Search Results (6,123)

The globe artichoke (Cynara cardunculus var. scolymus L.) is one of the most economically important vegetable crops in Spain. ‘Blanca de Tudela’ is the most widely grown and consumed cultivar and it is also highly valued on international export markets. Postharvest preservation is crucial for maintaining the quality of this highly perishable product. This study focused on comparing the effectiveness of two different postharvest strategies in preserving the quality of whole artichokes and extending their shelf-life: packaging in modified atmosphere packaging (MAP), and pre-cooling to 4 °C followed by MAP. In addition, one batch of artichokes was stored under refrigeration conditions, i.e., without packaging, at a temperature of 2 °C and a relative humidity of 85%. Parameters such as respiration rate, weight loss, firmness, total phenolic content, chlorophyll levels and visual sensorial quality were analyzed throughout 42 days of refrigerated storage at 2 °C. The results showed that non-packed artichokes exhibited rapid deterioration, with weight loss exceeding 45% and phenolic and chlorophyll content decreasing by over 50% and 78%, respectively, by the end of the storage period. In contrast, MAP drastically reduced quality deterioration, reduced weight loss to 2%, and preserved approximately 60% more phenolic compounds. The combined application of pre-cooling and MAP further enhanced preservation, reducing weight loss by an additional 25% compared to MAP alone and retaining nearly double the chlorophyll content. Thus, this treatment also ensured the highest preservation of phenolic compounds, with final values about 45% higher than MAP alone. Visual sensory assessment confirmed that both MAP and Pre-cooling + MAP maintained acceptable appearance and consumer-relevant quality parameters throughout storage. Overall, the results of this study indicate that MAP, particularly when combined with pre-cooling, effectively maintained the physical, biochemical, and sensory quality of whole ‘Blanca de Tudela’ artichokes over 42 days under cold storage conditions, demonstrating the potential of this integrated strategy to support postharvest preservation for long-distance export markets. Full article

Frequent heavy rainfalls due to climate change in recent years have led to an increasing incidence of severe damage, such as levee breaches. However, the integrity of levees is currently assessed by visual inspection, relying on the skill and experience of the overseeing engineers. Future work requires close monitoring of the external shape of levees and the implementation of quantitative assessments if abnormalities such as deformation are discovered. Therefore, the mobile mapping system (MMS), which uses a vehicle-mounted laser scanner to conduct surveys while moving, has attracted attention as a method for conducting high-precision surveys. However, the presence of blind spots in the laser irradiation indicates that there is no practical method for identifying areas that require countermeasures for the entire levee. In this paper, we discuss the appropriate position of laser irradiation that allows data acquisition down to the toe of the slope, and then propose a method of laser irradiation from a high altitude. Compared to previous laser surveys using vehicles, this method was able to obtain a high-density laser point cloud over the entire levee, demonstrating that it is possible to detect detailed deformations not only on the crest of the levee but also on the slope. Full article

Humans have an inherent connection to nature, and exposure to natural elements has been shown to reduce stress, improve mood, and support cognitive performance, forming the basis of biophilic design in the built environment. However, existing biophilic design guidance remains largely conceptual and offers limited evidence-based direction on how design properties should be applied. This scoping review addresses this gap by systematically mapping and synthesizing empirical evidence on indoor biophilic design interventions and their properties. Following PRISMA-ScR guidelines, 136 studies published between 2000 and 2025 were reviewed across seven intervention types, including green walls, indoor plants, window views, natural light, natural materials, water features, and nature-inspired visual references. Cross-category analyses identified design properties most consistently associated with restorative outcomes and human cognitive and physiological responses. The findings highlight the importance of moderate greenery levels, high-visibility placement, multi-sensory integration, and the enhanced restorative effects of combining multiple interventions. Contextual factors such as exposure duration and user characteristics were found to influence effectiveness. Based on these findings, the study introduces the Biophilic Intensity Matrix (BIMx), a matrix-based decision-support framework that supports early-stage design by helping designers select biophilic intervention types and compare their relative scale and intensity ranges according to exposure duration. Full article

Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Köppen–Geiger climate zones under high-emission Shared Socioeconomic Pathway (SSP) 5-8.5 climate scenarios. The Köppen–Geiger climate classification system is a practical tool that effectively captures climate types based on just two variables: temperature and precipitation. Monthly temperature and precipitation data from Climatic Research Unit (CRU,) ERA5-Land, and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles from 1951 to 2100 were used to generate climatic zone maps. CMIP6 models were evaluated against meteorological station data and ERA5-Land, with bias metrics used to identify the best-performing models for temperature and precipitation in Kazakhstan. Based on these results, two inter-model datasets were developed and used to generate Köppen–Geiger climate maps for high-emission scenarios for the 2061–2100 time period. This research resulted in two key outcomes. First, to facilitate this analysis, a Google Earth Engine (GEE) application was developed as an open accessible tool for dynamic visualization of Köppen–Geiger climate maps. Second, projected maps based on CMIP6 SSP5-8.5 scenario projections indicate that southern Kazakhstan may shift to BSh (Hot Semi-Arid) and Csa (Mediterranean) climates, and the southwest region of the country is projected to shift to a BWh (Hot Desert) climate. These projected Köppen–Geiger climate maps contributed to climate adaptation efforts by identifying regions at risk of desertification and aridification. This study provides a comprehensive analysis of climate zone transformations in Kazakhstan and offers a practical scalable geovisualization tool for monitoring climate change impacts. This allows users easy access to climate-related information and insights into data processing procedures. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

The pygmy hippopotamus (Choeropsis liberiensis, Morton, 1849) is classified as Endangered by the International Union for the Conservation of Nature (IUCN). Compared to other large, threatened mammals, this species remains relatively understudied and new findings indicate potential welfare concerns, emphasizing the need for further research on the species welfare in zoological institutions. One approach to improving welfare in captivity is through environmental enrichment. This study investigated the effects of olfactory enrichment on three individual pygmy hippopotamuses through behavioral analysis and heat-map visualization. Using continuous focal sampling, several behaviors were influenced by the stimuli, with results showing a general decrease in inactivity and an increase in environmental engagement and interaction, particularly through scenting behavior. To further enhance behavioral quantification, machine learning techniques were applied to video data, comparing manual and automated behavior classification using the pose estimation program SLEAP. Four behaviors Standing, Locomotion, Feeding/Foraging, and Lying Down were compared. A confusion matrix, time budgets, and Kendall’s Coefficient of Concordance (W) were used to assess agreement between methods. The results showed a strong and moderate agreement between manual and automated annotations, for the female and calf, respectively. This demonstrates the potential of automation to complement behavioral observations in future welfare monitoring. Full article

The quality of disposable bamboo chopsticks directly affects consumers’ usage experience and health safety. Therefore, quality inspection is particularly important, and multi-label classification of defects can better meet the refined demands of actual production. While ViT has made significant progress in visual tasks, it has limitations when dealing with extreme aspect ratios like bamboo chopsticks. To address this, this paper proposes an improved ViT model, C-ViT, introducing a convolutional neural network feature extraction module (CFE) to replace traditional patch embedding, making the input features more suitable for the ViT model. Moreover, existing loss functions in multi-label classification tasks focus on label prediction optimization, making hard labels difficult to learn due to their low gradient contribution. Therefore, this paper proposes a Hard Examples Contrastive Loss (HCL) function, dynamically selecting hard examples and combining label and feature correlation to construct a contrastive learning mechanism, enhancing the model’s ability to model hard examples. Experimental results show that on the self-built bamboo chopstick defect dataset (BCDD), C-ViT improves the mAP by 1.2% to 92.8% compared to the ViTS model, and can reach 94.3% after adding HCL. In addition, we further verified the effectiveness of the proposed HCL function in multi-label classification tasks on the VOC2012 public dataset. Full article

Object detection systems deployed in industrial environments are often constrained by limited annotation budgets, severe class imbalance, and heterogeneous visual conditions. Active learning (AL) aims to reduce labeling costs by selecting informative samples; however, existing strategies struggle to simultaneously ensure robust performance, rare-class coverage, and stability under realistic industrial constraints. In this work, we propose a rarity-aware, stratified AL framework for industrial object detection that explicitly aligns sample selection with class imbalance and annotation efficiency. The method relies on a composite image-level score that jointly captures model uncertainty, informativeness, and complementary diversity cues, while adaptively emphasizing rare classes. Crucially, a stratified querying mechanism is introduced to explicitly regulate class-wise sample allocation during selection, playing a key role in improving performance stability and rare-class coverage under severe imbalance, without sacrificing global informativeness. The proposed approach operates purely at the data-selection level, making it detector-agnostic and directly applicable to modern object detection pipelines. Experiments conducted on two real-world industrial datasets involving lobster and snow crab parts, using YOLOv10 and YOLOv12, demonstrate improved training stability and annotation efficiency across balanced, imbalanced, and noisy settings over multiple active learning cycles up to 15% labeled data. Complementary comparisons with fully supervised training further show that using only 45–65% of the labeled data is sufficient to retain more than 97% of full-supervision mAP@50 and over 90% of mAP@50:95. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

Accurate wildlife detection in Unmanned Aerial Vehicle (UAV)-captured imagery is crucial for biodiversity conservation, yet it remains challenging due to the visual similarity of species, environmental disturbances, and the small size of target animals. To address these challenges, this paper introduces YOLO-WL, a wildlife detection algorithm specifically designed for UAV-based monitoring. First, a Multi-Scale Dilated Depthwise Separable Convolution (MSDDSC) module, integrated with the C2f-MSDDSC structure, expands the receptive field and enriches semantic representation, enabling reliable discrimination of species with similar appearances. Next, a Multi-Scale Large Kernel Spatial Attention (MLKSA) mechanism adaptively highlights salient animal regions across different spatial scales while suppressing interference from vegetation, terrain, and lighting variations. Finally, a Shallow-Spatial Alignment Path Aggregation Network (SSA-PAN), combined with a Spatial Guidance Fusion (SGF) module, ensures precise alignment and effective fusion of multi-scale shallow features, thereby improving detection accuracy for small and low-resolution targets. Experimental results on the WAID dataset demonstrate that YOLO-WL outperforms existing state-of-the-art (SOTA) methods, achieving 94.2% mAP@0.5 and 58.0% mAP@0.5:0.95. Furthermore, evaluations on the Aerial Sheep and AI-TOD datasets confirm YOLO-WL’s robustness and generalization ability across diverse ecological environments. These findings highlight YOLO-WL as an effective tool for enhancing UAV-based wildlife monitoring and supporting ecological conservation practices. Full article

Surface defect evaluation in steel production demands both high inference speed and accuracy for efficient production. However, existing methods face two critical challenges: (1) the diverse dimensions and irregular morphologies of surface defects reduce detection accuracy, and (2) computationally intensive feature extraction slows inference. In response to these challenges, this study proposes an innovative network based on dual-branch feature enhancement and downsampling (DFED-Net). First, an atrous convolution and multi-scale dilated attention fusion module (AMFM) is developed, incorporating local–global feature representation. By emphasizing local details and global semantics, the module suppresses noise interference and enhances the capability of the model to separate small-object features from complex backgrounds. Additionally, a dual-branch downsampling module (DBDM) is developed to preserve the fine details related to scale that are typically lost during downsampling. The DBDM efficiently fuses semantic and detailed information, improving consistency across feature maps at different scales. A lightweight dynamic upsampling (DySample) is introduced to supplant traditional fixed methods with a learnable, adaptive approach, which retains critical feature information more flexibly while reducing redundant computation. Experimental evaluation shows a mean average precision (mAP) of 81.5% on the Northeastern University surface defect detection (NEU-DET) dataset, a 5.2% increase compared to the baseline, while maintaining a real-time inference speed of 120 FPS compared to the 118 FPS of the baseline. The proposed DFED-Net provides strong support for the development of automated visual inspection systems for detecting defects on steel surfaces. Full article

Oriented object detection constitutes a fundamental yet challenging task in Artificial Intelligence of Things (AIoT)-enabled maritime surveillance, where real-time processing of dense visual streams is imperative. However, existing detectors suffer from three critical limitations: sequential attention mechanisms that fail to capture coupled spatial–channel dependencies, unconstrained deformable convolutions that yield unstable predictions for elongated vessels, and center-based distance metrics that ignore angular alignment in sample assignment. To address these challenges, we propose JAOSD (Joint Attention-based Oriented Ship Detection), an anchor-free framework incorporating three novel components: (1) a joint attention module that processes spatial and channel branches in parallel with coupled fusion, (2) an adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization, and (3) an orientation-aware Adaptive Sample Selection strategy based on corner-aware distance metrics. Extensive experiments on three benchmarks demonstrate that JAOSD achieves state-of-the-art performance—94.74% mAP on HRSC2016, 92.43% AP₅₀ on FGSD2021, and 80.44% mAP on DOTA v1.0—while maintaining real-time inference at 42.6 FPS. Cross-domain evaluation on the Singapore Maritime Dataset further confirms robust generalization capability from aerial to shore-based surveillance scenarios without domain adaptation. Full article

This article presents a modular and replicable framework to assess spatial accessibility to essential public services in the Lazio Region (Italy). Current policies, framed within the EU Urban Agenda and the UN Sustainable Development Goals, emphasize improving accessibility rather than mobility, integrating land-use and transport planning, and supporting sustainable modes. The study adopts urban centres, densely populated sub-municipal units, as the main spatial unit to capture intra-municipal variability. Accessibility is measured as distance and travel time to the nearest education and healthcare facilities, for both private car and public transport, considering traffic conditions. Distances and times are computed using routing APIs and aggregated into service-specific indicators at urban-centre and municipal levels. Due to GTFS availability, the public transport analysis is restricted to the Province of Rome. Indicators are published as Linked Open Data following DCAT-AP, exposed via a SPARQL endpoint, and visualized through an interactive web map viewer. Results highlight pronounced disparities: car accessibility is relatively uniform, while public transport shows critical gaps in peripheral and mountainous areas. The framework enables transparent benchmarking and supports evidence-based, place-sensitive planning across different European contexts. Full article

This study investigates the state of the art related to the computational methods for solar cells. Numerical modeling is a basic pillar that is used to ensure the robust design of any device. In this paper, the results of a detailed science mapping-based analysis on the publications that focus on the “numerical modelling of solar cells” are presented. The query was conducted on the Web of Science for 2014–2024, and a subsequent filtering was performed. The results of this analysis provided the answers to the five research questions posed. The paper has been divided into two parts. In the first part, the literature search began with a broad examination, and 3259 studies were included in the analysis. To present the results in a visual form, graphs created using VOS viewer software have been used to identify the pattern of co-authorship, the geographical distribution of the authors, and the keywords most frequently used. In the second part, the analysis focused on three main aspects: (i) the influence of absorber layer thickness on optical absorption and device efficiency, (ii) the role of different ETL/HTL materials in charge transport, and (iii) the effect of illumination conditions on carrier dynamics and photovoltaic performance. By integrating the results across these dimensions, the study provides a comprehensive understanding of how these parameters collectively determine the efficiency and reliability of perovskite solar cells. Full article