33.8% (46.7 ± 13.3 to 30.9 ± 19.8; p < 0.01) and 55% (6.0 ± 2.2 to 2.7 ± 2.6; p < 0.01), respectively, at last follow-up. Conclusions: This study demonstrates the feasibility of performing complex, robotic-assisted ASD corrective surgery in a surgical specialty hospital, achieving significant correction of sagittal and coronal deformities, relieving patients’ symptoms, and offering efficiency and consistency to pedicle screw placement. This study demonstrates that a minimally invasive approach to complex deformity reconstruction reduces perioperative morbidity with decreased operative times, EBL, and LOS when compared to historic controls. This approach allows for the democratization of deformity care in that procedures typically reserved for large academic centers can be successfully accomplished at smaller institutions in optimized patients by qualified surgeons with appropriate perioperative support staff. Full article

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

Sodium-ion batteries (SIBs) have emerged as one of the most promising alternatives to lithium-ion systems, driven by the abundance and low cost of sodium resources as well as the urgent demand for sustainable large-scale energy storage. In recent years, remarkable advances have been achieved in electrode materials, electrolytes, and interfacial engineering, which have significantly improved the electrochemical performance of SIBs. Hard carbons and alloy-type anodes have shown encouraging progress in balancing capacity and stability, while layered oxides, polyanionic compounds, and Prussian blue analogues are leading candidates for cathodes due to their structural diversity and tunable redox properties. Concurrently, the development of advanced liquid and solid electrolytes, together with strategies to control the solid–electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI), is enhancing safety and long-term cycling. Despite these achievements, critical challenges remain, including limited energy density, volumetric expansion in alloying anodes, interfacial instability, and scalability issues. This review provides a comprehensive overview of the fundamental principles, recent material innovations, and failure mechanisms of SIBs, and highlights the current status of industrial progress led by companies such as Faradion, HiNa Battery, CATL, and Tiamat. Finally, future perspectives are discussed, emphasizing the role of sodium-ion technology in grid-scale storage, renewable energy integration, and sustainable battery recycling. By bridging academic advances and industrial development, this article outlines the roadmap toward the commercialization of sodium-ion batteries. Full article

The mainstream Daoism of the Tang Dynasty was the Highest Clarity Tradition, a paradigmatic form of Medieval Daoism. Meanwhile, the existence of Immortal Daoism, as an offshoot, can be regarded as an undercurrent of Tang Dynasty Daoism, embodying the historical diversity of Daoism during this period. As a paradigmatic figure among Tang Dynasty literati, Li Bai had religious beliefs and practices deeply imbued with Immortal Daoist concepts. His practices centered on three core elements: questing for the immortal realm in untamed mountain landscapes, cultivating spiritual essence through reclusive seclusion, and asserting a strong self-identity as an “ostracized transcendent.” A comparative analysis of works of the same genre reveals that Li Bai’s pursuit of Daoism centered on leaving this mortal coil as a transcendent, whereas the ultimate goal of Medieval Daoist postulants was “Dedao” (to achieve perfect harmony with the Dao). When interacting with such priests, Li Bai would actively adopt the terminology of Daoist scriptures to align with their perspectives and even visit Daoist monasteries for tangible benefits. In his personal writings, he favored imagery associated with Immortal Daoism. Li Bai’s preference for Immortal Daoism not only resolves long-standing academic debates concerning his relationship with Daoism but also stands as a concrete manifestation of the variety of Daoism in the Tang Dynasty, thereby providing a multi-dimensional perspective for the study of Daoist history. Full article