Search Results (3,179)

Change detection (CD) is a critical task in analyzing the geographic information changes in remote sensing images (RSIs), yet it still faces challenges such as complex background interference, multi-scale varying objects, and class imbalance between positive and negative samples. Recently, with the development of pre-training and fine-tuning techniques, transferring the general knowledge embedded in large-scale pre-trained visual foundation models (PVFMs) to various downstream tasks has attracted significant attention. However, when directly applying these PVFMs to CD tasks in RSIs, the domain knowledge differences often result in unsatisfactory outcomes. To address the above issues, we propose a novel hierarchical adapter framework to efficiently adapt PVFMs like FastSAM and Swin-Transformer for CD task in RSIs, namely ASS-CD. The proposed method leverages lightweight adapter modules with a cross-attention mechanism, which not only preserves the general knowledge of PVFMs but also integrates global and local information, significantly enhancing CD accuracy. Further, the convolutional block attention module (CBAM) is adopted to reduce interference from complex backgrounds and focus on multi-scale objects, and the hierarchical deep supervision module (HDSM) is utilized to impose deep supervision on multi-scale feature maps and compute the Dice loss, addressing the issue of class imbalance in CD datasets. The experimental results on three widely used datasets demonstrate that our ASS-CD achieves the state-of-the-art performance, with an approximately 5% improvement on the LEVIR-CD dataset compared to the other CD methods. Full article

The information perceived by producers plays a crucial role in the efficient management of the agricultural production chain, encompassing both the fertilization and marketing processes of the final product. The ability of farmers to make effective use of this information depends on their information behavior, the use of information technologies, and the adoption of up-to-date technical knowledge. However, small Ecuadorian producers face information gaps that limit their access to technical and commercial knowledge, which affects productivity and profitability. This study analyzed the informational competencies of small banana producers in the provinces of Guayas and Los Ríos, with the objective of identifying the causes of these gaps and their impact on fertilization and marketing. A structured survey was applied to small producers, evaluating five dimensions of information. In addition, soil analyses were conducted in 20 plantations to determine the correspondence between fertilization practices and banana nutritional requirements. The results showed that producers in Guayas presented more robust informational competencies, with greater recognition of information needs and active use of reliable sources. This was reflected in the fertilization practices more aligned with nutritional standards, where plantations in Guayas presented average values of 1.21 cmol(+)/L aluminum, 8.67 cmol(+)/L magnesium, and 0.87 cmol(+)/L potassium, largely complying with nutritional standards for banana cultivation. In contrast, growers in Los Ríos spent less time searching for information and evidenced deficiencies in soil nutrition. This study highlights the importance of strengthening knowledge transfer and improving agricultural communication systems as tools to close information gaps. It is recommended to implement inclusive public policies and training programs in the use of information technologies and sustainable practices. In addition, promoting the creation of collaborative platforms can optimize access to markets, facilitating the direct and efficient marketing of produce. Full article

Currently, the administration of innovation and technology, and the execution of technological projects (in this case, mechatronic projects) is, for all types of organizations, a challenge that requires the use of the creativity and initiative of its professionals, investing or implementing processes, machines, products, and services in such a way that inventions, designs, and prototypes provide solutions to environmental problems and facilitate society. Therefore, in innovation projects, it should be considered that it corresponds not only to the application of new technologies, but also to the generation of an outcome that is useful for the objective, quantifiable, and productive segment, as applied to mechatronic projects. Therefore, it is necessary and relevant to carry out a process of orderly development in the following phases: identification of need, ideation, development, construction, and verification of the final solution of these mechatronic projects. The above is turned towards a comprehensive design process around the academy, which for the purposes of this research takes place at the Universidad Autónoma del Caribe, which, according to the indicators of technological development and innovation, is positioned among the top ten positions at a national level (over 350 measured universities) in the DTI-Sapiens ranking, published every two years since 2017 by the consulting firm Sapiens Research and recognized by the international IREG Observatory. The Unit Department for the Administration and Execution of Technological Projects and Innovation: A Case Applied to Mechatronic Projects aims to achieve a balanced technological offer in the universe of R&D&I projects in mechatronics, among economic and social scientific values. In this way, it will be possible to consolidate links with the socioeconomic environment for the transfer of existing knowledge in HEIs, its exploitation by stakeholders, and the increase in the development of R&D&I projects, strengthening capacities in the UEES relationship for the transfer of know-how to companies. Full article

Cross-Domain Few-Shot Learning (CD-FSL) aims to empower machines with the capability to rapidly acquire new concepts across domains using an extremely limited number of training samples from the target domain. This ability hinges on the model’s capacity to extract and transfer generalizable knowledge from a source training set. Studies have indicated that the similarity between source and target-data distributions, as well as the difficulty of target tasks, determine the classification performance of the model. However, the current lack of quantitative metrics hampers researchers’ ability to devise appropriate learning strategies, leading to a fragmented understanding of the field. To address this issue, we propose quantitative metrics of domain distance and target difficulty, which allow us to categorize target tasks into three regions on a two-dimensional plane: near-domain tasks, far-domain low-difficulty tasks, and far-domain high-difficulty tasks. For datasets in different regions, we propose a Divide-and-Conquer Strategy (DCS) to tackle few-shot classification across various target datasets. Empirical results across 15 target datasets demonstrate the compatibility and effectiveness of our approach, improving the model performance. We conclude that the proposed metrics are reliable and the Divide-and-Conquer Strategy is effective, offering valuable insights and serving as a reference for future research on CD-FSL. Full article

HUG is the HELP-UnaG recombinant fusion protein featuring the typical functions of both HELP and UnaG. In HUG, the HELP domain is a thermoresponsive human elastin-like polypeptide. It forms a shield enwrapping the UnaG domain that emits bilirubin-dependent fluorescence. Here, we recapitulate the technological development of this bifunctional synthetic protein from the theoretical background of its distinct protein moieties to the detailed characterization of its macromolecular and functional properties. These pieces of knowledge are the foundations for HUG production and application in the fluorometric analysis of bilirubin and its congeners, biliverdin and bilirubin glucuronide. These bile pigments are metabolites that arise from the catabolism of heme, the prosthetic group of cytochromes, hemoglobin and several other intracellular enzymes engaged in electron transfer, oxygen transport and protection against oxygen free radicals. The HUG assay is a powerful, user-friendly and affordable analytical tool that alone supports research at each level of complexity or taxonomy of living entities, from enzymology, cell biology and pathophysiology to veterinary and clinical sciences. Full article

Objective. Early childhood exposure to violent media content represents an actionable target for preventive intervention. The associated risks for later aggressive behavior have been established in childhood, but few studies have explored widespread long-term associations with antisocial behavior. We investigate prospective associations between exposure to violent television content in early childhood and subsequent antisocial behavior in mid-adolescence. Method. Participants are 963 girls and 982 boys from the Quebec Longitudinal Study of Child Development (QLSCD) birth cohort. Parents reported the frequency of their child’s exposure to violent television content at ages 3.5 and 4.5 years. Four indicators of antisocial behavior were self-reported by participants at age 15 years. These indicators were linearly regressed on exposure to violent television content at ages 3.5 and 4.5 years. All analyses, stratified by sex, controlled for pre-existing and concurrent potential individual and family confounding variables. Results. For boys, preschool violent televiewing was associated with increases in proactive aggression (β = 0.065; 95% CI, 0.001 to 0.089), physical aggression (β = 0.074; 95% CI, 0.040 to 0.487), and antisocial behavior (β = 0.076; 95% CI, 0.013 to 0.140) by mid-adolescence. No prospective associations were found for girls. Conclusions. This study of typically developing children demonstrates long-term perils associated with early exposure to violent content in childhood. We observed risks for aggressive and delinquent behavior in boys, more than a decade later. Preventive intervention campaigns that target knowledge transfer to parents and communities regarding the potential insidious consequences of preschool exposure promise more optimal development in youth. Full article

Nigella sativa L. (generally known as black cumin) is a medicinal plant prized for its therapeutic and nutritional benefits. Its seed oil is used extensively in pharmaceuticals, nutraceuticals, cosmetics, and cooking. However, extracting oil to satisfy the world’s needs leaves behind plenty of solid residues. The seeds of Nigella are loaded with health-benefiting phytoconstituents, but so might their extraction residues. While much research on seeds and oil has been carried out, there is relatively little information about solid residue, particularly regarding health-benefiting phytoconstituents. Additionally, there is a knowledge gap relating to how phytoconstituents transfer from seeds to solid residue during oil extraction and any loss of key phytoconstituents that may occur during this transfer. Understanding the health-benefiting phytoconstituents in Nigella solid residue is crucial for unlocking its full potential for value-added applications in health and nutrition. Moreover, understanding the dynamics of these phytoconstituent transfers is essential for optimizing extraction processes and preserving the nutritional and therapeutic value of the derived products. Therefore, this study investigated the composition of the screw-press solid residues of different Nigella genotypes grown under similar environmental conditions. The results showed moderate variation in the levels of potential health-benefitting phytoconstituents in Nigella solid residues regarding total phenolic content (TPC) (720.5–934.8 mg GAE/100 g), ferric reducing antioxidant capacity (FRAP) (853.1–1010.5 mg TE/100 g), cupric reducing antioxidant capacity (CUPRAC) (3863.1–4801.5 mg TE/100 g), thymoquinone (TQ) (156.0–260.1 mg/100 g), saturated fatty acid (SFA) (2.0–2.2 mg/g), monounsaturated fatty acid (MUFA) (2.0–3.6 mg/g), and polyunsaturated fatty acid (PUFA) (8.2–12.1 mg/g). Notably, TPC, FRAP, and CUPRAC had high transfer rates into the solid residue (78.1–85.9%, 65.4–75.7%, and 84.5–90.4%, respectively), whereas TQ, SFA, MUFA, and PUFA showed lower transfer rates (15.9–19.3%, 7.5–8.9%, 12.0–18.3%, and 6.5–7.5%, respectively). When summing the values of individual phytoconstituents transferred into oil and solid residue from their respective seeds during processing, it was found that only 80.6–88.3% of TPC, 74.2–84.4% of FRAP, 86.3–92.3% of CUPRAC, 54.4–64.9% of TQ, 68.5–92.4% of SFA, 76.2–90.6% of MUFA, and 51.6–76.6% of PUFA were transferred from the total value present in their respective seeds. Full article

A review of the violent knife crime literature suggests that the experiential perspective is one which has not been addressed in academic study. The research presented hereafter aims to address this literary gap and generate transferable knowledge relevant to the lived experience of violent knife crime. The experiential study of the single case within psychological research involves detailed examination of a particular event. Participant ‘J’ is the survivor of an extremely violent attack, involving the use of a knife, in his own home. J’s experience was analysed using Interpretative Phenomenological Analysis with reference to elements of the lifeworld: temporality, spatiality, intersubjectivity, and embodiment. Three themes were identified: 1. switching from past to present tense when relaying traumatic experience; 2. The presence of redemption sequences; and 3. making sense as a temporal process, which included an additional two subthemes—‘The long journey’ and ‘Seeking belongingness’. This case emphasises that the traumatic event is conceptualised as one part of a longer journey towards recovery, and that recovery itself is central to the experience of violent knife crime. Finally, the need to understand recovery as temporal process highlights the need to provide victims with appropriate support in order to avoid negative outcomes. Full article

Exosomes are extracellular vesicles ranging from 30 to 150 nm in diameter that contain proteins, nucleic acids and other molecules. Produced by virtually all cell types, they travel throughout the body until they reach their target, where they can trigger a wide variety of effects by transferring the molecular cargo to recipient cells. In the context of ocular physiology, exosomes play a very important role in embryological development, the regulation of homeostasis and the immune system, which is crucial for normal vision. Consequently, in pathological situations, exosomes also undergo modifications in terms of quantity, composition and content, depending on the etiology of the disease. However, the mechanisms by which exosomes contribute to ocular pathology has not yet been studied in depth, and many questions remain unanswered. This review aims to summarize the most recent knowledge on the function of exosomes in the ocular system in healthy individuals and the role they play during pathological processes of a degenerative, infectious, neurodegenerative, vascular and inflammatory nature, such as keratoconus, keratitis, glaucoma, diabetic retinopathy and uveitis. Furthermore, given their unique characteristics, their potential as diagnostic biomarkers or therapeutic agents and their application in clinical ophthalmology are also explored, along with the main limitations that researchers face today in the field. Full article

Knowledge flow as the key to facilitating new technology production and diffusing innovation is crucial for achieving sustainable development. However, previous studies pay less attention to the type of knowledge in knowledge flow network construction, possibly leading to the deviation of conclusions. To fully show the panorama of knowledge flow, this study distinguishes between explicit and tacit knowledge based on the transfer of patent rights data and talent flow data, describes the spatial characteristics of flow networks and uses a multiple regression quadratic assignment procedure model to analyze the proximity mechanism of network formation in the Yangtze River Delta. We find that knowledge flow networks in the Yangtze River Delta cover a wide range but are extremely uneven, mainly concentrated along the Yangtze River and around Hangzhou Bay. In addition, the spatial structures of different types of knowledge flow networks vary. Different dimensions of proximity act in relatively consistent directions for both types of knowledge flows, with geographical and organizational proximity found to exert positive effects on facilitating knowledge flows while cognitive proximity has a negative impact. There is also a substitution effect between geographical proximity and organizational proximity, and a complementary effect with cognitive proximity. These findings provide significant implications for optimizing knowledge flow networks and promoting sustainable development. Full article

Artificial Intelligence (AI) has gained significant prominence in science education, yet its practical applications, particularly in teacher training, remain underexplored. Specifically, there is a lack of research on AI’s potential to support personalized professional development through automated analysis of classroom interactions and tailored feedback. As science teacher education requires skill development in complex scientific concepts within problem-based learning (PBL) contexts, there is a growing need for innovative, technology-driven instructional tools. AI-generated instructional videos are increasingly recognized as powerful tools for enhancing educational experiences. This study investigates the impact of AI-generated instructional videos, designed using established instructional design principles, on self-efficacy, task performance, and learning outcomes in science teacher education. Employing a within-subjects design, the current study included pre-test, post-test, and transfer assessments to evaluate learning durability and transferability, consistent with design-based research methodology. Moreover, this study compares the effectiveness of two AI-generated instructional video formats: one with an embedded preview feature allowing learners to preview key concepts before detailed instruction (video-with-preview condition) and another without this feature (video-without-preview condition). It specifically examines the role of preview features in enhancing these outcomes during training on scientific concepts with 55 Greek pre-service science teachers (n = 55; mean age 27.3 years; range 22–35). The results demonstrated that the videos effectively supported self-efficacy, task performance, and knowledge retention. However, no significant differences were observed between videos with and without preview features across all assessed metrics and tests. These findings also indicate that AI-generated instructional videos can effectively enhance knowledge retention, transfer, and self-efficacy, positioning them as promising assets in science teacher education. The limited impact of the preview feature highlights the need for careful design and evaluation of instructional elements, such as interactivity and adaptive learning algorithms, to fully realize their potential. Full article

In the era where digital technologies are becoming increasingly prevalent, it is anticipated that a majority of the global population will have at least a basic understanding of informatics. However, empirical evidence suggests that a significant portion of the global population remains digitally illiterate. This phenomenon is particularly pronounced in the case of the senior adult population. In light of the aforementioned challenges, this work integrates Computer Science Unplugged exercises, based on games and recreational activities without the use of computers, and L2T2L, a learning-by-teaching methodology whereby university students learn and then, in turn, teach that learning to other populations in a cascading manner. A case study was conducted in Lima, Peru, with the participation of 140 volunteers from centres for the elderly. Thirty-five students and one teacher from the Universidad Científica del Sur were responsible for initiating the transfer of knowledge from the university to the senior citizens, with the assistance of twelve individuals responsible for their care. The results demonstrate that the participants attained a commendable level of comprehension when attempting to complete all of the assigned tasks. Furthermore, the efficacy of L2T2L is evident in its adaptability and suitability for scenarios beyond those for which it was originally designed. Full article

Business incubators contribute to the growth of a country, and it is of great interest to deepen knowledge of the impact of incubation phases on the results of incubators to evaluate the effectiveness of developed incubation programs. The objective of this research was to propose a model that quantitatively related different incubation phases to the graduation rate of business incubators in Spain. A sample of 88 incubators was obtained. The survey included 42 items identified in different phases (spreading entrepreneurship, 9 items; pre-incubation, 9 items; basic incubation, 9 items; advanced incubation, 6 items; and graduation, 9 items) and four hypotheses relating to the existence of a positive influence from the startup incubation phases on the incubators results. These were validated by using a structural equation model (SEM) with five latent variables. Three of the four proposed hypotheses that linked startup pre-incubation (H2), basic incubation (H3), and advanced incubation (H4) with graduation rates in Spanish incubators were accepted. These startup incubation stages showed a positive influence on the startup graduation rate. The advanced incubation stage had a very strong relationship with the graduation rate (β = 0.543). Furthermore, a strong indirect effect between business incubation and the graduation rate, explaining 71% of the success of the incubators, was found. Proposals for improvement in each incubation phase to enhance the results of the business incubators are provided. Furthermore, future challenges that should be incorporated into the development of incubator programs, such as the social focus, the implementation of a training and monitoring model, an increase in network businesses, the internationalization of incubators with a globalized approach, the sustainability of the startup’s approach, and the transfer focus, are raised. Given the high variability of Spanish incubators and the wide sampling range, the model could be extended to other contexts with similar behavior within the sample range. Full article

Organizations face fundamental challenges in managing knowledge flows across complex networks, yet existing frameworks often lack quantitative tools for optimization. We develop a novel stochastic multi-scale model introducing knowledge flow viscosity (KFV) to analyze organizational learning dynamics. This model quantifies resistance to knowledge transfer using a time-varying viscosity tensor, capturing both continuous learning processes and discrete knowledge acquisition events. Through renormalization group analysis, we establish the existence of critical thresholds in knowledge diffusion rates, characterizing phase transitions in organizational learning capacity. Numerical simulations demonstrate that targeted reductions in communication barriers near these thresholds can significantly enhance knowledge flow efficiency. The findings provide a mathematical foundation for understanding multi-level knowledge flow dynamics, suggesting precise conditions for effective interventions to optimize learning in complex organizational systems. Full article

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development. Full article