Search Results (3,855)

In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcrafted rules or conventional machine learning models falter when the data scale and linguistic variety grow. GraphRAG, a retrieval-augmented generation (RAG) framework that splits documents into fixed-length chunks and then retrieves the most relevant ones for generation, offers a scalable alternative yet still suffers from fragmentation and semantic gaps that erode graph integrity. To resolve these issues, this paper proposes SC-LKM, a cybersecurity knowledge-graph construction method that couples the GraphRAG backbone with hierarchical semantic chunking. SC-LKM applies semantic chunking to build a cybersecurity knowledge graph that avoids the fragmentation and inconsistency seen in prior work. The semantic chunking method first respects the native document hierarchy and then refines boundaries with topic similarity and named-entity continuity, maintaining logical coherence while limiting information loss during the fine-grained processing of unstructured text. SC-LKM further integrates the semantic comprehension capacity of Qwen2.5-14B-Instruct, markedly boosting extraction accuracy and reasoning quality. Experimental results show that SC-LKM surpasses baseline systems in entity-recognition coverage, topology density, and semantic consistency. Full article

Due to its persistence and potential negative effects on ecosystems and human health, microplastic pollution in aquatic environments has become a major worldwide concern. Photocatalytic degradation is a sustainable manner to degrade microplastics to non-toxic by-products. In this review, comprehensive discussion focuses on the synergistic effects of various photocatalytic materials including TiO₂, ZnO, WO₃, graphene oxide, and metal–organic frameworks for producing heterojunctions and involving multidimensional nanostructures. Such mechanisms can include the generation of reactive oxygen species and polymer chain scission, which can lead to microplastic breakdown and mineralization. The advancements of material modifications in the (nano)structure of photocatalysts, doping, and heterojunction formation methods to promote UV and visible light-driven photocatalytic activity is discussed in this paper. Reactor designs, operational parameters, and scalability for practical applications are also reviewed. Photocatalytic systems have shown a lot of development but are hampered by shortcomings which include a lack of complete mineralization and production of intermediary secondary products; variability in performance due to the fluctuation in the intensity of solar light, limited UV light, and environmental conditions such as weather and the diurnal cycle. Future research involving multifunctional, environmentally benign photocatalytic techniques—e.g., doped composites or composite-based catalysts that involve adsorption, photocatalysis, and magnetic retrieval—are proposed to focus on the mechanism of utilizing light effectively and the environmental safety, which are necessary for successful operational and industrial-scale remediation. Full article

More than seventy-five years ago, R. Hanbury Brown and R. Q. Twiss performed the first experiments in quantum optics. At the outset, their results showed great promise for the field of astronomical science, featuring inexpensive hardware, immunity to atmospheric turbulence, and enormous interferometry baselines. This was put to good use for the determination of stellar diameters up to the present time. However, for two-dimensional imaging with faint objects, the integration times are prohibitive. Recently, in a sequence of papers, the present author developed a stochastic search algorithm to remove this roadblock, reducing millions of hours to minutes or seconds. Also, the author’s paper entitled “The Rise of the Brown-Twiss Effect” summarized the search algorithm and emphasized the mathematical proofs of the algorithm. The current algorithm is a sequence of six lines of code. The goal of the present article is to streamline the algorithm in the form of a discrete-time dynamic system and to reduce the size of the state space. The previous algorithm used initial conditions that were randomly assorted pixel intensities. The intensities were mutually statistically independent and uniformly distributed over the range $\left[0,\delta \right)$, where $\delta$ is a (very small) positive constant. The present formulation employs a transformation requiring the uniformly distributed phase of the fast Fourier transform of the cross correlations of the data as initial conditions. We shall see that this strategy results in the simplest discrete-time dynamic system capable for exploring the alternate features and benefits of compartmental nonnegative dynamic systems. Full article

Background: Dynamic computer-aided navigation systems are a real-time motion tracking technology widely applied in oral implantology and endodontics to enhance precision and reduce complications. However, their reliability, accuracy, and usability in dentoalveolar surgery and maxillary bone augmentation remain underinvestigated. Methods: A systematic review following PRISMA guidelines was conducted and registered on PROSPERO (CRD42024610153). PubMed, Scopus, Web of Science, and Cochrane Library databases were searched until October 2024 to retrieve English eligible studies, without restrictions on the publication year, on dynamic computer-assisted navigation systems in dentoalveolar and bone augmentation surgeries. Exclusion criteria were surgery performed without dynamic computer-assisted navigation systems; dental implant placement; endodontic surgery; and maxillo-facial surgery. The outcomes were reliability, accuracy, post-operative course, surgical duration, complications, patient- and clinician-reported usability, acceptability, and satisfaction. Included studies were qualitatively synthetized and judged using dedicated tools for the different study designs. Results: Twenty-nine studies with 214 patients were included, showing high reliability in dentoalveolar and bone augmentation surgeries comparable to or superior to freehand surgeries, higher accuracy in dentoalveolar surgery compared to maxillary bone augmentation, and reduced complication rates across all surgeries. While overall surgical duration slightly increased due to technology installation, operative time was reduced in third molar extractions. Patient-reported outcomes were poorly investigated. Clinician-reported outcomes were mixed, but difficulties in the differentiation of soft tissue from hard tissue were recorded, especially in sinus floor elevation. Conclusions: Dynamic computer-assisted navigation systems enhance accuracy and safety in dentoalveolar and bone augmentation surgery. Further studies are needed to assess the underinvestigated patient-reported outcomes and standardize protocols. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

To enhance the accuracy and reliability of large language models (LLMs) in regulatory question-answering tasks, this study addresses the complexity and domain-specificity of regulatory texts by designing a retrieval-augmented generation (RAG) testing framework. It proposes a dimensionality reduction-based semantic similarity measurement method and a retrieval optimization approach leveraging information reasoning. Through the construction of the technical route of the intelligent knowledge management system, the semantic understanding capabilities of multiple mainstream embedding models in the text matching of financial regulations are systematically evaluated. The workflow encompasses data processing, knowledge base construction, embedding model selection, vectorization, recall parameter analysis, and retrieval performance benchmarking. Furthermore, the study innovatively introduces a multidimensional scaling (MDS) based semantic similarity measurement method and a question-reasoning processing technique. Compared to traditional cosine similarity (CS) metrics, these methods significantly improved recall accuracy. Experimental results demonstrate that, under the RAG testing framework, the mxbai-embed-large embedding model combined with MDS similarity calculation, Top-k recall, and information reasoning effectively addresses core challenges such as the structuring of regulatory texts and the generalization of domain-specific terminology. This approach provides a reusable technical solution for optimizing semantic matching in vertical-domain RAG systems, particularly for MDSs such as law and finance. Full article

The cognitive cycle has been studied via cognitive architectures and by analyzing cognitive experiments. An emerging theoretical approach suggests that several automatic cognitive processes retrieve information, making it available to an internal agent, which in turn decides which information to access. Derived from this view, four phases of the cognitive cycle can be formulated and reproduced within a cognitive monitoring system. This exploratory work presents a new theory, Attention as Internal Action, and proposes a hypothesis about the relationship between an iteration of the cognitive cycle and a conscious motor action. The design of a continuous reaction time task is presented as a tool for quick cognitive evaluation. Via continuously provided user responses, the computational system behind the task adapts triggering stimuli based on the suggested hypothesis. Its software implementation was employed to assess whether a previously conducted simulation of the cognitive cycle’s time range aligned with empirical data. A control group was assigned to perform a separate simple reaction time task in a sequence of five days. The analysis showed that the experimental cognitive monitoring system produced results more closely aligned with the established understanding of the timing of the cognitive cycle than the control task did. Full article

Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a $2\times 2$ MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks. Full article

The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm⁻¹ range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Background: Electronic patient-reported outcomes (ePROs) enable real-time symptom monitoring and early intervention in oncology. Large language models (LLMs), when combined with retrieval-augmented generation (RAG), offer scalable Artificial Intelligence (AI)-driven education tailored to individual patient needs. However, few studies have examined the feasibility and clinical impact of integrating ePRO with LLM-RAG feedback during radiotherapy in high-toxicity settings such as head and neck cancer. Methods: This prospective observational study enrolled 42 patients with head and neck cancer undergoing radiotherapy from January to December 2024. Patients completed ePRO entries twice weekly using a web-based platform. Following each entry, an LLM-RAG system (Gemini 1.5-based) generated real-time educational feedback using National Comprehensive Cancer Network (NCCN) guidelines and institutional resources. Primary outcomes included percentage weight loss and treatment interruption days. Statistical analyses included t-tests, linear regression, and receiver operating characteristic (ROC) analysis. A threshold of ≥6 ePRO entries was used for subgroup analysis. Results: Patients had a mean age of 53.6 years and submitted an average of 8.0 ePRO entries. Frequent ePRO users (≥6 entries) had significantly less weight loss (4.45% vs. 7.57%, p = 0.021) and fewer treatment interruptions (0.67 vs. 2.50 days, p = 0.002). Chemotherapy, moderate-to-severe pain, and lower ePRO submission frequency were associated with greater weight loss. ePRO submission frequency was negatively correlated with both weight loss and treatment interruption days. The most commonly reported symptoms were appetite loss, fatigue, and nausea. Conclusions: Integrating LLM-RAG feedback with ePRO systems is feasible and may enhance symptom control, treatment continuity, and patient engagement in head and neck cancer radiotherapy. Further studies are warranted to validate the clinical benefits of AI-supported ePRO platforms in routine care. Full article

Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improving the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS tagging step and two representative sources of semantic information—specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluates all candidate keywords following some context-specific and semantic-aware criteria. For five state-of-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100% in terms of improved cases) and significantly (between 10.2% and 53.8%, with an average of 25.8%, in terms of F1-score and across all five methods), especially when all the three enhancement steps are used. Our results have profound implications considering the fact that our proposed approach can be easily applied to any AKE method with the standard output (candidate keywords and scores) and the ease to further extend it. Full article

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text. This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic. The research aims to develop a methodology for designing and developing an information system for pre- and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text. The research outlines two subtasks. As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others. As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users. The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients. Full article

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and have become persistent environmental contaminants due to their chemical stability. Water-soluble PFASs with fewer than ten carbon atoms, such as perfluorooctanoic acid (PFOA), are particularly concerning because of their high solubility in water, environmental mobility, and resistance to degradation. In this work, we present an eco-friendly Fe₃O₄ magnetic nanoparticle (MNP)-based platform for the detection and removal of PFOA from water. The synthesized iron oxide MNPs exhibit rapid and strong magnetic responsiveness, enabling efficient magnetic separation for both PFOA detection and removal. To optimize surface affinity for PFOA, we functionalized the MNPs with distinctive ligands, including polyethylene glycol (PEG), β-cyclodextrin (βCD), and dopamine (DA). Among these, PEG and DA showed notable binding affinity toward PFOA, as confirmed by infrared spectroscopy and colorimetric assays. After incubation with the functionalized MNPs followed by magnetic retrieval, we achieved over 90% PFOA removal efficiencies, demonstrating the potential for future research in PFAS remediation technologies. Importantly, the system was validated using deionized, tap, and lake water, all of which yielded comparable and promising results. This study provides a promising, eco-friendly, and recyclable nanomaterial platform for investigating the crucial role of surface chemistry in nanoparticle–PFAS interactions through ligand-mediated magnetic separation. Full article

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of kidney cancer and is often diagnosed at advanced stages. PIEZO1, a mechanosensitive ion channel, has been implicated in cancer progression, but its prognostic relevance in ccRCC remains unclear. This study aimed to evaluate the expression pattern of PIEZO1 in ccRCC and its association with clinicopathological characteristics and patient survival. Immunohistochemical analysis was performed on formalin-fixed, paraffin-embedded tumor tissues from 111 patients with ccRCC, along with 23 matched peritumoral non-cancerous tissues. Protein expression was quantified using the H-score system. Associations with tumor grade, staging, and overall survival (OS) were analyzed. mRNA expression data were retrieved from The Cancer Genome Atlas (TCGA) to validate the protein-level findings. Functional enrichment and pathway analyses were conducted to explore the biological context of PIEZO1-related gene expression. PIEZO1 showed predominantly cytoplasmic localization, with significantly lower expression in tumor tissues compared to adjacent non-malignant tissue (p < 0.0001). High PIEZO1 expression was correlated with higher tumor grade (p = 0.0147) and shorter OS (p = 0.0047). These findings were confirmed at the mRNA level in the TCGA cohort. Multivariate Cox regression analysis identified PIEZO1 as an independent prognostic factor for OS. In conclusion, PIEZO1 may serve as a clinically relevant biomarker in ccRCC. Its overexpression is associated with more aggressive tumor characteristics and poor prognosis, underscoring the need for further investigation into its functional role and potential as a therapeutic target. Full article