Search Results (377)

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

Sexual health constitutes a fundamental aspect of overall well-being, with direct implications for individual development and the broader social and economic progress of communities. Promoting environments that ensure sexual experiences free from coercion, discrimination, and violence is a key public health priority. Sexuality, in this regard, should be understood as an inherent dimension of human experience, shaped by biological, cultural, cognitive, and ideological factors. Accordingly, sexual health education requires a holistic and multidimensional approach that integrates sociocultural, biographical, and professional perspectives. This study aims to examine the level of knowledge and training in sexual health among nursing students and healthcare professionals, as well as to assess the extent to which sexual health content is incorporated into nursing curricula at Spanish universities. A scoping review was conducted using the Dialectical Structural Model of Care (DSMC) as the theoretical framework. The findings indicate a significant lack of knowledge regarding sexual health among both nursing students and healthcare professionals, largely due to educational and structural limitations. Furthermore, sexual health education remains underrepresented in nursing curricula and is frequently addressed from a narrow, fragmented biomedical perspective. These results highlight the urgent need for the comprehensive integration of sexual health content into nursing education. Strengthening curricular inclusion is essential to ensure the preparation of competent professionals capable of delivering holistic, inclusive, and empowering care in this critical area of health. Full article

Accurate dynamic hydrological models are often too complex and costly for the rapid, broad-scale screening necessitated for proactive land-use planning against increasing cloudburst risks. This paper demonstrates the use of GPT-4 to develop a GUI-based Python 3.13.2 application for geospatial flood risk assessments. The study used instructive prompt techniques to script a traditional stream and catchment delineation methodology, further embedding it with a custom GUI. The resulting application demonstrates high performance, processing a 29.63 km² catchment at a 1 m resolution in 30.31 s, and successfully identifying the main upstream contributing areas and flow paths for a specified area of interest. While its accuracy is limited by terrain data artifacts causing stream breaks, this study demonstrates how human–AI collaboration, with the LLM acting as a coding assistant guided by domain expertise, can empower domain experts and facilitate the development of advanced GIS-based decision-support systems. Full article

Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems. Full article

Landslide is one of the most damaging natural hazards, causing extensive damage to the infrastructure and threatening human life. Although advances have been made in landslide susceptibility assessment by objective explainable machine learning, the interpretability robustness of traditional single landslide susceptibility model is still low. The proposed interpretable hybrid model in this study overcomes these challenges and aims to enhance the stability of landslide susceptibility interpretability. The model integrates three base machine learning models—LightGBM, XGBoost, and Random Forest—using a heterogeneous category strategy, thereby enhancing the robustness of model interpretability. The hybrid model is interpreted using SHAP (Shapley Additive Explanations) values, which quantify feature contributions. A 10-fold cross-validation with the coefficient of variation (CV) metric reveals that the hybrid model outperforms individual base models in terms of interpretive robustness, yielding a lower CV value of 0.175 compared to 0.208 for LightGBM, 0.240 for XGBoost, and 0.207 for the Random Forest model. Although predictive accuracy remains comparable to the baseline models, the hybrid model provides more stable and reliable interpretability results for landslide susceptibility. It identifies the slope, elevation, and LS factor as the three most important factors for landslide susceptibility in Xi’an city. Furthermore, the quantitative nonlinear relationships between these predisposing factors and susceptibility were identified, providing empowering knowledge for the landslides risk prevention and urban planning in the regions vulnerable to landslides. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

Chronic kidney disease (CKD) significantly affects canine health, but the precise cellular mechanisms of this condition remain elusive. In this study, we used single-cell RNA sequencing (scRNA-seq) to profile renal cellular gene expression in a canine model of X-linked hereditary nephropathy (XLHN). Dogs with this condition exhibit juvenile-onset CKD similar to that seen in human Alport syndrome. Post-mortem renal cortical tissues from an affected male dog and a heterozygous female dog were processed to obtain single-cell suspensions. In total, we recovered up to 13,190 cells and identified 11 cell types, including major kidney cells and immune cells. Differential gene expression analysis comparing the affected male and heterozygous female dogs identified cell-type specific pathways that differed in a subpopulation of proximal tubule cells. These pathways included the integrin signaling pathway and the pathway for inflammation mediated by chemokine and cytokine signaling. Additionally, using machine learning-empowered digital cytometry, we deconvolved bulk mRNA-seq data from a previous canine study, revealing changes in cell type proportions across CKD stages. These results underline the utility of single-cell methodologies and digital cytometry in veterinary nephrology. Full article

Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”. Full article

Background/Objectives: High sodium consumption increases the risk of hypertension and cardiovascular disease. Although food remains the primary source of intake, elevated sodium levels in drinking water can further contribute to excessive intake, particularly in populations already exceeding recommendations. This review examines the extent to which national drinking water standards account for sodium-related health risks and aims to inform discussion on the need for enforceable, health-based sodium limits. Methods: National standards for unbottled drinking water in 197 countries were searched for using the WHO 2021 review of drinking water guidelines, the FAOLEX database, and targeted internet and AI searches. For each country, data were extracted for the document name, year, regulatory body, regulation type, sodium limit (if stated), and rationale. Socio-geographic data were sourced from World Bank Open Data. A descriptive analysis was conducted using Microsoft Excel. Results: Standards were identified for 164 countries. Of these, 20% (n = 32), representing 30% of the global population, had no sodium limit. Among the 132 countries with a sodium limit, 92% (n = 121) adopted the WHO’s palatability-based guideline of 200 mg/L. Upper limits ranged from 50 to 400 mg/L. Only twelve countries (9%) cited health as a rationale. Three countries—Australia, Canada, and the United States—provided a separate recommendation for at-risk populations to consume water with sodium levels below 20 mg/L. Conclusions: Globally, drinking water standards give inadequate attention to sodium’s health risks. Most either lack sodium limits or rely on palatability thresholds that are too high to protect health. Updating national and international standards to reflect current evidence is essential to support sodium reduction efforts. Health-based sodium limits would empower communities to better advocate for safe water. Amid rising water salinity, such reforms must be part of a broader global strategy to ensure universal and equitable access to safe, affordable drinking water as a basic human right. Full article

The Gaining–Sharing Knowledge-based (GSK) algorithm is a human-inspired metaheuristic that models how people learn and disseminate knowledge across their lifetime. It has shown promising results across a range of engineering optimization problems. However, one of its major limitations lies in the use of fixed parameters to guide the search process, which often causes the algorithm to get stuck in local optima. To address this challenge, we propose an Auto-Tuning Memory-based Adaptive Local Search (ATMALS) empowered GSK, that is, ATMALS-GSK. This enhanced version of GSK introduces two key improvements: adaptive local search and memory-driven automatic tuning of parameters. Rather than relying on fixed values, ATMALS-GSK continuously adjusts its parameters during the optimization process. This is achieved through a Gaussian distribution mechanism that iteratively updates the likelihood of selecting different parameter values based on their historical impact on the fitness function. This selection process is guided by a weighted moving average that tracks each parameter’s contribution to fitness improvement over time. To further reduce the risk of premature convergence, an adaptive local search strategy is embedded, facilitating the algorithm’s escape from local traps and guiding it toward more optimal regions within the search domain. To validate the effectiveness of the ATMALS-GSK algorithm, it is evaluated on the CEC 2011 and CEC 2017 benchmarks. The results indicate that the ATMALS-GSK algorithm outperforms the original GSK, its variants, and other metaheuristics by delivering greater robustness, quicker convergence, and superior solution quality. Full article

After decades of calls for more sustainable land use systems, there is still a lack of consensus on an appropriate way forward, especially for tropical and subtropical agroecosystems. Land Maxing utilises appropriate, community-based interventions to fortify and maximise the multiple, long-term benefits and interest flows from investments that rebuild all six essential capitals of sustainable development (natural, social, human, physical, financial and political/corporate will) for resource-poor smallholder communities in tropical and subtropical countries. Land Maxing adds domestication of overlooked indigenous food tree species, and the commercialization of their marketable products, to existing land restoration efforts while empowering local communities, enhancing food sovereignty, and boosting the local economy and overall production. These agroecological and socio-economic interventions sustainably restore and intensify subsistence agriculture replacing conventional negative trade-offs with fortifying ‘trade-ons’. Land Maxing is therefore productive, regenerative, remunerative and transformative for farming communities in the tropics and sub-tropics. Through the development of resilience at all levels, Land Maxing uniquely addresses the big global issues of environmental degradation, hunger, malnutrition, poverty and social injustice, while mitigating climate change and restoring wildlife habitats. This buffers subsistence farming from population growth and poor international governance. The Tropical Agricultural Association International is currently planning a programme to up-scale and out-scale Land Maxing in Africa. Full article

Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user’s consultation needs and levels of medical knowledge further complicates the chatbot’s ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots’ decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class. Full article

The transformation of organizational platformization provides a technological path and collaborative framework for sustainable development. In this context, platform-based human resource management (HRM) has attracted a lot of attention in academia and the industry, but there is a lack of in-depth research on what dimensions are included in the practice of platform-based HRM and how to measure it. Firstly, this study adopts a theory-based approach to decompose platform-based HRM practices into six functional dimensions, namely “adaptive employee recruitment”, “autonomous job design”, “empowering employee development”, “self-managed compensation management”, “team-based performance management” and “facilitating development planning”. Secondly, based on the scale development procedure, a measurement scale for platform-based HRM practices containing 22 items was developed and passed the reliability test. Finally, the paper conducted a predictive test of the scale with passion for harmonious work as the distal predictor variable and sense of self-determination as the proximal predictor variable, which confirmed the scale’s good predictability. This paper provides a quantifiable tool for related research on HRM in platform-based organizations and offers theoretical guidance and a reference model for building HRM empowerment systems in platform-based enterprises. At the same time, it also provides ideas and references for enterprises to practice platform-based human resources and achieve sustainable development. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

Understanding Earth as a complex, dynamic, and interconnected system is crucial to addressing the contemporary environmental challenges intensified in the Anthropocene. This article reviews foundational Earth System Science (ESS) developments, emphasizing its transdisciplinary nature and highlighting how it has evolved to address critical issues like climate change, biodiversity loss, and sustainability. Concurrently, Earth System Education (ESE) has emerged as an educational approach to foster holistic a understanding, environmental insights, and geoethical values among citizens. Integrating geoethics into ESE equips citizens with scientific knowledge and the ethical reasoning necessary for responsible decision making and proactive engagement in sustainability efforts. This article identifies system thinking and environmental insight as the key competencies that enable individuals to appreciate the interconnectedness of Earth’s subsystems and humanity’s role within this complex framework. This study advocates for embedding a holistic and geoethical view of the Earth system into formal and non-formal education, promoting inclusive, participatory, and action-oriented learning experiences. This educational shift is essential for empowering citizens to effectively address the environmental, social, and economic dimensions of sustainability, thereby fostering resilient, informed, and ethically responsible global citizenship in the Anthropocene. Full article