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This paper studies the durability of epoxy resin-based-carbon fiber reinforced polymer (EP-CFRP) under the combined action of aggressive environmental conditions and load. Environmental conditions such as high and low temperature cycles of −40-40 ℃ / −40-25 ℃ and humidity (water immersion and no water) are investigated in an unstressed state or loaded in about 30% or 60% of the initial ultimate load. The results indicate that both of the dual-factor coupling of “high and low temperature cycle-humidity” and the three-factor coupling of “high and low temperature cycle-humidity-load” have a greater impact on the durability of EP-CFRP. The tensile strength varies with the cycle of high and low temperature. The overall increase shows a simulated trend of first decreasing, then increasing, and then decreasing; however, the appearance of the peak and valley values are quite different. Humidity and load level have little effect on the tensile modulus of EP-CFRP The microcracks generated at the interface between the resin matrix and the fiber have been proved to be the main reason for the strength reduction at later stage. The coupling effect of humidity and load promotes the expansion of cracks and exacerbates the damage to EP-CFRP. According to the damage analysis, a non-linear fitting method is used to give the residual strength damage model of EP-CFRP after the coupling action of the three factors “high and low temperature cycle-humidity-load”.

Octa(dimethylethoxysiloxy) POSS (ODES) was synthesized successfully and used as the novel curing agent to prepare RTV silicone rubber (SROD) with outstanding mechanical properties and thermal stability. Compared with the silicone rubber cross-linked by tetraethoxysilane (SRTE), the novel RTV silicone rubber using octa(dimethylethoxysiloxy) POSS as a cross-linker had better mechanical, thermal, and optical properties. The highest tensile strength of SROD reached 1.26 MPa, which is three times that of SRTE. Besides, the decomposition temperature of 10% weight loss reached 507.7°C, exceeding that of SRTE by nearly 150°C. In addition, it was remarkable that due to the good compatibility of ODES with the silicone rubber matrix, the series of SROD showed good transmittance, greater than 87%. The thermal decomposition process of SROD was investigated by TGA coupled with real-time FTIR, and the results revealed the rigid structure and large steric hindrance of ODES that efficiently blocked the “backbiting” of the polysiloxy chains and delayed the end-induced ring decomposition, and consequently, improved the thermal stability of SROD significantly.

Six varieties of alkoxy polyhedral oligomeric silsesquioxanes (POSSs) featuring methoxy, ethoxy, n‐propoxy, isopropoxy, n‐butoxy, and n‐hexoxy as terminal groups were synthesized using the direct dehydrogenation condensation method. These POSSs were then incorporated as cross‐linking agents into hydroxy‐terminated polydimethylsiloxane (HPDMS) to create various formulations of room temperature vulcanized silicone rubbers (RTV SRs), denoted as SRM, SRE, SRP, SRI, SRB, and SRH. The morphology, thermal stability, and mechanical properties of the resulting SRs are analyzed using scanning electron microscopy (SEM), thermal gravimetric analysis (TGA) and universal tensile testing machine. The findings indicate that the incorporation of POSS into SRs results in significant enhancements in thermal stability and mechanical properties when compared to the control group (TEOS/SR). SRB and SRH exhibit the highest cross‐linking density and tensile strength. Specifically, SRB‐4 demonstrates the highest tensile strength at 2.66 MPa, representing an 8.6‐fold increase compared to TEOS/SR. The maximum decomposition rate temperature of SRP‐4 reaches 527°C and is 194°C higher than TEOS/SR. Because the alkoxy groups with different chain lengths on POSS have different chemical reactivity, they have a great effect on the dispersion of POSS in SRs, which affect the cross‐linking density, tensile strength, and thermal stability. [ABSTRACT FROM AUTHOR]

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method's effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method's potential for broader application in tunnel blasting assessments. [ABSTRACT FROM AUTHOR]

The targeting of suitable mixed grass species and seeding rates of native grass seed in the process of ecological restoration in alpine mining areas is unclear. Four kinds of native grass seed (Poa pratensis cv. Qinghai, Poa crymophila cv. Qinghai, Puccinellia tenuiflora cv. Tongde and Pedicularis kansuensis) were selected as experimental materials to set up mixed sowing tests in the Muli mining area, which were analyzed for changes in plant coverage, biomass, forage nutrient composition, and soil physicochemical properties under different mixed grass species and seeding rates, aiming to provide a data reference and theoretical basis for the screening of suitable mixed grass species and seeding rates for artificial grassland planting in alpine mining areas. The results showed that the mixed grass species and seeding rate (HF) of Poa pratensis cv. Qinghai + Poa crymophila cv. Qinghai + Puccinellia tenuiflora cv. Tongde + Pedicularis kansuensis had the highest vegetation coverage (97.33%). At the same time, the aboveground biomass of HF was the largest (356.27 g·m−2). The soil organic matter, total nitrogen, total phosphorus, and total potassium of HF increased by 37.05%, 28.11%, 34.68%, and 10.14%, respectively, compared with CK, and the difference was significant (p < 0.05). Principal component analysis was carried out on 23 indexes of vegetation and soil. It was found that nine indexes, including coverage, aboveground biomass, belowground biomass, soluble sugar, and soil organic matter content, were the key indexes of evaluation. By sorting the membership functions of the above indicators, it was found that among the 12 mixed grass species and seeding rates, the comprehensive evaluation value of HF was the highest (0.848). In summary, it is recommended that the mixed grass species and seeding rate of Poa pratensis cv Qinghai + Poa crymophila cv. Qinghai + Puccinellia tenuiflora cv. Tongde + Pedicularis kansuensis be adopted for ecological restoration in alpine mining areas; this mixed grass species and seeding rate can effectively promote plant growth and development and improve the physicochemical properties of the soil, which can improve the stability and sustainability of the artificial grassland in the alpine mining area. [ABSTRACT FROM AUTHOR]

In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework. [ABSTRACT FROM AUTHOR]

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise, but lack complex background image modeling capability; representation-based methods are good at defective region localization, but lack accuracy in defective region shape contour extraction; reconstruction-based methods detected defective region match well with the ground truth defective region shape contour, but are noisy. To combine the best of both worlds, we present an unsupervised Patch AutoEncoder-based Deep Image Decomposition (PAEDID) method for defective region segmentation. In the training stage, we learn the common background as a deep image prior by a patch autoencoder network. In the inference stage, we formulate anomaly detection as an image decomposition problem with the deep image prior and sparsity regularizations. By adopting the proposed approach, the defective regions in the image can be accurately extracted in an unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in simulation studies and an industrial dataset in the case study. [ABSTRACT FROM AUTHOR]