Search Results (8,387)

Background/Objectives: The first step in learning an alphabetic writing system is to establish letter–sound associations. This process is more difficult for children with dyslexia (DYS) than for typically developing (TD) children. Cerebral mechanisms underlying these associations are not fully understood and are expected to change during the training course. This study aimed to identify the neurophysiological correlates and developmental changes of visual letter processing in children with DYS compared to TD children, using event-related potentials (ERPs) during a letter/non-letter classification task. Methods: A total of 71 Russian-speaking children aged 7–11 years participated in the study, including 38 with dyslexia and 33 TD children. The participants were divided into younger (7–8 y.o.) and older (9–11 y.o.) subgroups. EEG recordings were taken while participants classified letters and non-letter characters. We analyzed ERP components (N/P150, N170, P260, P300, N320, and P600) in left-hemisphere regions of interest related to reading: the ventral occipito-temporal cortex (VWFA ROI) and the inferior frontal cortex (frontal ROI). Results: Behavioral differences, specifically lower accuracy in children with dyslexia, were observed only in the younger subgroup. ERP analysis indicated that both groups displayed common stimulus effects, such as a larger N170 for letters in younger children. However, their developmental trajectories diverged. The DYS group showed an age-related increase in the amplitude of early components (N/P150 in VWFA ROI), which contrasts with the typical decrease observed in TD children. In contrast, the late P600 component in the frontal ROI revealed an age-related decrease in the DYS group, along with overall reduced amplitudes compared to their TD peers. Additionally, the N320 component differentiated stimuli exclusively in the DYS group. Conclusions: The data obtained in this study confirmed that the mechanisms of letter recognition in children with dyslexia differ in some ways from those of their TD peers. This atypical developmental pattern involves a failure to efficiently specialize early visual processing, as evidenced by the increasing N/P150. Additionally, there is a progressive reduction in the cognitive resources available for higher-order reanalysis and control, indicated by the decreasing frontal P600. This disruption in neural specialization and automation ultimately hinders the development of fluent reading. Full article

Radiographically visible carotid artery calcifications are typically seen at the level of the C3–C4 cervical vertebrae and can be detected on panoramic dental radiographs. Their early identification is clinically relevant, as they represent a potential marker for increased risk of stroke. In this context, the present study proposes a deep learning method for automatic identification of carotid atheromas using MobileNetV2. From a publicly available dataset, 378 region-of-interest (ROI) images (640 × 320) were prepared and split into train/val/test = 264/57/57 with class counts train 157/107, val 34/23, test 34/23 (negatives/positives). Images underwent standardized preprocessing and on-the-fly augmentation; training used a two-stage scheme (backbone frozen “head” training followed by partial fine-tuning of the top layers), class-weighting, dropout = 0.3, batch normalization (BN) head, early stopping, and partial unfreezing (~70% of the backbone). The decision threshold was selected on validation by Youden’s J. On the independent test set, the model achieved an accuracy (ACC) of 94.7%, sensitivity (SEN) of 95,7%, specificity (SPE) of 0.941, area under the receiver operating characteristic curve (AUC) 0.963, and area under the precision–recall curve (AUPRC) of 0.968. Using a sensitivity-targeted threshold (SEN ≈ 0.80), the model yielded ACC = 91.2%, SEN = 82.6%, and SPE = 97.1%. These results support panoramic radiographs as an opportunistic screening modality for systemic vascular risk and highlight the potential of artificial intelligence (AI)-assisted methods to enable earlier identification within preventive healthcare. Full article

This paper addresses the problem of imbalance between coastal and inland areas and recognises the reuse of abandoned buildings as an evolutionary regeneration strategy which, through specific interventions linked by a system of routes for tourism and sport, can gradually trigger sustainable development on a regional scale. It presents research conducted in recent years on behalf of local administrations and continued in national and European projects. The reference context is the Abruzzo region, where coastal, hilly and mountainous areas are a short distance apart and include both densely built-up and populated urban centres and small depopulated towns surrounded by landscapes of high environmental value. The objective is to define, through the responsible use of built resources, viable and sustainable strategies for regeneration and rebalancing oriented towards the concept of transformative resilience. The methodology adopted is divided into phases and includes both theoretical developments and case study applications according to an approach that networks building restoration and reuse interventions in the region. The key results consist of defining a reuse logic that considers the regional territory as a whole, linking different resources, functions and environments. This logic, which envisages the organisation of new functions on a regional scale, emphasises the capacity of building reuse to produce positive effects on the territory and trigger socio-economic development dynamics. This research forms part of the experience underlying a project of significant national interest (PRIN 2022 TRIALs), which will provide guidelines for activating the transformative resilience capacities of inland areas of central Italy. Full article

Automated livestock monitoring in wide-area grasslands is a critical component of smart agriculture development. Devices such as Unmanned Aerial Vehicles (UAVs), remote sensing, and high-mounted cameras provide unique monitoring perspectives for this purpose. The high-resolution images they capture cover vast grassland backgrounds, where targets often appear as small, distant objects and are extremely unevenly distributed. Applying standard detectors directly to such images yields poor results and extremely high miss rates. To improve the detection accuracy of small targets in high-resolution images, methods represented by Slicing Aided Hyper Inference (SAHI) have been widely adopted. However, in specific scenarios, SAHI’s drawbacks are dramatically amplified. Its strategy of uniform global slicing divides each original image into a fixed number of sub-images, many of which may be pure background (negative samples) containing no targets. This results in a significant waste of computational resources and a precipitous drop in inference speed, falling far short of practical application requirements. To resolve this conflict between accuracy and efficiency, this paper proposes an efficient detection framework named CAMS-AI (Clustering and Adaptive Multi-level Slicing for Aided Inference). CAMS-AI adopts a “coarse-to-fine” intelligent focusing strategy: First, a Region Proposal Network (RPN) is used to rapidly locate all potential target areas. Next, a clustering algorithm is employed to generate precise Regions of Interest (ROIs), effectively focusing computational resources on target-dense areas. Finally, an innovative multi-level slicing strategy and a high-precision model are applied only to these high-quality ROIs for fine-grained detection. Experimental results demonstrate that the CAMS-AI framework achieves a mean Average Precision (mAP) comparable to SAHI while significantly increasing inference speed. Taking the RT-DETR detector as an example, while achieving 96% of the mAP_50–95 accuracy level of the SAHI method, CAMS-AI’s end-to-end frames per second (FPS) is 10.3 times that of SAHI, showcasing its immense application potential in real-world, high-resolution monitoring scenarios. Full article

In the fields of geographic information science and location-based services, the fusion of multisource Point-of-Interest (POI) data is of remarkable importance but faces several challenges. Existing matching methods, including those based on single non-spatial attributes, single spatial geometric features, and traditional hybrid methods with fixed rules, suffer from limitations such as reliance on a single feature and inadequate consideration of spatial context. This study takes Dongcheng District, Beijing, as the research area and proposes a POI-matching method based on multi-feature value calculation and a deep neural network (DNN) model. The method comprehensively incorporates multidimensional features such as names, addresses, and spatial distances. Additionally, the approach also incorporates an improved multilevel name association strategy, an address similarity calculation using weighted edit distance, and a spatial distance model that accounts for spatial density and regional functional types. Furthermore, the method utilizes a deep learning model to automatically learn POI entity features and optimize the matching rules. Experimental results show that the precision, recall, and F1 value of the proposed method achieved 97.2%, 97.0%, and 0.971, respectively, notably outperforming traditional methods. Overall, this method provides an efficient and reliable solution for geospatial data integration and POI applications, and offers strong support for GIS optimization, smart city construction, and scientific urban/town planning. However, this method still has room for improvement in terms of data source quality and algorithm optimization. Full article

Objectives: Post-extraction remodelling of hard and soft tissues results in volume reduction, leading to aesthetic challenges in planning prosthetic restorations, particularly in the anterior maxilla. This study assessed whether atraumatic vertical extraction, versus conventional extraction, could reduce postoperative volume loss and aesthetic compromises at the extraction site and adjacent teeth. Methods: Following randomized tooth extraction with unassisted healing in the test (Benex^® extraction, n = 10) and control group (conventional extraction, n = 10), postoperative scans were conducted at 30 days (t₁), 60 days (t₂), 90 days (t₃) and 12 months (t₄). Each scan was aligned with the baseline scan (t₀), and surface comparison was performed with five regions of interest (ROIs: central, mesial, distal, papilla mesial and papilla distal). Aesthetic parameters, including recession and Pink Esthetic Score (PES) of adjacent teeth, were clinically evaluated at each follow-up appointment. Statistical analysis used a mixed linear model accounting for confounding factors such as smoking, buccal bone integrity, gingival phenotype, and provisional use. Results: Both groups showed significant volume reduction from baseline to t₃ and t₄. The largest volume loss occurred in the central ROI in both test (t₄: −65.34 ± 36.89 mm³) and control group (t₄: −70.85 ± 30.96 mm³), with no significant difference between groups. A decline in PES and recession at the adjacent teeth was noted in both groups at 12 months. Conclusions: Both groups showed significant volume reduction with aesthetic impairment at the adjacent teeth’s soft tissue. Full article

Lightweight, lead-free polymer–mineral composites have attracted increasing interest as radiation-attenuating materials for applications where reduced mass and environmental compatibility are required. In this work, the $\gamma$-ray attenuation behavior of poly(ether ether ketone) (PEEK) reinforced with natural palygorskite and pumice was evaluated at filler concentrations of 10–40 wt%. Photon interaction parameters, including the linear attenuation coefficient ($\mu$), half-value layer (HVL), mean free path ($\lambda$), and effective atomic number ($Z_{\mathrm{eff}}$), were computed over the energy range 15 keV–15 MeV using the Phy-X/PSD platform and validated through full Geant4 Monte Carlo transmission simulations. At 15 keV, $\mu$ increased from $1.46{\mathrm{cm}}^{-1}$ for pure PEEK to $4.21{\mathrm{cm}}^{-1}$ and $8.499{\mathrm{cm}}^{-1}$ for the 40 wt% palygorskite- and pumice-filled composites, respectively, reducing the HVL from 0.69 cm to 0.24 cm and 0.11 cm. The corresponding $Z_{\mathrm{eff}}$ values increased from 6.5 (pure PEEK) to 9.4 (40 wt% palygorskite) and 15.3 (40 wt% pumice), reflecting the influence of higher-Z oxide constituents in pumice. At higher photon energies, the attenuation curves converged as Compton scattering became dominant, although pumice-filled PEEK retained marginally higher $\mu$ and shorter $\lambda$ up to the MeV region. These findings demonstrate that natural mineral fillers can enhance the photon attenuation behavior of PEEK while retaining the known thermal stability and mechanical performance of the polymer matrix as reported in the literature, indicating their potential use as lightweight, secondary radiation-attenuating components in medical, industrial, and aerospace applications. Full article

Power-equipment fault detection is challenging in real-world inspections due to subtle defect cues and cluttered backgrounds. This paper proposes an improved YOLOv12-based framework for multi-class power defect detection. We introduce a Prior-Guided Region Attention (PG-RA) module and design a Lightweight Residual Efficient Layer Aggregation Network (LR-RELAN). In addition, we develop a Dual-Spectrum Adaptive Fusion Loss (DSAF Loss) function to jointly improve classification confidence and bounding box regression consistency, enabling more robust learning under complex scenes. To support defect-oriented visual analytics and system interpretability, the framework further constructs Region of Interest (ROI) pseudo point clouds from detection outputs and compares two denoising strategies, Statistical Outlier Removal (SOR) and Radius Outlier Removal (ROR). A Python-based graphical prototype integrates image import, defect detection, ROI pseudo point cloud construction, denoising, 3D visualization, and result archiving into a unified workflow. Experimental results demonstrate that the proposed method improves detection accuracy and robustness while maintaining real-time performance, and the ROI pseudo point cloud module provides an intuitive auxiliary view for defect-structure inspection in practical applications. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives. Full article

Contrary to states such as Egypt or Morocco, the Jordanian state could not rely on long-lasting Islamic tradition and institutions at its creation and was exposed to the religious influence of its neighbours. The regime had to “invent” a Jordanian religious tradition, making Jordan a particularly interesting case for the study of the development of Islamic policies—central to a regime that significantly relies on religious legitimacy. This contribution based on fieldwork in Amman dives into the nationalisation of the Islamic education of Jordanian imams and preachers as a component of official Islam. It argues that the nationalisation of higher Islamic education is a crucial element of state control over religion and is inscribed in the regional competition over religious influence as much as in international considerations. It contributes to improving our understanding of the entanglement of the domestic promotion of official Islam and regional fight for religious influence. Full article

Automated detection of impact craters is essential for planetary surface studies, yet it remains a challenging task due to variable morphology, degraded rims, complex geological settings, and inconsistent illumination conditions. This study presents a novel crater detection methodology designed for large-scale analysis of Lunar Reconnaissance Orbiter Wide-Angle Camera (WAC) imagery. The framework integrates several key components: automatic region-of-interest (ROI) selection to constrain the search space, Canny edge detection to enhance crater rims while suppressing background noise, and a modified Hough transform that efficiently localizes elliptical features by restricting votes to edge points validated through local fitting. Candidate ellipses are then refined through a two-stage adjustment, beginning with L1-norm fitting to suppress the influence of outliers and fragmented edges, followed by least-squares optimization to improve geometric accuracy and stability. The methodology was tested on four representative Wide-Angle Camera (WAC) sites selected to cover a range of crater sizes (between ~1 km and 50 km), shapes, and geological contexts. The results showed detection rates between 82% and 91% of manually identified craters, with an overall mean of 87%. Covariance analysis confirmed significant reductions in parameter uncertainties after refinement, with standard deviations for center coordinates, shape parameters, and orientation consistently decreasing from the L1 to the L2 stage. These findings highlight the effectiveness and computational efficiency of the proposed approach, providing a reliable tool for automated crater detection, lunar morphology studies, and future applications to other planetary datasets. Full article

This study classifies corporate financial performance in countries in the Middle East and North Africa (MENA) region, addressing the critical need for accurate and early identification of high-, moderate-, and low-performance companies. The selection of the MENA region was driven by its significant economic growth, diverse market structures, and increasing attractiveness for foreign investment, which makes accurate financial performance assessment important. Despite the growing interest in AI applications for corporate financial performance, a research gap still persists. Existing studies focus primarily on bankruptcy and financial distress prediction in developed countries, with rather limited studies on multi-class financial performance classification in the MENA region. This study addresses a significant gap in the corporate financial performance evaluation literature, which is the lack of a robust, comparative evaluation of advanced DL techniques against conventional ML methods for multi-class corporate financial performance prediction using high-dimensional data. This study employs a design science research (DSR) approach by developing an evaluation analytics artifact that integrates structured preprocessing, dimensionality reduction, and comparative ML and DL modeling, following the relevance, design, and rigor cycles. By employing a design science research (DSR) methodology, the research used a dataset from the Compustat database, comprising 7971 firm-year observations from 2013 to 2024. A rigorous dimensionality reduction process, including pairwise correlation filtering, resulted in a final set of 15 key classification features. The study compared three machine learning techniques—random forests (RFs), support vector machines (SVMs), and eXtreme Gradient Boosting (XGBoost), against one deep learning technique, deep neural networks (DNNs), for classifying the corporate financial performance of MENA-region companies. The models were trained to classify corporations into three performance classes (low, moderate, and high), using the earnings per share (EPS) as the target variable. The empirical findings indicate that all four machine learning algorithms achieved meaningful predictive performance in classifying EPS-based corporate performance. Among the benchmark models, the support vector machine (SVM) and random forest (RF) classifiers produced stable and competitive results, indicating strong generalization capabilities across firms and periods. XGBoost consistently outperformed the traditional machine learning models, delivering the highest overall classification accuracy and superior discriminatory power, highlighting its effectiveness in capturing nonlinear relationships and complex feature interactions. Similarly, the deep neural network further improved classification performance relative to the benchmark models and exhibited comparable results to XGBoost, especially in modeling high-dimensional data. This superior performance can substantially enhance earnings performance classification through early performance deterioration and improvement identification, allowing more proactive strategic and operational decisions. Full article

Japan’s ongoing socio-spatial transformation has led to the decline of rural cultures and traditional rural landscapes (TRLs), necessitating alternative approaches to revitalize rural communities. Reinforcing inter-regional urban–rural connections could offer a path to rural revitalization and sustainable regional resilience. This study investigated such perspectives through a large-scale survey (n = 1704) and statistically analyzed the perceptual differences of residents across residential contexts regarding their cultural knowledge, daily practices, consumption preferences, and landscape recognition, represented by traditional tea culture in Shizuoka Prefecture, Japan. Results demonstrated significant residential-context-based differences. Although rural residents showed the deepest understanding and recognition of tea culture and landscapes, they failed to express such perceptual knowledge with confidence. By contrast, suburban residents presented moderate familiarity without deep understanding. Urban residents relied greatly on symbolic representations of rural culture and landscapes, but without distinct recognition. Although all groups showed high levels of interest in tea culture, they generally presented a lack of deep understanding regarding Zairai tea fields, a representative TRL in the region, indicating both its physical decline due to agricultural modernization and its diminishing cultural visibility. Overall, the findings of this study highlight the differentiated perceptions shaped by different residential contexts. By clarifying both perceptual commonalities and divergences that exist among these residential groups, this study provides a new perspective on reconstructing culturally rooted urban–rural connections to contribute to the revitalization of rural communities, culture, and the conservation of TRLs in the region. Full article

Recent years have shown that additive manufacturing is able to significantly increase the potential for enhancing lightweight structural design. In particular, strut-based lattices have attracted considerable research interest due to their promising mechanical performance in lightweight engineering applications. While the quasi-static properties of such lattices are relatively well established, their fatigue behavior remains insufficiently understood. This work presents a numerical investigation of the fatigue life of laser powder bed-fused strut-based lattices using the finite element method (FEM). Periodic AlSi10Mg lattice structures with two different unit cells, bcc and $f_{2}c{c}_z$, and three different aspect ratios were analyzed under uniaxial compression–compression loading. The stress-life approach was used to model the fatigue failure of the representative unit cells in the high-cycle fatigue region. The numerical predictions were compared with experimental results, showing good agreement between simulations and physical tests. The findings highlighted that the fatigue response was primarily governed by aspect ratio, unit cell topology, bulk material properties, and mean stress imposed by the load ratio. Moreover, stress concentrations arising from notch effects in the nodal regions were identified as critical fatigue crack initiation sites. Full article

Groundwater–surface water interactions were investigated in the Highwood River (3952 km²) and Sheep River watersheds (1568 km²), originating in the Rocky Mountains headwaters of the South Saskatchewan River (Alberta, Canada), to improve understanding of hydrological processes that potentially influence water use and vulnerability to climatic change in representative, alpine-fed mixed-use watersheds. Similar to adjacent regions of the Bow, Red Deer and Oldman watersheds, the upper reaches of these watersheds are sparsely populated with significant seasonal glacier and snowmelt influence, while the lower watersheds are currently under increasing water supply pressure from competing agricultural–municipal interests, with notable risk of flooding during high-flow events and drought during the growing season. Investigations included mapping of hydrologic and hydrogeologic controls (aquifers, buried channels, colluvial deposits, etc.,) and synoptic geochemical and isotopic surveys (δ²H, δ¹⁸O, δ¹³C-DIC, ²²²Rn) to characterize evolution in water type and seasonal progression in streamflow sources and underlying mechanisms. Our findings confirm seasonal progression in streamflow water sources, characterized by a pronounced snowmelt-dominated spring freshet, but with a sustained recession fed by colluvial, moraine, fluvial, and fractured bedrock sources. Seasonal isotopic variations establish that shallow groundwater sources are actively maintained throughout the spring freshet, often accounting for a dominant portion of streamflow, which indicates active displacement of groundwater storage by snowmelt recharge during spring melt. The contrast in the proportion of alpine contributions in each watershed suggests these systems may respond very differently to climate change, which needs to be carefully considered in developing sustainable water-use strategies for each watershed. Full article