Thermoreversible Self‐Assembly of Perfluorinated Core‐Coronas Cellulose‐Nanoparticles in Dry State

Self‐assembly of nanoparticles (NPs) forming unique structures has been investigated extensively over the past few years. However, many self‐assembled structures by NPs are irreversible, because they are generally constructed using their suspensions. It is still challenging for NPs to reversibly self‐assemble in dry state, let alone of polymeric NPs with general sizes of hundreds of nm. Herein, this study reports a new reversible self‐assembly phenomenon of NPs in dry state, forming thermoreversible strip‐like supermolecular structures. These novel NPs of around 150 nm are perfluorinated surface‐undecenoated cellulose nanoparticles (FSU‐CNPs) with a core‐coronas structure. The thermoreversible self‐assembled structure is formed after drying in the air at the interface between FSU‐CNP films and Teflon substrates. Remarkably, the formation and dissociation of this assembled structure are accompanied by a reversible conversion of the surface hydrophobicity, film transparency, and anisotropic properties. These findings show novel feasibility of reversible self‐assembly of NPs in dry state, and thereby expand our knowledge of self‐assembly phenomenon.     

Optimization of gear ratio and power distribution for a multimotor powertrain of an electric vehicle

The architecture and design of the propulsion system of electric vehicles are highly important for the reduction of energy losses. This work presents a powertrain composed of four electric motors in which each motor is connected with a different gear ratio to the differential of the rear axle. A strategy to reduce energy losses is proposed, in which two phases are applied. Phase 1 uses a divide-and-conquer approach to increase the overall output efficiency by obtaining the optimal torque distribution for the electric motors. Phase 2 applies a genetic algorithm to find the optimal value of the gear ratios, in which each individual of each generation applies Phase 1. The results show an optimized efficiency map for the output torque and speed of the powertrain. The increase in efficiency and the reduction of energy losses are validated by the use of numerical experiments in various driving cycles.     

Annealing‐Induced Hardening in Ultrafine‐Grained Ni–Mo Alloys

The influence of Mo alloying on annealing‐induced hardening in ultrafine‐grained (UFG) Ni is studied. The hardening observed after low temperature annealing is explained by the annihilation of mobile dislocations and a concomitant clustering of the remaining dislocations into low energy configurations. This study reveals that, with increasing Mo concentration, the hardening effect decreases as the Mo solute atoms hinder the annihilation and rearrangement of dislocations. This trend is the opposite to that observed in electrodeposited Ni–Mo alloys where the larger alloying element concentration yields a higher annealing‐induced strengthening effect. The difference is attributed to the different deformation mechanisms in UFG and nanocrystalline Ni–Mo alloys.

     

2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines

Two-sided assembly lines are a special type of assembly lines in which workers perform assembly tasks in both sides of the line. This type of lines is of crucial importance, especially in the assembly of large-sized products, like automobiles, buses or trucks, in which some tasks must be performed at a specific side of the product. This paper presents an approach to address the two-sided mixed-model assembly line balancing problem. First, a mathematical programming model is presented to formally describe the problem. Then, an ant colony optimisation algorithm is proposed to solve the problem. In the proposed procedure two ants ‘work’ simultaneously, one at each side of the line, to build a balancing solution which verifies the precedence, zoning, capacity, side and synchronism constraints of the assembly process. The main goal is to minimise the number of workstations of the line, but additional goals are also envisaged. The proposed procedure is illustrated with a numerical example and results of a computational experience that exhibit its superior performance are presented.     

On locally reversible languages

There exist several works that study the class of reversible languages defined as the union closure of 0-reversible languages, their properties and suitable representations. In this work we define and study the class of locally reversible languages, defined as the union closure of $k$-reversible languages. We characterize the class and prove that it is a local (positive) variety of formal languages. We also extend the definition of quasi-reversible automata to deal with locally reversible languages and propose a polynomial algorithm to obtain, for any given locally $k$-reversible language, a quasi-$k$-reversible automaton.     

Morphological segmentation and classification of marble textures at macroscopical scale

This paper presents a methodology mainly based on mathematical morphology to segment and classify colour images of polished samples of marbles at the macroscopic scale. The distinct textural regions are segmented through a watershed-based procedure, and the over-segmentation problem is surpassed with the merging of adjacent regions according to criteria based on the analysis of alternative gradients obtained in grey and colour images. The classification of the elementary textural units in three classes (vein, background and transition) is performed with a morphological partitioning method of feature space. The evaluation and analyses of the methodology and alternatives presented are performed on a set of polished Portuguese marbles.     

FTSET-a software tool for fault tolerance evaluation and improvement

It is commonly assumed that neural networks have a built-in fault tolerance property mainly due to their parallel structure. The international community of neural networks discussed these properties until 1994 and afterward the subject has been mostly ignored. Recently, the subject was again brought to discussion due to the possibility of using neural networks in areas where fault tolerance and graceful degradation properties would be an added value, like medical applications of nano-electronics or space missions. Nevertheless, the evaluation of fault tolerance and graceful degradation characteristics remained difficult because there were no systematic methods or tools that could be easily applied to a given Artificial Neural Networks application. The discussion of models is the first step for sorting ways of developing the fault tolerance capability and for building a tool that can evaluate and improve this characteristic. The present work proposes a fault tolerance model, presents solutions for improving it and introduces the Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks that evaluates and improves fault tolerance.