Charge Plasma Based Vertical Nanowire Tunnel Field Effect Transistor: Design and Sensitivity Analysis for Biosensing Application

Silicon - The device Dopingless Vertical Nanowire Tunnel Field Effect Transistor is simulated as a biosensor in this paper. The technique used is Charge Plasma for general doping in this device....     

Features of the exponential form of internal heat generation,Cattaneo–Christov heat theory on water-based graphene–CNT–titanium ternary hybrid nanofluid flow

To increase energy efficiency, the flow of fluids containing nanoparticles is crucial in industrial applications notably in nuclear reactors and nuclear system cooling. In light of this, this study examines the flow of a water-based ternary hybrid nanofluid (graphene, single-walled carbon nanotubes, and titanium dioxide) across a curved stretching sheet with suction. The non-Fourier heat flux model is also considered in the modeling. The existing partial differential equations are converted into ordinary differential equations through the use of similarity variables. These ordinary differential equations are then numerically solved using the Runge–Kutta–Fehlberg fourth- and fifth-order method along with a shooting approach. The collection of graphical findings for the key variables on the temperature and velocity profiles is investigated. Results reveal that the heat transport in ternary hybrid nanoliquid rises as the heat source/sink parameter rises. The Biot number influences the thermal profile positively, whereas the increasing curvature parameter values reduce heat transport. The curvature parameter has a positive impact on skin friction but the suction parameter has a negative impact on skin friction.     

Correction: Meta-heuristic endured deep learning model for big data classification: image analytics

Knowledge and Information Systems -     

Design and Analysis of Gate Overlapped/Underlapped NWFET Based Lable Free Biosensor

Silicon - In this paper, gate all around (GAA) nanowire P-channel FET label free biosensor is proposed with cavity. Proposed structure is label free so it doesn’t require selective material...     

Silver Hexafluoroantimonate‐Catalyzed Direct α‐Alkylation of Unactivated Ketones

A practically simple and direct α‐alkylation of unactivated ketones using benzylic alcohols has been achieved. The in situ formed acetals are the key for the success of the reaction. The catalyst, silver hexafluoroantimonate(V) (AgSbF₆) provides double activation by converting the ketone into an enol ether via acetal and generation of carbocationic center at the benzylic position of the benzylic alcohol. The alcohols include benzylic propargyl alcohols, cinnamyl alcohols, and diarylmethanols.

     

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren’t getting the most out of their land because they don’t use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.