Search Results (15,948)

In response to the plastic pollution crisis, bioplastics emerged as a sustainable alternative. However, low degradation rate and abiotic decomposition generate micro- and nanoplastics. These particles enter the food chain, establishing oral intake as a key route of human exposure. This review gathered studies on the biotransformation of bioplastics in the gastrointestinal tract and on their toxicity in human cells and murine models. Most studies focused on polylactic acid particles due to widespread use in food packaging. Under simulated gastrointestinal conditions in vitro, particles were modulated, resulting in cavity and pore formation, fragmentation, lipase competition, protein corona formation, and alterations in the gut microbiota (including Selenomonadaceae, Bifidobacterium, and Prevotellaceae). Also, particle breakdown increases surface area, enhancing interactions with biomeiolecules and causing higher in vitro and in vivo toxicity. Indeed, pro-inflammatory cytokine secretion, oxidative stress induction, and redox imbalance were found in both models. In mice, alterations in gut microbiota involving Bacillales indirectly mediated hepatotoxicity, leading to uric acid and triglyceride accumulation. Furthermore, microbiota adaptation over time was suggested with an increase in microorganisms and the potential conversion of L-lactic into harmful D-lactic acid. Despite limited studies, this review highlighted that ingested bioplastic-derived micro- and nanoplastics can lead to toxic effects. Full article

Reinforced Concrete (RC) structures experience progressive degradation over their service life due to mechanical loading and environmental exposure, leading to reduced bearing capacity and compromised structural safety. Incorporating discrete fibers into concrete mitigates crack propagation and enhances ductility, resulting in fiber-reinforced concrete (FRC) with superior fracture energy, durability, and sustainability characteristics. Despite these advantages, research on Structural Health Monitoring (SHM) techniques for FRC elements remains limited. The Electromechanical Impedance (EMI) method, which exploits piezoelectric transducers as both actuators and sensors, offers high sensitivity for detecting early-stage damage by monitoring variations in local mechanical impedance. This study investigates the effectiveness of a deep learning-enabled EMI framework for assessing the structural condition of FRC beams under flexural loading. A one-dimensional convolutional neural network (1D-CNN) is proposed to automatically extract salient features from high-frequency EMI signatures and classify structural health into three predefined states. The model is rigorously evaluated using specimen-invariant validation to ensure generalization across different FRC specimens, addressing a critical limitation of conventional cross-validation approaches in SHM research. Experimental tests on FRC beams instrumented with surface-bonded PZT transducers provide a dataset of 264 EMI responses for training and validation, enabling direct comparison between common and specimen-invariant validation schemes. The results demonstrate the superior robustness of the specimen-invariant approach and confirm the capability of the proposed 1D-CNN to identify flexural damage progression in FRC elements accurately. An ablation study further highlights the contribution of each architectural component to overall model performance. The findings underscore the potential of integrating EMI-based sensing with advanced deep learning models for reliable, automated, and scalable SHM of next-generation resilient concrete infrastructures. Full article

The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning and control smoothness near actuator limits. To address these issues, this paper develops a Beta-policy and PID-inspired augmented-state proximal policy optimization framework, termed BPAS-PPO, for the secondary cooling water system. The framework augments the state with proportional, integral, and derivative error features, adopts a Beta-distribution policy for bounded continuous-action generation, and uses a piecewise dense reward for the dual-loop tracking task. Simulation studies on an identified linear two-input two-output (TITO) model within the selected operating region show that the complete PID-augmented state yields the most effective training representation among the tested alternatives. Compared with PID, Fuzzy-PID, and Gauss-PPO, BPAS-PPO shows lower overshoot, shorter settling time, better setpoint tracking and disturbance rejection, and smoother control actions near actuator limits. The proposed framework is effective for the studied system within the selected operating region, while its performance beyond this region requires further validation. Full article

Shell-and-tube heat exchangers are critical components in oil refining, where their thermal and operational performance is strongly affected by fouling, corrosion-related deterioration, and deposit accumulation in tube-bundle cavities. This study investigates the technical condition of selected TK-type heat exchangers used in refinery services and proposes an integrated maintenance-oriented approach for the assessment and removal of severe deposits formed between tubes. The work first classifies heat-exchanger damage into structural and technological categories, emphasizing fouling as a key source of thermal performance degradation and operational inefficiency. A physical interpretation of compacted deposits is then combined with dynamic modeling to evaluate the response of the pollutant medium to pneumoshock excitation. Based on the analytical and simulation results, the main practical outcome of the study is the development of a pneumoshock cleaning device (PCD) for the mechanical removal of deposits from narrow inter-tube spaces. The proposed approach supports a more effective diagnosis of exchanger condition, helps identify suitable cleaning actions for heavily fouled bundles, and contributes to improved maintenance decision-making in refinery thermal systems, although quantitative before-and-after thermal performance validation is beyond the scope of the present study. As an applied developmental study, the work highlights the relevance of fouling-aware inspection and targeted cleaning technologies for extending equipment serviceability and supporting more reliable thermal management in industrial heat-exchange applications. Full article

In the field of drone navigation systems, a high-precision positioning solution can be provided by an integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS). But when satellite signals are interfered with or blocked by tall buildings, the errors of SINS will disperse rapidly due to the complex air and mechanical vibrations, leading to a serious degradation of navigation accuracy. To enhance the positioning performance in this situation, this paper proposes a hybrid information fusion method based on attention-augmented temporal convolutional network (TCN) for drone SINS/GNSS navigation system. A feature integration and prediction model is constructed to provide a pseudo-positioning reference for the integrated navigation filter during GNSS-denied periods, in which TCN is used to establish a predictive positioning error correction model based on inertial measurements and SINS data, while a self-attention model is incorporated to extract complex global drone motion features. The performance of the proposed method has been experimentally verified using Global Positioning System (GPS) and SINS data collected from real drone flight test. Comparison results among the proposed model, SINS with TCN, SINS with convergent Kalman filter (KF) prediction section and SINS-only indicate that the proposed method can effectively improve the drone positioning accuracy in specific GNSS-denied environments. Full article

Image stitching is a computational technique designed to align and seamlessly fuse multiple overlapping images into a single panoramic image with an extended field of view. It plays a critical role in diverse domains, including mobile photography, autonomous navigation, and visual perception systems. However, most conventional image stitching pipelines implicitly assume that the input images have been pre-corrected for geometric distortions, particularly radial distortion inherent to wide-angle and fisheye lenses. This assumption often fails in practice, as many consumer-grade cameras lack built-in correction or calibration support. Consequently, applying standard image stitching methods to the uncorrected imagery frequently degrades feature correspondence reliability and introduces visible geometric misalignments and seam discontinuities in the final panorama. To overcome these limitations, this paper introduces a task-driven joint iterative optimization framework for image stitching that unifies unsupervised radial distortion correction, distortion-aware feature registration, and seam-aware blending within a single cohesive optimization objective. Specifically, lens distortion parameters are explicitly modeled as learnable variables and embedded into both the geometric registration and seam optimization sub-problems. An efficient closed-loop optimization strategy is then employed to jointly refine distortion parameters, homography estimates, and optimal seam paths in an alternating, mutually reinforcing manner. Implementation-wise, we first propose a calibration-free initial radial distortion estimation method which leverages intrinsic image gradients and epipolar consistency to provide physically plausible initialization for subsequent optimization. During iteration, distortion parameters are progressively refined by integrating robust geometric constraints derived from current feature matches (via RANSAC-based consensus filtering) with photometric consistency cues. Extensive experiments on multiple public benchmarks featuring pronounced radial distortion demonstrate that our method achieves superior stitching fidelity using metrics including PSNR and SSIM. It also confirms enhanced feature matching stability, which outperforms both distortion-agnostic approaches and two-stage pipelines that decouple distortion correction from registration. Furthermore, comprehensive ablation studies quantitatively and qualitatively validate the functional necessity and synergistic contribution of each core module, confirming the design rationale and effectiveness of the proposed joint optimization architecture. Full article

This study proposes a coordinated allocation model for grid-forming energy storage and synchronous condensers considering transient stability constraints, with the following key aims: mitigate the continuous degradation of power systems’ capability to withstand inertia and the severe threats to dynamic rotor angle stability and frequency, while integrating renewable energy-centered frameworks using wind and photovoltaic power, and guarantee the secure and stable operation of transmitting power grids containing such bases. First, based on a virtual synchronous inertia quantification model of grid-forming energy storage and grid-forming wind and PV equipment, the inertia support capability of the renewable energy base is investigated. Subsequently, the impact of grid-forming equipment integration on transient rotor angle stability and frequency is studied, and a model of rotor angle stability and frequency constraints for the renewable energy base is established. Considering conditions such as investment cost constraints, transmission power constraints, and rotor angle stability and frequency constraints, a coordinated allocation model of grid-forming energy storage and synchronous condensers is formulated and solved to minimize the overall cost. Finally, the simulation verification results show that, compared with the configuration models that consider only the synchronous condenser or only the grid-forming energy storage, the proposed model reduces the comprehensive cost of the renewable energy base by 11.9% and 8.74%, respectively, reduces the minimized value of the power angle stability index by 80.95% and 78.95%, respectively, and meets the synchronous inertia demand of the renewable energy base throughout the period. Full article

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics. Full article

Extracellular vesicles (EVs) can disseminate replication-competent viral genomes complexed with selected host proteins, enabling stealth cell-to-cell transfer within lipid membrane-enclosed bubbles. In addition to complementing free-virion spread, EV-associated genomes can be protected from neutralizing antibodies and persist under conditions in which classical virion production decreases. Here, we propose a route-resolved framework in which interconnected cellular secretory pathways, including endoplasmic reticulum (ER) remodeling, multivesicular body (MVB) biogenesis, secretory autophagy, and plasma-membrane budding, jointly generate EV heterogeneity and create discrete opportunities for the capture, protection, and export of infectious cargo. We highlight reticulon-3 (RTN3), an ER-shaping protein, as an upstream regulator that can couple infection-induced ER microdomains to endosomal docking and to autophagy-linked trafficking decisions that bias intermediates toward secretion rather than degradation. Supporting this view, transmission electron microscopy of dengue virus-infected cells reveals extensive vesicular remodeling, including irregular MVBs adjacent to the plasma membrane and autophagosome-like double-membrane structures, consistent with altered vesicular routing following RTN3 perturbation. Collectively, these route-resolved, spatially organized spatio-organelle changes support a pathomechanistic model in which RTN3-mediated ER remodeling reshapes ER-endosome-autophagy trafficking interfaces, creating regulated decision points that can be leveraged to stratify infectious EV subsets (with infectivity-linked single-vesicle and quantitative proteomics approaches) and to inform host-directed strategies that curb non-lytic viral dissemination. Full article

To investigate the inevitable aging of composite insulators under the coupled effects of electrical, thermal, ice, and fog stresses, as well as to explore their aging mechanisms and residual strength prediction methods, this study collected operational insulator samples from four environmental regions: Tibet, Yunnan, Hunan Xuefeng Mountain, and Anhui/Chongqing. Mechanical properties, including tensile strength, elongation at break, and shear resistance, were tested. The results indicate that the degradation of mechanical performance in composite insulation components can be attributed to the synergistic interaction of operational environments and material characteristics, with the aging behavior of high-temperature vulcanized (HTV) silicone rubber exhibiting significant non-linearity. Based on existing research, molecular dynamics simulations were employed to construct microstructural models at different aging stages, and it was verified that main chain scission, reduced system density, and changes in the elemental chemical environment during aging are closely related to the degradation of material mechanical properties. Based on hyper-elastic constitutive theory and fracture mechanics, a quantitative method for assessing the comprehensive aging degree was proposed, with “service years” and “operational altitude” as the core dimensions. A negative exponential model was established to describe the strength degradation of silicone rubber materials. This model enables the non-destructive estimation of the residual mechanical strength of in-service insulators in complex regions without power interruption, providing a decision-making framework for grid operation and maintenance. Full article

Reconfigurable intelligent surfaces (RISs) have recently attracted attention as a potential solution for improving the reliability of optical wireless communication links, especially when direct transmission (DT) becomes severely degraded due to dynamic channel conditions. In this study, an RIS-assisted architecture based on a modulating retroreflector is proposed for underwater optical wireless communications (MRR-UOWC). In the considered system, both the DT path and the RIS-assisted path transmit the same information simultaneously at the same data rate. The propagation channels are modeled by taking into account propagation loss, Gamma–Gamma turbulence, and pointing error effects. At the receiver, the signals arriving through the direct path and the RIS-reflected path are coherently combined. To evaluate the effectiveness of this configuration, two diversity combining techniques, namely selection combining (SC) and maximum ratio combining (MRC), are investigated. Closed-form analytical expressions for the outage probability (${\mathrm{P}}_{\mathrm{out}}$), average bit-error rate (BER), and ergodic capacity ($\overline{C}$) are derived using the probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the end-to-end signal-to-noise ratio (SNR). The analysis indicates that jointly exploiting the DT and RIS-assisted links can provide noticeable performance gains by leveraging the complementary characteristics of the two propagation paths. Full article

Self-collision detection has become the dominant computational bottleneck in GPU-accelerated cloth simulation, as modern parallel solvers such as XPBD have drastically reduced the cost of position updates while leaving collision resolution largely unoptimized. Existing spatial partitioning methods treat all cloth regions uniformly, saturating GPU memory bandwidth despite the fact that the vast majority of the mesh surface remains geometrically flat and collision-free at any given frame. We propose a hierarchical self-collision culling framework built upon a resolution-independent discrete curvature metric derived from the $h^2$-normalized Laplace-Beltrami operator, integrated with a discrete Kirchhoff–Love shell model combining distance and dihedral bending constraints within XPBD. Unlike prior cache-dependent acceleration strategies, our method tightly couples curvature-driven geometric pruning with a fused GPU kernel design and shows that this stateless formulation is both faster and physically more reliable. Evaluated on meshes of $512\times 512$ and $1024\times 1024$ particles, our method achieves a $5.5\%$ and $9.7\%$ FPS improvement alongside a $34.9\%$ and $28.4\%$ reduction in active collision pairs, respectively, with qualitative validation via high-fidelity rendering confirming artifact-free self-contact and strict ground-plane non-penetration. Ablation results further reveal that temporal coherence, conventionally regarded as an optimization standard, strictly degrades both performance (FPS decrease of $1.4\%p$ to $1.9\%p$) and physical accuracy (penetration depth increase of $36.1\%$ to $100.0\%$ relative to the curvature-only stage) on RTX 3070 GPU, advocating for stateless per-frame geometric evaluation as the preferred design paradigm. Full article

This study presents a comparative investigation of target displacement demands, a fundamental indicator in the seismic performance assessment of reinforced concrete frame systems, within the framework of the Turkish Building Earthquake Code (TBEC-2018), American standards (ASCE 41), and European standards (Eurocode 8). To analyse the consistency in performance levels stipulated by different structural design codes, critical variables, including soil class, number of stories, concrete grade, frame span, and soft story at ground level, were parametrically defined. The impact of these variables on the target displacement demands of the structures was examined through a comparative lens. Nonlinear static pushover analyses based on fiber-based modelling were conducted using SeismoStruct software to determine displacement demands under different seismic code formulations across six distinct variables. The displacements obtained for each variable at identical seismic ground-motion levels were evaluated individually. Analytical results reveal that soil degradation significantly increases target displacements across all codes. At the same time, the presence of a high story affects structural ductility and displacement demands, with varying sensitivities across the codes. Notably, it was observed that TBEC-2018 yields more conservative displacement demands in certain spectral regions than those in ASCE 41 and Eurocode 8. The findings provide critical data for understanding the disparities in safety margins among international seismic design standards. Full article

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

Forest, shrubland, and grassland exhibit highly overlapping characteristics, and single-modal remote sensing data cannot simultaneously capture both spectral and structural information. Moreover, multimodal fusion learning of optical and SAR data faces challenges such as the lack of high-quality samples and difficulties in effective cross-modal feature fusion. Therefore, a high-resolution multimodal remote sensing feature dataset (GF23FSG) is constructed for the fine classification of forest, shrubland, and grassland, and a Cross-modal Adaptive Structure Fusion Network (CASFNet) is proposed. In response to the feature heterogeneity of optical and SAR, a cross-modal adaptive fusion module based on spatial alignment and a dynamic weight allocation strategy is proposed, which effectively enhances the learning of spectral–spectrum heterogeneous features. In addition, a multi-level auxiliary supervision mechanism is introduced to strengthen feature representation learning. Gradient constraints are further imposed on deep-level features to improve the model’s ability to capture and learn deep cross-modal representations, thereby effectively mitigating representation degradation during the feature fusion process. Experiments on the self-constructed GF23FSG dataset and the publicly available SEN12MS dataset achieve OA of 77.38% and 71.84%, respectively, demonstrating superior classification performance compared with SOTA methods. In addition, comparative analysis with public land cover products and field samples further confirm the reliability and generalization performance of the proposed dataset and model for the fine classification of forest, shrubland, and grassland. This study provides a new solution for the fine classification of forest, shrubland, and grassland from multimodal remote sensing images from the perspectives of dataset construction and methodological design. Full article