Search Results (6,286)

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

Meiotic recombination is a key evolutionary process that generates novel allele combinations during prophase I of meiosis, promoting genetic diversity and enabling the selection of desirable traits in livestock breeding. Although its molecular mechanisms are well-characterised in model organisms such as humans and mice, studies in African indigenous cattle, particularly South African breeds, remain scarce. Key regulators of recombination, including PRDM9, SPO11, and DMC1, play essential roles in crossover formation and genome stability, with mutations in these genes often linked to fertility defects. Despite the Bonsmara and Nguni breeds’ exceptional adaptability to arid and resource-limited environments, little is known about how recombination contributes to their unique genetic architecture and adaptive traits. This review synthesises the current knowledge on the molecular basis of meiotic recombination, with a focus on prophase I events and associated structural proteins and enzymes. It also highlights the utility of genome-wide tools, particularly high-density single nucleotide polymorphism (SNP) markers for recombination mapping. By focusing on the underexplored recombination landscape in South African beef cattle, this review identifies key knowledge gaps. It outlines how recombination studies can inform breeding strategies aimed at enhancing genetic improvement, conservation, and the long-term sustainability of local beef production systems. Full article

Teacher education plays a key role in promoting inclusion and educational equity, especially in contexts characterised by increasing socio-cultural diversity and technological advancement. In this framework, Universal Design for Learning (UDL) and digital technologies are presented as complementary and innovative strategies to create accessible, flexible, and motivating learning environments for all students. The study analysed the impact of UDL-focused learning activities and integrated Information and Communication Technologies (ICT). A comparative tool was applied before and after the intervention to measure the level of knowledge, perception, and digital competence of prospective teachers. Statistical analyses were carried out to evaluate the changes obtained. Findings reveal significant improvements in knowledge about UDL, as well as positive perceptions of ICT as a resource for inclusion. Participants demonstrated a greater understanding of UDL principles and strengthened their digital competences to design educational proposals adapted to diversity. The research confirms the value of integrating UDL and ICT in teacher training, fostering inclusive educational practices. It highlights the need to strengthen training programmes that respond to the current challenges of the education system. Full article

Background: Pediatric Crohn’s Disease (CD) affects more than physical health, influencing emotional well-being, social integration, and developmental milestones, with an impact on disease management. This study aimed to explore adolescents’ lived experiences with CD and identify factors influencing their motivation for self-management. Methods: A descriptive, cross-sectional qualitative study was conducted using a semi-structured, self-administered online questionnaire. Participants (n = 10) were adolescents with CD who had been diagnosed for over three years and were recruited from a tertiary pediatric gastroenterology center. Data included demographics, clinical characteristics, IMPACT-III (HRQOL), and PROMIS short forms. Open-ended responses underwent thematic analysis using the framework developed by Braun and Clarke. Results: Participants (80% female, median age 16.2 years, median disease duration 4.6 years) were all in clinical remission (median PCDAI = 2) and with good quality of life (median IMPACT-III = 80.7). Six themes emerged: (1) disease knowledge, (2) emotional responses, (3) coping and adaptation, (4) social support, (5) daily life and school impact, and (6) transition to adult care. Most participants demonstrated strong disease literacy and reported effective coping strategies. Emotional responses to diagnosis ranged from relief (60%) to distress (40%); relapses commonly triggered anxiety and fear. Therapeutic changes and disease monitoring were perceived as beneficial (100%) but with concern. Diagnostic procedures were viewed as burdensome by 70% of respondents. School performance and extracurricular participation were negatively affected in 40% during flares. Concerns regarding the future were reported by 40% of participants, with 30% believing that CD might limit life aspirations. While 60% managed their disease independently, 30% relied on parental support. All acknowledged the need for transition to adult care, though readiness varied. Conclusions: This study illustrates the overall impact of disease on pediatric CD patients. It reports significant emotional challenges and difficulties, as well as an impact on daily life, despite good disease knowledge. The findings underscore the importance of psychosocial well-being, ongoing mental health assessment, non-invasive monitoring, and holistic care, emphasizing the patient perspective, in managing pediatric CD. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. Full article

Background/Objectives: In Germany, the provision of healthcare for post-COVID patients primarily lies with general practitioners (GPs), who often lack the necessary knowledge and skills. As part of the PostCovidCare pilot study (PCC), case management software incorporating a symptom diary was introduced and piloted in n = 10 GP practices with n = 33 included patients involved (September 2022–March 2023). This study aimed to explore patients’ experiences. Methods: Semi-structured telephone interviews were transcribed and analyzed using qualitative content analysis. A total of n = 10 patient interviews were conducted (July–September 2023). Results: Patients’ experiences were heterogeneous. The service was largely structured, involving an extensive initial assessment, follow-up appointments, questionnaires, and support from medical assistants, but technical problems with the symptom diary occurred. The GP consultation played a prominent role. Positive aspects included being actively asked about their symptoms, being given a lot of time, initiating diagnostic and therapeutic measures, and having a closer relationship with their GP. Negative aspects included the time taken, resulting exhaustion, duplication of efforts, and insufficient involvement in the consultation process. Conclusions: The pilot study conducted at an early stage of the post-COVID era demonstrated the basic feasibility of case management in primary care from patients’ perspectives. In addition, for future projects, it is important to integrate patients into the design from the outset, adapt the software to users’ needs, and consider care providers’ perspectives. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article

Adaptive reuse of Mediterranean earthen houses offers a unique opportunity to fuse heritage preservation with sustainable development. This study introduces a comprehensive, sustainability-driven framework that reimagines these vernacular structures as culturally rooted and socially inclusive assets for contemporary living. Moving beyond conventional restoration, the proposed framework integrates environmental, socio-cultural, and economic sustainability across six core dimensions: ecological performance and material conservation, respectful functional transformation, structural resilience, cultural continuity and community engagement, adaptive flexibility, and long-term economic viability. Four geographically and culturally diverse case studies—Alhambra in Spain, Ghadames in Libya, the UCCTEA Chamber of Architects Main Building in North Cyprus, and Sheikh Hilal Beehive Houses in Syria—serve as testbeds to examine how earthen heritage can be reactivated in sustainable and context-sensitive ways. Through qualitative analysis, including architectural surveys, visual documentation, and secondary data, the study identifies both embedded sustainable qualities and persistent barriers, such as structural fragility, regulatory constraints, and socio-economic disconnects. By synthesizing theoretical knowledge with real-world applications, the proposed framework offers a replicable model for policymakers, architects, and conservationists aiming to bridge tradition and innovation. This research highlights adaptive reuse as a practical and impactful strategy for extending the life of heritage buildings, enhancing environmental performance, and supporting community-centered cultural regeneration across the Mediterranean region. Full article

Typo-morphology, as interpreted by the Italian School of Planning, provides an approach to investigate the relationship between built form and socio-cultural patterns in vernacular settlements. This study examines Harran, a heritage site in southeastern Türkiye known for its distinctive conic domed dwellings, to explore how typo-morphological analysis can inform culturally sensitive design and adaptive reuse approaches. Despite its historical significance and inclusion in the UNESCO tentative list, Harran faces insufficient documentation, fragmented governance, limited conservation, and increasing pressure from urbanization and natural disasters. Using multiple sources and fieldwork, the research reconstructs the morphological evolution of Harran through diachronic maps across compound, district, and town scales. Reflections from Alberobello, Italy, i.e., the sister city of Harran and a UNESCO-listed town with a similarly unique vernacular fabric, provide a comparative view to explore different heritage management approaches. Harran evolved through informal, culture-driven growth, whereas Alberobello followed a regulated path. While Alberobello benefits from planned development and institutional preservation, Harran faces partial abandonment and neglect. By positioning typo-morphology as a conceptual planning tool, this paper emphasizes the need for context-responsive, ethically grounded, and inclusive approaches to heritage planning and conservation. It argues for planning practices that are not only technically competent but also attuned to place-based knowledge, local identities, and the long-term sustainability of living heritage. Full article

We present a new method for trend analysis that integrates traditional foresight techniques with advanced data processing and artificial intelligence. It addresses the challenge of analyzing large volumes of information while preserving expert insight. The hybrid methodology combines computational analysis with expert validation across four phases: literature review, information systematization, trend identification, and analysis. Tools like Voyant Tools 2.6.18 and NotebookLMare used for semantic and statistical exploration. Among them, we highlight the use of the Defly Compass tool, a natural language processing tool based on semantic projections and developed by our team. The method produces mixed results, including both conceptual conclusions and quantifiable, reproducible outcomes adaptable to diverse contexts. Comparative case studies in agriculture, education, and public health identified key patterns within and across sectors. Cross-domain validation revealed universal trends such as digital infrastructure, data integration, and equity. Designed for accessibility, the method enables small, non-specialized teams to combine computational tools with expert knowledge for strategic decision making in complex environments. Full article

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with three statistical techniques: Multinomial Logistic Regression (MLR) assesses the influence of gender, age, and education on livestock ownership and livelihood choices; Multiple Correspondence Analysis (MCA) reveals patterns in institutional access and adaptive practices; and Stepwise Linear Regression (SLR) quantifies the relationship between resilience strategies and agricultural productivity. Findings show that demographic factors, particularly gender and education, along with access to veterinary services, drought-tolerant inputs, and community-based organizations, significantly shape resilience. However, trade-offs exist: strategies improving livestock productivity may reduce crop yields due to resource and labor competition. This study recommends targeted interventions, including gender-responsive extension services, integration of indigenous and scientific knowledge, improved infrastructure, and participatory governance. These measures are vital for strengthening resilience not only in West Pokot but also in other drought-prone ASAL regions across sub-Saharan Africa. Full article

Hippophae rhamnoides subsp. sinensis is extensively found in China, where the annual precipitation ranges from 400 to 800 mm. It is the most dominant species in natural sea buckthorn forests and the primary cultivar for artificial ecological plantations. Additionally, it exhibits significant nutritional and medicinal value, making it a renowned eco-economic tree species. Despite extensive research into its ecological functions and health benefits, the mitochondrial genome of this widespread species has not yet been published, and knowledge of the mitochondrial genome is crucial for understanding plant environmental adaptation, evolution, and maternal inheritance. Therefore, the complete mitochondrial genome was successfully assembled by aligning third-generation sequencing data to the reference genome sequence using the Illumina NovaSeq 6000 platform and Nanopore Prometh ION technologies. Additionally, the gene structure, composition, repeat sequences, codon usage bias, homologous fragments, and phylogeny-related indicators were also analyzed. The results showed that the length of the mitochondrial genome is 454,489 bp, containing 30 tRNA genes, three rRNA genes, 40 PCGs, and two pseudogenes. A total of 411 C-to-U RNA editing sites were identified in 33 protein-coding genes (PCGs), with higher frequencies observed in ccmFn, ccmB, nad5, ccmC, nad2, and nad7 genes. Moreover, 31 chloroplast-derived fragments were detected, accounting for 11.86% of the mitochondrial genome length. The ccmB, nad4L, and nad7 genes related to energy metabolism exhibited positive selection pressure. The mitochondrial genome sequence similarity between H. rhamnoides subsp. sinensis and H. tibetana or H. salicifolia was 99.34% and 99.40%, respectively. Fifteen shared gene clusters were identified between H. rhamnoides subsp. sinensis and H. tibetana. Phylogenetically, the Rosales order showed close relationships with Fagales, Fabales, Malpighiales, and Celastrales. These findings provide fundamental data for exploring the widespread distribution of H. rhamnoides subsp. sinensis and offer theoretical support for understanding the evolutionary mechanisms within the Hippophae genus and the selection of molecular breeding targets. Full article

Three-dimensional (3D) reconstruction has become a fundamental technology in applications ranging from cultural heritage preservation and robotics to forensics and virtual reality. As these applications grow in complexity and realism, the quality of the reconstructed models becomes increasingly critical. Among the many factors that influence reconstruction accuracy, the lighting conditions at capture time remain one of the most influential, yet widely neglected, variables. This review provides a comprehensive survey of classical and modern 3D reconstruction techniques, including Structure from Motion (SfM), Multi-View Stereo (MVS), Photometric Stereo, and recent neural rendering approaches such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), while critically evaluating their performance under varying illumination conditions. We describe how lighting-induced artifacts such as shadows, reflections, and exposure imbalances compromise the reconstruction quality and how different approaches attempt to mitigate these effects. Furthermore, we uncover fundamental gaps in current research, including the lack of standardized lighting-aware benchmarks and the limited robustness of state-of-the-art algorithms in uncontrolled environments. By synthesizing knowledge across fields, this review aims to gain a deeper understanding of the interplay between lighting and reconstruction and provides research directions for the future that emphasize the need for adaptive, lighting-robust solutions in 3D vision systems. Full article

The goal of on-device continual learning is to enable models to adapt to streaming data without forgetting previously acquired knowledge, even with limited computational resources and memory constraints. Recent research has demonstrated that weighted regularization-based methods are constrained by indirect knowledge preservation and sensitive hyperparameter settings, and dynamic architecture methods are ill-suited for on-device environments due to increased resource consumption as the structure scales. In order to compensate for these limitations, replay-based continuous learning, which maintains a compact structure and stable performance, is gaining attention. The limitations of replay-based continuous learning are (1) the limited amount of historical training data that can be stored due to limited memory capacity, and (2) the computational resources of on-device systems are significantly lower than those of servers or cloud infrastructures. Consequently, designing strategies that balance the preservation of past knowledge with rapid and cost-effective updates of model parameters has become a critical consideration in on-device continual learning. This paper presents an empirical survey of replay-based continual learning studies, considering the nearest class mean classifier with replay-based sparse weight updates as a representative method for validating the feasibility of diverse edge devices. Our empirical comparison of standard benchmarks, including CIFAR-10, CIFAR-100, and TinyImageNet, deployed on devices such as Jetson Nano and Raspberry Pi, showed that the proposed representative method achieved reasonable accuracy under limited buffer sizes compared with existing replay-based techniques. A significant reduction in training time and resource consumption was observed, thereby supporting the feasibility of replay-based on-device continual learning in practice. Full article