Influences of heating temperatures on physical properties, spray characteristics of bio-oils and fuel supply system of a conventional diesel engine

Alternative fuels need to satisfy the strict requirements of the use for diesel engines aiming at enhancing the performance and reducing pollutant emissions. The use of straight bio-oils for diesel engines entails improving their disadvantages such as high density, high surface tension and kinematic viscosity (tri-physical parameters). There have been some as-used methods for reduction of the above-mentioned negative effects related to straight bio-oil disadvantage, however, the adequately-heating method may be considered as a simple one helping the physical parameters of straight bio-oils to reach stable and highly-confident values which are close to those of traditional diesel fuel. As a consequence, the spray and atomization, combustion, performance, and emissions of diesel engines fueled with preheated bio-oils are improved. In this work, a study of the dependence of the density, surface tension and kinematic viscosity of coconut oil (a type of bio-oils) on temperatures (from 40-110oC) within a wide variety are conducted. In the first stage, the influence study of temperature on tri-physical parameters is carried out on the basis of experimental correlation and as-described mathematical equation. In the second stage, the influence study of tri-physical parameters on spray and atomization parameters including penetration length (Lb) and Sauter mean diameter (SMD), and the influence of tri-physical parameters on fuel supply system are investigated. The optimal range of temperature for the as-used bio-oils is found after analyzing and evaluating the obtained results regarding the physical properties and spray characteristics, as well as compared with those of diesel fuel. The confident level over 95% from the regression correlation equation between the above-mentioned tri-physical parameters and temperature is presented. Additionally, the measured spray parameters, the calculated values of frictional head loss and fuel flow rate are thoroughly reported. 

Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSO\textsubscript{d}. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSO\textsubscript{d} for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSO\textsubscript{d} and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealing in terms of the service disruption and computation time

Double-replica theory for evolution of genotype-phenotype interrelationship

The relationship between genotype and phenotype plays a crucial role in determining the function and robustness of biological systems. Here the evolution progresses through the change in genotype, whereas the selection is based on the phenotype, and genotype-phenotype relation also evolves. Theory for such phenotypic evolution remains poorly-developed, in contrast to evolution under the fitness landscape determined by genotypes. Here we provide statistical-physics formulation of this problem by introducing replicas for genotype and phenotype. We apply it to an evolution model, in which phenotypes are given by spin configurations; genotypes are interaction matrix for spins to give the Hamiltonian, and the fitness depends only on the configuration of a subset of spins called target. We describe the interplay between the genetic variations and phenotypic variances by noise in this model by our new approach that extends the replica theory for spin-glasses to include spin-replica for phenotypes and coupling-replica for genotypes. Within this framework we obtain a phase diagram of the evolved phenotypes against the noise and selection pressure, where each phase is distinguished by the fitness and overlaps for genotypes and phenotypes. Among the phases, robust fitted phase, relevant to biological evolution, is achieved under the intermediate level of noise (temperature), where robustness to noise and to genetic mutation are correlated, as a result of replica symmetry. We also find a trade-off between maintaining a high fitness level of phenotype and acquiring a robust pattern of genes as well as the dependence of this trade-off on the ratio between the size of the functional (target) part to that of the remaining non-functional (non-target) one. The selection pressure needed to achieve high fitness increases with the fraction of target spins.Comment: 15 pages, 7 figure

Theory for Adaptive Systems: Collective Robustness of Genotype-Phenotype Evolution

The investigation of mutually coupled dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding biological evolution and learning. We develop a general theory for such dynamics by extending dynamical mean field theory. We then apply our framework to biological systems whose fate is determined by the evolution of genotype-phenotype relationship. Here phenotypic evolution is shaped by stochastic gene-expression fast dynamics and is coupled to selection-based slow changes of genotypes encoding the network of gene regulations. We find dynamically robust patterns of phenotypes can be achieved under an intermediate level of external noise where the genotype-phenotype relation evolves in such a way that results in intrinsic out-of-equilibrium fluctuations of phenotypes even in the absence of that noise.Comment: 12 pages and 3 figure

Similar Triangles and Orientation in Plane Elementary Geometry for Coq-based Proofs

In plane elementary geometry, the concept of similar triangles not only forms an important foundation for trigonometry, but it also can be used to solve many geometric problems. The notion of orientation allows us to remove the usual ambiguities in presentation of object. In this paper, we present the formalization of these notions in Coq. We also introduce their properties and how they are applied to the proof of two theorems: the Ptolemy's theorem and the Intersecting Chords theorem

Design of a Decision-Aiding Model Between Subtractive Manufacturing and 3D-Printing

3D-printing is becoming more and more widely used in industry. As this happens, manufacturers are becoming unsure of when to use this new technology and when to trudge on with subtractive (conventional) manufacturing processes. Subtractive manufacturing processes are well-established within many manufacturing companies due to its high efficiencies and low costs. However, 3D-printing offers a greater level of customization, can be automated, and can easily have designs transferred via computer files. Each method has its respective advantages, however, each one also has its downfalls. Subtractive manufacturing produces unnecessary waste, is limited from creating certain geometries, and requires a skilled laborer to run the machines. 3D-printing can present a safety hazard due to its introduction of particles into the air, being slower at producing parts, and the design of a part being easily contained and compromised within a computer file. Since there are so many different advantages and disadvantages to each method, it is very difficult for a business to decide which form of manufacturing to use for any part. To solve this problem, we developed a decision-aiding model that will ask key questions that will determine whether form of manufacturing to use, and to do an economic analysis comparing the two forms of manufacturing and the time to manufacture each

A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks

We develop a mathematically rigorous framework for multilayer neural networks in the mean field regime. As the network's width increases, the network's learning trajectory is shown to be well captured by a meaningful and dynamically nonlinear limit (the \textit{mean field} limit), which is characterized by a system of ODEs. Our framework applies to a broad range of network architectures, learning dynamics and network initializations. Central to the framework is the new idea of a \textit{neuronal embedding}, which comprises of a non-evolving probability space that allows to embed neural networks of arbitrary widths. We demonstrate two applications of our framework. Firstly the framework gives a principled way to study the simplifying effects that independent and identically distributed initializations have on the mean field limit. Secondly we prove a global convergence guarantee for two-layer and three-layer networks. Unlike previous works that rely on convexity, our result requires a certain universal approximation property, which is a distinctive feature of infinite-width neural networks. To the best of our knowledge, this is the first time global convergence is established for neural networks of more than two layers in the mean field regime

Analysis of volumetric change of Hippocampus caused by Alzheimer's disease

Zájem o hipokampus prudce vzrostl poté co byla publikována jeho významnost v procesu učení a uchovávání informací. Zejména je velký zájem v oblasti změn jeho objemu a jejich vliv na Alzheimerovou chorobu. Pochopení struktury a funkcí hipokampu by přispělo k přesnější diagnóze této nemoci. V této práci byla vytvořena metoda segmentace hipokampu s využitím aktivních kontur. S jeho pomocí pak byla segmentována data složená jak ze zdravých, tak z nemocných pacientů. Výsledky segmentace pak byli statisticky analyzovány s využitím statistických metod jako jsou Kruskal-Walisův test, Mann-Whitneyův test. Výsledky těchto testů podporují na dané hladině významnosti alternativní hypotézu, která přisuzuje významnost rozdílu v objemu hipokampů mezi oběma studovanými skupinami.Interest in hippocampus increased sharply after his significance in the process of learning and retention of information was published. In particular, considerable interest was in its volume changes and their effect on Alzheimer’s disease. Understanding the structure and function hippocampus would contribute to a more accurate diagnosis of this disease. In this work was created a method of hippocampal segmentation using active contours. With its help, the data composed of both healthy and a diseased patients was segmented and the results were then statistically analyzed using statistical methods such as Kruskal-Walis test, Mann-Whitney test. The level of significance given by results of analysis supports alternative hypothesis that attaches significance of the difference in volume of the hippocampus between studied groups.