DSTC: DNS-based Strict TLS Configurations

Most TLS clients such as modern web browsers enforce coarse-grained TLS security configurations. They support legacy versions of the protocol that have known design weaknesses, and weak ciphersuites that provide fewer security guarantees (e.g. non Forward-Secrecy), mainly to provide backward compatibility. This opens doors to downgrade attacks, as is the case of the POODLE attack [18], which exploits the client's silent fallback to downgrade the protocol version to exploit the legacy version's flaws. To achieve a better balance between security and backward compatibility, we propose a DNS-based mechanism that enables TLS servers to advertise their support for the latest version of the protocol and strong ciphersuites (that provide Forward-Secrecy and Authenticated-Encryption simultaneously). This enables clients to consider prior knowledge about the servers' TLS configurations to enforce a fine-grained TLS configurations policy. That is, the client enforces strict TLS configurations for connections going to the advertising servers, while enforcing default configurations for the rest of the connections. We implement and evaluate the proposed mechanism and show that it is feasible, and incurs minimal overhead. Furthermore, we conduct a TLS scan for the top 10,000 most visited websites globally, and show that most of the websites can benefit from our mechanism

Spectral Properties of Non-Unitary Band Matrices

We consider families of random non-unitary contraction operators defined as deformations of CMV matrices which appear naturally in the study of random quantum walks on trees or lattices. We establish several deterministic and almost sure results about the location and nature of the spectrum of such non-normal operators as a function of their parameters. We relate these results to the analysis of certain random quantum walks, the dynamics of which can be studied by means of iterates of such random non-unitary contraction operators.Comment: updated version, to appear in Annales Henri Poincar

Design and evaluation of synthetic silica-based monolithic materials in shrinkable tube for efficient protein extraction

Sample pretreatment is a required step in proteomics in order to remove interferences and preconcentrate the samples. Much research in recent years has focused on porous monolithic materials since they are highly permeable to liquid flow and show high mass transport compared with more common packed beds. These features are due to the micro-structure within the monolithic silica column which contains both macropores that reduce the back pressure, and mesopores that give good interaction with analytes. The aim of this work was to fabricate a continuous porous silica monolithic rod inside a heat shrinkable tube and to compare this with the same material whose surface has been modified with a C(18) phase, in order to use them for preconcentration/extraction of proteins. The performance of the silica-based monolithic rod was evaluated using eight proteins; insulin, cytochrome C, lysozyme, myoglobin, β-lactoglobulin, ovalbumin, hemoglobin, and bovine serum albumin at a concentration of 60 μM. The results show that recovery of the proteins was achieved by both columns with variable yields; however, the C(18) modified silica monolith gave higher recoveries (92.7 to 109.7%) than the non-modified silica monolith (25.5 to 97.9%). Both silica monoliths can be used with very low back pressure indicating a promising approach for future fabrication of the silica monolith inside a microfluidic device for the extraction of proteins from biological media

_Trichoderma pseudokoningii_ Rifai isolation from Egyptian immunocompromised cattle with _Mycobacterium bovis_ infection

Recently, _Trichoderma_ species have emerged as potent fungal pathogens in immunocompromised humans. We report the first three cases of _Trichoderma pseudokoningii_ Rifai pulmonary infection in the Egyptian tuberculous dairy cattle with _Mycobacterium bovis_, from the heart of a generalized bovine TB in a cow over 5 years old, a mediastinal lymph node of pulmonary TB in a cow of 3 years old, and a lung of mixed pulmonary and digestive BTB in a cow of 4 years old. We have also developed a pathogenisity test technique for _Trichoderma pseudokoningii_ Rifai infection in 3 G. pigs by intraperitoneal injection of 2 G. pigs with mixed infection of _Mycobacterium bovis_ and _Trichoderma pseudokoningii_ Rifai; death of both animals 14 days, thereafter, and by injection of 1 G. pig with single infection of _Trichoderma pseudokoningii_ Rifai; death of animal 21 days, thereafter. We did not report any animal case along review of literature

Spectral Clustering for Optical Confirmation and Redshift Estimation of X-ray Selected Galaxy Cluster Candidates in the SDSS Stripe 82

We develop a galaxy cluster finding algorithm based on spectral clustering technique to identify optical counterparts and estimate optical redshifts for X-ray selected cluster candidates. As an application, we run our algorithm on a sample of X-ray cluster candidates selected from the third XMM-Newton serendipitous source catalog (3XMM-DR5) that are located in the Stripe 82 of the Sloan Digital Sky Survey (SDSS). Our method works on galaxies described in the color-magnitude feature space. We begin by examining 45 galaxy clusters with published spectroscopic redshifts in the range of 0.1 to 0.8 with a median of 0.36. As a result, we are able to identify their optical counterparts and estimate their photometric redshifts, which have a typical accuracy of 0.025 and agree with the published ones. Then, we investigate another 40 X-ray cluster candidates (from the same cluster survey) with no redshift information in the literature and found that 12 candidates are considered as galaxy clusters in the redshift range from 0.29 to 0.76 with a median of 0.57. These systems are newly discovered clusters in X-rays and optical data. Among them 7 clusters have spectroscopic redshifts for at least one member galaxy.Comment: 15 pages, 7 figures, 3 tables, 1 appendix, Accepted by Journal of "Astronomy and Computing

Numerical Solution of Fuzzy Differential Equations Based on Taylor Series by Using Fuzzy Neural Networks

In this paper a new method based on learning algorithm of Fuzzy neural network and Taylor series has been developed for obtaining numerical solution of fuzzy differential equations.A fuzzy trial solution of the fuzzy initial value problem is written as a sum of two parts.The first part satisfies the fuzzy initial condition,it contains Taylor series and involves no fuzzy adjustable parameters.The second part involves a feed-forward fuzzy neural network containing fuzzy adjustable parameters (the fuzzy weights).Hence by construction,the fuzzy initial condition is satisfied and the fuzzy network is trained to satisfy the fuzzy differential equation . In comparison with existing similar neural networks,the proposed method provides solutions with high accuracy.Finally , we illustrate our approach by two numerical examples