Search Results (4,084)

Understory-cultivated Panax ginseng possesses high pharmacological and economic value; however, its visual quality grading predominantly relies on subjective manual assessment, constraining industrial scalability. To address challenges including fine-grained morphological variations, boundary ambiguity, and complex natural backgrounds, this study proposes Ginseng-YOLO, a lightweight and deployment-friendly object detection model for automated ginseng grade classification. The model is built on the YOLOv11n (You Only Look Once11n) framework and integrates three complementary components: (1) C2-LWA, a cross-stage local window attention module that enhances discrimination of key visual features, such as primary root contours and fibrous textures; (2) ADown, a non-parametric downsampling mechanism that substitutes convolution operations with parallel pooling, markedly reducing computational complexity; and (3) Slide Loss, a piecewise IoU-weighted loss function designed to emphasize learning from samples with ambiguous or irregular boundaries. Experimental results on a curated multi-grade ginseng dataset indicate that Ginseng-YOLO achieves a Precision of 84.9%, a Recall of 83.9%, and an mAP@50 of 88.7%, outperforming YOLOv11n and other state-of-the-art variants. The model maintains a compact footprint, with 2.0 M parameters, 5.3 GFLOPs, and 4.6 MB model size, supporting real-time deployment on edge devices. Ablation studies further confirm the synergistic contributions of the proposed modules in enhancing feature representation, architectural efficiency, and training robustness. Successful deployment on the NVIDIA Jetson Nano demonstrates practical real-time inference capability under limited computational resources. This work provides a scalable approach for intelligent grading of forest-grown ginseng and offers methodological insights for the design of lightweight models in medicinal plants and agricultural applications. Full article

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

In recent years, the application of eye-tracking technology in urban studies has garnered increasing attention from researchers across various disciplines. This study aims to provide a comprehensive review of the current applications of eye-tracking technology in these urban environments through a systematic literature analysis. Our findings indicate that eye-tracking technology has played a significant role in exploring visual preferences and the restorative effects of urban streets, as well as the visual preferences and restorative potential of urban landscapes. Certain visual elements in streets and parks, such as artificial and natural elements, can elicit different psychological and visual responses from people. This is of great reference value for understanding how urban street and park design can better meet people’s visual preferences and exert the therapeutic effects of urban streets and parks. Moreover, characterised by its portability and reliability, eye-tracking technology has significant advantages in capturing real-time visual behaviour and cognitive responses in natural urban settings and can become a powerful tool for future research. Furthermore, eye-tracking technology holds great potential for extending its applications to other urban public spaces, such as plazas, waterfront areas, and urban greenways. This expansion can provide deeper insights into how people interact with and perceive various urban environments, ultimately contributing to more effective urban planning and design strategies. Full article

Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and process optimization, illustrating how intelligent visual inspection systems and real-time data analysis contribute to increased operational efficiency and higher safety standards. The research methodology combines an in-depth analysis of industrial case studies, including one from the frozen dough industry, with a systematic review of the current literature on machine vision technologies in manufacturing. The findings highlight the potential of such systems to reduce human error, maintain consistent product quality, minimize material waste, and promote safer and more adaptable work environments. This study offers valuable insights into the integration of advanced visual technologies within human-centered production environments, while also addressing key challenges and future opportunities for innovation and technological evolution. Full article

During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM_2.5 and carbon dioxide concentrations in which two were installed indoors and one outdoors at two residential apartments in Central Europe (Budapest, Hungary). In our research, we present a methodology to support the evaluation of indoor sensors by utilizing official outdoor monitoring data, leveraging the fact that indoor spaces are frequently ventilated and thus influenced by outdoor conditions. We compared six-year measurement data (January 2017–December 2022) with outdoor concentrations provided by the Hungarian Air Quality Monitoring Network (HAQM). However, the well-known low spatial representativeness and high spatio-temporal variability of PM_2.5 in city environments made this evaluation problematic, which needed to be addressed before comparison. Here we quantify the spatial heterogeneity of the HAQM PM_2.5 data for a maximum of eight stations. Then, based on the carbon dioxide readings of the AirVisual Pro units, data filtering was performed for the AirVisual 1 and AirVisual 2 sensors located in indoor environments to identify ventilated periods (nearly 10,000 ventilated events) for the AirVisual 1 and AirVisual 2 sensors, respectively, for the comparison of indoor and outdoor PM_2.5 concentrations. The AirVisual 3 sensor was placed in a garden storage, and the measurements taken there were considered outdoor values throughout. Finally, four heterogeneity criteria were set for the HAQM data to filter conditions that were assumed to be comparable with the indoor sensor data. The results indicate that the spatial heterogeneity was indeed detectable, and in approximately 50–60% of the cases, the readings could be considered as non-representative to single location comparison, but the results depend on the selected homogeneity criteria. The AirVisual and HAQM comparison indicated relatively low sensitivity to heterogeneity criteria, which is a promising result that can be exploited. AirVisual sensors generally overestimated PM_2.5, but this bias could be corrected with a simple linear adjustment. Slopes changed across sensors (0.83–0.85 for AirVisual 1, 0.48–0.53 for AirVisual 2, and 0.70–0.73 for AirVisual 3), indicating general overestimation and correlations from moderate to high (R² = 0.45–0.89) depending on the device. In contrast, when we compared the measurements only with data from the nearest reference station, we obtained a weaker match and slopes that did not match those calculated by taking into account homogeneity criteria. This research contributes to the proliferation of citizen science and supports the application of LCSs in indoor conditions. Full article

Background: Functional Neurological Disorder (FND) encompasses conditions with neurological symptoms inconsistent with structural pathology, arising instead from complex interactions between psychological, biological, and social factors. Despite growing research, the etiological and risk factor landscape remains only partially understood, complicating diagnosis and treatment. Objective: This systematic review maps risk factors for major FND subtypes such as functional seizures (psychogenic non-epileptic seizures or PNES), functional cognitive disorder (FCD), functional movement disorders (FMD), functional weakness and sensory disturbances, functional visual symptoms, and functional gait abnormalities by categorizing predisposing, precipitating, and perpetuating influences. Methods: A systematic search of PubMed, PsycINFO, Scopus, and Web of Science initially identified 245 records. After removal of 64 duplicates, 181 studies were screened by title and abstract. Of these, 96 full texts were examined in detail, and finally 23 studies met the predefined inclusion criteria. Data were extracted and analyzed thematically within a biopsychosocial framework, with results summarized in subtype-specific profiles. Results: Childhood adversity, especially emotional, physical, or sexual abuse, emerged as a robust and consistent predisposing factor across PNES cohorts. Psychiatric history (notably anxiety, depression, and PTSD), neurodevelopmental traits (more frequent in FCD), and personality patterns such as alexithymia and somatization also contributed to vulnerability. Precipitating influences included acute psychological stress, intrapersonal conflict, or concurrent medical illness. Perpetuating factors comprise maladaptive illness beliefs, avoidance behaviors, insufficient explanation or validation by healthcare providers, and secondary gains related to disability. While several risk factors were shared across subtypes, others appeared subtype-specific (trauma was especially associated with PNES, whereas neurodevelopmental traits were more characteristic of FCD). Conclusions: FND arises from a dynamic interplay of predisposing, precipitating, and perpetuating factors, with both shared and subtype-specific influences. Recognizing this heterogeneity can enhance diagnostic precision, guide tailored intervention, and inform future research into the neurobiological and psychosocial mechanisms underlying FND. Full article

Background: Multiple Myeloma (MM) is a malignant plasma cell dyscrasia that progresses through the consecutive asymptomatic, often undiagnosed, precancerous stages of Monoclonal Gammopathy of Undetermined Significance (MGUS) and Asymptomatic Multiple Myeloma (SMM). MM is characterized by low survival rates, severe complications and drug resistance; therefore, understanding the molecular mechanisms of progression is crucial. This study aims to detect genetic mutations, both germline and somatic, that contribute to disease progression and drive tumorigenesis at the final stage of MM, using samples from patients presenting MGUS or SMM, and newly diagnosed MM patients. Methods: Mutations were identified through a fully computational pipeline, implemented in a Linux and RStudio environment, applied to each patient sequence, obtained through single-cell RNA-sequencing (scRNA-seq), separately. Structural and functional mutation types were identified by stage, along with the affected genes. The analysis included quality control, removal of the Unique Molecular Identifiers (UMIs), trimming, genome mapping and result visualization. Results: The findings revealed frequent germline and somatic mutations, with distinct structural and functional patterns across disease stages. Mutations in key genes were identified, pointing to molecules that may play a central role in carcinogenesis and disease progression. Notable examples include the HLA-A, HLA-B and HLA-C genes, as well as the KIF, EP400 and KDM gene families, with the first four already confirmed. Comparative analysis between the stages highlighted molecular transition events from one stage to another. Emphasis was given to novel genes discovered in newly diagnosed MM patients, that might contribute to the tumorigenesis that takes place. Conclusions: This study contributes to the understanding of the genetic basis of plasma cell dyscrasias and the transition events between the stages, offering insights that could aid in early detection and diagnosis, guide the development of personalized therapeutic strategies, and improve the understanding of mechanisms responsible for resistance to existing therapies. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article

Background: Public beliefs about dietary risks, such as excessive salt intake, are often not isolated misconceptions but part of structured cognitive systems. This study aimed to explore how individuals organize their beliefs and misperceptions regarding salt-related health consequences. Material and Methods: Using data from an international online survey, we applied a system of multivariate proportional odds logistic regression (POLR) models to estimate conditional associations among beliefs about salt’s links to various diseases—including cardiovascular, metabolic, renal, neuropsychiatric, and mortality outcomes. In addition, exploratory and confirmatory factor analyses (EFA and CFA) were conducted to identify and validate latent constructs underlying the belief items. Beliefs were modeled as interdependent, controlling for latent constructs, sociodemographics, and self-reported health awareness. Statistically significant associations (p < 0.05) were visualized via a heatmap of beta coefficients. Results: Physicians showed almost universal agreement that salt contributes to hypertension (µ = 0.97), compared to non-medical respondents (µ = 0.85; p < 0.0001). Beliefs about mortality (µ = 1.55 for MDs vs. 0.99 for non-medical; p < 0.0001) emerged as central hubs in the belief network. Strong inter-item associations were observed, such as between hypertension and heart failure (β = −0.39), and between obesity and type 2 diabetes (β = −0.94). Notably, cognitive gaps were found, including a lack of association between atrial fibrillation and stroke, and non-reciprocal links between hypertension and heart failure. Conclusions: Beliefs about the health effects of salt are structured and sometimes asymmetrical, reflecting underlying reasoning patterns rather than isolated ignorance. Understanding these structures provides a systems-level view of health literacy and may inform more effective public health communication and education strategies. Full article

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

This paper investigates technological pathways for the conversion of coal-fired power plants toward sustainable energy sources, using an integrated multi-criteria decision-making approach that combines Proknow-C, AHP, and PROMETHEE. Eight alternatives were identified: full conversion to natural gas, full conversion to biomass, coal and natural gas hybridization, coal and biomass hybridization, electricity and hydrogen cogeneration, coal and solar energy hybridization, post-combustion carbon capture systems, and decommissioning with subsequent reuse. The analysis combined bibliographic data (26 scientific articles and 13 patents) with surveys from 14 energy experts, using Total Decision version 1.2.1041.0 and Visual PROMETHEE version 1.1.0.0 software tools. Based on six criteria (environmental, structural, technical, technological, economic, and social), the most viable option was full conversion to natural gas (ϕ = +0.0368), followed by coal and natural gas hybridization (ϕ = +0.0257), and coal and solar hybridization (ϕ = +0.0124). These alternatives emerged as the most balanced in terms of emissions reduction, infrastructure reuse, and cost efficiency. In contrast, decommissioning (ϕ = −0.0578) and carbon capture systems (ϕ = −0.0196) were less favorable. This study proposes a structured framework for strategic energy planning that supports a just energy transition and contributes to the United Nations Sustainable Development Goals (SDGs) 7 and 13, highlighting the need for public policies that enhance the competitiveness and scalability of sustainable alternatives. Full article

Regenerative design (RD) is attracting attention as a concept that goes beyond sustainability. However, RD has been criticized as an overly theoretical and abstract approach. This study constructs a multiscale RD approach in urban areas by combining the theoretical frameworks of an adaptive planning approach based on the complex adaptive systems (CAS) theory and transformative capacities (TC) through the case study of Shimokita-Senrogai. The study’s main contribution is to materialize the process for a multiscale RD approach in urban areas, where it is difficult to reach consensus among diverse stakeholders immediately. The main finding is identifying the necessary conditions for implementing an RD approach that enhances TC by adapting to urban uncertainties from global climate change to local civic dynamics through the agency of more-than-human actor networks. Based on these, this study proposes a methodology to visualize actors, their activity ranges, bases, and ecosystemic flows across multiple territorial scales beyond the development site and its vicinity. Full article

Background: The growing environmental crisis, driven by population increases and rapid urban development, has amplified the need for sustainable and ecological design approaches. Biomimicry, drawing inspiration from nature’s forms, processes, and systems, offers promising solutions in this context. Particularly in landscape architecture, biomimicry supports the integration of esthetics with ecological responsibility. Methods: This study presents a bibliometric analysis using the Scopus database to quantitatively assess the relationship between biomimicry and sustainable/ecological design within landscape architecture. A stepwise search strategy was applied, and the Biblioshiny tool within the version 4.2.1 of Bibliometrix package in RStudio 2024.04.1+748 software was used for data analysis and visualization. Results: A total of 1634 documents were identified under the keyword “biomimicry,” among which 210 addressed sustainability and/or ecological design. However, only three studies explicitly connected biomimicry, sustainable/ecological principles, and landscape architecture. Keyword trends, publication years, and country-level contributions were also examined. Conclusions: The findings highlight a substantial gap in the literature on the integration of biomimicry within sustainable landscape architecture. This underscores the need for further interdisciplinary research and practice that incorporates biomimetic principles to promote ecological innovation in landscape design. Full article

RNA sequencing is a high-throughput sequencing method essential for unbiased detection and characterization of known and emerging plant viruses. Its high sensitivity makes it particularly well-suited for identifying low-abundance viral sequences, even in asymptomatic plants or those affected by complex, mixed infections. Here, we conducted a metatranscriptomic survey of Petunia hybrida plants from the Bulgarian market, both symptomatic and asymptomatic, and their corresponding in vitro plantlets. Viruses were detected in all tested samples demonstrating that visual symptoms are not a reliable indicator of infection. The viromes were dominated by petunia vein clearing virus (PVCV, Petuvirus venapetuniae), cucumber mosaic virus (CMV, Cucumovirus CMV), and tomato aspermy virus (TAV, Cucumovirus TAV), along with bacteriophages and fungus-associated viruses. However, the PVCV and CMV abundance was elevated in in vitro samples, possibly due to cutting-induced activation and/or prolonged cultivation. Phylogenetic analysis of the Bulgarian CMV, TAV, and PVCV isolates highlights their genetic links to strains from a wide geographic range and diverse hosts, emphasizing the potential for virus movement and genetic exchange among plant viruses across regions and species. It also suggests that petunias may contribute to the transmission dynamics of viruses within ornamental trade networks. These findings also emphasize the phytosanitary risks to horticulture and establish a basis for further investigation into plant virus ecology. Full article