entropy

摘要： In order to use the framework of general system theory(GST)to unify the three mechanics subjects of classical mechanics,quantum mechanics,and relativistic mechanics,a new general system theory(NGST)is developed based on a new ontology of ether and minds as the fundamental existences in the world.Based on this new ontology,many fundamental concepts have been detected to be ambiguously defined nowadays and particularly lack of ontological support.In our previous work,some of the fundamental concepts such as universe,world,time,space,matter,ether,mind,life,field,force have been redefined.The purpose of this paper is to clarify the concepts of energy,heat,work,entropy,and information in our NGST.This is an important and necessary step in the development of the NGST.

摘要： 1.Entropy and its implications in human disease and aging Entropy,which is a thermodynamic property or an interpreta-tion of the second law of thermodynamics,was first defined in 1865 by the German physicist Rudolph Clausius[1].It eventually evolved into a general scientific concept that is of universal and paramount importance in three aspects.First,entropy is a measur-able physical property that is commonly associated with a state of chaos,disorderliness,randomness,or uncertainty of any systems[2].Second,entropy is a measure of the amount of energy that is unavailable to do work[2].Third,the universe or an isolated sys-tem always obeys“the principle of entropy increase”that irre-versible or spontaneous processes can occur only in the direction of entropy increase—that is,the direction of increasing chaos,dis-order,randomness,or uncertainty[2].

摘要： In a recent publication it was shown that homonuclear scalar couplings in directly detected protein NMR spectra can be“decoupled”using deep neural networks,including cases where existing methods fail[1].The work harkens back to the introduction of maximum entropy and non-uniform sampling,and it elegantly illustrates how new approaches can be devised in the conceptualization of NMR experiments,freeing researchers from conventional thinking and approaches.The work opens up a new era in biomolecular NMR spectroscopy,where experimental design is tailored towards processing with deep neural networks.(https://doi.org/10.1021/jacs.1c04010).

摘要： The first part of this paper is a condensed synthesis of the matter presented in several previous ones. It begins with an argumentation showing that the first and second laws of thermodynamics are incompatible with one another if they are not connected to relativity. The solution proposed consists of inserting the Einstein mass-energy relation into a general equation that associates both laws. The second part deals with some consequences of this new insight and its possible link with gravitation. Despite a slight modification of the usual reasoning, the suggested hypothesis leads to a simplification and extension of the thermodynamic theory and to the idea that relativity is omnipresent around us.

摘要： Dear Editor,This letter develops two new self-training strategies for domain adaptive semantic segmentation,which formulate self-training into the processes of mining more training samples and reducing influence of the false pseudo-labels.Particularly,a self-training strategy based on entropy-ranking is proposed to mine intra-domain information.

摘要： In this work, we propose an original approach to the thin-layer identification of secondary metabolites (terpenes) based on the acquisition of multicomponent images integrating terpenes to be identified. Its principle consists initially of segmentation by region of each component of the image based on the attribute tuples or colors of each region of the digital image. Then we proceeded to the calculations of region parameters such as standard deviation, entropy, average pixel color, eccentricity from an algorithm on the matlab software. These values allowed us to build a database. Finally, we built an algorithm for identifying secondary metabolites (terpenes) on the basis of these data. The relevance of our method of identifying or recognizing terpenes has been demonstrated compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two terpenes having the same frontal ratio. The robustness of our method with respect to the identification of linalool, limonene was tested.

摘要： In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values atR = 91.20 in red, atB = 213.21 in blue showed the presence of samidin in the extract. The color entropy values HG = 5.25 in green and HB = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.

摘要： In this article,we calculate various topological invariants such as symmetric division degree index,redefined Zagreb index,VL index,first and second exponential Zagreb index,first and second multiplicative exponential Zagreb indices,symmetric division degree entropy,redefined Zagreb entropy,VL entropy,first and second exponential Zagreb entropies,multiplicative exponential Zagreb entropy.We take the chemical compound named Proanthocyanidins,which is a very useful polyphenol in human’s diet.They are very beneficial for one’s health.These chemical compounds are extracted from grape seeds.They are tremendously anti-inflammatory.A subdivision formof this compound is presented in this article.The compound named subdivided grape seed Proanthocyanidins is abbreviated as SGSP_(3).This network SGSP_(3),is converted and modeled into its mathematical graphical formation with the support of the latest mathematical tools.We have also developed many closed formulas for the measurement of entropy for the general chemical structure of the subdivided grape seed Proanthocyanidins network.The achieved outcomes can be correlated with the chemical version of SGSP_(3) to get a better understanding of its biological as well as physical features.

摘要： In this paper,sine trigonometry operational laws(ST-OLs)have been extended to neutrosophic sets(NSs)and the operations and functionality of these laws are studied.Then,extending these ST-OLs to complex neutrosophic sets(CNSs)forms the core of thiswork.Some of themathematical properties are proved based on ST-OLs.Fundamental operations and the distance measures between complex neutrosophic numbers(CNNs)based on the ST-OLs are discussed with numerical illustrations.Further the arithmetic and geometric aggregation operators are established and their properties are verified with numerical data.The general properties of the developed sine trigonometry weighted averaging/geometric aggregation operators for CNNs(ST-WAAO-CNN&ST-WGAO-CNN)are proved.A decision making technique based on these operators has been developed with the help of unsupervised criteria weighting approach called Entropy-ST-OLs-CNDM(complex neutrosophic decision making)method.A case study for material selection has been chosen to demonstrate the ST-OLs of CNDM method.To check the validity of the proposed method,entropy based complex neutrosophic CODAS approach with ST-OLs has been executed numerically and a comparative analysis with the discussion of their outcomes has been conducted.The proposed approach proves to be salient and effective for decision making with complex information.

摘要： With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease globally.Breast cancer is the most widely affected cancer worldwide,with an increased death rate percentage.Due to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast cancer.Early detection of breast cancer will reduce the death rate worldwide.The early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive rate.This paper proposed an efficient and optimized deep learning-based feature selection approach with this consideration.This model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm rate.Transfer learning is used in the extraction of features initially.Na ext,a convolution neural network,is used to extract the features.The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant features.The optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant images.The feature extraction and classification process used two datasets,breast,and MIAS.Compared to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

摘要： This paper adopts entropy method to calculate comprehensive performance of sample companies,in order to fully reflect the performance level of the sample companies. Then it applies empirical research totest relationship between ownership structure and comprehensive performance. Ownership structure is to determine the basis for corporate governance structure, and the finalefficiency of corporate governance is reflected in thecompany's operating performance. Therefore, ownership structure is related to corporate performance.