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Practical strategy for the thermal evaluation of film-cooled blade is of great importance to the gas turbine community. Due to the physical or methodology limitations, it is difficult to evaluate the blade's thermal performance at simulated engine conditions. The present study proposed novel focal-sweep-based phosphor thermometry for blade cooling inspection. While Mg4FGeO6:Mn (MFG) served as the temperature sensor to quantify the blade temperatures as well as simulated the TBC effect, the focal sweep method was adopted to overcome the optical constraints in cascade testing. The obtained MFG results of microstructures, jet impingement, and anti-erosion test demonstrated that the MFG phosphor is robust enough to simulate the thermal insulation effect of TBC and can withstand high-speed flow erosion. Furthermore, the proposed strategy clearly captured the blade temperature distributions (mainstream at T_(0,8)=~850 K) with high spatial resolution, which was then successfully remapped onto the three-dimensional twisted blade. Additional comparisons with the thermocouples demonstrated that the simulated-TBC has a thermal insulation effect of about 68K. This study addressed the common problems of phosphor thermometry in blade cooling evaluation, offering a practical strategy for future thermal diagnostics of the gas turbine.

This article investigates the size effect of segmental tunnel linings on the deformation behavior and the damage evolution mechanism using the Finite Element Method (FEM). A series of models with different diameters are established under the same loading condition to control the variable. The bolts and the rebar apply the elastic-plastic model. The Concrete Damage Plasticity model has been considered for the concrete lining. The vertical convergence deformation, internal force of these models, and damage to the segment body are selected for comparison. The number and location of the yielded bolts are explored for the analysis of the overall structure stiffness and damage evolution mechanism. In terms of plastic hinge theory, an extended discussion is carried out to explain the differences in damage evolution mechanisms in different-sized linings. The results show the large-diameter lining rings have a relatively greater increasing rate of convergence deformation. Furthermore, fewer plastic hinges emerged in large-scale lining rings before the structures reached their ultimate strength. According to the experimental results, we can know that the failure of large-diameter has the characteristic of more brittle damage than that of small-diameter one and these large-size structures are prone to become unstable geometry.

This study explored the role of the long non-coding RNA (lncRNA) XIST (X-inactive specific transcript) as a driver of RA pathogenesis, with a particular focus on the ability of this lncRNA to interact with GATA1 and CCN6. The GSE83147and GSE181614 datasets were downloaded for analysis. XIST and CCN6 expression were assessed in synovial fibroblasts (SFs) and in both normal cartilage samples and those from RA patients, with the relationship between XIST and CCN6 additionally being examined. XIST and CCN6 were respectively knocked down or overexpressed in SFs to establish their regulatory roles in these cells in the context of RA. Further studies of the regulatory interplay between XIST, GATA1, and CCN6 were then performed through RNA immunoprecipitation, RNA pull-down, gain-of-function, loss-of-function, and luciferase reporter assays. In addition, RA model rats were established and used to measure the production of TNF-α, IL-6, and IL-8 and to subject tissues from these animals to histopathological examination. RA patient synovial tissues and SFs exhibited XIST and CCN6 upregulation. The knockdown of XIST suppressed SF migratory, proliferative, invasive, and angiogenic activity, while CCN6 knockdown partially reversed the ability of XIST to influence these phenotypic outcomes in vitro and in vivo. XIST bound to GATA1 within SFs, thus promoting enhanced CCN6 transcription. Knocking down XIST alleviated RA-related pathological damage, synovial injury, and inflammatory response induction in rats. The binding of XIST to GATA1 leads to CCN6 upregulation, driving RA pathogenesis by altering SF proliferation and angiogenic activity, suggesting that this pathway may represent a viable target for therapeutic intervention.

The uterosacral ligament (USL) is the main structure of physiological apical support, and USL suspension is one of the most commonly used methods for native tissue apical suspension. Structures surrounding the USL are complex, and the USL itself is difficult to identify, especially the sacral portion of the USL. Laparoscopy improves visualization, but exposure of the USL is still unsatisfactory.In this study, we report a simple method for exposing and suturing the USL laparoscopically, with step-by-step instructions, well-presented figures and videos. The key techniques are shown as follows: keeping tension on the USL with a uterine manipulator, dissecting the space medial to the USL, exposing the portion of the USL near the sacrum through the natural space, and then suturing it medially and connecting it directly to the posterior cervix.95 consecutive patients have undergone this modified USLS and none had serious perioperative complication.In this way, the USL anatomy is exposed well, which may make placement of sutures in USL suspension safe and effective.

The realization of sustainable social rental housing is regarded as an important policy to solve the housing burden, but social rental housing is often unsustainable. This study assesses the sustainability of social rental housing. However, the decision-making models, such as the classical decision-making hierarchy (AHP) used in the current study, conflict with the resolution of the complex influence of the relationship between evaluation dimensions/criteria. Based on this, we construct a new hybrid multi-attribute decision-making model (MADM) combining the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Fuzzy DEMATEL-based analytic network process (DANP) and the modified VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for the sustainability assessment of government-led social rental housing (SRH) using Taiwanese experts as the subject of the investigation. The Fuzzy DEMATEL found that economic sustainability (D1), as the core dimension, affects environmental (D3) and social sustainability (D2). Furthermore, criteria with the “cause” position, including the reasonable development model (C2), the provision of living facilities and services needed (C5), the adoption of environmentally friendly technology and equipment (C10), guaranteed rent and lease (C1), the acquisition of working skill and employment opportunities (C6) and space planning and design (C3), should receive more attention from decision makers because improving these can further enhance the sustainability of the overall scheme. Combined with the DANP weight and modified VIKOR, we also indicate that these criteria should be optimized to make the evaluation more sustainable for a selected social housing case in Taiwan. The research also further proposes management strategy implications.

PDF file, 68K, Outgrowth of basal and luminal MaECs from Ctrl-GFP, MFCKO-GFP and MFCKD-GFP mice.

PDF file, 81K, Outgrowth of unsorted MaECs isolated from Cre Ctrl-GFP, Ctrl-GFP and MFCKO-GFP mice.

PDF file, 82K, Characterization of distinct cellular populations for expression of luminal marker CK8 and basal marker CK5 using immmunofluorescence.

PDF file, 140K.

PDF file, 80K, Outgrowth from Ctrl (FAKf/f) MaECs infected with Ad-LacZ or Ad-Cre.

PDF file, 492K, Genotyping of recipient mammary fat pads following transplantation as well as TUNEL assay for luminal and basal acini sections derived from Ctrl, MFCKO, and MFCKD mice.

PDF file, 624K, PyMT-induced tumor cells have a luminal cellular origin and FAK inhibitor treatment does not induce significant apoptosis in SUM149 and SUM159 breast cancer cells.

PDF file, 128K, Acinar colony and mammasphere formation for MaECs deleted of FAK in vivo or in vitro.

With the rapid development of information technology, binary memory may not meet the requirements of future information and communication technology, hence multilevel memory is widely studied. Herein, a donor-acceptor-type polymer poly[2,7-9-(heptadecan-9-yl)-9H-carbazole-alt-7H-benzimidazo[2,1-a]benz[de]isoquinolin-7-one] (PCz-BMBI) was synthesized by the Suzuki cross-coupling reaction and characterized. The organic resistive random access memory (ORRAM) devices with non-volatile ternary memory behavior, based on PCz-BMBI and PCz-BMBI:CuO NPs active layers were fabricated by spin-coating technology. The I-V characteristics of the devices were studied, and the effect of the embedding ratio of CuO nanoparticles (NPs) on the memory performance of the devices was discussed. The results indicate that the switching ratios of all devices with CuO NPs embedded were improved, and their first threshold voltages were lowered. Furthermore, the devices fabricated at an embedding ratio of 7 wt% CuO NPs exhibited optimal memory performance with a current ratio of ON2/ON1/OFF of 105.3:102.3:1, low threshold voltages of -0.50 V/-1.60 V, a retention time of 104 s, and excellent stability over 103 cycles. In addition, the storage mechanism of the devices is discussed.

Graph convolutional network (GCN) has shown potential in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate the new node. The available GCN-based methods fail to understand the global and contextual information of the graph. To address this deficiency, a novel semisupervised network based on graph sample and aggregate-attention (SAGE-A) for HSIs' classification is proposed. Different from the GCN-based method, SAGE-A adopts a multilevel graph sample and aggregate (graphSAGE) network, as it can flexibly aggregate the new neighbor node among arbitrarily structured non-Euclidean data and capture long-range contextual relations. Inspired by the convolution neural network (CNN) self-attention mechanism, the proposed network uses the graph attention mechanism to characterize the importance among spatially neighboring regions, so the deep contextual and global information of the graph can be learned automatically by focusing on important spatial targets. Extensive experimental results on different real hyperspectral data sets demonstrate the performances of our proposed method compared with the state-of-the-art methods.