Search Results (23,392)

Aero-engine casings with excellent impact resistance are a practical requirement for ensuring the safe operation of aero-engines. In this paper, we report on numerical simulations of broken rotating blades impacting aluminum honeycomb sandwich casings under different temperatures and optimization of structural parameters. Firstly, an impact test system with adjustable temperature was established. Restricted by the temperature range of the strain gauge, ballistic impact tests were carried out at 25 °C, 100 °C, and 200 °C. Secondly, a finite element (FE) model including a pointed bullet and an aluminum honeycomb sandwich plate was built using LS-DYNA. The corresponding simulations of the strain–time curve and damage conditions showed good agreement with the test results. Then, the containment capability of the aluminum honeycomb sandwich aero-engine casing at different temperatures was analyzed based on the kinetic energy loss of the blade, the internal energy increment of the casing, and the containment state of the blade. Finally, with the design objectives of minimizing the casing mass and maximizing the blade kinetic energy loss, the structural parameters of the casing were optimized using the multi-objective genetic algorithm (MOGA). Full article

Thermostable cellulases and xylanases have broad acceptance in food, feed, paper and pulp, and bioconversion of lignocellulosics. Thermophilic fungi serve as an excellent source of thermostable enzymes. This study characterized four endo-β-1,4-glucanases (two glycoside hydrolase (GH) family 5 and two GH7 members) and four endo-β-1,4-xylanases (two GH10 and two GH11 members) from thermophilic fungus Thielavia terrestris, along with one GH10 endo-β-1,4-xylanase each from thermophilic fungus Chaetomium thermophilum and mesophilic fungus Chaetomium globosum. Comparative analysis was conducted against three previously reported GH10 endoxylanases: two thermostable enzymes from the thermophilic fungus Humicola insolens and thermophilic bacterium Halalkalibacterium halodurans, and one mesophilic enzyme from model fungus Neurospora crassa. The GH10 xylanase TtXyn10C (Thite_2118148; UniProt G2R8T7) from T. terrestris demonstrated high thermostability and activity, with an optimal temperature of 80–85 °C. It retained over 60% of its activity after 2 h at 70 °C, maintained approximately 30% activity after 15 min at 80 °C, and showed nearly complete stability following 1 min of exposure to 95 °C. TtXyn10C exhibited specific activity toward beechwood xylan (1130 ± 15 U/mg) that exceeded xylanases from H. insolens and H. halodurans while being comparable to N. crassa xylanase activity. Furthermore, TtXyn10C maintained stability across a pH range of 3–9 and resisted trypsin digestion, indicating its broad applicability. The study expands understanding of enzymes from thermophilic fungi. The discovery of the TtXyn10C offers a new model for investigating the high activity-stability trade-off and structure-activity relationships critical for industrial enzymes. Full article

Microalgal biomass has emerged as a valuable and nutrient-rich source of novel plant-based foods of the future, with several demonstrated benefits. In addition to their green and health-promoting characteristics, these foods exhibit bioactive properties that contribute to a range of physiological benefits. Photoautotrophic microalgae are particularly important as a source of food products due to their ability to biosynthesize high-value compounds. Their photosynthetic efficiency and biosynthetic activity are directly influenced by light conditions. The primary goal of this study is to track the changes in the light requirements of various high-value microalgae species and use advanced systems to regulate these conditions. Artificial intelligence (AI) and machine learning (ML) models have emerged as pivotal tools for intelligent microalgal cultivation. This approach involves the continuous monitoring of microalgal growth, along with the real-time optimization of environmental factors and light conditions. By accumulating data through cultivation experiments and training AI models, the development of intelligent microalgae cell factories is becoming increasingly feasible. This review provides a concise overview of the regulatory mechanisms that govern microalgae growth in response to light conditions, explores the utilization of microalgae-based products in plant-based foods, and highlights the potential for future research on intelligent microalgae cultivation systems. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets. Full article

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods. Full article

Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due to complex structure–property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R² values of 0.964 for training and 0.801 for testing. SHAP analysis provided unprecedented mechanistic insights, revealing that PVA molecular weight dominates mechanical performance (SHAP importance: 84.94) through chain entanglement and crystallization mechanisms, followed by degree of hydrolysis (72.46) and cross-linking parameters. The interpretability analysis identified optimal parameter ranges and critical feature interactions, elucidating complex non-linear relationships and reinforcement mechanisms. By addressing the “black box” limitation of machine learning, this approach enables rational design strategies and mechanistic understanding for next-generation multifunctional hydrogels. Full article

CRISPR technology, which is derived from the bacterial adaptive immune system, has transformed traditional genetic engineering techniques, made strain engineering significantly easier, and become a very versatile genome editing system that allows for precise, programmable modifications to a wide range of microbial genomes. The economies of fermentation-based manufacturing are changing because of its quick acceptance in both academic and industry labs. CRISPR processes have been used to modify industrially significant bacteria, including the lactic acid producers, Clostridium spp., Escherichia coli, and Corynebacterium glutamicum, in order to increase the yields of bioethanol, butanol, succinic acid, acetone, and polyhydroxyalkanoate precursors. CRISPR-mediated promoter engineering and single-step multiplex editing have improved inhibitor tolerance, raised ethanol titers, and allowed for the de novo synthesis of terpenoids, flavonoids, and recombinant vaccines in yeasts, especially Saccharomyces cerevisiae and emerging non-conventional species. While enzyme and biopharmaceutical manufacturing use CRISPR for quick strain optimization and glyco-engineering, food and beverage fermentations benefit from starter-culture customization for aroma, texture, and probiotic functionality. Off-target effects, cytotoxicity linked to Cas9, inefficient delivery in specific microorganisms, and regulatory ambiguities in commercial fermentation settings are some of the main challenges. This review provides an industry-specific summary of CRISPR–Cas9 applications in microbial fermentation and highlights technical developments, persisting challenges, and industrial advancements. Full article

The widespread discharge of industrial and urban waste has led to significant increases in the environmental concentrations of numerous chemical substances. This work presents the development of a simple and environmentally friendly dispersive liquid–liquid microextraction (DLLME) method based on a hydrophobic natural deep eutectic solvent (NADES) for the determination of selected compounds from benzotriazole, benzothiazole, paraben, and UV filter families in wastewater samples. Of the twelve NADES formulations evaluated, those composed of a 4:1 molar ratio of thymol and menthol presented the highest extraction efficiencies. The influence of key experimental variables such as the pH of the aqueous sample, the ratio of NADES phase to sample volume, and the extraction time on the extraction efficiency was investigated using a multivariate optimization. Under optimal conditions, relative standard deviations below 15% and recoveries for spiked wastewater samples ranged between 82 and 108%, demonstrating the suitability of the method for routine water-quality monitoring. The sustainability and practicality of the developed method was evaluated using the assessment tools ChlorTox, AGREEprep, AGRRE, and BAGI, obtaining scores of 0.005 g in the NADES-DLLME method, 0.70, 0.52, and 72.5, respectively, demonstrating that the method is green and reliable. Full article

A novel single-walled carbon nanotube (SWNT), silver (Ag) nanoparticle, and zinc benzene carboxylate (ZnBDC) metal–organic framework (MOF) composite was synthesised and systematically characterised to develop an efficient platform for mercury ion (Hg²⁺) detection. X-ray diffraction confirmed the successful incorporation of Ag nanoparticles and SWNTs without disrupting the crystalline structure of ZnBDC. Meanwhile, field-emission scanning electron microscopy and energy-dispersive spectroscopy mapping revealed a uniform elemental distribution. Thermogravimetric analysis indicated enhanced thermal stability. Electrochemical measurements (cyclic voltammetry and electrochemical impedance spectroscopy) demonstrated improved charge transfer properties. Electrochemical sensing investigations using differential pulse voltammetry revealed that the SWNTs/Ag@ZnBDC-modified glassy carbon electrode exhibited high selectivity toward Hg²⁺ ions over other metal ions (Cd²⁺, Co²⁺, Cr³⁺, Fe³⁺, and Zn²⁺), with optimal performance at pH 4. The sensor displayed a linear response in the concentration range of 0.1–1.0 nM (R² = 0.9908), with a calculated limit of detection of 0.102 nM, slightly close to the lowest tested point, confirming its high sensitivity for ultra-trace Hg²⁺ detection. The outstanding sensitivity, selectivity, and reproducibility underscore the potential of SWNTs/Ag@ZnBDC as a promising electrochemical platform for detecting trace levels of Hg2+ in environmental monitoring. Full article

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article

This study, in response to the optimization needs of fall risks for the elderly in the context of cultural and tourism integration in Hebei Province, China, established a quantitative correlation system between ten gait parameters and ten types of spatial risk factors. By collecting gait data (Qualisys infrared motion capture system, sampling rate 200 Hz) and spatial parameters from 30 elderly subjects (with mild, moderate, and severe functional impairments), a multi-level regression model was established. This study revealed that step frequency, step width, and step length were nonlinearly associated with corridor length, door opening width, and step depth (R² = 0.53–0.68). Step speed, ankle dorsiflexion, and foot pressure were key predictive factors (OR = 0.04–8.58, p < 0.001), driving the optimization of core spatial factors such as threshold height, handrail density, and friction coefficient. Step length, cycle, knee angle, and lumbar moment, respectively, affected bed height (45–60 cm), switch height (1.2–1.4 m), stair riser height (≤35 mm), and sink height adjustment range (0.7–0.9 m). The prediction accuracy of the ten optimized values reached 86.7% (95% CI: 82.1–90.3%), with Hosmer–Lemeshow goodness-of-fit x² = 7.32 (p = 0.412) and ROC curve AUC = 0.912. Empirical evidence shows that the graded optimization scheme reduced the fall risk by 42–85%, and the estimated fall incidence rate decreased by 67% after the renovation. The study of the “abnormal gait—spatial threshold—graded optimization” quantitative residential layout optimization provides a systematic solution for the data-quantified model of elderly-friendly residential renovations. Full article

Breast milk is the optimal nutrition for infants, yet lactation insufficiency remains a common cause of early breastfeeding cessation. Moringa oleifera has been traditionally used as a galactagogue due to its rich micronutrient and phytosterol content. This systematic review assessed the effects of Moringa leaf supplementation on prolactin levels and breast milk volume in postpartum mothers with lactation insufficiency. A systematic search following PRISMA guidelines, was conducted for randomized controlled trials involving healthy postpartum women supplemented with Moringa oleifera. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. Eight studies met the inclusion criteria, with intervention durations ranged from 3 to 10 days. Moringa supplementation increased significantly breast milk volume by up to 400 mL/day compared to controls. Serum prolactin levels also rose significantly with a mean increase of 231.72 ng/mL Most studies exhibited low to moderate risk of bias, though one study exhibited high risk due to lack of binding and subjective outcome measurement. Moringa oleifera leaf supplementation appears to enhance lactation by increasing milk volume and prolactin levels in postpartum mothers. However, further longer-term studies are needed to establish optimal dosing, sustained effectiveness, and safety. Full article

Dens invaginatus is a developmental dental anomaly characterized by the infolding of the enamel organ into the dental papilla during early odontogenesis. This process leads to a broad spectrum of anatomical variations, ranging from minor enamel-lined pits confined to the crown to deep invaginations extending through the root, occasionally communicating with periodontal or periapical tissues. The internal complexity of affected teeth presents diagnostic and therapeutic challenges, particularly in severe forms that mimic root canal systems or are associated with pulpal or periapical pathology. Maxillary lateral incisors are most frequently affected, likely due to their unique developmental timeline and morphological susceptibility. Although various classification systems have been proposed, Oehlers’ classification remains the most clinically relevant due to its simplicity and correlation with treatment complexity. Recent advances in diagnostic imaging, especially cone beam computed tomography (CBCT), have revolutionized the identification and classification of these anomalies. CBCT-based adaptations of Oehlers’ classification allow for the precise assessment of invagination extent and pulpal involvement, facilitating improved treatment planning. Contemporary therapeutic strategies now include calcium-silicate-based cement sealing materials, endodontic microsurgery for inaccessible anatomy, and regenerative endodontic procedures for immature teeth with necrotic pulps. Emerging developments in artificial intelligence, genetic research, and tissue engineering promise to further refine diagnostic capabilities and treatment options. Early detection remains critical to prevent complications such as pulpal necrosis or apical disease. A multidisciplinary, image-guided, and patient-centered approach is essential for optimizing clinical outcomes in cases of dens invaginatus. Full article