8{}^8Be Decay Anomaly and Light Z′Z'

In this proceedings, we discuss a light (17 MeV) $Z'$ solution to the anomaly observed in the decay of Beryllium-8 by the Atomki collaboration. We detail an anomaly free model with minimal particle content which can satisfy all other experimental constraints with gauge couplings $\mathcal{O}(10^{-4})$.Comment: Prepared for the 2019 EW session of the 54th Rencontres de Moriond, talk presented by Simon Kin

Neutral SU(2) Gauge Extension of the Standard Model and a Vector-Boson Dark-Matter Candidate

If the standard model of particle interactions is extended to include a neutral SU(2)_N gauge factor, with SU(3)_C x SU(2)_L x U(1)_Y x SU(2)_N embedded in E_6 or [SU(3)]^3, a conserved generalized R parity may appear. As a result, we have the first example of a possible dark-matter candidate X_1 which is a non-Abelain vector boson. Using current data, its mass is predicted to be less than about 1 TeV. The associated Z' of this model, as well as some signatures of the Higgs sector, should then be observable at the LHC (Large Hadron Collider).Comment: 10 pages, 1 figure; version accepted in PL

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%

Diagnosis of Blood Cells Using Deep Learning

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00

Synthesis, characterization, and antimicrobial properties of novel double layer nanocomposite electrospun fibers for wound dressing applications

Herein, novel hybrid nanomaterials were developed for wound dressing applications with antimicrobial properties. Electrospinning was used to fabricate a double layer nanocomposite nanofibrous mat consisting of an upper layer of poly(vinyl alcohol) and chitosan loaded with silver nanoparticles (AgNPs) and a lower layer of polyethylene oxide (PEO) or polyvinylpyr- rolidone (PVP) nanofibers loaded with chlorhexidine (as an antiseptic). The top layer containing AgNPs, whose purpose was to protect the wound site against environmental germ invasion, was prepared by reducing silver nitrate to its nanoparticulate form through interaction with chitosan. The lower layer, which would be in direct contact with the injured site, contained the antibi- otic drug needed to avoid wound infections which would otherwise interfere with the healing process. Initially, the upper layer was electrospun, followed sequentially by electrospinning the second layer, creating a bilayer nanofibrous mat. The morphology of the nanofibrous mats was studied by scanning electron microscopy and transmission electron microscopy, showing successful nanofiber production. X-ray diffraction confirmed the reduction of silver nitrate to AgNPs. Fourier transform infrared spectroscopy showed a successful incorporation of the material used in the produced nanofibrous mats. Thermal studies carried out by thermogravi- metric analysis indicated that the PVP–drug-loaded layer had the highest thermal stability in comparison to other fabricated nanofibrous mats. Antimicrobial activities of the as-synthesized nanofibrous mats against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans were determined using disk diffusion method. The results indicated that the PEO–drug-loaded mat had the highest antibacterial activity, warranting further attention for numerous wound-healing applications.QUST-CAS-SPR-14\15-

Mechanical Thrombectomy for Acute Ischemic Stroke in Metastatic Cancer Patients: A Nationwide Cross-Sectional Analysis

BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) is the standard treatment for large vessel occlusion (LVO) acute ischemic stroke. Patients with active malignancy have an increased risk of stroke but were excluded from MT trials. METHODS: We searched the National Readmission Database for LVO patients treated with MT between 2016-2018 and compared the characteristics and outcomes of cancer-free patients to those with metastatic cancer (MC). Primary outcomes were all-cause in-hospital mortality and favorable outcome, defined as a routine discharge to home (regardless of whether home services were provided or not). Multivariate regression was used to adjust for confounders. RESULTS: Of 40,537 LVO patients treated with MT, 933 (2.3%) had MC diagnosis. Compared to cancer-free patients, MC patients were similar in age and stroke severity but had greater overall disease severity. Hospital complications that occurred more frequently in MC included pneumonia, sepsis, acute coronary syndrome, deep vein thrombosis, and pulmonary embolism (P\u3c0.001). Patients with MC had similar rates of intracerebral hemorrhage (20% vs. 21%) but were less likely to receive tissue plasminogen activator (13% vs. 23%, P\u3c0.001). In unadjusted analysis, MC patients as compared to cancer-free patients had a higher in-hospital mortality rate and were less likely to be discharged to home (36% vs. 42%, P=0.014). On multivariate regression adjusting for confounders, mortality was the only outcome that was significantly higher in the MC group than in the cancerfree group (P\u3c0.001). CONCLUSION: LVO patients with MC have higher mortality and more infectious and thrombotic complications than cancer-free patients. MT nonetheless can result in survival with good outcome in slightly over one-third of patients

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

© 2019 The Authors Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events

Study the Effect of Epoxy Additives on Some Physical Properties of Asphalt Cement

Abstract:In the series of study on the asphalt cement to improve properties of asphalt cement several polymers materials are used for this purpose. In this study Epoxy used at different percentage by weight (1, 2, 4 and 6) %. Epoxy was added in three cases, pure Epoxy and the other two cases were mixing of Catalyst-Epoxy by weight in the two ratio (1:3) and (1:4).The effect of additives on the asphalt cement properties are evaluated by penetration test (ASTM D-5)(10), softening point test (ASTM D-2398)(10), and Kinematics viscosity test (ASTM D-2170)(10). Temperature susceptibility of asphalt cement was evaluated by using Penetration viscosity number ( PVN ). The study shows that Epoxy, 1:3 Epoxy and 1:4 Epoxy additives have more effect on the physical properties of asphalt cement and make asphalt softer than original asphalt cement, the additives reduced the temperature susceptibility, but Epoxy have more reduction in the susceptibility of asphalt cement compared with 1:3 Epoxy and 1:4