Anomalous single production of fourth family up type quark associated with neutral gauge bosons at the LHC

From the present limits on the masses and mixings of fourth family quarks, they are expected to have mass larger than the top quark and allow a large range of mixing of the third family. They could also have different dynamics than the quarks of three families of the Standard Model. The single production of the fourth family up type quark t' has been studied via anomalous production process pp-> t'VX (where V=g,Z,\gamma) at the LHC with the center of mass energy of 7 and 14 TeV. The signatures of such process are discussed within both the SM decay modes and anomalous decay modes of t' quarks. The sensitivity to anomalous coupling kappa/Lambda=0.004 TeV^(-1) can be reached at sqrt(s)=14 TeV and L_(int)=100 pb^(-1).Comment: 15 pages, 9 figure

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

Combination of polymeric superplasticizers, water repellents and pozzolanic agents to improve air lime-based grouts for historic masonry repair

This paper presents the experimental procedure to develop air lime-based injection grouts including polymeric superplasticizers, a water repellent agent and pozzolanic agents as additives. Research focuses on the development of grouts to improve various characteristics simultaneously combining different additions and admixtures. Aiming to improve the injectability of the grouts, in this study different polymeric superplasticizers were added, namely polycarboxylated-ether derivative (PCE), polynaphthalene sulfonate (PNS) and condensate of melamine-formaldehyde sulfonate (SMFC). Sodium oleate was also used as a water repellent agent to reduce the water absorption. The enhancement of the strength and setting time was intended by using microsilica and metakaolin as pozzolanic mineral additions. Compatibility between the different admixtures and action mechanism of the different polymers were studied by means of zeta potential and adsorption isotherms measurements. Diverse grout mixtures were produced and investigated assessing their injectability, fluidity, stability, compressive strength, hydrophobicity and durability. This research leads to several suitable mixtures produced by using more than one component to enhance efficiency and to provide better performance of grouts. According to the results, the grout composed of air lime, metakaolin, sodium oleate and PCE was found the most effective composition improving the mechanical strength, injectability and hydrophobicity

A Large Hadron Electron Collider at CERN

This document provides a brief overview of the recently published report on the design of the Large Hadron Electron Collider (LHeC), which comprises its physics programme, accelerator physics, technology and main detector concepts. The LHeC exploits and develops challenging, though principally existing, accelerator and detector technologies. This summary is complemented by brief illustrations of some of the highlights of the physics programme, which relies on a vastly extended kinematic range, luminosity and unprecedented precision in deep inelastic scattering. Illustrations are provided regarding high precision QCD, new physics (Higgs, SUSY) and electron-ion physics. The LHeC is designed to run synchronously with the LHC in the twenties and to achieve an integrated luminosity of O(100) fb$^{-1}$. It will become the cleanest high resolution microscope of mankind and will substantially extend as well as complement the investigation of the physics of the TeV energy scale, which has been enabled by the LHC

Lime-based rendering mortars with photocatalytic and hydrophobic agents: assessment of the water repellency and biocide effect

Different rendering mortars were prepared by mixing air lime and air lime-pozzolanic nanosilica with TiO2 and sodium oleate as, respectively, photocatalytic and water repellent agents, added in bulk. The aim of the work was to design and obtain new rendering mortars with improved durability focusing in the reduction of the water absorption of these materials and in their self-cleaning and biocide effect. To achieve a better distribution of the TiO2 particles, which was expected to enhance their efficiency, different dispersing agents were also incorporated to the fresh mixtures. Four diverse polycarboxylate ethers superplasticizers and a poly-naphthalene-sulfonate were tested. Workability and fluidity of the fresh rendering mortars were determined to guarantee the applicability of the final products. Water contact angle was monitored with the aim of assessing the hydrophobicity of the mortars lent by the water repeller. The biocide effect was studied by means of the culture of a strain of Pseudomonas fluorescens. The colonization of the mortars’ surface was analyzed by determining the number of colonies forming units (CFU) after several days subjecting the samples to suitable T and RH conditions. At the same time, the surface of the mortars was irradiated with solar light to activate the photocatalyst. Results showed the efficiency of the sodium oleate in reducing the water uptake of the rendering mortars. Good compatibility between the water repellent agent, the pozzolanic additive and some of the polycarboxylate superplasticizers was observed. The presence of the photocatalyst was found to be very effective in preventing microbiological colonization

Predicting In Vivo Anti-Hepatofibrotic Drug Efficacy Based on In Vitro High-Content Analysis

Background/Aims Many anti-fibrotic drugs with high in vitro efficacies fail to produce significant effects in vivo. The aim of this work is to use a statistical approach to design a numerical predictor that correlates better with in vivo outcomes. Methods High-content analysis (HCA) was performed with 49 drugs on hepatic stellate cells (HSCs) LX-2 stained with 10 fibrotic markers. ~0.3 billion feature values from all cells in >150,000 images were quantified to reflect the drug effects. A systematic literature search on the in vivo effects of all 49 drugs on hepatofibrotic rats yields 28 papers with histological scores. The in vivo and in vitro datasets were used to compute a single efficacy predictor (Epredict). Results We used in vivo data from one context (CCl4 rats with drug treatments) to optimize the computation of Epredict. This optimized relationship was independently validated using in vivo data from two different contexts (treatment of DMN rats and prevention of CCl4 induction). A linear in vitro-in vivo correlation was consistently observed in all the three contexts. We used Epredict values to cluster drugs according to efficacy; and found that high-efficacy drugs tended to target proliferation, apoptosis and contractility of HSCs. Conclusions The Epredict statistic, based on a prioritized combination of in vitro features, provides a better correlation between in vitro and in vivo drug response than any of the traditional in vitro markers considered.Institute of Bioengineering and Nanotechnology (Singapore)Singapore. Biomedical Research CouncilSingapore. Agency for Science, Technology and ResearchSingapore-MIT Alliance for Research and Technology Center (C-185-000-033-531)Janssen Cilag (R-185-000-182-592)Singapore-MIT Alliance Computational and Systems Biology Flagship Project (C-382-641-001-091)Mechanobiology Institute, Singapore (R-714-001-003-271

Computational Modelling of Tissue-Engineered Cartilage Constructs

Cartilage is a fundamental tissue to ensure proper motion between bones and damping of mechanical loads. This tissue often suffers damage and has limited healing capacity due to its avascularity. In order to replace surgery and replacement of joints by metal implants, tissue engineered cartilage is seen as an attractive alternative. These tissues are obtained by seeding chondrocytes or mesenchymal stem cells in scaffolds and are given certain stimuli to improve establishment of mechanical properties similar to the native cartilage. However, tissues with ideal mechanical properties were not obtained yet. Computational models of tissue engineered cartilage growth and remodelling are invaluable to interpret and predict the effects of experimental designs. The current model contribution in the field will be presented in this chapter, with a focus on the response to mechanical stimulation, and the development of fully coupled modelling approaches incorporating simultaneously solute transport and uptake, cell growth, production of extracellular matrix and remodelling of mechanical properties.publishe