Browse Articles

Gut microbiota profiles of peninsular Malaysian populations are associated with urbanization and lifestyle

Geochemical characteristic of different-lithofacies source rocks and its implications for ultradeep hydrocarbon exploration in the lower cambrian Yuertus formation, Tarim basin

A novel Fractional fuzzy approach for multi-criteria decision-making in medical waste management

Efficient numerical analysis of nonlinear liquid dispersion pattern drops using Taylor wavelet collocation

Abstract In this article, the Taylor wavelet collocation method (TWCM) is presented for solving the nonlinear Rosenau–Hyman equation, which models nonlinear dispersion in liquid drop formation. TWCM outperforms existing methods, such as the Hermite wavelet and other semi-analytical approaches, by providing higher computational efficiency and accuracy. The differential equation is converted into a system of algebraic equations, solved using the Broyden-Quasi Newton algorithm. Numerical examples demonstrate the reliability and robustness of TWCM in handling nonlinear dispersion patterns. All calculations and visualizations are performed using Matlab, showcasing the method’s effectiveness and advancement in analyzing nonlinear liquid dispersion.

To explore the targeting of toluidine blue: low molecular dextran-40 to the lymphatic system and its effect assessment

Waste minimization strategies for environmental sustainability analysis of MABAC based on schweizer-sklar prioritized approach for circular bipolar fuzzy systems

AI image enhancement for failure analysis in 3D quantum information technology

Abstract 3D integration and miniaturization techniques get more widely used in conventional integrated circuits but also represent crucial ingredients for future quantum computing devices. This consolidates the need for efficiently detecting increasingly small defects on wafer size. Here we present a time-efficient and accurate way of measuring, localizing and statistically classifying defects down to the micrometer regime, utilizing a combination of scanning acoustic microscopy (SAM), You Only Look Once object detection, semantic segmentation and a machine learning super-resolution (ML-SR) approach. In particular, we test the capabilities of different ML-SR approaches to enable self-supervised quality enhancement of the measured image data. We reveal that the developed AI-powered workflow enhances time-efficiency by a factor of around 4x and 6x for the TSV and delamination analysis, respectively. Yet, the mentioned approach is not limited to SAM image data but presents a general way for speeding-up failure analysis in various fields.

Generative artificial intelligence in entrepreneurship education enhances entrepreneurial intention through self-efficacy and university support

Impact of LULC changes on small watershed morphometry and delineation of sustainable conservation strategies for degraded transboundary rivers

Tactile sensation and attractiveness of hair bundles in the combing process

Finite element analysis of maxillary arch distalization using skeletal anchorage at three different application regions

Abstract This study aimed to evaluate total maxillary arch distalization using three different skeletal anchorage systems—Mini Screw-Assisted Keleş Slider (MKS), infrazygomatic crest (IZC) screw, and maxillary tuberosity (MT) screw—through finite element analysis (FEA). A 3D cranio-maxillary model including dentition, periodontal ligament, and alveolar bone was constructed. For the MKS and IZC groups, forces were applied at three vertical heights (0 mm, 3 mm, and 6 mm apically), while the MT group involved three directional applications: buccal, palatal, and combined bucco-palatal. This design yielded nine distinct simulation scenarios. Tooth movements were assessed along the x (transverse), y (sagittal), and z (vertical) axes, and Von Mises stress distributions were analyzed in surrounding structures. In the MKS group, the first scenario showed the greatest molar crown displacement, while the third had the highest root-level movement. The ninth MT scenario yielded the most palatal crown displacement of incisors, while the sixth IZC scenario showed the greatest root movement. Apical force applications (MKS and IZC at 3–6 mm) allowed controlled displacement suited for Class II Division 2 malocclusions. In contrast, archwire-level and MT scenarios produced patterns favorable for Class II Division 1 cases. Anchorage type and force direction significantly affected distalization outcomes.

Lactate-related gene signatures predict prognosis and immune profiles in esophageal squamous cell carcinoma

Lactose fermenting enteroinvasive Escherichia coli from diarrhoeal cases confers enhanced virulence

Investigating the correlation between ultrasonic pulse velocity and compressive strength in polyurethane foam concrete

Abstract Using waste polyurethane foam as a partial replacement for natural coarse aggregates in concrete provides an eco-friendly solution by reducing waste and conserving natural resources. However, the strength behavior of polyurethane foam concrete differs from conventional concrete. To ensure effective design and quality control in the field, the viability of non-destructive testing methods for finding out the in situ mechanical properties of polyurethane foam concrete must be evaluated. This study establishes a correlation between compressive strength and ultrasonic pulse velocity (UPV) test to predict the compressive strength of polyurethane foam concrete using UPV test results. An experimental study was conducted on concrete specimens with varying percentages of polyurethane foam replacing natural coarse aggregate, ranging from 10 to 60% in 10% increments. The control concrete mix was 100% natural coarse aggregate without polyurethane foam. The properties of the specimens were evaluated after curing for 7, 14, and 28 days. It also examines polyurethane foam concrete workability, density, and microstructural properties. The findings show that the UPV and compressive strength of polyurethane foam concrete were lower than those of the control mix concrete for all replacement levels and curing ages. The empirical relationships between compressive strength and UPV were found to be exponential, with high correlation values ranging from 0.9012 to 0.9998. The predicted values and the experimentally measured results were compared in order to confirm the accuracy of the empirical equations for compressive strength prediction.

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture

Abstract Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 unruptured, 40 ruptured) using semi-automatic morphological analysis and high-resolution, image-based blood flow simulations from 3D rotational angiography. Advanced morphological and hemodynamic parameters were extracted, with clustering applied to address multicollinearity. Univariate logistic regression identified cluster representatives, and forward selection highlighted the maximum height, Neck inflow rate, and Non-sphericity index as rupture predictors, though only the latter two were significant. Clinical variables like age, sex, and comorbidities were also assessed but failed to predict rupture risk. The full model showed overfitting, with a pseudo-R2 of 0.142 on the training set but only 0.032 on the test set. A simplified model using just Neck inflow rate and Non-sphericity index performed similarly poorly (pseudo-R2 of 0.034). Multiple machine learning classifiers were evaluated, with similar performance across models, supporting the model-independence of the results. Overall, neither morphological, hemodynamic, nor clinical variables reliably predicted rupture risk, highlighting the limitations of current methods and underscoring the need for prospective studies and multimodal approaches that integrate imaging biomarkers and compare small and large aneurysms for better risk stratification.

A high-precision segmentation method based on UNet for disc cutter holder of shield machine

Corneal safety assessment of germicidal far UV-C radiation

Abstract Far UV-C radiation (200–240 nm) is a promising alternative to conventional UV-C for disinfection in occupied spaces, offering strong germicidal efficacy with reduced skin risk. However, its ocular safety remains unclear, as most studies relied only on non-human corneal models with physiological differences. This study investigated UV-induced DNA damage in the epithelium, stroma, and endothelium of ex vivo human corneas and porcine corneas, and reconstructed human cornea epithelium (RHCE) using immunohistochemistry. Samples were exposed to 222 nm, 233 nm, 254 nm, and broadband UV-B (280–400 nm) radiation in the presence of real human tears. Compared to human corneas (26 μm mean epithelium thickness), porcine corneas (110 μm) and RHCE (79 μm), showed reduced UV penetration. In human corneas with a thin epithelium, far UV-C exposure led to epithelial and anterior stromal damage, underscoring the epithelium’s protective function. Optical properties using porcine corneas confirmed the immunohistological findings, validating wavelength-dependent penetration depths. Simulations suggest that in intact human corneas, damage-relevant intensity of 222 nm light reaches the middle of the epithelium, while for 233 nm, it reaches the basal layer. These findings support the relative safety of far UV-C, especially 222 nm, for intact corneas. However, potential DNA damage accumulation after repeated exposures underscores the need for further research on long-term ocular effects.

Numerical simulation on residual axial compression bearing capacity of square in square CFDST columns after lateral impact

A transfer learning based deep neural network adaptive controller for the Furuta pendulum subject to uncertain disturbance signals

FMI and 2D seismic integration for fractured basement reservoir assessment, Geisum area, Gulf of Suez

Abstract Fractured basement reservoirs represent critical contributors to global hydrocarbon production, with lithologically heterogeneous systems such as weathered granites serving as economically viable targets. In the tectonically active Gulf of Suez rift basin, fractured basement units are increasingly recognized as high-potential reservoirs for hydrocarbon exploration. This study investigates the Geisum Oil Field, a prolific southern Gulf of Suez hydrocarbon province, where basement-hosted production challenges conventional reservoir paradigms. A multidisciplinary approach combining advanced geophysical well logs (including Formation MicroImager [FMI] and resistivity anisotropy analysis) with 2D seismic interpretation was employed to (1) delineate conductive fracture networks, (2) quantify fracture aperture distributions, and (3) resolve structural controls on reservoir heterogeneity. Results identify three dominant fracture orientations—NE–SW, NW–SE, and ENE–WSW—aligned with regional stress regimes. Quantitative analysis reveals a maximum fracture aperture of ~ 0.7 mm within the uppermost basement interval, correlating with enhanced porosity (φ) and permeability (k) zones. Fault intersection geometries were found to amplify fracture density, creating interconnected conduits that optimize reservoir quality. However, kinematic analysis of fault systems highlights potential compartmentalization risks, as insufficient fault seal integrity may permit hydrocarbon migration along reactivated fault planes. These findings underscore the dual role of tectonic fracturing in basement reservoirs: while fracture networks enhance storage and flow capacity, dynamic fault systems necessitate rigorous seal evaluation to mitigate leakage hazards. This work provides a framework for de-risking basement reservoir exploration in rift-related settings globally.