Search

stract: Fake products are a big problem since it's difficult to tell the difference between real and fake goods in today's environment. However, some people create these false goods for financial gain alone, without considering how they will affect customers or how they will influence a company's reputation, earnings, or sales. With blockchain technology, it is possible to verify the validity of a product and determine if it is real or false. Blockchain is a popular technology that is used in many applications. Information is maintained in the form of blocks in numerous databases that are connected by chains using blockchain technology, and no permission from outside users is needed. The immutability and security of blockchain technology are advantages.

FeCrYZ (Y = Ti, Zr, & Hf and Z = Sn, and Sb) quaternary Heusler alloys for data storage, energy conversion and optoelectronics applications.

Geopolymers are promising cement replacement materials as their use results in a considerable reduction of CO2 emissions. Geopolymer Fly ash (GF) is a widely used geopolymer due to its low cost and waste management achievement. The compressive strength of GF depends on variables such as curing time, curing temperature, NaOH molarity, the ratio of sodium silicate to sodium hydroxide, the ratio of fly ash to alkaline solution, etc. Artificial Neural Networks are employed to predict the strength of GF due to their accurate prediction capability as well as saving time and cost of experimental work. The obtained Root Mean Square Error (RMSE) and correction coefficient (R2) values were 4.47 and 0.972 respectively. The results illustrate the ability of the ANN model to be used as an efficient tool in predicting the compressive strength and determining the optimal values of GF parameters. The maximum strength of GF was observed for 2 hours curing time at 100°C, molarity of 10, fly ash to alkaline solution ratio of 3, and sodium silicate to sodium hydroxide ratio of 1.

Storm Motors Is An E-Commerce Company Who Act As Mediators Between Parties Interested In Selling And Buying Pre-Owned Cars. They Have Recorded Data About The Seller And Car Details, Registration Details, Web Advertisement Details, Make And Model Information And Price. The Company Wishes To Develop An Algorithm To Predict The Price Of Pre-Owned Cars Based On Various Attributes Associated With The Car To Make A Sale Quickly, If The Price Is Reasonable And Satisfies Both The Seller And Buyer, By Comparing The Price Of Various Car Models Based On Car Features To Improve Their Business. In This Paper, We Have Conducted A Comparative Study Using Machine Learning Algorithms Like Linear Regression And Random Forest Algorithms Which Is Implemented With Jupyter Note Book. The Study Shows That Linear Regression Algorithm Performance Is More Than Random Forest Algorithm. We Have Also Experimented With Auto Ai Experimentation In Ibm Cloud Watson Studio, Which Automatically Builds The Best Predictive Model By Comparing With Other Algorithm, With Accurate Measures. In This Auto Ai Experiment We Have Found That Linear Regression Is Performing Better Than Ridge Algorithm And Random Forest. The Main Objective Of This Paper Is To Find The Best Predictive Model For Predicting Pre-Owned Car Price.

Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient’s health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object Notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based Sematic (SS) ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the Message Queuing Telemetry Transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.

The characteristics of magnetohydrodynamics hybrid nanofluid flow in an irregular wall are explored theoretically. Since the movement of nanoparticles significantly impacts the flow, the Brownian motion and thermophoresis effect are incorporated in this investigation. The flow model is formulated through the non-dimensionalized nonlinear partial differential equations which are solved by means of the finite difference method. Using a judicial combination of quasilinearization technique and Thomas algorithm, the system of equations is solved for the velocity variation, heat transfer, and concentration of hybrid nanofluid. The range of parameters considered in the study is varied as required to obtain the physically meaningful flow situations. The present study is restricted only to positive values of the physical parameters. The wall shear stress, heat, and mass transfer gradients on the surface are also computed. The various results show that the effect of the magnetic field makes the boundary layer thinner and enhances the heat transfer rate. It is also found that the thermophoresis effect and Brownian motion are capable to reduce friction near the wavy wall. In addition, deduction in the values of the magnetic parameter, the volume fraction of nanoparticles, and amplitude of wavy wall result into enhancement in the heat transfer rate. The physical dynamics behind these interesting result is discussed in detail.

There is a significant increase in the number of mucormycosis cases in the setting of the coronavirus disease 2019 (COVID-19) pandemic. This study was undertaken to understand the clinical profile of such patients and the risk factors associated with increased mortality of this already deadly infection.A retrospective observational study was conducted by including microbiologically confirmed cases of mucormycosis with the background of COVID-19 infection (COVID-19-associated mucormycosis [CAM]). Data was segregated into those of survivors versus non-survivors and the two groups were analyzed for various risk factors. Early and late CAM were also compared.The case fatality rate was 21.73% (5/23 patients). Case fatality in early CAM was 33.3% versus 9.1% in late CAM. Rhino-orbital-cerebral mucormycosis (Early CAM had a higher case fatality rate. Patients had better recovery rates with sequential or dual antifungal treatment. The raised incidence and mortality in the COVID-19 pandemic is probably related to the COVID-19-induced immunosuppression with associated diabetes and excessive use of steroids.

Fabrication of electrode with graphene-based material has received widespread interest in the specialization of energy-related devices. Especially in supercapacitors, engineering electrode material with graphene shows promising performance with enhanced energy and power density. In this work, graphene-nickel sulphide (G-NiS) hybrid nanocomposite was synthesized successfully by solvothermal method. The phase purity of the materials were determined from XRD studies. SEM image reveals deposition of NiS nanoparticles over the surface of graphene oxide layer forming a flower-like structure. The G-NiS nanocomposite electrode shows an excellent specific capacitance of 409.2F/g at low current density of 5 A/g in the three electrode cell. A symmetric supercapacitor based on G-NiS nanocomposite exhibited a high specific capacitance of 136.2F/g at 5 A/g and excellent cycling stability with coulombic efficiency of 98.1% after 1000 cycles. The high performance of the nanocomposite is mainly attributed to the presence of the NiS nanoparticles deposited on the graphene surface which readily produce more active sites for electron transport. Thus, the as prepared G-NiS nanocomposite could be an advanced supercapacitor electrode material.

The tight-binding linear muffin-tin orbital with the atomic sphere approximation (TB-LMTO-ASA) method was used to investigate the possibility of half-metallic ferromagnetism in CdI2-type MX2 (M = V, Nb; X = Cl, Br, I) compounds. The spin-polarization calculations show that VX2 (X = Cl, Br, I) compounds exhibit half-metallic ferromagnetism with a magnetic moment of 3.0 µB per formula unit as expected at their equilibrium volume. Their half-metallic behavior persists within a small range of volume compression. However, the NbX2 (X = Cl, Br, I) compounds show non-magnetic behavior at their equilibrium volume, whereas these compounds become ferromagnets under large volume expansion. In addition to this, NbX2 (X = Cl, Br, I) compounds exhibit half-metallic property too. The variation of lattice constant with the magnetic moment for VX2 (X = Cl, Br, I) compounds is also analyzed. The magnetism arises mainly from the cationd-like states. The ground-state properties like equilibrium lattice parameters, bulk modulus, the heat of formation, magnetic moments, spin-flip-gap, and minority spin-band-gap are calculated and compared with available results. These materials may be useful in spintronic devices and other applications.

The structural, electronic, magnetic, and transport properties of LiMgPdSb-type novel quaternary Heusler alloys XRuCrZ (X = Co, Ni, Rh, and Pd; Z = Si and Ge) have been studied employing the first-principles calculation derived from density functional theory. From the total energy calculations, it is found that all these alloys are stable in the cubic α phase, and spin-polarized calculations show that all the alloys favor a ferromagnetic state. In addition, it is ascertained that XRuCrZ (X = Co and Rh; Z = Si and Ge) exhibits half-metallic ferromagnetic property. In addition, the temperature-dependent transport properties have been studied. The calculated dimensionless figure of merit for CoRuCrSi is 0.43. Our results show that these alloys will be suitable for data storage and energy conversion applications.