Length to diameter ratio effect on heat transfer performance of simple and compound angle holes in thin-wall airfoil cooling

Heat transfer coefficients on a flat plate surface downstream a row of simple and compound angle cylindrical holes are investigated using high-resolution thermographic liquid crystal technique. A variation of flow parameters including blowing ratio, and geometry parameters including compound angle and length-to-diameter ratio are examined. Blowing ratios (M) ranging from 0.3 to 2, length to diameter ratios (L/D) from 0.5 to 5, and two compound angle (β: 0°, 45°) are employed composing a test matrix of 70 test cases. Detailed local, spanwise averaged, and area averaged heat transfer coefficients hf/h0 are presented to illustrate the effect of length-to-diameter ratio and compound angle. The film cooling performance is also evaluated using NHFR method and Δφ method by combining adiabatic film effectiveness and heat transfer coefficient data. Results indicate that Δφ method has superiority in evaluating film cooling performance due to its direct reflection of temperature reduction by film protection

Experimental investigation of wall thickness and hole shape variation effects on full-coverage film cooling performance for a gas turbine vane

The effects of wall thickness and hole shape variation on a full-coverage film cooled turbine vane are investigated in a stationary and linear cascade utilizing the pressure sensitive paint technique. The varied wall thickness produces hole length-to-diameter ratio (L/D) in a range from L/D = 2 to 5, and holes tested include simple angle hole, compound angle hole, and fan-shaped hole. Five rows of holes are provided on the pressure side while three rows of holes are provided on the suction side, with six rows of cylindrical holes drilled on the leading edge to construct showerhead film cooling. The tested blowing ratios for the showerhead, pressure side, and suction side range from 0.25 to 1.5, with a density ratio of 1.5. The freestream Reynolds number is 1.35 × 105, based on the axial chord length and the inlet velocity, with a freestream turbulence intensity level of 3.5% at the cascade inlet. The results indicate that the wall thickness variation produces significant influence on the pressure side film cooling effectiveness, while only marginal effect on the showerhead and suction side film cooling. Also observed is that the fan-shaped hole generates the highest film cooling effectiveness on pressure or suction side. Also discussed is the surface curvature effect, combining with effects of wall thickness and hole shape variations, on the film cooling effectiveness in comparison to the flat-plate data

Domain Generalization via Balancing Training Difficulty and Model Capability

Domain generalization (DG) aims to learn domain-generalizable models from one or multiple source domains that can perform well in unseen target domains. Despite its recent progress, most existing work suffers from the misalignment between the difficulty level of training samples and the capability of contemporarily trained models, leading to over-fitting or under-fitting in the trained generalization model. We design MoDify, a Momentum Difficulty framework that tackles the misalignment by balancing the seesaw between the model's capability and the samples' difficulties along the training process. MoDify consists of two novel designs that collaborate to fight against the misalignment while learning domain-generalizable models. The first is MoDify-based Data Augmentation which exploits an RGB Shuffle technique to generate difficulty-aware training samples on the fly. The second is MoDify-based Network Optimization which dynamically schedules the training samples for balanced and smooth learning with appropriate difficulty. Without bells and whistles, a simple implementation of MoDify achieves superior performance across multiple benchmarks. In addition, MoDify can complement existing methods as a plug-in, and it is generic and can work for different visual recognition tasks.Comment: 11 pages, 6 figures, Accepted by ICCV 202

Efficient Attribute-Based Smart Contract Access Control Enhanced by Reputation Assessment

Blockchain's immutability can resist unauthorized changes of ledgers, thus it can be used as a trust enhancement mechanism to a shared system. Indeed, blockchain has been considered to solve the security and privacy issues of the Internet of Things (IoT). In this regard, most researches currently focus on the realization of various access control models and architectures, and are working towards making full use of the blockchain to secure IoT systems. It is worth noting that there has been an increasingly heavy pressure on the blockchain storage caused by dealing with massive IoT data and handling malicious access behaviors in the system, and not many countermeasures have been seen to curb the increase. However, this problem has not been paid enough attention. In this paper, we implement an attribute-based access control scheme using smart contracts in Quorum blockchain. It provides basic access control functions and conserves storage by reducing the number of smart contracts. In addition, a reputation-based technique is introduced to cope with malicious behaviors. Certain illegal transactions can be blocked by the credit-assessment algorithm, which deters possibly malicious nodes and gives more chance to well-behaved nodes. The feasibility of our proposed scheme is demonstrated by doing experiment on a testbed and conducting a case study. Finally, the system performance is assessed based on experimental measurement

The Inhibitory Role of B7-H4 in Antitumor Immunity: Association with Cancer Progression and Survival

B7-H4 is one of the most recently identified members of B7 superfamily of costimulatory molecules serving as an inhibitory modulator of T-cell response. B7-H4 is broadly expressed in human peripheral tissues and inducibly expressed in immune cells. The expression of B7-H4 has been observed in various types of human cancer tissues, and its soluble form has been detected in blood samples from cancer patients. However, its precise physiological role is still elusive, as its receptor has not been identified and the expression levels are not consistent. This paper summarizes the pertinent data on the inhibitory role of B7-H4 in antitumor immunity and its association with cancer progression and survival in human patients. The paper also discusses the clinical significance of investigating B7-H4 as potential markers for cancer diagnosis and prognosis, and as therapeutic targets

Downregulation of developmentally regulated endothelial cell locus-1 inhibits the growth of colon cancer

Developmentally regulated endothelial cell locus-1 (Del1) is an embryonic angiogenic factor expressed in early embryonic endothelial cells, but recently has been found to be expressed in some forms of cancers including colon and breast cancers, and melanoma, and human cancer cell lines. Overexpression of Del1 accelerates tumor growth by enhancing vascular formation, implying Del1 may be a potential target for anti-angiogenic cancer therapy. The study aims to investigate whether downregulation of Del1 could inhibit the growth of tumors established in nude Balb/c mice by subcutaneous implantation of human LS-174T colon cancer cells. The shRNA expression vectors targeting human Del1, and vascular endothelial growth factor (VEGF) were constructed. Gene transfection of Del1-shRNA downregulated expression of Del1 in LS-174T cells in vivo and in vitro, but did not alter the proliferative or survival properties of cells in vitro. Gene transfection of VEGF-shRNA downregulated expression of both VEGF and Del1 in LS-174T cells in vivo and in vitro. Both Del1-shRNA and VEGF-shRNA gene therapies exhibited anti-tumor activities and they also showed a synergistic effect in suppressing growth of colon tumors by anti-angiogenesis and anti-proliferation. Although further investigation to clarify the mechanisms explaining the role of Del1 in tumor growth, and the interaction between VEGF and Del1, is required, the results indicate that downregulation of Del1 presents a potent therapeutic strategy to combat colon cancer

LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors

Inspired by the outstanding zero-shot capability of vision language models (VLMs) in image classification tasks, open-vocabulary object detection has attracted increasing interest by distilling the broad VLM knowledge into detector training. However, most existing open-vocabulary detectors learn by aligning region embeddings with categorical labels (e.g., bicycle) only, disregarding the capability of VLMs on aligning visual embeddings with fine-grained text description of object parts (e.g., pedals and bells). This paper presents DVDet, a Descriptor-Enhanced Open Vocabulary Detector that introduces conditional context prompts and hierarchical textual descriptors that enable precise region-text alignment as well as open-vocabulary detection training in general. Specifically, the conditional context prompt transforms regional embeddings into image-like representations that can be directly integrated into general open vocabulary detection training. In addition, we introduce large language models as an interactive and implicit knowledge repository which enables iterative mining and refining visually oriented textual descriptors for precise region-text alignment. Extensive experiments over multiple large-scale benchmarks show that DVDet outperforms the state-of-the-art consistently by large margins