Search Results (10,316)

Current space exploration focuses on returning to the Moon to expand space exploration capacity by improving technology. The long-term presence of humans and robots on the Moon requires the development of durable habitats for space missions. In recent decades, in situ resource utilization (ISRU) for construction materials has been recognized as a viable option. However, the addition of nanomaterials, which exhibit a high strength-to-weight ratio, has not been incorporated with the ISRU framework in space missions. This paper investigates the impact of carbon nanotubes (CNTs) on lunar simulant-based geopolymers’ compressive strength and water retention. The evaluation of water retention indicates another potential in water recapturing capability. In this study, CNTs can enhance the mechanical properties of lunar simulant-based geopolymer. Two lunar simulants were used, representing the Highland and Mare regions of the Moon. Experimental variables included CNT concentration, four curing regimes (ambient curing, two oven-curing methods, and microwave radiation), and dispersion time in aqueous solutions. Results showed that CNTs can positively influence both strength gain and water retention during curing regimes, but the extent of influence appears to be dependent on simulant type and curing regime. The Highland simulant consistently outperformed the Mare simulant in oven-curing regimes from a strength perspective, regardless of CNT presence. The strength benefits of CNTs were more pronounced at ambient curing temperatures. Even under poor curing conditions—where water availability may be limited at temperatures of 80 °C—CNTs aid in retaining water within the geopolymer matrix, leading to improved strength compared to counterparts. Under the same conditions, a higher concentration of CNTs further confirmed their role in water retention during geopolymerization, with consistently greater water retention observed in samples containing CNTs. Additionally, microwave radiation was explored as an alternative to conventional oven drying, showing potential for reducing curing duration. Finally, the findings suggest that combining CNTs and microwave radiation could enhance water recovery and reuse, contributing to the development of high-strength infrastructure materials on the Moon with reduced energy and cost requirements. Full article

Background/Objectives: Cancer patients undergoing chemotherapy often face challenges that reduce their physical function and quality of life. Technological resources offer innovative solutions for physical rehabilitation, but the extent of their application in this context remains unclear. This scoping review aims to explore and map the various technological tools used to support physical rehabilitation in cancer patients during chemotherapy, focusing on their potential to improve outcomes and enhance patient care. Methods: A scoping review was conducted following the Joanna Briggs Institute (JBI) guidelines and the PRISMA-ScR framework. Comprehensive searches were performed in the MEDLINE, CINAHL, Scopus, SPORTDiscus, and COCHRANE databases. The included studies focused on the technological resources used in physical rehabilitation for cancer patients undergoing chemotherapy. Data extraction followed the World Health Organization’s “Classification of Digital Health Interventions v1.0” to categorize the technologies. Results: A total of 32 studies met the inclusion criteria. The most commonly used technologies included wearable devices (16 studies), web-based platforms and telerehabilitation systems (7 studies), mHealth applications (6 studies), virtual reality (2 studies), and exergaming (3 studies). These tools were designed to enhance physical function, manage treatment-related symptoms, and improve overall quality of life. Wearable devices were particularly effective for monitoring physical activity, while web-based platforms and mHealth applications supported remote rehabilitation and patient engagement. Conclusions: Technological resources offer significant opportunities for personalized rehabilitation interventions in cancer patients undergoing chemotherapy. However, further research is needed to evaluate the long-term effectiveness, cost-efficiency, and clinical integration of these tools to ensure broader accessibility and sustainable impact. Full article

The Amazon Region (AR), with its vast biodiversity and rich natural resources, presents a unique opportunity for the development of sustainable polymer composites (PCs) reinforced with residues from both timber and agro-extractivism industries. This study explores the potential of Amazonian residues, such as sawdust, wood shavings, and agro-industrial by-products such as açaí seeds and Brazil nut shells, to enhance the mechanical, thermal, and environmental properties of polymer composites. By integrating these natural materials into polymer matrices, significant improvements in the composite performance were achieved, including increased tensile strength, thermal stability, and biodegradability. The study also highlights the environmental and economic benefits of using these residues, promoting waste reduction and supporting a circular economy in the region. Through case studies and detailed analyses, this study demonstrates the feasibility and advantages of incorporating Amazonian residues into composites for a wide range of applications, from construction materials to consumer goods. This approach not only adds value to the by-products of Amazonian industries, but also contributes to the global effort toward sustainable material development. Full article

Distribution network reconfiguration (DNR) plays a vital role in enhancing network sustainability by optimizing its topology. This process achieves key objectives such as reducing power losses, improving voltage profiles, balancing loads, and increasing network reliability, aligning with sustainability metrics. Depending on the goals and equipment available, reconfiguration may be applied for short-term or long-term durations. Long-term or static reconfiguration suits both conventional switches and traditional as well as modern networks. In modern networks equipped with remote-control switches, however, reconfiguration can be implemented rapidly to meet specific operational objectives. This study provides a comprehensive review of recent advancements in network reconfiguration, categorizing methods into four groups: heuristic, metaheuristic, conventional, and modern approaches. Each category is broadly defined and compared, with applications discussed for both static and dynamic reconfiguration. Dynamic reconfiguration is highlighted as a key area for future exploration in smart and modern distribution networks. This article serves as a resource for engineers and researchers, helping them select the most suitable method based on network equipment and performance goals. Full article

The rise in abusive language on social media is a significant threat to mental health and social cohesion. For Bengali speakers, the need for effective detection is critical. However, current methods fall short in addressing the massive volume of content. Improved techniques are urgently needed to combat online hate speech in Bengali. Traditional machine learning techniques, while useful, often require large, linguistically diverse datasets to train models effectively. This paper addresses the urgent need for improved hate speech detection methods in Bengali, aiming to fill the existing research gap. Contextual understanding is crucial in differentiating between harmful speech and benign expressions. Large language models (LLMs) have shown state-of-the-art performance in various natural language tasks due to their extensive training on vast amounts of data. We explore the application of LLMs, specifically GPT-3.5 Turbo and Gemini 1.5 Pro, for Bengali hate speech detection using Zero-Shot and Few-Shot Learning approaches. Unlike conventional methods, Zero-Shot Learning identifies hate speech without task-specific training data, making it highly adaptable to new datasets and languages. Few-Shot Learning, on the other hand, requires minimal labeled examples, allowing for efficient model training with limited resources. Our experimental results show that LLMs outperform traditional approaches. In this study, we evaluate GPT-3.5 Turbo and Gemini 1.5 Pro on multiple datasets. To further enhance our study, we consider the distribution of comments in different datasets and the challenge of class imbalance, which can affect model performance. The BD-SHS dataset consists of 35,197 comments in the training set, 7542 in the validation set, and 7542 in the test set. The Bengali Hate Speech Dataset v1.0 and v2.0 include comments distributed across various hate categories: personal hate (629), political hate (1771), religious hate (502), geopolitical hate (1179), and gender abusive hate (316). The Bengali Hate Dataset comprises 7500 non-hate and 7500 hate comments. GPT-3.5 Turbo achieved impressive results with 97.33%, 98.42%, and 98.53% accuracy. In contrast, Gemini 1.5 Pro showed lower performance across all datasets. Specifically, GPT-3.5 Turbo excelled with significantly higher accuracy compared to Gemini 1.5 Pro. These outcomes highlight a 6.28% increase in accuracy compared to traditional methods, which achieved 92.25%. Our research contributes to the growing body of literature on LLM applications in natural language processing, particularly in the context of low-resource languages. Full article

The Developmental Origins of Health and Disease (DOHaD) framework has highlighted the role of maternal and paternal health on disease risk in offspring and across generations. Although adolescence is increasingly recognised as a key DOHaD window where interventions may have the greatest impact in breaking the cycle of non-communicable diseases, data around the recognition of this concept in adolescents remain limited. Previous work by our group found that the understanding of DOHaD-related concepts among adolescents in New Zealand was low, including some adolescents showing disagreement with key DOHaD concepts. This qualitative study aimed to explore DOHaD perspectives and understandings among a group of adolescents who responded to the survey using semi-structured focus groups and interviews (n = 12). Four core themes were identified: 1. knowledge of DOHaD and DOHaD-related terminology; 2. understanding different life course windows for DOHaD interventions; 3. recognising that DOHaD-related information needs to be accessible for adolescents; and 4. the importance of developing context-specific resources for adolescents. Adolescents in this study indicated that they had not heard of DOHaD or related terminology. Although the majority recognised that there were many important life stages for potential interventions, there was a strong emphasis on adolescence as a key window of opportunity. Adolescents suggested that more could be done in schools to help promote awareness and understanding of DOHaD-related concepts during the later years of schooling. The development of future resources needs to be contextually specific for adolescents to ensure increased uptake of information during this important developmental window. Full article

Moroccan Darija, a dialect of Arabic, presents unique challenges for natural language processing due to its lack of standardized orthographies, frequent code switching, and status as a low-resource language. In this work, we focus on detecting offensive language in Darija, addressing these complexities. We present three key contributions that advance the field. First, we introduce a human-labeled dataset of Darija text collected from social media platforms. Second, we explore and fine-tune various language models on the created dataset. This investigation identifies a Darija RoBERTa-based model as the most effective approach, with an accuracy of 90% and F1 score of 85%. Third, we evaluate the best model beyond accuracy by assessing properties such as correctness, robustness and fairness using metamorphic testing and adversarial attacks. The results highlight potential vulnerabilities in the model’s robustness, with the model being susceptible to attacks such as inserting dots (29.4% success rate), inserting spaces (24.5%), and modifying characters in words (18.3%). Fairness assessments show that while the model is generally fair, it still exhibits bias in specific cases, with a 7% success rate for attacks targeting entities typically subject to discrimination. The key finding is that relying solely on offline metrics such as the F1 score and accuracy in evaluating machine learning systems is insufficient. For low-resource languages, the recommendation is to focus on identifying and addressing domain-specific biases and enhancing pre-trained monolingual language models with diverse and noisier data to improve their robustness and generalization capabilities in diverse linguistic scenarios. Full article

Singapore’s aquaculture sector is critical to achieving the nation’s ‘30 by 30’ food security goal, which aims to produce 30% of its nutritional needs locally by 2030. However, the sector faces several significant challenges. Limited land and water resources, high operational costs, disease outbreaks, reliance on imported seedstock, and environmental impact are among the key issues. Additionally, the industry struggles with a shortage of skilled manpower and high dependency on foreign labour. This study explores these challenges in detail and suggests potential solutions to enhance the sustainability and productivity of Singapore’s aquaculture. Innovative farming techniques such as recirculating aquaculture systems (RASs) and vertical farming, advanced water quality management, and the adoption of renewable energy sources are recommended to address space and cost constraints. Developing local breeding facilities, enhancing education and training programs, and adopting sustainable practices are also crucial. The establishment of a national hatchery and increased investment in research and development (R&D) are essential for long-term growth. By implementing these strategies, Singapore can overcome the challenges in its aquaculture sector and ensure a sustainable future for local food production. Full article

This review paper explores the integration of artificial intelligence (AI) in career and technical education (CTE). CTE is an educational domain often overlooked in discussions about teaching and learning and notably omitted in the extant literature about AI’s application in educational settings. Although much of the existing literature focuses on AI in K-12 and higher education, CTE faces distinct challenges and opportunities in both education and the application of AI because CTE programming is more hands-on and industry-connected. This paper, grounded in Diffusion of Innovations theory, examines AI tool adoption processes among CTE educators by analyzing both barriers and opportunities. Key findings suggest that while AI offers significant benefits, its adoption is hindered by systemic factors. This paper contributes to the literature by highlighting the importance of contextualizing AI adoption within the distinct pedagogical practices and industry partnerships of CTE. It emphasizes the need for targeted strategies that address CTE-specific challenges, including robust infrastructure, equitable resource distribution, and fostering a culture of innovation among educators. The implications of this work underscore AI’s potential to bridge the gap between education and workforce demands, positioning CTE programs as critical sites for preparing students for the next phase of workforce under Industry 5.0. Full article

Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classification algorithms to segment and identify pavement types and surface materials in this scenario. Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. The methodology involves two experiments: the first uses free prompting with random street-view images, employing Grounding Dino and SAM algorithms to assess performance across categories. The second experiment evaluates standardized pavement classification using the Deep Pavements dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. The study presents open resources, such as the Deep Pavements dataset and a fine-tuned CLIP-based model, demonstrating a significant improvement in the true positive rate (TPR) from 56.04% to 93.5%. Our findings highlight both the potential and limitations of current open-vocabulary algorithms and emphasize the importance of diverse training datasets. This study advances urban feature mapping by offering a more intuitive and accurate approach to geospatial data extraction, enhancing urban accessibility and mobility mapping. Full article

This experimental study explores the possibility of using an existing Trombe wall as a space for year-round cultivation to increase building resource efficiency. To do so with the least cost to the building, a small 0.75 m²/5.45 m³ Trombe wall cavity space was retrofitted with shelves placed behind the glazing, additional ventilation, and a watering network to be able to grow 400 hydroponic Kratky basil plants in individual glass jars. Historical thermal observations made at the site over a year-long timespan were contrasted with the experimental readings. When fully equipped, the Trombe wall’s thermal mass increased by 51%, which had a balancing effect on the system, lowering the average daily thermal oscillations from 35.41 °C to 17.88 °C. The living plants and water have also had significant cooling (26.99 °C to 22.91 °C) and humidifying (39.88 to 47.74%) effects. The system’s energy efficiency, however, decreased from 26 to 18% (absorption) and from 85 to 46 (dissipation), lowering its energy contribution to the building by about 30%. The average plant’s lifespan within the Trombe wall was 46 days, with 15% of the specimens surpassing the 100-day mark. Over the course of a year, 20.55 kg of edible greens were grown in the Trombe wall. The experiment has shown that it is possible to grow the plants inside the Trombe wall cavity during the warmer half of the year, revealing many possible ways to improve the space’s comfort, yields, and energy efficiency. Full article

Innovation is an eternal theme of human development, and green innovation efficiency serves as the basis for achieving innovation-driven development in a country or region, as well as an important aspect of ecological civilization construction. In this context, based on the panel data of 30 Chinese provinces during 2003–2020, this study explores the effect of housing price bubbles on green innovation efficiency by using a global SBM-DEA model with unexpected outputs and a two-way fixed effects model. The results show that housing price bubbles considerably reduced green innovation efficiency, which is also verified by a series of robustness and endogeneity tests. Heterogeneity tests show that housing price bubbles in eastern and high human capital regions have a significantly higher inhibitory effect on green innovation efficiency than that in the central and western regions and low human capital regions. The mechanism test shows that housing price bubbles have reduced green innovation efficiency by intensifying the mismatch of labor and capital between regions. Moreover, high housing prices will further deepen the negative impact of housing price bubbles on green innovation efficiency, while expanding economic openness will help alleviate the negative impact. Therefore, to effectively enhance regional green innovation efficiency, we put forward a series of policy measures in terms of strengthening the adjustment of housing policies, optimizing the resource allocation structure, and implementing differentiated environmental control tools. Full article

There are a series of problems in the drilling process of deep heterogeneous formations, such as severe drilling string vibration, slow rock-breaking drilling speed, and the short practical working life of drill bits. It is urgent to develop supporting technical methods for breakthroughs. Based on the main characteristics of the drilling environment in deep formations and the results of drilling string dynamics research in recent years, a technical equipment design concept was proposed to use the vibration of the drill string during the drilling process in heterogeneous formations to improve the hydraulic energy of the bottom hole drilling fluid and thus improve the drilling speed. A technical equipment research and development design was carried out to use the vibration energy of the drill string to enhance the injection energy of the bottom hole drilling medium, and a vibration reduction and energy enhancement device for the bottom hole drill string was developed; we conducted on-site acceleration effect testing, and the research results showed that the vibration of the drill string contains enormous energy, which can be converted into rock-breaking acceleration energy; The designed vibration reduction and energy enhancement device for the bottom hole drilling string can enhance the injection energy of drilling fluid, and reduce the harm of preparing a string vibration; The vibration reduction and energy enhancement device of the bottom hole drill string can significantly improve the drilling speed. The research results have opened up new directions for deep well acceleration technology and provided equipment support for accelerating the exploration and development of deep oil and gas resources. It is recommended to strengthen further the research on the basic theory of downhole drill string vibration, the acceleration technology equipment based on drill string vibration, and vibration acceleration methods. Full article

As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased. Full article

The increasing global burden of morbidity and mortality from chronic diseases related to poor diet quality, coupled with the unsustainable depletion of vital planetary resources by current food production systems, threatens future food security and highlights the urgent need to transition to high-quality plant-based diets as a viable solution to mitigate economic, health, and environmental challenges. Taking into consideration the significant role that fermented vegetables may play as a sustainable, healthy, long-lasting, and plant-based nutritional resource, this narrative review analyzes their production and benefits. For this purpose, the mechanisms of the fermentation process are explored, along with the importance of probiotic cultures in plant-based fermented foods, and with the implications of fermentation on food safety within the broader framework of low-impact, organic, plant-derived nutrition. Additionally, the health benefits of fermented vegetables and probiotics are examined, including their effects on mental health. Vegetable fermentation is a versatile method for enhancing food preservation, nutritional quality, and safety. This ancient practice prolongs the shelf life of perishable items, reduces the toxicity of raw ingredients, and improves digestibility. Specific starter cultures, particularly lactic acid bacteria, are essential for controlling fermentation, ensuring safety, and maximizing health benefits. Fermented vegetables, rich in probiotics, support gut health and immune function. Emerging research indicates their potential to alleviate adverse mental health symptoms such as stress and anxiety, highlighting their significance in modern dietary guidelines and chronic health management. Full article