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In this study, different mass concentrations (0%, 1%, 2%, 3%, and 4%, based on starch mass) of tamarind seed polysaccharides (TSP) were added to endow potato starch (PS) bread with gluten-like properties in order to improve the processing performance and texture quality of gluten-free bread. The results indicated that adding TSP increased the peak viscosity of PS, improve its viscoelasticity, and endow it with gluten-like network structural characteristics. With increasing TSP concentration, the baking loss rate of gluten-free bread significantly decreased from 26.01% to 20.36%, and the specific volume increased from 1.27 mL/g to a maximum value of 3.19 mL/g. The addition of TSP significantly improved the texture characteristics of bread, as evidenced by a reduction in its hardness, cohesiveness, chewiness and resilience, resulting in a softer texture. Adding TSP delayed the staling of bread, thereby extending the shelf life. Principal component analysis (PCA) showed strong correlations between bread quality and baking performance and gelatinization characteristics of PS. This study has expanded the application of TSP in food field and provides new ideas and a theoretical basis for the development of gluten-free starch products.

To address the issues of significant object discrepancies, low similarity, and image noise interference between source and target domains in object detection, we propose a supervised learning approach combined with knowledge distillation. Initially, student and teacher models are jointly trained through supervised and distillation-based approaches, iteratively refining the inter-model weights to mitigate the issue of model overfitting. Secondly, a combined convolutional module is integrated into the feature extraction network of the student model, to minimize redundant computational effort; an explicit visual center module is embedded within the feature pyramid network, to bolster feature representation; and a spatial grouping enhancement module is incorporated into the region proposal network, to mitigate the adverse effects of noise on the outcomes. Ultimately, the model undergoes a comprehensive optimization process that leverages the loss functions originating from both the supervised and knowledge distillation phases. The experimental results demonstrate that this strategy significantly boosts classification and identification accuracy on cross-domain datasets; when compared to the TFA (Task-agnostic Fine-tuning and Adapter), CD-FSOD (Cross-Domain Few-Shot Object Detection) and DeFRCN (Decoupled Faster R-CNN for Few-Shot Object Detection), with sample orders of magnitude 1 and 5, increased the detection accuracy by 1.67% and 1.87%, respectively. [ABSTRACT FROM AUTHOR]

Video anomaly detection (VAD) plays a crucial role in fields such as security, production, and transportation. To address the issue of overgeneralization in anomaly behavior prediction by deep neural networks, we propose a network called AMFCFBMem-Net (appearance and motion feature cross-fusion block memory network), which combines appearance and motion feature cross-fusion blocks. Firstly, dual encoders for appearance and motion are employed to separately extract these features, which are then integrated into the skip connection layer to mitigate the model's tendency to predict abnormal behavior, ultimately enhancing the prediction accuracy for abnormal samples. Secondly, a motion foreground extraction module is integrated into the network to generate a foreground mask map based on speed differences, thereby widening the prediction error margin between normal and abnormal behaviors. To capture the latent features of various models for normal samples, a memory module is introduced at the bottleneck of the encoder and decoder structures. This further enhances the model's anomaly detection capabilities and diminishes its predictive generalization towards abnormal samples. The experimental results on the UCSD Pedestrian dataset 2 (UCSD Ped2) and CUHK Avenue anomaly detection dataset (CUHK Avenue) demonstrate that, compared to current cutting-edge video anomaly detection algorithms, our proposed method achieves frame-level AUCs of 97.5% and 88.8%, respectively, effectively enhancing anomaly detection capabilities. [ABSTRACT FROM AUTHOR]

Next generation fuel-efficient jet engines are running hotter presenting a structural challenge for the exhaust systems and structures adjacent to the engines. A conventional and affordable titanium alloy with superior oxidation resistance provides significant weight reductions and associated cost savings by eliminating the need for high density material systems such as nickel-base superalloys for service temperatures in between current titanium and nickel, enabling major technology advancement in high temperature aerospace applications. This paper presents an overview of Arconic’s engineered material ARCONIC-THORTM to address the needs of future aerospace systems.

The aeroengine and airframe applications of titanium alloys are often limited by their insufficient oxidation resistance to the aggressive environment at higher temperatures. A high temperature oxidation resistant titanium alloy (Arconic-THORTM) has been developed. This alloy is an alpha-beta alloy with superior oxidation resistance and improved creep resistance. The oxidation weight gain of Arconic-THORTM is much lower than Ti-6242 and Beta 21s in the temperature range up to 750˚C. The room and elevated temperature properties of Arconic-THORTM are comparable to those for Ti-6242. Arconic-THORTM also shows superior, post-thermal-exposure tensile strength, ductility, and fatigue properties, and is capable of being cold formed, hot formed, and superplastic formed, welded and heat treated to different product geometries. The microstructures and mechanical properties for sheet manufactured from the production ingots are presented and discussed in this study.

Utilization of gamma titanium aluminide alloys in aerospace and automotive/industrial applications has placed significant demand on melting sources for products to be used in cast, wrought, and direct-machining applications. There is also an increased demand for input stock used in gas atomization of powders. Current technologies used in ingot manufacturing include plasma arc melting, vacuum arc melting, and induction skull melting + centrifugal casting. Subsequent processing may include forging, re-melting + casting, or machining directly into components. Over the past six years, Arconic Engineered Structures has developed a robust melting method using plasma cold-hearth melting technology, including the design and implementation of a new 3-torch system to produce Ti-48-2-2 cast bars. General discussions concerning plasma cold-hearth melting, manufacturing challenges, and metallurgical attributes associated with cast Ti-48-2-2 bars will be reviewed. Emphasis will be on understanding the impact of hot isostatic pressing on internal voids, residual stress cracking and resulting mechanical properties.

Background and Objectives: Notch is a fascinating signaling pathway. It is extensively involved in tumor growth, cancer stem cells, metastasis, and treatment resistance and plays important roles in metabolic regulation, tumor microenvironment, and tumor immunity. However, the role of Notch in ovarian cancer (OC) has yet to be fully understood. Therefore, this study systematically described the expression, mutation, and copy number variation of genes in the Notch signaling pathway in OC and evaluated the relationship between gene mutation and Overall Survival (OS) prognosis. Materials and Methods: Notch risk score (NTRS) was established by univariate Cox regression analysis combined with Lasso regression analysis, and the efficacy of NTRS in predicting prognosis and immunotherapy response in patients with OC was verified. We further assessed the correlations of NTRS with clinical features, immune infiltration level, immune checkpoint expression, and immune characteristics. Additionally, differential expression and functions of the fourteen signature genes were confirmed via vitro assays. Results: The results showed that Notch genes (NTGs) were markedly differentiated between tumor and normal tissues, which may help to explain the high heterogeneity in the biological characteristics and therapeutic outcomes of human OC. A Notch risk (NTR) prognostic model based on 11 key NTGs was successfully constructed. Tumors with high Notch risk scores (NTRS) were independently associated with shorter overall survival and poorer immunotherapy outcomes. We further assessed the correlations of NTRS with immune characteristics. The results showed that NTGs play a key role in regulating the tumor immune microenvironment. Additionally, we validated the baseline and induced expressions of 14 prognosis-related NTGs in our own OC samples. In vitro assays confirmed that the knockdown of NCOR2 and APH1B and overexpression of HEY2 and SKP2 could inhibit the proliferation, invasion, and migration of OC cells. Conclusions: These findings emphasize that Notch multilayer changes are associated with the prognosis of patients with OC and the characteristics of immune cell infiltration. Our predictive signature may predict the prognosis and immunotherapy response of OC patients in an independent manner. NCOR2, APH1B, HEY2, and SKP2 may more prominently represent important indicators to improve patient prognosis. [ABSTRACT FROM AUTHOR]