Granulomatous orchitis. A case report and review of the literature

We developed a method for determination of motor conduction along the mandibular and sensory conduction along the lingual and inferior alveolar nerves in 10 controls and 6 patients with lingual neuropathy following lower wisdom tooth extraction. Patients with lingual neuropathy had reduced/absent or delayed compound sensory action potentials and normal conduction along the fibers of the inferior alveolar nerve and mandibular nerve. The method provides a useful electrophysiological means of evaluating lingual nerve lesions.     

Synthesis of Chlorinated and Non-chlorinated Polyols from Model Cross-Metathesis Modified Triacylglycerols

Three pure triacylglycerols (TAG) containing decenoic acid (D), and stearic acid (S), were hydroxylated into chlorinated and non-chlorinated polyols and studied in detail. D is a fatty moiety that can result from the cross metathesis of small olefins with common vegetable oils such as soybean oil. A fundamental understanding relating chemical composition and derived structure, particularly the number and position of the hydroxyl groups, to physical properties was established allowing us to add some perspective to the growing body of knowledge on industrially relevant polyol and polyurethane systems produced with metathesis-modified TAG (MTAG). The hydroxyl value, crystallization and melting behaviors, thermal degradation behavior of the polyols were directly related to their peculiar shortened and primary functionality inherited from the parent MTAG. The effect of regiochemistry on the physical properties of the polyols was investigated with the chromatography fractions of the trichlorinated polyol of propane-1,2,3-triyl tris(dec-9-enoate), giving an unhindered insight into the role of short and terminal functionality of MTAG polyols that will help select the optimal isomer composition for designer polyurethane materials.     

Equivalent network representation of boundary conditions involving generalized trial quantities

Virtual adjustable sources are introduced in equivalent network representation of boundary conditions. For this purpose, integral equations are to be solved simple application of analog Kirchoff’s and Ohm’s laws. These adjustable sources represent generalized trial quantities. In order to illustrate this proposed approach, equivalent network representation of lossy planar transmission lines with arbitrary metallization thickness is presented.     

Equivalent network representation of boundary conditions involvinggeneralized trial quantities-application to lossy transmission lineswith finite metallization thickness

The derivation of integral equations for solving boundary conditions by mere application of analog Kirchhoff's and Ohm's laws is used. Generalized trial quantities are introduced as virtual adjustable sources in the equivalent network representation of boundary conditions. The lossy conductor domain of a planar transmission line is represented by a particular two-port. Thus, metallic losses can be evaluated for any metallization thickness without restricting the conductor modeling to a simple surface impedance approximation. In this paper, this two-port model is discussed and numerical results relative to a lossy coplanar waveguide (CPW) are presented. These results are in very good agreement with those obtained from the mode-matching technique and with other experimental data available in the literature. The size of matrices involved in the calculation of losses is twice as large as that in the lossless case. Moreover, the authors' formulation can be easily applied to superconducting planar transmission lines     

Lipid‐derived monoamide as phase change energy storage materials

One of the major shortcomings of current organic phase change materials (PCMs) is their relatively low melting points, typically below 80°C, which limits their integration into thermal energy storage (TES) systems. The present work was aimed at developing lipid‐derived PCMs with increased melting points which would be suitable for TES applications requiring higher melting points without compromising other key properties such as enthalpy. The introduction of an amide group into the structure of linear saturated fatty acids was used as a means to increase intermolecular interactions and therefore crystallization and melting points. A series of six linear monoamides with differing chain length and symmetry about the amide group were investigated for thermal stability, thermal transition, flow behavior, and crystal structure to establish the structure‐property relationships relevant to TES. The presence of the highly polar amide group in the aliphatic fatty acid–derived molecules resulted in notable improvement in performance compared with the analogous monofunctional molecules: Increases in melting points (79°C‐96°C) and high enthalpies of fusion (155‐201 J/g) were recorded. Fundamental relationships between structure, processing, and macroscopic physicochemical properties, never before elucidated, were revealed in the study. The study revealed a step‐like variation of macroscopic properties: a surprising outcome of the competition between intermolecular attractions, symmetry effects, and mass transfer limitations. The predictive structure‐function relationships established in this work will allow the straightforward engineering of monoamide architectures that can extend the range of organic PCMs and deliver thermal properties desirable for TES applications.     

Machine learning techniques to predict sensitive patterns to fault attack in the Java Card application

Fault attack represents one of the serious threats against Java Card security. It consists of physical perturbation of chip components to introduce faults in the code execution. A fault may be induced using a laser beam to impact opcodes and operands of instructions. This could lead to a mutation of the application code in such a way that it becomes hostile. Any successful attack may reveal a secret information stored in the card or grant an undesired authorisation. We propose a methodology to recognise, during the development step, the sensitive patterns to the fault attack in the Java Card applications. It is based on the concepts from text categorisation and machine learning. In fact, in this method, we represented the patterns using opcodes n-grams as features, and we evaluated different machine learning classifiers. The results show that the classifiers performed poorly when classifying dangerous sensitive patterns, due to the imbalance of our data-set. The number of dangerous sensitive patterns is much lower than the number of not dangerous patterns. We used resampling techniques to balance the class distribution in our data-set. The experimental results indicated that the resampling techniques improved the accuracy of the classifiers. In addition, our proposed method reduces the execution time of sensitive patterns classification in comparison to the SmartCM tool. This tool is used in our study to evaluate the effect of faults on Java Card applications.     

Reverse engineering Java Card and vulnerability exploitation: a shortcut to ROM

International Journal of Information Security - Secure elements store and manipulate assets in a secure way. The most attractive assets are the cryptographic keys stored into the memory that can be...     

Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem. This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi-objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take into account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method outperforms two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2 and NSGA II.