Irregularity Finding in Roads Conditions using Data Mining: A Survey

Road conditions play a vital role now days. Irregularity in road surface can cause accidents, vehicle failure and discomfort in drivers and passengers. Governments spend lots of amount every year in maintenance of roads for keeping roads in proper condition. But more maintenance work can increase the traffic, causing disturbance in road users. To avoid disturbances caused by road irregularity,this system can detect road irregularity using Smartphone sensors. The approach is based on data mining. In this, it used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final outputs show that it is possible to find out road irregularity

EFFECT OF TOLL-LIKE RECEPTOR INHIBITOR IMIQUIMOD ON IL1R1 INTERACTION WITH IL-1RA AND ITS SNP VARIANT-AN IN SILICO APPROACH

Objective: Interleukin 1 receptor antagonist (IL1Ra) acts as an antagonist to Interleukin 1 beta (IL1β) signalling in maintaining homeostasis. A loss of function due to Single Nucleotide Polymorphism (SNP) occurrence in IL1Ra can lead to dysregulated state as seen in autoimmune disease pathogenesis. The current study aims at achieving conformational stability in the IL1R1-IL1Ra_SNP complex by introducing a ligand into the region apart from the active site of Interleukin 1 receptor type1 (IL1R1).Methods: Protein-protein docking was performed using ClusPro, for IL1R1 with IL1β, IL1Ra and IL1Ra_SNP variant to study the difference in the interaction between the complexes. A known inhibitor, Imiquimod was docked using Glide, to the Il1R1-IL1Ra_SNP complex using flexible docking and the change in surface energy was calculated.Results: Binding Interactions show that IL1Ra binds more avidly to IL1R1 than IL1β. Conformational instability was seen in the IL1R1-IL1Ra_SNP complex. The difference in the amino acid interactions between the IL1R1 with IL1Ra and IL1Ra_SNP variant further illustrated the change in binding residues and hydrogen bond formation. Upon docking of an appropriate ligand to the IL1R1-IL1Ra_SNP complex, the conformational stability of the IL1R1-IL1Ra_SNP complex enhanced considerably suggesting a possible mechanism for treating SNP-induced conformational instability.Conclusion: Toll-like receptors like IL1R1 have many binding pockets apart from its active site. No strategies have yet been reported in targeting them for correcting conformational instability induced by SNP. Through our study, it was observed that the conformational instability of IL1R1-IL1Ra_SNP complex decreased upon introducing an appropriate small molecule.Keywords: IL1β, IL1R1, IL1Ra, SN

A Study of Attack Detection and Localization Scheme Using Enhanced Hash Technique

Security plays an vital role in wireless sensor networks. The nodes are deployed in the physical environment. Hackers may easily access the data. In order to provide security, The Advanced Encryption Standard (AES) algorithm has developed into an option for various security services. Sensor nodes collect the data from the environment and send to sink. But attackers corrupt data while transmitting therefore data security is main concern of wireless sensor network (WSN). Owing to the increasing popularity of wireless sensor networks, they have become attractive targets for malicious attacks. Due to the ad-hoc nature and openness of wireless sensor networks, they are susceptible to the identity based attack. In this paper, we study on a process of named Attack Detection and Localization Scheme to detect and localize the identity based attacks. An improved algorithm for hashing has been proposed. We named it as Effective Hashing Technique (EHT).It generates the Hash keys to differentiate an attacker from a normal node and to reduce the occurrences of any false positives or negatives. Also, our localization algorithm efficiently finds out the position estimates for the nodes

Design and investigation of negative capacitance–based core‐shell dopingless nanotube tunnel field‐effect transistor

Investigation and analysis of a ferroelectric material–based dopingless nanotube tunnel field-effect transistor are conducted using a lead zirconate titanate (PZT) gate stack to induce negative capacitance in the device. Landau–Khalatnikov equations are used in deriving the parameter values of the ferroelectric material to ensure accurate results. The nanotube structure of the tunnel field-effect transistor allows for better electrostatic control owing to its gate-all-around structure. Incorporation of negative capacitance further reduces the voltage supply requirement and power consumption of the structure while simultaneously improving switching. In addition, the device is studied for varying thicknesses of the dielectric PZT material. The threshold voltage of the device under study was calculated as 0.281 V, and the average subthreshold slope of the device was reduced to 18.271 mV/decade, far below the thermionic limit of 60 mV/decade

TCAD device modeling and simulation study of organic field effect transistor-based pH sensor with tunable sensitivity for surpassing Nernst limit

A dual-gate organic field effect transistor (DG-OFET)-based pH sensor is proposed that will be able to detect the variations in the aqueous (electrolyte) medium. In this structure, a source-sided underlap technique with a dual-gate sensing approach has been used. The change in ON-current (ION) was observed due to parallel examination of electrolytes in two gates underlapping the region of the structure. For the evaluation of the sensitivity of DG-OFET, the change in the drain current was exploited for different pH and corresponding charge densities utilizing 2D physics-based numerical simulation. The simulation results were extracted with the help of the software package Silvaco TCAD-ATLAS. The simulated results display that the proposed DG-OFET shows significantly higher sensitivity for high-k dielectrics. The voltage sensitivity achieved by DG-OFET with SiO2 as a dielectric in our work is 217.53 mV/pH which surpasses the Nernst Limit nearly four times. However, using a high-k dielectric (Ta2O5) increases it further to 555.284 mV/pH which is more than nine times the Nernst Limit. The DG-OFET pH sensor has a lot of potential in the future for various flexible sensing applications due to its flexibility, being highly sensitive, biocompatible and low-cost

Implementation of gate-all-around gate-engineered charge plasma nanowire FET-based common source amplifier

This paper examines the performance of a Gate-Engineered Gate-All-Around Charge Plasma Nanowire Field Effect Transistor (GAA-DMG-GS-CP NW-FET) and the implementation of a common source (CS) amplifier circuit. The proposed GAA-DMG-GS-CP NW-FET incorporates dual-material gate (DMG) and gate stack (GS) as gate engineering techniques and its analog/RF performance parameters are compared to those of the Gate-All-Around Single-Material Gate Charge Plasma Nanowire Field Effect Transistor (GAA-SMG-CP NW-FET) device. Both Gate-All-Around (GAA) devices are designed using the Silvaco TCAD tool. GAA structures have demonstrated good gate control because the gate holds the channel, which is an inherent advantage for both devices discussed herein. The charge plasma dopingless technique is used, in which the source and drain regions are formed using metal contacts and necessary work functions rather than doping. This dopingless technique eliminates the need for doping, reducing fluctuations caused by random dopants and lowering the device’s thermal budget. Gate engineering techniques such as DMG and GS significantly improved the current characteristics which played a crucial role in obtaining maximum gain for circuit designs. The lookup table (LUT) approach is used in the implementation of the CS amplifier circuit with the proposed device. The transient response of the circuit is analyzed with both the device structures where the gain achieved for the CS amplifier circuit using the proposed GAA-DMG-GS-CP NW-FET is 15.06 dB. The superior performance showcased by the proposed GAA-DMG-GS-CP NW-FET device with analog, RF and circuit analysis proves its strong candidature for future nanoscale and low-power applications

Competition through capacity investment under asymmetric existing capacities and costs

This paper discusses the way that different operational characteristics including existing capacity, scale economies, and production policy have an important influence on the capacity outcomes when firms compete in the market place. We formulate a game-theoretical model where each firm has an existing capacity and faces both fixed and variable costs in purchasing additional capacity. Specifically, the firms simultaneously (or sequentially) make their expansion decisions, and then simultaneously decide their production decisions with these outputs being capacity constrained. We also compare our results with cases where production has to match capacity. By characterizing the firms’ capacity and production choices in equilibrium, our analysis shows that the operational factors play a crucial role in determining what happens. The modeling and analysis in the paper gives insight into the way that the ability to use less production capacity than has been built will undermine the commitment value of existing capacity. If a commitment to full production is not possible, sinking operational costs can enable a firm to keep some preemptive advantage. We also show that the existence of fixed costs can introduce cases where there are either no pure strategy equilibrium or multiple equilibria. The managerial implications of our analysis are noted in the discussion. Our central contribution in this paper is the innovative integration of the strategic analysis of capacity expansion and well-known (s,S)(s,S) policy in operations and supply chain theor

Characterizing Prostate Cancer Risk Through Multi-Ancestry Genome-Wide Discovery of 187 Novel Risk Variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe