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Extended Three Dimensional Analysis of Building Systems is abbreviated as ETABS. The significant objective of this program is to make multi-story structures in a purposeful way. The productive plan and development of quake safe designs is significant everywhere. ETABS was used to analyse and design a multi-story residential structure with the lateral loading effect of an earthquake. IS 1893-part2:2002 and IS 456:2000 were used to design this project. This analysis takes into account harsh earthquake zones, and reactions are evaluated using soil type-II conditions. Important phrases: Etabs programme , Seismic Analysis of G+12 storey RC frame structure.

We use nonstandard analysis to study the problem of expressing a Gaussian integral in terms of the limiting behavior of a sequence of spherical integrals. Peterson and Sengupta proved that if a Gaussian measure $\mu$ has full support on a finite-dimensional Euclidean space, then the expected value of a bounded measurable function on that domain can be expressed as a limit of integrals over spheres $S^{n-1}(\sqrt{n})$ intersected with certain affine subspaces of $\mathbb{R}^n$. This allows one to realize the Gaussian Radon transform of such functions as a limit of spherical integrals. Using nonstandard analysis, we study such limits in terms of Loeb integrals over a single hyperfinite dimensional sphere. This nonstandard geometric approach generalizes the known limiting result for bounded continuous functions to the case when the Gaussian measure is not necessarily fully supported. We also present an asymptotic linear algebra result needed in the above proof., Comment: 26 pages, 3 figures, updated with submitted version (corrected minor mistakes and edited exposition slightly at a few places)

As a result of the rapid developments in digital technologies, smart cities have been adorned with several digital gadgets based on the Internet of Things (IoT). The essential part of smart city operations is IoT communications. IoT devices generate, process, and exchange a large amount of security-critical and privacy-sensitive data regularly, resulting in attractive targets for threats and attacks. Group-oriented communication, such as data gathering and area monitoring, is critical in the IoT World. It enables users to manage various IoT devices simultaneously. Conventional one-to-one authentication techniques donot consider the resource constraints of IoT devices in grouped communication. They also do not solve the issue of massive machinecommunication (mMTC) scalability. Many to Many (M2M)authentication approach of group authentication makes grouporientedcommunication and mMTC very secure. The lower timecomplexity of Group-based authentication (GBA) makes theseprotocols very popular for efficient and secured communication.This paper uses a polynomial-based group authentication schemeand membership verification to ensure efficient and threat-freecommunication among IoT devices. Bi-variate polynomials havebeen used instead of single variable functions in the proposedscheme. The protected nature of the bi-variate polynomial makesthe proposed scheme very secure and reliable. Furthermore,establishing the session key makes the proposed scheme effectiveand efficient. Security analysis of the proposed work shows itsefficiency over existing schemes.

Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.

Summary Background Oral anticoagulation reduces the rate of systemic embolism for patients with atrial fibrillation by two-thirds, but its benefits for patients with previous intracranial haemorrhage are uncertain. In the Start or STop Anticoagulants Randomised Trial (SoSTART), we aimed to establish whether starting is non-inferior to avoiding oral anticoagulation for survivors of intracranial haemorrhage who have atrial fibrillation. Methods SoSTART was a prospective, randomised, open-label, assessor-masked, parallel-group, pilot phase trial done at 67 hospitals in the UK. We recruited adults (aged ≥18 years) who had survived at least 24 h after symptomatic spontaneous intracranial haemorrhage, had atrial fibrillation, and had a CHA2DS2-VASc score of at least 2. Web-based computerised randomisation incorporating a minimisation algorithm allocated participants (1:1) to start or avoid long-term (≥1 year) full treatment dose open-label oral anticoagulation. The participants assigned to start oral anticoagulation received either a direct oral anticoagulant or vitamin K antagonist, and the group assigned to avoid oral anticoagulation received standard clinical practice (antiplatelet agent or no antithrombotic agent). The primary outcome was recurrent symptomatic spontaneous intracranial haemorrhage, and was adjudicated by an individual masked to treatment allocation. All outcomes were ascertained for at least 1 year after randomisation and assessed in the intention-to-treat population of all randomly assigned participants, using Cox proportional hazards regression adjusted for minimisation covariates. We planned a sample size of 190 participants (one-sided p=0·025, power 90%, allowing for non-adherence) based on a non-inferiority margin of 12% (or adjusted hazard ratio [HR] of 3·2). This trial is registered with ClinicalTrials.gov (NCT03153150) and is complete. Findings Between March 29, 2018, and Feb 27, 2020, consent was obtained at 61 sites for 218 participants, of whom 203 were randomly assigned at a median of 115 days (IQR 49–265) after intracranial haemorrhage onset. 101 were assigned to start and 102 to avoid oral anticoagulation. Participants were followed up for median of 1·2 years (IQR 0·97–1·95; completeness 97·2%). Starting oral anticoagulation was not non-inferior to avoiding oral anticoagulation: eight (8%) of 101 in the start group versus four (4%) of 102 in the avoid group had intracranial haemorrhage recurrences (adjusted HR 2·42 [95% CI 0·72–8·09]; p=0·152). Serious adverse events occurred in 17 (17%) participants in the start group and 15 (15%) in the avoid group. 22 (22%) patients in the start group and 11 (11%) patients in the avoid group died during the study. Interpretation Whether starting oral anticoagulation was non-inferior to avoiding it for people with atrial fibrillation after intracranial haemorrhage was inconclusive, although rates of recurrent intracranial haemorrhage were lower than expected. In view of weak evidence from analyses of three composite secondary outcomes, the possibility that oral anticoagulation might be superior for preventing symptomatic major vascular events should be investigated in adequately powered randomised trials. Funding British Heart Foundation, Medical Research Council, Chest Heart & Stroke Scotland.

Sir Syed’s vision to bring together fragmented Muslim efforts for education led to the foundation of Muhammadan Educational Conference. This vision was passionately taken forward by Mohsin-ul-Mulk. He was a man of pragmatic approach and took initiatives to keep the spirit of the conference alive during difficult times. Sir Syed and Mohsin-ul Mulk may have been in disagreement on certain issues, but most of the times they found a meeting ground in their common passion for the betterment of Muslim community. Mohsin-ul Mulk’s untiring efforts not only kept the conference active but also extended its scope to make it an all India phenomenon.

The aim of this paper is to investigate the role of international financial integration into financial market development of Euro area countries. Annual dataset from 1998 to 2014 by using multiple regression method. The study focuses on financial integration on determining the impact on financial market development. Overall results confirming the significant positive and negative effect of international financial integration (Stock traded& share price and stock turnover ratio, respectively) while insignificant positive andnegative effect of financial integration (financial assets and liabilities and share price volatility, respectively) on financial market development. The finding provides strong evidence of achieving higher financial market development due to the drivers of financial integration.

Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.

PolyCystic Ovary Syndrome (PCOS) is one of the most common causes of female infertility, affecting a large number of women of reproductive age, even continuing far beyond the childbearing years. This hormonal disorder may further lead to the risk of other long-term complications. Considering the powerful recognition abilities of the probabilistic nature of ensemble-based gradient boosting algorithms, particularly in the field of the medical domain, we propose the use of Extreme Gradient Boosting, XGBoost, for early detection of PCOS. To strongly support an effective classification performance, we have resampled our data using a combination of SMOTE(Synthetic Minority Oversampling Techniques) & ENN (Edited Nearest Neighbour), to solve class imbalance and data outliers issues. Also, by exploiting popular statistical correlation methods, ANOVA Test Chi-Square Test, we have identified 23 most significant metabolic and clinical parameters that best classify PCOS conditions. Finally, we experimented with our model on a benchmark dataset collected from Kaggle to justify the effectiveness of our proposed findings where the Extreme Gradient Boosting classifier outperformed all other classifiers with a 10 Fold Cross-validation score of 96.03 % all over, along with a 98% Recall in the detection of patients not having PCOS, which outperforms all the existing recent methods where the numerical data-driven diagnosis of PCOS have been studied on this particular dataset.

Breast cancer has been identified as one of the most common invasive cancers and the second leading cause of cancer death among women. The survival rates have, however, improved dramatically in recent years, thanks to the advances in the screening and treatment process, hugely depending on how early the disease was detected. Along with the physicians, this had also initiated researchers all over the globe to dedicate themselves to extensive research to produce automated diagnosis strategies for breast cancer. Realizing the extraordinary potential of machine learning-based models in the biomedical domain, a large number of diagnosis methods have been proposed in this direction. In our study, we propose a hybrid unique machine learning framework that integrates individual prediction probabilities from 3 machine learning (Logistic Regression, Support Vector Machine, and K Nearest Neighbors) classifiers, then enhances the performance of these 3 classifiers through hybridization, stacking a gradient boosting algorithm over the combination of these classifiers which ultimately results in a 10 Fold Cross Validation Score of 98.4%, Recall of 100% and Precision of 97.3%. Besides, to handle the class imbalance problem we have incorporated SMOTE(Synthetic Minority Oversampling Technique) for minority classes and also Robust Scaling for normalization to deal with outliers in the dataset. In our proposed hybrid solution, we successfully adopted the breast cancer domain in every stage of our framework, starting from data pre-processing, feature extraction and finally classification. Our framework outperformed some recent state of the art studies in the breast cancer domain.

Cloud computing is the latest technology. Provides various on-demand services and online for network services, platform services, data storage, etc. Many organizations are not thrilled with using cloud services due to data security concerns, as the data resides on the cloud service provider's servers. To address this problem, various researchers around the world have applied various approaches to strengthen the security of data stored in cloud computing. The latest development in the field of cryptography is DNA encryption. It arose after the disclosure of the computational ability of deoxyribonucleic acid (DNA). DNA encryption uses DNA as a computational tool along with various molecular techniques to manipulate it. Due to the large storage capacity of DNA, this field is becoming very promising. This paper used a layered DNA encryption method for the data encryption and decryption process. Using the four DNA bases (A, C, G, T), we generate dynamic DNA tables to replace the message characters with a dynamic DNA sequence. The implementation of the proposed approach is performed in Python and the experimental results are verified. The resulting encrypted text contains information that will provide greater security against intruder attacks.

Megacities of the world are going through the unprecedented challenge of increasing congestion and frequent traffic jams. There is an immediate demand for a fast, safe, and efficient mode of mass mobility, especially in the densely populated cities of the world. Personal air vehicles could play a vital role in solving the above problems by blending the speed and efficiency of an airplane with the cost and convenience of a car. This paper discusses the initial sizing approach of a personal air vehicle. The objective is to size an electric vertical takeoff and landing (eVTOL) aircraft for the range of 200 km and payload of 200 kg. A dataset of various ongoing eVTOLs under development is used for the initial estimate of weight sizing. A sensitivity analysis is also carried out to study the effect of some design parameters which influence the selection of the final configuration. This study is based on the preliminary investigation and meant to serve as initial steps in the process of problem formulation for multidisciplinary design optimization of the system of transport.

Breast cancer has become a symbol of tremendous concern in the modern world, as it is one of the major causes of cancer mortality worldwide. In this regard, breast ultrasonography images are frequently utilized by doctors to diagnose breast cancer at an early stage. However, the complex artifacts and heavily noised breast ultrasonography images make diagnosis a great challenge. Furthermore, the ever-increasing number of patients being screened for breast cancer necessitates the use of automated end-to-end technology for highly accurate diagnosis at a low cost and in a short time. In this concern, to develop an end-to-end integrated pipeline for breast ultrasonography image classification, we conducted an exhaustive analysis of image preprocessing methods such as K Means++ and SLIC, as well as four transfer learning models such as VGG16, VGG19, DenseNet121, and ResNet50. With a Dice-coefficient score of 63.4 in the segmentation stage and accuracy and an F1-Score (Benign) of 73.72 percent and 78.92 percent in the classification stage, the combination of SLIC, UNET, and VGG16 outperformed all other integrated combinations. Finally, we have proposed an end to end integrated automated pipelining framework which includes preprocessing with SLIC to capture super-pixel features from the complex artifact of ultrasonography images, complementing semantic segmentation with modified U-Net, leading to breast tumor classification using a transfer learning approach with a pre-trained VGG16 and a densely connected neural network. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer., Comment: Accepted for publication as a Research Paper (Journal Article) in Biomedical Signal Processing and Control, Elsevier

Cloud computing has significantly benefited today’s environment of IT industry through its mobility, sustainability, security, cost savings and several other important features. The risk of data loss on cloud in case of dealing with hardware and software is relatively small. However, the issue of data security becomes imminent when we are storing personal data on the cloud which is not transparent to users. In this paper, we introduce a new entity in terms of a virtual machine that provides services and assurance beyond service level agreement (SLA). In the proposed model, a role of data handling and security is assured with association of third party auditor (TPA) by the virtual machine. We further demonstrate the applied technique for encryption, decryption and integrity verification modules. We also upgrade the entropy of the advanced encryption standard (AES) with a variant of secret sharing scheme in the environment of cloud simulator.

Mixed spectral responses from different ground materials often create confusions in complex remote sensing scenes and restrict classification performance. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection of spectral signatures. In this paper, we propose a method to integrate unmixing into a deep feature learning model in order to classify hyperspectral data. We propose to generate superpixels from the abundance estimations of the underlying materials of the image obtained from an unsupervised endmember extraction algorithm called vertex component analysis (VCA). The mean abundances of the superpixels are then used as features for a deep classifier. Our proposed deep model, formulated as a joint convolutional neural network and recurrent neural network, receives significant spectral-spatial information in the data to produce better and powerful features and achieve improved classification performance than several alternative methods.

High Altitude Airships (HAAs) offer tremendous potential as long-endurance relocatable aerial platforms for several strategic and commercial applications. Design, analyses, and optimization of HAAs involves a complex interplay of various disciplines, and hence a multidisciplinary approach is essential. This paper describes a methodology to obtain the optimal design of an HAA meeting the requirements of onboard payload and power. The methodology couples six mutually interacting disciplines, viz., Environment, Geometry, Energy, Structure, Aerodynamics, and Thermal. The design problem is posed in a multidisciplinary optimization framework involving eleven design variables drawn from these six disciplines, and optimal solutions are obtained using Genetic Algorithm. The methodology obtains the optimal envelope shape, layout of the solar array, and altitude of operation, and determines the most critical day of operation. To demonstrate the efficacy of methodology, the optimal solutions are obtained for five different geographical locations of deployment, and compared with those for a standard envelope shape. A comparative study of these solutions is carried out to highlight the importance of thermal considerations in design optimization. Since the problem involves mutually conflicting disciplines; a multi-objective optimization involving Aerodynamics and Structures are also carried out. It is noticed that operating parameters and thermal behavior have a significant effect on design.

This paper describes a methodology for determining the optimal envelope shape that minimizes the solar array area of a high altitude airship to be deployed at a specified geographical location. It identifies the critical day of the year and the optimal altitude of deployment on that day, and determines the smallest area of solar array that is just sufficient to meet all the operating requirements. This ensures that the airship can be deployed round the clock throughout the year at that specific geographical location, without any power shortfall. The methodology involves eleven design variables whose values are affected by parameters stemming from four disciplines, viz., Environment, Geometry, Aerodynamics, and Energy. Candidate envelope profiles are generated using a standard shape generation algorithm, and their volumetric drag coefficient is estimated using a surrogate based aerodynamic model. Envelope shapes corresponding to minimum solar area obtained by coupling the methodology to an optimizer based on Genetic Algorithms. To study the effect of the season of deployment, optimal envelope shapes were obtained for deployment on three specific days of the year (viz., Winter solstice, Summer solstice and Equinox) for an assumed altitude of deployment. It was seen that area of solar arrays needed for the airship were 4–9% lower than that for the baseline airship envelope. However, when the altitude of deployment and day of operation were allowed to vary, the optimal envelope shapes obtained had 35% lower solar area.

Designing a protocol for the protection of sensitive information from possible thefts, losses, and damages is an important area of research in information security. Secret sharing scheme (SSS) allows a secret to be recovered from a specific set of information. The idea of secret sharing, is to divide the secret into several pieces (or shares) so that secret can be recovered from distributed shares. When shares are released to the network, there is chance to get shares by the adversaries. One of the solution of this problem is protected secret sharing (PSS). In the PSS, shares are used to reconstruct the secret and to establish a secure key between every pair of shareholders. In this paper, we have studied and analyzed the protected secret sharing scheme, and we have proposed an extension of the PSS scheme by using the bivariate polynomial. Where, each shareholder has a pair of shares - one is to reconstruct the secret, and another is for the verification of adversaries.

Background: Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy.Methods: RESTART was a prospective, randomised, open-label, blinded-endpoint, parallel-group trial at 122 hospitals in the UK that assessed whether starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. For this prespecified subgroup analysis, consultant neuroradiologists masked to treatment allocation reviewed brain CT or MRI scans performed before randomisation to confirm participant eligibility and rate features of the intracerebral haemorrhage and surrounding brain. We followed participants for primary (recurrent symptomatic intracerebral haemorrhage) and secondary (ischaemic stroke) outcomes for up to 5 years (reported elsewhere). For this report, we analysed eligible participants with intracerebral haemorrhage according to their treatment allocation in primary subgroup analyses of cerebral microbleeds on MRI and in exploratory subgroup analyses of other features on CT or MRI. The trial is registered with the ISRCTN registry, number ISRCTN71907627.Findings: Between May 22, 2013, and May 31, 2018, 537 participants were enrolled, of whom 525 (98%) had intracerebral haemorrhage: 507 (97%) were diagnosed on CT (252 assigned to start antiplatelet therapy and 255 assigned to avoid antiplatelet therapy, of whom one withdrew and was not analysed) and 254 (48%) underwent the required brain MRI protocol (122 in the start antiplatelet therapy group and 132 in the avoid antiplatelet therapy group). There were no clinically or statistically significant hazards of antiplatelet therapy on recurrent intracerebral haemorrhage in primary subgroup analyses of cerebral microbleed presence (2 or more) versus absence (0 or 1) (adjusted hazard ratio [HR] 0·30 [95% CI 0·08–1·13] vs 0·77 [0·13–4·61]; pinteraction=0·41), cerebral microbleed number 0–1 versus 2–4 versus 5 or more (HR 0·77 [0·13–4·62] vs 0·32 [0·03–3·66] vs 0·33 [0·07–1·60]; pinteraction=0·75), or cerebral microbleed strictly lobar versus other location (HR 0·52 [0·004–6·79] vs 0·37 [0·09–1·28]; pinteraction=0·85). There was no evidence of heterogeneity in the effects of antiplatelet therapy in any exploratory subgroup analyses (all pinteraction>0·05).Interpretation: Our findings exclude all but a very modest harmful effect of antiplatelet therapy on recurrent intracerebral haemorrhage in the presence of cerebral microbleeds. Further randomised trials are needed to replicate these findings and investigate them with greater precision.Funding: British Heart Foundation.