An Intelligent Online Vehicle Tyre Pressure Monitoring System

This paper aims at designing an intelligent online tyre pressure monitoring system (TPMS). The objective of this work is to display the tyre pressure of the tyre and also give the indication about the quality. The data corresponding to the tyre pressure is obtained with a high precision MEMS pressure sensor. The output of the MEMS pressure sensor is amplified and transmitted to the processing unit placed on the dash of the vehicle using wireless communication (RF). The processing is carried on using fuzzy logic algorithms on LabVIEW platform. The output pressure is displayed along with the indicator representing the quality. Indicator is green when the tyre pressure is in the desired range specified by the manufacturer. Yellow when the pressure has dropped and need to be inflated. Red indicates tyre pressure is below the safety driving conditions. After testing and validating the entire system using LabVIEW. The entire code is converted to verilog code and dumped on to FPGA chip (Spartan 3E) using FPGA module of LabVIEW with CompactRIO, for implementation of FPGA chip on real time system.DOI:http://dx.doi.org/10.11591/ijece.v2i3.23

Prediction of Groundwater Quality Index in the Selected Divisions of Srikakulam Using Artificial Neural Networks Approach

Applicability of artificial neural network (ANN) modelling in predicting the water quality index (WQI) and in turn to ascertain the suitability of the water for human consumption has been presented in the paper. In the light of the present study, seventy-nine (79) groundwater samples were collected from two mandals (divisions) Veeraghattam (VGT) and Palakonda (PLKD) and analyzed for physicochemical parameters during the pre-monsoon and post-monsoon seasons of 2015 and 2016. In computing the WQI, physicochemical parameters such as  pH, EC, TDS, TH, Ca, Mg, chlorine, fluoride, nitrite, DO and TA have been considered. From the results it was found that the WQI varies from 43.9 to 46.5 and 31.4 to 34.7 in VGT and PLKD divisions respectively. ANN tool in MATLAB has been used to predict the WQI. Back propagation methodology and LM algorithm has been chosen for the study. To train the network, physicochemical parameters have been given as inputs and the already computed WQI values as output. A particular season has been chosen for testing the network. After simulating the network, the results obtained were compared with the experimental value and found to have an error of 0.6%. It is inferred that the chosen model fits apt for the prediction of WQI in the present study

Designer carbon nanotubes for contaminant removal in water and wastewater: A critical review

The search for effective materials for environmental cleanup is a scientific and technological issue of paramount importance. Among various materials, carbon nanotubes (CNTs) possess unique physicochemical, electrical, and mechanical properties that make them suitable for potential applications as environmental adsorbents, sensors, membranes, and catalysts. Depending on the intended application and the chemical nature of the target contaminants, CNTs can be designed through specific functionalization or modification processes. Designer CNTs can remarkably enhance contaminant removal efficiency and facilitate nanomaterial recovery and regeneration. An increasing number of CNT-based materials have been used to treat diverse organic, inorganic, and biological contaminants. These success stories demonstrate their strong potential in practical applications, including wastewater purification and desalination. However, CNT-based technologies have not been broadly accepted for commercial use due to their prohibitive cost and the complex interactions of CNTs with other abiotic and biotic environmental components. This paper presents a critical review of the existing literature on the interaction of various contaminants with CNTs in water and soil environments. The preparation methods of various designer CNTs (surface functionalized and/or modified) and the functional relationships between their physicochemical characteristics and environmental uses are discussed. This review will also help to identify the research gaps that must be addressed for enhancing the commercial acceptance of CNTs in the environmental remediation industry

Protein coated gold nanoparticle synthesis: A mathematical model approach for proteasome inhibition

Cancer is characterized by the uncontrolled cell division and proliferation. As compared to normal cells, the NF-kB signaling pathway is highly active in multiple myeloma as well as in many other cancers. Inhibition of this pathway has been shown to undermine the survival of myeloma cells, making NF-kB an attractive therapeutic target. NF-kB is activated by ubiquitin proteasome complex which ubiquitinates and degrades its inhibitor, I-kB. Inhibiting I-kB degradation leaves NF-kB in an inactive state and hence proteasomal inhibitors have great potential in cancer therapy. The reduction in NFkB activity by proteasome inhibition reduces the cell proliferation and induces the apoptosis of multiple myeloma cells. The only proteasome inhibitor, bortezomib had been approved by US ? FDA and few drugs under clinical trials to treat multiple myeloma. Here we propose ubiquitin tagged monoclonal antibody to Gold nanoparticles (Ub-AuNPs) with core diameter of <2 nm has comparable dimensions of proteasome subunits, can evolve as potential novel proteasomal inhibitors (PIs). The gold acts as a theranostic agent and it is having excellent biocompatibility where it directly attached to the highly oxidative stress of cancerous cells. In this model as the monoclonal antibody is already ubiquitinated it easily finds its way to proteasome of myeloma cells. Further we use a mathematical model to analyze how these monoclonal antibody associated with gold nanoparticles enter to the target site for proteasome inhibition. And also gives an idea about proposed mechanism of inhibition by invading the proteasome and block the lumen of proteasomes, further inhibiting the degradation of I-kB. Thus, PIs has emerged as a powerful strategy for treatment of multiple myeloma (MM)

In silico screening for the interaction of small molecules with their targets and evaluation of therapeutic efficacy by free online tools

Pharmaceutical chemistry deals with the process of isolating organic compounds from natural sources or chemically synthesizing them in order to explore potential drugs. Drugs are small molecules, used to prevent or treat various diseases. Of several lead molecules, only few of them reach clinical trial phases and emerge as effective drugs, whereas the majority will be eliminated at different stages. On the other hand, due to the lack of proper identification of their pharmacokinetic properties and biological potential, many small molecules fail to reach this stage. This could be because of the fact that it is either time consuming and costly or there is full of uncertainty due to lack of analyses that are necessary for the confirmation. In the post-genomic era, computational methods have been implemented in almost all stages of drug research and development owing to the drastic increase in the available knowledge about small molecules and the target biomacromolecule. This includes identifying the suitable and specific targets for drug candidates, lead discovery, lead optimization and ultimately preclinical phases. In this context, numerous websites have become highly valuable and influence the drug development and discovery process. Here, we have attempted to bring together some of the online computational approaches and tools that are available to facilitate research efforts in the field of drug discovery and drug design. The output information from these tools is extremely helpful in selecting and deciding about the future direction or specific path needed to be followed by the researchers. These computational methods are indeed help to focus the intended research in the right direction. As detailed in this review, the information provided about the servers and methods should be useful throughout the process of screening of synthesized or chemical database originated small molecules to find the appropriate targets along with the active sites without depending on any commercial tools or time-consuming and costly assays. It should however be remembered that the bioinformatics-based prediction cannot completely replace the wet lab data of chemical compounds or specific assays

Stochastic state-feedback control using homotopy optimization and particle filtering

In this paper, a method of designing control inputs for stochastic nonlinear processes under state-feedback is proposed. The objective is to determine a control input that minimizes the expected value of the integral of error between the set-point and the states. Since the states may not be measured, they are estimated using a particle filtering algorithm. The optimal control design is then reformulated as a parameter estimation problem using control vector parameterization where the inputs are considered as a nonlinear function of the error between the state estimates and the set-point. The parameters are then computed through a homotopy based optimization method. The control performance resulting from proposed homotopy based optimization method is compared with that of direct optimization and an existing nonlinear control method on a Solid Oxide Fuel Cell (SOFC) stack model. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Error Bounds for Identification of a Class of Continuous LTI Systems

The main problem in identification of continuous LTI systems is the lack of derivative information of the outputs. If all the derivatives are known exactly, a least squares approach is sufficient to obtain the parameter estimates. In this paper, we propose a method which can provide theoretical bounds on the error in the parameter estimates assuming only a few derivatives are known accurately. The error bounds are given for the finite data case as opposed to the asymptotic regimes considered in existing identification approaches. The method is based on transforming the differential equation into the Laplace domain to obtain a linear-in-parameter form for the ODE parameters. As the system is not well conditioned, the method of Tikhonov Regularization is applied to find an approximate solution. Since, exact derivative information is seldom known in practice, B-spline approximation is incorporated in the simulation study where the accuracy of method is demonstrated

Antiproliferative potential, quantitative structure-activity relationship, cheminformatic and molecular docking analysis of quinoline and benzofuran derivatives

Quinoline and benzofuran moieties are commonly used for the synthesis of therapeutically beneficial molecules and drugs since they possess a wide range of pharmacological activities including potent anticancer activity as compared to other heterocyclic compounds. Many of well-known antimalarial, antimicrobial, anti-helminthic, analgesic, anti-inflammatory, antiprotozoal, and antitumor compounds contain quinoline/benzofuran skeleton. The aim of this study was to analyze ten new quinoline and eighteen benzofuran derivatives for carcinoma cell line growth inhibition and to predict possible interactions with the target. The anticancer activity of these compounds against colon cancer (HCT-116) and triple-negative breast cancer (MDA-MB-468) cell lines was determined and performed molecular docking to predict the possible interactions. Among ten quinoline derivatives, Q1, Q4, Q6, Q9, and Q10 were found to be the most potent against HCT-116 and MDA-MB-468 with IC50 values ranging from 6.2-99.6 and 2.7-23.6 μM, respectively. Using the IC50 values, a model equation with quantitative structure activity relationship (QSAR) was generated with their descriptors such as HBA1, HBA2, kappa (1, 2 and 3), Balaban index, Wiener index, number of rotatable bonds, log S, log P and total polar surface area (TPSA). The effect of benzofuran derivatives was moderate in cytotoxicity tests and hence only quinolines were considered for further analysis. The molecular docking indicated the mammalian / mechanistic target of rapamycin (mTOR), Topoisomerase I and II as possible targets for these molecules. The predicted results obtained from QSAR and molecular docking analysis of quinoline derivatives showed high correlation in comparison to the results of the cytotoxic assay. Overall, this study indicated that quinolines are more potent as anticancer agents compared to benzofurans. Further, compound Q9 has emerged as a lead molecule which could be the base for further development of more potent anticancer agents