Search Results (41,269)

Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, but many RAG systems represent knowledge either as flat text chunks or as automatically constructed indexing graphs. This assumption is incomplete for curated thematic corpora, including religious scriptures, legal codes, clinical guidelines, educational taxonomies, policy documents, and library classification systems, where domain experts have already organized knowledge into thematic paths and citeable canonical units. This paper investigates how RAG can exploit such expert-authored structures while pruning evidence to a compact and query-specific set. We conduct a critical survey supported by a bibliometric analysis of 2815 Scopus-indexed RAG-related records exported on 26 May 2026, of which 2809 records were retained after duplicate removal. The bibliometric results indicate rapid growth in RAG research but limited explicit consolidation around curated thematic paths, canonical evidence units, or thematic path-guided evidence pruning. We therefore propose ThemePath-RAG, a retrieval framework that retrieves curated thematic paths as high-recall semantic routes, expands candidate canonical evidence, and applies query-aware scoring and global pruning before generation. To assess operational feasibility, we implement ThemePath-RAG for Qur’anic question answering and compare it with a Vector RAG baseline on 150 paired questions using RAGAS context relevance with gpt-4o-mini as the LLM evaluator. Both methods return approximately three final ayat per question. Vector RAG achieves higher mean context relevance than ThemePath-RAG ($0.920$ versus $0.798$; $p<0.001$). Thus, the proof of concept establishes the feasibility of thematic-path-guided retrieval and identifies evidence-selection challenges, rather than demonstrating superiority over conventional vector retrieval. The paper clarifies the framework’s relationship to GraphRAG, LightRAG, HippoRAG, PathRAG, ontology-based RAG, and AI-augmented bibliometric systems, and outlines a language-matched, multi-baseline evaluation agenda for future cross-domain validation. Full article

Infertility affects millions worldwide, with estimates indicating that 1 in 6 people of reproductive age will experience it in their lifetime. Globally, infertility impacts between 12.6–17.5% of couples of reproductive age. Recently, machine learning (ML) has garnered significant attention in biomedical research, enabling creation of predictive models that can personalize disease treatment based on measurable variables, thereby aiding in the development of diagnostic tools. In this study, 28 predictor variables were selected preliminarily; after a multicollinearity test, 20 predictors were selected for the classification task and modelled as a binary supervised classification problem. Seven ML algorithms were evaluated, including Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, Naïve Bayes, K-Nearest Neighbour, and Extreme Gradient Boosting (XGBoost). Statistical analysis showed that anti-Müllerian hormone (AMH) can serve as a biomarker for diagnosing PCOS and evaluating ovarian reserve. Female fertility has been associated negatively with waist circumference (r = –0.35), systolic blood pressure (r = –0.30), poor ovarian reserve (r = –0.28), and triglycerides (r = –0.33), suggesting a possible link between these metabolic factors and female infertility. Among the models tested, Naïve Bayes and Logistic Regression provided the most reliable and generalizable performance. The incorporation of SHapley Additive exPlanations (SHAP) analysis enhanced the interpretability of the models, identifying polyendocrine metabolic ovarian syndrome (PMOS, previously known as polycystic ovarian syndrome—PCOS), AMH, poor ovarian reserve, menstrual cycle irregularity, systolic blood pressure, body mass index (BMI), fasting glucose, and triglycerides as the most influential predictors of female fertility. However, future studies incorporating data from multiple centres, comprising a larger, more representative population, and using more interpretable models could enhance the reliability of ML in clinical decision-making. Full article

In this paper, we propose a novel generative adversarial network (GAN)-based joint localization and mapping (JLAM) method using angle difference of arrival (ADOA) measurements for millimeter-wave (mmWave) communication systems. The proposed method adopts a deep auto-encoder neural network as the discriminator of the GAN and models the generator as an explicit geometric ADOA function of the access point (AP) positions and the mobile terminal (MT) position, rather than as a conventional black-box neural network. By exploiting the two-dimensional distribution characteristics of high-dimensional ADOA vectors collected at a large number of random and unknown MT positions, the proposed method learns the ADOA data distribution and transforms it into the AP geometric topology. Then, the MT positions and the indoor map are estimated based on the recovered physical and virtual AP topology. The simulation results show that, under the representative setting with $N=2000$ measured ADOA vectors and $\sigma =2^{\circ }$ AOA measurement noise, the proposed method achieves an average localization error of about 0.25 m, compared with about 0.60 m for the JADE algorithm, corresponding to an error reduction of approximately $58\%$. The proposed method also provides more accurate room boundary estimation than JADE, confirming its effectiveness for mmWave JLAM. Full article

The highly pathogenic H5 subtype avian influenza viruses (AIVs) pose persistent threats to the poultry industry and public health owing to their high lethality and pandemic potential. Migratory wild birds play a pivotal role in the global dissemination and genetic reassortment of the virus, serving as both natural reservoirs and long-distance vectors that drive its spatiotemporal spread. However, the extent and evolutionary drivers of antigenic divergence among H5 AIVs circulating in wild birds in East China remain poorly understood. Here, we aim to characterize the evolutionary dynamics and antigenic divergence of H5 AIVs isolated from wild birds in East China between 2013 and 2022. Whole-genome sequencing and phylogenetic analysis revealed that the isolates belonged to multiple clades, including 2.3.2.1 and 2.3.4.4, and encompassed the H5N1, H5N6, and H5N8 subtypes. Key amino acid site analysis showed that the glycosylation site patterns in the HA and NA proteins varied among clades, with some strains exhibiting gains or losses of glycosylation sites, while certain strains had acquired mutations associated with mammalian adaptation. Cross-hemagglutination inhibition (HI) assays combined with antigenic cartography demonstrated that the majority of the isolates were antigenically well-matched with the contemporaneous vaccine strains used in China, indicating that these vaccines effectively covered the predominant circulating antigenic variants at the time. Nevertheless, potential antigenic mismatches were still observed between some circulating strains and these vaccine strains. These findings suggest that wild birds in East China may contribute to the regional movement and diversification of H5 AIVs, highlighting the value of sustained surveillance for early warning and vaccine strain evaluation. Full article

Background/Objectives: The glucocorticoid receptor (GR) is essential for regulating liver energy balance, metabolism, and inflammation. Stress and other factors can impair its function, resulting in glucocorticoid-resistant metabolic liver disease. GR mainly exists in two forms: the glucocorticoid-binding isoform, GRα, and the non-binding isoform, GRβ. The GRβ isoform typically exhibits minimal signaling activity beyond its role as a dominant-negative regulator of GRα, thereby decreasing glucocorticoid responsiveness and potentially causing resistance. Methods: To explore GRβ signaling independent of GRα, we developed mice with adenovirus-induced overexpression of GRβ (GRβ-Ad) and control mice with a vector (Vec-Ad). After five days on a standard diet, these mice received either vehicle or dexamethasone treatment. Liver tissues were collected, and we performed RNA sequencing and advanced PamGene kinome analysis to detect pathway changes in GRβ-Ad mice compared with controls. Results: Significant increases were observed in the expression of genes that inhibit fatty acid oxidation, inflammation, and liver cancer development. There was also a marked difference in serine/threonine kinase activity between GRβ-Ad and control mice. Conclusions: The findings suggest that elevated GRβ levels affect kinase pathways that modulate glucocorticoid signaling, disrupt liver lipid metabolism, and are associated with cancer pathways. Further research is needed to determine whether GRβ functions similarly in humans and to assess its potential contribution to hepatocellular carcinoma (HCC). Full article

Existing image–text retrieval methods often compute cross-modal similarity using global single-vector representations. Although efficient for coarse semantic alignment, such compressed representations are limited when textual queries involve fine-grained semantics, including objects, attributes, relations, and their compositional structures. This paper focuses on fine-grained text-to-image retrieval and proposes Q-GrAM, a retrieval-oriented adaptation of the BLIP-2 Q-Former. Instead of treating Q-Former queries as a homogeneous set, Q-GrAM partitions a fixed query budget into semantically differentiated groups. A text-guided router assigns token-level semantic demands to query groups, while query conditional initialization modulates each group according to group-level textual summaries. The resulting grouped visual query features are matched with text tokens through a group-aware late interaction scorer, and auxiliary routing balance and inter-group diversity regularization are introduced to stabilize semantic specialization. Experiments on MS-COCO 5K, Flickr30K, and Flickr30K-CFQ show that Q-GrAM achieves strong text-to-image retrieval performance against both global embedding baselines and representative fine-grained image–text matching methods, while maintaining competitive bidirectional retrieval performance. These results demonstrate the effectiveness of structured, text-conditioned Q-Former query specialization for fine-grained text-driven image search. Full article

Brain tumor classification from magnetic resonance imaging (MRI) remains challenging in settings where only image-level labels are available and tumor classes exhibit overlapping visual characteristics. In this study, we consider the publicly available Brain Tumor MRI Dataset from Kaggle, a four-class dataset composed of 2D MRI slices belonging to the categories glioma, meningioma, pituitary tumor, and no tumor. Accordingly, the proposed framework is formulated as a slice-based multiclass classification approach rather than a volumetric 3D analysis pipeline. We propose a lightweight and interpretable framework that integrates handcrafted multiscale MRI descriptors, an artificial neural network (ANN), Bee Colony Optimization (BCO)-based neural architecture search, and fuzzy softmax confidence modeling. Each MRI slice is represented by a compact 9-dimensional feature vector derived from intensity, local entropy, and gradient magnitude computed globally and over non-overlapping spatial blocks. The ANN design problem is formulated as a discrete–continuous optimization task, where BCO is employed to optimize network architecture and training hyperparameters by maximizing validation macro-$F_1$. To quantify predictive reliability, the softmax outputs are interpreted as fuzzy class memberships and further analyzed using maximum membership, normalized entropy, decision margin, and ambiguity measures, enabling confidence-aware reliability assessment. These fuzzy confidence descriptors enable confidence-threshold-based selective classification and rejection of low-confidence predictions. Across repeated runs, the optimized BCO-ANN achieved a mean test accuracy of $0.781\pm 0.009$, mean macro-$F_1$ of $0.775\pm 0.010$, mean Brier score of $0.319\pm 0.012$, and mean Expected Calibration Error (ECE) of $0.0273\pm 0.0080$, compared with $0.748\pm 0.011$, $0.738\pm 0.013$, $0.352\pm 0.010$, and $0.0446\pm 0.0071$ for the baseline ANN, respectively. Under confidence-threshold-based rejection, selective macro-$F_1$ increased to $0.820\pm 0.009$ at $\tau =0.55$ and to $0.874\pm 0.020$ at $\tau =0.85$, with the expected reduction in coverage. These results indicate that the proposed framework provides a transparent and reproducible approach for optimization-aware and confidence-aware multiclass brain tumor MRI classification in a lightweight handcrafted feature setting. Full article

Private investment is critical for post-conflict economic recovery, yet evidence on how specific fiscal policy instruments, such as taxation, borrowing composition, and expenditure types, affect domestic and foreign investment in a post-conflict set-up remains limited. This study examines whether disaggregated fiscal policies are associated with crowding in or out private investment in Rwanda, a post-conflict economy characterized by constrained fiscal space, shallow credit markets, and evolving institutions. Using a Vector Error Correction Model (VECM), on quarterly data spanning 1996 Q1–2024 Q4, the analysis captures long- and short-run dynamics between disaggregated fiscal variables, institutional quality, and private investment. The results indicate that direct taxes and domestically financed debt are negatively associated with both domestic and foreign private investment. Externally financed capital spending, on the other hand, is associated with a crowding-in effect, stimulating both local and foreign investment. Lagged measures of institutional quality also enhance investment outcomes, highlighting the conditional role of government in shaping fiscal transmission. These findings demonstrate that fiscal effects are instrument-specific, depending on funding sources and composition, and mediated by institutional and macroeconomic conditions. By integrating disaggregated fiscal analysis with institutional context, this study provides empirically grounded insights for designing fiscal strategies that support private sector-led recovery and sustainable growth in post-conflict and resource-constrained economies. Full article

In this paper, we provide a new Riemann curvature formula in homogeneous Finsler geometry. Meanwhile, we prove that a homogeneous Finsler metric is Landsberg if and only if its connection operator induces Killing vector fields for a Hessian metric. As an application, we prove that any homogeneous Landsberg sphere with dimension bigger than 1 and constant flag curvature must be Riemannian. Full article

This study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops under nitrogen (N) fertilization treatments, irrigation (I), and their interaction (N × I). The U-Net model outperformed all other methods, achieving accuracies for crop nitrogen status of 65–99% in N, 84–100% in I, and 41–82% in N × I treatments, with variation due to different input data. Supervised machine learning also performed well, with Support Vector Machine achieving 53–87, 66–86, and 32–66% respectively, and Random Forest 61–96, 70–81, and 33–65%. Unsupervised K-means yielded the lowest accuracies (47–58, 9–65, and 8–34%), demonstrating necessity of substantial supervision to delineate crop nitrogen and water status. These findings were confirmed by repeated analyses of UAV imagery acquired later in the growing season with consistent results. Comparable classification performance was observed for crop water status and leaf area index at both time points. Despite being demonstrated in a single-field, single-crop framework, the results provide proof of concept for applying deep learning classifiers to detect subtle nitrogen and water stress under field conditions in precision agriculture. Future research could test diverse agroecosystems and growing seasons, alternative deep learning algorithms, and sensor data fusion to improve classification accuracies. Full article

Given the heterogeneity in data on heat pump performance curves across manufacturers, selecting the appropriate one for more detailed studies is complex. Machine learning (ML) techniques can be very helpful in this endeavor. In this case, four techniques were used: artificial neural networks (ANN), support vector machines (SVM), Gaussian Process (GP), and decision trees (DT), to predict HP performance maps. These four techniques were then applied to a hybrid installation consisting of an air-water HP boiler and a biomass boiler modeled with TRNSYS and connected in series. Performance maps were generated using TRNSYS type 581. Key aspects, including overall efficiency, emissions, lifetime costs, and design and control parameters, were then analyzed. The study found that the coefficient of variation of root-mean-square error (CVRMSE) was 14.9% for the DT model, 11.4% for the ANN, 11.1% for the SVM, and 10.7% for the GP model. The GP model was ultimately used to develop an HP performance map due to its highest accuracy, and comparisons with baseline data revealed significant differences in efficiency, operational costs, and emissions, among others. Full article

In this paper, we propose a lightweight authentication and identification protocol based on different phosphorescent Strontium aluminate color labels. The excitation sources are pulsed UV LEDs emitting at 365 nm and 385 nm, causing different RGB-dependent time responses of the label that are measured using a smartphone recording at 30 FPS. The rise and decay time responses as measured by the red (R), green (G) and blue (B) pixels were separately analyzed and were found to follow a power law with individual parameters depending on the excitation wavelength, pulse duration and duty cycle, which serve as security features and are suitable for authentication purposes in IoT applications. Our solution uses simple cryptographic functions such as HMAC and XOR. We performed a security analysis of our protocol to prove its resistance to known attack vectors. The proposed scheme has minimal computation and communication costs and can be deployed on resource-constrained Internet of Things devices. Full article

The development of a magnetic composite based on a silicone matrix containing carbonyl iron (CI), combined with the Frequency Selective Surface (FSS) for radiation-attenuating material (RAM) applications in the X-band (8.2–12.4 GHz), is presented in this work. Four FSS geometries were investigated: square, circular, triangular, and hexagonal. The electromagnetic properties, namely relative electrical permittivity and magnetic permeability, were characterized using a vector network analyzer employing both waveguide and free-space measurement techniques. The attenuation performance was evaluated through reflection loss (RL) measurements and numerically simulated using FEKO software. The stability of the attenuation performance was also assessed for different wave incidence angles (0° to 45°), demonstrating a robust average peak attenuation of −32.1 dB at 11.18 GHz, with optimal resonance values reaching as low as −60.34 dB at an incidence angle of 30°, in good agreement with the simulation results. The results indicate that the capacitive and inductive behavior associated with FSS geometries plays a key role in tailoring the electromagnetic response, demonstrating the effectiveness of FSS-based magnetic composites for controlled attenuation performance. Full article

Feline panleukopenia is a highly contagious and often fatal disease in cats caused by the feline panleukopenia virus (FPV), a member of the Parvoviridae family. Despite the widespread use of vaccination, FPV remains a significant threat to both domestic and wild felid populations worldwide, particularly in young or unvaccinated animals. Effective vaccination strategies are therefore essential for controlling the disease and reducing mortality. Current vaccines, including modified live and inactivated vaccines, have demonstrated substantial efficacy in inducing protective immunity; however, several challenges remain, such as maternal antibody interference, vaccine failure, and safety concerns in certain animal populations. Recent advances in vaccine technology have spurred the development of next-generation FPV vaccines, including recombinant vectors, DNA vaccines, virus-like particle (VLP) vaccines, and novel delivery platforms. Among these, probiotic-based vaccine vectors have garnered growing interest due to their favorable safety profiles, mucosal immunogenicity, and suitability for oral administration. These systems may provide innovative approaches for inducing both systemic and mucosal immune responses against FPV. This review summarizes the current understanding of the immunological mechanisms underlying protection against FPV infection and discusses the progress made in FPV vaccine development. Furthermore, it highlights the major challenges associated with current vaccination strategies and explores emerging vaccine platforms, including probiotic vector-based vaccines, as promising tools for future disease control. Improved vaccine design and optimized immunization strategies will be crucial for enhancing the prevention of feline panleukopenia in the future. Full article

The transcriptional regulation of the cannabinoid receptor 1 (CB1R) by promoter/enhancer elements and transcription factors is an area of cannabinoid research that has historically been understudied. To map the promoter region of the human CNR1 gene (the gene encoding CB1R), a 997-base-pair fragment from the sequence upstream of the CNR1 gene was cloned into a secreted luciferase reporter vector, and a series of deletion fragments were constructed. The transcriptional activity of these constructs was tested in human cell lines from three tissues: neuronal tissue (SHSY5Y), kidney tissue (HEK293T), and colonic epithelium (HCT116). Through this mapping, we have identified two key regulatory regions within the promoter. Increased levels of cAMP suppressed reporter expression from the full-length promoter fragment in all three cell lines, and in silico modeling predicts potential cAMP response elements (CRE) within one of the key regulatory sequences. Additionally, the minimal promoter region for CNR1 also appears to be in the second regulatory region identified, and in silico modeling predicts BRE and INR elements within this sequence. These findings begin to unravel the mechanisms by which CNR1 is transcriptionally regulated. Full article