Search Results (2,815)

The agro-industry is among the largest methane emitters, posing a critical challenge for sustainability. In rural areas, producers lack effective technologies to manage daily organic waste. Anaerobic digestion (AD) offers a circular pathway by converting waste into biogas and biofertilizers; however, its adoption is limited by inappropriate designs and insufficient operational control. Theoretical-applied research addresses these barriers by improving the design and operation of small-scale biodigesters, elevating pH and Electrical Conductivity (EC) from passive indicators to first-order control variables. Based on the design of a compact biodigester previously validated in the Chillón Valley and replicated in Huaycán under a utility model patent process (INDECOPI, Exp. 001087-2025/DIN), a stoichiometric NaHCO${}_3$ strategy with joint pH–EC monitoring was formalized, defining operational windows (pH 6.92–6.97; EC 6200–6300 $\mathsf{\mu }$ S/cm and dose–response curves (0.3–0.4 kg/day for 3–4 day) to buffer VFA shocks and preserve methanogenic ionic strength. The system achieved stable productions of 370–462 L/day, surpassing the theoretical potential of 352.88 L/day calculated by Buswell’s equation. A multivariable predictive model (linear, quadratic, interaction terms pH × EC, temperature, and loading rate) was developed and validated with field data: R² = 0.78; MAPE = 2.7%; MAE = 11.2 L/day; RMSE = 13.8 L/day; r = 0.89; residuals normally distributed (Shapiro–Wilk p = 0.79). The proposed approach enables daily decision-making in low-instrumentation environments and provides a replicable and scalable pathway for the safe valorization of organic waste in rural areas. The design consolidates the shift from reactive to proactive and co-optimized pH–EC control, laying the foundation not only for standardized protocols and training in rural systems but also for improved environmental sustainability. Full article

Temperature and strain rate play a crucial role in determining the mechanical properties of metals. These critical parameters are typically assessed using the split Hopkinson pressure bar (SHPB) test. However, previous studies have seldom considered the coupled influence of temperature and strain rate on dynamic mechanical behavior, thereby reducing the accuracy of constitutive models. To accurately characterize the dynamic mechanical behavior of 34CrNi3MoA low-alloy steel, a new constitutive model combining temperature and strain rate was developed. Firstly, SHPB experiments under varying temperatures and strain rates were designed to obtain actual stress–strain curves. The results indicate that the mechanical properties of 34CrNi3MoA low-alloy steel are significantly influenced by both temperature and strain rate. True stress has a significant temperature-softening effect within the temperature range of 25 °C to 600 °C, while the flow stress in the yield stage increases with rising strain rate. Secondly, a novel constitutive model was established by integrating a correction function. The model comprises three components: a strain rate-strengthening function influenced by temperature, a temperature-softening function influenced by strain rate, and a strain-hardening correction function accounting for the coupling of temperature and strain rate. Comparing the mean relative error, the new model significantly improves accuracy compared to the original Johnson–Cook (J-C) model. Full article

Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization. Full article

Background: Monkeypox (Mpox) is a re-emerging zoonotic disease caused by the monkeypox virus (MPXV). Transmission occurs primarily through direct contact with lesions or contaminated materials, with sexual transmission playing a significant role in recent outbreaks. In 2022, Mpox triggered a major global outbreak and was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO), prompting renewed interest in effective control strategies. Methods: This study developed a compartmental SEIR-based model to assess the epidemiological impact of key interventions, including vaccination and isolation, while incorporating critical epidemiological parameters. Sensitivity analyses were conducted to examine (1) disease dynamics in relation to the basic reproduction number, and (2) how different parameters influence the curve of symptomatic infections. Real-world continental-scale data were used to validate the model and identify the parameters that most significantly affect epidemic progression and potential control of Mpox. Results: Results showed that the basic reproduction number was most influenced by the recovery rate, vaccination rate, vaccine effectiveness, and transmission rates of symptomatic and asymptomatic individuals. In contrast, the progression of symptomatic cases was highly sensitive to the case fatality rate and incubation rate. Conclusions: These findings highlight the importance of integrated public health strategies combining vaccination, isolation, and early transmission control to mitigate future Mpox outbreaks. Full article

Fatigue fracture is the predominant failure mode in welded joints, where complex stress distributions and stress gradient effects at critical joint regions present major challenges for fatigue design. In civil engineering, the diversity of welded joint configurations, large structural spans, and complex loading conditions make it essential to investigate the influence of geometrical size effects on fatigue performance to ensure structural safety. This study focuses on welded T-joints and examines how variations in web plate thickness, weld toe size, and welding angle affect their fatigue behavior through experimental testing. The results show that fatigue life curves fitted using the Mises stress amplitude exhibit higher accuracy than those based on the normal stress amplitude used in current design codes. Pearson correlation analysis indicates that the influences of the geometrical parameters on fatigue life are mutually independent. Furthermore, analysis of the coefficient of variation reveals that welding angle has the greatest effect on fatigue life, whereas weld toe size exerts the least influence. Full article

The complex jet-ground interactions of Short Take-off and Vertical Landing (STOVL) aircraft are critical to flight safety and performance, yet studying them with traditional full-scale wind tunnel tests is prohibitively expensive and time-consuming, hindering design optimization. This study addresses this challenge by developing and numerically verifying a “pressure ratio–momentum–geometry” multi-dimensional similarity framework, enabling accurate and efficient scaled-model analysis. Systematic simulations of an F-35B-like configuration demonstrate the framework’s high fidelity. For a representative curved nozzle configuration (e.g., the F-35B three-bearing swivel duct nozzle, 3BSD), across scale factors ranging from 1:1 to 1:15, the plume deflection angle remains stable at 12° ± 1°. Concurrently, axial force (F) and mass flow rate (Q) strictly follow the square scaling relationship ($F\propto 1/n^2, Q\propto 1/n^2)$, with deviations from theory remaining below 0.15% and 0.58%, respectively, even at the 1:15 scale, confirming high-fidelity momentum similarity, particularly in the near-field flow direction. Second, a 1:13.25 scale aircraft model, constructed using Froude similarity principles, exhibits critical parameter agreement (intake total pressure and total temperature) of the prototype-including vertical axial force, lift fan mass flow, and intake total temperature—all less than 1.5%, while the critical intake total pressure error is only 2.2%. Fountain flow structures and ground temperature distributions show high consistency with the full-scale aircraft, validating the reliability of the proposed “pressure ratio–momentum–geometry” multi-dimensional similarity criterion. The framework developed herein has the potential to reduce wind tunnel testing costs and shorten development cycles, offering an efficient experimental strategy for STOVL aircraft research and development. Full article

Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing language rules remains a complex problem. This paper addresses two key challenges: (1) The over-correction behavior of language models, particularly on semantically deficient input, can result in both recognition errors and loss of critical information. (2) Character misalignment in visual features, which affects recognition accuracy. To address these problems, we propose a Deformable-Alignment-based Dual Correction Mechanism (DADCM) for STR. Our method includes the following key components: (1) We propose a visually guided and language-assisted correction strategy. A dynamic confidence threshold is used to control the degree of language model intervention. (2) We designed a visual backbone network called SCRTNet. The net enhances key text regions through a channel attention module (SENet) and applies deformable convolution (DCNv4) in deep layers to better model distorted or curved text. (3) We propose a deformable alignment module (DAM). The module combines Gumbel-Softmax-based anchor sampling and geometry-aware self-attention to improve character alignment. Experiments on multiple benchmark datasets demonstrate the superiority of our approach. Especially on the Union14M-Benchmark, where the recognition accuracy surpasses previous methods by 1.1%, 1.6%, 3.0%, and 1.3% on the Curved, Multi-Oriented, Contextless, and General subsets, respectively. Full article

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at nominal doses of 0–10 Gy and evaluated using a MATLAB (R2024b)-based tool that allows for both manual and automated ROI selection. The calibration curves were modeled with a second-order polynomial and rational model, and performance was assessed using statistical measures. The study found that the rational model fits better than the polynomial model. Additionally, the automatic ROI approach outperformed the manual method in both models, resulting in higher calibration accuracy and reproducibility (R² = 0.999, RMSE = 0.118 Gy, MAE = 0.103 Gy vs. R² = 0.986, RMSE = 0.448 Gy, MAE = 0.388 Gy). Although manual ROI occasionally produced greater dose–response slopes at higher doses, it was more susceptible to operator bias and film non-uniformity. In contrast, automatic ROI reduced variability by consistently picking homogeneous sections, resulting in steady curve fitting across the entire dose range. Furthermore, a companion module transformed calibrated films into 2D false-color maps and 3D dosage surfaces, allowing for visual assessment of dose uniformity, detection of scanner-related aberrations, and quantitative verification for quality assurance. These findings demonstrate that automated ROI selection provides a more stable and reproducible foundation for film calibration in HDR brachytherapy, minimizing operator dependency while facilitating routine clinical quality assurance. Full article

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications. Full article

Aortic stenosis (AS) is a common degenerative valvular disease in elderly patients, causing obstruction of left ventricular outflow and presenting with symptoms such as angina, syncope, and heart failure. Although surgical aortic valve replacement (SAVR) remains the gold standard, its high perioperative risk in frail patients has led to the adoption of transcatheter aortic valve implantation (TAVI) as a less invasive and effective alternative. The transfemoral (TF) access route is generally preferred, but severe peripheral arterial disease may limit its feasibility. We report the case of a 71-year-old woman with critical AS complicated by multiple comorbidities, including extensive vascular calcifications, a porcelain aorta, and significant subrenal abdominal aortic stenosis. Multimodal imaging, including computed tomography, was essential for procedural planning, revealing complex iliofemoral anatomy unsuitable for conventional device passage without intervention. Intravascular lithotripsy (IVL) was used to disrupt calcific plaques and facilitate safe vascular access. The TAVI procedure was successfully performed under local anesthesia via TF access using a 65 cm GORE® DRYSEAL Flex Introducer Sheath (W. L. Gore & Associates, Flagstaff, AZ, USA) (18-Fr). After balloon valvuloplasty performed over a SAFARI2™ Pre-Shaped TAVI Guidewire, Extra Small (Boston Scientific, Marlborough, MA, USA) Curve in the left ventricle, a self-expanding Medtronic Evolut™ FX 26 (Medtronic, Minneapolis, MN, USA)mm transcatheter valve was implanted. Postoperative imaging confirmed optimal valve function and vascular integrity without complications. This case highlights the role of IVL as an innovative adjunctive technique enabling TF-TAVI in patients with challenging vascular anatomy, thereby expanding treatment options for high-risk individuals with severe AS. Full article

The critically endangered African wild ass is found in low population densities and there may be as few as 600 individuals in the Danakil Desert of Ethiopia and Eritrea. An understanding of suitable habitats is important for prioritizing the conservation and management of the African wild ass. In this study, we recorded presence locations of the African wild ass and independently prepared environmental covariates to identify suitable habitats using the maximum entropy (Maxent) model. Model performances were high, with the area under the curve (AUC) values of 0.927 and 0.950 for wet and dry seasons, respectively. The predicted moderately suitable habitat area extent was greater during the wet season (15,223 km²) than during the dry season (6052 km²). Precipitation, temperature, and distance from water sources were vital variables for the wet season, while distance from water sources and distance from the settlements were important determinant covariates for the dry season. This information prioritizes where protected areas should be established for African wild ass conservation and also indicates potential new undocumented locations to guide surveys in the Danakil Desert of the Afar Region, Ethiopia. Full article

Background: Vascular calcification has been highlighted as a prognostic factor for perioperative thrombosis but a protective factor for late restenosis in lower limb peripheral artery disease (LLPAD). The aim of this study was to investigate the association between calcification and twelve-month primary patency in patients with stenting of the common femoral artery (CFA) and its bifurcation for atheromatous stenosis. Materials/Methods: This single-center retrospective study analyzed consecutive limbs (n = 90) that underwent CFA stenting for symptomatic lesions between January 2018 and January 2023. Calcification was assessed using dedicated computed tomography angiography analysis software (EndoSize; Therenva), with blinded evaluation of volume (mm³) and density (Hounsfield Units) across three anatomically distinct zones: proximal CFA (Zone 1); distal CFA (Zone 2); and bifurcation segments (Zone 3). The primary endpoint was twelve-month primary patency, defined as a peak systolic velocity ratio (PSVR) < 2.4 on duplex ultrasound without target lesion revascularization. Secondary endpoints included predictors of restenosis using multivariable logistic regression. Results: Ninety cases of CFA stenting for LLPAD (lower limb peripheral artery disease) were analyzed. A total of 78.9% of CFA lesions were treated for claudication and 21.1% for critical limb-threatening ischemia (CLTI). Lesions were distributed as Azema types I (1%), II (43%), and III (56%). At twelve-month follow-up, primary patency (PSVR < 2.4) was achieved in 77.4% of limbs. Patent CFA stenting demonstrated significantly higher median calcification density in Zone 2 compared to those with restenosis (1122 [IQR: 903–1248] vs. 858 [788–987] HU; p = 0.006; q = 0.021 after false discovery rate correction). ROC curve analysis identified a density threshold of 800 HU with a 76% reduction in restenosis risk (OR 0.24; 95% CI: 0.08–0.72; p = 0.011). Bootstrap validation (1000 replications) confirmed threshold stability at 821 HU (95% CI: 656–990 HU). Conclusions: In this exploratory study, dense calcification (≥800 HU) in the distal CFA appears to be protective against twelve-month restenosis following stenting. These findings suggest that calcification density may serve as a valuable predictor for patient selection and procedural planning in CFA interventions. Full article

Background: Patients with adolescent idiopathic scoliosis (AIS) require effective bracing to control curve progression. However, most traditional spinal braces commonly pose challenges in terms of undesired bulkiness and restricted mobility. Recent advancements have focused on innovative brace designs, utilizing novel materials and structural configurations to improve wearability and functionality. However, it remains unclear how effective these next-generation braces are biomechanically compared to traditional braces. Objectives: This review aimed to analyze the design features of next-generation AIS braces and assess their biomechanical effectiveness via reviewing contemporary studies. Methods: Studies on newly designed scoliosis braces over the past decade were searched in databases, including Web of Science, PubMed, ScienceDirect, Wiley, EBCOHost and SpringerLink. The Joanna Briggs Institute Critical Appraisal Checklist for Cohort Studies was adopted to evaluate the quality of the included studies. The data extracted for biomechanical effect analysis included brace components/materials, design principle, interfacial pressure, morphological changes, and intercomparison parameters. Results: A total of 19 studies encompassing 12 different kinds of braces met the inclusion/exclusion criteria. Clinical effectiveness was reported in 14 studies, with an average short-term Cobb angle correction of 25.4% (range: 12.41–34.3%) and long-term correction of 18.22% (range: 15.79–19.3%). This result aligned broadly with the previously reported efficacy of the traditional braces in short-term cases (range: 12.36–31.33%), but was lower than the long-term ones (range: 23.02–33.6%). Two included studies reported an interface pressure range between 6.0 kPa and 24.4 kPa for novel braces, which was comparable to that of the traditional braces (4.8–30.0 kPa). Additionally, five of six studies reported the trunk asymmetric parameters and demonstrated improvement in trunk alignment. Conclusions: This study demonstrates that most newly designed scoliosis braces could achieve comparable biomechanical efficacy to the conventional designs, particularly in interface pressure management and Cobb angle correction. However, future clinical adoption of these novel braces requires further improvements of ergonomic design and three-dimensional correction, as well as more investigation and rigorous evidence on the long-term treatment outcomes and cost-effectiveness. Full article

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

Background/Objectives: The spinal cord is a critical component of the central nervous system that transmits neural signals between the brain and the body’s peripheral regions through its nerve roots. Despite being partially protected by the vertebral column, the spinal cord remains highly vulnerable to trauma, tumors, infections, and degenerative or inflammatory disorders. These conditions can disrupt neural conduction, resulting in severe functional impairments, such as paralysis, motor deficits, and sensory loss. Therefore, accurate and comprehensive spinal cord segmentation is essential for characterizing its structural features and evaluating neural integrity. Methods: In this study, we propose a fully automated method for segmentation of the cervical spinal cord in sagittal magnetic resonance (MR) images. This method facilitates rapid clinical evaluation and supports early diagnosis. Our approach uses a Swin-Unet architecture, which integrates vision transformer blocks into the U-Net framework. This enables the model to capture both local anatomical details and global contextual information. This design improves the delineation of the thin, curved, low-contrast cervical cord, resulting in more precise and robust segmentation. Results: In experimental studies, the proposed Swin-Unet model (SWU1), which uses transformer blocks in the encoder layer, achieved Dice Similarity Coefficient (DSC) and Hausdorff Distance 95 (HD95) scores of 0.9526 and 1.0707 mm, respectively, for cervical spinal cord segmentation. These results confirm that the model can consistently deliver precise, pixel-level delineations that are structurally accurate, which supports its reliability for clinical assessment. Conclusions: The attention-enhanced Swin-Unet architecture demonstrated high accuracy in segmenting thin and complex anatomical structures, such as the cervical spinal cord. Its ability to generalize with limited data highlights its potential for integration into clinical workflows to support diagnosis, monitoring, and treatment planning. Full article