Investigating the Ethernet and Boolean Logic

2016 ◽  
Vol 3 (1) ◽  
pp. 194
Author(s):  
Hetty Rohayani ◽  
Erick Fernando ◽  
Derist Touriano
Keyword(s):  
Machine Learning ◽  
Wireless Communication ◽  
Sensor Network ◽  
Large Scale ◽  
Access Point ◽  
Boolean Logic ◽  
Symmetric Encryption ◽  
Access Points ◽  
Full Control ◽  
Assembly Instructions

<p>Write-ahead logging to work. After years of research that are typical in Moore's Law, validate multi-processor investigation, which embodies the principles of intuitive machine learning. Our focus in this paper is not on whether a symmetric encryption can be made metamorphic, probabilistic, adaptive, efficient wearable, and interposable, but rather the introduction of new wireless communication (SCHAH). The properties SCHAH highly dependent on the assumptions inherent in our framework; in this section, we consider the methodology which consists of n access point. Implementation of our applications are replicated, symbiosis, and with large scale yangmemiliki full control over homegrown database, as may be necessary in order to control and access points are not compatible. A collection of shell scripts contains about 85 x86 assembly instructions. Where the engine and is fully compliant courseware follows the sensor network evaluation, although SCHAH not able to give a lot of kernel at a time. </p>

scholarly journals Optimasi Jaringan Internet Di Gedung Teknik Elektro Politeknik Negeri Sriwijaya

2019 ◽  
Vol 4 (1) ◽  
pp. 95
Author(s):  
Suci Tri Lestari ◽  
Suroso Suroso ◽  
Ibnu Ziad
Keyword(s):  
Wireless Communication ◽  
Service Quality ◽  
Access Point ◽  
Local Network ◽  
Electromagnetic Signal ◽  
Signal Quality ◽  
Access Points ◽  
The World ◽  
Selection Of ◽  
Basic Necessity

Nowadays wireless communication has become a basic necessity for the community. WiFi (wireless fidelity) One of them, which is a local network that uses an electromagnetic signal that works at a frequency of 2.4 GHz. Tool to implement WiFi is Access point (AP). The toughest bully in the world of WiFi is known for interference. Interference is the use of the same frequency or channel on a network. Interference can cause service quality to decline so that it is less optimal in its usage, therefore the optimization needs to the network so that users can use the network without constraints. From the results of the research that has been done, the optimization is done by evaluating the selection of the channel from each access point studied with the access point that was concluded that the optimization of the results of all access points experienced Improved signal quality as in AP 1 prior to optimized (-64.0) dBm to (-56.0) dBm category good and after optimized (-56.0) dBm to (-48) dBm category excellent.

scholarly journals Light Fidelity (Li - Fi) Access Point Selection for Heterogeneous Network with Non Overlapping Li - Fi Access Points

2020 ◽  
Vol 8 (5) ◽  
pp. 2101-2104
Keyword(s):  
Wireless Communication ◽  
Data Transmission ◽  
Heterogeneous Network ◽  
Data Communication ◽  
Access Point ◽  
Data Rate ◽  
Electromagnetic Spectrum ◽  
Coverage Area ◽  
Access Points ◽  
Handover Process

Data transmission is a part of our life in today’s scenario. Data communication can be carried out by wired technology and wireless technology. Radio frequency is a part of the electromagnetic spectrum, which is used for wireless communication. Many technologies are used for wireless communication such as Wi-Fi, Blue Tooth, Ad-hoc, and etc. These are all transmitting data through radio spectrum for short distance. New technology called as Li-Fi (Light-Fidelity) is going to be a major wireless communication technology in future. In the proposed system a heterogeneous network has been consider as a system model. Li-Fi technology is having the capacity of low coverage area. So, non movable wireless devices are getting better usage of Li-Fi technology in an indoor environment. For mobile devices need more number of Li-Fi access points. When the user moves from one access point to another access point covering range, the service should be continued without break. System handover is a major issue in the Li-Fi technology. If the user goes out of the range of light emission area, then the user can get the service through Wi-Fi access point. Proposed system focusing on the handover process based on the measured handover efficiency value. Data transmission can be disturbed due to some power failure or LED fault. In this situation immediately the entire load should be handled by the Wi-Fi access point. In such a situation data rate is coming down than the threshold value. So handover process will be handled by the common controller. Data rate is observed when the number of user increased and for variation in channel gain and the simulation results are analysed.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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