Search Results (48,495)

Settlement control for tunnel–terminal co-construction projects remains undefined, despite the growing trend of integrating multiple transportation modes within large-scale transport hubs. This study investigates a large underground structure passing beneath an airport terminal, combining field investigations, statistical analyses, and finite element simulations to examine differential settlement behavior under non-uniform loading conditions. The key contribution of this work is the proposal of a differential settlement control standard, defined by the tangent of the rotation angle between adjacent column foundations, with a recommended value of 1/625. Case analysis at cross-section E–E shows that the measured maximum tangent rotation angle was 1/839, corresponding to base slab settlements of 40.5 mm and 33.1 mm for the high-speed railway and metro structures, respectively. Application of the proposed 1/625 criterion yields allowable maximum base slab settlements of 55.28 mm for the high-speed railway and 44.83 mm for the metro, with differential settlement limits of 7.5 mm and 3.13 mm. Numerical simulations confirm the validity of this standard, ensuring the structural integrity of co-constructed systems and providing practical guidance for future airport terminal–tunnel integration projects. Full article

In the context of increasingly stringent vehicle emission regulations, computer-aided engineering has been indispensable for optimizing the design and the operational strategies of emission control systems. This paper proposes a new filtration model for particulate filters that enables the accurate estimation of solid particle number emissions above 10 and 23 nm in diameter (SPN10 and SPN23, respectively). The model incorporates a persistent slip factor and a linear filtration efficiency of cake layers into the unit collector model proposed by Konstandopoulos and Johnson. This enhancement captures PM escape phenomena, such as a passage through interconnected large pores in filter walls. Simulations using a 1D + 1D two-channel framework with the proposed model successfully reproduced experimental results of SPN10 and SPN23 emissions downstream of a miniature gasoline particulate filter (GPF) tested with a synthetic particle generator. The model was also able to represent the observed continuous emissions during a cake filtration mode. Additional simulations using the same model parameters showed good agreement with experimental data of SPN10 and SPN23 emissions downstream of a full-size GPF tested with a gasoline direct injection (G-DI) engine under 5 steady-state operating conditions. The simulations revealed that particles in the 10–100 nm size range dominated the downstream SPN emissions despite their high filtration efficiency, whereas particles in the 100–200 nm size range were less significant. The proposed model is expected to contribute to the GPF developments to comply with the stringent emission regulations of the upcoming Euro 7. Full article

Nitrate leaching from agricultural soils is a major contributor to groundwater contamination and non-point source pollution. Controlling this loss remains challenging due to the complexity of soil–water–nutrient interactions under intensive farming practices. Biochar, a porous, carbon-rich material derived from biomass pyrolysis, has emerged as a promising amendment for nitrate mitigation. This review summarizes recent advances in understanding the roles of biochar in nitrate retention and transformation in soils, including both direct mechanisms—such as surface adsorption, ion exchange, and pore entrapment—and indirect mechanisms—such as enhanced microbial activity, soil structure improvement, and root system development. Field and laboratory evidence shows that biochar can reduce NO₃⁻-N leaching by 15–70%, depending on its properties, soil conditions, and application context. However, inconsistencies in performance due to differences in biochar types, soil conditions, and environmental factors remain a major barrier to widespread adoption. This review also suggests current knowledge gaps and research needs, including long-term field validation, biochar material optimization, and integration of biochar into precision nutrient management. Overall, biochar presents a multifunctional strategy for reducing nitrate leaching and promoting sustainable nitrogen management in agroecosystems. Full article

Alpha-1 antitrypsin deficiency (AATD) represents a paradigmatic genetic disorder with well-characterized hepatic manifestations but relatively underexplored pulmonary implications. While liver involvement has been extensively reviewed, the underlying mechanisms of lung disease progression remain poorly understood, particularly regarding immunological pathways and inflammatory processes. The pathophysiology involves defective alpha-1 antitrypsin (AAT) production, including AAT variants that induce neutrophil elastase activity, causing progressive alveolar destruction and sustained inflammation, leading to emphysema, as one of the main components of chronic obstructive pulmonary disease (COPD). AATD and smoking represent major risk factors for COPD, the third leading cause of death worldwide at present. In AATD patients, neutrophils, which constitute the majority of circulating leukocytes, become dysregulated. Under normal conditions, cells perform essential functions, including phagocytosis and neutrophil extracellular trap formation (NETosis); in AATD, however, they accumulate excessively in alveolar spaces due to impaired elastase control. The accumulation of Z-AAT polymers within epithelial cells creates a pathological cycle, acting as chemoattractants that sustain pro-inflammatory responses and contribute to chronic obstructive pulmonary disease development. In addition, monocytes, representing a smaller fraction of leukocytes, migrate to inflammatory sites and differentiate into macrophages while secreting AAT with anti-inflammatory properties. However, in PiZZ patients, this protective mechanism fails, as polymer accumulation within cells reduces both AAT secretion and the number of protective human leukocyte antigen(HLA)-DR-monocyte subsets. In particular, macrophages demonstrate remarkable plasticity, switching between pro-inflammatory M1 (classically activated macrophages) and tissue-repairing M2 (alternatively activated macrophages) phenotypes based on environmental cues. In AATD, this adaptive capability becomes compromised due to intracellular polymer accumulation, leading to impaired phagocytic function and dysregulated cytokine production and ultimately perpetuating chronic inflammation and progressive tissue damage. Recent advances in induced pluripotent stem cell (iPSC) technology have facilitated alveolar epithelial cell (AEC) generation, in addition to the correction of AATD mutations through gene editing systems. Despite the limitations of AAT correction, iPSC-derived organoid models harboring AATD mutations can deliver important insights into disease pathophysiology, while gene editing approaches help demonstrate causality between specific mutations and observed phenotypes. Therefore, in this review, we investigated recent studies that can serve as tools for gene editing and drug development based on recently developed iPSC-related technologies to understand the pathogenesis of AATD. Full article

The self-starting capability of straight-bladed H-type Darrieus Vertical Axis Wind Turbines (VAWTs) remains a major constraint for deployment, particularly in urban, low speed, and turbulent environments. We conducted a systematic review of technological strategies to improve self-starting, grouped into five categories: (1) aerodynamic airfoil design, (2) rotor configuration, (3) passive flow control, (4) active flow control, and (5) incident flow augmentation. Searches in Scopus and IEEE Xplore (last search 20 August 2025) covered the period from 2019 to 2026 and included peer-reviewed journal articles in English reporting experimental or numerical interventions on H-type Darrieus VAWTs with at least one start-up metric. From 1212 records, 53 studies met the eligibility after title/abstract screening and full-text assessment. Data were synthesized qualitatively using a comparative thematic approach, highlighting design parameters, operating conditions, and performance metrics (torque and power coefficients) during start-up. Quantitatively, studies reported typical start-up torque gains of 20–30% for airfoil optimization and passive devices, about 25% for incident-flow augmentation, and larger but less certain improvements (around 30%) for active control. Among the strategies, airfoil optimization and passive devices consistently improved start-up torque at low TSR with minimal added systems; rotor-configuration tuning and incident-flow devices further reduced start-up time where structural or siting constraints allowed; and active control showed the largest laboratory gains but with uncertain regarding energy and durability. However, limitations included heterogeneity in designs and metrics, predominance of 2D-Computational Fluid Dynamics (CFDs), and limited 3D/field validation restricted quantitative pooling. Risk of bias was assessed using an ad hoc matrix; overall certainty was rated as low to moderate due to limited validation and inconsistent uncertainty reporting. In conclusions, no single solution is universally optimal; hybrid strategies, combining optimized airfoils with targeted passive or active control, appear most promising. Future work should standardize start-up metrics, adopt validated 3D Fluid–Structure Interaction (FSI) models, and expand wind-tunnel/field trials. Full article

Photodynamic therapy (PDT) is a promising, minimally invasive treatment for cervical cancer, but its efficacy is significantly limited by hypoxia—oxygen deficiency in the tumour microenvironment. The aim of this study was to present strategies to counteract hypoxia in PDT using the latest nanotechnologies. Based on a review of the literature available in PubMed/MEDLINE, Scopus, and Web of Science databases, covering the period from January 2024 to March 2025, nine original in vivo studies were identified that investigated the use of nanoparticle-based strategies to overcome hypoxia and enhance the efficacy of PDT in cervical cancer. A variety of approaches to improve tumour oxygenation are described, including the catalytic decomposition of hydrogen peroxide (H₂O₂) with manganese oxide (MnO₂), the use of bimetallic nanozymes (e.g., Au₂Pt), and FeOOH structures and oxygen storage and control systems (e.g., endoperoxides). Strategies to reduce oxygen consumption by cancer cells, such as nitric oxide (NO) release or inhibition of mitochondrial oxidative phosphorylation, are also discussed. The review shows that appropriately designed nanoparticles can effectively counteract hypoxia, enhancing the efficacy of PDT by intensifying reactive oxygen species (ROS) generation and modulating HIF-1α factor expression. The strategies presented here have the potential to significantly improve the efficacy of photodynamic therapy in the treatment of cervical cancer, especially under conditions of limited oxygen availability. Full article

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article

This paper proposes a design and control methodology for a Quasi-Y-Source impedance source inverter (QS-YSI) as a power electronics interface for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) applications in the context of virtual power plants (VPPs). The work presents an analysis of bidirectional power transfer using Electric Vehicles (EVs) to supply power to the utility grid, businesses, and homes, thereby acting as distributed energy resources. The proposed QS-YSI topology supports both V2G and G2V operation while providing reactive power compensation and enabling the decoupled tracking of active power (P) and reactive power (Q), demonstrating the capability of EVs to return energy to the grid and to provide ancillary services such as power factor correction. The key contributions are a detailed control design methodology that includes pulsating DC-link voltage regulation, inverter output current reference tracking in the synchronous $dq$ reference frame considering DC-link voltage dynamics, and a modified Pulse Width Modulation (PWM) technique for effective decoupling of DC link and inverter output current control. Finally, the feasibility and validity of the proposed approach are demonstrated through simulations of the complete system under nominal conditions and experiments conducted considering a small-scale prototype. Full article

Autonomous driving technology is developing rapidly, particularly in vision-based approaches that rely on cameras to monitor the environment. However, one of the critical challenges for autonomous vehicles is the ability to adapt to different weather conditions, as environmental factors such as clouds, fog, rain, sand, shine, snow, and the sunrise significantly impact their perceptual capabilities. Control strategies of the vehicles must be dynamically adjusted based on real-time weather conditions to ensure safe and efficient driving. For the strategies, we developed a novel weather perception model to improve the adaptability of autonomous driving systems. The model is more lightweight than an existing study, as it is computationally efficient with enhanced performance. Moreover, the model detects a weather type, improving its robustness, and providing reliable weather awareness for autonomous driving. Full article

WRKY transcription factors play an important role in transcriptional reprogramming associated with plant abiotic stress responses. In this study, the role of Solanum tuberosum (S. tuberosum; St) WRKY transcription factors StWRKY4 and StWRKY56 were explored in response to salt stress by generating transgenic potato lines using RNAi. The results showed that the total chlorophyll content in transgenic StWRKY4 was 6.1 mg/g at 200 mM after 35 days; however, in StWRKY56, an elevated 12.6 mg/g total chlorophyll was observed which indicated different operating mechanisms of these StWRKY transcription factors under salt stress. Proline content increased to 1.0 mg/g in StWRKY4 while it decreased to 0.54 mg/g in StWRKY56 as compared to their respective control plants after 35 days at 200 mM of salt stress. For Na⁺/K⁺ ratios, StWRKY4 and StWRKY56 showed 32.3 and 5.5 values, respectively, in silenced plants under similar conditions. This shows contrasting trends in StWRKY4 and StWRKY56 for Na⁺/K⁺. However, the expression analyses of StSOS1s were found to be upregulated, whereas for StNHX3s these were found to be downregulated in StWRKY4 and StWRKY56 under salt stress. Thus, this study, for the first time, demonstrated the different but critical roles of StWRKY4 and StWRKY56 for fine-regulating salt stress tolerance in complex signaling network of potato plant. Full article

Selenium is an important trace element with certain antioxidant effects. Nano-selenium, as a novel selenium source, has the advantages of strong biological activity, high absorption efficiency, and low toxicity. The aim of the present study was to compare the protective effects of sodium selenite and nano-selenium on intestinal oxidative stress induced by hydrogen peroxide (H₂O₂) in mice. A total of 60 female mice were randomly divided into 6 groups with 10 replicates per group and 1 mouse per replicate (n = 10). The first three groups were as follows: the Control group (C), fed with basal diet; the sodium selenite group (SS), basal diet + 0.3 mg·kg⁻¹ sodium selenite; and the nano-selenium group (NS), basal diet + 0.3 mg·kg⁻¹ nano-selenium. The latter three groups (CH, SSH, NSH) were fed the same diet as the former three groups, but the last 10 days of the experiment were fed with drinking water containing 0.3% H₂O₂ to induce oxidative stress. The results showed that under normal conditions, the supplementation with sodium selenite or nano-selenium decreased the spleen index of mice; sodium selenate up-regulates GPX3 expression in the ileum, and increases T-SOD in the colon of mice; and nano-selenium up-regulated GPX1 expression but decreased T-AOC in the jejunum. After drinking water treated with H₂O₂, H₂O₂ increased the expression of intestinal inflammatory factors and selenium proteins, such as IL-1β and SOD in jejunum, IL-1β, NF-κB, IL-10, TXNRD1, TXNRD2, GPX1, GPX3, GPX4, and CAT in ileum, and IL-1β and SOD in colon. At the antioxidant level, H₂O₂ decreased T-AOC in the jejunum. In the H₂O₂ treatment, sodium selenite and nano-selenium increased the ratio of VH to CD (VH/CD) in jejunum; sodium selenite up-regulated the expression of TXNRD1 in jejunum, down-regulated the expression of GPX3 in ileum, at the antioxidant level, decreased the T-SOD and T-AOC in colon, and increased the content of MDA in ileum; and nano-selenium down-regulated the expression of TXNRD1 in colon. At the same time, the expression of IL-1β, NF-κB, IL-10, TXNRD1, TXNRD2, GPX1, GPX4, and CAT can be restored to normal levels by selenium supplementation. According to the results, drinking H₂O₂ induced intestinal oxidative stress in mice to a certain extent, and selenium supplementation mitigated the destructive effect of H₂O₂ on the intestinal morphology of mice jejunum and restored the level of related inflammatory factors, and had a positive effect on antioxidants. Full article

The integrity of subsea oil and gas pipelines is essential for offshore safety and environmental protection. Conventional leak detection approaches, such as manual inspection and indirect sensing, are often costly, time-consuming, and prone to subjectivity, motivating the development of automated methods. In this study, we present a deep learning-based framework for detecting underwater leaks using images acquired in controlled experiments designed to reproduce representative conditions of subsea monitoring. The dataset was generated by simulating both gas and liquid leaks in a water tank environment, under scenarios that mimic challenges observed during Remotely Operated Vehicle (ROV) inspections along the Brazilian coast. It was further complemented with artificially generated synthetic images (Stable Diffusion) and publicly available subsea imagery. Multiple Convolutional Neural Network (CNN) architectures, including VGG16, ResNet50, InceptionV3, DenseNet121, InceptionResNetV2, EfficientNetB0, and a lightweight custom CNN, were trained with transfer learning and evaluated on validation and blind test sets. The best-performing models achieved stable performance during training and validation, with macro F1-scores above 0.80, and demonstrated improved generalization compared to traditional baselines such as VGG16. In blind testing, InceptionV3 achieved the most balanced performance across the three classes when trained with synthetic data and augmentation. The study demonstrates the feasibility of applying CNNs for vision-based leak detection in complex underwater environments. A key contribution is the release of a novel experimentally generated dataset, which supports reproducibility and establishes a benchmark for advancing automated subsea inspection methods. Full article

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions. Full article

Newcastle disease (ND) is a highly contagious and economically significant viral infection that affects poultry globally, with recurrent outbreaks occurring even among vaccinated flocks in Egypt. Caused by the Newcastle disease virus (NDV), the disease results in substantial losses due to high mortality rates, decreased productivity, and the imposition of trade restrictions. This study aimed to develop a rapid, sensitive, and field-deployable diagnostic assay based on real-time reverse transcription recombinase-aided amplification (RT-RAA) for the detection of all NDV genotypes in clinical avian specimens. Primers and an exo-probe were designed based on the most conserved region of the NDV matrix gene. After testing ten primer combinations, the pair NDV RAA-F1 and RAA-R5 demonstrated the highest sensitivity, detecting as low as 6.89 EID₅₀/mL (95% CI). The RT-RAA assay showed excellent clinical sensitivity and specificity, with no cross-reactivity to other common respiratory pathogens such as avian influenza virus, infectious bronchitis virus, Mycoplasma gallisepticum or infectious laryngotracheitis virus. All 25 field samples that were tested positive by real-time RT-PCR, including those with high CT values (~35), were detected by RT-RAA in 2–11 min, indicating superior sensitivity and speed. The assay requires only basic equipment and can be performed under isothermal conditions, making it highly suitable for on-site detection in resource-limited or rural settings. The successful implementation of RT-RAA can improve NDV outbreak response, support timely vaccination strategies, and enhance disease control efforts. Overall, the assay presents a promising alternative to conventional diagnostic methods, contributing to the sustainability and productivity of the poultry sector in endemic regions. Full article

With the increasing integration of renewable energy sources and power electronic converters, Grid-Forming (GFM) technologies such as Virtual Synchronous Generators (VSGs) have emerged as key enablers of future power systems. However, conventional VSG control strategies with fixed parameters often fail to maintain transient stability under dynamic grid conditions. This paper proposes a novel adaptive GFM control framework based on physics-informed reinforcement learning, targeting transient power angle stability in systems with high renewable penetration. An adaptive controller, termed the 3N-D controller, is developed to periodically update the virtual inertia and damping coefficients of VSGs based on real-time system observations, enabling anticipatory adjustments to evolving operating conditions. The controller leverages a reinforcement learning architecture embedded with physical priors, which captures the high-order differential relationships between rotor angle dynamics and control variables. This approach enhances generalization, reduces data dependency, and mitigates the risk of local optima. Comprehensive simulations on the IEEE-39 bus system with varying VSG penetration levels validate the proposed method’s effectiveness in improving system stability and control flexibility. The results demonstrate that the physics-embedded GFM strategy can significantly enhance the transient stability and adaptability of future power grids. Full article