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In this study, the consequence of cell-size, cell-wall-thickness and cell circularity together on the compressive performance of closed-cell aluminum foam was analyzed experimentally and with FEM simultaneously. The closed-cell metal foams (CAFs) with varying cell sizes of 55% porosity were synthesized through stir casting technique. The granular metallic calcium is used as a stabilizing/thickening agent, and TiH2 powder used as a foaming agent. The uniaxial compression tests were performed to investigate the compressive deformation behavior of CAFs. The simulation studies were carried out by taking the assumption similar to experimental constraints. The circular-shaped cells were created in FE half symmetrical model of varying cell sizes (in 2-Dimensional). The cell sizes used in FE models are 1.5 mm, 2.5 mm, 2.8 mm and of 3.5 mm. While in experimental samples, different cell sizes obtained are 1.65 mm, 2.47 mm, 2.88 mm and 3.59 mm. In both of the investigations, it is observed that the energy absorption capacity and plateau strength decrease with an increase in cell sizes or vice versa. A similar effect was also observed with an increase in cell wall thickness. The obtained FE results are acceptable and comparable with the experimental for each cell size model. The deformation mechanism was analyzed using deformed sample images, which were captured during the deformation as well as during the different stages in the FEA models.

Diagnosis of epilepsy primarily involves understanding cautious patient history and assessment of EEG (Electro Encephalography), which is an essential diagnostic support tool. It captures the electrical activity in the brain, which enables the neurologist to look for the presence of epileptiform patterns for which brain waves (Delta, Theta, Alpha, Beta, and Gamma) are studied thoroughly. The Delta (0–4 Hz), Theta (4–8 Hz), and Alpha (8

In the present study, closed-cell aluminum foams of varying porosities (65–75%) were produced through the stir casting technique using TiH2 (1 wt.%) as a foaming agent. Under the uniaxial compressive loading, the compressive stress-strain responses of these foams were examined. The finite element (FE) models of the foam were elaborated using the ABAQUS FE simulation software and realized via the ABAQUS Explicit solver. Within the test domain, the experimental results were compared with the FE-predicted ones for the same porosities and both yielded similar stress-strain responses. The energy absorption capacity, yield stress, and densification strain values of AFs obtained through FE simulation were in good agreement with the experimental results, while the predicted plateau stress values differed from the latter by no more than 4–5%.

Wireless sensor networks (WSNs) have a large number of existing applications and is continuously increasing. Thus it is envisioned that WSN will become an integral part of our life in the near future. Direct propagation, chain formation, cluster creation are various techniques by which data is communicated by sensor nodes to the sink. It has been proved that Clustering is an efficient and scalable method to utilize the energy of sensor nodes efficiently. Optimal election of cluster heads is an NP (non deterministic polynomial time)-Hard problem. In our proposed work, a multi-objective optimization algorithm, non dominated sorting genetic algorithm-II based clustering in wireless sensor networks has been proposed. Energy conservation, network lifetime, coverage and load balancing are the four conflicting objective functions used. Our proposed algorithm handles all of these multiple objectives simultaneously. To reduce the computational complexity of the algorithm, efficient non-dominated level update mechanism for sorting has been used, which eliminates the need of applying non dominated sorting from scratch every time. The algorithm returns a solution set consisting of multiple non dominated solutions, wherein every solution is a best solution according to some objective function, in a single run, from which any solution can be chosen based on user preferences. According to our simulation carried on MATLAB, the proposed approach outperforms the established clustering algorithms in terms of network characteristics such as network lifetime, energy consumption and number of packets received.

Compressive deformation responses of closed-cell AlSi12Mg1Cu1 (LM-13) alloy foam (AFs) was analyzed through the help of ABAQUS Simulation software. An Approximate 2D model was made for closed-cell metal foam specimen of 60% porosity. Required porosity for the Closed Cell Metal foam was obtained by creating circular voids in the Model. The finite element (FE) Results were validated with the experimental results that were calculated from the output data of the computerized UTM machine. By comparing numerical outcomes and experimental data, the modelling and simulation technique was calibrated. Density and dimension of the FE model were kept similar to the experimental specimen. Plateau Stress ( σ pl ) , Energy absorption capacity ( E ab ) and densification strain ( e d ) of the experimental and FE outcomes were calculated and compared. The results show the approximately similar trend of compressive behavior and predicted FE results were found satisfactory.

This article overviews importance of interdisciplinary technologies, which are facilitating tremendous growth for the bioinformatics applications. A summary in brief on development of bioinformatics areas and related technologies is presented. Emphasis mainly is given to discuss role of computer professionals and some computational tools to support the bioinformatics objectives. Challenges and issues faced by expected technological developments are also highlighted in view to adopt strategies for futuristic developments of bioinformatics.

This article reviews and discusses emerging applications of object-oriented programming languages. A history in brief on development of object-oriented analysis and methods eventually leading for important soft programming techniques are discussed. Emphasis mainly is given to discuss and differentiate popular object-oriented programming languages such as; C++, Python and Java. The pros and cons of these programming languages are also highlighted in view of their futuristic developments and possible application areas.

Vehicular ad-hoc networks (VANETs) have suffered from many risks in the past, such as security privilege and authentication because attackers/hackers always try to disturb the network and break the communication services. The series of attacks that have broken secured communication are network jammer, source sink, and MAC crack, and these attacks are on as denial of service (DoS) attacks. The proposed method is based on the master chock filter concept for the filtration of packets during busy traffic. The IP-CHOCK protocol is implemented along with routing protocol employed in the VANET. The performance of the proposed protocol was evaluated using network simulator NS-2.34. The protocol includes blocking the source IP originator by the DoS attacks and checking the prevention of TCP/UDP flooding and IP sniffing attacks. The evaluation of the protocol was based on the mobility node’s interaction and utilization of bandwidth. The simulation results were analyzed with two different scenarios; first scenario includes 20 mobile nodes and second scenarios with 50 mobile nodes. These two scenarios were performed on the highway transportation condition. The results showed that the throughput, packet delivery ratio, packet loss time, and packet receiving ratio have improved as compared to random IP-trackback protocol. Hence, indicates that the proposed protocol is efficient for intelligent transportation system in VANET.

In the present investigation, a two-dimensional (2D) finite element (FE) model of closed-cell AlSi12Mg1Cu1 alloy foam (AAFs) of 0.35 relative density of different cell shapes, i.e., square, hexagonal, octagonal, and circular shaped cell established with the help of ABAQUS simulation software. Experimentation is also done for 0.35 relative density metal foam to report its compressive behavior. The plateau stress σ p l $\left(\right.{\sigma }_{pl}$ ) and energy absorption capacity (E ab) of AAFs (ρ rd = 0.35) properties are calculated from the plotted stress–strain graph and a significant effect of cell shape on compressive performance observed. By SEM characterization, it was noted that the experimental specimen also had near circular cells. The strength and energy absorption values are calculated for square-shape, circular-shaped, octagonal-shaped, and hexagonal-shaped cells in descending order from maximum to minimum. A convergence study is also carried out concerning the number of elements to obtain the most accurate FE results.

To address the issue of water contamination with the usage of dyes and undesired bacterial growth on food products, different ZnO nanostructures have been studied in present manuscript. XRD technique has investigated the crystal purity of ZnO nanostructures. The alteration in chemical structural parameters (bond vibration and spring constant etc.) confirms their structure level modification by fourier transform infrared spectroscopy (FTIR). The maximum efficiency of methylene blue dye degradation has been observed in nanosheet (∼96.42%) as compared to nanosphere (∼95.45%) & nanorod (∼87.12%) morphology. In addition, the antibacterial activity of ZnO nanospheres for E. coli and S. aureus bacteria shows inhibition diameter of 10 mm & 12 mm is credited due to generation of active oxidizing agents as compared to other reported morphologies. Present studied ZnO nanomaterials embolden researchers to investigate the unexplored aspects of ZnO nanomaterials to ameliorated efficiency in photocatalytic and antibacterial applications.

Software Defined Network (SDN) is the most significant model for the administration of large-scale complex systems, which may require reconfigurations occasionally. It involves the separation of the network control plane and the forwarding plane, where a control plane controls many devices. In this way, the switches forwards packets by the rules defined in the flow table by the controller. Despite the fact, SDN is a new area, it has pulled in a lot of consideration from both the scholar world and industry. This paper investigates the multi-controller placement problem from the viewpoint of cost and load balance factors. The paper proposes an algorithm named Load Balancing with ECP-LL that places controllers by using clustering. The algorithm uses Shannon entropy which avoids poor local minima problems.

Background: Time series forecasting methods play a critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred to as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.Objective: The aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series.Comparison: The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values.Result: The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent methods of time-series prediction (ARIMA).Conclusion: The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt-Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.

Internet of Things is a norm which has expanded very swiftly with high magnitude of heterogeneity and functionalities. Security and privacy became the prime factors of Internet of Things due to unsecured character of wireless communication. Thus, because of unsecured network, it is easy for invaders to trace and find the position of nodes during communication and leak the information. Issues related to location information may include sharing of information, storage, sensing, and processing which can be used by external entities in different contexts, i.e. contexts can be: technical, legal, and social. These issues make privacy a major concern. Here, the research this article presents notions of existing privacy models and the amplified techniques using a random path. The article then describes possible solutions to preserve the location of nodes with less transmission time. Results of proposed scheme depict effectual behavior of the approach.

Analysis of EEG (Electroencephalography) for the detection of interictal activity amidst of artefacts for supporting the diagnosis of epilepsy is a time-consuming process that requires high expertise and experienced neurologist. The objective of the work is automated identification interictal activity in order to assist neurologists and also reduce the time consumed in visual inspection. For this work, four cases distinguishing interictal from a controlled activity are considered from the Bonn database. Non-linear properties from the complete signal such as correlation dimensions and properties such as approximate entropy, sample entropy and fuzzy approximate entropy from the specific sub-bands of frequency range A5 (0–2.7 Hz), D5 (2.71–5.4 Hz), D4 (5.4–10.8 Hz), D3 (10.85–21.7 Hz), and D2 (21.7–43.4 Hz) are used. Backpropagation neural network is used as a classifier. Performance parameters accuracy, sensitivity, and specificity are calculated. Ten-fold cross method is used as a validation method. For case 1, case 2, case 3, and case 4, the highest accuracy of 99.9%, 99%, 98.5%, and 99% has been achieved, respectively. The problem of interictal identification activity still remains unmapped to some extent this work focuses on the same and in this work we have achieved good performance measures in context to the same.

Wireless sensor network (WSN) are used to sense, process, and send important information relating to surrounding environment. In these network, one of the important issue is timely deliver...

The Internet live streaming applications use different sets of network protocols and individual application layer decisions for delivering multimedia flows, once they have different network and application requirements. As a consequence, the nodes of the live streaming systems cannot cooperate among them to share a given live streaming flow, increasing network resource consumption due to the duplication of a same streaming flow. For that reason, we have proposed the Global Media Transmission Protocol (GMTP), which decouples the functions for rendering the media data to the application user from the functions for disseminating the media flow over the network. The GMTP protocol executes P2P algorithms at the network and transport layers to allow streaming servers to manage a network of cooperative routers and efficiently distribute a live multimedia flow. The streaming servers orchestrate the partnerships among routers based on the corresponding available bandwidth of a known transmission channel, determined according to the demand from the client nodes. The GMTP executes a network-assisted congestion control algorithm to calculate the available bandwidth of the transmission channels, resulting in more accurate information about the network conditions. We evaluated the GMTP in different network scenarios and in this paper we discuss the performance of the GMTP implementation on the Linux kernel, showing that our approach is feasible for the Internet.

There have been numerous advancements in mobile wireless communication since the last few decades. This innovation consists of a number of generations and is still in progress. The journey of mobile wireless communication started with 1G followed by 2G, 3G, 4G, and under research upcoming generation 5G. In this paper, an attempt has been made to contribute to the upcoming mobile generation (5G) by uncovering security risks in the upcoming 5G network and finding vulnerabilities in the 5G network. We have also thrown some light on the new emerging technology 5G, its concept, requirements, features, benefits, and hardware and software requirements.

An asymmetric coplanar waveguide-fed branched multistubs resonators loaded printed antenna is presented for multiband operation. By incorporating an asymmetrical long and short L-shaped branched stubs, the lower resonances for 2.4 and 3.5 GHz bands are achieved while the higher resonance for 5.5 GHz band is achieved by embedding horizontal stub on asymmetric CPW-fed printed antenna. By placing multistubs resonators at suitable location on asymmetric CPW-fed printed antenna, it is likely to excite the triple resonances at 2.44/3.66/5.25 GHz frequencies and antenna exhibits the bandwidths of 5.73% (140 MHz, 2.36–2.50 GHz), 16.98% (630 MHz, 3.40–4.03 GHz), and 14.65% (800 MHz, 5.06–5.86 GHz), respectively. The design procedure and antenna characteristics of the proposed multistubs loaded multiband antenna are presented.

As applications of the wireless sensor network (WSN) continuously increasing, the communications of wireless sensor technologies have become the most emerging fields of wireless communication technologies. When we use sensor networks in a remote environment, health monitoring, and machine automation, security of these networks will become the main concern. The main focus of this work is to make sensor networks secure by ensuring authentication, integrity, and confidentiality. The proposed approach used IPsec with sensor network protocols. In this work, IPsec has been used to provide security and integrity for the sensor network. IPsec, the protocol of the network layer, provides connectionless integrity, data origin authentication, protection against replay attacks, and confidentiality. In this chapter, some changes will be made in the IPsec protocol suite for using it with WSN.

The study examines the suitable climatic windows in the face of climate change under limited irrigations under sub temperate climate. The field experiment in Randomized Block Design, comprising of four dates of sowing viz., October 20, November 10, November 30 and December 20, and three genotypes viz., HPW-249, HPW-155 and HPW-42 in silty clay loam, acidic soil was conducted during Rabi session for two years at CSKHPKV, Palampur, Himachal Pradesh. The days taken to complete different phenophases were statistically at par amongst varieties, whereas DAS to vegetative stage and heading maturity were observed minimum reflecting lowest DAS for maturity in 20 th December sowing. The growth and yield attributes viz., plant height, number of tiller m -2 , number of grains tillers -1 and 1000-grain weight was highest in variety HPW-249 during both the years. Amongst sowing environments, October 20 th sown crop gave significantly highest values of growth and yield during both the years. The periodic LAI recorded similar upto 60 DAS and thereafter, the highest values were recorded in HPW-249 upto physiological maturity. Amongst sowing environments, 20th October sown gave highest LAI during entire ontogeny of crop likewise yield attributes, grain and straw yield was highest in 20 th October sown crops. Amongst variety HPW-249 gave highest grain yield to the tune of 43.0 in 2011-12 and 45.6 q/ha in 2012-13. The economic returns and benefit cost ratio were also significantly highest in HPW-249 (Rs. 75568 in 2011-12 and 81219 in 2012-13). Likewise, grain yield, net returns and B: C ratio were significantly highest in 20th October sown crop compared to subsequent dates of sowing.

Graphical abstract Fig. 2: Pictorial description of the stack based ensemble ARIMA model., Background Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. Methods We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. Result The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. Conclusion Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

Summary Essentials A venous thromboembolism (VTE) diagnostic strategy is economical compared to imaging alone. Applied a VTE diagnostic strategy to a D-dimer multicenter study for cost-minimization analysis. Average diagnostic test costs for patients were significantly lower with a diagnostic strategy. Implementation of a VTE diagnostic strategy reduces the diagnostic costs for a hospital. Background The burden of healthcare costs has substantially risen in the last few decades. One possible contributing factor to this increase are the diagnostic approaches for venous thromboembolism (VTE) using only imaging to exclude a diagnosis of VTE. Objective To demonstrate cost minimization in the diagnosis of VTE by comparing standalone imaging (computed tomography pulmonary angiography and compression ultrasonography) to a published VTE diagnostic strategy incorporating assessment of pre-test probability and D-dimer testing. Methods We retrospectively reviewed data from a multicenter diagnostic accuracy study of a D-dimer reagent where consecutive outpatients (n=747) with suspected VTE, including both pulmonary embolism (n=346) and deep venous thrombosis (n=401) were evaluated. By applying a VTE diagnostic strategy and using the proportion of patients that were diagnosed as VTE-negative (n=137 for PE; n=120 for DVT), we developed a cost calculator to compare the average diagnostic test cost per suspected VTE patient, both before and after the implementation of the VTE diagnostic strategy. Results Implementation of the VTE diagnostic strategy reduced the average diagnostic test cost for a suspected PE patient by 38% and for a suspected DVT patient by 24%. Assuming the proportion of VTE suspected patients to be 30% PE and 70% DVT, the weighted average reduction in the diagnostic test cost per suspected VTE patient was 32%. Conclusion Implementation of a VTE diagnostic strategy can allow hospitals to reduce costs without compromising patient safety.

The effectiveness of wireless sensor networks depend on the essential routing protocol. Due to the change in the application requirement, it may vary the characteristics of a routing protocol. As scalability (i.e., varying the number of nodes by considering another parameter constant) is an important issue which occurs due to an increase in the number of nodes. Thus, any routing protocol should work effectively, when scalability factor is considered. In this paper, we analyze the scalability issues of various hierarchical based routing based protocol. Here we have categorised scalability in two classes – varying the number of nodes and varying the size of terrain. We have performed analytical simulation in MATLAB which showed the limitation and capability of the existing WSN routing protocols (i.e. LEACH, PEGASIS, TBC, GSTEB,CAMP, HEEMP) against scalability. It is found that except PEGASIS all other aforementioned routing protocols are scalable.

Background: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. Methods: This work addresses the classification problem for two groups; Group 1: “inter-ictal vs. ictal” for which case 1(C-E), and case 2(D-E) are included and Group 2; “activity from controlled vs. inter-ictal activity” considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. Results: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. Conclusion: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

Wireless sensor networks is a collection of many low power sensing devices having local processing and wireless communication capabilities. In these wireless networks, energy constraint is significant. To provide communication between sensor devices, many routing protocols have been proposed. In each routing protocol, their main task is to transmit data (via single hop or multi-hop) towards the sink. Hence, location of the sink has a vital impact on the various factors like performance of the routing protocol. As sink can be static and mobile, but in many applications, sink mobility is not possible. In this paper, we examine the performance of extanttree and clustered based routing WSN protocol, when the different location of a static sink (i.e., at centre, corner and far) is deployed. We perform analytical simulation in terms of networks lifetime. We test the existing network based routing algorithm (i.e. HEEMP, CAMP, GSTEB, TBC, PEGASIS, LEACH) extensively. For each routing algorithm we have evaluated the impact of sink locality. The experimental results show that the HEEMP, CAMP algorithm outperforms other existing algorithms.

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.