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Background: The estimation of gender using hand dimensions is a crucial role in forensic investigation and legal sciences. Objectives: In this study, the mean score of hand measurements have been derived and linear discriminant analysis has been applied to predict gender from hand dimensions. An Independent t-test has been employed to know discrimination between male and female. The accuracy of gender estimation has been derived and checked by ROC curve. Material and Methods: A sample of 150 participants has been collected for the completion of this study, where 78 males and 72 females were found aged between 18 and 24 years. The breadths and lengths of left and right hands of the participants have been measured. Results: Respondents’ mean score of length and breadth of left hand were found 17.69 ± 1.30 and 7.56 ± 1.17, respectively. It was found to be significantly different in the respondents’ mean scores of males and females. Conclusions: The hand dimensions of males were higher than females. The prediction of a number of females and males were 76 and 74, respectively, and its accuracy was observed 0.87. This research can be applied for larger sample and would be helpful in predicting gender discrimination among adults through hand dimensions.

Electroencephalography (EEG) signals are a combination of complex pattern sequences,that are periodic in nature. These pattern sequences include a gamma waves that indicates deep thinking behaviour, a beta wave sequence that indicates busy and active mind status, an alpha wave segment which indicates reflective and restful behaviour, a theta wave which is an indicative of drowsiness, and a delta wave which indicates sleeping & dreaming conditions. Features like frequency changes, amplitude changes, pattern changes, etc. are used to identify chronic, ischemic and other diseases related to the brain. In order to classify these wave patterns into brain diseases like epilepsy, a series of high complexity signal processing operations are needed to be executed in tandem. These operations include signal pre-processing, feature extraction, feature selection, classification into epileptic & non-epileptic seizure and post-processing.A large variety of algorithms are developed by researchers for each of these operations. Performance of these algorithms varies largely w.r.t. the number of leads used for EEG capture, filtering efficiency, feature extraction & selection efficiency, and classifier efficiency.Thus, it becomes ambiguous for researchers and system designers to select the best possible algorithm set for their application. the proposed AMVAFEx model uses Multiple Neural Networks for definite EEG grouping.

Video summarization is a field of signal processing which deals with removing redundant information from a sequence of frames. In order to design an efficient video summarization model, it is imperative that frame features must be analyzed, and their similarity must be evaluated. This process would assist in aggregation of frames that have similar feature sets, and then use this information to reduce frame redundancy. Design of an efficient feature extraction and analysis model must be done in order to improve efficiency of video summarization. Models that aim at improving this efficiency, tend to improve the computational complexity of summarization, thereby limiting their applicability. In order to remove this drawback, The proposed event-based model for long video summarization uses a combination of LSTM based CNN with feature variance method for estimation of keyframes. The model works is 2-phases, wherein in the first phase various events are recognized from the video using LSTM based CNN model; while in the second phase these events are individually summarized using variance-based threshold engine. The proposed event classification-based LSTM CNN model with variance-based summarization outperforms other models, and achieves 8% improvement in terms of compression ratio.

The current work focuses on the need for automation in agriculture through IoT technology. Automated farming aims to monitor and control various parameters that can help increase agriculture productivity. The proposed system provides a technical solution to a variety of problems such as water demand, humidity, temperature and humidity of complex plants, fire warning and maintenance check on unwanted agricultural inputs including timely and adequate electricity supply. In the proposed system a Wi-Fi module is used that automatically notifies the alert and unwanted stay or sign-in display of real-time data that can be seen and accessed online using IoT technology from anywhere in the world. The system is equipped with a solar panel that provides conditions for different environmental forces and the performance of various sensors is available to meet your desired expectations.

A field experimental trial entitled “Effect of Nitrogen and Zinc levels on Yield and Economics of Mustard (Brassica juncea L.)” was undertaken during Rabi season (2021-2022) at Crop Research Farm, Department of Agronomy, SHUATS, Prayagraj (Allahabad) (U.P.). The size of experimental field is 243m2 and soil of experimental field was sandy loam in texture, nearly neutral in soil reaction (pH 7.1), low in organic carbon (0.28 %), available Nitrogen (225 kg/ha.), available Phosphorus (19.50 kg/ha.) and available Potassium (213 kg/ha.). The treatments consisted of 3 levels of Nitrogen N1 (60 kg/ha.), N2(80 kg/ha.), N3 (100 kg/ha.) and 3 levels of Zinc Zn1 (5 kg/ha.), Zn2 (10 kg/ha.) and Zn3 (15 kg/ha.). The experiment was laid out in Randomized Block Design with 9 treatments and 3 replications. The results revealed that the application of Nitrogen 100 kg/ha. + Zinc 15 kg/ha. recorded maximum siliqua length (5.20 cm), number of siliquae per plant (398.90), number of seeds per siliqua (24.40), test weight (3.53 g). The economic analysis demonstrates that Nitrogen 100 kg/ha. + Zinc 15 kg/ha. treatment produced higher grain yield (1.89 t/ha.), stover yield (3.11 t/ha.), gross returns (103650.00 INR/ha.), net returns (70264.08 INR/ha.) and B:C ratio (2.10). The major challenges experienced throughout the research work were mostly related to the use of nitrogen and zinc.

Introduction: The rib osteomyelitis is a very rare entity and it hardly accounts for 1% of all cases of osteomyelitis. In this case report, we are presenting a case of acute osteomyelitis of rib in a very young child, with a previous history of mediocre trauma over the chest wall. Case Report:It is a case report of a young boy, who had sustained the blunt injury over the chest wall. The X-ray was unremarkable. After sometime, he presented to the hospital with the pain over the chest wall. Now, the X-ray showed the signs of rib osteomyelitis. Conclusion: In children, the clinical presentation of rib osteomyelitis is very non-specific. Sometimes, the injury while playing, which is very usual in this age group may create the confusion. Hence, it may need high index of suspicion by the physician to include is as a possible diagnosis. Keywords: Rib, osteomyelitis, chest wall.

Kidney bean (Phaseolus vulgaris) productivity and nutritional quality are declining due to less nutrient accessibility, poor soil health, and indigent agronomic practices in hilly regions, which collectively led to a fall in farmer’s income, and to malnutrition in consumers. Addressing such issues, the present investigation was designed to assess the impact of Pseudomonas jesenii MP1 and Pseudomonas palleroniana N26 treatment on soil health, microbial shift, yield, and nutrient status of the kidney bean in the Harsil and Chakrata locations of Indian Central Himalaya. P. jesenii MP1 and P. palleroniana N26 were characterized as cold adaptive PGPR as they possessed remarkable in vitro plant growth promoting traits. Further, field trial study with PGPR treatments demonstrated remarkable and prolific influence of both strains on yield, kidney bean nutrient status, and soil health at both geographical locations, which was indicated with improved grain yield (11.61%–23.78%), protein (6.13%–24.46%), and zinc content (21.86%–61.17%) over control. The metagenomic study revealed that use of bioinoculants also concentrated the nutrient mobilizing and plant beneficial microorganisms in the rhizosphere of the kidney bean. Moreover, correlation analysis also confirmed that the plant growth-promoting traits of P. jesenii MP1 and P. palleroniana N26 are the basis for improved yield and nutrient status of the kidney bean. Further, cluster and principal component analysis revealed that both P. jesenii MP1 and P. palleroniana N26 exhibited pronounced influence on yield attributes of the kidney bean at both the locations. At the Harsil location, the P. jesenii MP1-treated seed demonstrated highest grain yield over other treatments, whereas at Chakarata, P. jesenii MP1, and P. palleroniana N26 treatment showed almost equal enhancement (~23%) in grain yield over control. The above results revealed that these bioinoculants are efficient plant growth promoters and nutrient mobilizers; they could be used as green technology to improve human health and farmer’s income by enhancing soil health, yield, and nutrient status of the kidney bean at hilly regions.

We present the numerical results of the structure and dynamics of the self-bound ground state arising solely because of the presence of beyond mean field quantum fluctuation in spin-orbit coupled binary Bose-Einstein condensates in one dimension. Depending upon spin-obit (SO) and Rabi couplings, we observe that the ground state exhibits either quantum-bright (plane) or quantum-stripe soliton nature. We find an analytical soliton solution for non-zero SO coupling that matches quite well with the numerical results. Further, we investigate the dynamical stability of these solitons by adopting three protocols, such as (i) adding initial velocity to each component, (ii) quenching the SO and Rabi coupling parameters at initial and finite time, and (iii) allowing collision between the two spin-components by giving equal and opposite direction velocity to them. Many interesting dynamical features of the solitons, like, multi-fragmented, repelling, and breathing in time and space-time, are observed. For given Rabi coupling frequency, the breathing frequency of the soliton increases upon the increase in SO coupling, attaining a maximum at the critical SO coupling where the phase transition from the bright to stripe soliton occurs. We observe that the maximum breathing frequency exhibits power law dependence on the Rabi coupling frequency with an exponent $\sim 0.16$., Comment: 17 pages, 18 figures, typos corrected, clarity of some figures got enhanced

Each round of clustering generally uses a single method to perform the task of clustering. For the enhancement of network capability to better manage the network resources, we extend a new multi-clustering algorithm (EEFMCP) in this paper. We apply a fuzzy inference system to define the various rules in each execution round. Three types of input/ output combinations define the complete working of the clustering protocol. For each node, the different execution round adopts a specific input/output combination to calculate the chance value for cluster head (CH). Multiple input/ output combination of fuzzy variables has better control over the network dynamism. The network feature drastically changes due to the depletion of the energy of sensor nodes. A new closeness index is proposed and utilized for better CH selection. We compare EEFMCP with Low Energy Adaptive Clustering Hierarchy (LEACH), Cluster Head Election mechanism using Fuzzy logic (CHEF), Fuzzy Energy-Aware Unequal Clustering Algorithm (EAUCF), and Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH), the distinct algorithms useful for clustering in WSN. The substantial simulation work shows that the EEFMCP always performs better for different simulation scenarios.

The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.

Global terrorist activities increase with the evolution of various social media sites such as Facebook, Twitter, etc. Various organizations use a wide scope of network capabilities of social media to broadcast their information, propaganda, as well communicate their strategic objectives. So, by analyzing such growing terrorist activity over online social media using mining and analysis, various valuable insights can be predicted. This chapter approaches an effective way of analyzing such activities by identifying nearest nodes in the network. The terrorist network mining algorithm has assisted by successfully achieving terrorist activities and their behavior on nodes of social network using centrality algorithm. The algorithm works in two phases: 1) fuzzification of data to measure centrality between nodes in the network and 2) applying genetic approach for the optimization of data and to increase the searching capability for appropriate cluster centers.

In the present article, the model of forced flow and heat transfer in a non-Newtonian Reiner-Rivlin fluid between two infinite rotating disks in the presence of a magnetic field has been studied analytically. We converted the higher-order, partial differential equations (PDEs) into a system of nonlinear ordinary differential equations (ODEs) of flow and heat transfer by using the Von Karman similarity transformation. Homotopy Perturbation Method (HPM) is adopted to solve the system of nonlinear ODEs associated with boundary conditions. The viscous dissipation function is included in the energy equation. Therefore, the differential equation of the energy equation involves the Eckert number. The influence of physical parameters like Reynolds number, visco-inelastic parameter, forced parameter, Prandtl number, magnetic field on radial, transverse, axial velocity components, and temperature profile are discussed in detail. A comparison of the results obtained by HPM is made with numerical results, which proves the accuracy of HPM. The Nusselt number on the lower and upper disks are computed and presented graphically. The shear and couple stress on the lower disk is also discussed. The Maple software package is used for numeric and symbolic calculation of velocity components and temperature profiles.

The two proposed design microstrip patch antenna as a sensor are analysed with the help of CST software for detecting the moisture. The proposed design is T-Shape rectangular shape with slotted in the ground plane and without slotted in the ground plane. It is operated at two frequencies 2.2GHz and 4.6GHz. It is designed on FR4 substrate and thickness is 1.676mm. The moisture can be detected with the help of oven drying technique. It will be helpful for measuring the moisture content. This technique is very easy, less time consuming, versatile and compact. The ground parameter of the proposed design is 20mm, as well as the length and width of the patch dimensions are 15mm and 10mm, which will be very portable. The designs is simulated on CST software and gets the good return loss, which is -16dB at 2.2GHz and -45dB at 4.6GHz. The gain is also the important parameter of the sensor; the proposed slotted ground plane design gain is 4.99dB at 4.6GHz as well as without slotted ground plane design gain is 5.1dB at 4.6GHz. It will be fabricated and measure the moisture in rice grains with the help of vector network analyser. The calibration equation and regression equation is also the important role of detecting the moisture content.

The microgrid with renewable energy sources (RES) is capable to fulfill the local energy demand, and can inject the remaining energy in to power grid at distribution level. It can mitigate the problem of greenhouse gases emission too. This paper discussed the optimal design and simulation of grid connected micro grid for a residential building of the Gwalior, Madhya Pradesh region, considering solar photovoltaic system. A model is proposed and simulated using Homer energy software. Simulation results reveals that the proposed residential microgrid is cost effective as compared to the utility grid in fulfilling the full load demand. It is calculated that the cost of solar generation is 0.081 $/kWh while for the grid it is 0.099 $/kWh for the considered region. Further, impact of proposed grid on the environment is also analyzed, it is also observed the proposed grid design inject the negligible harmful greenhouse gases in the environment.

The morphological and visual properties of Zinc Magnesium Aluminate (Zn0.67 Mg0.3 Al2O4) nanophosphor impured with Dysprosium (Dy3+) of 0.03 mol were reported in the present paper. Urea (NH2-CO-NH2) was used as fuel, Zn0.67Mg0.3 Al2O4(Pure)& Zn0.67Mg0.3 Al2O4 doped with 0.03 mol of Dysprosium (Dy3+) nanophosphors were synthesized through a solution combustion technique. The emergence of Zn1−xMgx Al2O4(Pure) & Zn1−x−yMgxAl2O4:YDy (x = 0.3 & Y = 0.03 mol) were confirmed by urea-nitrate incorporation through combustion synthesis manner at 550 °C. The morphological properties of Pure and impure ZnMgAl2O4 nanocrystallites were investigated through X-ray diffraction (XRD) having Cu Kα [40 kV, 15 mA X-ray] at λ = 1.541862°A, the stage recognization and interposed stage emergence were established through the appearance of the heterojunctions peak at 2θ = 36.68◦ and Fourier Transform Infrared (FT-IR) Spectroscopy (PEService). An Optical energy gap(Eg) of 1.499 eV calculated by Tauc Plot Method confirmed that Zn1-x-yMgxAl2O4, may be semiconducting materials. The result proved that the Zn-MgAl2O4 with an average particle size of 25.12 nm and 24.90 nm for pure and impure nanophosphors are crystalline in nature. The nanophosphors size were calculated by Scherer’s equation.

A triple-band antenna integrated with three band-pass filters for 1.6 Gigahertz, 2.4 Gigahertz and 5.2 Gigahertz is presented. The Filtering antenna structure consists of three coupled line band-pass filters and a planar tri-band antenna with the partial ground of 9 mm × 25 mm. The proposed integrated system provides good filtering performance and these three frequencies are extremely helpful for L band, S band and C band applications. The projected antenna filter is fabricated on FR-4 epoxy substrate and its dielectric constant is 4.4, and thickness is 1.6mm

The morphological and visual properties of Magnesium Zinc Aluminate (Mg0.67Zn0.3 Al2O4) nanophosphor impured with Dysprosium (Dy3+) of 0.03 mol were reported in the present paper. Urea (NH2-CO-NH2) was used as fuel, Mg0.67Zn0.3 Al2O4(Pure)& Mg0.67Zn0.3 Al2O4 doped with 0.03 mol of Dysprosium (Dy3+) nanophosphors were synthesized through a solution combustion technique. The emergence of Mg1-xZnx Al2O4(Pure) & Mg1−x−yZnx Al2O4:YDy(x = 0.3 & Y = 0.03 mol) were confirmed by urea-nitrate incorporation through combustion synthesis manner at 550◦C. The morphological properties of Pure and impure MgZnAl2O4 nanocrystallites were investigated through X-ray diffraction (XRD) having Cu Kα [40 kV,15 mA X-ray] at λ = 1.541862°A,the stage rec- ognization and interposed stage emergence were established through the appearance of the heterojunctions peak at 2θ = 36.73◦ and Fourier Trans- form Infrared (FT-IR) Spectroscopy(PEService).An Optical energy gap(Eg) of 1.23 eV calculated by Tauc Plot Method confirmed that Mg1−x−yZnxAl2O4, may be semiconducting materials.The result proved that the MgZnAl2O4 with an average particle size of 28.34 nm and 21.99 nm for pure and impure nanophosphors are crystalline in nature.The nanophosphors size were calculated by Scherer’s equation.

Pure and nano-ZnO doped PVDF-PMMA polymer blend-nanocomposites have been investigated for charge transfer effects in electrochemical sensors. Fourier transform infrared spectroscopy (FTIR) was used to characterize and elucidate the structure of the blended and doped polymers. Polymer microstructure and morphology were studied to establish the structure activity relations. Cyclic voltammetry (CV) furnishing information as a function of energy scan was the method of choice to test electronic properties of the modified polymers. Efficiency of CV results from its approach of rapidly observing the redox behavior of the analyte over a wide potential range. Electrode reactions of the intermediates and the products formed during the forward scan have been studied for sulphonamide azomethines. Comparative electro-activity of the PVDF-PMMA polymer blend-nanocomposite electrets electrode with the glassy carbon electrode was performed.

Background: Despite recent advances in understanding spinal mechanics, injury mechanisms, improved instrumentation, better imaging techniques and improved rehabilitative care, the management of spinal cord injury patients remain controversial. In this study we try to analyse neurological outcomes in cases of incomplete spinal cord injuries (SCI) of dorsolumbar spine treated by decompression and posterior fixation at our centre. Methods: 36 patients with incomplete SCI due to dorsolumbar fractures which were operated in our centre between May 2017 and January 2019 were included in our study. The neurological status of these cases was assessed using American Spinal Injury Association (ASIA) Impairment Scale before and after surgery and at follow ups of 1 month, 2 month, 3 month and 6 months. Results: The mean age of patients in our study was 33.7 years with most patients in 26-40 years that is the young working age group. There were 22 males and 14 females. Most common cause leading to SCI was found to be fall from height. 24 cases out of 36 showed improvement of 1 or more grades in their ASIA Impairment Scale. Conclusion: Operative fixation of dorsolumbar fractures achieves early mobilisation and rehabilitation in cases of Incomplete SCI. Our study indicates that neurological status also improves in such patients after operative fixation.

Objective: To evaluate the functional and radiological outcome of balloon kyphoplasty and to endorse the unilateral single balloon extrapedicular kyphoplasty as practically more feasible and safer method in comparison to the conventional methods.Methods: Totally, 81 patients were presented to our center with osteoporotic vertebral compression fracture. Among these, 59 patients (61 vertebrae) were enrolled with stable wedge osteoporotic compression fracture. Pre-operatively percentage of vertebral height loss and kyphotic angle were calculated and single balloon extrapedicular kyphoplasty was performed in all cases.Results: Postoperatively, anterior vertebral height improved to 79.61% of normal subjects. In our study, the mean segmental kyphosis correction following balloon kyphoplasty was 14.27°. Overall incidence of cement leak in our study was 15.25%.Conclusion: Although we encountered the few difficulties, but this technique holds the safety and feasibility measures. Furthermore, it is effective in restoring anterior vertebral height, alignment and angle of kyphosis.

BackgroundWhile the biomarkers of COVID-19 severity have been thoroughly investigated, the key biological dynamics associated with COVID-19 resolution are still insufficiently understood.Main bodyWe report a case of full resolution of severe COVID-19 due to convalescent plasma transfusion in a patient with underlying multiple autoimmune syndrome. Following transfusion, the patient showed fever remission, improved respiratory status, and rapidly decreased viral burden in respiratory fluids and SARS-CoV-2 RNAemia. Longitudinal unbiased proteomic analysis of plasma and single-cell transcriptomics of peripheral blood cells conducted prior to and at multiple times after convalescent plasma transfusion identified the key biological processes associated with the transition from severe disease to disease-free state. These included (i) temporally ordered upward and downward changes in plasma proteins reestablishing homeostasis and (ii) post-transfusion disappearance of a particular subset of dysfunctional monocytes characterized by hyperactivated Interferon responses and decreased TNF-α signaling.ConclusionsMonitoring specific subsets of innate immune cells in peripheral blood may provide prognostic keys in severe COVID-19. Moreover, understanding disease resolution at the molecular and cellular level should contribute to identify targets of therapeutic interventions against severe COVID-19.

Preparation and characterization of pure and nano-ZnO doped PVDF-PMMA polymer blend-nanocomposites for investigating the charge storage and transfer effects have been studied. Fourier transform infrared (FTIR) was used for characterization and structure elucidation of the blended polymers. Polymer microstructure, chain conformation and polymer morphology were studied to establish the structure activity relations. Cyclic voltammetry (CV) was chosen to study the electronic properties of the modified polymers as it furnishes information as a function of an energy scan. The effectiveness of CV results from its capability of rapidly observing the redox behavior of the analyte over a wide potential range. Electrode reactions of the intermediates and the products formed during the forward scan have been studied for sulphonamide analogues of synthesized azomethines. Comparative electro activity of plain glassy carbon electrode and the PVDF-PMMA polymer blend-nanocomposite electrets modified electrode was studied.

In construction cement mortar is consumption is about 30% of the volume of masonry. The durability and strength of masonry depends upon the strength of mortar also. In masonry mortar is laid between the courses of bricks hence its good compressive strength is essential requirement. In this investigation the compressive strength of cement mortar is tested with Nano Aluminum oxide. The water cement ratio is taken 0.5 and cement sand ratio is taken 1:3 by weight. It is found that while Nano aluminum oxide added 0.5–2% of the weight of cement compressive strength increases up to 16%.

Compressive strength and tensile strength determination is essential for any engineering material used in construction work. In this experimental work tensile strength of mortar is determined with Nano ZnO. The water cement ratio is taken 0.5 and cement fine aggregate ratio is 1:3. It is found that while Nano zinc oxide added 0.5–2% strength increases at 0.5 and 1% at higher curing.

A total of 144 patients of temporary loop ileostomies were treated with early closure at 4 to 6 weeks from its construction. It was found to be feasible, safe and advisable specially on select group of patients with low rate of complications and the results can be compared to classical delayed closure. METHODS This study was carried out in Department of Surgery, Patna Medical College on patients of waiting for closure of loop ileostomies done for both in emergency and routine settings. After a careful selection of cases depending on nutritional status and absence of active infection, closure of ileostomy was done 4 to 6 weeks after the index operation. The post-operative course was closely observed and followed up. RESULTS A total of 144 patients underwent early closure at 4-6 weeks from the construction of ileostomy with the mean duration of closure of 38.2 days. In most of the patients the intestinal loops were mobilised without any significant difficulty and intestinal continuity restored with linear staplers. Most common post-operative complication was wound infection in 38.9% of cases. Other complications included prolonged ileus in 2.8% cases, enterocutaneous fistula in 5.6% and incisional hernia in 6.9% of the cases. None of the patients developed intra peritoneal collection or significant anastomotic leak requiring re laparotomy. No mortality was recorded in the study subjects. The patients were discharged from the hospital ranging from 7-23 days with the average of 9.2 days from the time of stoma closure. Overall, the results may be comparable to those of the delayed conventional closure but at the same time much more convenient to the patient.

The MIMO technology fulfils the present demand of higher data rate and capacity in smart cities. The single antenna in a transmitter and receiver system has low data rate and some reliabilities issues which can be easily solved by the use of multiple antenna wireless technology. The connectivity issue also creates problem in smart cities due to large users. The use of MIMO technology has significant advantages which fulfils all the present needs of wireless technology. In this paper a wideband MIMO antenna has been designed which covers 4.59-5.68 GHz WLAN band. The MIMO antenna resonates at 5.14 GHz and produced 1 GHz of bandwidth. The overall size of the antenna is 32 x 20 mm2. FR4 material is chosen for substrate and PEC material is considered for radiator and ground of MIMO antenna. The proposed antenna has isolation of less than -11.8 dB in the entire proposed frequency band. The proposed MIMO antenna gain of 2.59 dBi and directivity of 3 dBi was obtained. The ECC of less than 0.015 is observed in the frequency band while at resonant frequency 5.14 GHz it was 0.0002. The other antenna performance parameters for MIMO antenna were also discussed

Humans are utilizing one of the biggest networks of social communication by the web, the development of internet gives a way to the rise of the Online social network that assists users to communicate and accommodate between the users, thus sharing and conveying their emotions and information in an online social network. As users in the online social network are growing and performing several activities, they want trusted users or sources. Trusted users or source of information is one of the primary requirements due to security and privacy of the information. Finding future trusted user is called trust prediction. Several tools are available for predicting trust between users in an online social network. The main aim of trust prediction is to find the trust association between the users in the online social network. In the following study, researchers have at reviewed the diverse trusted-link prediction techniques and evaluated them on the basis of the provided standard dataset provided to us

A microstrip patch sensor is designed and analysis for determination of moisture content in Hevea Rubber Latex. The microstrip patch sensor was designed to operate at microwave frequency range 2.8GHz, 7.8GHz and 8.8GHz on FR4 substrate with 4.4 permittivity and 1.676mm thickness. The width and length of the rectangular patch antenna was 30mm and 35mm, respectively as well as defected ground structure in the ground and microstrip line feed used because of easy to feed and low spurious feed radiation with the simulated by using the CST software. The actual values of moisture content will be obtained by using standard oven drying method and measured by the vector network analyzer

The proposed design is analysis for determination of moisture content at operated microwave frequency 1.7GHz with the good return loss -15dB. The proposed design will be fabricated on FR-4 substrate which is low cost as well as substrate with 4.3 permittivity and 1.676mm thickness. The width and length of the proposed design was 40mm and 40mm, respectively as well as defected ground structure (DGS) in the ground and microstrip line feed used because of easy to feed and low spurious feed radiation with the simulated by using the CST software. The actual values of moisture content will be obtained by using standard oven drying method and measured by the vector network analyzer

Cement mortar is unavoidable part of any construction. Strength and durability of mortar is necessary for long life of structure. At the time of preparation of mortar, various admixtures are added for enhancement of its various engineering properties. Addition of admixtures alters it workability, strength, etc. In this experimental study, it is observed that by adding some nano-materials, the flow value of the mortar decreases, but by increasing the content of the nanoparticles, workability increases slightly. Similarly by adding polymer PVA, the workability increases than the control mix, when content of polymer is 2% of cement. The workability of mortar increases with addition of nano and polymer when both are added.

The predominant goal of autonomic computing is to manage the computer networks and software systems using some adaptive technologies to implement the services efficiently. Such a computing model becomes very useful in the networking scenarios, where a variety of services are offered to a wide range of audience with different user specifications such as cloud. Autonomic cloud computing (ACC) allows the cloud provider to manage the cloud services intelligently in an autonomous way with little or no human intervention. But, from implementation point of view, ACC faces numerous challenges such as availability, reliability, integration, security, interoperability, and resource management. Based on the literature study, it has been found that management and availability of cloud computing resources are the two major challenges. This chapter firstly offers a review of the resource management (provisioning and scheduling) techniques in an ACC environment. Secondly, the security-based methods for resource management to achieve the availability are discussed. In addition to this, an analysis of the relevant solutions in each category is also provided.

In this paper, the Zagreb index is used to develop the clustering protocol for Wireless Sensor Network. Zagreb index utilizes the node density feature of a wireless sensor network. Node density has many inherent features and also these feature changes dynamically with a decrease in the number of nodes alive. Network performance suffers significantly in terms of hot-spot issues arise due to multi-hop communication. Mathematical analyses of a chemical compound at the molecular level are efficiently performed by using the Zagreb index. Zagreb ratio, a new parameter is proposed to calculate threshold value. The ratio of the second Zagreb index to the first Zagreb index is used to calculate the threshold of a node for CH selection. The proposed algorithm (ZIISEP) is compared with two other algorithms namely low energy adaptive clustering hierarchy protocol (LEACH), and Power efficient gathering in sensor information systems (PEGASIS). Simulation results show that ZIISEP performs better than LEACH, and PEGASIS in all situations like the first node die (FND), half node dies (HND), the last node dies (LND), and average energy spent per round (AVG_PR).

Fuzzy clustering generally uses more than one input parameters to determine specific output parameters for cluster head (CH) selection. The output parameters are restricted to one or two parameters for a combination of certain specific input parameters. Different combination of input parameters computes different output values. In this paper, we extend a new multi-input multi-output clustering algorithm (OCSSP) to properly utilize the node's energy and improve the longevity of the network. Rules of the fuzzy inference system are the basis for a better outcome of the network. The rules are finalized by performing many rounds of simulations. There are three input parameters and three output parameters to completely define the clustering scenario. We perform double restriction on the size of each cluster to load balance the overall network properly. The capability to bear the load by a sensor node changes rapidly with the depletion of energy. The new proposal to restrict the size of a cluster performs better CH nomination. We compare OCSSP with Low Energy Adaptive Clustering Hierarchy (LEACH), and Distributed Unequal Clustering using Fuzzy logic (DUCF) the well-known clustering algorithms and valuable to perform clustering in WSN. The extensive simulation work proves that the OCSSP achieves significant performance gain.

Alzheimer's Disease (AD) is one of the most common types of diseases amongst older adults. The primary reason for death in senior citizens is Alzheimer's related. To prevent Alzheimer's and provide early treatment, we have to accurately diagnosis Alzheimer's Disease and its prophase, which is called Mild Cognitive Impairment (MCI) in the healthcare sector. In this study, we have used seven machine learning classification methods for the prediction of Alzheimer's Disease. To recognize the type or stage of disease, it is essential to classify medical data and potentially develop a prediction model or system. The framework that we have developed consists of machine learning methods with Particle Swarm Optimization (PSO) and has been successfully applied to the classification of AD and dementia. For the prediction of Alzheimer's Disease, we have used seven machine learning Algorithms such as Support Vector Machine Classification, Random Forest Classification, XgBoost Classifier, Decision Tree Classification, Adaboost Classifier, K-Neighbour Classifier, and Logistic Regression. Our best-proposed method is the Random Forest Classifier, which achieves the greatest accuracy of 85.71%.

We report the electronic structure and magnetic properties of Co