In Session 15, the reason why many quanta have spin 1/2 was demonstrated by applying circuit theory to the Dirac equation. In this session, we attempt to apply circuit theory to the Dirac equation from a different viewpoint and determine a model in which electrons and neutrinos are not bound to other quanta and exist independently. We also determine a model of neutrons and protons that have spin 1/2 and can be bound to other quanta and show that this model represents the elements of an asymmetric LC ladder circuit, which can be treated as particles.
Feature extraction is crucial for underwater target classification and recognition, and this paper aims to propose a scheme for accurately classifying the material features of underwater targets. Finite element simulation software COMSOL is used to obtain echo signals, and then Auto Regressive (AR) coefficient, cepstrum feature, spectral peak and its frequency of Smoothing Pseudo Wigner-Ville Distribution (SPWVD) of echo signals are extracted as the input of the pattern recognition network. The classification results show that the proposed scheme is able to effectively differentiate among four types of materials: metal, brick, plastic and rubber. The scheme also shows good robustness for the target size and shape, and the proposed scheme has the potential to be applied to the practical underwater target classification.
One dimensional (1D) spectral amplitude coding-optical code division multiple access (SAC-OCDMA) systems are generally known by a strict limitation in the number of users that can simultaneously connect. A main solution to overcome this drawback consists of constructing a two-dimensional (2D) structure using a new hybrid spectral/spatial code through combining a one dimensional new zero cross correlation code (NZCC) to a one dimenional multi-diagonal code (1D MD) to easily provide null cross-correlation properties and totally remove the multiple access interferences. Accordingly, this research paper describes an easy and fast technique to construct a hybrid two-dimensional NZCC-MD code based on a non diagonal matrix for SAC-OCDMA systems with direct spectral/spatial dimension (SDD) detection technique and water-filling (WF) optimization. The novel code presents many advantages such as less complex structure, acceptable code length as well as the possibility to construct it for any weight value. The proposed code demonstrates a very good performance for systems with high debit and high simultaneous user number keeps a less complex structure.
Abstract Background Abdominal Paracentesis drainage (APD) is a useful treatment for acute pancreatitis (AP) patient with pancreatitis associated ascitic fluid, however, researches seldom mentioned whether every patient benefit from this treatment. Here, we described a machine learning model to predict the outcomes of APD on certain AP patients. Methods The EHR data of 464 AP patients admitted between 2014 to 2020 were used in our study in a de-identified way. A machine learning model using random forest algorithm was established and validated under the stratified 10 fold cross validation strategy. The patients were labelled as “apd_cure” and “apd_serious” group according to their outcome, and the accuracy, sensitivity, specificity, positive prediction value, negative prediction value and ROC curve as well as its area under curve were used to value the efficacy of the model. A logistic regression model was established in the same strategy to compared their predictability. Results The random forest model has an excellent overall properties in predicting the outcomes of APD treatment for the AUC was 0.703 ± 0.118 [95%CI 0.64–0.77]. The accuracy, specificity and NPV (Negative Predictive Value) of the model was 0.786 ± 0.038, 0.940 ± 0.037 and 0.817 ± 0.037, respectively, indicates the model was more able to correctly classify patients who improved after APD treatment. The sensitivity and PPV(Positive Predictive Value) of the model was 0.208 ± 0.144 and 0.486 ± 0.232, which means that the model has insufficient ability to identify patients who may be more likely to have a worsening condition after APD treatment. Finally, the random forest model was statistically better than logistic regression model in accuracy and specificity. Conclusion The random forest model described in this study is a validated model in predicting the outcome of APD treatment on acute pancreatitis patients. It has higher overall performance than the logistic regression model. We hope it may help doctors choose treatment options appropriately and may enhance treatment efficacy in this group of patients.
