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Stable copper nanoparticles as potential antibacterial agent against aquaculture pathogens and human fibroblast cell viability
The developments of green nanotechnology are generating interest of researchers towards synthesis of copper nanoparticles due to their increasing application towards the biomedical field. The utilization of phytochemicals in plant extracts have become a valuable trend in the synthesis of nanoparticles as they possess dual nature of reducing and stabilizing agents. In this work a simple and rapid biosynthesis route for producing stable fenugreek copper nanoparticles (FCuNPs) using Trigonella foenum-graecum is demonstrated and assessed its antibacterial activity against gram negative Vibrio species. The characterization of synthesized FCuNPs was carried out using UVvis spectrophotometer and the SPR of FCuNPs is observed at 350 nm. TEM, HRTEM SAED analysis was done to evaluate the morphology and size of FCuNPs. FTIR spectra of both the plant extract and FCuNPs were recorded in order to study the interaction of phytochemicals with FCuNPs. The antibacterial activity of biosynthesized FCuNPs was tested against V. vulnificus, V. harveyi and V. parahaemolyticus using agar well diffusion technique. Since this method of synthesizing copper nanoparticles does not involve any harmful chemicals, the FCuNPs produced are more biocompatible and were used to evaluate human skin fibroblast cell line by Alamar Blue reduction assay. The outcomes of this report will surely provide a new path in the field of nanotechnology and nano medicine where there is a significant need of antibacterial and cell viability studies. Hence, FCuNPs can be powerful therapeutic materials in numerous biomedical applications, which are to be discovered in the near prospective. 2021 Elsevier Ltd -
Stacked LSTM and Kernel-PCA-based Ensemble Learning for Cardiac Arrhythmia Classification
Cardiovascular diseases (CVD) are the most prevalent causes of death and disability worldwide. Cardiac arrhythmia is one of the chronic cardiovascular diseases that create panic in human life. Early diagnosis aids physicians in securing life. ECG is a non-stationary physiological signal representing the heart's electrical activity. Automated tools to detect arrhythmia from ECG signals are possible with Machine Learning (ML). The ensemble learning technique combines the power of two or more classifiers to solve a computational intelligence problem. It enhances the performance of the models by fusing two or more models, which extremely increases its strength. The proposed ensemble Machine learning amalgamates the potency of Long Short-Term Memory (LSTM) and ensemble learning, opening up a new direction for research. In this research work, two novel ensemble methods of Extreme Gradient Boosting-LSTM (EXGB-LSTM) are developed, which use LSTM as a base learner and are transformed into an ensemble learner by coalescing with Extreme Gradient Boosting. Kernel Principal Component Analysis (K-PCA) is a significant non-linear dimensionality reduction technique. It can manage highdimensional datasets with various features by lowering the dimensionality of the data while retaining the most crucial details. It has been applied as a preprocessing step for feature reduction in the dataset, and the performance of EXGB-LSTM is tested with and without K-PCA. Experimental results showed that the first method, fusion of EXG-LSTM, has reached an accuracy of 92.1%, Precision of 90.6%, F1-score of 94%, and Recall of 92.7%. The second proposed method, KPCA with EXGB-LSTM, attained the highest accuracy of 94.3%, with a precision of 92%, F1-score of 98%, and Recall of 94.9% for multi-class cardiac arrhythmia classification. (2023), (Science and Information Organization). All Rights Reserved. -
Stakeholders' pedagogical preferences for teaching 'marketing' in management education
This study has been realized that there is a dire need for re-thinking, particularly obvious for matters of assessment and its relation to the current focus on teaching marketing. A descriptive design of the research was used where convenient sampling has been followed for data collection. In order to achieve the purpose, it was decided to collect independent opinions of students, teachers, and professionals. Analysis has been done through descriptive statistics and Spearman's rank correlation. As a result, a significant difference between the stakeholders' perceptions about the pedagogy for teaching marketing in management education was identified. 2021 Ecological Society of India. All rights reserved. -
Stand-Up: The Comic Public Sphere in India
Comic performances might take a subversive form, especially in an autocratic regime where used as an instrument of expression by the oppressed, the silenced, the unseen, and the unheard, thereby offering a political critique of the state, economy, and systemic failures. This article discusses the ability of contemporary Indian stand-up comic performances to undermine hegemony. The article begins with a theoretical evaluation of the comic in the public sphere, moves to a brief survey of South Asian forms of comic performances, and links these concerns to performances by two contemporary comedy collectives, Aisi Taisi Democracy (ATD) and East India Comedy (EIC). The article then concludes with the possible complications of ATD and EICs contribution to a robust public sphere. Copyright 2023 (Rashi Bhargava and Samarth Singhal). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at http://ijoc.org. -
Star formation around three co-moving HAeBe stars in the Cepheus Flare
Context. The presence of three more Herbig Ae/Be (HAeBe) candidates in the Cepheus Flare within a 1.5 radius centered on HD 200775 suggests that star formation is prevalent in a wider region of the LDN 1147/1158, LDN 1172/1174, and LDN 1177 clouds. A number of young stellar objects (YSOs) are found to be distributed toward these cloud complexes along with the HAeBe stars. Various star formation studies clearly indicate ongoing low-mass star formation inside the clouds of this region. Sources associated with less near-infrared excess and less H? emission raise the possibility that more low-mass YSOs, which were not identified in previous studies, are present in this region. Aims. The aim is to conduct a search for additional young sources that are kinematically associated with the previously known YSOs and to characterize their properties. Methods. Based on the Gaia DR2 distances and proper motions, we found that the HAeBe candidates BD+681118, HD 200775, and PV Cep are all spatially and kinematically associated with previously known YSOs. Based on the Gaia DR2 data, we identified a number of co-moving sources around BD+681118. These sources are characterized using optical and near-infrared color-color and color-magnitude diagrams. Results. We estimated a distance of 3407 pc to the whole association that contains BD+681118, HD 200775, and PV Cep. Based on the distance and proper motions of all the known YSOs, a total of 74 additional co-moving sources are found in this region, of which 39 form a loose association surrounding BD+681118. These sources are predominantly M-type sources with ages of ?10 Myr and no or very little near-infrared excess emission. The distribution of co-moving sources around BD+681118 is much more scattered than that of sources found around HD 200775. The positive expansion coefficients obtained via the projected internal motions of the sources surrounding BD+681118 and HD 200775 show that the co-moving sources are in a state of expansion with respect to their HAeBe stars. A spatiooral gradient of these sources toward the center of the Cepheus Flare Shell supports the concept of star formation triggered by external impacts. 2021 ESO. -
Star formation in a massive spiral galaxy with a radio-AGN
We present an analysis of new VLT/MUSE optical imaging spectroscopic data of 2MASX J23453268-0449256 (J2345-0449), a nearby (z = 0:0755) massive (Mstellar = 4*1011 M) spiral galaxy. This is a particularly interesting source for a study of active galactic nucleus (AGN) feedback since it hosts two pairs of bright, giant radio jets and a massive, luminous X-ray halo, but it has no massive bulge. The galaxy has a 24 kpc wide ring of molecular gas, and a source-averaged star formation rate that is factors 30 to 70 lower than expected from the Kennicutt-Schmidt law. With MUSE, we have analyzed the stellar continuum and bright optical line emission and have constrained the spatially resolved past and present star formation on scales of approximately 1 kpc. More than 93% of the stellar mass formed ?10 Gyrs ago including in the disk. Optical emission from the AGN is very faint and contributes 2% of the continuum around the nucleus at most. Most line emission is predominantly excited by shocks and old stellar populations except in 13 young star-forming regions that formed ?11 Myrs ago, of which only seven are associated with the molecular ring (the others are at larger radii). They avoid a region of high electron densities aligned with the radio source, and form stars at efficiencies that are comparable to those in normal spiral galaxies. We discuss the implications of our findings for the regulation of star formation in galaxies through AGN feedback in the absence of competing mechanisms related to the presence of a massive stellar bulge, such as morphological quenching. The Authors 2023. -
Star-forming, rotating spheroidal galaxies in the GAMA and SAMI surveys
The Galaxy And Mass Assembly (GAMA) survey has morphologically identified a class of 'Little Blue Spheroid' (LBS) galaxies whose relationship to other classes of galaxies we now examine in detail. Considering a sample of 868 LBSs, we find that such galaxies display similar but not identical colours, specific star formation rates, stellar population ages, massto- light ratios, and metallicities to Sd-Irr galaxies. We also find that LBSs typically occupy environments of even lower density than those of Sd-Irr galaxies, where ?65 per cent of LBS galaxies live in isolation. Using deep, high-resolution imaging from VST KiDS and the new Bayesian, 2D galaxy profile modelling code PROFIT, we further examine the detailed structure of LBSs and find that their Ssic indices, sizes, and axial ratios are compatible with those of low-mass elliptical galaxies. We then examine SAMI Galaxy survey integral field emission line kinematics for a subset of 62 LBSs and find that the majority (42) of these galaxies display ordered rotation with the remainder displaying disturbed/non-ordered dynamics. Finally, we consider potential evolutionary scenarios for a population with this unusual combination of properties, concluding that LBSs are likely formed by a mixture of merger and accretion processes still recently active in low-redshift dwarf populations.We also infer that if LBS-like galaxies were subjected to quenching in a rich environment, they would plausibly resemble cluster dwarf ellipticals. 2019 The Author(s). -
Static analysis tool for identification of permission misuse by android applications
Android is one of the most important and widely used mobile operating systems in the world. The Android operating system utilizes the permission-based model, which permits Android applications to get user data, framework data, gadget data and other assets of Smartphone. These permissions are affirmations declared by the developer of an application. The permissions granted varies from one application to another, depending on its functionality. During installation, permissions to access the resources of the smartphone are requested by apps. Once the client grants the permission, the apps are allowed to access the granted resources as per its requirement. Android OS is susceptible to different security issues owing to the loopholes in security. This paper mainly focuses on identifying how the permissions granted to a specific application is misused by another application using SharedUserID. The paper also proposes a security tool that identifies a list of applications which are misusing the permissions in a user's Android smartphone. The viability of the tool is tested by using a Proof-of-Concept (PoC) implementation of the security tool. Research India Publications. -
Static perfect fluid space-Time and paracontact metric geometry
The main purpose of this paper is to study and explore some characteristics of static perfect fluid space-Time on paracontact metric manifolds. First, we show that if a K-paracontact manifold M2n+1 is the spatial factor of a static perfect fluid space-Time, then M2n+1 is of constant scalar curvature-2n(2n + 1) and squared norm of the Ricci operator is given by 4n2(2n + 1). Next, we prove that if a (?,?)-paracontact metric manifold M2n+1 with ? >-1 is a spatial factor of static perfect space-Time, then for n = 1, M2n+1 is flat, and for n > 1, M2n+1 is locally isometric to the product of a flat (n + 1)-dimensional manifold and an n-dimensional manifold of constant negative curvature-4. Further, we prove that if a paracontact metric 3-manifold M3 with Q? = ?Q is a spatial factor of static perfect space-Time, then M3 is an Einstein manifold. Finally, a suitable example has been constructed to show the existence of static perfect fluid space-Time on paracontact metric manifold. 2022 World Scientific Publishing Company. -
STATIC PERFECT FLUID SPACE-TIME ON ALMOST KENMOTSU MANIFOLDS
In this work, we intend to investigate the characteristics of static perfect fluid space-time metrics on almost Kenmotsu manifolds. At first we prove that if a Kenmotsu manifold M is the spatial factor of static perfect fluid space-time then it is ?-Einstein. Moreover, if the Reeb vector field ? leaves the scalar curvature invariant, then M is Einstein. Next we consider static perfect fluid space-time on almost Kenmotsu (?, ?)0-manifolds and give some characteristics under certain conditions. 2021 Bulgarian Academy of Sciences. All rights reserved. -
Static voltage stability of reconfigurable radial distribution system considering voltage dependent load models
This paper presents the static voltage stability analysis of RDS. Initially the performance of RDS is evaluated using backward/forward load flow considering voltage-dependent load modeling. Later, the load flow solution is used for determining the static voltage stability of the system. The analysis is performed for different type of loads such as constant power, constant current, constant impedance, residential, industrial, commercial, agricultural and electric vehicle loads. The simulations are performed for standard and optimal reconfigured topology of standard IEEE 33-bus test system. The comparative study reveals the importance of load type and topology while assessing the static stability analysis of radial distribution systems. 2020, International Information and Engineering Technology Association. -
Statistic analysis of IPL match score and winner inning wise using machine learning algorithms
This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning. 2024, Taru Publications. All rights reserved. -
Statistical analysis of stagnation-point heat flow in Williamson fluid with viscous dissipation and exponential heat source effects
This analysis explores the effect of the novel exponential space-dependent heat generation factor on the stagnation-point Williamson fluid flow over a stretchable surface. The heat transport phenomenon is carried out by the addition of viscous and Ohmic dissipations. Similarity transformations are applied to the nonlinear system of partial differential expressions that arise by the flow. The nonlinear ordinary differential system hence obtained is solved to visualize the role of different constraints graphically. Statistical methods such as correlation, probable error, and regression are utilized. The probable error is evaluated to calculate the reliability of the computed correlation factors. The study reveals that the velocity phenomenon is reduced by incrementing the Weissenberg parameter. The velocity of the hydromagnetic liquid is lesser than the velocity of magnetohydrodynamic fluid flow. Also, the higher heat generation factor gives a boost to the temperature of the flowing material. 2020 Wiley Periodicals LLC -
Statistical and experimental studies of MoS2/g-C3N4/TiO2: a ternary Z-scheme hybrid composite
Abstract: A ternary photocatalyst, MoS2/g-C3N4/TiO2, was prepared using layered and exfoliated MoS2, g-C3N4, and TiO2 viahydrothermal and wet chemical method. It was characterized using various methods to evaluate the structural, morphological and optical properties. Successful incorporation of g-C3N4 and TiO2into MoS2 was confirmed by X-ray photoelectron spectroscopy, and the formation of heterojunctions among MoS2, g-C3N4 and TiO2 particles was established by transmission electron microscopy. These hybrid composites exhibited excellent efficiency in the degradation of malachite green dye. The composite can be recycled four times without loss of photoactivity. The remarkable improvement in photocatalytic efficiency was because of the synergism among the three nanoparticlesthrough the Z-scheme pathway which allows separation of electronhole pairs and makes MoS2/g-C3N4/TiO2 an outstanding material in the fields of photocatalysis and water treatment. The optimized experimental conditions for the degradation of the dye were assessed by the BoxBehnken design of the response surface methodology. Graphical abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
Statistical features from frame aggregation and differences for human gait recognition
Human gait recognition, an alternate biometric technique, received significant attention in the last decade. As many gait recognition applications require real-time response, the primary concern is to design efficient and straightforward gait features for human recognition. In this work, two novel gait features are proposed. Both features are designed by exploring the dynamic variations of different body parts during a gait cycle. The first feature set is based on one-against-all gait frame differences for person identification. This novel approach divides each frame in a gait cycle to blocks, compute the block sum, and then find the difference of respective block sum between the first frame and the rest. The second feature set is defined on the first-order statistics of the normalized sum of the frames in a cycle. Two other existing features- Centroid of Silhouette frames and feature values defined on Change Energy Images are also considered. Feature level fusion is realized by considering the different combinations of the four types of features. Experiments carried out with the CASIA Gait Dataset B demonstrated the proposals merit with high recognition accuracy. The outcome of the investigations is promising when compared to recent contributions. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
Statistical features learning to predict the crop yield in regional areas
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
Statistical tests for key strength identification in cryptography
The cryptographic study involves three algorithms, one for Encryption of Plain text to Cipher text, one for Decryption for Cipher text back to Plain text and third for the generation of the Key. Key generation algorithm works on the principle of Randomness. In this work, the randomness of Key is studied by using Statistical methods like Runs Up & Runs Down test, Runs (Above and Below the mean), Chi Square test & Auto correlation test for its usability in Cryptographic study. 2020 IJSTR. -
Steering through the pandemic: narrative analysis of school leader experiences in India
The COVID ?19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Stereotype threat and psychological wellbeing in children of prisoners
Stereotypes are ideas that one holds regarding individuals because of their membership to a specific group. The current research was undertaken to study stereotype perception and stereotype threat on the psychological wellbeing of children of prisoners. Eight children of prisoners in the age range of 17 to 25 (females = 4), whose fathers were in prison for more than a year, participated in the study. Semi-structured interview method was used to collect data from the participants and data were analyzed using thematic network analysis method. Results revealed that children of prisoners strongly perceive stereotypes against them and they conform to stereotypes. Further to this, it was found that stereotype threat had positive effects in terms of increased goal-directed behavior and negative effects in terms of reduced happiness and increased feelings of anger and sadness. These Undings are discussed in light of previous literature on the effects of stereotypes on behavior. 2019 International Journal of Criminal Justice Sciences (IJCJS). -
Sterlite Technologies Ltd.: To Buy or Not to Buy is the Question
[No abstract available]