A computational study of the chemokine receptor CXCR1 bound with interleukin-8

CXCR1 is a G-protein coupled receptor, transducing signals from chemokines, in particular the interleukin-8 （1L8） molecules. This study combines homology modeling and molecular dynamics simulation methods to study the structure of CXCRI-IL8 complex. By using CXCR4-vMIP-II crystallography structure as the homologous template, CXCRI-IL8 complex structure was constructed, and then refined using all-atom molecular dynamics simulations. Through extensive simulations, CXCRI-IL8 binding poses were investigated in detail. Furthermore, the role of the N-terminal of CXCR1 receptor was studied by comparing four complex models differing in the N-terminal sequences. The results indicate that the receptor N-terminal affects the binding of IL8 significantly. With a shorter N-terminal domain, the binding of IL8 to CXCR1 becomes unstable. The homology modeling and simulations also reveal the key receptor-ligand residues involved in the electrostatic interactions known to be vital for complex formation.

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

Applying Graph Theory and Mathematical-Computational Modelling to Study a Neurophysiological Circuit

The aim of the present study is to contribute to the knowledge about the functioning of the neuronal circuits. We built a mathematical-computational model using graph theory for a complex neurophysiological circuit consisting of a reverberating neuronal circuit and a parallel neuronal circuit, which could be coupled. Implementing our model in C++ and applying neurophysiological values found in the literature, we studied the discharge pattern of the reverberant circuit and the parallel circuit separately for the same input signal pattern, examining the influence of the refractory period and the synaptic delay on the respective output signal patterns. Then, the same study was performed for the complete circuit, in which the two circuits were coupled, and the parallel circuit could then influence the functioning of the reverberant. The results showed that the refractory period played an important role in forming the pattern of the output spectrum of a reverberating circuit. The inhibitory action of the parallel circuit was able to regulate the reverberation frequency, suggesting that parallel circuits may be involved in the control of reverberation circuits related to motive activities underlying precision tasks and perhaps underlying neural work processes and immediate memories.

Computational investigations on polymerase actions in gene transcription and replication:Combining physical modeling and atomistic simulations

Polymerases are protein enzymes that move along nucleic acid chains and catalyze template-based polymerization reactions during gene transcription and replication. The polymerases also substantially improve transcription or replica- tion fidelity through the non-equilibrium enzymatic cycles. We briefly review computational efforts that have been made toward understanding mechano--chemical coupling and fidelity control mechanisms of the polymerase elongation. The polymerases are regarded as molecular information motors during the elongation process. It requires a full spectrum of computational approaches from multiple time and length scales to understand the full polymerase functional cycle. We stay away from quantum mechanics based approaches to the polymerase catalysis due to abundant former surveys, while ad- dressing statistical physics modeling approaches along with all-atom molecular dynamics simulation studies. We organize this review around our own modeling and simulation practices on a single subunit T7 RNA polymerase, and summarize commensurate studies on structurally similar DNA polymerases as well. For multi-subunit RNA polymerases that have been actively studied in recent years, we leave systematical reviews of the simulation achievements to latest computational chemistry surveys, while covering only representative studies published very recently, including our own work modeling structure-based elongation kinetic of yeast RNA polymerase II. In the end, we briefly go through physical modeling on elongation pauses and backtracking activities of the multi-subunit RNAPs. We emphasize on the fluctuation and control mechanisms of the polymerase actions, highlight the non-equilibrium nature of the operation system, and try to build some perspectives toward understanding the polymerase impacts from the single molecule level to a genome-wide scale.

Computational and experimental verification of a wide-angle metamaterial absorber

A metamaterial absorber is computed numerically and measured experimentally in a 150-THz^300-THz range.The measured absorber achieves high absorption rate for both transverse electric（TE） and transverse magnetic（TM） polarizations at large angles of incidence.An absorption sensor scheme is proposed based on the measured absorber and the variations of surrounding media.Different surrounding media are applied to the surface of the absorption sensor（including air,water,and glucose solution）.Measured results show that high figure of merit（FOM） values are obtained for different surrounding media.The proposed sensor does not depend on the substrate,which means that it can be transplanted to different sensing platforms conveniently.

Computational Modeling of Government Policy to Attain Long-Term Higher Voter Turnout for Sustainable Democracy

People power is the fundamental concept of democracy and power of the people is exercised though voting. People decide who should be elected to make decisions for them. However, if people do not properly participate in the voting process and only two-thirds of all eligible voters participate in an election, the democratic institution loses its credibility and becomes vulnerable. This paper investigates various changes in voting institutions throughout the USAwith a simulation model that analyzes the efficacy of such methods to attain higher voter turnout.

Computational Evaluation of Selectivity of Triazole-and Amide-Based Drug Candidates, Flavanone Derivatives and Synthesized Steroid Compounds for Treatment of Diabetes Type II

Inhibition of 11βHSD1 （11-beta-hydroxysteroid dehydrogenase 1） is a promising strategy in drug treatment of diabetes. Several 11βHSDI inhibitors have been proposed; however, their selectivity to 11βHSD1 over its isozyme 11βHSD2 （11-beta-hydroxysteroid dehydrogenase 2） has not been fully reported. The authors sought to provide a short list of top potent and selective compounds along with their detailed binding modes and pharmacophore models, Molecular docking was used for initial screening of a set of 23 potent inhibitors reported by previous experimental studies. After that, selected promising entries were reassessed by molecular dynamics simulations, followed by hydrogen bond analysis. Pharmacophore models of all drug candidates and binding modes of some selected drugs were analyzed. Among the 23 compounds, only four inhibitors were identified as potent and selective drug candidates. Binding energies, 3D pharmacophores and binding modes of the four compounds with 11βHSDI are also discussed in detail in this study.

Computational studies on magnetism and ferroelectricity

Magnetics,ferroelectrics,and multiferroics have attracted great attentions because they are not only extremely im-portant for investigating fundamental physics,but also have important applications in information technology.Here,recent computational studies on magnetism and ferroelectricity are reviewed.We first give a brief introduction to magnets,fer-roelectrics,and multiferroics.Then,theoretical models and corresponding computational methods for investigating these materials are presented.In particular,a new method for computing the linear magnetoelectric coupling tensor without applying an external field in the first principle calculations is proposed for the first time.The functionalities of our home-made Property Analysis and Simulation Package for materials(PASP)and its applications in the field of magnetism and ferroelectricity are discussed.Finally,we summarize this review and give a perspective on possible directions of future computational studies on magnetism and ferroelectricity.

Computational Study on Melting Process Using Smoothed Particle Hydrodynamics

Recently, smoothed particle hydrodynamics (SPH) method has become popular in computational fluid dynamic and heat transfer simulation. The simplicity offered by this method made some complex system in physics such as moving interface in multiphase flow, heat conductivity jumping in multiple material boundaries and many geometrical difficulties become relative easy to calculate. We will treat a relative easy example of melting process to test the method in solving fluid motion equation coupled by heat transfer process. The main heat transfer processes are caused by solid-liquid (medium to medium) heat diffusion and convection. System interaction with ambient temperature can be modeled by gas surrounding fluid-solid system. For the ambient temperature, we proposed surface particle heat transfer governed by convectional heat flux. Using local particle number density value as surface detection method, we applied cooling and heating to surface particle on the melting ice cube and water system. The simulation result is also verified by experiment.

GENERALIZED FORELLI-RUDIN TYPE OPERATORS BETWEEN SEVERAL FUNCTION SPACES ON THE UNIT BALL OF C^(N)

In this paper,we investigate sufficient and necessary conditions such that generalized Forelli-Rudin type operators T_(λ,τ,k),S_(λ,τ,k),Q_(λ,τ,k)and R_(λ,τ,k)are bounded between Lebesgue type spaces.In order to prove the main results,we first give some bidirectional estimates for several typical integrals.

Dual-Defect Engineering Strategy Enables High-Durability Rechargeable Magnesium-Metal Batteries

Rechargeable magnesium-metal batteries(RMMBs)are promising next-generation secondary batteries;however,their development is inhibited by the low capacity and short cycle lifespan of cathodes.Although various strategies have been devised to enhance the Mg^(2+)migration kinetics and structural stability of cathodes,they fail to improve electronic conductivity,rendering the cathodes incompatible with magnesium-metal anodes.Herein,we propose a dual-defect engineering strategy,namely,the incorporation of Mg^(2+)pre-intercalation defect(P-Mgd)and oxygen defect(Od),to simultaneously improve the Mg^(2+)migration kinetics,structural stability,and electronic conductivity of the cathodes of RMMBs.Using lamellar V_(2)O_(5)·nH_(2)O as a demo cathode material,we prepare a cathode comprising Mg_(0.07)V_(2)O_(5)·1.4H_(2)O nanobelts composited with reduced graphene oxide(MVOH/rGO)with P-Mgd and Od.The Od enlarges interlayer spacing,accelerates Mg^(2+)migration kinetics,and prevents structural collapse,while the P-Mgd stabilizes the lamellar structure and increases electronic conductivity.Consequently,the MVOH/rGO cathode exhibits a high capacity of 197 mAh g^(−1),and the developed Mg foil//MVOH/rGO full cell demonstrates an incredible lifespan of 850 cycles at 0.1 A g^(−1),capable of powering a light-emitting diode.The proposed dual-defect engineering strategy provides new insights into developing high-durability,high-capacity cathodes,advancing the practical application of RMMBs,and other new secondary batteries.

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

Influences of Co-Flow and Counter-Flow Modes of Reactant Flow Arrangement on a PEMFC at Start-Up

To investigate the influences of co-flowand counter-flowmodes of reactant flowarrangement on a proton exchange membrane fuel cell(PEMFC)during start-up,unsteady physical and mathematical models fully coupling the flow,heat,and electrochemical reactions in a PEMFC are established.The continuity equation and momentum equation are solved by handling pressure-velocity coupling using the SIMPLE algorithm.The electrochemical reaction rates in the catalyst layers(CLs)of the cathode and anode are calculated using the Butler-Volmer equation.The multiphase mixture model describes the multiphase transport process of gas mixtures and liquid water in the fuel cell.After validation,the influences of co-flow and counter-flow modes on the PEMFC performance are investigated,including the evolution of the current density,flow field,temperature field,and reactant concentration field during start-up,as well as the steady distribution of the current density,reactant concentration,andmembrane water content when the start-up stabilizes.Co-flow and counter-flow modes influence the current density distribution and temperature distribution.On the one hand,the co-flow mode accelerates the start-up process of the PEMFC and leads to a more evenly distributed current density than the counter-flow mode.On the other hand,the temperature difference between the inlet and outlet sections of the cell is up to 10.1℃ under the co-flow mode,much larger than the 5.0℃ observed in the counter-flow mode.Accordingly,the counter-flowmode results in a more evenly distributed temperature and a lower maximum temperature than the co-flow case.Therefore,in the flow field design of a PEMFC,the reactant flow arrangements can be considered to weigh between better heat management and higher current density distribution of the cell.

The joint Laplace transforms for killed diffusion occupation times

The approach of Li and Zhou(2014)is adopted to find the Laplace transform of occupation time over interval(0,a)and joint occupation times over semi-infinite intervals(-∞,a)and(b,∞)for a time-homogeneous diffusion process up to an independent exponential time e_(q)for 0<a<b.The results are expressed in terms of solutions to the differential equations associated with the diffusion generator.Applying these results,we obtain explicit expressions on the Laplace transform of occupation time and joint occupation time for Brownian motion with drift.

Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

With the high level of proliferation of connected mobile devices,the risk of intrusion becomes higher.Artificial Intelligence(AI)and Machine Learning(ML)algorithms started to feature in protection software and showed effective results.These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’malware,especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly.In this article,we present a framework for mobile malware detection based on a new dataset containing new categories of mobile malware.We focus on categories of malware that were not tested before by Machine Learning algorithms proven effective in malware detection.We carefully select an optimal number of features,do necessary preprocessing,and then apply Machine Learning algorithms to discover malicious code effectively.From our experiments,we have found that the Random Forest algorithm is the best-performing algorithm with such mobile malware with detection rates of around 99%.We compared our results from this work and found that they are aligned well with our previous work.We also compared our work with State-of-the-Art works of others and found that the results are very close and competitive.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

Research of caged dynamics of clusters center atoms in Pd_(82)Si_(18) amorphous alloy

To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research object to further elucidate the underlying mechanism of caged dynamics from multiple perspectives,including the cage's lifetime,atomic local environment,and atomic potential energy.The results reveal that Si atoms exhibit a pronounced cage effect due to the hindrance of Pd atoms,resulting in an anomalous peak in the non-Gaussian parameters.An in-depth investigation was conducted on the caged dynamics differences between fast and slow Si atoms.In comparison to fast Si atoms,slow Si atoms were surrounded by more Pd atoms and occupied lower potential energy states,resulting in smaller diffusion displacements for the slow Si atoms.Concurrently,slow Si atoms tend to be in the centers of smaller clusters with coordination numbers of 9 and 10.During the isothermal relaxation process,clusters with coordination numbers 9 and 10 have longer lifetimes,suggesting that the escape of slow Si atoms from their cages is more challenging.The findings mentioned above hold significant implications for understanding the caged dynamics.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.