Search Results (50)

Currently, fifth-generation (5G) communication has emerged as the most promising candidate for next-generation railway-dedicated communication systems. Condition monitoring of 5G networks is critical for ensuring the continuity and reliability of train–ground communications. In this paper, a real-time monitoring technology is proposed, which is based on generalized channel characteristics extracted from received Demodulation Reference Signals (DM-RSs). Furthermore, a corresponding monitoring system has been developed based on the Radio Frequency System on Chip (RFSoC). Experimental results demonstrate that the proposed condition monitoring system exhibits excellent performance: it can accurately measure key network metrics (including field strength, multipath components, and frequency offset) and enable real-time monitoring of the operational condition of 5G radio access networks (RAN) and on-board terminals. Future work will focus on integrating the monitoring system into on-board terminals. Full article

Progressive Fibrosing Interstitial Lung Disease (PF-ILD) is a severe phenotype of Interstitial Lung Disease (ILD) with a poor prognosis, typically requiring prolonged clinical observation and multiple CT examinations for diagnosis. Such requirements delay early detection and treatment initiation. To enable earlier identification of PF-ILD, we propose ILD-Slider, a parameter-efficient and lightweight deep learning framework that enables accurate PF-ILD identification from a limited number of CT slices. ILD-Slider introduces anatomy-based position markers (PMs) to guide the selection of representative slices (RSs). A PM extractor, trained via a multi-class classification model, achieves high PM detection accuracy despite severe class imbalance by leveraging a peak slice mining (PSM)-based strategy. Using the PM extractor, we automatically select three, five, or nine RSs per case, substantially reducing computational cost while maintaining diagnostic accuracy. The selected RSs are then processed by a slice-level 3D Adapter (Slider) for PF-ILD identification. Experiments on 613 cases from The University of Osaka Hospital (UOH) and the National Hospital Organization Osaka Toneyama Medical Center (OTMC) demonstrate the effectiveness of ILD-Slider, achieving an AUPRC of 0.790 (AUROC 0.847) using only five automatically extracted RSs. ILD-Slider further validates the feasibility of diagnosing PF-ILD from non-contiguous slices, which is particularly valuable for real-world and public datasets where contiguous volumes are often unavailable. These results highlight ILD-Slider as a practical and efficient solution for early PF-ILD identification. Full article

In complex environments, traditional device-free localization (DFL) methods based on received signal strength (RSS) encounter difficulties in simultaneously achieving high accuracy and efficiency due to multipath effects and noise interference. These methods typically depend on convex sparsity regularization, which, despite its computational convenience, is insufficient in capturing the sparsity of signals. In contrast, non-convex sparsity regularization methods, while theoretically more capable of approximating ideal sparsity, are associated with higher computational complexity and a greater likelihood of getting stuck in local optima. To address these issues, this study proposes a synthetic model based on a novel weakly convex penalty function called Springback. This model combines a compression term (${\ell }_1$) that promotes sparsity and a rebound term (${\ell }_2$) that preserves signal amplitude, adjusting parameters to balance sparsity and computational complexity. Furthermore, to tackle the low efficiency of traditional synthetic models when dealing with large-scale data, we introduce a Springback-transform model based on an analytical transform learning framework. This model can directly extract sparse features from signals, avoiding the complex computational processes inherent in traditional synthetic models. Both models are solved using a difference of convex algorithm (DCA), significantly improving positioning accuracy and computational efficiency. Experimental results demonstrate that the proposed models exhibit high accuracy, low positioning error, and a short computation time across various environments, outperforming other state-of-the-art models. These achievements offer a new solution to the problem of DFL in complex environments, with high practical value and application prospects. Full article

Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic of spatial RSS fingerprint images makes it challenging to effectively extract subtle fingerprint features. To address this issue, this paper proposes an RSS-based indoor positioning method that combines enhanced spatial frequency fingerprint representation with fusion learning. First, bicubic interpolation is applied to improve image resolution and reveal finer spatial details. Then, a 2D fast Fourier transform (2D FFT) converts the enhanced spatial images into frequency domain representations to supplement spectral features. These spatial and frequency fingerprints are used as dual-modality inputs for a parallel convolutional neural network (CNN) model with efficient multi-scale attention (EMA) modules. The model extracts modality-specific features and fuses them to generate enriched representations. Each modality—spatial, frequency, and fused—is passed through a dedicated fully connected network to predict 3D coordinates. A coordinate optimization strategy is introduced to select the two most reliable outputs for each axis (x, y, z), and their average is used as the final estimate. Experiments on seven public datasets show that the proposed method significantly improves positioning accuracy, reducing the mean positioning error by up to 47.1% and root mean square error (RMSE) by up to 54.4% compared with traditional and advanced time–frequency methods. Full article

Understanding how disasters are framed in news media is critical to unpacking the socio-political dynamics of crisis communication. However, empirical research on narrative variation across disaster types and geographies remains limited. This study addresses that gap by examining whether media outlets adopt distinct narrative structures based on disaster type and country. We curated a large-scale dataset of 20,756 disaster-related news articles, spanning from September 2023 to May 2025, aggregated from 471 distinct global news portals using automated web scraping, RSS feeds, and public APIs. The unstructured news titles were transformed into structured representations using GPT-3.5 Turbo and subjected to unsupervised topic modeling using Latent Dirichlet Allocation (LDA). Five dominant latent narrative topics were extracted, each characterized by semantically coherent keyword clusters (e.g., “wildfire”, “earthquake”, “flood”, “hurricane”). To empirically evaluate our hypotheses, we conducted chi-square tests of independence. Results demonstrated a statistically significant association between disaster type and narrative frame (${\chi }^2=25,280.78$, p < 0.001), as well as between country and narrative frame (${\chi }^2=23,564.62$, p < 0.001). Visualizations confirmed consistent topic–disaster and topic–country pairings, such as “earthquake” narratives dominating in Japan and Myanmar and “hurricane” narratives in the USA. The findings reveal that disaster narratives vary by event type and geopolitical context, supported by a mathematically robust, scalable, data-driven method for analyzing media framing of global crises. Full article

Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients. Methods: From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index. Results: The study included 126 BC patients with predominantly “mass”-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10–8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33–56.98]). Radiomic and clinical–radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (p < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical–radiomic model. Conclusions: Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone. Full article

For fine-grained recognition, capturing distinguishable features and effectively utilizing local information play a key role, since the objects of recognition exhibit subtle differences in different subcategories. Finding subtle differences between subclasses is not straightforward. To address this problem, we propose a weakly supervised fine-grained classification network model with Local Diversity Guidance (LDGNet). We designed a Multi-Attention Semantic Fusion Module (MASF) to build multi-layer attention maps and channel–spatial interaction, which can effectively enhance the semantic representation of the attention maps. We also introduce a random selection strategy (RSS) that forces the network to learn more comprehensive and detailed information and more local features from the attention map by designing three feature extraction operations. Finally, both the attention map obtained by RSS and the feature map are employed for prediction through a fully connected layer. At the same time, a dataset of ancient towers is established, and our method is applied to ancient building recognition for practical applications of fine-grained image classification tasks in natural scenes. Extensive experiments conducted on four fine-grained datasets and explainable visualization demonstrate that the LDGNet can effectively enhance discriminative region localization and detailed feature acquisition for fine-grained objects, achieving competitive performance over other state-of-the-art algorithms. Full article

Total mixed ration silage has been used as a strategy to optimize the use of dry and wet feed in ruminant feeding. Another promising technique is silage reallocation, which allows producers to divide the ensiled material in large silos into smaller units that can be easily transported and marketed. Thus, this study aimed to improve food preservation through the development of total mixed rations (TMRs) based on relocated sorghum silage (RSS) and cactus pear for sheep diets. A completely randomized design was used with five treatments (0, 15, 25, 30, and 35% RSS inclusion on a dry matter basis) and five replicates. Ninety days after ensiling, the silos were opened. The fermentation characteristics, nutritional aspects, aerobic stability, and microbial populations of TMR silages were evaluated. The inclusion of RSS showed a quadratic effect on pH, density, permeability, lactic acid bacteria and yeast counts, and total carbohydrates (p < 0.05). It reduced gas and effluent losses, porosity, ammonia nitrogen, buffer capacity, ash, crude protein, ether extract, and non-fibrous carbohydrates (p < 0.05) while increasing dry matter, neutral and acid detergent fiber, hemicellulose, and cellulose contents (p < 0.05). There was an interaction effect between the levels of RSS inclusion and exposure times to air on CO₂ and dry matter content (p < 0.05). Regarding carbohydrate fractionation, there was a reduction in fraction A + B1 (non-fibrous carbohydrates) and an increase in fractions B2 (fibrous carbohydrates from the cell wall and of slow ruminal availability, susceptible to the effects of the passage rate) and C (indigestible neutral detergent fiber) (p < 0.05). For protein fractionation, a quadratic effect was observed for fractions A (non-protein nitrogen) and C (insoluble protein, indigestible in the rumen and intestine), an increase in fraction B1 (soluble protein rapidly degraded in the rumen) + B2 (insoluble protein with intermediate degradation rate in the rumen), and a reduction in fraction B3 (insoluble protein with slow degradation rate in the rumen) (p < 0.05) as RSS levels increased. Under the experimental conditions, it is recommended to include up to 30% RSS in the total mixed ration silage to improve microbiological characteristics, reduce gas and effluent losses, and increase dry matter recovery and nutritional aspects of silage when associated with cactus pear. Full article

Background: Neurosurgery demands exceptional precision due to the brain’s complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon’s ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Results: Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. Conclusions: This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS’s clinical utility and cost-effectiveness. Full article

Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the prevention of groundwater pollution and overexploitation. Moreover, the development of a novel localization strategy project in wireless sensor groundwater networks aims to address the challenge of optimizing sensor location in relation to the monitoring process so as to extract the maximum quantity of information with the minimum cost. In this study, the improved hybrid butterfly artificial gorilla troop optimizer (iHBAGTO) technique is applied to optimize nodes’ position and the analysis of the path loss delay, and the RSS is calculated. The hybrid of Butterfly Artificial Intelligence and an artificial gorilla troop optimizer is used in the multi-functional derivation and the convergence rate to produce the designed data localization. The proposed iHBAGTO algorithm demonstrated the highest convergence rate of 99.6%, and it achieved the lowest average error of 4.8; it consistently had the lowest delay of 13.3 ms for all iteration counts, and it has the highest path loss values of 8.2 dB, with the lowest energy consumption value of 0.01 J, and has the highest received signal strength value of 86% for all iteration counts. Overall, the Proposed iHBAGTO algorithm outperforms other algorithms. Full article

Cultural tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for peak visitor time in Riyadh, a city in Saudi Arabia. The main objective of the framework is to improve the cultural tourism of Riyadh by considering various factors to help in improving CT based on recommendation system (RS). Primarily, the map data and cultural event dataset were processed for location, such as grouping with Kriging interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes were processed with word embedding. Meanwhile, the social network sites (SNS) data like reviews and news were extracted with an external application programming interface (API). The review data were processed with keyword extraction and word embedding, whereas the news data were processed with score value estimation. Lastly, the data were fused, corresponding to a historical site, and given to the Multi-Quadratic-Long Short-Term Memory (MQ-LSTM) recommendation system (RS); also, the recommended result with the map was stored in a database. Lastly, the database security was maintained with locality sensitive hashing (LSH). From the experimental evaluation with multiple databases including the Riyadh Restaurants 20K dataset, the proposed recommendation model achieved a recommendation rate (RR) of 97.22%, precision of 97.7%, recall of 98.27%, and mean absolute error (MAE) of 0.0521. This result states that the proposed RS provides higher RR and reduced error compared to existing related RSs. Thus, by attaining higher performance values, the proposed model is experimentally verified. Full article

In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices. Full article

Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP). The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations. Full article

High-speed gas/vapour jets injected into a cross-moving sonic liquid signifies a vital phenomenon which bears useful applications in environmental and energy processes. In the present experimental study, a pulsating jet of supersonic steam was injected into cross-flowing water. Circulation zones of opposite vorticity owing to the interaction between the steam jet and cross-water flow were found. However, a large circulation appeared in front of the nozzle exit. Also, most small circulation regions were observed at higher water-flow rates (>2 m³/s). Among the prime mixing variables (i.e., turbulence kinetic energy (TKE) and Reynolds shear stress (RSS)), the RSS estimations backed a small diffusive phenomenon within a region far from the nozzle exit. Further information extracted from the PIV images indicated the existence of Kelvin–Helmholtz (KH) instabilities. The counter-rotating vortex pairs (CVPs) appeared to be significant in the region close to the nozzle exit, and they exhibited leeward side folds. Moreover, the effects of the operating conditions on the pressure recovery and mixing efficiency as well as the penetration and the separation height were evaluated to determine the optimisation of the phenomenon. By applying extreme difference analysis, the mixing efficiency was found as the most influential parameter. Full article

Introduction: Nowadays, the risk stratification of preterm birth (PTB) and its prediction remain a challenge. Many risk factors associated with PTB have been identified, and risk scoring systems (RSSs) have been developed to face this challenge. The objectives of this systematic review were to identify RSSs for PTB, the variables they consist of, and their performance. Materials and methods: Two databases were searched, and two authors independently performed the screening and eligibility phases. Records studying an RSS, based on specified variables, with an evaluation of the predictive value for PTB, were considered eligible. Reference lists of eligible studies and review articles were also searched. Data from the included studies were extracted. Results: A total of 56 studies were included in this review. The most frequently incorporated variables in the RSS included in this review were maternal age, weight, history of smoking, history of previous PTB, and cervical length. The performance measures varied widely among the studies, with sensitivity ranging between 4.2% and 92.0% and area under the curve (AUC) between 0.59 and 0.95. Conclusions: Despite the recent technological and scientifical evolution with a better understanding of variables related to PTB and the definition of new ultrasonographic parameters and biomarkers associated with PTB, the RSS’s ability to predict PTB remains poor in most situations, thus compromising the integration of a single RSS in clinical practice. The development of new RSSs, the identification of new variables associated with PTB, and the elaboration of a large reference dataset might be a step forward to tackle the problem of PTB. Full article