Search Results (11,897)

This study examines the diversity of terminologies associated with assistive technology (AT), a crucial field that promotes autonomy and inclusion for people with disabilities. Although the wide use of assistive technology is observed in the literature, a variety of terms are often used interchangeably, which hinders research, technological development, and the formulation of public policies. In this sense, this systematic review aimed to identify, categorise, and analyse the diversity of terms used to describe AT in the scientific literature, contributing to greater conceptual clarity and supporting structured and interdisciplinary development in the field. A comprehensive search was conducted in July 2024 across the Scopus, Web of Science, and PubMed databases, covering publications from 1989 to 2024. Eligible studies were peer-reviewed journal articles in English that conceptually defined at least one AT-related term. The selection process followed the PRISMA 2020 guidelines and included studies from Q1 and Q2 journals to ensure academic rigour. A total of 117 studies were included out of 11,941 initial records. Sixteen distinct terms were identified and grouped into five clusters based on semantic and functional similarities: Cluster 1—Technologies for assistance and inclusion. Cluster 2—Functional assistive devices. Cluster 3—Assistive interaction interfaces. Cluster 4—Assistive environmental technologies. Cluster 5—Assistive systems. A complementary meta-analysis revealed geographic and temporal trends, indicating that terms such as “assistive technology” and “assistive device” are globally dominant. In contrast, others, like “enabling technology,” are more context-specific and emerging. The findings contribute theoretically by providing a structured framework for understanding AT terminology and practically by supporting the design of public policy and interdisciplinary communication. Full article

Introduction: With the emergence of the expert patient and the expansion of health literacy, the importance of planning and building health technologies aimed at teaching and training health professionals, especially nurses, due to their activities with patients in Primary Health Care, with the aim of meeting the real and constant demands of the expert patient, is evident. Methods: Methodological study with a quantitative approach. The course was constructed based on a scope review, scientific reference, and observational visits during the months of September 2021 and August 2022. For validation, an organized electronic form was used with general information about the research and items of the course constructed for later evaluation by the judges with the three-point Likert scale and with the application of the Delphi Technique between the months of September and October 2022; for the agreement of the judges, the Content Validation Coefficient > 0.8 was considered. Results: Based on the content selected in the scope review, the reference contribution, and the observational visits, the course was constructed. Nine judges participated in the validation stage in Delphi I with a total Content Validation Coefficient above 0.90 and with some suggestions for modifications and improvements pointed out by them. In Delphi II, six judges evaluated the course, resulting in a total Content Validation Coefficient of 0.99. Conclusions: The course developed was considered valid to support the training of Primary Health Care nurses in the formation of the expert patient, with a view to promoting patient autonomy in self-care management, optimizing Primary Health Care, and reducing unnecessary hospital admissions. Full article

The use of remote sensing technologies for mapping forest fires has experienced significant growth in recent decades, driven by advancements in remote sensors, processing platforms, and artificial intelligence algorithms. This study presents a review of 192 scientific articles published between 1990 and 2024, selected using PRISMA criteria from the Scopus database. Trends in the use of active and passive sensors, spectral indices, software, and processing platforms as well as machine learning and deep learning approaches are analyzed. Bibliometric analysis reveals a concentration of publications in Northern Hemisphere countries such as the United States, Spain, and China as well as in Brazil in the Southern Hemisphere, with sustained growth since 2015. Additionally, the publishers, journals, and authors with the highest scientific output are identified. The normalized burn ratio (NBR) and the normalized difference vegetation index (NDVI) were the most frequently used indices in fire mapping, while random forest (RF) and convolutional neural networks (CNN) were prominent among the applied algorithms. Finally, the main technological and methodological limitations as well as emerging opportunities to enhance fire detection, monitoring, and prediction in various regions are discussed. This review provides a foundation for future research in remote sensing applied to fire management. Full article

Seeds of Cucurbitaceae crops represent a promising yet underexplored source of bioactive compounds with potential applications beyond nutrition, particularly in the cosmetics industry. This review examines the seeds of Citrullus lanatus (watermelon), Cucumis melo (melon), and Cucurbita pepo (pumpkin), focusing on their biochemical composition and evaluating their functional value in natural cosmetic development. Although these fruits are widely consumed, industrial processing generates substantial seed by-products that are often discarded. These seeds are rich in polyunsaturated fatty acids, proteins, carbohydrates, and phytochemicals, positioning them as sustainable raw materials for value-added applications. The incorporation of seed-derived extracts into cosmetic formulations offers multiple skin and hair benefits, including antioxidant activity, hydration, and support in managing conditions such as hyperpigmentation, acne, and psoriasis. They also contribute to hair care by improving oil balance, reducing frizz, and enhancing strand nourishment. However, challenges such as environmental instability and low dermal permeability of seed oils have prompted interest in nanoencapsulation technologies to improve delivery, stability, and efficacy. This review summarizes current scientific findings and highlights the potential of Cucurbitaceae seeds as innovative and sustainable ingredients for cosmetic and personal care applications. Full article

The activities of birds present increasing challenges in agriculture, aviation, and environmental conservation. This has led to economic losses, safety risks, and ecological imbalances. Attempts have been made to address the problem, with traditional deterrent methods proving to be labour-intensive, environmentally unfriendly, and ineffective over time. Advances in artificial intelligence (AI) and the Internet of Things (IoT) present opportunities for enabling automated real-time bird detection and repellence. This study reviews recent developments (2020–2025) in AI-driven bird detection and repellence systems, emphasising the integration of image, audio, and multi-sensor data in IoT and edge-based environments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used, with 267 studies initially identified and screened from key scientific databases. A total of 154 studies met the inclusion criteria and were analysed. The findings show the increasing use of convolutional neural networks (CNNs), YOLO variants, and MobileNet in visual detection, and the growing use of lightweight audio-based models such as BirdNET, MFCC-based CNNs, and TinyML frameworks for microcontroller deployment. Multi-sensor fusion is proposed to improve detection accuracy in diverse environments. Repellence strategies include sound-based deterrents, visual deterrents, predator-mimicking visuals, and adaptive AI-integrated systems. Deployment success depends on edge compatibility, power efficiency, and dataset quality. The limitations of current studies include species-specific detection challenges, data scarcity, environmental changes, and energy constraints. Future research should focus on tiny and lightweight AI models, standardised multi-modal datasets, and intelligent, behaviour-aware deterrence mechanisms suitable for precision agriculture and ecological monitoring. Full article

Obesity, a multifactorial metabolic syndrome driven by genetic–epigenetic crosstalk and environmental determinants, manifests through pathological adipocyte hyperplasia and ectopic lipid deposition. With the limitations of conventional anti-obesity therapies, which are characterized by transient efficacy and adverse pharmacological profiles, the scientific community has intensified efforts to develop plant and fungal polysaccharide therapeutic alternatives. These polysaccharide macromolecules have emerged as promising candidates because of their diverse biological activities and often act as natural prebiotics, exerting beneficial effects through multiple pathways. Plant and fungal polysaccharides can reduce blood glucose levels, alleviate inflammation and oxidative stress, modulate metabolic signaling pathways, inhibit nutrient absorption, and reshape gut microbial composition. These effects have been shown in cellular and animal models and are associated with mechanisms underlying obesity and related metabolic disorders. This review discusses the complexity of obesity and multifaceted role of plant and fungal polysaccharides in alleviating its symptoms and complications. Current knowledge on the anti-obesity properties of plant and fungal polysaccharides is also summarized. We highlight their regulatory effects, potential intervention pathways, and structure–function relationships, thereby providing novel insights into polysaccharide-based strategies for obesity management. Full article

Modern beef cattle reproductive management faces increasing challenges due to the growing global demand for beef. Reproductive efficiency is a critical factor determining the productivity and profitability of beef cattle operations. Optimal reproductive performance in a beef cattle herd is achieved when each cow produces one calf per year, maintaining a calving interval of 365 days. However, this goal is difficult to achieve, as the gestation period in beef cows lasts approximately 280 days, leaving only 80–85 days for successful conception. Traditional methods, such as visual estrus detection, are becoming increasingly unreliable due to expanding herd sizes and the subjectivity of visual observation. Additionally, silent estrus—where ovulation occurs without noticeable behavioral changes—further complicates the accurate estrous-based identification of the optimal insemination period. To enhance reproductive efficiency, advanced technologies are increasingly being integrated into cattle management. Sensor-based monitoring systems, including accelerometers, pedometers, and ruminoreticular boluses, enable the precise tracking of activity changes associated with the estrous cycle. Furthermore, infrared thermography offers a non-invasive method for detecting body temperature fluctuations, allowing for more accurate estrus identification and optimized timing of insemination. The use of these innovative technologies has the potential to significantly improve reproductive efficiency in beef cattle herds and contribute to overall farm productivity and sustainability. The objective of this review is to examine advancements in smart technologies applied to beef cattle reproductive management, presenting commercially available technologies and recent scientific studies on innovative systems. The focus is on sensor-based monitoring systems and infrared thermography for optimizing reproduction. Additionally, the challenges associated with these technologies and their potential to enhance reproductive efficiency and sustainability in the beef cattle industry are discussed. Despite the benefits of advanced technologies, their implementation in cattle farms is hindered by financial and technical challenges. High initial investment costs and the complexity of data analysis may limit their adoption, particularly in small and medium-sized farms. However, the continuous development of these technologies and their adaptation to farmers’ needs may significantly contribute to more efficient and sustainable reproductive management in beef cattle production. Full article

Anaerobic digestion (AD) is a sustainable and widely adopted technology for the treatment of organic solid wastes (OSWs). However, AD efficiency varies significantly across different substrates, primarily due to differences in the microbial community and metabolic pathways. This review provides a comprehensive summary of the AD processes for four types of typical OSWs (i.e., sewage sludge, food waste, livestock manure, and straw), with an emphasis on their universal characteristics across global contexts, focusing mainly on the electron transfer mechanisms, essential microbial communities, and key metabolic pathways. Special attention was given to the mechanisms by which substrate-specific structural differences influence anaerobic digestion efficiency, with a focused analysis and discussion on how different components affect microbial communities and metabolic pathways. This study concluded that the hydrogenotrophic methanogenesis pathway, TCA cycle, and the Wood–Ljungdahl pathway serve as critical breakthrough points for enhancing methane production potential. This research not only provides a theoretical foundation for optimizing AD efficiency, but also offers crucial scientific insights for resource recovery and energy utilization of OSWs, making significant contributions to advancing sustainable waste management practices. Full article

This study contributes to scientific theory by analyzing the models proposed within the framework of circular agriculture to determine how the three dimensions of sustainability—environmental, economic, and social—are integrated into their implementation. A systematic review was conducted on articles published between 2016 and 2025, indexed in the Scopus and Web of Science databases, as well as the relevant grey literature. The methodology employed an extensive content analysis designed to minimize bias, applying filters related to specific knowledge areas to delimitate the search scope and enhance the precision of the research. The findings reveal that the research on circular agriculture models is predominantly grounded in the principles of the circular economy and its associated indicators. Moreover, these models tend to focus on environmental metrics, often neglecting a comprehensive exploration of the social and economic dimensions of sustainable development. It can be concluded that a significant gap persists in the literature regarding the circularity of agriculture and its socio-economic impacts and the role of regulatory frameworks, aspects that future research must address in order to achieve sustainability in circular agriculture. Full article

Lactoferrin (LF) is an 80 kDa iron-binding glycoprotein primarily found in milk, saliva, tears, and nasal secretions. LF is well known for its antibacterial and immunomodulatory effects. However, the extraction of LF from milk is inadequate for large-scale therapeutic applications, presenting a challenge for economic mass production. Recombinant protein expression systems offer a solution to overcome this challenge and efficient production of LF. This review discusses recent progress in the translational research of LF gene transfer and biopharming, focusing on different expression systems such as bacteria, yeast, filamentous fungi, transgenic crops, and animals as well as purification methods. The optimization of expression yields, prospects for genetic engineering, and biotechnology to enhance LF production for biomedical applications are emphasized. This review systematically sourced the literature from 1987 to 2025 from leading scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Despite ongoing debates, progress in this field indicates a viable path towards the effective use of LF in therapeutic settings. Full article

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity decay, structural optimization, and the design and application of key materials as well as their performance within battery systems. Addressing these issues requires systematic theoretical foundations and scientific guidance. Numerical modeling has emerged as a powerful tool for investigating the complex physical and electrochemical processes within flow batteries across multiple spatial and temporal scales. It also enables predictive performance analysis and cost-effective optimization at both the component and system levels, thus accelerating research and development. This review provides a comprehensive overview of recent progress in the modeling of ARFBs. Taking the all-vanadium redox flow battery as a representative example, we summarize the key multiphysics phenomena involved and introduce corresponding multi-scale modeling strategies. Furthermore, specific modeling considerations are discussed for phase-change ARFBs, such as zinc-based ones involving solid–liquid phase transition, and hydrogen–bromine systems characterized by gas–liquid two-phase flow, highlighting their distinctive features compared to vanadium systems. Finally, this paper explores the major challenges and potential opportunities in the modeling of representative ARFB systems, aiming to provide theoretical guidance and technical support for the continued development and practical application of ARFB technology. Full article

Avocado (Persea americana), originally from Mesoamerica, has emerged as a focus of intense scientific and industrial interest due to its unique combination of nutritional richness, bioactive potential, and technological versatility. Its pulp, widely consumed across the globe, is notably abundant in monounsaturated fatty acids, especially oleic acid, which can comprise over two-thirds of its lipid content. In addition, it provides significant levels of dietary fiber, fat-soluble vitamins such as A, D, E and K, carotenoids, tocopherols, and phytosterols like β-sitosterol. These constituents are consistently associated with antioxidant, anti-inflammatory, glycemic regulatory, and cardioprotective effects, supported by a growing body of experimental and clinical evidence. This review offers a comprehensive and critical synthesis of the chemical composition and functional properties of avocado, with particular emphasis on its lipid profile, phenolic compounds, and phytosterols. It also explores recent advances in environmentally sustainable extraction techniques, including ultrasound-assisted and microwave-assisted processes, as well as the application of natural deep eutectic solvents. These technologies have demonstrated improved efficiency in recovering bioactives while aligning with the principles of green chemistry. The use of avocado-derived ingredients in nanostructured delivery systems and their incorporation into functional foods, cosmetics, and health-promoting formulations is discussed in detail. Additionally, the potential of native cultivars and the application of precision nutrition strategies are identified as promising avenues for future innovation. Taken together, the findings underscore the avocado’s relevance as a high-value matrix for sustainable development. Future research should focus on optimizing extraction protocols, clarifying pharmacokinetic behavior, and ensuring long-term safety in diverse applications. Full article

Amidst global climate change, carbon emissions across the edible vegetable oil supply chain are critical for sustainable development. This paper systematically reviews the existing literature, employing life cycle assessment (LCA) to analyze key factors influencing carbon footprints at stages including cultivation, processing, and transportation. It reveals the differential impacts of fertilizer application, energy structures, and regional policies. Unlike previous reviews that focus on single crops or regions, this study uniquely integrates global data across major edible oils, identifying three critical gaps: methodological inconsistency (60% of studies deviate from the requirements and guidelines for LCA); data imbalance (80% concentrated on soybean/rapeseed); weak policy-technical linkage. Key findings: fertilizer emissions dominate cultivation (40–60% of total footprint), while renewable energy substitution in processing reduces emissions by 35%. Future efforts should prioritize multidisciplinary integration, enhanced data infrastructure, and policy scenario analysis to provide scientific insights for the low-carbon transformation of the global edible oil industry. Full article

This systematic literature review investigates how the concept of time is taught and learned in early mathematics education. While young children are commonly expected to learn how to tell time, this review explores what additional aspects should be emphasised to foster a deeper and more sustainable understanding of time. Using the EBSCO database, 36 relevant articles published up to December 2024 were identified. To cover different aspects related to the teaching and learning of time, peer-reviewed scientific articles as well as practice-based reports were included in the search. A majority of the articles focused on clock reading as an aspect of time. The aspects duration, sequencing, and measurement of time also frequently appeared whereas expressions of time, or cross-disciplinary aspects were seldom mentioned. Drawing on the findings, this review proposes a comprehensive framework outlining key aspects that should be included in early mathematics education to support the teaching and learning of time. Full article