Search Results (450)

Background and Objectives: Hereditary angioedema (HAE) represents a specific form of life-threatening inborn errors of immunity. Current guidelines recommend regular assessment of the disease burden, disease control and quality of life. This study describes the profile of HAE patients in Slovakia, disease control, quality of life, states of anxiety and depression, and socioeconomic situation. Materials and Methods: We used a set of standardized questionnaires—AE-QoL, AECT, HADS and Socioeconomic Status Questionnaire, and a non-standardized questionnaire—to describe the characteristics of the population. Results: We collected data on 56.44% (57 out of 101) of HAE adult patients registered in Slovakia. Moderate to severe HAE was present in 61.40% of patients; 73.68% were on long-term prophylactic treatment; and 19.30% received rescue treatment due to an acute HAE attack during the last 4 weeks. Most patients achieved lower AE-QoL scores, indicating a good quality of life. The AECT score indicated well-controlled disease in 91.23% of patients. Anxiety and/or depression scores were higher than normal in 17.54% of patients. Patients with HAE earned less than the average population, but most of them were economically active with relatively low rates of presenteeism and absenteeism. Only a minority of patients used social system benefits. Patients were exclusively cared for by relatives. Conclusions: The QoL scores achieved in all three standardized questionnaires indicate a good quality of life of HAE patients in Slovakia, which is associated with a high and specialized standard of care. Anxiety and/or depression were present in 17.54% of patients. Direct patients costs and social care costs are low, but there is an indirect socioeconomic burden on patients and their families. Full article

Background: Liberia modernized vaccination data systems in 2023–2025 by piloting a District Health Information System (DHIS2)-based Digital Vaccination Registry (Electronic Immunization Registry, EIR) to address the limitations of paper-based workflows and of a proprietary COVID-19 electronic platform (offline gaps, lack of unique identifiers, performance issues and cost). Objective: To assess a pilot platform by evaluating training, registry use and device management, utility for routine immunization, vaccine logistics and Adverse Events Following Immunization (AEFI) data, and routine immunization data quality in the DHIS2 mobile application compared with paper registers. Methods: Using the Public Health Informatics Institute’s Collaborative Requirements Development Methodology, stakeholders defined requirements, trained users and implemented a pilot. Mixed methods were used; a mini data audit was performed, and qualitative data were collected across 19 facilities in Montserrado, Gbarpolu and Grand Bassa. Seventy-eight health workers were trained to use the DHIS2 mobile application. Results: The future state design replaces paper aggregation steps with real-time mobile entry to a national registry and dashboard. Dual entry persisted during high-volume periods. The mini data audit found discrepancies between facility paper registers and DHIS2-EIR entries for child enrollment data and, Bacillus Calmette Guérin and Diphtheria–Pertussis–Tetanus dose administration records Participants attributed these discrepancies to internet and device problems and challenges navigating the system. Participants requested a training manual, improved connectivity at point of service, integration with supportive supervision, additional staff and system features (field to record hospital number, automated next visit date, and vaccination status prompts). Conclusions: Lessons from the pilot will inform country-wide implementation, including planned linkage with electronic birth and death registration to enable a unique child identifier and reduce manual errors and delays. Full article

To address the existing challenges in mold slag thickness measurement—such as the susceptibility of contact sensors to high-temperature degradation and the limitation of non-contact methods to detecting only the upper slag surface—this study proposes an integrated approach that fuses millimeter-wave radar and eddy current sensors for measuring mold slag thickness in a continuous casting mold. The method innovatively combines two sensing principles: the millimeter-wave radar employs an improved FFT-CZT² high-precision ranging algorithm to perform high-resolution scanning of the solid slag upper surface, reconstructing its topography (error: $\pm$1 mm), while Mel-frequency cepstral coefficients (MFCC) are applied to extract features from the radar intermediate-frequency signals, combined with an enhanced PSO-BP neural network algorithm to predict the thickness of the solid slag layer (error: $\pm$5 mm). Concurrently, an eddy current sensor monitors the liquid slag–molten steel interface position (error: $\pm$1 mm). Through dual-sensor data fusion, the upper surface topography data and solid slag thickness obtained from the radar are spatially registered in three dimensions with the molten steel level information derived from the eddy current sensor. This integration ultimately enables the non-contact synchronous measurement of three key parameters within the mold: solid slag layer thickness, liquid slag layer thickness inversion, and molten steel level. Furthermore, by reconstructing the upper slag surface morphology, the method successfully resolves practical issues such as uneven material distribution, local material deficiency, or excessive feeding. Preliminary experimental verification confirms that the proposed method maintains stable performance even under high-temperature and complex environmental conditions. It thus provides a real-time, accurate, and full-cross-section monitoring solution for mold slag in continuous casting, offering significant practical value for the development of smart steel plants. Full article

Background and Objectives: The authors aimed to describe their experience performing frameless stereotactic biopsies using an Autoguide Robotic Platform and to compare the outcomes with a frame-based stereotactic technique. We would like to emphasize the importance of this study, as it is the first in the literature to use sodium fluorescein for confirmation in intracranial biopsies taken with a Stealth Autoguide Robotic Platform. Materials and Methods: We retrospectively evaluated 30 patients who underwent a stereotactic intracranial biopsy between June 2018 and March 2024. The patients were divided into two groups: The robotic biopsy group (n = 15) underwent a frameless image-guided stereotactic intracranial biopsy with a Stealth Autoguide Robotic Platform and optical neuronavigation system (Stealth-Station S8, Medtronic, Minneapolis, MN, USA) using intraoperative sodium fluorescein. The frame-based (Integra, CRW, New Jersey, USA) stereotactic biopsy group (n = 15) underwent a stereotactic biopsy with the use of a stereotactic planning system (Atlas Integra Software, NJ, USA and Brainlab AG, Munich, Germany) without sodium fluorescein. Preoperative MRI and CT scans were performed in all the patients. Their external cranial anatomy was registered using either facial tracing or O-Arm (Medtronic Sofamor Danek, Inc., Memphis, TN, USA). Results: The robotic biopsy group demonstrated a diagnostic yield of 93.3% (14/15), while the frame-based group achieved 100% (15/15), with no significant difference (p = 0.609). The mean calculated tip error in the robotic biopsy group was 0.42 ± 0.19 mm (range: 0.1–0.7 mm) and the postoperative targeting accuracy in the frame-based biopsy group was 0.51 ± 0.23 mm (range: 0.2–0.9 mm), with no significant difference (p = 0.287). The robotic biopsy group demonstrated a significantly shorter mean surgical time (40.26 ± 6.13 vs. 52.47 ± 8.92 min, p = 0.002). Conclusions: Both frame-based and robotic-assisted stereotactic biopsy techniques achieve comparable diagnostic accuracy and targeting precision. However, a robotic biopsy significantly reduces the surgical time compared to a frame-based technique. The use of intraoperative sodium fluorescein is a valuable adjunct method for confirming that biopsy specimens are obtained from the intended target site. Full article

Background: Accurate dietary assessment is vital for preventing malnutrition in aging populations, particularly in home-care settings. Although Large Multimodal Models (LMMs) for nutrient estimation are evolving, their nutrient-specific accuracy requires rigorous validation. Methods: Fifteen standardized hospital meals were photographed under controlled conditions (90-degree angle, 500 lux). Ground truth values were determined by direct weighing. Estimates for energy and macronutrients were performed by 10 registered dietitians (RDs) and 10 AI models (including ChatGPT-4o and Gemini 1.5 Pro). Accuracy was assessed using Pearson’s correlation, Mean Absolute Error (MAE), and Bland–Altman analysis to quantify systematic bias. Results: For energy and carbohydrates, RDs and top-performing AI models (notably ChatGPT-4o and Gemini 1.5 Pro) demonstrated practical accuracy (r > 0.8, frequently within ±10% range). However, accuracy for protein and lipids was significantly lower across all AI models. Specifically, all AI models exhibited a substantial systematic overestimation of lipids (Mean Bias > +20%, p < 0.01), highlighting a critical “invisible nutrient” bias. Conclusions: Current AI tools show potential for caloric and carbohydrate monitoring but struggle with lipid and protein density. These findings emphasize the need for human–AI collaboration (“human-in-the-loop”) and the integration of cooking metadata to improve clinical utility in geriatric nutrition. Full article

Targeting resource-constrained Internet of Things (IoT) devices, this paper proposes Stuck-at-Fault Physical Unclonable Function (SAF-PUF), a lightweight Resistive Random-Access Memory (RRAM)-based PUF that exploits the intrinsic addresses of manufacturing-induced SAF defects as a stable entropy source. By using the coordinates of Stuck-at-1 (SA1) cells to seed a 32-bit Linear Feedback Shift Register (LFSR), SAF-PUF generates robust, variable-length responses with zero Bit Error Rate (BER) across a wide temperature range from −40 °C to 125 °C, without any error-correction circuitry. Experimental results based on 100,000 Challenge–Response Pairs (CRPs) demonstrate strong resilience against machine learning (ML) attacks, with prediction accuracies of logistic regression (LR), support vector machines (SVM), neural networks (NN) and convolutional neural networks (CNNs) remaining close to 50%. Moreover, a “use-then-conceal” mechanism is introduced to enhance post-authentication security, enabling response obfuscation with minimal cell reconfiguration. These features make SAF-PUF a high-security, low-overhead hardware root of trust suitable for IoT applications. Full article

Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification. Full article

This paper presents a register-transfer-level (RTL) fault injection study of the LEON3 processor’s internal memory subsystem under single-event upsets (SEUs). The analysis targets four key components: the instruction cache (I-cache), data cache (D-cache), AHB bus control interface, and memory controller (MCTRL), all of which are unprotected in the standard LEON3 configuration. Using the NETFI+ fault injection framework, multi-cycle SEUs are injected into sequential elements across these blocks while executing a memory-intensive benchmark. The results show that the AHB interface is extremely fragile, with every fault causing execution failure. The memory controller, though architecturally invisible, frequently induces precise SPARC V8 traps such as window overflow and illegal instruction through indirect data-path corruption. The data cache is identified as the primary source of silent data corruption (SDC), while the instruction cache exhibits partial natural masking but remains susceptible to control-flow errors. These findings highlight the disproportionate impact of unprotected protocol and controller logic on system reliability and inform targeted hardening strategies for LEON3-based embedded systems in radiation-prone environments. Full article

Surveying and register administration are core to land administration, and accordingly, land surveying and registration are essential to socio-economic development due to their potential accuracy and efficiency. Until now, customary land surveying and registration have relied on human input, which is a situation that undermines efficiency and is prone to errors in data handling. During the last decade, the exponential growth in artificial intelligence (AI), in particular, geospatial artificial intelligence (GeoAI), has provided new methodologies that can overcome these deficiencies. This review examines AI in cadastral management by analyzing technical solutions and trends across three areas including data collection, modeling, and common applications. This review aims to provide a comprehensive survey of the current use of AI in cadastral management to the extent of defining a future research avenue. Based on the comprehensive review of literature, this study has reached the following three conclusions. (1) Automated extraction of parcel boundaries has been achieved through deep learning in data collection and processing, removing the bottlenecks of manual interpretation. Models such as convolutional neural networks (CNNs) and Transformers have been used for pixel-level semantic segmentation of high-resolution remote sensing images, leading to significant improvements in efficiency and accuracy. (2) Non-spatial data have been processed with natural language processing techniques to automatically extract information and construct relationships, thus overcoming the limitations of paper-based archives and traditional relational databases. (3) Deep learning models have been applied to automatically detect parcel changes and to enable integrated analysis of spatial and non-spatial data, which has supported the transition of cadastral management from two-dimensional to three-dimensional. However, several challenges remain, including differences in multi-temporal data processing, spatial semantic ambiguity, and the lack of large-scale, high-quality annotated data. Future research can focus on improving model generalization, advancing cross-modal data fusion, and providing recommendations for the development of a reliable and practical intelligent cadastral system. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Recent advances in artificial intelligence have made power efficiency a primary objective in system design. In this context, stochastic computing (SC), which processes probabilistic bitstreams using simple logic, and spiking neural networks (SNNs), a neuromorphic paradigm, have gained prominence as alternative approaches. This study proposes a Stochastic Computing Neural Network (SC-NN) framework that minimizes the intrinsic errors of stochastic computing and leverages the isomorphism between one-count operations on bitstreams and spike-rate computations in spiking neural networks, yielding improvements in accuracy and hardware efficiency. In contrast to earlier studies that utilized independent random number sequences of 10 bits or higher, our study employed a practically implementable 8-bit linear feedback shift Register (LFSR)-based pseudo-random bitstream. Using 4 taps and 255 seeds improves the realism of the hardware. Despite the inherent accuracy ceiling of pseudo-random sequences, the proposed method achieves higher accuracy. Applied to an 8-bit SC-based neural network accelerator, the proposed design improves accuracy by 35% over a conventional FSM baseline, while reducing power and area by 43.8% and 17.2%, respectively, and decreasing delay by 5.5%. These improvements translate to a 2.3× enhancement in the Figure of Merit (FoM), which was further verified through physical layout and FPGA results. Overall, this work introduces a new paradigm that enables simultaneous gains in accuracy and efficiency for low-power AI by suppressing the error sources and embedding the structural similarity between SNNs and SC into the design. Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

Illustrated digital picture books are widely used for second-language reading and vocabulary growth. We ask how close current generative AI (GenAI) tools are to producing such books on demand for specific learners. Using the ChatGPT-based Learning And Reading (C-LARA) platform with GPT-5 for text/annotation and GPT-Image-1 for illustration, we ran three pilot studies. Study 1 used six AI-generated English books glossed into Chinese, French, and Ukrainian and evaluated them using page-level and whole-book Likert questionnaires completed by teachers and students. Study 2 created six English books targeted at low-intermediate East-Asian adults who had recently arrived in Adelaide and gathered student and teacher ratings. Study 3 piloted an individually tailored German mini-course for one anglophone learner, with judgements from the learner and two germanophone teachers. Images and Chinese glossing were consistently strong; French glossing was good but showed issues with gender agreement, register, and naturalness of phrasing; and Ukrainian glossing underperformed, with morphosyntax and idiom errors. Students rated tailored English texts positively, while teachers requested tighter briefs and curricular alignment. The German pilot was engaging and largely usable, with minor image-consistency and cultural-detail issues. We conclude that for well-supported language pairs (in particular, English–Chinese), the workflow is close to classroom/self-study usability, while other language pairs need improved multi-word expression handling and glossing. All resources are reproducible on the open-source platform. We adopt an interdisciplinary stance which combines aspects taken from computer science, linguistics, and language education. Full article

Terrestrial laser scanning (TLS) point clouds require high-precision registration and georeferencing to be used effectively. Only then can data from multiple stations be integrated and transformed from the instrument’s local coordinate system into a common, stable reference frame that ensures temporal consistency for further analyses of displacement and deformation. The article demonstrates the validation of an innovative referencing system devised to improve the reliability and accuracy of registering and georeferencing TLS point clouds. The primary component of the system is openable reference spheres, whose centroids can be directly and precisely determined using surveying methods. It also includes dedicated adapters: tripods and adjustable F-clamps with which the spheres can be securely mounted on various structural components, facilitating the optimal distribution of the reference markers. Laboratory tests with four modern laser scanners (Z+F Imager 5010C, Riegl VZ-400, Leica ScanStation P40, and Trimble TX8) revealed sub-millimetre accuracy of sphere fit and form errors, along with the sphere distance error within the acceptance threshold. This confirms that there are no significant systematic errors and that the system is fully compatible with various TLS technologies. The registration and georeferencing quality parameters demonstrate the system’s stability and repeatability. They were additionally verified with independent control points and geodetic levelling of the centres of the spheres. The system overcomes the critical limitations of traditional reference spheres because their centres can be measured directly using surveying methods. This facilitates registration and georeferencing accuracy on par with, or even better than, that of commercial targets. The proposed system serves as a stable and repeatable reference frame suitable for high-precision engineering applications, deformation monitoring, and longitudinal analyses. Full article

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools to improve dietary quality and social connection among community-dwelling older adults with frailty. Methods: A non-randomized, quasi-experimental study was conducted among 29 older adults (≥65 years) in Tokyo, Japan. Participants were assigned to the intervention (n = 11) or control (n = 18) group. The 3-month intervention included weekly photo uploads of meals via an AI-based dietary application providing automated image analysis and personalized feedback, supervised by registered dietitians, along with peer communication through a group chat. The primary outcome was dietary quality. The secondary outcomes included body weight, body mass index (BMI), skin carotenoid score, and loneliness. Results: The adjusted Japanese Food Guide Spinning Top Score at 3-month follow-up was 49.0 (standard error [SE] = 2.6) and 39.5 (SE = 2.0) in the intervention and control groups, respectively. The adjusted mean difference between groups was +9.5 (95% confidence interval: 2.3 to 16.7, p = 0.01). After using analysis of covariance for adjusting for respective baseline values, age, education status, and antihypertension drug use, no statistically significant between-group differences were observed at 3-month follow-up for any secondary outcomes. Conclusions: AI-based dietary intervention and peer communication effectively improved dietary quality among older adults, highlighting the potential of such an intervention to promote healthier eating habits in this population. Full article