Search Results (6,184)

The quality of rebar construction is a critical factor that significantly affects the structural stability of reinforced concrete structures. Various automated inspection technologies have been developed to overcome the limitations of conventional labor-intensive inspection methods. However, owing to the complex geometry of rebar arrangements and challenging site conditions, existing approaches still face difficulties in achieving the high accuracy and real-time performance required for practical applications. To address these limitations, this study proposes an automated rebar-spacing inspection technique based on color recognition of painted tie wires with the aim of improving the efficiency and accuracy of data recognition and processing. Field experimental results demonstrated that the use of fluorescent-green tie wires in the HSV color space minimized false detections and achieved a high average recognition rate of 92.6% with the identification of optimal threshold ranges. Furthermore, by utilizing tie-wire intersection coordinates, the stable identification of rebar arrangement conditions and reliable estimation of rebar spacing were achieved, even under conditions with missing data. The proposed automated inspection method can enable more efficient data acquisition and processing under complex construction site conditions while providing accurate and reliable inspection results. Full article

Background: Mediastinal paragangliomas (MPs) are rare, highly vascular neuroendocrine tumors whose surgical resection is the gold standard but carries a high risk of perioperative complications due to the MPs’ proximity to major cardiovascular structures with potential life-threatening hemorrhage. Due to their rarity, the literature consists primarily of case reports. Our systematic review aims to synthesize the evidence from the last two decades to propose a standardized, multidisciplinary approach to the diagnosis and surgical management of MPs. Methods: A systematic literature review was conducted from 2005 to 2025. Studies reporting on surgically resected adult mediastinal paragangliomas were included. Patient demographic data, diagnostic workup, surgical approaches, and outcomes were extracted and analyzed descriptively. Results: Analysis of 79 patients from 75 papers revealed a median age of 50 years (female predominance of 62%). Most tumors were in the middle mediastinum (51.9%). Sternotomy was the most common surgical approach (44.3%), with cardiopulmonary bypass utilized in 27.8% of cases. Postoperative complications occurred in 28% of patients, with left vocal cord palsy (12.7%) being most frequent. The median follow-up was 12 months. All percentages refer to the number of patients. Conclusions: Surgical removal is the standard of care for curative treatment of MPs. However, surgical treatment requires meticulous planning within a multidisciplinary team to reduce the risk of perioperative complications. The choice of surgical approach—open, minimally invasive, or with circulatory support—depends on tumor site, size, and vascular involvement. This review consolidates existing evidence of MPs’ surgical management, aiming to mitigate the significant risks associated with surgery. Lifelong follow-up is essential due to the potential for recurrence. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

This study examined the association between radio frequency (RF) data and ships in a congested area, with a focus on the Busan Port region in South Korea. RF datasets consisting of one L-band, four S-band, and two X-band frequencies were used in conjunction with Automatic Identification System (AIS) and Small Fishing Vessel Tracking System (V-Pass) data collected during the corresponding RF data periods. A distance-based association approach was applied, and the RF–ship association ratio (RSAR) was estimated as the ratio between the number of AIS-reported vessels associated with RF data and the total number of AIS-reported vessels present within the time period. The results indicate low overall RSAR in the congested region, with 6.5% for L-band, 1.7–24.6% for S-band, and 7.7–17.2% for X-band. Under stable high-pressure conditions (101.4–102.2 kPa) and light breeze conditions (0.9–3.6 m/s), atmospheric impacts on the RSAR can be considered minimal. Moreover, to indicate the relationship between the acquired RF signal and vessel congestion, a congestion index (CI) was derived from AIS and V-Pass data using a spatial grid-based method. The CI density maps within the congested region indicate that the acquired RF signals exist dominantly in low-congested areas. Full article

Background: Age-related macular degeneration (AMD) is a major cause of vision loss, yet publicly available Optical Coherence Tomography (OCT) datasets lack demographic diversity, particularly from South Asian populations. Existing datasets largely represent Western cohorts, limiting AI generalizability. Moreover, raw OCT volumes contain redundant spatial information and speckle noise, hindering efficient analysis. Methods: We introduce BanglaOCT2025, a retrospective dataset collected from the National Institute of Ophthalmology and Hospital (NIOH), Bangladesh, using Nidek RS-330 Duo 2 and RS-3000 Advance systems. We propose a novel preprocessing pipeline comprising two stages: (1) A constraint-based centroid minimization algorithm automatically localizes the foveal center and extracts a fixed 33-slice macular sub-volume, robust to retinal tilt and acquisition variability; and (2) A self-supervised volumetric denoising module based on a Flip-Flop Swin Transformer (FFSwin) backbone suppresses speckle noise without requiring paired clean reference data. Results: The dataset comprises 1585 OCT volumes (202,880 B-scans), including 857 expert-annotated cases (54 DryAMD, 61 WetAMD, and 742 NonAMD). Denoising quality was evaluated using reference-free volumetric metrics, paired statistical analysis, and blinded clinical review by a retinal specialist, confirming preservation of pathological biomarkers and absence of hallucination. Under a controlled paired evaluation using the same classifier with frozen weights, downstream AMD classification accuracy improved from 69.08% to 99.88%, interpreted as an upper-bound estimate of diagnostic signal recoverability rather than independent generalization. Conclusions: BanglaOCT2025 is the first clinically validated OCT dataset representing the Bengali population and establishes a reproducible fovea-centric volumetric preprocessing and restoration framework for AMD analysis, with future validation across independent and multi-centre test cohorts. Full article

Bridges are a crucial part of infrastructure, but many are in urgent need of maintenance. Digital methods like bim and Digital Twinning can support this process but depend on digital models that are often missing for existing structures. Automating the reconstruction of these models from existing documentation, such as construction drawings, is essential to accelerate digital adoption. Addressing a key step in the reconstruction process, this paper presents an end-to-end pipeline for extracting bridge cross-sections from drawings. First, the YOLOv8 network locates and classifies the cross-sections within the drawing. The results are then processed by the segmentation model sam, which generates pixel-wise masks without requiring task-specific training data. This eliminates the need for manual mask annotation and enables straightforward adaptation to different cross-section types, making the approach broadly applicable in practice. Finally, a global optimization algorithm fits parametric templates to the masks, minimizing a custom loss function to extract geometric parameters. The pipeline is evaluated on 33 real-world drawings and achieves a median parameter deviation of −2.2 cm and 2.4 cm, with an average standard deviation of 35.4 cm. Full article

Background/Objectives: Accurate hydration assessment is critical for optimizing performance and preventing heat-related complications in ultra-endurance athletes. This study evaluated the agreement and reliability between urine reagent strips and refractometry for field-based hydration assessment via urine-specific gravity (USG) in ultra-trail runners. Methods: Thirty-four ultra-trail runners (22 males, 12 females; mean age 43.71 ± 11.50 years) participated during The Coastal Challenge, a 241-km multi-stage ultra-trail competition. Urine samples were collected before and after the first two stages (Stage 1: 41 km, 1071 m elevation; Stage 2: 40 km, 1828 m elevation). USG was measured using semi-quantitative urine reagent strips (Combur10Test M) and a handheld digital refractometer (Palm Abbe™). Agreement was assessed via paired t-tests, Pearson and Spearman correlations, intraclass correlation coefficients, and Bland-Altman plots across four measurement time points. Results: Strong agreement existed between methods with correlation coefficients of 0.92–0.99 (p < 0.01) within the hydration range typical of well-prepared ultra-endurance athletes (USG 1.010–1.020). No significant differences were found between devices at any time point (all p > 0.05). Bland-Altman analyses revealed minimal mean bias (range: −0.002 to +0.001 g/mL) and narrow limits of agreement, with fewer than 5% of values falling outside limits. Both methods detected significant increases in USG from pre- to post-stage (p < 0.01), indicating exercise-induced hypohydration. Conclusions: Semi-quantitative urine reagent strips and handheld refractometers demonstrate strong agreement for hydration assessment in ultra-trail runners under field conditions when not severely hypohydrated, supporting their interchangeable use for practical monitoring. Full article

Advanced glycation end products (AGEs) are important biomarkers associated with diabetes and metabolic disorders; yet existing detection methods are invasive and unsuitable for frequent monitoring. This study aimed to develop a non-invasive and portable AGEs detection device, optimize strategies for mitigating pigmentation-related interference, and evaluate its feasibility for metabolic assessment. The proposed system employs a 365 nm ultraviolet LED excitation source, an optical filter assembly integrated into an ergonomic dark chamber, and an eyelid-signal-based algorithm to suppress ambient light and skin pigmentation interference. Simulation experiments were conducted to evaluate the influence of different pigment colors and skin tones on fluorescence measurements. A clinical study was performed in 200 participants, among whom 42 underwent concurrent serum AGEs measurement as the reference standard. Predictive models combining corneal fluorescence signals and body mass index (BMI) were constructed and evaluated. The results indicated that purple and blue pigments introduced greater interference, whereas green and pink pigments had minimal effects. Device-derived AGEs estimates demonstrated good agreement with serum AGEs, with a mean error below 8%. A hybrid model incorporating BMI achieved improved predictive accuracy compared with single-parameter models. Participants with high-AGE dietary habits exhibited elevated fluorescence signals and BMI. These findings suggest that the proposed device enables stable and accurate non-invasive AGEs assessment, with potential utility for metabolic monitoring. Incorporating lifestyle-related parameters may further enhance predictive performance and expand clinical applicability. Full article

Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments. Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns, we propose employing reinforcement learning (RL) to address dynamic decision-making challenges. However, traditional RL methods often struggle to adapt to significant market volatility, primarily by focusing on adjusting existing asset weights. Different from traditional RL methods, the multi-agent reinforcement learning correctable strategy (MAC) developed in this study detects and replaces potentially harmful assets with familiar alternatives, ensuring a resilient response to market crises. Utilizing the multi-agent reinforcement learning model, MAC empowers individual agents to maximize portfolio returns and minimize risk separately. During training, MAC strategically replaces assets to simulate market changes, allowing agents to learn risk-identification through uncertainty estimation. During testing, MAC detects potentially harmful assets and replaces them with more reliable alternatives, enhancing portfolio stability. Experiments conducted on a real-world US Exchange-Traded Fund (ETF) market dataset demonstrate MAC’s superiority over standard RL-based PM methods and other baselines, underscoring its practical efficacy for real-world applications. Full article

Carrots, rich in carotenoids and other bioactive compounds, are a promising raw material for value-added applications such as nutricosmetics. Nutricosmetics represent a rapidly expanding segment of the beauty and wellness industry, driven by rising consumer interest in natural ingredients and health-focused products. However, the use of carrots as bio-ingredients in nutricosmetics remains limited due to a disconnect between production systems, scientific research, and market expectations. This study integrates bibliometric, social media, consumer-survey, market-trend, and foreign-trade analyses to identify the key gaps hindering the valorization of carrots within this industry. A systematic literature analysis showed a strong emphasis on postharvest quality and bioactive characterization (approximately 70% of the dominant thematic focus), with minimal attention to commercialization or circular-economy frameworks (less than 5%). Social media results revealed that public discourse is dominated by culinary and gardening themes (around 82% of extracted mentions), with very limited awareness of cosmetic or wellness applications (below 10%). Google Trends demonstrated moderate global growth in interest in nutricosmetics (approximately 28% increase over the analyzed period), with higher activity concentrated in Spanish-speaking countries (about 63% of the top interest locations). Consumer surveys in Colombia (n = 191) indicated that 70.16% of respondents were unfamiliar with the term “nutricosmetics,” though 54.45% reported consuming such products once definitions were provided, revealing a latent market potential. Trade analysis highlighted Colombia’s dependence on high-value imported ingredients, despite existing capacity to export value-added goods. Together, these findings reveal structural gaps between research, industry, and consumer awareness, offering a roadmap for positioning carrots as a viable ingredient for value-added applications in the nutricosmetics sector. Full article

Reconfigurable intelligent surface (RIS) demonstrates significant potential in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) wireless communication systems. However, the introduction of RIS leads to a substantial number of parameters in the channel matrix, making channel estimation highly challenging. By exploiting the sparsity of mmWave channels, compressed sensing algorithms, such as the orthogonal matching pursuit (OMP) algorithm, can significantly reduce the pilot overhead. Nevertheless, traditional OMP algorithms typically require extensive prior knowledge about the number of effective paths, which is often difficult to obtain. To address this problem, we propose a novel multi-user joint correlation allocation (MUJCA) algorithm, which requires only minimal and easily measurable prior information. Our key idea is to divide the RIS coverage area into multiple sub-regions, each associated with a known number of scatterers, which is a pre-measured quantity, with users distributed within these sub-regions. Then, the MUJCA algorithm exploits joint correlation of multiple users to facilitate sparse channel recovery and transforms it back into the spatial channel. Simulation results show that the proposed MUJCA achieves higher channel estimation accuracy than existing benchmark algorithms. Full article

Polyurethane-based implantable devices (PUIDs) delivered via catheter are increasingly used in structural heart interventions; however, limited in vivo data exist regarding their long-term biostability and biological safety. This study evaluated a balloon-shaped implant made of Pellethane^®, a polyether-based polyurethane, designed as a three-dimensional intracardiac spacer and deployed via percutaneous femoral vein access. The device was chronically positioned adjacent to the tricuspid valve annulus in seven pigs for 24 weeks. Explanted devices and surrounding tissues were evaluated through material characterization (SEM, GPC, FT-IR, and ¹H-NMR) and histological analysis. SEM and FT-IR confirmed preserved surface morphology and chemical bonds, GPC showed stable molecular weight, and ¹H-NMR revealed intact urethane and ether linkages. Materials characterization revealed no evidence of hydrolytic or oxidative degradation, indicating structural stability of the devices. Histological analysis showed stable device positioning with minimal thrombosis or inflammatory response. Biocompatibility was confirmed via ISO 10993-1:2018 Standard (International Organization for Standardization (ISO): Geneva, Switzerland, 2018), and extractable substances were evaluated under exhaustive extraction conditions specified by ISO 10993-18:2020 (International Organization for Standardization (ISO): Geneva, Switzerland, 2020), with no toxicologically significant findings. These findings support the long-term biostability and biological safety of the PUIDs in dynamic cardiac environments, informing future design criteria for catheter-delivered cardiovascular devices. Full article

Trust in machine-learning models is critical for deployment by users, especially for high-risk tasks such as healthcare. Model trust involves much more than performance metrics such as accuracy, precision, or recall. It includes user readiness to allow a model to make decisions. Model trust is a multifaceted concept commonly associated with the stability of model predictions under variations in training data, noise, algorithmic parameters, and model explanations. This paper extends existing model trust concepts by introducing a novel Model Sureness measure. Some alternatively purposed Model Sureness measures have been proposed. Here, Model Sureness quantitatively measures the model accuracy stability under training data variations. For any model, this is carried out by combining the proposed Bidirectional Active Processing and Visual Knowledge Discovery. The proposed Bidirectional Active Processing method iteratively retrains a model on varied training data until a user-defined stopping criterion is met; in this work, this criterion is set to a 95% accuracy when the model is evaluated on the test data. This process further finds a minimal sufficient training dataset required for a model to satisfy this criterion. Accordingly, the proposed Model Sureness measure is defined as the ratio of the number of unnecessary cases to all cases in the training data along with variations of these ratios. Higher ratios indicate a greater Model Sureness under this measure, while trust in a model is ultimately a human decision based on multiple measures. Case studies conducted on three benchmark datasets from biology, medicine, and handwritten digit recognition demonstrate a well-preserved model accuracy with Model Sureness scores that reflect the capabilities of the evaluated models. Specifically, unnecessary case removal ranged from 20% to 80%, with an average reduction of approximately 50% of the training data. Full article

The exponential growth of the digital economy has transformed data centers into major energy consumers, yet their inflexible power consumption patterns and substantial waste heat generation pose significant challenges to grid stability and carbon neutrality targets. Existing energy management strategies often overlook the deep coupling potential between computing workload flexibility, thermal dynamics, and carbon trading mechanisms, leading to suboptimal resource utilization. To address these issues, this study proposes a collaborative low-carbon economic scheduling strategy for data center microgrids. A multiple-dimensional coupling framework is established, integrating a queuing theory-based model for delay-tolerant workload shifting and a heat-determined computing mechanism for active waste heat recovery (WHR). Furthermore, a mixed-integer linear programming (MILP) model is formulated, incorporating a linearized tiered carbon trading mechanism to facilitate source–load coordination. Simulation results demonstrate that the proposed strategy achieves a dual optimization of economic and environmental benefits, reducing total operating costs by 11.7% while minimizing carbon emissions to 6879 kg compared to baseline scenarios. Additionally, by leveraging temperature aware load migration, the daily weighted power usage effectiveness (PUE) is optimized to 1.2607. These findings quantify the marginal benefits of load flexibility under tiered pricing, providing insights for operators to balance service timeliness and energy efficiency in next generation green computing infrastructure. Full article

Potential tropospheric noise is a critical factor that undermines the effectiveness of deformation monitoring in Synthetic Aperture Radar Interferometry (InSAR) technologies. In most scenarios, many point targets within the InSAR deformation monitoring area either do not undergo deformation or exhibit only minimal deformation trends. The phases of densely distributed stable points can effectively respond to spatial tropospheric delays, particularly turbulent atmospheric phases. This study proposes a data-driven InSAR atmospheric correction method by exploring how to use these densely stable InSAR time series to model atmospheric phase delays. Our focus is on selecting stable InSAR time series point targets and evaluating the impact of different densities of stable points on atmospheric correction performance. Analysis of 645 interferograms derived from 217 Sentinel-1A SAR images, spanning from 13 June 2017 to 15 November 2024, demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 70%, 59%, and 69% compared to the terrain-related linear approach, the General Atmospheric Correction Online Service, and common scene stacking methods, respectively. In addition, simulation data and leveling data were used to validate the proposed method. This article does not develop an independent InSAR atmospheric correction method. Instead, the proposed approach starts with the InSAR deformation time series, allowing for easy integration into existing InSAR workflows and widely used atmospheric correction strategies. It can serve as a post-processing tool to improve InSAR time series analysis. Full article