Search Results (938)

This contribution presents an integrated methodological pipeline for digital documentation and virtual reconstruction of large-scale urban archaeological excavations, developed through the Amba Aradam case study (Metro C line, Rome). The excavation revealed a 2nd-century A.D. military complex extending over 4770 m² at depths reaching 20 m, documented through multiple photogrammetric campaigns (2016–2018) as structures were progressively excavated and removed. We established an empirically validated texture density standard (1.26 mm²/texel) for photorealistic digital replicas suitable for immersive HMD and desktop exploration, with an explicit texture density calculation formula ensuring reproducibility. The temporal integration workflow merged 3D snapshots acquired across three excavation campaigns while maintaining geometric and chromatic consistency. Semantic documentation, through the extended matrix framework, recorded Virtual Stratigraphic Units linking archaeological evidence, comparative sources, and interpretative reasoning (paradata) for transparent virtual reconstruction. The complete pipeline, implemented through open-source 3DSC 1.4 and EMtools add-ons for Blender and Metashape v0.9 (available on GitHub), addresses specific challenges of documenting complex stratigraphic contexts within active construction environments where in situ preservation is not feasible. The spatial integration of the digital replica with previous archaeological data illuminated the urban evolution of Rome’s military topography during the 2nd–3rd centuries A.D., demonstrating the essential role of advanced digital documentation in contemporary urban archaeology. Full article

With the rapid advancements in technology, there has been a noticeable increase in phishing attacks that exploit users by impersonating trusted entities. The primary attack vectors include fraudulent websites and carefully crafted emails. Early detection of such threats enables the more effective blocking of malicious sites and timely user warnings. One of the key elements in phishing detection is identifying the entity being impersonated. In this article, we conduct a comparative analysis of methods for detecting phishing websites that rely on website screenshots and recognizing their impersonation targets. The two main research objectives include binary phishing detection to identify malicious intent and multiclass classification of impersonated targets to enable specific incident response and brand protection. Three approaches are compared: two state-of-the-art methods, Phishpedia and VisualPhishNet, and a third, proposed in this work, which uses perceptual hash similarity as a baseline. To ensure consistent evaluation conditions, a dedicated framework was developed for the study and shared with the community via GitHub. The obtained results indicate that Phishpedia and the Baseline method were the most effective in terms of detection performance, outperforming VisualPhishNet. Specifically, the proposed Baseline method achieved an F1 score of 0.95 on the Phishpedia dataset for binary classification, while Phishpedia maintained a high Identification Rate (>0.9) across all tested datasets. In contrast, VisualPhishNet struggled with dataset variability, achieving an F1 score of only 0.17 on the same benchmark. Moreover, as our proposed Baseline method demonstrated superior stability and binary classification performance, it should be considered as a robust candidate for preliminary filtering in hybrid systems. Full article

Large language models (LLMs) have rapidly transformed artificial intelligence applications across industries, yet their integration into production systems has unveiled critical security vulnerabilities, chief among them prompt injection attacks. This comprehensive review synthesizes research from 2023 to 2025, analyzing 45 key sources, industry security reports, and documented real-world exploits. We examine the taxonomy of prompt injection techniques, including direct jailbreaking and indirect injection through external content. The rise of AI agent systems and the Model Context Protocol (MCP) has dramatically expanded attack surfaces, introducing vulnerabilities such as tool poisoning and credential theft. We document critical incidents including GitHub Copilot’s CVE-2025-53773 remote code execution vulnerability (CVSS 9.6) and ChatGPT’s Windows license key exposure. Research demonstrates that just five carefully crafted documents can manipulate AI responses 90% of the time through Retrieval-Augmented Generation (RAG) poisoning. We propose PALADIN, a defense-in-depth framework implementing five protective layers. This review provides actionable mitigation strategies based on OWASP Top 10 for LLM Applications 2025, identifies fundamental limitations including the stochastic nature problem and alignment paradox, and proposes research directions for architecturally secure AI systems. Our analysis reveals that prompt injection represents a fundamental architectural vulnerability requiring defense-in-depth approaches rather than singular solutions. Full article

Using a method inspired by quantum principles, this study estimates the composition of various types of tax contributions expected from foreign trade operations. The estimation approach is proposed considering the superposition of expectations and disturbances—fundamental elements of quantum methods—that add complexity to the forecasts of tax collections. For instance, the contributions of international trade-related taxes may be determined not only by the country’s degree of regional integration but also by the composition of tax revenue that depends on the kind and use of merchandise. Using the case of Mexico’s imports, the methodology illustrates how the expectations of collecting certain taxes—like the General Import Tariff (GIT) and the Value Added Tax (VAT)—would be impacted by fluctuations in others—such as the Special Tax on Production and Services (STPS). The hypothesis of this study is that, through the proposed quantum-inspired methodology, it is possible to establish future scenarios of tax revenue compositions while maintaining fiscal consistency by anticipating potential outcomes in the adjustments of contributions if the recently proposed fiscal reform is approved by the Mexican Government. This work contributes to the academic literature on public finance management by advancing a methodology that can support the strategic formulation of fiscal expectations and policy. Full article

As precision agriculture places higher demands on real-time field weed detection and recognition accuracy, this paper proposes a multi-scale information-enhanced weed detection algorithm, MIE-YOLO (Multi-scale Information Enhanced), for precision agriculture. Based on the popular YOLO12 (You Only Look Once 12) model, MIE-YOLO combines edge-aware multi-scale fusion with additive gated blocks and two-stage self-distillation to boost small-object and boundary detection while staying lightweight. First, the MS-EIS (Multi-Scale-Edge Information Select) architecture is designed to effectively aggregate and select edge and texture information at different scales to enhance fine-grained feature representation. Next, the Add-CGLU (Additive-Convolutional Gated Linear Unit) pyramid network is proposed, which enhances the representational power and information transfer efficiency of multi-scale features through additive fusion and gating mechanisms. Finally, the DEC (Detail-Enhanced Convolution) detection head is introduced to enhance detail and refine the localization of small objects and fuzzy boundaries. To further improve the model’s detection accuracy and generalization performance, the DS (Double Self-Knowledge Distillation) strategy is defined to perform double self-knowledge distillation within the entire network. Experimental results on the custom Weed dataset, which contains 9257 images of eight weed categories, show that MIE-YOLO improves the F1 score by 1.9% and the mAP by 2.0%. Furthermore, it reduces computational parameters by 29.9%, FLOPs by 6.9%, and model size by 17.0%, achieving a runtime speed of 66.2 FPS. MIE-YOLO improves weed detection performance while maintaining a certain level of inference efficiency, providing an effective technical path and engineering implementation reference for intelligent field inspection and precise weed control in precision agriculture. The source code is available on GitHub. Full article

In both research and industrial settings, it is often necessary to expand the input/output channels of measurement instruments using relay-based multiplexer boards. In research activities in particular, the need for a highly flexible and easily configurable solution frequently leads to the development of customized systems. To address this challenge, we developed a system optimized for automated direct current (DC) measurements. The result is based on a $4\times 4$ switching platform that simplifies measurement procedures that require instrument routing. The platform is based on a custom-designed circuit board controlled by a microcontroller. We selected bistable relays to guarantee contact stability after switching. We finally developed a system architecture that allows for straightforward expansion and scalability by connecting multiple platforms. We share both the hardware design source files and the firmware source code on GitHub with the open-source community. This work presents the design and development of the proposed system, followed by the performance evaluation. Finally, we present a test of our designed system applied to a specific case study: the DC analysis of complex resistive networks through multi-point resistance measurements using only a single voltmeter and current source. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field. Full article

Quantifying tissue, molecular, and structural integrity is essential for biobank development. However, current assessment methods either involve destructive testing that depletes valuable biospecimens or rely on manual evaluations, which are not scalable and lead to interindividual variation. To overcome these challenges, we present PathQC, a deep-learning framework that directly predicts the tissue RNA Integrity Number (RIN) and the extent of autolysis from hematoxylin and eosin (H & E)-stained whole-slide images of normal tissue biopsies. Advancing over prior QC methods focused on imaging quality control, PathQC provides sample-quality control through the direct quantification of molecular integrity (RIN) and structural degradation (autolysis). PathQC first extracts morphological features from the slide using a recently developed digital pathology foundation model (UNI), followed by a supervised model that learns to predict RNA Integrity Number and autolysis scores from these morphological features. PathQC is trained on and applied to the Genotype-Tissue Expression (GTEx) cohort, which comprises 25,306 non-diseased post-mortem samples across 29 tissues from 970 donors, when paired ground-truth RIN and autolysis scores were available. Here, PathQC predicted RIN with an average Pearson correlation of 0.47 and an autolysis score of 0.45, with notably high performance using adrenal gland tissue (R = 0.82) for RIN and colon tissue (R = 0.83) for autolysis. We provide a pan-tissue model for predicting RIN and autolysis scores for new slides from any tissue type (GitHub). Overall, PathQC enables a scalable assessment of tissue molecular and structural integrity from routine H & E images, enhancing biobank quality control and retrospective analyses across 29 tissues and multiple collection sites. Full article

Autonomous surface vehicles (ASVs) have played a crucial role in various areas, including oceanographic research, environmental monitoring, and asset inspection. However, the high cost and proprietary nature of many platforms limit accessibility. Thus, this work introduces a low-cost, fully open-source ASV platform designed to support a wide range of applications, from academic research to community-driven monitoring projects, bridging the existing gap between low-cost prototyping and naval architecture-based ASV development. Featuring a modular 2 m hull design, the vehicle integrates off-the-shelf components and open-source software to ensure affordability, flexibility, and ease of replication. Field tests were conducted on Ilha do Fundão (Fundão Island), located within the campus of the Federal University of Rio de Janeiro (UFRJ), Brazil. All design files and code are released on GitHub (version 1.0.0) to encourage adoption and collaborative improvement. Full article

Background: Limited cutaneous systemic sclerosis (lcSSc) is an autoimmune disease with a wide range of different biomarkers, while inflammation-based ratios have been less extensively investigated. This study aimed to evaluate the associations between inflammation-based ratios, disease-specific parameters, and endothelial dysfunction, as well as to assess the predictive role of inflammation-based ratios in lcSSc. Methods: A total of 38 lcSSc patients and 38 matched controls with primary Raynaud’s phenomenon were analyzed at baseline regarding inflammation-based ratios, lcSSc-specific parameters, and parameters of endothelial dysfunction. LcSSc patients were prospectively observed during a 3-year follow-up period in which lcSSc complications were recorded annually. Results: LcSSc patients had a significantly higher neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio (MLR), fibrinogen-to-albumin ratio, monocyte/high-density lipoprotein (HDL) ratio, and neutrophil/HDL ratio versus controls (all p < 0.05). During follow-up, the MLR, C-reactive protein (CRP)/albumin ratio, monocyte/HDL ratio, and neutrophil/HDL ratio increased significantly (all p < 0.05) in lcSSc patients. The monocyte/HDL ratio correlated positively with the DETECT score step 2 (r = 0.453, p = 0.032) and negatively with the UCLA SCTC GIT total score (r = −0.469, p = 0.024). The CRP/albumin ratio correlated significantly with the EUSTAR index (r = 0.473, p = 0.024) and the fibrinogen-to-albumin ratio correlated with asymmetric dimethylarginine (r = 0.452, p = 0.044). The MLR and CRP/albumin ratio were associated with development of pulmonary arterial hypertension (p = 0.036, p = 0.006), and the lymphocyte/HDL ratio was associated with newly developed interstitial lung disease (p = 0.004). Conclusions: Readily available inflammation-based ratios may reflect vascular and inflammatory activity and could contribute to risk stratification for pulmonary complications in lcSSc; however, these exploratory findings require confirmation in larger cohorts. Full article

Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate information not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then executed on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user-interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025. Full article

Forest foods play a vital role in enhancing dietary diversity, human health, and the sustainable use of forest ecosystems. However, structured and machine-readable resources that systematically describe their taxonomic and nutritional attributes remain scarce. To fill this gap, we introduce ForestFoodKG, a comprehensive resource that integrates taxonomic hierarchy and nutritional composition of 1191 forest food items. The resource consists of two components—(i) the ForestFoodKG dataset, containing standardized taxonomic and nutritional records across seven biological levels, and (ii) the ForestFoodKG Knowledge Graph (ForestFoodKG-KG), which semantically links forest food entities using named entity recognition and relation extraction. The constructed graph comprises 4492 entities and 14,130 semantic relations, providing a structured foundation for intelligent querying, nutrition analytics, and ecological informatics. All data were manually verified and made publicly available in CSV format on GitHub. ForestFoodKG serves as the first structured knowledge base for forest foods, promoting data-driven research in nutrition science, sustainable forestry, and knowledge-based decision-making. Full article

Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminator augmentation (ADA) for three-dimensional (3D) StyleGAN2 to improve generative performance on limited volumetric data. The proposed 3D StyleGAN2-ADA redefines all 2D operations for volumetric processing and incorporates the full set of original augmentation techniques. Experiments are conducted on the NoduleMNIST3D dataset of lung CT scans containing 590 voxel-based samples across two classes. Two augmentation pipelines are evaluated—one using color-based transformations and another employing a comprehensive set of 3D augmentations including geometric, filtering, and corruption augmentations. Performance is compared against the same network and dataset without any augmentations at all by assessing generation quality with Kernel Inception Distance (KID) and 3D Structural Similarity Index Measure (SSIM). Results show that volumetric ADA substantially improves training stability and reduces the risk of a mode collapse, even under severe data constraints. A strong augmentation strategy improves the realism of generated 3D samples and better preserves anatomical structures relative to those without data augmentation. These findings demonstrate that adaptive 3D augmentations effectively enable high-quality synthetic medical image generation from extremely limited volumetric datasets. The source code and the weights of the networks are available in the GitHub repository. Full article

Nanosecond-pulsed Nd:YAG laser ablation was investigated as a method for removing Al Ti-based hard coatings deposited on WC–Co hardmetal inserts. Systematic variation in laser parameters identified conditions for complete coating removal while preserving substrate integrity. The laser was operated at 532 nm, under a range of fluences (0.1–11.7 J/cm²), pulse delays (20–180 µs), and pulse numbers (1–300). LIBS qualitative monitoring enabled precise ablation progress by identifying Ti, Al, and O layers, and later the detection of Co and W signals. Scanning electron microscopy (SEM/EDS) and optical profilometry confirmed that 5–10 pulses at intermediate delays (60–80 µs, 4.8–7.1 J/cm²) provided complete removal of ~18 µm-thick coatings while maintaining substrate integrity. In contrast, higher energies and excessive pulses caused localized melting and surface irregularities. These results demonstrate that Nd:YAG laser ablation, especially when coupled with LIBS, offers a precise, fast, and environmentally alternative to conventional chemical stripping methods for the refurbishment and recycling of cutting tools. Full article