Browse Articles
Discover research articles across all indexed journals
Defending endangered trees against climate change and hungry goats
Numerical modeling of coupled stress-fracture evolution in water-resisting key strata during longwall mining
Final Analysis of a Study of Etranacogene Dezaparvovec for Hemophilia B
A lightweight YOLO11n seg framework for real time surface crack detection with segmentation
Abstract The recognition of superficial cracks is essential to ensure the safety, durability, and longevity of civil infrastructure such as bridges, pavements, tunnels, and buildings. Traditional crack detection methods have been largely based on manual inspections and classical image processing techniques, including edge detection, thresholding, and morphological operations. With the rapid advancement of computer vision and deep learning, significant progress has been made in automating crack detection. To gain insight into previous research, we reviewed some studies from the past few years and identified YOLO11 as the most suitable model for crack detection tasks. In this study, we propose a deep learning-based framework for surface crack detection using the Crack-Seg dataset and the YOLO11n-seg architecture. Experimental results demonstrate that YOLO11n-seg achieves strong performance on the Crack-Seg dataset. The suggested model reaches a Precision of 78.8%, which is comparable to heavy baselines. Our results show that the suggested lightweight model, with just 2.8 million parameters, has a Box mAP@50 of 76.2% with a Mask mAP@50 of 58.7%. Most importantly, the model reaches an inference rate of 3.6ms for each image (on Tesla T4), allowing for ultra-fast processing in highly automated inspection systems. These findings establish a new benchmark for edge-deployable crack recognition, demonstrating the possibility that the YOLO11n-seg architecture may provide acceptable segmentation performance with lower computational cost than large, traditional methods.
Medicare’s Role in Fighting Chronic Disease
SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks
Palbociclib for Hormone-Receptor–Positive, HER2-Positive Advanced Breast Cancer
Marvellous microbes, memory and the multiverse: Books in brief
Influence of ageing time on the microstructural and mechanical behaviour of Al-Si-Mg/coconut shell ash metal matrix composite
A Randomized Trial of Tenecteplase in Acute Central Retinal Artery Occlusion
Sunyaev–Zeldovich detection of hot intracluster gas at redshift 4.3
One-year prospective study of a HEMA-based hydrophobic acrylic trifocal IOL (Clareon PanOptix): visual performance, patient-reported outcomes, and optic clarity
Case 4-2026: An 80-Year-Old Woman with Cough and Hypoxemia
GNSS evaluation of GRACE-assimilated water storage models over 89 river basins worldwide
Abstract The gravity recovery and climate experiment (GRACE) and GRACE follow-on (GFO) gravity observations have significantly improved our understanding of the terrestrial water cycle. However, GRACE-assimilated (GA) hydrological models still differ significantly. This paper uses global navigation satellite system (GNSS) data to assess two global GA datasets: Global land water storage release 2 (GLWS2.0) and catchment land surface model GRACE data assimilation (CLSM-DA). From 2004 to 2019, the mean annual amplitude of equivalent water thickness (EWT) of these datasets differs by more than 25 mm over 40% of the modeled land area, and the timing of peak water storage diverges by as much as 30-days across 50% of their domain. We compare the modeled hydrological loading vertical displacement predicted from these models with GNSS uplift data to compare and contrast the model quality. Using river basin boundary information from 89 rivers, we cluster 9,163 global GNSS stations, each with at least three years of daily data. Results show that CLSM-DA generally agrees better with GNSS data across more river basins. Its 100–300 mm larger annual water variation accounts for better agreement in Africa, Southeast Asia, and parts of South America. In regions like the Western United States and Eastern Europe, where both models estimate similar annual amplitudes, CLSM-DA’s 30–60 day phase delay improves alignment with GNSS. This evaluation also reveals key limitations in both models, especially during extreme hydrological events such as droughts, and highlights the value of geodetic observations in advancing GA hydrological modeling.
The Hypertension Control Paradox — Why Is America Stuck?
Anti-inflammatory and cancer chemopreventive potential of essential oils from some cultivated plants in Egypt
Abstract This study aimed to investigate the biological activity of essential oils (EOs) of Artemisia abrotanum , Lavandula dentata , Cymbopogon citratus and Laurus nobilis as anti-inflammatory and cancer chemopreventive activities. The anti-inflammatory activity was evaluated using lipopolysaccharides-induced nitric oxide (NO) inhibition on murine macrophage cells (RAW264.7) and cancer chemo-preventive influence was assessed in vitro utilizing Hepa1c1c7 murine carcinoma cells. The EO of A. abrotanum has the most potential activity toward inhibition of NO release, recording 96.6 ± 0.1%, as estimated by Greiss assay. Followed by EOs of L. dentata and L. nobilis with 63.6 ± 0.11 and 37.0 ± 0.23%, respectively. At the protein expression level, western blotting technique was used to evaluate the expression of iNOS. The EO of A. abrotanum at 100 µg/ml exhibited a very high impact on inhibiting iNOS expression, followed by EOs of L. dentata and L. nobilis . On the other hand, pre-screening concentration (100 µg/ml) revealed that the EOs of L. dentata and A. abrotanum have moderate potency to induce expression of chemo-preventive marker NQO1. The results revealed that the EO of A. abrotanum had strong anti-inflammatory activity. While the EOs of L. dentata and A. abrotanum have moderate potency to induce cancer chemoprevention.
Aspirin in Patients Receiving Oral Anticoagulation
Medial temporal lobe atrophy is associated with age and pathologies, especially small vessel disease
Abstract Visual assessment of medial temporal lobe atrophy (MTA) in the clinical workup of cognitive impairment is traditionally corrected for age since MTA increases with age. In addition, common pathologies in the elderly such as amyloid, tau, alpha-synuclein and TDP-43 accumulation as well as white matter hyperintensities representing small vessel disease may affect the association between MTA and age. We investigated this in 949 cognitively unimpaired (CU) and 854 cognitively impaired (CI) individuals focusing on amyloid, tau and alpha-synuclein that at present can be measured in vivo in plasma, CSF or using PET. MTA was associated with age also when these aforementioned pathologies were accounted for. WMH was the strongest and most consistent predictor and mediated 32–41% of the association between age and MTA. Secondly, an age-independent cut-off for distinguishing between Aβ- CU and Aβ + CI was derived from 195 CU participants with low levels of pathology. Accuracy, sensitivity and specificity were comparable for our age-independent and previously published age-adjusted cut-offs. In summary, age and WMH emerged as the most prominent factors associated with MTA. Our age-independent cut-off for MTA performed in line with the best performing age-adjusted cut-off, suggesting our more parsimonious proposal could be useful in a clinical setting.