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Analysis of Reinforced Concrete Structure Subjected to Blast Loads Without and with Carbon Fibres
In the past few decades, the terrorist attack on buildings has significantly increased. Blast loads due to explosions cause severe damage to the buildings structural and non-structural elements which may also lead to progressive collapse of the building. Hence, there is a need for the structures to be analysed and designed for blast loads in addition to the conventional loads. An investigation is undertaken to minimize the damage of a G+3 storied building and by improving the mechanical properties such as compressive strength, nonlinear behaviour of M40 grade concrete by adding carbon fibres in different dosages. A finite element model of G+3 storied building has been created using Ansys/LS Dyna to analyse the structure subjected to a blast load with charge weights of 50 kg, 100 kg, 150 kg at 3000 mm standoff distance. The lateral deflections and strains of the structure are determined for different charge weights to study the behaviour of the structure when subjected to blast loads. The addition of carbon fibres has improved the behaviour of structure by reducing the strains and deflections and optimum dosage of fibres is also determined in this paper. 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved. -
Analysis of Routing Protocols in MANET Networks
The scientific article is a review and comparative analysis of routing protocols for MANETs. The study examines the main protocols connected to mobile ad hoc networks such as B.A.T.M.A.N, BMX7, OLSRv1, Babel and provides a detailed analysis of their characteristics, advantages and disadvantages. To empirically evaluate performance, tests were carried out in a network simulator. The results of the study allow us to draw conclusions about the effectiveness and reliability of each of the monitoring protocols under various operating conditions of MANET. This article is a valuable contribution to the field of MANET research and can be used in the development of new technologies and solutions for mobile wireless networks. The work is relevant and practically significant because it helps researchers and engineers make informed decisions when choosing the optimal routing protocol in MANET networks. The results obtained can be useful in the design of mobile applications, emergency communication systems, transport management and other areas where the efficient operation of wireless networks is important. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysis of secure cloud storage provisioning for medical image management system
Medical images are considered to be the most sensitive images as it contains various health related sensitive information of an individual and it is necessary for the health care organization to maintain the sensitivity of these images without anybody misusing these data. When these images are transferred digitally through a network in order to store it in cloud for easy access for the authorities of the health care system, it is important to compress and encrypt these images to reduce the size and safeguard the information before storing and make sure that these images are transferred securely. In this paper, we use Huffman Coding technique in order to compress the image for easy transmission and to consume less storage space in cloud. To maintain the confidentiality of these images Blowfish encryption methodology is used. Once the image undergoes compression and encryption, the encrypted image is transferred and stored in a cloud storage. IAEME Publication. -
Analysis of SH-waves Propagating in Multiferroic Structure with Interfacial Imperfection
This article presents the study of wave mechanics in a multiferroic structure having imperfection in the structures interface. This article reflects the study of shear horizontal (SH) wave propagation in a layered cylindrical structure consisting of thin layers of different materials (reinforced material and piezomagnetic material) with an imperfect interface. The interface considered between both materials is mechanically imperfect. Dispersion relations are achieved analytically. Distinct graphs are drawn (numerically) to exhibit the influence of parameters like rotation, initial stress, and mechanically imperfect parameters on phase velocity. Numerical results are drawn analytically and explained for each affecting distinct parameters for materials and interface. Parametric results on the phase velocities yield a significant conclusion of which some are: (a) Performance of Piezo with reinforcement material have an influential impact on wave velocity. (b) The mechanical imperfection affects the significantly on wave velocity (c) The Reinforcement/PM stiffening can monotonically up the velocity of phase velocity. 2022 Published by Semnan University Press. -
Analysis of Social Media Marketing Impact on Customer Behaviour using AI & Machine Learning
The study of client behaviour has been revolutionized by the combination of social media marketing with cutting-edge technology like Artificial Intelligence (AI) and Machine Learning (ML) in today's age of digital transformation. This study delves into the complex interplay between AI/ML, consumer involvement, and social media marketing methods. Our research exposes crucial insights via careful data collecting, sentiment analysis, and the construction of prediction models. By stressing the importance of catering content to individual interests, AI-driven customization emerges as a potent tool, increasing user engagement by 18%. Analysis of online sentiment shows how important it is to keep people feeling good about a business; postings with positive feelings get 30% more likes and comments on average. Accurate and time-saving insights from machine learning models provide up new avenues for optimizing marketing's use of available resources. As a result of the study's conclusions, companies will be able to better connect with their customers, use their resources more efficiently, and behave ethically moving forward. Promising new developments in the subject include the next steps, which include sophisticated AI models, temporal dynamics analysis, and investigation of long-term consequences, ethical issues, and multichannel techniques. This study helps companies, marketers, and policymakers better understand the convergence of technology and marketing in today's ever-changing digital world so that they may better serve their customers and build a successful brand over time. 2024 IEEE. -
Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting
In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. 2024 Bentham Science Publishers. -
Analysis of students' preferences for teachers based on performance attributes in higher education
Faculty evaluation is widely used not only for the appraisal of their performance, but also for curriculum innovation and development. There are many techniques to perform faculty evaluation. But these techniques do not address all the factors essential for evaluating a faculty. These evaluations are subjective in nature and found to be controversial as students' expectations vary. This hinders the main motive of faculty evaluation. To overcome this problem, there is a need to identify a suitable method to perform faculty evaluation. In this paper, the Conjoint Analysis, a mathematical statistics technique is used to analyze the major aspects that the students are expecting from their faculty. This technique increases the fairness in the appraisal process so that teaching can be made fun and effective. This research is a novel attempt that applies conjoint analysis to identify the major aspects of teaching in students' perspective. The proposed idea can be adapted to any domain where the customers' choice is valued particularly in Cloud computing services. 2019 Mithula G P, Arokia Paul Rajan R. -
Analysis of supervised and unsupervised technique for authentication dataset
Traditional methods of data storage vary from the present. These days data has become more unstructured and requires to be read contextually. Data Science provides a platform for the community to perform artificial intelligence and deep learning methodologies on large volumes of structured and unstructured data. In the era of artificial intelligence, AI is showing it's true potential by addressing social causes and automation in various industries such as automobile, medicine and smart buildings, healthcare, retail, banking, and finance service are some of the deliverables. From a variety of sources and flooding data, AI and machine learning are finding real-world adoption and applications. The nature of the data models is trial and error and is prone to change with their discoveries for the specific problem and this is the case with the different algorithms used. In this paper, we apply machine learning algorithms such as unsupervised learning k-means, bat k-means and supervised learning decision tree, k-NN, support vector machine, regression, discriminant analysis, ensemble classification for data set taken from UCI repository, phishing website, website phishing, Z- Alizadeh Sani and authentication datasets. Authentication dataset is generated for testing Single Sign-on which learns from data by training to make predictions. 2018Rahul K. Dubey, P. K. Nizar Banu. -
Analysis of the chemical properties and high-temperature rheological properties of MDI modified bio-asphalt
As an environmentally friendly material, bio-oil is employed to partially replace non-renewable petroleum asphalt, but its addition weakens the high-temperature non-deformability of petroleum asphalt. Therefore, the 4,4?-diphenylmethane diisocyanate (MDI) was employed as a chemical modifier of bio-asphalt to improve its high temperature rheological properties. The MDI with addition of 0.5%, 1%, 2%, 4% by weight, and the bio-oil with addition of 12% were used to obtain the MDI modified bio-asphalts. The chemical reaction mechanism between the MDI and bio-asphalt was analyzed by employing the Fourier-transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. Meanwhile, the rotational plate viscosity (RPV) test, the temperature sweep test, and the multiple stress creep and recovery (MSCR) test were employed to evaluate the high-temperature rheological properties of the MDI modified bio-asphalts. Moreover, the relationships between the chemical reaction mechanism and high-temperature rheological parameters of MDI modified bio-asphalt were established. Test results show that a nucleophilic addition reaction occurred between the MDI and the active hydrogen of bio-asphalt to form urethane chains, which increased the content of macromolecular polymers in the bio-asphalt. The MDI increased the G*/sin? (rutting factor) and the E(?) (visco-flow activation energy) of the bio-asphalt, but decreased its permanent strain and Jnr (non-recoverable creep compliance). Therefore, the MDI modifier effectively enhanced the permanent non-deformability of the bio-asphalt. Both IUrethane and LMS were positively correlated with the rutting factor, viscosity and 1/Jnr, and had significant correlations at a significance level of 0.05. Furthermore, the optimal ratio of MDI to bio-oil was determined to be 1:6 by mass. 2020 Elsevier Ltd -
Analysis of the Effectiveness of a Two-Stage Three-Phase Grid-Connected Inverter for Photovoltaic Applications
This paper proposes a two-stage three-phase grid-connected inverter for photovoltaic applications. The proposed inverter topology consists of a DC-DC boost converter and a three-phase grid-connected inverter. The DC-DC boost converter is used to boost the low voltage DC output of the PV array to a high voltage DC level that is suitable for feeding into the grid-connected inverter. The three-phase grid-connected inverter is used to convert the high voltage DC output of the boost converter into a three-phase AC output that is synchronized with the grid voltage. The proposed inverter topology offers several advantages over traditional single-stage inverters. Firstly, the DC-DC boost converter allows for the use of a smaller, more efficient inverter in the second stage, reducing the overall cost of the system. Secondly, the use of a boost converter allows for the maximum power point tracking of the PV array, which can increase the overall efficiency of the system. The proposed inverter topology offers improved control of the grid current, reducing the impact of the PV system on the grid. The proposed topology has been simulated using MATLAB/Simulink and the results show that the system is capable of delivering a high-quality three-phase AC output with low harmonic distortion. The Author(s). Publisher: University of Tehran Press. -
Analysis of the Performance of a 5-Level Modular Multilevel Inverter for a Solar Grid-Connected System
The main purpose of a multilevel inverter is to combine numerous levels of DC voltage to create a nearly sinusoidal voltage. The synthesized output waveform has more stages as the number of levels rises, creating a staircase ripple which resembles the preferred waveform. As the number of voltage levels rises, the output waves harmonic distortion diminishes and eventually approaches zero. In particular, the performance analysis of a five-level inverter with variable loads is highlighted in this paper. This topology has fewer devices than traditional multilevel inverters for the same five output levels, which makes it more affordable due to lesser driver circuits. The proposed modular five level topology is simulated using both high frequencies switching pulse width modulation and basic frequency switching modulation techniques. The output voltage, current waveform, and total harmonic distortion are examined and compared using simulink to confirm the viability of the modular multilevel inverter topology. 2024, TUBITAK. All rights reserved. -
Analysis of the Performance of VAR Models as a tool for Market Risk
The International Journals Research Journal of Social Science & Management Vol.2, No.10 pp.74-83. ISSN No. 2251-1571 -
Analysis of the photo-thermal excitation in a semiconducting medium under the purview of DPL theory involving non-local effect
Non-local theory comprises a unique characteristics by analyzing the effects of all points of the body on a single point of the material. The present study enlightens the propagation of photo-thermal waves in a semiconductor by adopting the two phase lag theory of thermoelasticity in the frame of non-local effect. Normal mode analysis has been employed to obtain the exact expressions of the field quantities such as temperature, components of the displacement, carrier density, and components of the stress. Each field quantity is found to be influenced by the non-local parameter as well as phase lags. Quantitative results are determined in the time-domain by adopting a suitable technique of Laplace transform inversion which exhibit the influence of the non-locality effect on the distributions of field variables. Significant differences have been attributable to the studied fields due to the non-locality effect. Also, computational results are compared with the corresponding results obtained by using single phase lag theory proposed by Lord and Shulman (LS model)LS model single phase lag model (LS model). 2022, Springer Nature B.V. -
Analysis of the spread of infectious diseases with the effects of consciousness programs by media using three fractional operators
In this chapter, the mathematical model spread of infectious diseases exemplifying the effects of awareness programs by media is studied with the help of newly proposed fractional operators. The solution for the system of equations exemplifying the model is obtained with the help of the q-homotopy analysis transform technique (q-HATT). The projected method is an elegant amalgamation of the q-homotopy analysis scheme and the Laplace transform. Three fractional operators are employed in this study to show their essence in generalizing the models associated with power-law distribution: kernel singular, nonlocal, and nonsingular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and converges for the solution is derived with Banach space. The projected scheme springs the series solution rapidly convergent, and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional-order, the physical nature has been captured in plots. 2022 Elsevier Inc. All rights reserved. -
Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet
In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely TiO 2, Cu and Ag into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid Cu-TiO 2/ H 2O. Thus, forming the ternary nanofluid Ag-Cu-TiO 2/ H 2O. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing. 2023, The Author(s). -
Analysis of the UAV Flight Logs in Order to Identify Information Security Incidents
The article discusses issues related to the analysis of the UAV flight logs to identify information security incidents that occurred during flights. Existing methods and tools for analyzing logs are described, and sources for obtaining logs are presented. In the main part of the article, first, the parameters important for the analysis are highlighted. The features of analyzing the values in the flight logs for the detection of two types of attacksGPS Spoofing and GPS Jamming are also given. For this purpose, the parameters that are most important for the detection of each of these attacks have been identified, systems of equations have been compiled to analyze these parameters, the calculations of which make it possible to detect the fact of attacks with high efficiency. The paper also presents the developed software that implements a number of functions that allow automating the analysis of flight logs, as well as determining the presence of information security incidents that occurred during the flight. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE. -
Analysis of Unintelligible Speech for MLLR and MAP-Based Speaker Adaptation
Speech Recognition is the process of translating human voice into textual form, which in turn drives many applications including HCI (Human Computer Interaction). A recognizer uses the acoustic model to define rules for mapping sound signals to phonemes. This article brings out a combined method of applying Maximum Likelihood Linear Regression (MLLR) and Maximum A Posteriori (MAP) techniques to the acoustic model of a generic speech recognizer, so that it can accept data of people with speech impairments and transcribe the same. In the first phase, MLLR technique was applied to alter the acoustic model of a generic speech recognizer, with the feature vectors generated from the training data set. In the second phase, parameters of the updated model were used as informative priors to MAP adaptation. This combined algorithm produced better results than a Speaker Independent (SI) recognizer and was less effortful for training compared to a Speaker Dependent (SD) recognizer. Testing of the system was conducted with the UA-Speech Database and the combined algorithm produced improvements in recognition accuracy from 43% to 90% for medium to highly impaired speakers revealing its applicability for speakers with higher degrees of speech disorders. 2021, Springer Nature Singapore Pte Ltd. -
Analysis of unsteady flow of blood conveying iron oxide nanoparticles on melting surface due to free convection using Casson model
Iron oxide nanoparticles have great importance in future biomedical applications because of their intrinsic properties, such as low toxicity, colloidal stability, and surface engineering capability. So, blood containing iron oxide nanoparticles are used in biomedical sciences as contrast agents following intravenous administration. The current problem deals with an analysis of the melting heat transfer of blood consisting iron nanoparticles in the existence of free convection. The principal equations of the problem are extremely nonlinear partial differential equations which transmute into a set of nonlinear ordinary differential equations by applying proper similarity transformations. The acquired similarity equalities are then solved numerically by Runge-Kutta Felhsberg 45th-order method. The results acquired are on the same level with past available results. Some noteworthy findings of the study are: the rate of heat transfer increases as the Casson parameter increases and also found that the temperature of the blood can be controlled by increasing or decreasing the Prandtl number. Hence, we conclude that flow and heat transfer of blood have significant clinical importance during the stages where the blood flow needs to be checked (surgery) and the heat transfer rate must be controlled (therapy). 2020 Wiley Periodicals LLC -
Analysis of value and growth styles of investing : A study on nifty 100 index stocks of NSE /
Asian Journal Of Research In Business Economics And Management, Vol.7, Issue 5, pp.165-177, ISSN: 2249-7307.