Search Results (28,510)

This article focuses on the research and development of innovative polymer-concrete composites for the manufacture of precision machine tool frames and critical mechanical engineering components. The relevance of this work stems from the need to replace traditional cast iron and cement concrete with materials with superior damping properties and thermal stability. The polymer matrix used in this study was ED-20 epoxy-diane resin, modified with (FAM) furan resin and cured with polyethylenepolyamine (PEPA), which together ensured minimal linear shrinkage (less than 0.5–1%) during polymerization. The focus was on the effect of multimodal filler distribution, including quartz sand, gabbro, and basalt, as well as reinforcing additives such as silicon carbide and fiberglass, on the final performance characteristics of the material. Experimental studies determined the key physical and mechanical parameters of the obtained samples. The results showed that the optimized composition (Smp_001) exhibited compressive strength up to 92.3 MPa, significantly exceeding that of standard high-strength concrete. It was established that the use of silicon carbide and glass fiber promotes the formation of a dense heterogeneous microstructure characterized by extremely low porosity (1.2–2.5%) and record-low water absorption (less than 0.05%). These characteristics guarantee high dimensional stability of the frames during prolonged contact with process fluids and cutting fluids. The scanning electron microscopy (SEM) and (EDS) energy dispersive X-ray spectroscopy methods confirmed the dense packing and high degree of interaction of the polymer matrix with the crystalline phases of the filler. This condition of the interfacial boundaries guarantees stable stress transfer throughout the entire volume of the material, which minimizes the risk of local damage during operation. The study confirmed that the developed material has vibration damping properties 6–10 times more effective than gray cast iron, a critical factor in improving machining accuracy on modern metal-cutting machines. The scientific novelty of the study lies in its substantiation of the synergistic effect of the combined use of basalt fillers and silicon carbide to achieve the precision properties of a structural material. Its practical significance is confirmed by the possibility of producing large-scale parts by casting without the need for complex finishing, opening up new prospects for modernizing the machine tool industry. Full article

Depleted hydrocarbon reservoirs (DHRs), particularly depleted gas reservoirs (DGRs), are increasingly regarded as promising candidates for geologic carbon storage (GCS). However, their low abandonment pressure poses significant thermo-hydraulic challenges during the injection of cold, high-pressure CO₂. In such non-isothermal conditions, complex processes may occur, including Joule–Thomson (J-T) cooling, hydrate formation, salt precipitation, and thermal fracturing, all of which may affect storage performance. This study presents an integrated assessment of the impact of CO₂ injection into DGRs on the three key pillars of GCS: capacity, injectivity, and containment. The analysis integrates laboratory experiments conducted at our institute, simplified analytics and numerical simulations to assess the governing physical mechanisms. The findings indicate that the cold CO₂ injection can enhance effective storage capacity during the injection phase. This is attributed to the increase in fluid density and the delay in pressure buildup. However, the post-injection thermal equilibrium may result in pressure rebound. The CO₂ injectivity has been demonstrated to be significantly impacted by the near-wellbore thermal effects. While thermo-induced fracturing may enhance injectivity, it poses potential risks to wellbore and caprock integrity. The process of hydrate formation depends on the local temperature and petrophysical conditions, with dynamic factors further reducing the likelihood of pore plugging. Salt precipitation has been found to be less critical under typical DGR conditions with low initial water saturation, although having the potential to become significant in the presence of water influx and/or cyclic injection. The findings provide a technical basis for enhancing the engineering design, accelerating the certification process, and ensuring the safe operation of future GCS projects in DGRs. Full article

To address the challenges of time-dependent error divergence in Strapdown Inertial Navigation Systems (SINS) and the insufficient accuracy of traditional terrain matching algorithms in feature-sparse flat terrain environments, this paper proposes an intelligent terrain-aided navigation method integrating an Improved Osprey Optimization Algorithm (IOOA), Distribution Estimation, and Q-learning. Utilizing terrain information entropy as a robust matching metric, the algorithm establishes a two-phase evolutionary framework comprising Lévy flight-based random search (exploration phase) and elite-guided Gaussian Estimation of Distribution (exploitation phase). By introducing a Q-learning mechanism to adaptively regulate exploration parameters, an intelligent balance between population diversity and convergence speed is achieved. Under a unified computational benchmark, systematic multi-scenario simulations were conducted using datasets from simulated moderately undulating foothill terrain, the Libyan Sahara, and the real Digital Elevation Model (DEM) of the Junggar Basin in Xinjiang, China. Experimental results demonstrate that, compared to traditional TERCOM and mainstream swarm intelligence algorithms, the proposed algorithm drastically reduces positioning errors in the aforementioned complex terrains and significantly enhances matching accuracy. Robustness and real-time performance tests indicate that the algorithm achieves an average single-match processing time of only 0.08 s and maintains error variability as low as ±0.83 m under random perturbations. Furthermore, an ablation study confirms the necessity of the multi-strategy fusion mechanism in suppressing local optima entrapment and non-convergent oscillations. This study validates the engineering feasibility of the algorithm under conditions of low computational dependency, providing an effective technical approach for high-precision autonomous navigation in GPS-denied environments. Full article

To address challenges such as severe occlusion caused by the dense growth of blueberry fruits in natural environments, complex backgrounds, and the limited computational resources of agricultural edge devices, this study proposes BR-DETR-Prune, a lightweight object detection model oriented towards edge computing environments. Based on the RT-DETR architecture, the model introduces a PConv-based FasterNet as the backbone network, which effectively reduces memory access latency and floating-point operation costs. Furthermore, it utilizes a “Gather-and-Distribute” (GD) mechanism to reconstruct the feature fusion neck. Through the unified aggregation and multi-branch distribution of global information, it significantly enhances the model’s feature extraction capability for dense and overlapping targets. An AIFI-RepBN encoder is designed, integrating re-parameterization technology into the attention module to further reduce computational redundancy. For lightweight processing, a random channel pruning strategy based on the “Lottery Ticket Hypothesis” is adopted to perform structural compression and fine-tuning on the model, achieving a significant reduction in the number of parameters while inversely improving accuracy. The experimental results demonstrate that BR-DETR-Prune achieves an mAP@0.5 of 97.1% on a self-built blueberry dataset, with only 15.52 M parameters and a computational load reduced to 34.0 GFLOPs. Its comprehensive performance is superior to mainstream models such as YOLOv8, YOLO11, and the original RT-DETR. Particularly, deployment testing on the NVIDIA Jetson Orin Nano Super embedded edge computing platform reveals that the model achieves a real-time inference speed of 20.5 FPS under FP16 precision, exhibiting smooth detection frames and strong robustness against occlusion. This study provides an effective optimization solution for the deployment of high-precision Transformer architectures on low-computational-power devices, offering an efficient and reliable visual perception approach for automated blueberry harvesting and yield estimation. Full article

Slump-type gravity-flow deposits are extensively developed in the Jiufotang Formation of the Naiman Sag, representing a core frontier for deep-water subtle hydrocarbon reservoir exploration. However, these deposits exhibit strong internal reservoir heterogeneity, while their diagenetic mechanisms are complex and their development pattern remains unclear. Integrating macroscopic and microscopic investigation of cores, scanning electron microscopy (SEM), micro-CT, and high-pressure mercury injection capillary pressure (MICP) data, a systematic study was conducted on the petrological characteristics and diagenesis of the gravity-flow reservoirs. The results indicate that the lithology is dominated by feldspathic lithic sandstones with low compositional maturity. The present-day reservoir quality is governed by the high spatiotemporal coupling between deposition and burial diagenesis. Compaction is the absolute dominant diagenetic factor driving the densification of these reservoirs. The strong compaction resistance, derived from a low argillaceous matrix content and a well-developed grain-supported framework, is the key to the formation of high-quality reservoirs. Furthermore, three distinct diagenetic pathways are revealed: the “high-energy freezing—rigid pore preservation” pathway controls the development of high-quality exploration sweet spots; the “shear mixing—plastic pore reduction” pathway forms low-permeability transitional reservoirs; and the “viscous suspension—compaction densification” pathway indicates widespread tight sandstone exploration targets. Full article

Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article

Owing to their reliance on detailed mathematical modeling, traditional control methods encounter challenges such as high control complexity and low precision when applied to high-speed flight vehicle control under strongly disturbed atmospheric conditions. To address this limitation, this study introduces a data-driven neural network mapping approach into the field of flight vehicle control. By excavating the underlying patterns in operational data and leveraging the nonlinear mapping capability of neural networks, accurate prediction and generation of control commands are achieved, thereby eliminating the dependence on precise mathematical models and offering a novel solution for complex control problems. Building on this foundation, a self-organizing map (SOM) radial basis function (RBF) neural network is proposed. Leveraging the competitive learning mechanism of SOM, it performs adaptive clustering on input samples, dynamically optimizes the number of clusters to determine the number of hidden-layer nodes in RBF, and adopts the SOM cluster centers as the centers of RBF basis functions. This design enables the one-click data-driven determination of both the number of nodes and their corresponding center vectors, significantly simplifying the network structure design process. Meanwhile, to address inherent limitations of this network, such as suboptimal output weights, unoptimized width functions, and the inherent drawbacks of the traditional Moth-Flame Optimization (MFO) algorithm, an Adaptive Enhanced Moth-Flame Optimization (AEMFO) algorithm is developed, drawing inspiration from biological swarm intelligence. By integrating strategies such as adaptive spiral update and elite opposition-based learning, it balances the global exploration and local exploitation capabilities, and performs targeted optimization of the RBF width parameters and output-layer weights. This optimization significantly enhances the accuracy of the network in mapping attitude-control commands in strongly disturbed environments, providing robust support for the stable attitude control of high-speed flight vehicles. Finally, simulation results demonstrate that the proposed method achieves high control accuracy for flight vehicle attitude control under strongly disturbed environments. Full article

Gestational diabetes mellitus (GDM) is increasingly recognized as a complex pathology rooted in systemic and organelle-level dysfunction, specifically involving chronic low-grade inflammation (CLGI), mitochondrial impairment, and endoplasmic reticulum (ER) stress. Central to this pathophysiology is mitochondrial dysfunction, characterized by reduced respiration, impaired metabolic flexibility, and dysregulated fission/fusion machinery, which fuels a self-perpetuating cycle of reactive oxygen species (ROS) production. Concurrently, chronic ER stress triggered by hyperglycemia and lipotoxicity activates the unfolded protein response (UPR), further amplifying redox imbalance through the Endoplasmic Reticulum Oxidoreductin 1/Protein Disulfide Isomerase (ERO1/PDI) axis and bridging metabolic toxicity to inflammation via c-Jun N-terminal kinase (JNK) and nuclear factor kappa-light-chain–enhancer of activated B cells (NF-κB) signaling. The Advanced Glycation Endproducts (AGEs) and the Receptor for Advanced Glycation Endproducts (RAGE) axis act as a molecular catalyst that sequester antioxidants and drive pro-inflammatory feedback loops. These converging mechanisms culminate in profound placental maladaptation, including structural abnormalities like chorangiosis and functional defects in nutrient transport mediated by hyperactive mechanistic target of rapamycin complex 1 (mTORC1) signaling. This review article provides insight into recent evidence to elucidate the meta-inflammatory environment of GDM, where modest but sustained elevations in biomarkers like Interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) disrupt redox homeostasis and impair insulin signaling pathways through the activation of stress-sensitive kinases. By integrating these molecular perspectives, the article underscores the necessity of targeting the systemic inflammatory and oxidative continuum spanning pre-conception to the antenatal period through lifestyle interventions and emerging therapeutic strategies to mitigate GDM risk and improve maternal–fetal outcomes. Full article

Background: Local recurrence of rectal cancer is a challenging problem for patients and clinicians. Surgical resection is associated with good outcomes if R0 margins are achieved; however, it is often complex, requires suitable patient fitness, and is associated with long term physical and psychological consequences. Meanwhile, continuing technical advances in radiotherapy have enabled the delivery of highly conformal treatment, thereby enabling dose escalation or pelvic reirradiation to be safely considered—either as definitive management or in the neoadjuvant setting—for patients with locally recurrent rectal cancer. Pelvic reirradiation may refer to patients who have received primary rectal radiotherapy with the aim of neoadjuvant downstaging or reducing the risk of locoregional recurrence, versus radiotherapy for a previous unrelated non-rectal pelvic malignancy. Methods: A literature search of pelvic reirradiation for non-metastatic, locally recurrent rectal cancer was conducted for full text articles published over the last 20 years. Additional papers were identified within the references of these papers. Studies focusing on non-rectal cancers, and patients having primary radiotherapy for locally recurrent rectal cancer were excluded. Due to the heterogenicity of the data, no meta-analysis was performed. Results: A total of 15 papers were included, containing a cohort of 840 patients. Several reirradiation modalities were reported, including external beam radiotherapy, brachytherapy, stereotactic ablative radiotherapy and heavy particle therapy (carbon ion). Carbon ion radiotherapy was the most common reirradiation treatment modality utilised with a median cumulative dose of 70.4 Gray (Gy). Treatment response, defined as either complete or partial improvement in tumour size, was only reported in seven studies, and varied from 14 to 88%. Overall 3-year survival was also variable with rates reported between 18 and 85%. These observations may be due to variation in patient selection, treatment intent, and technique. Pelvic reirradiation was associated with acceptable toxicity, low rates of G3+ toxicity, and improved symptom control. Conclusions: Our review describes the multitude of approaches to pelvic reirradiation for locally recurrent rectal cancer. Reviewing the radiobiological and patient outcomes is challenging in view of the degree of heterogeneity in patient selection, treatment approach, and reported outcomes. However, there is consensus that pelvic reirradiation—either for long term control or to downstage prior to definitive surgery—is feasible with potential utility in this setting. Full article

While predictive maintenance increasingly relies on artificial intelligence, strict dependence on cloud computing introduces network latency and demands continuous connectivity, creating critical bottlenecks for time-sensitive industrial applications. To overcome this, we introduce a novel hybrid edge-cloud architecture, which allows deploying an ultra-low-power microcontroller (ESP32-S3) without dedicated AI acceleration hardware to perform complete, operational, predictive maintenance on ultra-constrained embedded hardware. The edge model is optimized to be very small to ensure that increasing model complexity does not cause inference latency to exceed 100 ms or make real-time operation infeasible. We created a very compact INT8-quantized neural network to perform the simultaneous classification of faults and estimation of Time-to-Failure (TTF) with a deterministic mean inference time of 42.3 ms. It dynamically estimates prediction confidence, processes high-confidence predictions locally, and offloads uncertain predictions to a higher-capacity cloud model, and recovers 97.3% of the cloud accuracy gain at 92% of the cloud latency budget. An asymmetric loss function penalizes over-prediction of the remaining useful life, and thus it provides conservative and safe warnings of fault. Operators’ interpretability is improved with Shapley Additive exPlanations (SHAP) and natural-language recommendations. Network outages of up to 50% have not influenced the safety-critical fault recall (above 0.924), so graceful degradation is reached when the network is used in real time in industrial applications. The edge-first with adaptive cloud fallback approach is demonstrated to be technically feasible for a full predictive maintenance workflow—including inference, confidence fusion, and explainability on a low-cost commercial microcontroller. Full article

Urban expressway projects are critical components of modern transportation infrastructure, yet their construction quality is often threatened by multi-source, latent, and dynamic risks. Traditional expert-driven risk identification methods frequently suffer from subjective bias and low efficiency, failing to meet the rigorous management requirements of complex engineering environments. To address these challenges, this study proposes a robust risk assessment framework integrating Large Language Models (LLMs) and the Delphi method within a Bayesian Network (BN) structure. First, LLM technology is leveraged to perform semantic mining on extensive engineering texts, including construction specifications and project reports, to pre-identify potential risk factors. Second, the Delphi method is applied through multiple rounds of expert consultation to refine a comprehensive inventory comprising 32 risk factors across five dimensions: personnel, machinery, materials, methods, and environment. Finally, a BN-based evaluation model is developed, utilizing forward inference, backward diagnosis, and sensitivity analysis to quantify risk levels and pinpoint critical risk drivers. The framework was empirically validated using the T Expressway Project in Hangzhou as a case study. Results demonstrate that the model effectively transforms empirical management into precise, data-driven diagnosis, providing project managers with a quantitative tool for optimizing construction quality control and decision making in complex urban bridge projects. Full article

Selenium (Se), a vital trace element, plays a significant role in maintaining vascular health and may offer protective effects against atherosclerosis. Its actions are mediated through Se-dependent selenoenzymes and selenoproteins, which enhance antioxidant defense, modulate inflammatory responses, and promote autophagy. These processes collectively help prevent cellular senescence—a state associated with age-related vascular decline characterized by oxidative stress, DNA damage, pro-inflammatory activity, and endothelial dysfunction. Epidemiological evidence consistently shows that low Se status is associated with increased risk of atherosclerotic cardiovascular disease within a narrow concentration range. However, clinical trials have not demonstrated clear reductions in cardiovascular events or mortality with Se supplementation alone. Overall, current evidence indicates that Se modulates key mechanisms involved in vascular aging and atherosclerosis, particularly redox balance, immune activation, and vascular cell homeostasis. This comprehensive review summarizes current epidemiological, clinical, and experimental research on the role of Se in cardiovascular health. It underscores Se’s potential as a promising strategy for the prevention and treatment of atherosclerosis, while also acknowledging the complexities and nuances of its effects on vascular health. A deeper understanding of the cellular and molecular mechanisms involved could pave the way for targeted interventions aimed at reducing the burden of atherosclerotic cardiovascular disease. Full article

Artificial intelligence (AI) is increasingly reshaping musculoskeletal (MSK) imaging across the entire imaging pathway. This narrative review summarizes current AI applications in MSK radiology across four domains: acquisition and reconstruction, detection and triage, characterization and quantification, and prognosis and decision support. AI-based reconstruction has enabled faster MRI acquisitions, improved denoising and artifact reduction, and supported low-dose CT imaging while preserving diagnostic quality. Fracture detection and triage currently represent the most mature clinical applications, particularly in emergency settings. AI is also promoting a shift from qualitative interpretation to quantitative imaging phenotyping through automated assessment of body composition, cartilage, bone density, degenerative spine disease, skeletal maturity, and lesion heterogeneity. Emerging applications in prognostic modeling, implant evaluation, and multimodal risk stratification remain promising but less mature. Broader clinical implementation is still limited by restricted interpretability, dataset bias, insufficient prospective validation, regulatory complexity, and unresolved medico-legal issues. Overall, AI should be viewed as a tool to augment, not replace, radiological expertise. Full article

Sulfur-containing medium-carbon microalloyed steel blooms are widely used for high-load automotive components, and reducing surface defects is important for improving product yield and lowering downstream processing costs. To address surface defects such as star cracks and microcracks in the continuous casting of these steel blooms, this study redesigned the mold flux on the basis of the steel’s solidification characteristics and crack susceptibility and carried out a twin-strand industrial comparative casting trial. Thermodynamic and thermophysical analyses indicated that the relatively high contents of S, Mn, and Ti/N in the steel promoted the precipitation of MnS and TiN–MnS complex inclusions along grain boundaries, severely weakening grain boundary cohesion. Meanwhile, the high specific heat capacity and low thermal conductivity further intensified thermal stress concentration in the solidifying shell, rendering the steel highly susceptible to cracking. Evaluation of the originally used mold flux (Flux A) revealed that its high melting temperature (1189 °C), long melting time (106 s), high break temperature (1170 °C), and poor crystallization behavior resulted in an excessively thin liquid slag layer (<5 mm) within the mold, making it difficult to provide adequate lubrication and stable heat transfer; these were key external factors inducing surface defects. Accordingly, the optimized mold flux (Flux B) was designed and prepared by increasing the basicity from 0.95 to 1.1, raising the Al₂O₃ content from 9.48% to 11.16%, increasing the F content from 4.93% to 5.58%, and reducing the carbon content from 13.85% to 6.97%. The rheological and crystallization properties of the flux were optimized in a coordinated manner, allowing uniform heat transfer through the crystalline slag layer while maintaining adequate lubrication. Industrial comparative trials demonstrated that Flux B stabilized the liquid slag layer at 8–10 mm, increased slag consumption to 0.56 kg/t, and significantly reduced surface defects such as star cracks and microcracks on blooms. The ultrasonic testing acceptance rate for rolled products increased to 98.6%, thereby meeting stringent quality requirements for the continuous casting of sulfur-containing, medium-carbon, microalloyed steel blooms. Full article

Objectives: This study aimed to evaluate very low HBV DNA viral load below the limit of quantification and to identify correlational factors in different patient groups, including individuals with chronic hepatitis B (CHB), occult HBV infection (OBI), and others. Methods: We retrospectively analyzed 390 patients with very low-level viremia (VLLV). HBV DNA levels were measured in plasma samples using real-time quantitative PCR (qPCR). Serological markers were evaluated in serum samples using chemiluminescence microparticle immunoassay (CMIA). Demographic variables, HBV serological markers (anti-HBs, anti-HBe, anti-HBc), and DNA results were evaluated. Results: The study included 193 CHB patients with maintained virological suppression and 197 patients in the other group; of which, 60 patients had occult hepatitis B infection (HBV DNA positive, HBsAg negative) and 137 had no occult hepatitis B infection. Very low viral load was more common in men (53.3%) and in individuals aged ≥50 years (63.3%). In univariate analysis, OBI was associated with anti-HBe (odds ratio (OR) = 2.874, 95% CI: 1.255–6.579, p = 0.013), and anti-HBc seropositivity (OR = 5.750; 95% CI: 2.626–12.591, p < 0.001). In multivariate analysis, anti-HBe positivity and anti-HBc positivity were independently associated with OBI. Anti-HBs positivity was independently and inversely associated with OBI. Conclusions: In patients with VLLV cohort, anti-HBc and anti-HBe seropositivity were independently associated with detectable but unquantifiable HBV DNA. Although anti-HBe positivity reflects reduced viral replication, it does not indicate complete viral suppression and may be detected at very low viremia levels, especially in occult HBV infection. These findings highlight the complex interplay between viral replication dynamics and host immune responses across the VLLV spectrum, characterize the serological landscape associated with detectable but unquantifiable HBV DNA, and warrant validation in prospective studies. Full article