Sharp-edged orifice plate’s wall pressure characteristics

952-956The minimum wall pressure coefficient of orifice plate and its wall pressure distribution relating to tunnel’s and dissipater’s safety are important indices for this energy dissipater design. In the present paper, the minimum wall pressure coefficient of sharp-edged orifice plate was analyzed; meanwhile, the characteristics of sharp-edged orifice plate wall pressure distribution were also analyzed. The research result has shown that the minimum wall pressure coefficient was mainly dominated by the contraction ratio of the orifice plate. The less is the contraction ratio of the orifice plate, the larger is the minimum wall pressure coefficient. The effect of orifice plate’ thickness on the minimum wall pressure coefficient was not obvious and could be neglected. When Reynolds number is more than 105, it has little impact on the minimum wall pressure coefficient. Wall pressure begins to drop down dramatically before 0.5D orifice plate, reaches minimum at the end of orifice plate, and then recovers normally when flows arrival at 3D away after orifice plate. An empirical expression was presented to calculate the minimum wall pressure coefficient. Experimental data illuminate that calculation results by using empirical expression coincided with experiment results

Distance between plugs in multi-stage plug discharge tunnel

1472-1475For multi-stage plug discharge tunnel, the distance between the upper and the lower plug affects plug’s energy dissipation capacity, and it is also an important index in multi-stage plug design. In the present paper, the reasonable distance between the upper and the lower plug in multi-stage plug discharge tunnel are researched by physical model experiment. The research result shows that when the contraction ratio is in the scope of 0.4~ 0.8, the reasonable distance between the upper and the lower plug in multi-stage plug discharge tunnel is equal to or more than 5.5D

An efficient intelligent algorithm based on WSNs of the drug control system

U radu se predlaže novi algoritam, ACORS-ANNDPF za WSNs (bežične senzorske mreže), u svrhu povećanja stope uporabe WSNs i produženja životnog ciklusa Iot-a (Interneta stvari). Razvijen na temelju algoritma kolonije mrava, ovaj se poboljšani algoritam može primijeniti na izbor optimalne putanje i prepoznavanje optimalnog čvora za usmjeravanje u slučaju gubljenja čvora usmjeravanja. Kako bi se smanjilo vrijeme utrošeno na premiještanje skupine mreža, algoritam neuronske mreže odabire pokazatelje u skladu s aktualnim aplikacijskim okruženjem i podešava ih u svrhu optimiziranja podataka skupine. Nakon toga, autor provodi nekoliko simulacijskih eksperimenata i uspoređuje predloženi algoritam s drugim algoritmima. Rezultati pokazuju da se predloženim algoritmom osigurava visoka učinkovitost energije i balansirana potrošnja energije. Prema tome, zaključeno je da se predloženim algoritmom može poboljšati brzina uporabe mreže i povećati prijenosna funkcija mreže.A new algorithm, ACORS-ANNDPF for WSNs, is proposed in this paper to improve the utilization rate of WSNs and prolong the life cycle of the IoT. Developed on the basis of ant colony algorithm, the improved algorithm is applicable to the selection of the optimal path and identification of the optimal routing node in the case of losing the routing node. To reduce the time spent on transferring network packets, the indices are selected by the neural network algorithm in light of the actual application environment and adjusted to optimize the fusion of packet data. After that, the author carries out several simulation experiments and compares the proposed algorithm with other algorithms. The results demonstrate that the proposed algorithm ensures high energy efficiency and balanced energy consumption. Therefore, it is concluded that the proposed algorithm can improve network utilization rate and lead to better network transmission performance

Cellular Automata Based Modeling for Evaluating Different Bus Stop Designs in China

A cellular automaton model is proposed to simulate mixed traffic flow composed of motor vehicles and bicycles near bus stops. Three typical types of bus stops which are common in China are considered in the model, including two types of curbside bus stops and one type of bus bay stops. Passenger transport capacity of three types of bus stops, which is applied to evaluate the bus stop design, is calculated based on the corresponding traffic flow rate. According to the simulation results, the flow rates of both motor vehicles and bicycles exhibit phase transition from free flow to the saturation one at the critical point. The results also show that the larger the interaction between motor vehicle and bicycle flow is near curbside bus stops, the more the value of saturated flows drops. Curbside bus stops are more suitable when the conflicts between two flows are small and the inflow rate of motor vehicles is low. On the contrary, bus bay stops should be applied due to their ability to reduce traffic conflicts. Findings of this study can provide useful suggestions on bus stop selection considering different inflow rate of motor vehicles and bicycles simultaneously

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report

Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a benchmark for future studies to reference. Methods and Materials: The Generative Pre-trained Transformer (GPT)-4 application programming interface (API) was implemented as a Digital Imaging and Communications in Medicine (DICOM) storage server, which upon receiving a structure set DICOM file, prompts GPT-4 to re-label the structure names of both target volumes and normal tissues according to the AAPM TG-263. Three disease sites, prostate, head and neck, and thorax were selected for evaluation. For each disease site category, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50) and 50 patients were randomly selected for evaluation. Structure names that were considered were those that were most likely to be relevant for studies utilizing structure contours for many patients. Results: The overall re-labeling accuracy of both target volumes and normal tissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and 96.9% respectively. Re-labeling of target volumes was less accurate on average except for prostate - 100%, 93.1%, and 91.1% respectively. Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.Comment: 20 pages, 5 figures, 1 tabl

Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics

We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs. We developed an exam consisting of 100 radiation oncology physics questions based on our expertise at Mayo Clinic. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. ChatGPT (GPT-4) outperformed all other LLMs as well as medical physicists, on average. The performance of ChatGPT (GPT-4) was further improved when prompted to explain first, then answer. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups. In evaluating ChatGPTs (GPT-4) deductive reasoning ability using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."), ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote. This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants

A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells

Development of an accurate and automated algorithm to completely segment cervical cells in Pap images is still one of the most challenging tasks. The main reasons are the presence of overlapping cells and the lack of guiding mechanism for the convergence of ill-defined contours to the actual cytoplasm boundaries. In this paper, we propose a novel method to address these problems based on level set method (LSM). Firstly, we proposed a morphological scaling-based topology filter (MSTF) and derived a new mathematical toolbox about vector calculus for evolution of level set function (LSF). Secondly, we combine MSTF and the mathematical toolbox into a multifunctional filtering algorithm 2D codimension two-object level set method (DCTLSM) to split touching cells. The DCTLSM can morphologically scale up and down the contour while keeping part of the contour points fixed. Thirdly, we design a contour scanning strategy as the evolution method of LSF to segment overlapping cells. In this strategy, a cutting line can be detected by morphologically scaling the union LSF of the pairs of cells. Then, we used this cutting line to construct a velocity field with an effective guiding mechanism for attracting and repelling LSF. The performance of the proposed algorithm was evaluated quantitatively and qualitatively on the ISBI-2014 dataset. The experimental results demonstrated that the proposed method is capable of fully segmenting cervical cells with superior segmentation accuracy compared with recent peer works