Search Results (987)

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

Lunar dust remains one of the most critical unresolved challenges to long-duration lunar missions. Its sharp, abrasive, and electrostatically charged particles are easily inhaled and can penetrate deep into the lungs, reaching the bloodstream and the brain. Despite airlocks and HEPA filtration systems, dust will inevitably infiltrate lunar habitats and threaten astronaut health. We present a novel patent protected helmet design. This system uses a multilayered, synergistic mitigation approach combining mechanical and electrostatic defenses. The mechanical system delivers HEPA-filtered, ionized air across the user’s face, while the electrostatic barrier repels charged particles away from the respiratory zone. These two systems work together to prevent dust from entering the user’s breathing space. Designed for use inside lunar habitats, this helmet represents a potential solution to an unaddressed, life-threatening problem. It allows astronauts to eat, talk, and sleep while maintaining a protected respiratory zone and provides targeted inhalation-level protection in an environment where dust exposure is otherwise unavoidable. This concept is presented at Technology Readiness Level 2 (TRL 2) to prompt early engagement and feedback from the scientific and engineering communities. Full article

With the increasing penetration of wind and photovoltaic (PV) power in modern power systems, accurate power forecasting has become crucial for ensuring grid stability and optimizing dispatch strategies. This study focuses on multiple wind farms and PV plants, where three deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and a hybrid Transformer–BiLSTM model—are constructed and systematically compared to enhance forecasting accuracy and dynamic responsiveness. First, the predictive performance of each model across different power stations is analyzed. The results reveal that the LSTM model suffers from systematic bias and lag effects in extreme value ranges, while Bi-LSTM demonstrates advantages in mitigating time-lag issues and improving dynamic fitting, achieving on average a 24% improvement in accuracy for wind farms and a 20% improvement for PV plants compared with LSTM. Moreover, the Transformer–BiLSTM model significantly strengthens the ability to capture complex temporal dependencies and extreme power fluctuations. Experimental results indicate that the Transformer–BiLSTM consistently delivers higher forecasting accuracy and stability across all test sites, effectively reducing extreme-value errors and prediction delays. Compared with Bi-LSTM, its average accuracy improves by 19% in wind farms and 35% in PV plants. Finally, this paper discusses the limitations of the current models in terms of multi-source data fusion, outlier handling, and computational efficiency, and outlines directions for future research. The findings provide strong technical support for renewable energy power forecasting, thereby facilitating efficient scheduling and risk management in smart grids. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

This study investigated the Yongning Fortress ruins in Taiyuan through a comprehensive analytical approach employing scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), laser particle size analysis, X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), and ion chromatography (IC). The research focused on elucidating the disturbance mechanisms and environmental impacts induced by the root systems of five representative herbaceous species on rammed earth structures. The results demonstrated distinct, species-specific disturbance patterns. Melica roots created three-dimensional network damage, Artemisia capillaris primarily caused deep root penetration, Fallopia aubertii exhibited coupled physical–chemical effects, Convolvulus arvensis induced shallow horizontal expansion damage, while Cirsium formed a heterogeneous structure characterized by dense taproots and loose lateral roots. Environmental conditions, particularly moisture content, significantly influenced disturbance intensity. All root activities led to common deterioration processes, including particle rounding, gradation degradation, and formation of organic–mineral composites. Notably, vegetation markedly altered soluble salt distribution patterns, with Cirsium increasing total salt content to 3.7 times that of undisturbed rammed earth (0.48%), while sulfate ion concentration (1.16 × 10⁻³) approached hazardous thresholds. The study established a theoretical framework linking plant traits, disturbance mechanisms, and environmental response, and proposed risk-based zoning strategies for preservation. These outcomes provide significant theoretical foundations and practical guidance for the scientific conservation of rammed earth heritage sites. Full article

Acute pulmonary embolism (APE) remains a significant cause of mortality and morbidity despite increasing prophylaxis for deep venous thrombosis (DVT). The IVC filter is a temporary or permanent intravascular device that traps migrating thrombi from their origin in the pelvis or a lower limb into the pulmonary vasculature, thereby preventing significant APE. The current and longstanding indications for placing an IVC filter are in patients with documented lower extremity DVT and acute APE who also have absolute contraindications to anticoagulation or have experienced an acute, hemodynamically unstable APE requiring ventilatory and vasoactive support, with limited cardiovascular reserve. Updated guidelines have led to a significant rise in IVC filter placements for specific therapeutic indications of venous thromboembolism compared to prophylactic use. Meta-analyses show that IVC filter placement is associated with a lower risk of subsequent APE but an increased risk of DVT. However, there appears to be no significant reduction in APE-related mortality and no change in all-cause mortality. Early complications after IVC filter placement typically relate to procedural issues and include bleeding or infection at the venous access site, development of arteriovenous fistulas, accidental arterial puncture, and post-procedural access site hematoma or thrombosis. Additional early complications include IVC filter malposition, incomplete expansion, IVC penetration, or guidewire entrapment. Delayed complications may involve DVT below the filter, IVC occlusion due to the filter, IVC filter migration, fracture of one of the IVC filter components, IVC rupture, or IVC thrombosis. Retrieval of IVC filters by simple, advanced, or open techniques should be considered after weighing the risk-to-benefit for the individual patient. Deployment of the IVC filter remains an important component of interventional APE management within the narrow indications currently proposed. Current guidance recommends that an untethered temporary IVC filter should be placed and retrieved once the contraindication to anticoagulation is resolved. Full article

Background/Objectives: Natural products exhibit significant immunomodulatory potential but face severe efficacy loss in three-dimensional (3D) tumor models. This review comprehensively examines the penetration–activity trade-off and proposes integrated strategies for developing effective natural product-based cancer immunotherapies. Methods: We analyzed formulation strategies across three natural product categories (hydrophobic, macromolecular, stability-sensitive), evaluating penetration enhancement versus activity preservation in spheroids, organoids, and advanced 3D platforms. Results: Tumor spheroids present formidable barriers: dense extracellular matrix (33-fold increased fibronectin), pH gradients (7.4 → 6.5), and extreme cell density (6 × 10⁷ cells/cm³). While nanoparticles, liposomes, and cyclodextrins achieve 3–20-fold penetration improvements, biological activity frequently declines through conformational changes, incomplete release (10–75%), and surface modification interference. Critically, immune cells remain peripheral (30–50 μm), questioning deep penetration pursuit. Patient-derived organoids display 68% predictive accuracy, while emerging vascularized models unveil additional complexity. Food and Drug Administration (FDA) Modernization Act 2.0 enables regulatory acceptance of these advanced models. Conclusions: Effective therapeutic outcomes depend on maintaining immunomodulatory activity in peripherally-located immune cell populations rather than achieving maximum tissue penetration depth. Our five-stage evaluation framework and standardization protocols guide development. Future priorities include artificial intelligence-driven optimization, personalized formulation strategies, and integration of multi-organ platforms to bridge the critical gap between enhanced delivery and therapeutic efficacy. Full article

Tissue repair is a significant challenge in biomedical research. Traditional treatments face limitations such as donor shortage, high costs, and immune rejection. Recently, magnetic-responsive materials, particularly magnetic nanoparticles have been introduced into tissue engineering due to their ability to respond to external magnetic fields, generating electrical, thermal, and mechanical effects. These effects enable precise regulation of cellular behavior and promote tissue regeneration. Compared to traditional physical stimulation, magnetic-responsive material-mediated stimulation offers advantages such as non-invasiveness, deep tissue penetration, and high spatiotemporal precision. This review summarizes the classification, fabrication, magnetic effects and applications of magnetic-responsive materials, focusing on their mechanisms and therapeutic effects in neural and bone tissue engineering, and discusses future directions. Full article

Driven by the “double carbon” goals, the penetration rate of distributed photovoltaics (PV) in distribution networks has increased rapidly. However, the continuous growth of distributed PV installed capacity poses significant challenges to the carrying capacity of distribution networks. Reinforcement learning (RL), with its capability to handle high-dimensional nonlinear problems, plays a critical role in analyzing the carrying capacity of distribution networks. This study constructs an evaluation model for distributed PV carrying capacity and proposes a corresponding quantitative evaluation index system by analyzing the core factors influencing it. An optimization scheme based on deep reinforcement learning is adopted, introducing the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the evaluation model. Finally, simulations on the IEEE-33 bus system validate the good feasibility of the reinforcement learning approach for this problem. Full article

Hyperbolic reflection features from ground-penetrating radar (GPR) data have been recognized as essential indicators for detecting hidden defects in the asphalt pavement. Computer vision and deep learning algorithms have been developed to detect and enhance the hyperbolic features of hidden defects. However, migrating existing hyperbolic feature detection methods using raw GPR data results in inaccurate predictions. Pre-processing raw GPR data using straightforward image processing methods could enhance the reflection features for fast and accurate hyperbolic detection during real-time GPR measurements. This study proposed accessible and straightforward image processing methods as GPR data preprocessing steps (such as the Sobel edge detector and histogram equalization) to assist existing computer vision algorithms for reflection feature enhancement during the GPR survey. Field tests were conducted, and several image processing methods with existing standard image processing libraries were applied. The proposed regions of the identified hyperbola signal-to-noise ratio (RIHSNR) were used to quantify the enhancement performance of hyperbolic feature detectability. Applying Sobel edge detection and Otsu’s thresholding to GPR data significantly improves detection accuracy and speed: mAP@0.5 rises from 0.65 to 0.85 for Faster R-CNN and from 0.72 to 0.88 for CBAM-YOLOv8 using the proposed computer vision methods as preprocessing steps. At the same time, inference time drops to 30 ms and 25 ms, respectively. Full article

Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution. Full article

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally pursued small investigation areas with measurements made in direct contact with target structures and for small depths. Detecting deep piles presents specific challenges, and surveys conducted from the ground level may be unsuccessful. To reach great depths, medium-low frequencies must be used, but this choice results in lower resolution. Furthermore, the pile signals may be masked when they are located beneath massive reinforced foundations, which act as an electromagnetic shield. Finally, GPR equipment looks for differences in the dielectric of the material, and the signals recorded by the GPR will be very weak when the differences in the physical properties of the investigated media are modest. From these weak signals, it is difficult to identify information on the differences in the subsurface media. In this paper, we are illustrating an exploration on plinth foundations, supported by drilled piles, submerged in soil, extensive, deep and uninformed. Pulse GPR prospecting was performed in common-offset and single-fold, bistatic configuration, exploiting the exposed faces of an excavation around the foundation. In addition, three velocity tests were conducted, including two in common mid-point and one in zero-offset transillumination, in order to explore the range of variation in relative dielectric permittivity in the investigated media. Thanks to the innovative survey on the excavation faces, it is possible to perform profiles perpendicular to the strike direction of the interface. The electromagnetic backscattering analysis approach allowed us to extract the weighted average frequency attribute section. In it, anomalies emerge in the presence of drilled piles with four piles with an estimated diameter of 80 cm. Full article

As high-end oil and gas equipment, the high-temperature high-pressure (HTHP) adaptability and intelligence level of Rotary Steerable Systems (RSS) directly determine the development efficiency of deep unconventional resources. This paper reviews the technological breakthroughs and industrial chain synergy pathways of domestic RSS in China, with core conclusions as follows: (1) domestic technologies represented by the CG STEER system have achieved stable operation at 150 °C, high build rates of 15.3°/30 m, and reservoir penetration rates of 98.7%, with key indicators reaching international advanced levels; (2) collaborative innovations in material system reconstruction, hybrid steering mechanisms, and vibration suppression technology have reduced single-well drilling cycles by 50%; (3) industrial chain synergy effects are significant: a 95% localization rate reduced the cost per bottom hole assembly (BHA) run to CNY 2 million, and the “Penta-Helix” innovation model increased patent sharing rates to >60%; (4) breakthroughs in 175 °C high-temperature chips and downhole intelligent decision-making algorithms are urgently needed. This study provides technological paradigms and industrial upgrading pathways for the autonomous development of drilling equipment for extreme conditions. Recognizing the need for comprehensive improvement, the revised manuscript will strengthen three key aspects: (1) supplementing systematic comparisons between domestic technologies and international benchmarks in terms of HTHP adaptability and intelligent control; (2) elaborating technical details of hybrid steering mechanisms and vibration suppression technologies to clarify their innovation in industrial processes; (3) adding case studies of autonomous decision-making systems in ultra-deep wells to verify the practical effectiveness of the proposed methods. These revisions aim to address the current limitations and enhance the scientific rigor of the study. Full article

Ground-penetrating radar (GPR) is an important geophysical technique in subsurface detection. However, traditional numerical simulation methods such as finite-difference time-domain (FDTD) face challenges in accurately simulating complex heterogeneous mediums in real-world scenarios due to the difficulty of obtaining precise medium distribution information and high computational costs. Meanwhile, deep learning methods require excessive prior information, which limits their application. To address these issues, this paper proposes a novel two-step forward modeling strategy for GPR data of metal pipes. The first step employs the proposed Polarization Self-Attention Image Translation network (PSA-ITnet) for image translation, which is inspired by the process where a neural network model “understands” image content and “rewrites” it according to specified rules. It converts scene layout images (cross-sectional schematics depicting geometric details such as the size and spatial distribution of underground buried metal pipes and their surrounding medium) into simulated clutter-free GPR B-scan images. By integrating the polarized self-attention (PSA) mechanism into the Unet generator, PSA-ITnet can capture long-range dependencies, enhancing its understanding of the longitudinal time-delay property in GPR B-scan images. which is crucial for accurately generating hyperbolic signatures of metal pipes in simulated data. The second step uses the Polarization Self-Attention Style Transfer network (PSA-STnet) for style transfer, which transforms the simulated clutter-free images into data matching the distribution and characteristics of a real-world underground heterogeneous medium under unsupervised conditions while retaining target information. This step bridges the gap between ideal simulations and actual GPR data. Simulation experiments confirm that PSA-ITnet outperforms traditional methods in image translation, and PSA-STnet shows superiority in style transfer. Real-world experiments in a complex bridge support structure scenario further verify the method’s practicability and robustness. Compared to FDTD, the proposed strategy is capable of generating GPR data matching real-world subsurface heterogeneous medium distributions from scene layout models, significantly reducing time costs and providing an efficient solution for GPR data simulation and analysis. Full article

Background/Objectives: Conventional therapeutic strategies exhibit limited efficacy against pancreatic cancer, primarily due to its profoundly hypoxic tumor microenvironment and dense fibrotic stroma. Chemodynamic therapy (CDT) holds promise; however, its application in pancreatic cancer is restricted by insufficient endogenous hydrogen peroxide (H₂O₂) levels and the activation of protective autophagy in response to oxidative stress. Methods: To overcome these obstacles, we developed a tumor microenvironment-responsive, pancreatic cancer-targeted CDT nanoamplifier—H-MnO₂/GOX&CQ-iRGD—comprising a hollow mesoporous MnO₂ shell co-loaded with glucose oxidase (GOX) and chloroquine (CQ), and surface-functionalized with the tumor-penetrating peptide iRGD. GOX catalyzes glucose oxidation to generate H₂O₂, enhancing Fenton-like reactions. CQ suppresses autophagy induced by oxidative stress, thereby alleviating therapy resistance. The iRGD peptide targets integrin α_vβ₃, which is overexpressed on pancreatic cancer cells and tumor vasculature, promoting deep tumor penetration and enhanced delivery efficiency. Results: We comprehensively characterized the nanoplatform’s physicochemical properties, tumor microenvironment triggered degradation, controlled drug release, glucose-driven H₂O₂ generation, and hydroxyl radical production in vitro. Cellular studies assessed nanoparticle uptake, intracellular H₂O₂ production, autophagy inhibition, and cytotoxicity. In vivo experiments further demonstrated effective tumor targeting and significant therapeutic outcomes in pancreatic cancer models. Conclusions: This nanoplatform addresses major barriers of CDT—namely, insufficient H₂O₂ levels, autophagy-mediated resistance, and limited intratumoral penetration—offering a promising strategy for pancreatic cancer treatment. Full article