Explore the vast range of titles available.

Both Xp11 translocation renal cell carcinomas and the corresponding mesenchymal neoplasms are characterized by a variety of gene fusions involving TFE3 . It has been known that tumors with different gene fusions may have different clinicopathologic features; however, further in-depth investigations of subtyping Xp11 translocation-associated cancers are needed in order to explore more meaningful clinicopathologic correlations. A total of 22 unusual cases of Xp11 translocation-associated cancers were selected for the current study; 20 cases were further analyzed by RNA sequencing to explore their TFE3 gene fusion partners. RNA sequencing identified 17 of 20 cases (85%) with TFE3 -associated gene fusions, including 4 ASPSCR1/ASPL-TFE3 , 3 PRCC-TFE3 , 3 SFPQ/PSF-TFE3 , 1 NONO-TFE3 , 4 MED15-TFE3 , 1 MATR3-TFE3 , and 1 FUBP1-TFE3 . The results have been verified by fusion fluorescence in situ hybridization (FISH) assays or reverse transcriptase polymerase chain reaction (RT-PCR). The remaining 2 cases with specific pathologic features highly suggestive of MED15-TFE3 renal cell carcinoma were identified by fusion FISH assay. We provide the detailed morphologic and immunophenotypic description of the MED15-TFE3 renal cell carcinomas, which frequently demonstrate extensively cystic architecture, similar to multilocular cystic renal neoplasm of low malignant potential, and expressed cathepsin K and melanotic biomarker Melan A. This is the first time to correlate the MED15-TFE3 renal cell carcinoma with specific clinicopathologic features. We also report the first case of the corresponding mesenchymal neoplasm with MED15-TFE3 gene fusion. Additional novel TFE3 gene fusion partners, MATR3 and FUBP1 , were identified. Cases with ASPSCR1-TFE3 , SFPQ-TFE3 , PRCC-TFE3 , and NONO-TFE3 gene fusion showed a wide variability in morphologic features, including invasive tubulopapillary pattern simulating collecting duct carcinoma, extensive calcification and ossification, and overlapping and high columnar cells with nuclear grooves mimicking tall cell variant of papillary thyroid carcinoma. Furthermore, we respectively evaluated the ability of TFE3 immunohistochemistry, TFE3 FISH, RT-PCR, and RNA sequencing to subclassify Xp11 translocation-associated cancers. In summary, our study expands the list of TFE3 gene fusion partners and the clinicopathologic features of Xp11 translocation-associated cancers, and highlights the importance of subtyping Xp11 translocation-associated cancers combining morphology, immunohistochemistry, and multiple molecular techniques.

Steel slag aggregate concrete (SAC) is widely recognized as a high-performance and sustainable construction material. However, its broader structural application has been impeded by the limited development of reliable constitutive models. Building upon the well-established non-uniform hardening plasticity theory, this study proposes a comprehensive theoretical framework to establish a stress–strain relationship model for SAC under complex stress states. To this end, a multiaxial elastoplastic constitutive model for SAC is developed through the following steps: (1) The Guo–Wang failure criterion is employed as the bounding surface, from which a yield criterion is formulated to capture the characteristic mechanical responses of SAC under multiaxial loading; (2) Based on fundamental plasticity theory, the stress–strain relationship is derived by integrating the proposed yield function with a non-associated flow rule using a Drucker–Prager-type plastic potential function, while ensuring consistency conditions are satisfied; (3) A parameter calibration methodology is introduced and applied using experimental data from uniaxial and multiaxial tests on SAC; (4) A numerical implementation scheme is developed in MATLAB 2024a, and the model is validated through computational simulations. The validation results confirm that the proposed model reliably captures the stress–strain behavior of SAC under complex loading conditions. Overall, this study not only delivers a robust multiaxial constitutive model for SAC, but also offers a systematic modeling approach that may serve as a reference for the further development of constitutive theories for steel slag-based concretes and their broader application in structural engineering.

This paper investigated novel predefined-time stability theorems for time-delayed fuzzy Cohen-Grossberg neural networks. A novel predefined-time stability lemma was introduced via a newly developed inequality-based analytical framework.The theoretical results demonstrated that, compared to existing stability criteria in the literature, is provided more precise estimation of settling time boundaries, but also effectively reduced conservatism. To validate the effectiveness of the proposed lemma, the stability theorem was applied to the synchronization control problem of fuzzy Cohen-Grossberg neural networks (FCGNNs).To address this, an adaptive control strategy was proposed, employing a discontinuous state-feedback approach for the response neural network. Rigorous algebraic criteria was established to ensure synchronization within the specified time frame, in line with prior discussions. The effectiveness of the proposed synchronization method was empirically verified through numerical case studies.

The delayed coalescence of drops with the interface between a moving aqueous layer and an oil phase is investigated in a novel flow channel. Drops are released onto oil–aqueous interfaces moving at velocities from$0~\\text{cm}~\\text{s}^{-1}$up to$3.4~\\text{cm}~\\text{s}^{-1}$. The evolution of the drop shape, the film thickness between the drop and the bulk liquid, and the velocities of the drop surface and the bulk interface were measured with planar laser-induced fluorescence. As the interface speed increases, the drop coalescence is delayed. This is attributed to the lubrication pressure that develops in the draining film. This pressure was calculated by using the drop shape and the tangential velocities of the drop surface and the bulk interface, and was shown to increase with the interface velocity. The film forming between the drop and the bulk liquid has a dimple shape, symmetric about the centreline. With increasing interface velocity, the dimple shifts to the front part of the drop, resulting locally in a low pressure, which leads to film rupture. As the film breaks, ‘oil drops on a string’ formations are entrained into the water phase, which is rarely seen when a drop coalesces with a stationary liquid–liquid interface. The velocity fields in the drop were investigated with particle image velocimetry. It is found immediately after reaching the interface that the drops accelerate to reach the interface speed. Initially there is a strong internal circulation in the drops, which decays quickly as the drops approach the speed of the interface.

Magnesium powder was used as an expansion agent to solve the problem caused by the large linear shrinkage of phosphoric acid-based geopolymers(PAG). The linear shrinkage of the hardened PAG increased with a maximum of 3.54%. The maximum compressive strength of the geopolymer can reach 89.3 MP. When the liquid-solid ratio is 1.70. 0.1%-0.4% magnesium powder was added, the linear shrinkage of hardened PAG decreased, and the shrinkage could be compensated basically when 0.3% magnesium powder was added. The shrinkage of phosphoric acid base polymers is mainly self-shrinkage, which is caused by the reduction of polymer volume after the reaction. XRD and FTIR results showed that magnesium is not involved in the bonding reaction, and eventually, magnesium phosphate is formed. This research provides a valuable reference for the study of shrinkage characteristics and expansion agents of PAG.

Geopolymers, as sustainable alternatives to conventional cement, face application limitations due to pronounced drying shrinkage. This study systematically investigates the effects of cement incorporation (0–40%) on the drying shrinkage mitigation and performance evolution of metakaolin-based geopolymers (MKBGs) through multi-scale characterization of mechanical properties, reaction kinetics, and pore structure refinement. Key findings reveal that 10% cement addition optimally reduces drying shrinkage through pore structure densification and elastic modulus enhancement. The cement–geopolymer hybrid system exhibited a distinctive dual-reaction mechanism: cement hydration produced C-S-H gels that refined the pore structure while simultaneously competing with and delaying the geopolymerization kinetics, as demonstrated by the extended duration of the reaction exotherm. However, cement contents exceeding 20% induce detrimental self-desiccation shrinkage, resulting in net shrinkage amplification. Microstructural analysis confirms that the optimal 10% cement dosage achieves synergistic phase evolution, with N-A-S-H and C-S-H gels co-operatively improving mechanical strength and dimensional stability. This work provides quantitative guidelines for designing shrinkage-resistant geopolymer composites through controlled cement hybridization.

This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.

During the oilfield development process, various factors can cause different types of reservoir damage, leading to reduced oil well production or even shutdown, and decreased water injection capacity in water wells, resulting in significant economic losses for the oilfield. However, formation damage control measures must be based on the quantitative diagnosis of the types and degrees of reservoir damage. This paper establishes a spatiotemporal evolution numerical model for 12 common types of damage during the oil and gas exploration and development process, based on the material balance theory and Fick’s diffusion law in reservoir damage processes. This model achieves numerical simulation of the degree of various reservoir damage types in different spatiotemporal domains. The overall damage degree of a specific well’s evolution with time and space is further simulated in simple superposition way. The sequential core flow experiment was carried out in the laboratory, and then compared with the calculation results. The accuracy is above 90%. Finally, using field test data, the simulation results show a 95% or higher degree of agreement with the actual field measurements, proving that the reservoir damage spatiotemporal evolution quantitative simulation technology established in this paper has high accuracy and practicality.

The Aphis gossypii is a major threat of cotton worldwide due to its short life cycle and rapid reproduction. Chemical control is the primary method used to manage the cotton aphid, which has significant environmental impacts. Therefore, prioritizing eco-friendly alternatives is essential for managing the cotton aphid. The ladybird, Hippodamia variegata , is a predominant predator of the cotton aphid. Its performance in cotton plantation is directly linked to chemical communication, where volatile compounds emitted from aphid-infested plants play important roles in successful predation. Here, we comprehensively studied the chemical interaction between the pest, natural enemy and host plants by analyzing the volatile profiles of aphid-infested cotton plants using gas chromatography-mass spectrometry (GC-MS). We then utilized the identified volatile compounds in electrophysiological recording (EAG) and behavioral assays. Through behavioral tests, we initially demonstrated the clear preference of both larvae and adults of H. variegata for aphid-infested plants. Subsequently, 13 compounds, namely α-pinene, cis -3-hexenyl acetate, 4-ethyl-1-octyn-3-ol, β-ocimene, dodecane, E-β-farnesene, decanal, methyl salicylate, β-caryophyllene, α-humulene, farnesol, DMNT, and TMTT were identified from aphid-infested plants. All these compounds were electrophysiologically active and induced detectable EAG responses in larvae and adults. Y-tube olfactometer assays indicated that, with few exceptions for larvae, all identified chemicals were attractive to H. variegata , particularly at the highest tested concentration (100 mg/ml). The outcomes of this study establish a practical foundation for developing attractants for H. variegata and open avenues for potential advancements in aphid management strategies by understanding the details of chemical communication at a tritrophic level.

The research status of photocatalytic and thermal insulation coatings at home and abroad was summarized. The different types of photocatalytic coatings were summarized. The factors influencing the photocatalytic coatings, such as the activity of photocatalytic coatings, the load of photocatalytic coatings, and the dispersion process, were expounded. The type and property of insulation coatings and the effects of fillers on the properties of insulation coatings were summarized. At present, the primary problem of photocatalysis and heat preservation and heat insulation building coatings in China is lack of research and development of dispersants and composite materials. The evaluation system of photocatalysis, heat preservation and heat insulation need to be improved. It is of significance for the multi-functional coatings with the performance of luminescent catalytic insulation.