Search Results (58,227)

Texture-modified foods (TMFs) are essential for individuals with dysphagia, yet conventional formulations often lack structural consistency, nutritional density, and sensory appeal. Three-dimensional (3D) food printing offers new opportunities to tailor texture and composition. This study developed 3D-printed TMFs based on a lentil-carrot matrix and formulated with pea protein isolate (PPI), a curcumin-enriched oleogel (O), or their combination (PPI–O), and compared them with a commercial dysphagia thickener reference. Printability was assessed through extrusion force measurements and dimensional deviation analysis. Texture profile analysis (TPA), International Dysphagia Diet Standardisation Initiative (IDDSI) tests, moisture and protein content determination, color measurements, and preliminary sensory evaluation were conducted. PPI-containing formulations required higher extrusion forces but showed improved dimensional stability, hardness, cohesiveness, and gumminess compared with the oleogel-only sample, likely due to the formation of a stronger protein network. In contrast, the oleogel-only formulation exhibited lower mechanical resistance and a more pronounced melting perception, reflecting the lubricating effect of the lipid-based matrix. Protein content significantly increased with PPI incorporation, and curcumin-enriched oleogel also markedly influenced color parameters. All samples were classified as compatible with IDDSI Level 5. The hybrid PPI–O formulation provided a balanced combination of printability, structural fidelity, enhanced protein content, and suitable textural properties. These findings suggest that extrusion-based 3D printing may represent a promising approach for designing plant-based TMFs for dysphagia-oriented foods. Full article

The apple rust fungus Gymnosporangium yamadae (G. yamadae) secretes effector proteins into host apple leaf cells to facilitate parasitism. Among these, the candidate effector GyHRb12 was found to localize to the nucleus upon transient expression in Nicotiana benthamiana leaf cells, although its functional role remained unclear. Subsequent investigations demonstrated that overexpression of GyHRb12 protein decreases plant cell resistance and attenuates the transcription of multiple antifungal-related genes. Using a yeast two-hybrid screen, MdRPS20, a component of the 30S ribosomal subunit, was identified as an interactor of GyHRb12. Proteomic analysis revealed that GyHRb12 modulates the expression of proteins involved in protein translation processes, which may be mediated by changes in ribosomal abundance. Notably, mutating the 14th amino acid in MdRPS20 disrupted its interaction with GyHRb12, underscoring the critical role of this residue in effector recognition and subsequent suppression of host immunity. Collectively, these findings demonstrate that G. yamadae employs a nuclear-localized effector to target a ribosomal subunit protein, thereby reprogramming host translation activity and suppressing host immunity. Full article

The growing demand for lightweight and cost-effective vehicular armor systems has driven the development of hybrid multilayer architectures capable of improving ballistic resistance while reducing structural mass. This study evaluates the ballistic performance of a functionally graded aluminum–Kevlar–cabuya fiber composite system designed for vehicle door protection. A combined experimental–numerical framework was implemented, integrating ballistic testing according to NIJ 0108.01 and STANAG 4569 Level 1 standards with explicit dynamic finite element modeling based on the Johnson–Cook constitutive formulation for AA5083-H32. The multilayer configuration (25 mm aluminum/15 mm Kevlar 29/15 mm treated cabuya composite) successfully resisted 9 × 19 mm and 5.56 × 45 mm FMJ threats without complete perforation. Numerical simulations predicted a maximum back-face deformation of 52.75 mm under 9 mm impact, showing strong agreement with the experimental measurements (mean ± SD, n = 3). Post-impact microstructural analysis revealed a sequential energy dissipation mechanism governed by plastic deformation of the aluminum layer, Kevlar fibrillation and fragment retention, and controlled micro-cracking within the treated cabuya backing layer. With an areal density of 140.87 kg/m², the system achieved a 19% weight reduction compared with conventional steel-based solutions. These results demonstrate the structural-scale feasibility of integrating treated cabuya fiber composites as active energy redistribution layers in certified hybrid vehicular armor systems. Full article

The complex architecture and stacking patterns of deepwater turbidite channel sandbodies introduce significant uncertainty in injector–producer connectivity. This uncertainty affects both the mechanisms and the quantitative evaluation of the waterflood sweep. In this study, a representative reservoir in the Niger Delta Basin is selected as a case study. Injector–producer well groups are first classified into three connectivity patterns—coeval, cross-stage, and hybrid based on geological and seismic constraints. Time-lapse seismic data are then interpreted to delineate sweep morphology and to infer the controlling mechanisms associated with each pattern. Coeval connectivity exhibits a relatively uniform and continuous front advance with minimal barriers. Cross-stage connectivity shows fragmented swept regions with pronounced bypassing, and localized preferential breakthrough caused by discontinuous sandbodies and pervasive barriers. Hybrid connectivity is characterized by intermediate behavior, combining features of both patterns. To translate these mechanistic differences into quantitative metrics for development evaluation, an oil–water relative permeability ratio correlation for low viscosity oil is established that remains valid across the full water cut range, thereby overcoming the limitations of conventional semi-log linear correlations at both low and ultra-high water cut stages. Based on this framework, a production data-driven predictive model for waterflood sweep efficiency is derived using production data and steady state flow theory. The model is validated across well groups representing different connectivity patterns. Field application yields a consistent ranking of sweep efficiency: coeval > hybrid > cross-stage, with group average values of 0.86, 0.80, and 0.70, respectively. These results agree with the mechanistic interpretation derived from time-lapse seismic analysis. The proposed methodology provides a practical quantitative framework for evaluating injector–producer connectivity and comparing development strategies in deepwater turbidite channel reservoirs. Full article

To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides a high-quality data foundation for the detection network. Second, a LWCSP module is introduced. This module integrates hybrid convolution and shuffle operations. It reduces the model’s parameter count and computation. Simultaneously, it maintains strong feature representation capability. A good balance between detection performance and efficiency is achieved. Finally, an improved SWise-IoU is proposed to optimize the bounding box regression in YOLOv7-tiny. This method dynamically evaluates sample quality. It enables differentiated gradient adjustment for samples of different qualities. This promotes sufficient learning of sample features by the model, thereby improving detection accuracy. Experimental results show that the proposed model delivers strong performance on a public bridge crack dataset. Compared to the baseline, the mAP@0.5 is 12.1 higher, and model size, parameter count, and FLOPs are reduced by 7.3%, 8.03%, and 10%, respectively. The final model size is only 11.4 MB, and mAP@0.5 is 86.1%, suitable for a real-time crack detection task. Full article

Phonocardiogram (PCG) signals offer a non-invasive, low-cost screening tool for cardiovascular diseases. However, their noisy and non-stationary nature makes automated classification challenging, and traditional methods often fail to capture complex spectral-temporal patterns. This study proposes a multimodal deep learning architecture for the binary classification of heart sounds (Healthy vs. Unhealthy). The hybrid model integrates Convolutional Neural Networks (CNNs), Transformer encoders, and Bidirectional Long Short-Term Memory (BiLSTM) networks with an attention mechanism. It utilizes an early-fusion feature extraction pipeline combining MFCCs, Mel-spectrograms, and Chroma descriptors. To ensure robust evaluation and prevent data leakage, SMOTE is applied exclusively to the training folds within a strict zero-leakage, patient-wise 5-fold cross-validation protocol. The proposed framework demonstrates exceptional performance, achieving an average accuracy of 91.67%, a sensitivity of 80.95%, a specificity of 94.46%, and an AUC-ROC of 96.50%. An ablation study confirms that integrating Transformer and BiLSTM modules significantly enhances diagnostic stability over baseline CNNs. Furthermore, with exactly 858,434 parameters (3.27 MB) and interpretable attention maps, this highly optimized model provides a robust assistive solution suitable for deployment in digital stethoscopes and mobile telemedicine systems. Full article

The aviation sector faces the challenge of reducing emissions while meeting growing demand for passenger transport. Recent research has proposed a hybridised axial compressor concept using a vaneless, counter-rotating configuration with independently electrically driven rotors. Earlier work showed the aerodynamic feasibility of this approach and identified the need for extended compressor maps to capture performance variations with hybridisation degree and speed ratio. This study explores the operational potential of such compressors in greater depth, focusing on how variable rotor speeds can unlock aerodynamic benefits and expand the operating envelope for hybrid-electric propulsion in regional aircraft and rotorcraft. Using mean line analysis, it is shown that independently driven rotors can operate effectively across a wide range of speed ratios. This flexibility enables the compressor to maintain high efficiency over diverse operating conditions, including part-load scenarios, typical of hybrid-electric missions. Independent speed control also offers a means of actively managing compressor stability. Compared to the conventional design the operating range can be significantly increased without relying on traditional stability measures such as variable stator vanes or bleed valves, reducing system weight and complexity. In this way the operating range of the hybrid compressor could be increased by up to 50%, while the number of blade rows could be reduced by up to 30% and the mass flow range increased by up to 33%. Together with the potential efficiency gains of counter-rotating concepts, this underscores its promise for future low-emission propulsion systems. Full article

Structural health monitoring (SHM) of machinery represents a topic of significant engineering interest. In machine design, the development of diagnostic systems capable of tracking structural integrity during operation is essential, provided that such systems do not interfere with or compromise the machine’s performance. This paper presents a SHM designed to reconstruct, in real time, the full deformation field of a machine from discrete measurements collected at a limited number of locations. The proposed approach combines modal reconstruction techniques with mode selection criteria, further enhanced by the integration of a supervised machine learning classifier. This hybrid framework enables the continuous reconstruction of the structural deformation at each timestep of the measured signal. The methodology is validated on a benchmark geometry, a rectangular plate, by comparing finite element simulations with reconstructions obtained through the proposed modal–machine learning strategy. Results demonstrate the capability of the approach to approximate the global deformation with high fidelity from sparse measurements, establishing a foundation for the extension of the method to more complex machine geometries. The study highlights the potential of integrating data-driven and physics-based techniques for SHM, paving the way for future diagnostic tools in advanced mechanical systems. Full article

In this study, a novel “Physics-Informed Hybrid Machine Learning” framework was developed to model and optimize the complex combustion and carbon-based emission characteristics of Cu₂O nano-additive doped diesel fuel. To reduce reliance on purely empirical correlations, the proposed framework integrates alterations in fuel physical properties into the prediction loop, thereby enhancing physical consistency and model generalizability. The methodology comprises data pre-processing, modeling via Gaussian Process Regression (GPR) with an Automatic Relevance Determination (ARD) kernel, and multi-objective optimization using NSGA-II. Experimental tests were conducted at a constant engine speed of 2000 rpm under varying load conditions. The developed hybrid model exhibited high predictive accuracy, particularly for performance metrics and gaseous emissions (e.g., R² > 0.95 for BSFC and CO). ARD-based feature importance analysis confirmed that nano-additive dosage plays a critical role in the fine-tuning of emissions. Crucially, the optimization algorithm identified a nano-additive dosage of ~29 ppm and an engine load of 15.5 Nm as the optimal operating point for the simultaneous improvement of performance and carbonaceous emissions. This finding, exploring the unmeasured design space, demonstrates the framework’s capability to discover optimal conditions beyond discrete experimental points. Full article

The Hybrid Smart Energy Community (HySEC) model is an integrated framework for optimizing hybrid renewable energy systems, unifying BIM, IoT, and data-driven modeling, as an innovative approach for the energy transition. A Revit—Twinmotion BIM model, enriched with topographic, CAD, and real-image data, enhances spatial accuracy and stakeholder communication, while a digital–physical architecture linking sensors, gateways, edge devices, and cloud platforms enables decentralized peer-to-peer communication and real-time monitoring. The framework is applied to a smart energy community composed of a hydropower–wind–solar PV system serving six buildings (48.8 MWh/year), supported by high-resolution hourly Open-Meteo data. A NARX neural network trained on 8760 hourly observations achieves an MSE of 2.346 at epoch 16, providing advanced predictive capability. Benchmarking against HOMER demonstrates clear advantages in grid exports (15,130 vs. 8274 kWh/year), battery cycling (445 vs. 9181 kWh/year), LCOE (€0.09 vs. €0.180/kWh), IRR (9% vs. 6%), payback (8.7 vs. 10.5 years), and CO₂ emissions (−9.4 vs. 101 tons). These results confirm HySEC as a conceptually flexible solution that strengthens energy autonomy, supports heritage site rehabilitation, and promotes sustainable rural development. Full article

The rapid development of Autonomous Underwater Vehicles (AUVs) has increased the demand for propulsion systems that balance thrust density, hydrodynamic efficiency, and acoustic discretion. This study presents a comprehensive numerical investigation of the performance of the Blue Robotics T500 thruster, embedded within the RAS-HA-X25 AUV’s internal conduit. Using transient Computational Fluid Dynamics (CFD) within the OpenFOAM framework, this research assesses the propulsive characteristics of the thruster across six distinct outlet geometries, including convergent jet nozzles and multi-lobed “daisy” configurations. To improve computational efficiency for parametric design, a calibrated actuator disc model was developed and validated against resolved-rotor simulations, revealing a 15 discrepancy attributed to tip leakage and hub vortex effects. Results show that at the operational advance ratio ($J=0.167$), the 60 mm convergent nozzle is the optimal configuration for maximising thrust, achieving a peak net thrust of 42 N. In contrast, the daisy-type lobed geometries, while causing a 50 reduction in absolute thrust compared to a standard cylindrical pipe, significantly homogenise the exit-plane velocity distribution and reduce swirl intensity. These findings indicate that lobed terminations provide a viable mechanism for reducing hydroacoustic signatures, offering a strategic “stealth” advantage for low-observable underwater platforms where acoustic discretion is prioritised over pure thrust density. This study establishes a robust methodology for optimising embedded propulsion modules in next-generation autonomous and hybrid underwater vehicles. Full article

This paper investigates the AWJ machinability of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) fiber/epoxy-based hybrid composites by analyzing key machinability metrics such as kerf width (KW), material removal rate (MRR), and surface roughness (Ra). Various process parameters including CNT weight percentage, CNT diameter, stand-off distance, and traverse speed have been varied to optimize the machining performance. Experimental analysis suggested that increasing the CNT weight percentage significantly enhanced material hardness, thereby reducing both the MRR and surface roughness. Moreover, adjusting the stand-off distance and traverse speed further improved the machinability of the composite. ANOVA results highlighted that CNT weight percentage was a significant factor, accounting for 94.17% of the variation in MRR and 93.72% of the variation in surface finish, while the stand-off distance influenced 87.03% of the variation in kerf width. Additionally, response surface methodology (RSM) was utilized to develop predictive models that estimated KW, MRR, and Ra with error rates of 2.95%, 2.23%, and 5.65%, respectively. These insights offer a valuable framework for tailoring the AWJ process to achieve optimal machining outcomes in HRS/CNT/epoxy composite materials Full article

Decarbonizing the construction sector requires high-volume replacement of Portland clinker with non-calcined supplementary cementitious materials (SCMs). This study investigates white cement pastes incorporating raw Algerian diatomite—a silica-rich biogenic mineral—at substitution levels from 40% to 95% (5% increments) and a fixed water-to-binder ratio of 0.5. The target application is ultra-lightweight, multifunctional composites for non-structural uses such as decorative panels and partition elements. Increasing diatomite content progressively reduced bulk density from 1.483 g/cm${}^3$ (D40) to 0.557 g/cm${}^3$ (D95) and increased porosity. 28-day compressive strength decreased monotonically from 16 MPa (D40) to 2.4 MPa (D95) as clinker dilution intensified. Ultrasonic pulse velocity dropped from 6205 m/s to 1495 m/s, reflecting progressive pore development and confirming the material’s lightweight potential. Statistically significant strength gains beyond 28 days were recorded ($+25.87\%$ for compression, p-value$<0.05$), evidencing delayed pozzolanic activity. These results confirm that raw, non-calcined diatomite is a viable SCM for eco-efficient, low-density construction systems. To overcome the extrapolation instability of purely data-driven approaches, a Meta-Avrami Hybrid Framework was developed. It anchors Gradient Boosting residual learning to a sigmoidal Avrami hydration kernel. The model achieved high predictive accuracy ($R^2\approx 0.999$, $\mathrm{RMSE}\approx 0.010$) under 10-fold cross-validation. Generalization was well-controlled, with a low overfitting gap ($\Delta R^2=0.0226$) and stable fold-to-fold performance ($\mathrm{Std}=0.0204$). These metrics confirm suitability for unseen mix designs. This is particularly relevant for service-life assessment of partition panels and lightweight façade elements, where long-term performance guarantees are required. The physics-informed architecture ensures asymptotic strength stabilization up to a 10-year horizon (amplification ratios 1.03–1.05). This prevents the non-physical divergence observed in polynomial and power-law hybrids (ratios 1.36–1.70). The framework provides a reliable and interpretable tool for service-life design of sustainable low-carbon cementitious systems. Full article

Treatment wetlands (TWs) are increasingly deployed as nature-based solutions for water and wastewater management due to their cost-effectiveness, operational simplicity, and provision of wider ecosystem benefits. The UK has been at the forefront of TW application since the 1980s. This study evaluated their development and performance within Southern Water, a water utility in the UK. In total, 35 sewage treatment sites have incorporated TWs since 1991, primarily for tertiary treatment and stormwater overflow control. Performance data were available for 16 sites, comprising 14 horizontal subsurface flow (HSSF) and two surface flow (SF) wetlands. HSSF wetlands achieved substantial reductions in TSSs (up to 97%), ${\mathrm{N}\mathrm{H}}_4^{+}$ (up to 99%), and BOD5 (up to 92%). The COD removal showed more variance (0–62%) in the studied sites. In contrast, SF wetlands provided moderate reductions in TSSs (17–79%) and COD (36–67%) but were less effective for ${\mathrm{N}\mathrm{H}}_4^{+}$ and BOD5 (14–65%). The TWs operated by Southern Water currently serve more than 100,000 people and illustrate the expanding role of such systems in meeting wastewater treatment needs. However, challenges and further research are needed, including risks of media clogging, the evaluation of emerging micropollutants treatment, and inconsistent maintenance. To address these, the study highlights opportunities for innovation through hybrid and aerated designs, advanced monitoring, and a more detailed understanding of plant–microbe interactions. The findings emphasise both the potential and future research needs of TWs and support their continued integration into wastewater management strategies under evolving environmental and regulatory pressures. Full article

Developing far-offshore wind power integrated with hydrogen production represents a critical pathway for China’s energy decarbonization. However, the investment prospects of off-grid offshore wind-to-hydrogen projects remain highly uncertain due to volatile technology costs and hydrogen prices, complicating the evaluation of project value and optimal timing. To address the oversimplified treatment of electrolyzer operation and the limited consideration of alkaline electrolyzers in the existing studies, this paper proposes an integrated assessment framework that combines time-series operational simulation with real options analysis. A detailed dynamic model of an alkaline (ALK)–proton exchange membrane (PEM) hybrid configuration is developed to simulate the coordinated hydrogen production under fluctuating wind power. Technical learning effects and stochastic hydrogen price processes are incorporated, and the least-squares Monte Carlo method is applied to determine the optimal investment strategies. A case study of a planned far-offshore wind farm in Guangdong indicates that, compared with a standalone PEM configuration, the hybrid configuration reduces the levelized hydrogen cost by about 15%, increases the investment value by up to 17 times under slow technological progress, and brings forward the optimal investment year by five years, from 2039 to 2034. Sensitivity analysis shows that expected hydrogen prices and discount rates dominate the investment outcomes. Full article