Search Results (19,812)

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

During large-scale pavement construction, the preparation of SBS-modified asphalt typically produces large amounts of harmful fumes. The emergence of deodorants can effectively alleviate the problem of smoke emissions during the asphalt manufacturing process. On the basis of ensuring the original road performance, exploring more suitable dosages and types of deodorant is urgently needed. Five commercial deodorants were evaluated using an asphalt smoke collection system, and UV-visible spectrophotometry (UV) was employed to screen the deodorants based on smoke concentration. Gas chromatography–mass spectrometry (GC-MS) was used to quantitatively analyze changes in harmful smoke components before and after adding two deodorants. Subsequently, the mechanisms of action of the two different types of deodorants were analyzed microscopically using fluorescence microscopy. Finally, the performance of bitumen and asphalt mixtures after adding deodorants was evaluated. The results showed that deodorant A (reactive type) and D (adsorption type) exhibited the best smoke suppression effects, with optimal addition rates of 0.6% and 0.5%, respectively. Deodorant A reduced benzene homologues by nearly 50% and esters by approximately 40%, while deodorant D reduced benzene homologues by approximately 70% and esters by approximately 60%, without producing new toxic gases. Both deodorants had a minimal impact on the basic properties of bitumen and the road performance of asphalt mixtures, with all indicators meeting technical specifications. This research provides a theoretical basis for the effective application of deodorants in the future, truly enabling a transition from laboratory research to large-scale engineering applications in the construction of environmentally friendly roads. Full article

Embedded systems in IoT are expected to be run by reliable, resource-efficient software. Devices on the edge are typically required to communicate with central nodes, and in some setups with each other, constituting a distributed system. The Erlang language, favored for its constructs that support building fault-tolerant, distributed systems, offers solutions to these challenges. Its dynamic type system and higher-level abstractions enable fast development, while also featuring tools for building highly available and fault-tolerant applications. To study the viability of using Erlang in embedded systems, we analyze the solutions the language offers, contrasting them with the challenges of developing embedded systems, with a particular focus on resource use. We measure the footprint of the language’s constructs in executing tasks characteristic of end devices, such as gathering, processing and transmitting sensor data. We conduct our experiments with constructs and data of varying sizes to account for the diversity in software complexity of real-world applications. Our measured data can serve as a basis for future research, supporting the design of the software stack for embedded systems. Our results demonstrate that Erlang is an ideal technology for implementing software on embedded systems and a suitable candidate for developing a prototype for a real-world use case. Full article

Multimodal knowledge graphs have recently been successfully applied to tasks such as those relating to information retrieval, question and answer, and recommender systems. In this study, we propose a dual-path fine-tuning mechanism technique with a low-rank adapter and an embedded cueing layer, aiming to improve the generalization and accuracy of the model in analogical reasoning tasks. The low-rank fine-tuning (LoRA) technique with rank-stable scaling factor is used to fine-tune the MKGformer model, and a cue-embedding layer is innovatively added to the input layer, which enables the model to better grasp the scale of the relationship between entities according to the dynamic cue vectors during the fine-tuning process and ensures that the model achieves the best results during training. The experimental results show that the R-MKG model improves several evaluation indexes by more than 20%, which is significantly better than the traditional DoRA and FA-LoRA methods. This research provides technical support for multimodal knowledge graph analogical reasoning. We hope that our work will bring benefits and inspire future research. Full article

Wheeled rovers are widely used in lunar and planetary exploration missions owing to their mechanical simplicity and energy efficiency. However, they face serious mobility challenges on sloped soft terrain, especially in terms of sideslip and loss of attitude angle when traversing across slopes. Previous research proposed using wheelbase extension/contraction and intentionally sinking wheels into the ground, thereby increasing shear resistance and reducing sideslip. Building upon this concept, this study proposes a novel recovery method that integrates beam extension/contraction and Archimedean screw-shaped wheels to enable lateral movement without rotating the rover body. The beam mechanism allows for independent wheel movement, maintaining stability by anchoring stationary wheels during recovery. Meanwhile, the helical structure of the screw wheels helps reduce lateral earth pressure by scraping soil away from the sides, improving lateral drivability. Driving experiments on a sloped sandbox test bed confirmed that the proposed 2DPPL (two-dimensional push-pull locomotion) method significantly reduces sideslip and prevents a drop in attitude angle during slope traversal. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity decay, structural optimization, and the design and application of key materials as well as their performance within battery systems. Addressing these issues requires systematic theoretical foundations and scientific guidance. Numerical modeling has emerged as a powerful tool for investigating the complex physical and electrochemical processes within flow batteries across multiple spatial and temporal scales. It also enables predictive performance analysis and cost-effective optimization at both the component and system levels, thus accelerating research and development. This review provides a comprehensive overview of recent progress in the modeling of ARFBs. Taking the all-vanadium redox flow battery as a representative example, we summarize the key multiphysics phenomena involved and introduce corresponding multi-scale modeling strategies. Furthermore, specific modeling considerations are discussed for phase-change ARFBs, such as zinc-based ones involving solid–liquid phase transition, and hydrogen–bromine systems characterized by gas–liquid two-phase flow, highlighting their distinctive features compared to vanadium systems. Finally, this paper explores the major challenges and potential opportunities in the modeling of representative ARFB systems, aiming to provide theoretical guidance and technical support for the continued development and practical application of ARFB technology. Full article

Addressing global challenges such as climate change and inequality requires convergence competencies that enable learners to devise sustainable solutions. Such competencies have been emphasized in Science, Technology, Engineering, Mathematics (STEM) fields, but empirical research and assessment tools tailored to Humanities, Arts, and Social Sciences (HASS) remain scarce. This study aimed to develop and validate a self-assessment tool to measure convergence competencies among HASS learners. A three-round Delphi survey with domain experts was conducted to evaluate and refine an initial pool of items. Items with insufficient content validity were revised or deleted, and all retained items achieved a Content Validity Ratio (CVR) of ≥0.800, with most scoring 1.000. The validated instrument was administered to 455 undergraduates participating in a convergence education program. Exploratory factor analysis identified five key dimensions: Convergent Commitment, Future Problem Awareness, Future Efficacy, Convergent Learning, and Multidisciplinary Inclusiveness, explaining 69.72% of the variance. Confirmatory factor analysis supported the model’s goodness-of-fit (χ² (160) = 378.786, RMSEA = 0.054, CFI = 0.952), and the instrument demonstrated high internal consistency (Cronbach’s α = 0.919). The results confirm that the tool is both reliable and valid for diagnosing convergence competencies in HASS contexts, providing a practical framework for interdisciplinary learning and reflective engagement toward sustainable futures. Full article

Computer vision techniques such as three-dimensional digital image correlation (3D-DIC) and three-dimensional point tracking (3D-PT) have demonstrated broad applicability for monitoring the conditions of large-scale engineering systems by reconstructing and tracking dynamic point clouds corresponding to the surface of a structure. Accurate stereophotogrammetry measurements require the stereo cameras to be calibrated to determine their intrinsic and extrinsic parameters by capturing multiple images of a calibration object. This image-based approach becomes cumbersome and time-consuming as the size of the tested object increases. To streamline the calibration and make it scale-insensitive, a multi-sensor system embedding inertial measurement units and a laser sensor is developed to compute the extrinsic parameters of the stereo cameras. In this research, the accuracy of the proposed sensor-based calibration method in performing stereophotogrammetry is validated experimentally and compared with traditional approaches. Tests conducted at various scales reveal that the proposed sensor-based calibration enables reconstructing both static and dynamic point clouds, measuring displacements with an accuracy higher than 95% compared to image-based traditional calibration, while being up to an order of magnitude faster and easier to deploy. The novel approach has broad applications for making static, dynamic, and deformation measurements to transform how large-scale structural health monitoring can be performed. Full article

Reactive oxygen species (ROS) are often seen solely as harmful byproducts of oxidative metabolism, yet evidence reveals their paradoxical roles in both promoting and inhibiting cancer progression. Despite advances, precise context-dependent mechanisms by which ROS modulate oncogenic signaling, therapeutic response, and tumor microenvironment dynamics remain unclear. Specifically, the spatial and temporal aspects of ROS regulation (i.e., the distinct effects of mitochondrial versus cytosolic ROS on the PI3K/Akt and NF-κB pathways, and the differential cellular outcomes driven by acute versus chronic ROS exposure) have been underexplored. Additionally, the specific contributions of ROS-generating enzymes, like NOX isoforms and xanthine oxidase, to tumor microenvironment remodeling and immune modulation remain poorly understood. This review synthesizes current findings with a focus on these critical gaps, offering novel mechanistic insights into the dualistic nature of ROS in cancer biology. By systematically integrating data on ROS source-specific functions and redox-sensitive signaling pathways, the complex interplay between ROS concentration, localization, and persistence is elucidated, revealing how these factors dictate the paradoxical support of tumor progression or induction of cancer cell death. Particular attention is given to antioxidant mechanisms, including NRF2-mediated responses, that may undermine the efficacy of ROS-targeted therapies. Recent breakthroughs in redox biosensors (i.e., redox-sensitive fluorescent proteins, HyPer variants, and peroxiredoxin–FRET constructs) enable precise, real-time ROS imaging across subcellular compartments. Translational advances, including redox-modulating drugs and synthetic lethality strategies targeting glutathione or NADPH dependencies, further highlight actionable vulnerabilities. This refined understanding advances the field by highlighting context-specific vulnerabilities in tumor redox biology and guiding more precise therapeutic strategies. Continued research on redox-regulated signaling and its interplay with inflammation and therapy resistance is essential to unravel ROS dynamics in tumors and develop targeted, context-specific interventions harnessing their dual roles. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

In an increasingly complex global context, higher education faces the challenge of preparing professionals who are innovative, committed, and socially responsible. Entrepreneurial competence is particularly prominent among the key skills required to meet this goal, given its significant personal and social impact. This study examines how final-year undergraduate students at the University of Deusto (Spain) perceive the importance of entrepreneurial competence—defined as a set of transversal skills, knowledge, and attitudes enabling initiative and opportunity recognition across various contexts—rather than entrepreneurial competence strictly understood as business creation. The sample included 267 students from different faculties. Descriptive, comparative, and ordinal logistic regression analyses (SPSS) were used. The results show that, while entrepreneurial competence was given significant importance, it was ranked comparatively low relative to other competencies. Significant differences by gender were observed, with women rating entrepreneurial competence more highly than men. The faculty variable showed slight disparities, and there were no relevant differences between campuses. These findings highlight the need to reinforce the integration of entrepreneurial competence into educational curricula on a transversal basis, adapting the teaching of this competence to the sociocultural context of students, as well as the need to increase students’ awareness of the importance of entrepreneurial competence. It is proposed that further research should focus on the relationships between intrapreneurship, gender, and academic disciplines, in order to enrich entrepreneurial competence education and its impact on the employability and social commitment of students. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article