Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.

SIMULATION AND ANALYSIS OF WAVE GROUPS IN A WAVE FLUME

Using the trial-error method, for a given wave spectrum, wave trains with different wave groupness, which is defined by the groupness factor GF have been simulated in a flume. The statistic characteristics are analyzed for some of these wave trains in this paper.

STABILITY ANALYSIS OF DATA BUOY AT SEA

A data buoy, when moored at the sea, will be influenced by different environmental factors such as wind and wave current. For this reason, the stability of a data buoy is of great importance. This paper describes the analysis and calculation of the stability of a data buoy which has a weight suspended from its bottom under normal working conditions and the influence of the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy when it is capsized. The paper also provides the curves which show the influence of the size of the weight and the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy. All these are of great reference value for the design and use of a data buoy at sea and for the design of similar floating bodies. The results from calculations agree with those from the model experiment.

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation functions, such as ReLU, logistic (sigmoid), and Tanh. Both methodologies produced optimal models with high-level accuracy rates. The first methodology involves a list of supervised machine learning algorithms, including KNN, SVM, Adaboost, Logistic Regression, Naive Bayes, and Decision Tree algorithms. They achieved accuracy rates of 86%, 89%, 89%, 81%, 79%, and 99%, respectively. The author clearly explained that Decision Tree algorithm is not suitable for the dataset at hand due to overfitting issues. Therefore, it was discarded as an optimal model to be used. However, the latter methodology (Neural Network) demonstrated the most stable and optimal accuracy, achieving over 87% accuracy while adapting well to real-life situations and requiring low computing power overall. A performance assessment and evaluation were carried out based on a confusion matrix report to demonstrate feasibility and performance. The author concluded that the performance of the model in real-life situations can advance not only the medical field of science but also mathematical concepts. Additionally, the advanced preprocessing approach behind the model can provide value to the Data Science community. The model can be further developed by employing various optimization techniques to handle even larger datasets related to heart failures. Furthermore, different neural network algorithms can be tested to explore alternative approaches and yield different results.

WAVE IMPACT THEORY AND SCALE EFFECT

In this paper the theoretical analysis and calculating method for the maximal impact caused by a jet are presented and the formulas for calculation of the maximal impact of waves acting on the slope are developed. Through the comparison between the results of model tests with a model scale of 1:10 and those of prototype tests conducted in the same way in Big Wave Flume, Hannover, West Germany, the effects of model scale on the measurement of wave impact on slopes are described. In addition, a distribution regularity of the maximal impact pressure is also given.

SILTATION INVESTIGATION IN LIANYUN HARBOR BY USING γ-RAY DENSITY GAUGE

The principle, method and mersuring equipment in studying siltaton in the Lianyun Harbor by using the γ-ray density gauge are described in this paper. From field observation and analyses, some primary principles concerning the infill rate, the distribution of silt density with depth and the consolidation rate and change of the shear strength of the silt have been found out.

Determination of Shaft Capacity for Pile under Cyclic Axial Loading in Clay

For load-controlled and displacement-controlled test data of piles cyclically axiallly loaded in clay, the displacement conditions are suggested for determining the shaft capacity. According to the suggested displacement conditions, not only the results of shaft capacity from laboratory model piles are close to those from in-situ piles, but also the results of load-controlled tests are in satisfactory agreement with those of displacement-controlled test. Moreover, based on the test data of laboratory model piles in combination with the test data published, the paper suggests the values of the normalized shaft capacity of piles under a variety of static and cyclic loading combinations.

Wind-Wave Frequency Spectra of New Dalian Port

This paper, based on wave data measured with Type 956 Directional WAVE-TRACK Buoy System at new Dalian Port for a whole year, analyses the properties of the wave frequency spectra, and derives the formula for the frequency spectrum of wind-waves.

FACT:An Air-Ground Communication Framework for Seeding Quality Control of Aircraft

A new type of air-ground communication application framework named FACT(framework for air-ground communication technology with weather-modification aircraft)is presented to track and command weather-modification aircraft to perform ideal cloud seeding.FACT provides a set of solutions from three perspectives,namely,onboard,onground and air-to-ground,with the core purpose of solving the problems of the rapid exchange of information,contract analysis and identifying potential seeding areas when flight plans and meteorological conditions change.On board,the observed data are processed centrally and transmitted downward through air-to-ground communication.The real-time application and sharing of aircraft detection data are strengthened on the ground,and potential areas of operation are automatically identified based on ground data.The communication between the air and the ground achieves a technical breakthrough by realizing double satellite links,adaptive data transmission and VPN channel encryption.Additionally,an application based on FACT is designed and implemented for the real-time command of weather-modified aircraft.This approach has become the key air-to-ground communication system support for more than 40 Chinese aircraft and the big data service support center of airborne data to ensure improved operation of weather-modification aircraft in China.