Performance Evaluation of Public Bus Transportation by Using DEA Models and Shannon’s Entropy:An Example From a Company in a Large City of China

The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis(DEA)model and Shannon’s entropy.This company operates 37 main routes on the backbone roads.Thus,it plays a significant role in public transportation in the city.According to bus industry norms,an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands.For passenger satisfaction,passenger waiting time and passenger-crowding degree are considered,and they are undesirable indicators.To describe such indicators,a superefficient DEA model is constructed.With this model,by using actual data,efficiency is evaluated for each bus route.Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results.Also,sensitivity analysis is presented.Therefore,the results are meaningful for the company to improve its operations and management.

Informational Noise＇s Entropy

Assessment of quantity of information that is present in own noises of EE （electronic elements）--informational noise＇s entropy--using spectrum distribution of noises＇ probability of real elements is proposed. It is shown that informational noise＇s entropy as opposed to used differential entropy of continuous signals defines the quantity of qualitative information related to the features of element＇s structure. Proposed quantitative assessment of information can be used for calculation of information contained in the own noises of other technical systems.

A Modified Genetic Algorithm for Product Family Optimization with Platform Specified by Information Theoretical Approach

Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, and offer opportunities for superior overall design. An information theoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variables selection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposed and developed for optimizing the corresponding product family in a single stage, simultaneously determining the optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform and values of unique variables are found for each product in the second stage. An example of design of a family of universal motors was used to verify the proposed method.

An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific patterns.Therefore, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

Motoyosi Sugita—A “Widely Unknown” Japanese Thermodynamicist Who Explored the 4th Law of Thermodynamics for Creation of the Theory of Life

The purpose of this paper is to introduce to you, the Western people, nowadays a “widely unknown” Japanese thermodynamicist by the name of Motoyosi Sugita and his study on the thermodynamics of transient phenomena and his theory of life. This is because although he was one of the top theoretical physicists in Japan before, during and after WWII and after WWII he promoted the establishment of the biophysical society of Japan as one of the founding members, he himself and his studies themselves have seemed to be totally forgotten nowadays in spite that his study was absolutely important for the study of life. Therefore, in this paper I would like to present what kind of person he was and what he studied in physics as a review on the physics work of Motoyosi Sugita for the first time. I will follow his past studies to introduce his ideas in theoretical physics as well as in biophysics as follows: He proposed the bright ideas such as the quasi-static change in the broad sense, the virtual heat, and the field of chemical potential etc. in order to establish his own theory of thermodynamics of transient phenomena, as the generalization of the Onsager-Prigogine’s theory of the irreversible processes. By the concept of the field of chemical potential that acquired the nonlinear transport, he was seemingly successful to exceed and go beyond the scope of Onsager and Prigogine. Once he established his thermodynamics, he explored the existence of the 4th law of thermodynamics for the foundation of theory of life. He applied it to broad categories of transient phenomena including life and life being such as the theory of metabolism. He regarded the 4th law of thermodynamics as the maximum principle in transient phenomena. He tried to prove it all life long. Since I have recently found that his maximum principle can be included in more general maximum principle, which was known as the Pontryagin’s maximum principle in the theory of optimal control, I would like to explain such theories produced by Motoyosi Sugita as detailed as possible. And also I have put short history of Motoyosi Sugita’s personal life in order for you to know him well. I hope that this article helps you to know this wonderful man and understand what he did in the past, which was totally forgotten in the world and even in Japan.

Influence of high configuration entropy on damping behaviors of Ti-Zr-Hf-Ni-Co-Cu high entropy alloys

TiNi-based shape memory alloys(SMAs)have been used as damping materials to eliminate noise and mechanical vibration.However,their application is limited by low working temperatures and damping capacity.In this work,two novel Ti-Zr-Hf-Ni-Co-Cu high entropy shape memory alloys(HESMAs)with different transformation temperatures and damping properties were investigated.The results show that Ti_(25)Zr_(8)Hf_(17)Ni_(30)Co_(5)Cu_(15) has superior damping performance arising from martensitic transformation,shape memory effect(thermal cycle at constant load)as well as superelasticity.Compared to traditional TiNi-based SMAs,the as-cast HESMAs exhibit a much higher ultrahigh yield strength(∼2 GPa)and storage modulus(∼50 GPa).The high configuration entropy of the HESMAs with high uneven internal stress and severe lattice distortion is revealed as the underlying mechanisms governing distinctive damping performance.The effects of high configuration entropy and microheterogeneity on the martensitic transforma-tion behavior and damping performance of HESMAs are clarified in this work,which provides a basis for designing alloys with superior damping properties.

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Approximating Conditional Density Functions Using Dimension Reduction

We propose to approximate the conditional density function of a random variable Y given a dependent random d-vector X by that of Y given θ^τX, where the unit vector θ is selected such that the average Kullback-Leibler discrepancy distance between the two conditional density functions obtains the minimum. Our approach is nonparametric as far as the estimation of the conditional density functions is concerned. We have shown that this nonparametric estimator is asymptotically adaptive to the unknown index θ in the sense that the first order asymptotic mean squared error of the estimator is the same as that when θ was known. The proposed method is illustrated using both simulated and real-data examples.

Plant functional and phylogenetic turnover correlate with climate and land use in the Western Swiss Alps

Aims Understanding the relative importance of historical and environ-mental processes in the structure and composition of communities is one of the longest quests in ecological research.Increasingly,researchers are relying on the functional and phylogeneticβ-diversity of natural communities to provide concise explanations on the mechanistic basis of community assembly and the drivers of trait variation among species.The present study investigated how plant functional and phylogeneticβ-diversity change along key environmental and spatial gradients in the Western Swiss Alps.Methods Using the quadratic diversity measure based on six functional traits-specific leaf area,leaf dry matter content,plant height,leaf carbon content,leaf nitrogen content and leaf carbon to nitrogen content alongside a species-resolved phylogenetic tree-we relate variations in climate,spatial geographic,land use and soil gradients to plant functional and phylogenetic turnover in mountain commu-nities of the Western Swiss Alps.Important Findings Our study highlights two main points.First,climate and land-use factors play an important role in mountain plant community turnover.Second,the overlap between plant functional and phy-logenetic turnover along these gradients correlates with the low phylogenetic signal in traits,suggesting that in mountain land-scapes,trait lability is likely an important factor in driving plant community assembly.Overall,we demonstrate the importance of climate and land-use factors in plant functional and phyloge-netic community turnover and provide valuable complementary insights into understanding patterns ofβ-diversity along several ecological gradients.