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Starting from the roots of teacher education: Inclusion of educational neuroscience in teacher training in India
Educational neuroscience has warranted much research and showed much promise in recent times. However, there is a lack of effective bridging between research in this field and its implementation in actual classroom settings. In order to bridge this gap, a key strategy is to include educational neuroscience in teacher training and education. The current study uses a survey method to assess student-teachers' awareness, opinions, and openness toward educational neuroscience concepts and techniques. This study examines a snowball sample of 83 Indian student-teachers who joined the Bachelor of Education program in 2018, 2019, and 2020. The results of this study indicate that although most student-teachers are aware of certain techniques, they are unaware that it is part of educational neuroscience. Further, the student-teachers also showed interest in learning such techniques and considered it relevant and useful in classroom teaching. Finally, this study also highlights the role of decision-makers in including educational neuroscience in the B.Ed. program, possibly as an optional paper. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Startups' empowerment of employees: An analysis using VOSviewer
The purpose of this study is to evaluate and consolidate existing research on the factors that affect the performance of employees in startup companies. Specifically, it focuses on factors such as empowerment, motivation, dedication, leadership styles, and self-determination. This study aims to understand how these factors influence employee performance in the context of startup organizations. The theoretical framework of this study is based on the HR value chain model. The HR value chain model demonstrates how HR practices can benefit organizations by showing the various HR processes that support a company's goals. Additionally, the study incorporates psychological factors related to employee performance, such as autonomy, competence, and relatedness. The methodology section mentions that the research primarily covers management-related articles from 2010 to 2022. The research involved reviewing primary studies by searching computerized databases and selected journal articles from specific websites. VOSviewer 1.6.18 was used to analyze eligible articles, and bibliometric networks were generated to connect keywords to relevant articles. The study's findings indicate that empowerment, motivation, commitment, and other traits are linked to the success of new startup businesses. Effective leadership tactics play a crucial role in creating a healthier, happier work environment with dedicated employees. It appears that the study identified 20 papers, including 17 research articles, that establish connections between empowerment and startup performance. The use of VOSviewer's overlay bibliometric networks helped visualize these connections. 2024, Malque Publishing. All rights reserved. -
State of local governance and urban development problems: A study of Bengaluru
[No abstract available] -
State of the art MOF-composites and MXene-composites: Synthesis, fabrication and diverse applications
The composites of MXene and Metal-organic frameworks (MOFs) have gained considerable attention recently due to their synergic properties. MOFs are advantageous as it offers high surface area, good stability, and tunable chemical structure, MXenes, on the other hand, provide superior ion-transport characteristics, high conductivity, large surface-to-volume ratio, and facile modification strategies. Composite fabrication enables tuning the desired properties of the individual materials and propounds good stability and enhanced performance. This review assembles and describes the composites of MXene and MOF in various fields of application like energy storage devices, sensors, medicinal, separation membranes, medicinal, and photocatalysis in a comprehensible way. Here we first outline the characteristics of MOFs and MXenes along with a comparative study of different synthesis routes for the fabrication of the composites. The review also encompasses a detailed discussion on different industries and applications that the MXene and MOF composites are subjected to. Finally, the review provides future perspective for designing and development of newer composites as well as scope for further industrial approach. 2024 Elsevier Ltd -
State-of-art Techniques for Classification of Breast Cancer: A Review
Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. -
State-of-the-Art and Upcoming Trends in IoT-Enabled Smart Cities
Modern cities tremendous development of urbanization necessitates smart responses to pressing problems like mobility, medical care, power, and civil construction. The Internet of Things (IoT), which can use sustainable data and communication innovations, is evolving into the foundation for the upcoming trends of smart cities. To meet the demands of the expanding populace, several demands of the smart city must be taken into account. The IoT expansion has greatly generated a variety of study avenues for the smart city on the flip side of developing innovation. The suggested research proposal offers the analytic network procedure (ANP) for analyzing smart cities while maintaining in mind application instances of the smart city. In complicated circumstances when there are ambiguous options, the ANP technique performs effectively. The projected methods experimental findings demonstrate its viability for use case-based evaluation of IoT-enabled smart cities. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
State-of-the-Art of AI-Driven Smart Technologies
Emergence as a transformative force has redefined the contours of technological innovation across industries and societies. AI-driven smart technologies-ranging from intelligent personal assistants and autonomous vehicles to predictive healthcare systems and smart infrastructure-have transitioned from futuristic concepts to integral components of everyday life. The convergence of other and 5G networks has accelerated intelligent capabilities, including perceiving, reasoning, learning, and real-time. These technologies are designed not merely to automate tasks but to personalize experiences, optimize utilization, and operational efficiencies across multiple domains. The rise of smart technologies is rooted in the rapid growth of algorithms, neural networks, cognitive computing, and autonomy. In the domain of consumer electronics, AI evolution is where thermostats work collaboratively to offer convenience, security, and energy efficiency. These systems can learn user preferences over time, automate routines, and alerts recommendations. 2026 by IGI Global Scientific Publishing. -
Static Analysis and Machine Learning for Runtime Library Detection in Linux Binaries
The upsurge of malware targeting Internet of Things (IoT) devices demands effective approaches. This work announces a new method, stimulated by MANTILLA, which influences machine learning models. Through a prominence on architecture-independent characteristics from binary procedures, the system progresses its competence to differentiate among several libraries as well as architectures. Classification accuracy is further enhanced by employing a majority voting technique such that the output of the model is robust and reliable. Besides the machine learning-based classification, the paper incorporates a malware detection module based on signature matching. This two-pronged approach enables the system to cross-check discovered runtime libraries against a large database of pre-collected malware signatures. By marking possible security threats according to this comparison, the system greatly increases its ability to identify malicious binaries, thus offering an added layer of security for IoT devices. This unification of detection and classification mechanisms plays an important role in dealing with the changing nature of malware threats. Although encouraging results were obtained through this project, more evaluation should be done for comparison of the efficiency of KNN with other models, for example, Random Forest. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
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. -
Stationary wrap around the sketchbook /
Patent Number: 202141054657, Applicant: Jersiah Jeyaraj.
The professionals and student community belonging to the art and architecture industry travel to different places. They carry pen/pencils and notebooks in additional pouches or bags for documentation and creative activities. But it isn't easy to search their tools and materials while it gets scattered inside of bags. We believe and respect the value of time since we notice they lose time searching and organizing their tools and materials to start the work. -
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 Ecological Mathematical Model Based on Data Warehouse
Persistence of ecosystems, existence and stability of periodic and almost periodic solutions, and global attractiveness are important research contents in ecological mathematical theory. This article takes the ocean as an example to illustrate. The marine ecological model management system integrates marine technology, Internet technology and database technology. The purpose is to collect, organize and analyze mathematical models related to marine ecosystems, integrate them according to certain classification principles, and store them in the form of text. In the database, the query of the database according to the important parameters in the mathematical model or the classification of the mathematical model is provided on the Internet, and the queried mathematical model is displayed on the screen through the browser. This paper adopts the method of data warehouse. How to effectively use resources is an important aspect of whether to take the initiative in competition. Data warehouse can play the characteristics of information processing and has broad application prospects in the face of competition in the field of telecommunications. 2023 IEEE. -
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 Data Analysis of Anticorrosion and Antifouling: Unveiling Insights from Performance and Trends
This chapter provides intended insights about the current antifouling and anticorrosion conventional coatings, each with a distinct quality, that fall short of fully satisfying the contemporary requirements of material preservation in a marine environment. To solve these issues and accomplish the required goals, a number of novel terminologies and procedures have been investigated and several recently published research works were referred to and briefly examined in order to provide appropriate conclusions. In order to explore new methodologies that provide total protection for materials from biofouling and corrosion in maritime environments, this study looks into the complex realm of antifouling and anticorrosion methods. 2024 Scrivener Publishing LLC. -
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.