Search Results (1,409)

The SCANVIR^® project is a regional initiative aimed at accelerating the elimination of hepatitis C virus (HCV) by reaching high-risk populations outside traditional healthcare settings. Launched in 2017 in Limoges and later expanded to Poitiers and Bordeaux, the project organized dedicated screening and treatment days in 43 facilities taking care of intravenous drug users, migrants, and prisoners in Nouvelle-Aquitaine. These events involved multidisciplinary teams and advanced diagnostic tools, including rapid tests for HCV, HBV, and HIV; FibroScan^® for liver assessment; and GeneXpert^® for on-site HCV RNA detection. Patients also received counseling on risk prevention, addiction, psychosocial support, and treatment when needed. Between 2017 and 2024, SCANVIR^® screened 1664 patients, with 98.9% accepting FibroScan^®. Anti-HCV antibodies were detected in 23.4% of participants, among whom 41.5% (N = 162) had a replicative profile. Of these, 83% initiated treatment and 80% were cured or were still undergoing therapy. FibroScan^® assessments showed advanced fibrosis in 17% of patients, severe fibrosis in 7.2%, and severe steatosis in 18%. By promoting a “Test, Treat, Prevent” strategy, SCANVIR^® proved cost-effective in diagnosing and treating individuals distant from care structures, highlighting the value of integrating education and prevention into liver disease screening. SCANVIR^® is an officially registered European trademark. Full article

The valorization of dredged sediments represents a major environmental and logistical challenge, particularly in the context of forthcoming regulations restricting their marine disposal. This study investigates the potential of untreated dredged sediments as sustainable raw materials for geopolymer binder development, with the dual objective of sustainable sediment management and reduction in cement-related environmental impact. Dredged sediments from the Grand Port Maritime de Bordeaux (GPMB) were activated with sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃), both alone and in combination, with supplementary aluminosilicate and calcium-rich co-products, to assess their reactivity and effect on binder performance. A multi-scale experimental approach combining mechanical testing, calorimetry, porosity analysis, Scanning Electron Microscopy and Energy-Dispersive Spectroscopy (SEM–EDS), X-ray diffraction (XRD), Thermogravimetric Analysis (TGA), and solid-state Nuclear Magnetic Resonance (NMR) was employed to challenge the commonly assumed inert behavior of sediments within geopolymer matrices, to elucidate gel formation mechanisms, and to optimize binder formulation. The results show that untreated sediments actively participate in alkali activation, reaching compressive strengths of up to 5.16 MPa at 90 days without thermal pre-treatment. Calcium-poor systems exhibited progressive long-term strength development associated with the formation of homogeneous aluminosilicate gels and refined microporosity, whereas calcium-rich systems showed higher early age strength but more limited long-term performance, linked to heterogeneous gel coexistence and increased total porosity. These findings provide direct evidence of the intrinsic reactivity of untreated dredged sediments and highlight the critical role of gel chemistry and calcium content in controlling long-term performance. The proposed approach offers a viable pathway for low-impact, on-site sediment valorization in civil engineering applications. Full article

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R² values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article

As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve a Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum, and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI), and in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37–39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in a wheat blast hotspot district, Meherpur, Bangladesh. Training 52 diverse stakeholders validated the kit’s field readiness, with 88% of trainees endorsing its user-friendly design. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site genomic surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as an early warning modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers. Full article

Greenhouse pesticide application often suffers from low droplet deposition uniformity and health risks to operators. A self-propelled overhead rail air-assisted sprayer has been designed. The mathematical model based on droplet movement and the DPM are used to analyze the equipment’s working principle. Deposition surfaces at 0.4, 0.5, 0.6, and 0.7 m were used to examine the effects of travel speed, external airflow, and spray angle on droplet deposition uniformity. Through one-way analysis of variance, all variables reached a significant level (p < 0.001). Simulation results identified the optimal operating parameters: travel speed of 0.3 m/s, external air-flow velocity of 0.3 m/s, and spray angle of 5°, resulting in droplet deposition densities of 719, 586, 700, and 839 droplets/cm², with a coefficient of variation of 14.57%. The sediment variation coefficients of both the on-site test results and the simulation results were within 10%, which proved the reliability of the numerical simulation. In conclusion, the device proposed in this study effectively enables targeted fog spraying for multi-layer crops in greenhouses, significantly improving pesticide utilization, reducing application costs, and minimizing environmental pollution. It offers reliable technical support for greenhouse pest control operations. Full article

The extraction of the light stripe center is a pivotal step in line-structured light vision measurement. This paper addresses a key challenge in the online measurement of train wheel treads, where the diverse and complex profile characteristics of the tread surface lead to uneven gray-level distribution and varying width features in the stripe image, ultimately degrading the accuracy of center extraction. To solve this problem, a region-adaptive multiscale method for light stripe center extraction is proposed. First, potential light stripe regions are identified and enhanced based on the gray-gradient features of the image, enabling precise segmentation. Subsequently, by normalizing the feature responses under Gaussian kernels with different scales, the locally optimal scale parameter (σ) is determined adaptively for each stripe region. Sub-pixel center extraction is then performed using the Hessian matrix corresponding to this optimal σ. Experimental results demonstrate that under on-site conditions featuring uneven wheel surface reflectivity, the proposed method can reliably extract light stripe centers with high stability. It achieves a repeatability of 0.10 mm, with mean measurement errors of 0.12 mm for flange height and 0.10 mm for flange thickness, thereby enhancing both stability and accuracy in industrial measurement environments. The repeatability and reproducibility of the method were further validated through repeated testing of multiple wheels. Full article

As sustainable tourism gains global momentum, extended reality (XR) technologies have emerged as important tools for enhancing visitor experiences at overburdened World Heritage Sites while mitigating physical deterioration through non-consumptive engagement. However, existing research on immersive technologies in heritage tourism has largely relied on single-cultural samples and has paid limited attention to theoretically grounded boundary conditions in post-adoption behaviour. To address these gaps, this study extends the Expectation–Confirmation Model (ECM) by incorporating cultural distance (CD) and prior visitation experience (PVE) as moderating variables, and empirically tests the proposed framework using a mixed domestic–international sample exposed to an on-site XR application at the Badaling Great Wall World Heritage Site. Data were collected immediately after the XR experience and analysed using structural equation modelling. The results validate the core relationships of ECM while identifying significant moderating effects. Cultural distance attenuates the positive effects of confirmation on perceived usefulness as well as the effect of perceived usefulness on continuance intention, while prior visitation experience weakens the influences of enjoyment and visual appeal on satisfaction. These findings establish important boundary conditions for ECM in immersive heritage contexts. From a practical perspective, the study demonstrates that high-quality, culturally responsive XR can complement physical visitation and support sustainable conservation strategies at large-scale linear heritage sites. Full article

Seismic action significantly affects the mechanical properties and failure characteristics of bridge pile foundations, soft rocks, and sediments. This study, by integrating shaking table tests, numerical simulations, and on-site monitoring, systematically analyzed the influence mechanisms of seismic intensity, sediment characteristics, and pile foundation layout on structural responses. Tests show that the 2.5-layer rock–sand pile exhibits nonlinear bearing degradation under seismic force: when the seismic acceleration increases from 0 to 100 m/s², the bearing capacity of the pile foundation decreases by 55.3%, and the settlement increases from 3.2 mm to 18.5 mm. When the acceleration is ≥2 m/s², the cohesion of the sand layer is destroyed, causing a semi-liquefied state. When it is ≥10 m/s², the resistance loss reaches 80%. The increase in pore water pressure leads to dynamic settlement. When the seismic acceleration is greater than 50 m/s², the shear modulus of the sand layer drops below 15% of its original value. The thickness of the sediment has a nearly linear relationship with the reduction rate of the bearing capacity. When the thickness increases from 0 to 1.4 cm, the reduction rate rises from 0% to 55.3%. When the thickness exceeds 0.8 cm, it enters the “danger zone”, and the bearing capacity decreases nonlinearly with the increase in thickness. The particle size is positively correlated with the reduction rate. The liquefaction risk of fine particles (<0.1 mm) is significantly higher than that of coarse particles (>0.2 mm). The load analysis of the pile cap shows that when the sediment depth is 140 cm, the final bearing capacity is 156,187.2 kN (reduction coefficient 0.898), and the maximum settlement is concentrated at the top point of the pile cap. Under the longitudinal seismic load of the pile group, the settlement growth rate of the piles containing sediment reached 67.16%, triggering the dual effect of “sediment–earthquake”. The lateral load leads to a combined effect of “torsional inclination”, and the stress at the top of the non-sediment pile reaches 6.41MPa. The seismic intensity (PGA) is positively correlated with the safety factor (FS) (FS increases from 1.209 to 37.654 when 10 m/s²→100 m/s²), while sediment thickness (h) is negatively correlated with FS (FS decreases from 2.510 to 1.209 when 0.05 m→0.20 m). The research results reveal the coupled control mechanism of sediment characteristics, seismic parameters, and pile foundation layout on seismic performance, providing key parameters and an optimization basis for bridge design in high-intensity areas. Full article

Pancreatic function tests are used to determine the presence of chronic pancreatitis, particularly in the early stage of the disease. Chymotrypsin is an indicator of pancreatic function and is thus related to pancreatic diseases. However, these methods often require specific equipment and cannot always meet on-site analysis requirements. Consequently, a highly sensitive detection method needs to be developed. This research employed graphene oxide modified with NHS sensors and peptides (RRHFFGC: Arginine-Arginine-Histidine-Phenylalanine-Phenylalanine-Glycine-Cysteine) tagged with gold nanoclusters (Au NCs) for the detection of chymotrypsin. The N-Hydroxysuccinimide-(Polyethylene Glycol)₄-Dibenzocyclooctyne (NHS-PEG₄-DBCO) and graphene oxide (GO)-N₃ click reaction yielded GO-NHS material, appropriate for fluorescence quenching. The peptide chain was accurately broken with the introduction of chymotrypsin, and the Au NCs were situated far from the GO-NHS surface. The detection limit was 2.014 pg/mL. The results showed that the detection method had high sensitivity in comparison with the previous studies. This method is relevant to real samples due to its potential efficacy. Therefore, it is a promising method in the biomedical field. Full article

In reinforced concrete structures, the quality of rebar sleeve connections directly impacts the structure’s safety reserve and durability. However, quality inspection is complicated by the periodic distribution of stirrups, concrete obstruction, and noise interference, presenting challenges for assessing sleeve connection integrity. This paper proposes a training-free, interpretable framework for automated rebar sleeve connection quality inspection, leveraging point cloud semantic filtering and geometric a priori segmentation. The method constructs a polar-cylindrical framework, employing hierarchical semantic filtering to eliminate stirrup layers. Geometric a priori instance segmentation techniques are then applied, integrating θ histograms, Kasa circle fitting, and axial bridging domain constraints to reconstruct each longitudinal rebar. Sleeve detection occurs within the rebar coordinate system via radial profile analysis of length, angular coverage, and stability tests, subsequently stratified into two layers and parameterised. Sleeve projections onto column axes calculate spacing and overlap area percentages. Experiments using 18 BIM-TLS paired datasets demonstrate that this method achieves zero residual error in stirrup detection, with sleeve parameter accuracy reaching 98.9% in TLS data and recall at 57.5%, alongside stable runtime transferability. All TLS datasets meet the quality requirements of rebar sleeve connection spacing ≥35d and percentage of overlap area ≤50%. This framework enhances on-site quality inspection efficiency and consistency, providing a viable pathway for digital verification of rebar sleeve connection quality. Full article

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications. Full article

Many industrial sectors are concerned about microbiological contamination and the associated risk of microbiologically influenced corrosion (MIC). This applies in particular to the transmission and storage of fuels in the refining industry. Exceeding a certain level of these contaminants poses a serious risk to fuel quality and can cause storage and pipeline infrastructure corrosion. This situation requires an urgent evaluation of microorganism levels in the fuel to avert such detrimental consequences. Diesel fuels containing biofuel additives are particularly susceptible to these phenomena. Traditional detection methods are limited by low sensitivity, high costs, and long turnaround times, making them unsuitable for quick, on-site, and real-time detection and monitoring. A novel approach involves the application of microwave dielectric testing to quantify microbial load in diesel fuel. Microwave dielectric spectroscopy offers a non-destructive, label-free solution, providing rapid information on microorganism presence. Combined with chemometric techniques, it effectively estimates total microorganism counts in diesel fuel. Measurement in the X-band range (8.2–12.4 GHz) takes a few seconds. Calibration with known bacterial and fungal concentrations (10³ to 10⁷ CFU/mL) and principal component analysis (PCA) of the spectroscopic data allow for clear differentiation of contamination levels, categorizing them from acceptable to hazardous. The sensitivity limit of the proposed method corresponds to a bacterial concentration of 10³ CFU/mL. Full article

Spontaneous coal combustion accounts for more than 90% of mine fires, and at the same time, the ‘dual carbon’ strategy requires fire prevention and extinguishing materials to have both low-carbon and environmentally friendly functions. To meet on-site application needs, a composite gel with fast injection, flame retardant, and CO₂ adsorption functions was developed. PVA-PEI-PAC materials were selected as the gel raw materials, and an orthogonal test with three factors and three levels was used to optimize the gelation time parameters to identify the optimal formulation. The microstructure of the gel, CO₂ adsorption performance, as well as its inhibition rate of CO, a marker gas of coal spontaneous combustion, and its effect on activation energy were systematically characterized through SEM, isothermal/temperature-programmed/cyclic adsorption experiments, and temperature-programmed gas chromatography. The results show that the optimal gel formulation is 14% PVA, 7% PEI, and 5.5% PAC. The gel microstructure is continuous, dense, and rich in pores, with a CO₂ adsorption capacity at 30 °C and atmospheric pressure of 0.86 cm³/g, maintaining over 76% efficiency after five cycles. Compared with raw coal, a 10% gel addition reduces CO release at 170 °C by 25.97%, and the temperature-programmed experiment shows an average CO inhibition rate of 25% throughout, with apparent activation energy increased by 14.96%. The gel prepared exhibited controllable gelation time, can deeply encapsulate coal, and can efficiently adsorb CO₂, significantly raising the coal–oxygen reaction energy barrier, providing an integrated technical solution for mine fire prevention and extinguishing with both safety and carbon reduction functions. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

Childhood overweight and obesity is a growing public health challenge in Indonesia, affecting approximately one in five school-aged children. Because children spend substantial time at school and frequently obtain meals and snacks from on-site canteens, these settings represent an important opportunity for nutrition-focused interventions. As an initial step towards understanding factors influencing canteen stocking decisions, we assessed perceived taste, acceptability, and feasibility of healthier local foods and beverages from the perspectives of canteen owners and school administrators (n = 10) across five primary schools (n = 2 urban, n = 3 rural) in Magelang, Indonesia. Participants completed in-person taste tests of selected food and beverage options and participated in in-depth interviews exploring drivers of stocking decisions. IndoMilk (multi-cereal, reduced-sugar dairy beverage) received the most favorable taste ratings and was perceived as the most feasible option to sell, followed by sate telur puyuh (braised quail eggs) and sate buah (fresh fruit skewers). In contrast, gethuk (cassava/coconut cake) and polo pendem (steamed tubers with boiled peanuts) were viewed as less appealing to children and unlikely to be sold. Participants identified children’s taste preferences, affordability, visual appeal, and profitability as key considerations influencing stocking decisions, while perceptions of nutrition varied. Findings from this pilot study highlight contextual factors shaping school canteen food environments and may inform future interventions aimed at introducing healthier options while accounting for children’s preferences and canteen operational constraints. Full article