Search Results (879)

Background/Objectives: Nicotine-free electronic cigarettes (NFECs) are becoming increasingly popular, especially among youth and non-smokers, yet their effects on the gastrointestinal tract (GIT) remain poorly understood. This systematic review synthesizes available in vitro, in vivo, and limited human evidence on NFEC-associated changes in gastrointestinal health and function. Methods: Literature searches were conducted in Medline, Web of Science, Cochrane, and Scopus in July 2025, following PRISMA guidelines. Eligible studies examined NFEC effects on any GIT segment, including the oral cavity, liver, intestines, and microbiome. Data on study design, exposure characteristics, and main outcomes were extracted and narratively synthesized. Results: Of 111 identified records, 94 full-text articles were retrieved, and 21 studies met the inclusion criteria. Most were preclinical, with only one human pilot study. Evidence from oral cell and microbial models suggests that NFEC aerosols can induce pro-inflammatory cytokine production, impair cell viability, and disrupt microbial metabolism through their base constituents (propylene glycol, vegetable glycerine, and flavourings). Animal studies indicate possible hepatic oxidative stress, altered lipid metabolism, and gut barrier dysfunction, with some data suggesting more pronounced steatosis in nicotine-free exposures compared to nicotine-containing counterparts. Microbiome studies report reduced tight junction expression and altered neutrophil function. Conclusions: Current evidence is limited and predominantly preclinical but indicates that NFEC exposure can affect multiple aspects of gastrointestinal health. Robust longitudinal and interventional human studies are urgently needed to determine the clinical relevance of these findings and to inform regulation and public health policy. Full article

The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers complementary and enriched spectral–spatial information, providing novel pathways for crop growth stage recognition in complex agricultural scenarios. However, the lack of publicly available multimodal datasets specifically designed for rice growth stage identification remains a significant bottleneck that limits the development and evaluation of relevant methods. To address this gap, we present RiceStageSeg, a multimodal benchmark dataset captured by unmanned aerial vehicles (UAVs), designed to support the development and assessment of segmentation models for rice growth monitoring. RiceStageSeg contains paired centimeter-level RGB and 10-band multispectral (MS) images acquired during several critical rice growth stages, including jointing and heading. Each image is accompanied by fine-grained, pixel-level annotations that distinguish between the different growth stages. We establish baseline experiments using several state-of-the-art semantic segmentation models under both unimodal (RGB-only, MS-only) and multimodal (RGB + MS fusion) settings. The experimental results demonstrate that multimodal feature-level fusion outperforms unimodal approaches in segmentation accuracy. RiceStageSeg offers a standardized benchmark to advance future research in multimodal semantic segmentation for agricultural remote sensing. The dataset will be made publicly available on GitHub v0.11.0 (accessed on 1 August 2025). Full article

The increasing use of Arabic and Urdu on social media platforms, particularly Twitter, has created a growing need for robust Named Entity Recognition (NER) systems capable of handling noisy, informal, and code-mixed content. However, both languages remain significantly underrepresented in NER research, especially in social media contexts. To address this gap, this study makes four key contributions: (1) We introduced a manual entity consolidation step to enhance the consistency and accuracy of named entity annotations. In the original datasets, entities such as person names and organization names were often split into multiple tokens (e.g., first name and last name labeled separately). We manually refined the annotations to merge these segments into unified entities, ensuring improved coherence for both training and evaluation. (2) We selected two publicly available datasets from GitHub—one in Arabic and one in Urdu—and applied two novel strategies to tackle low-resource challenges: a joint multilingual approach and a translation-based approach. The joint approach involved merging both datasets to create a unified multilingual corpus, while the translation-based approach utilized automatic translation to generate cross-lingual datasets, enhancing linguistic diversity and model generalizability. (3) We presented a comprehensive and reproducible pseudocode-driven framework that integrates translation, manual refinement, dataset merging, preprocessing, and multilingual model fine-tuning. (4) We designed, implemented, and evaluated a customized XLM-RoBERTa model integrated with a novel attention mechanism, specifically optimized for the morphological and syntactic complexities of Arabic and Urdu. Based on the experiments, our proposed model (XLM-RoBERTa) achieves 0.98 accuracy across Arabic, Urdu, and multilingual datasets. While it shows a 7–8% improvement over traditional baselines (RF), it also achieves a 2.08% improvement over a deep learning (BiLSTM = 0.96), highlighting the effectiveness of our cross-lingual, resource-efficient approach for NER in low-resource, code-mixed social media text. Full article

Due to its extensive applications, aerial image object detection has long been a hot topic in computer vision. In recent years, advancements in unmanned aerial vehicle (UAV) technology have further propelled this field to new heights, giving rise to a broader range of application requirements. However, traditional UAV aerial object detection methods primarily focus on detecting predefined categories, which significantly limits their applicability. The advent of cross-modal text–image alignment (e.g., CLIP) has overcome this limitation, enabling open-vocabulary object detection (OVOD), which can identify previously unseen objects through natural language descriptions. This breakthrough significantly enhances the intelligence and autonomy of UAVs in aerial scene understanding. This paper presents a comprehensive survey of OVOD in the context of UAV aerial scenes. We begin by aligning the core principles of OVOD with the unique characteristics of UAV vision, setting the stage for a specialized discussion. Building on this foundation, we construct a systematic taxonomy that categorizes existing OVOD methods for aerial imagery and provides a comprehensive overview of the relevant datasets. This structured review enables us to critically dissect the key challenges and open problems at the intersection of these fields. Finally, based on this analysis, we outline promising future research directions and application prospects. This survey aims to provide a clear road map and a valuable reference for both newcomers and seasoned researchers, fostering innovation in this rapidly evolving domain. We keep track of related works in a public GitHub repository. Full article

Infrastructure as code (IaC) is essential for modern cloud development, enabling teams to define, deploy, and manage infrastructure in a consistent and repeatable manner. As organizations migrate to Azure, selecting the right approach is crucial for managing complexity, minimizing errors, and supporting DevOps practices. This paper examines the use of Azure Bicep with GitHub Actions to automate infrastructure deployment for an application in the Azure cloud. It explains how Bicep improves readability, modularity, and integration compared to traditional ARM templates and other automation tools. The solution utilizes a modular Bicep design to deploy resources, including virtual networks, managed identities, container apps, databases, and AI services, with environment-specific parameters for development, QA, and production. GitHub Actions workflows automate the building, deployment, and tearing down of infrastructure, ensuring consistent deployments across environments. Security considerations include managed identities, private networking, and secret management in continuous integration (CI) and continuous delivery (CD) pipelines. This paper provides a detailed architectural overview, workflow analysis, and implementation guidance to help teams adopt a robust, automated approach to Azure infrastructure deployment. By leveraging automation tooling and modern DevOps practices, organizations can streamline delivery and maintain secure, maintainable cloud environments. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

This paper introduces PyBEP, a Python-based tool for the automated and optimized selection of open-circuit potential (OCP) curves and calculation of stoichiometric cycling ranges for lithium-ion battery electrodes based on open-circuit voltage (OCV) measurements. Thereby, it overcomes key challenges in traditional approaches, which are often time-intensive and susceptible to errors due to manual curve digitization, data inconsistency, and coding complexities. The originality of PyBEP arises from the systematic integration of automated electrode chemistry identification, quality-controlled database usage, refinement of the results using incremental capacity methodology, and simultaneous optimization of multiple electrode parameters. The PyBEP database leverages high-quality, curated OCP data and employs differential evolution optimization for precise OCP determination. Validation against literature data and experimental results confirms the robustness and accuracy of PyBEP, consistently achieving precision of 10 mV or better. PyBEP also offers features like electrode chemical composition identification and quality enhancement of measurement data, further extending the battery modeling functionalities without the need for battery disassembly. PyBEP is open-source and accessible on GitHub, providing a streamlined, accurate resource for the battery research community, making PyBEP a unique and directly applicable toolkit for electrochemical researchers and engineers. Full article

Accurate forecasting of tropical cyclone (TC) tracks is critical for disaster preparedness and risk mitigation. While traditional numerical weather prediction (NWP) systems have long served as the backbone of operational forecasting, they face limitations in computational cost and sensitivity to initial conditions. In recent years, deep learning (DL) has emerged as a promising alternative, offering data-driven modeling capabilities for capturing nonlinear spatiotemporal patterns. This paper presents a comprehensive review of DL-based approaches for TC track forecasting. We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. To enable rigorous performance comparison, we introduce a Unified Geodesic Distance Error (UGDE) metric that standardizes evaluation across diverse studies and lead times. Based on this metric, we conduct a critical comparison of state-of-the-art models and identify key insights into their relative strengths, limitations, and suitable application scenarios. Building on this framework, we conduct a critical cross-model analysis that reveals key trends, performance disparities, and architectural tradeoffs. Our analysis also highlights several persistent challenges, such as long-term forecast degradation, limited physical integration, and generalization to extreme events, pointing toward future directions for developing more robust and operationally viable DL models for TC track forecasting. To support reproducibility and facilitate standardized evaluation, we release an open-source UGDE conversion tool on GitHub. Full article

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article

Hydrogen sulfide (H₂S) can be produced from the metabolism of foods containing sulfur in the gastrointestinal tract (GIT). At low doses, H₂S regulates the gut microbial community and supports GIT health, but depending on dose, age, and individual health conditions, it may also contribute to inflammatory responses and gut barrier dysfunction. Controlling H₂S production in the GIT is important for maintaining a healthy gut microbiome. However, research on this subject is limited due to the gaseous nature of the chemical and the difficulty of accessing the GIT in situ. In the present ex vivo experiment, we used a single-dose sodium sulfide preparation (SSP) as a H₂S precursor to test the effect of H₂S on the human gut microbiome across different age groups, including breastfed infants, toddlers, adults, and older adults. Metagenomic sequencing and metabolite measurements revealed that the development of the gut microbial community and the production of short-chain fatty-acids (SCFAs) were age-dependent; that the infant and the older adult groups were more sensitive to SSP exposure; that exogeneous SSP suppressed SCFA production across all age groups, except for butyrate in the older adult group, suggesting that H₂S selectively favors specific gut microbial processes. Full article

NetworkX is a free Python library for graphs and networks and is used in many applications and projects to find the shortest path in path planning scenarios. For dense graphs, the library provides the Floyd–Warshall algorithm for shortest paths and the A* (“A-Star”) algorithm for shortest paths and path lengths. However, several extensions have been proposed to improve the A*, but they are not included in the library. In this context, this paper presents a set of implementations improving the A*, such as the IDA*, D* Lite, SMA*, Bidirectional A* and RTA*. The goal or challenge is to address the limitations of the A* in specific scenarios, such as searching for an optimal path repeatedly or when confronted with memory limitations, as exemplified by the NetworkX library. To do this, we first review the literature of the usage and general application of NetworkX in different domains of applicability and then explore their usage in a shortest path context. By reviewing and validating the usage of A* and extensions in Python using the NetworkX framework, the implementations were submitted to the network environment validation and passed the tests. We have also done the benchmarking of the A*, comparing it with the new ones, and concluded the better efficiency of the A* extensions in tri-objective scenario parameters (length, cost and toll). Despite the extensive utilisation of A* and its notable efficacy in identifying optimal paths, its performance is suboptimal in specific scenarios, such as when confronted with memory constraints and dynamic environments. Almost every algorithm outperformed or matched the A* in the fields that were developed to have an advantage, demonstrating the quality and robustness of the implemented algorithms. As a contribution and to foster further research in this shortest path specific context field, the dataset and Python code of the algorithms are available in a GitHub opensource repository. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

The singular biodiversity of the Brazilian Caatinga inspires innovative solutions in food science. In this study, we evaluated the prebiotic potential of honeys produced by Apis mellifera in the Pajeú hinterland, Pernambuco, Brazil (Caatinga Biome), with different floral origins: Mastic (Aroeira), Mesquite (Algaroba), and mixed flowers. These were used to formulate synbiotic and alcoholic beverages fermented by Saccharomyces boulardii CNCM I-745. Static and dynamic simulations of the human gastrointestinal tract (GIT) were used, as well as physicochemical, rheological, and microbiological analyses. The results revealed that honey positively influences the viability and resilience of probiotic yeast, especially honey with a predominance of Algaroba, which promoted the highest survival rate (>89%) even after 28 days of refrigeration and in dynamic in vitro simulation of the GIT (more realistic to human physio-anatomical conditions). The phenolic composition of the honeys showed a correlation with this tolerance. The use of complementary methodologies, such as flow cytometry, validated the findings and highlighted the functional value of these natural matrices, revealing an even greater longevity potential compared to conventional microbiological methodology. The data reinforces the potential of the Caatinga as a source of bioactive and sustainable compounds, proposing honey as a promising non-dairy synbiotic vehicle. This work contributes to the appreciation of the biome and the development of functional food products with a positive social, economic, and ecological impact. Full article

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article

In this paper we examine the OpenJDK library implementation of the ConcurrentLinkedQueue. We use model checking to verify that it behaves according to the algorithm it is based on: Michael and Scott’s fast and practical non-blocking concurrent queue algorithm. In addition, we develop a simple concurrent queue specification in CSP and verify that Michael and Scott’s algorithm satisfies it. We conclude that both the algorithm and the implementation are correct and both conform to our simpler concurrent queue specification, which we can use in place of either implementation in future verification tasks. The complete code is available on GitHub. Full article