A hybrid model for information security risk assessment

Many industry standards and methodologies were introduced which has brought forth the management of threats assessment and risk management of information assets in a systematic manner. This paper will review and analyze the main processes followed in IT risk management frameworks from the perspective of the threat analysis process using a threat modeling methodology. In this study, the authors propose a new assessment model which shows that systematic threat analysis is an essential element to be considered as an integrated process within IT risk management frameworks. The new proposed model complements and fulfills the gap in the practice of assessing information security risks

Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors

We report on the implementation of an algorithm-based spectrometer capable of reconstructing the spectral shape of materials in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) wavelengths using only experimental photocurrent measurements from quantum dot infrared photodetectors (QDIPs). The theory and implementation of the algorithm will be described, followed by an investigation into this algorithmic spectrometer's performance. Compared to the QDIPs utilized in an earlier implementation, the ones used here have highly varying spectral shapes and four spectral peaks across the MWIR and LWIR wavelengths. It has been found that the spectrometer is capable of reconstructing broad spectral features of a range of bandpass infrared filters between wavelengths of 4 and 12 mu m as well as identifying absorption features as narrow as 0.3 mu m in the IR spectrum of a polyethylene sheet

The influence of microsatellite polymorphisms in sex steroid receptor genes ESR1, ESR2 and AR on sex differences in brain structure.

The androgen receptor (AR), oestrogen receptor alpha (ESR1) and oestrogen receptor beta (ESR2) play essential roles in mediating the effect of sex hormones on sex differences in the brain. Using Voxel-based morphometry (VBM) and gene sizing in two independent samples (discovery n ​= ​173, replication ​= ​61), we determine the common and unique influences on brain sex differences in grey (GM) and white matter (WM) volume between repeat lengths (n) of microsatellite polymorphisms AR(CAG)n, ESR1(TA)n and ESR2(CA)n. In the hypothalamus, temporal lobes, anterior cingulate cortex, posterior insula and prefrontal cortex, we find increased GM volume with increasing AR(CAG)n across sexes, decreasing ESR1(TA)n across sexes and decreasing ESR2(CA)n in females. Uniquely, AR(CAG)n was positively associated with dorsolateral prefrontal and orbitofrontal GM volume and the anterior corona radiata, left superior fronto-occipital fasciculus, thalamus and internal capsule WM volume. ESR1(TA)n was negatively associated with the left superior corona radiata, left cingulum and left inferior longitudinal fasciculus WM volume uniquely. ESR2(CA)n was negatively associated with right fusiform and posterior cingulate cortex uniquely. We thus describe the neuroanatomical correlates of three microsatellite polymorphisms of steroid hormone receptors and their relationship to sex differences

Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation

Background and Objective: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE. Methods: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures. Results: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data. Conclusions: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation

Simulation of Beam-Beam Effects and Tevatron Experience

Effects of electromagnetic interactions of colliding bunches in the Tevatron had a variety of manifestations in beam dynamics presenting vast opportunities for development of simulation models and tools. In this paper the computer code for simulation of weak-strong beam-beam effects in hadron colliders is described. We report the collider operational experience relevant to beam-beam interactions, explain major effects limiting the collider performance and compare results of observations and measurements with simulations.Comment: 23 pages, 17 figure

Beam instrumentation for the Tevatron Collider

The Tevatron in Collider Run II (2001-present) is operating with six times more bunches and many times higher beam intensities and luminosities than in Run I (1992-1995). Beam diagnostics were crucial for the machine start-up and the never-ending luminosity upgrade campaign. We present the overall picture of the Tevatron diagnostics development for Run II, outline machine needs for new instrumentation, present several notable examples that led to Tevatron performance improvements, and discuss the lessons for future colliders

Notes on a paper of Mess

These notes are a companion to the article "Lorentz spacetimes of constant curvature" by Geoffrey Mess, which was first written in 1990 but never published. Mess' paper will appear together with these notes in a forthcoming issue of Geometriae Dedicata.Comment: 26 page

An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients

Tomato: a crop species amenable to improvement by cellular and molecular methods

Tomato is a crop plant with a relatively small DNA content per haploid genome and a well developed genetics. Plant regeneration from explants and protoplasts is feasable which led to the development of efficient transformation procedures. In view of the current data, the isolation of useful mutants at the cellular level probably will be of limited value in the genetic improvement of tomato. Protoplast fusion may lead to novel combinations of organelle and nuclear DNA (cybrids), whereas this technique also provides a means of introducing genetic information from alien species into tomato. Important developments have come from molecular approaches. Following the construction of an RFLP map, these RFLP markers can be used in tomato to tag quantitative traits bred in from related species. Both RFLP's and transposons are in the process of being used to clone desired genes for which no gene products are known. Cloned genes can be introduced and potentially improve specific properties of tomato especially those controlled by single genes. Recent results suggest that, in principle, phenotypic mutants can be created for cloned and characterized genes and will prove their value in further improving the cultivated tomato.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types