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The present study explores the optical properties of retinal molecules in presence of small gold cluster (Au14), where the cluster motifs are varied through Oh, D2h and C4v symmetries. The constructive interference of the transition dipole moments (TDMs) in retinal-gold nanoconjugates is responsible for most intense electronic transitions. The charge transfer transition from the occupied Au cluster orbital to the unoccupied retinal orbital is playing significant role in the spectral shift of the conjugates. The vibrational frequency analysis of 11-cis retinal and its conjugate (Au14 in D2h symmetry) reveal that the different vibrational modes associated with the isomerization process are red shifted in the conjugate. The conjugation of isolated retinal with gold probes makes the process of 11-cis to all-trans isomerization thermodynamically favorable. The entire evaluation reflects the effect of gold cluster in the spectral tuning of retinal molecules and reveals its relevant consequences on photoisomerization and hence the process of vision.

The character of non-bonding molecular orbitals (and the related spin-density) is addressed in the general circumstance of translationally symmetric graphenic strips. Besides a systematic characterization of such possible strips, couplings are addressed between boundary-localized unpaired spins, including the case when opposite sides of a strip are two different types of (translationally symmetric) boundaries.

Each Android application requires accumulations of permissions in installation time and they are considered as the features which can be utilized in permission-based identification of Android malwares. Recently, ensemble feature selection techniques have received increasing attention over conventional techniques in different applications. In this work, a cluster based voted ensemble voted feature selection technique combining five base wrapper approaches of R libraries is projected for identifying most prominent set of features in the predictive modeling of Android malwares. The proposed method preserves both the desirable features of an ensemble feature selector, accuracy and diversity. Moreover, in this work, five different data partitioning ratios are considered and the impact of those ratios on predictive model are measured using coefficient of determination (r-square) and root mean square error. The proposed strategy has created significant better outcome in term of the number of selected features and classification accuracy.

Worms that can contaminate hundreds of thousands of web hosts pose a significant risk to the web. Online worms pose a significant risk to the protection of Internet infrastructure. The recent Intrusion Detection system (IDS) monitor edge network DMZs to determine and filter system malicious. While an IDS can help defend the hosts on the nearby advantage networking of its at denial and conciliation of system, it cannot on your own successfully intervene to halt as well as overturn the spreading of novel Internet worms. Age group of the worm signatures needed by an IDS—the byte patterns desired for monitored website traffic to determine worms—today involves non-trivial man labor and hence substantial delay: as networking operators identify anomalous conduct, they conversate with each other and personally learn package traces to develop a worm signature. However, treatment needs to happen soon in an epidemic to halt a worm's spread. In this paper, an automated signature model process is proposed for “polymorphic worms”. We implemented Principal Component Analysis (PCA) to discover by far the most important data that is actually discussed between polymorphic mask situations, and to use them as signatures. The experimental consequences indicate where the PCA effectively recognized “polymorphic worms by zero false positives as well as low false negatives.”

In Internet infrastructure, information security is the most major concern for any individual or any organization. The general regular signature-based approach fails to detect unknown attacks promptly. Our research approach construes a unique honeypot system which will be able to detect the suspected traffic from other ordinary traffic and captures relevant particulars about different worm operation without attacker’s information. Our structural approach is automated and generates a high-quality signature for mainly unknown polymorphic worms. We are using a probabilistic approach to generate an attack pattern which deals with low-false alarm. Our observation with artificially produced polymorphic worms illustrates the proposed setup can be qualified to improve the efficiency of the intrusion detection system signature database and also able to increase the possibility of encounter different kinds of polymorphic worms with high expertise.

The appearance of SARS-CoV-2 has resulted ~19000 deaths and ~423000 infections worldwide as of March 24, 2020. Coronavirus spike (S) glycoproteins hooks on target cells and binds to the angiotensin-converting enzyme 2 (ACE2) receptor. Recent researches speculated that residues 331 to 524 of the S glycoprotein of the receptor binding domain (RDB) of the spike is the most crucial target and this side was very important for computational docking. In the present study we have considered a series of saikosaponins and molecular docking was performed. Most of the docked molecules bind favorably to the RDB region of the spike glycoprotein and among them Saikosaponin B4 is the best inhibitor.

The amount of malware has been rising drastically as the Android operating system enabled smartphones and tablets are gaining popularity around the world in last couple of years. One of the popular methods of static detection techniques is permission/feature-based detection of malware through the AndroidManifest.xml file using machine learning classifiers. Ignoring important features or keeping irrelevant features may specifically cause mystification to classification algorithms. Therefore, to reduce classification time and improve accuracy, different feature reduction tools have been used in past literature. Community detection is one of the major tools in social network analysis but its implementation in the context of malware detection is quite rare. In this article, the authors introduce a community-based feature reduction technique for Android malware detection. The proposed method is evaluated on two datasets consisting of 3004 benign components and 1363 malware components. The proposed community-based feature reduction technique produces a classification accuracy of 98.20% and ROC value up to 0.989.

In this letter, a possible reason behind selective host-guest organization in the initial stage of sI methane hydrate nucleation is provided, through density functional theory based calculations. In doing so, we have connected earlier experimental and theoretical observations on the structure and energetics of sI methane hydrate to our findings. Geometry and relative stability of small (H 2 O) 5 and (H 2 O) 6 clusters, presence of CH 4 guest, integrity and cavity radius of (H 2 O) 20 and (H 2 O) 24 , as well as the weak van der Waals type of forces, particularly dispersion interaction, are major factors responsible for initial formation of methane encapsulated dodecahedron cavity over tetrakaidecahedron.

We have investigated four different already synthesized meta- and para-connected diradicals to get their magnetic exchange coupling constants (J) through generalized valence bond (GVB) approach and also with Hartree-Fock (HF) theory with a view to establish the superiority of GVB method over HF theory for the prediction of J. In doing so, at first optimization of these molecules are done at B3LYP level of density functional theory using the 6-31++g(d) basis set. With these optimized geometries, J values are computed at GVB method along with TZV basis set and these values are compared with those of the J values obtained using HF/TZV level of theory. Nonetheless, J values found in both the theoretical processes are also compared with the experimental findings. Thus, the supremacy of the wavefunction based GVB method in predicting J is established in comparison with the HF method.

A big data cluster consists of number of network-connected computers. A big data cluster offers a huge data store and processing power. End users submit both data and application to the cluster. All the computers called nodes in the cluster work together to give the result from the data. During data processing, lots of process run on different nodes and exchange data. The data exchange is done via regular network protocols. During processing, one or multiple computers may not participate well due to its bad hardware or operating system health. Some computers may receive known network attack like DOS and thus slow down the performance of the cluster. Some other computers may receive unknown attacks generated by the big data job itself. Therefore, the system requires a mechanism to detect such nodes under attack or the nodes generating attacks and isolate thereafter. To detect this attack, we need to analyze the cumulative network traffic of all the nodes in the cluster. Therefore, we must collect such network traffic of all the nodes participating in data processing job simultaneously. This work is to present an efficient testbed for external or internal attack generation and dataset creation for different attacks. The proposed architecture captures network traffic from all nodes of the cluster and stores them for attack detection in near future.

In the recent era, human are ruled by digital media. It can be said that for improvised Human Computer Interaction (HCI) process and security purposes, a robust authentication framework is the target of all researchers. Any CHAP or PAP based and biometric authentication techniques have been evolved in a great way to serve this purpose. Along with these widely used techniques, a new research domain Affective Computation (AC) has taken place with a different approach towards engaging human emotions into a farm computational model. In this work, a robust and adaptive framework for a Human Emotional Mapper has been introduced in details through which human emotional signature can be tracked. This emotional signature pattern can be used for authentication as well as improvised HCI.

Cloud computing is the most latest developments in the IT industry also known as on-demand computing. This technology attracting different organization because of its advantages like throughput, scalability, easy access etc. Beside this it has a big challenge of security and privacy issues. In this paper, we review different security challenges in cloud computing like Trust, authenticity, confidentiality, encryption, key management, multitenancy, data splitting, virtual machine security and we also have discussed how to overcome these issues.

Current paper analyzes different existing approaches for solving issues with Real Time Big Data Processing . On the background of identified inherent functional requirements of Real Time as well as Big Data processing system, comparative studies are carried out on all major technology solutions in the entire spectrum and conclusive decisions are made in order to adopting an option for design and development of a novel and Real Time Big Data Processing architecture.

Current paper presents the design details of a Real Time Streaming API based on Apache Storm. Theoretically, Apache Storm is known for extremely low latency in processing of streaming messages, but the factor of low latency alone is not typically accounted for a strict real time processing system. Hence the research work presented here throws light on how a true real time big data processing system could effectively be designed and developed having its deep root in Apache Storm API.

The domain of aromaticity spans a wide range of molecules, from polycyclic aromatic hydrocarbons, heterocycles to all-metal systems. Here, in silico we demonstrate the aromaticity in C2B2F4, extending beyond the limit of conventional aromatic molecules. This molecule gains the magic number of six π-electrons through an unusual electronic contribution from exo-cyclic atoms. The stability of the molecule is established through density functional theory, ab initio calculations as well as molecular dynamics simulation.

l -Serine is supposed to be a nonessential amino acid and is indispensable to many neurodevelopmental symptoms. It acts as a vital metabolic precursor in different biological processes. Adequate supply of l -serine is then earnestly necessary for healthy neural development. Herein we investigate the complexation behaviour of l -serine into the hydrophobic cavity of β-cyclodextrin (β-CD). The synthesized 1:1 inclusion complex (IC) was studied and analyzed through various spectroscopic techniques (UV, FT-IR, Raman, XRD, 1H NMR, COSY, NOESY, SEM, TGA, etc.) as well as through molecular docking and molecular dynamics computations. Hydrogen bonding and van der Waal's interaction are found to be the chief driving force for the formation of such an inclusion complex. Analysis of experimental and theoretical results reveals the formation of a stable IC that is indicative of the applicability of β-CD as a vehicle for l -serine delivery.

In this paper, fractality and stationarity of average Sensex value of stock market have been investigated by enlightening the self-similarity characteristics and nature of frequency oscillation of the two crucial constraints, namely, the average Sensex values of BSE and NASDAQ. The time series of these parameters between 31st Oct 2007 and 1st Nov 2017, of Bombay Stock Exchange (BSE) and American stock exchange (NASDAQ) have been chosen for extracting the nature of scaling (fractality) and stationary behavior using statistical techniques. Methodologies like General Hurst Estimation (GHE) and Higuchi’s Fractal dimension (HFD) have been used to compute Hurst Exponent for the time series. According to experimental results, the said time series exhibits SRD - Short Range Dependent anti-persistent character. The stationarity behavior of the data-series has been determined using Continuous Wavelet Transform (CWT) method. Both the time series have been found to exhibit non-stationarity.

Two vital statistics of wireless network namely peak hour call initiated and call drop have been chosen to examine the self similarity and stationarity behaviour of typical wireless network data in this paper. The scaling pattern and nature of fluctuating frequency are exposed through these two parameters. For exposing the scaling nature of the time series that has been taken from the period 3rd March, 2005 to 31st October, 2015, from the local mobile switching server. Statistical methodologies like Rescaled Analysis (R/S) and General Hurst Estimation (GHE) method are being used to detect the scaling nature of the data-series. Both the time series represent the Short Range Dependency (SRD) and anti-persistency behaviour. The stationarity or non-stationarity behaviour of the time series have been examined by Kwiatkowski Phillips Schmidt Shin (KPSS) test and Continuous Wavelet Transform (CWT). Here both the time series shows non stationarity behaviour.

This paper presents a Physical Cell Identity (PCI) self-distribution algorithm for Self Organizing Network (SON). The PCI is used to identify different cells in Long Term Evolution Advanced (LTE-A) network using Self-configuration technique. The PCI assignment of cells would be quite complex for the restricted number of PCI and the heterogeneous structures of LTE-A network. Hence, the PCI self-distribution problem is mapped to a minimal spanning tree (MST) in this paper. A combined MST and graph coloring algorithm is proposed, MATLAB is used in order to optimize the PCI reuse distance and decrease the collision, confusion and multiplexing interference of SON.

The bond-stretch isomers are characterized by a principal change in the bond-length with the rest of the molecule being unaltered. The electronic structure regulates the bond stretch isomerism phenomenon in which has been investigated with density functional theory, ab initio CASSCF, highly efficient n-electron valence state perturbation theory and multireference configuration interaction calculations. Two isomers are distinguished on different potential energy surfaces and the corresponding avoided crossing is also studied in details. The bonding pattern in two isomers are analysed through adaptive natural density partitioning analysis and quantum theory of atoms in molecules analysis. The bonds in both the isomers primarily involve the 2p orbitals, which overlap face-to-face in long-bond isomer. Whereas, in-plane π-bonding occurs at the short-bond isomer leading to unusual bent bond. © 2015 Wiley Periodicals, Inc.

Feature selection is the process of grouping most significant set of features which reduces dimensionality and generates most analytical result. Choosing relevant attributes are a critical issue for competitive classifiers and for data reduction also. This work proposes a hybrid feature selection technique based on Rough Set Quick Reduct algorithm with Community Detection scheme. The proposed technique is applied in Android malware detection domain and compared with the performances of existing feature selectors. It produces highest average classification accuracy of 97.88% and average ROC values up to 0.987.

In this work, the effects of ligand field strength as well as the metal coordination geometry on magnetic anisotropy of pentacoordinated CoII complexes have been investigated using a combined experimental and theoretical approach. For that, a strategic design and synthesis of three pentacoordinate CoII complexes [Co(bbp)Cl2]·(MeOH) (1), [Co(bbp)Br2]·(MeOH) (2), and [Co(bbp)(NCS)2] (3) has been achieved by using the tridentate coordination environment of the ligand in conjunction with the accommodating terminal ligands (i.e., chloride, bromide, and thiocyanate). Detailed magnetic studies disclose the occurrence of slow magnetic relaxation behavior of CoII centers with an easy-plane magnetic anisotropy. A quantitative estimation of ZFS parameters has been successfully performed by density functional theory (DFT) calculations. Both the sign and magnitude of ZFS parameters are prophesied well by this DFT method. The theoretical results also reveal that the α → β (SOMO–SOMO) excitation contributes almost entir...

In this note, we have theoretically evaluated second-order nonlinear optical (NLO) responses of two structural isomeric forms of three different green fluorescent protein chromophores (viz. cyan fluorescent protein (CFP), orange variant of Discosoma coral (DsRed) and Zoanthus sp. (zFP)) coupled bis-TEMPO based photomagnetic high-spin diradicals by measuring the second-order effect of first molecular hyperpolarizabilities. The magnetic character and certain biological applicabilities of these diradicals are investigated theoretically in one of our recent papers (Bhattacharya et al., 2013). The second-order total molecular hyperpolarizabilities of these molecules are correlated with their respective HOMO–LUMO energy gaps (ΔEH-L) and also with the extent of the π-network of the couplers. Relations between ΔEH-L, chemical hardness, NLO response and exchange energy (Bhattacharya et al., 2013) are made. The applicability of these diradicals in the light of NLO properties are also highlighted in this note. Here these resulting fluoroprotein coupled diradicals can switch back and forth between two isomeric states of different colors upon irradiation by light of appropriate wavelength. Nonetheless, the magnitude of second-order NLO responses is different for different isomeric forms; hence we made a correlation between the color codes with the magnitude of second-order NLO responses. Thence with just simple visual inspection one can understand the extent of second-order of NLO response for these diradicals.

Three different pairs of isomeric trans and corresponding cis forms of fluoroprotein chromophore (FPC) fragments coupled bis-2,2′,6,6′-tetramethylpiperidinyloxy (TEMPO) based diradicals have been designed, and subsequently the magnetic properties of these diradicals have been investigated. We have used blue shifted variants of green fluorescent protein (cyan fluorescent protein or CFP), orange variant of Discosoma coral (DsRed) and yellow mutant of Zoanthus sp. (zFP) chromophore fragments as spin couplers. Along with the gas phase magnetic exchange coupling constant ( J ), the zero field splitting parameters (ZFSs) of all the diradicals in water and also in blood plasma medium have been calculated. We have found the all molecules to be ferromagnetic in nature with trans diradicals having lower spin–spin ZFS parameter (| D |) and the higher magnetic exchange coupling constants values than their respective fluorescent cis isomers. This has also been noted that due to the presence of the fluoroprotein chromophores, the resulting diradicals can switch back and forth between the isomeric trans and cis forms upon irradiation of the proper wavelength of light. The time dependent density functional (TDDFT) technique is used to measure the actual wavelength of this conversion for these diradicals. A comparison between the evaluated ZFS values with other nitroxide based synthesized molecular systems which are in use as magnetic resonance imaging (MRI) contrast agents (MRICAs) indicates that if these diradicals are synthesized, they can be useful as MRICAs. A relation between the distance and the spin–spin ZFS parameter is also made. To support this fact, we have computed the solvent accessible surface area, molecular volume, the surface to volume ratio, and molecular diameter of each diradical species to address their potentiality to act as contrast agents in MRI. Finally the ability of these diradicals to act as MRICAs is further justified with the help of molecular electrostatic potential surfaces.

Big data analytics is a process of examining large set of data and extracting only the useful information out of it for further research. Data that is generated these days doesn’t follow any particular structure. Data can be structured, un-structured or semi-structured. Data can be in form of text, image, video, live streams etc. It is a challenging job to handle such data for data mining, web mining and text mining or environmental research works. In this paper we have discussed about the step by step process of big analytics and the relevant challenges while applying on real life events. Also this paper provides a comparative study on the popular data mining algorithms which are generally used for big analytics.

Mobile malwares have been rising in scale as Android operating system enabled smart phones are getting popularity around the world. To fight against this outburst of Android malwares, different static and dynamic malware detection methods have been proposed. One of the popular methods of static detection technique is permission based detection of malwares through AndroidManifest.xml file using machine learning classifiers. However, the comparison of different machine learning classifiers on different data sets has not been fully cultivated by existing literatures. In this work we propose a framework which extracts the permission features of manifest files, generates feature vectors and uses different machine learning classifiers of a Data Mining Tool, Weka to classify android applications. We evaluate our method on a set of total 170 applications (100 benign, 70 malwares) and results show that highest TPR rate is 96.70 % while accuracy is up to 77.13 % and highest F1 score is 0.8583.

The effect of an external electric field on the magnetic anisotropy of a single-molecule magnet has been investigated, for the first time, with the help of DFT. The application of an electric field can alter the magnetic anisotropy from "easy-plane" to "easy-axis" type. Excitation analysis performed through time-dependent DFT predicts that the external electric field facilitates metal to π-acceptor ligand charge transfer, leading to uniaxial magnetic anisotropy and concomitant spin Hall effect in a single molecule.