Search Results (400)

The ancient city of Gela (built in the 7th century BCE) is located in the southern sector of the Sicily Island (Southern Italy) on a Pleistocene marine terrace near the mouth of the Gela River. Gela was one of the most important Greek colonies in the Mediterranean Sea, strategically positioned at the crossroads of the major maritime trade routes and with a rich production of cereals thanks to the fertile Gela River alluvial plain. To reconstruct the coastal and environmental configuration during the Greek period and to improve the understanding of the location of the harbour basin, a multidisciplinary approach was applied to a sector of the Gela River alluvial–coastal plain. This area, located very close to the ancient city, is known as Conca (Italian for “Basin”) and was identified through the analysis of historical and modern maps as well as aerial photographs. The multidisciplinary approach includes geomorphology (derived from maps and aerial photos), stratigraphy (boreholes and archeological trench), paleoecology (ostracoda, foraminifera and fossil contents of selected layers), geochronology (¹⁴C dating of selected organic materials) and archeology (historical sources and maps, pottery fragments extracted from boreholes and trench layers). The main results show that this area was occupied by lower shoreface environments in the time intervals between 4.4 and 2.8 ka, which progressively transitioned to upper shoreface environments until the Greek age. During the Roman period, these environments were significantly reduced due to repeated alluvial sedimentation of the Gela River transforming the area into fluvial–marshy environments. A time interval of aeolian sand deposition was recorded in the upper part of the coastal stratigraphical succession, which can be related to climatic conditions with high aridity. Available data show that marine environments persisted in the Conca sector during the Greek age, allowing hypothesizing the presence of an ancient harbour in this area. The depth of the Greek age marine environments is estimated to be between 4.5 and 7 m below the current ground level. Further investigation, mainly based on geophysical and stratigraphical methods, will be planned aimed at identifying the presence of buried archeological targets. Full article

Periocular recognition is essential when full-face images cannot be used because of occlusion, privacy constraints, or sensor limitations, yet in many deployments, only periocular images are available at run time, while richer evidence, such as archival face photos and textual metadata, exists offline. This mismatch makes it hard to deploy conventional multimodal fusion. This motivates the notion of conditional biometrics, where auxiliary modalities are used only during training to learn stronger periocular representations while keeping deployment strictly periocular-only. In this paper, we propose Multi-Anchor Conditional Periocular Embedding (MACPE), which maps periocular, facial, and textual features into a shared anchor-conditioned space via a learnable anchor bank that preserves periocular micro-textures while aligning higher-level semantics. Training combines identity classification losses on periocular and face branches with a symmetric InfoNCE loss over anchors and a pulling regularizer that jointly aligns periocular, facial, and textual embeddings without collapsing into face-dominated solutions; captions generated by a vision language model provide complementary semantic supervision. At deployment, only the periocular encoder is used. Experiments across five periocular datasets show that MACPE consistently improves Rank-1 identification and reduces EER at a fixed FAR compared with periocular-only baselines and alternative conditioning methods. Ablation studies verify the contributions of anchor-conditioned embeddings, textual supervision, and the proposed loss design. Full article

To assess the efficiency of vision–language models in detecting and classifying carious and non-carious lesions from intraoral photo imaging. A dataset of 172 annotated images were classified for microcavitation, cavitated lesions, staining, calculus, and non-carious lesions. Florence-2, PaLI-Gemma, and YOLOv8 models were trained on the dataset and model performance. The dataset was divided into 80:10:10 split, and the model performance was evaluated using mean average precision (mAP), mAP50-95, class-specific precision and recall. YOLOv8 outperformed the vision–language models, achieving a mean average precision (mAP) of 37% with a precision of 42.3% (with 100% for cavitation detection) and 31.3% recall. PaLI-Gemma produced a recall of 13% and 21%. Florence-2 yielded a mean average precision of 10% with a precision and recall was 51% and 35%. YOLOv8 achieved the strongest overall performance. Florence-2 and PaLI-Gemma models underperformed relative to YOLOv8 despite the potential for multimodal contextual understanding, highlighting the need for larger, more diverse datasets and hybrid architectures to achieve improved performance. Full article

This review examines scientific and engineering strategies for adapting bituminous and asphalt concrete materials to the highly diverse climates of Central Asia. The region’s sharp gradients—from arid lowlands to cold mountainous zones—expose pavements to thermal fatigue, photo-oxidative aging, freeze–thaw cycles, and wind abrasion. Existing climatic classifications and principles for designing thermally and radiatively resilient pavements are summarized. Special emphasis is placed on linking binder morphology, rheology, and climate-induced transformations in composite bituminous systems. Advanced characterization methods—including dynamic shear rheometry (DSR), multiple stress creep recovery (MSCR), bending beam rheometry (BBR), and linear amplitude sweep (LAS), supported by FTIR, SEM, and AFM—enable quantitative correlations between phase composition, oxidative chemistry, and mechanical performance. The influence of polymeric, nanostructured, and biopolymeric modifiers on stability and durability is critically assessed. The review promotes region-specific material design and the use of integrated accelerated aging protocols (RTFOT, PAV, UV, freeze–thaw) that replicate local climatic stresses. A climatic rheological profile is proposed as a unified framework combining climate mapping with microstructural and rheological data to guide the development of sustainable and durable pavements for Central Asia. Key rheological indicators—complex modulus (G*), non-recoverable creep compliance (Jnr), and the BBR m-value—are incorporated into this profile. Full article

Activity-based protein profiling (ABPP) has emerged as a powerful chemical proteomics approach for profiling active amino acid residues, mapping functional proteins, and guiding covalent drug development in complex biological systems. Recent methodological advances have produced several novel formats, including tandem orthogonal proteolysis-ABPP (TOP-ABPP), isotopic tandem orthogonal proteolysis-ABPP (IsoTOP-ABPP), and competitive IsoTOP-ABPP, enabling broader target identification and quantitative analysis for varied experimental purposes. In parallel, chemical probe design has evolved to selectively target specific amino acid residues, such as cysteine (Cys), lysine (Lys), and histidine (His), and to incorporate photoaffinity labeling (PAL) functionalities for capturing transient or weak protein-ligand interactions. Additionally, the integration of cleavable linkers with diverse cleavage mechanisms, including acid/base-mediated, redox-mediated, and photo irradiation mechanisms, has enhanced probe versatility and downstream analytical workflows. This review summarizes recent advances in ABPP methodologies and the design of activity-based probes and PAL probes, emphasizing their implications for future work in chemical biology. Full article

In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems. Full article

Cover crop mixes play an important role in enhancing soil health, nutrient turnover, and ecosystem resilience; yet, maintaining even seed dispersion and planting uniformity is difficult due to significant variances in seed physical and aerodynamic properties. These discrepancies produce non-uniform seeding and species separation in drill hoppers, which has an impact on stand establishment and biomass stability. The thousand-grain weight is an important measure for determining cover crop seed quality and yield since it represents the weight of 1000 seeds in grams. Accurate seed counting is thus a key factor in calculating thousand-grain weight. Accurate mixed-seed identification is also helpful in breeding, phenotypic assessment, and the detection of moldy or damaged grains. However, in real-world conditions, the overlap and thickness of adhesion of mixed seeds make precise counting difficult, necessitating current research into powerful seed detection. This study addresses these issues by integrating deep learning-based computer vision algorithms for multi-seed detection and counting in cover crop mixes. The Canon LP-E6N R6 5D Mark IV camera was used to capture high-resolution photos of flax, hairy vetch, red clover, radish, and rye seeds. The dataset was annotated, augmented, and preprocessed on RoboFlow, split into train, validation, and test splits. Two top models, YOLOv5 and YOLOv7, were tested for multi-seed detection accuracy. The results showed that YOLOv7 outperformed YOLOv5 with 98.5% accuracy, 98.7% recall, and a mean Average Precision (mAP 0–95) of 76.0%. The results show that deep learning-based models can accurately recognize and count mixed seeds using automated methods, which has practical applications in seed drill calibration, thousand-grain weight estimation, and fair cover crop establishment. Full article

The study of the linguistic landscape is a key area for mapping the linguistic and cultural characteristics of university campuses. This attention is manifest in the language choice employed in the signage on campus, which serves as a physical indicator of these institutions’ linguistic policies and practices. The following paper will present a multi-faculty study conducted at Széchenyi István University in Hungary. The objective of this research is to address the question of how internationalization is explicitly manifested in the institution. A further aim of this investigation was to determine to what extent foreign languages, especially English and German, are represented in the texts found at the university, and what functions these texts perform. Therefore, mixed-method research was conducted in the university’s central academic buildings and their immediate surroundings, during which photos of the signage were taken, analysed, and systematically categorized. This research yielded a comprehensive understanding of the university’s linguistic landscape and revealed the lack of a coherent foreign language policy at the university. The results can provide relevant information for consciously (re)designing the linguistic landscape of the university studied and can help other universities to plan their language policies. Full article

Anopheles stephensi is an invasive and deadly malaria vector with the ability to use artificial containers as larval habitats. This ability is unique for malaria vectors in Africa and requires distinct surveillance strategies for early detection and rapid response. In this study, we trained a variety of artificial intelligence (AI) image recognition algorithms, using thousands of smartphone photos of laboratory-authenticated An. stephensi and seven endemic mosquito species, to develop a citizen science-friendly tool for An. stephensi detection. In Antananarivo, Madagascar, citizen science observations of >132 Anopheles spp. larvae from multiple artificial containers—including one closeup photo of a larva, from a tire—were submitted via NASA’s GLOBE Observer app in March 2020 and discovered years later. Given that genetic testing was no longer possible, this photo was used as a proof-of-concept to determine whether the AI species identification could be used on citizen science-generated images. The tire larva was classified as An. stephensi by all 11 species models, which yielded high accuracy and confidence (up to 99.34%) and included a false positive rate of <1%. Furthermore, explainable AI (XAI) heat maps led to the discovery of dark spots in abdominal segment VI corresponding to testes, corroborating a separate classification of the tire larva as male by the sex model. All available evidence suggests that AI image identification would have flagged this larva as a suspect An. stephensi, which could have been submitted to a molecular laboratory for further confirmation. Results demonstrate the power of integrating citizen science and AI—for which we provide free online tools—as a low-cost signal for malaria programs to confirm and respond to, and as complementary surveillance to fill the critical knowledge gaps in the distribution of invasive An. stephensi across Africa and beyond. Full article

Artificial intelligence algorithms for automated archaeological site detection have been scarcely applied in the Andean highlands, regions that preserve a significant amount of surface archaeological architecture but have not yet been fully explored or mapped due to the difficult terrain. This paper presents a case study of the application of convolutional neural networks (CNNs) to automatically identify archaeological architecture in the Azapa valley in the Arica y Parinacota region of Chile. Using a high-resolution and big regional-scale archaeological geodatabase created through a systematic and detailed photo-interpretation survey of satellite imagery and fieldwork, our study demonstrates the efficiency of CNN-based automated detection in identifying archaeological stone structures such as roundhouses and corrals in the Chilean highlands. After outlining the technical protocol for automated detection, we present the results and discuss the potential of our AI model for archaeological mapping in arid highland environments, from a regional to a more extended and global perspective. Full article

Bio-waste from potato shell agro-waste-based photocatalyst is introduced using potato shell integrated with Fe₃O₄ nanoparticles as a novel photocatalyst for photo-Fenton oxidation reaction. The catalyst was prepared via thermal activation of biochar, followed by co-precipitation of magnetite nanoparticles, resulting in a stable and reusable material. X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques augmented with the energy dispersive X-ray spectroscopy (EDX) analysis with elemental mapping were used to assess the prepared sample. The prepared material, PtS200–Fe₃O₄, is then applied for oxidizing Procion Blue dye using biochar-supported magnetite catalyst. The oxidation process was evaluated under varying operational parameters, including pH, temperature, catalyst loading, oxidant dosage, and dye concentration. Results revealed that the system achieved complete dye removal within 20 min at 60 °C and pH 3, demonstrating the strong catalytic activity of the composite. Furthermore, the kinetic modeling is evaluated and the data confirmed that the degradation followed first-order kinetics. Also, the thermodynamic parameters indicated low activation energy with PtS200–Fe₃O₄ composite in advanced oxidation processes. The system sustainability is also assessed, and the reusability test verified that the catalyst retained over 70% efficiency after six consecutive cycles, highlighting its durability. The study confirms the feasibility of using biochar-supported magnetite as a cost-effective, eco-friendly, and efficient catalyst for the treatment of textile effluents and other dye-contaminated wastewater. Full article

Reliable and representative ground data is fundamental for accurate crop classification using satellite imagery. This study demonstrates a structured approach to ground truth planning in the Bareilly district, Uttar Pradesh, where wheat is the dominant crop. Pre-season spectral clustering of Sentinel-2 Level-2A NDVI time-series data (November–March) was applied to identify ten spectrally distinct zones across the district, capturing phenological and land cover variability. These clusters were used at the village level to guide spatially stratified and optimized field sampling, ensuring coverage of heterogeneous and agriculturally significant areas. A total of 197 ground truth points were collected using the iCrops mobile application, enabling standardized and photo-validated data collection with offline functionality. The collected ground observations formed the basis for random forest supervised classification, enabling clear differentiation between major land use and land cover (LULC) classes with an overall accuracy of 91.6% and a Kappa coefficient of 0.886. The findings highlight that systematic ground data collection significantly enhances the reliability of remote sensing-based crop mapping. The outputs serve as a valuable resource for agricultural planners, policymakers, and local stakeholders by supporting crop monitoring, land use planning, and informed decision-making in the context of sustainable agricultural development. Full article

This study introduces a novel photocatalyst derived from pumpkin peel bio-waste, calcined at 200 °C and incorporated with magnetite nanoparticles to form a hybrid PK-P/Fe₃O₄ catalyst. The material was characterized using X-ray diffraction (XRD), diffuse reflectance spectra (DRS), and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX) mapping to confirm its structure and elemental distribution. The catalyst was applied for the photo-Fenton degradation of Synozol Red KHL dye under natural solution conditions (pH 5.7). Optimal parameters were achieved with a 20 mg/L catalyst and 200 mg/L H₂O₂, resulting in complete dye removal within 25 min of irradiation. The PK-P/Fe₃O₄ catalyst exhibited excellent reusability, retaining 72% removal efficiency after 10 successive cycles. Kinetic analysis confirmed a first-order model, while thermodynamic evaluation revealed a non-spontaneous, endothermic process with a low activation energy barrier, indicating energy-efficient dye degradation. These findings highlight the potential of bio-waste-derived PK-P/Fe₃O₄ as a sustainable, low-cost, and highly effective catalyst for treating dye-polluted wastewater under photo-Fenton conditions. Full article

Multi-camera systems (MCSs) are pivotal in aviation surveillance and autonomous navigation due to their wide coverage and high-resolution sensing. However, challenges such as complex setup, time-consuming data acquisition, and costly testing hinder research progress. To address these, we introduce MCS-Sim, a photo-realistic MCSsimulator for UAV visual perception research. MCS-Sim integrates vision sensor configurations, vehicle dynamics, and dynamic scenes, enabling rapid virtual prototyping and multi-task dataset generation. It supports dense flow estimation, 3D reconstruction, visual simultaneous localization and mapping, object detection, and tracking. With a hardware-in-loop interface, MCS-Sim facilitates closed-loop simulation for system validation. Experiments demonstrate its effectiveness in synthetic dataset generation, visual perception algorithm testing, and closed-loop simulation. Here we show that MCS-Sim significantly advances multi-camera UAV visual perception research, offering a versatile platform for future innovations. Full article

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or $A^{*}$) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article