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Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts
The Big Five Inventory-10 (BFI-10; Rammstedt & John, 2007) is one of many short versions of personality inventories that measure the Big Five trait dimensions. Short versions of scales often present methodological challenges as a trade-off for their convenience. Based on samples from 28 countries (N = 10,560), the current study investigated inter-item correlations estimated using Omega coefficients within each of the five personality characteristics measured by the BFI-10. Results showed that inter-item correlations were significantly lower, in the sample data from non-Western countries compared with the Western countries, for three of the five personality traits, specifically Conscientiousness, Extraversion, and Emotional Stability. Our findings indicate that the psychometric challenges exist across different cultures and traits. We offer recommendations when using short-item scales such as BFI-10 in survey research. 2022 Elsevier Ltd -
Comparison of Various Types of Lubrication During Hard Turning of H13 Tool Steel by Analysing Flank Wear Using ANOVA
Hard machining of components has been a new attraction in the field of manufacturing, as it avoids the need for multiple cost inculcation processes for a single part. Hard machining attracts a wide attention to the researchers because of the usage of hard tools, tougher machinery and enormous quantities of cutting fluids. Optimized use of any of these functionaries can result in reduction of cost as well as safer and clean working environments. In this research new cutting fluid reduction processes were compared along with the use of hard metal inserts. These two methods suggest an enormous amount of cost reduction along with cleaner shop floor. Minimal quantity lubrication (MQL) and minimal cutting fluid application (MCFA) capacities in cutting fluid reduction as mentioned by various researchers in past two decades. These methods were compared in this research paper for finding out the best possible system. Flank wear is considered as a crucial parameter in hard machining as the wear rate affects other deserving product qualities such as surface finish and job profiles. In this research tungsten carbide coated hard metal inserts were used instead of conventional CBN or diamond tipped tools, which are of higher in price margin. The study comprised of Taguchis L9 orthogonal array, which was advised by previous researchers as good tool for optimisation. MQL and MCFA assisted experimentation were performed with same cutting conditions, which were then again compared with dry hard machining and wet machining. Influence of each input parameters where critically evaluated using ANOVA. The results revealed that a promising reduction in tool wear was noticed in MCFA assisted hard machining. 2020, Springer Nature Singapore Pte Ltd. -
COMPARISON OF VIOLENCE IN TELEVISION ANIMATION IN THE PAST TWO DECADES AND IF THERE IS AN EFFECT ON THE MINDS ON THE VIEWERS
Cartoons are the most popular form of entertainment for children. There are very few children who do not watch cartoons. Cartoons are animated figures which carry a story along with it. Every child is exposed to cartoons in some way or the other and whether we except it or not cartoons do have an impact on the minds of the viewers. That??s why most of the cartoons for children are sweet and innocent. But there are a few cartoons that have violence in them. This violence at times can affect the minds and the thinking process of the child. Over the past two decades violence in cartoons has increased. So this study compares the cartoons in the two decades and the increase and the effect it has on the minds. The study to a certain extent proved that the sample population believes that the violence that they view affects them in some way or the other. -
Comparisons of Stock Price Predictions Using Stacked RNN-LSTM
This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG. -
Compendium of Qubit Technologies inQuantum Computing
Quantum computing is information processing based on the principles of quantum mechanics. Qubits are at the core of quantum computing. A qubit is a quantum state where information can be encoded, processed, and readout. Any particle, sub-particle, or quasi-particle having a quantum phenomenon is a possible qubit candidate. Ascendancy in algorithms and coding demands knowledge of the specificities of the inherent hardware. This paper envisages qubits from an information processing perspective and analyses core qubit technologies. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Competitive and contagion effect of initial public offerings in India: An empirical study
This study aims to empirically examine the impact of initial public offerings (IPOs) on the equity share prices of industry rivals. The cross-industry sample comprises 13 companies across six different industries in the Indian market. The study investigates four key variables: the stock returns of industry rivals before and after the IPO of a new market entrant, as well as the daily traded volume of both the market entrant and industry rivals in the days following the IPO. The analysis reveals a significant association between the stock prices of industry rivals before and after the listing date of a market entrant, as evidenced by the adoption of three distinct time windows. However, no significant relationship is observed between the daily traded volume of market entrants and industry rivals. The results reveal the presence of the competitive and contagion effect and the lack of active capitalisation of this short-term phenomenon by investors. 2023 The Author -
COMPETITIVE MARKETING IN INDIA: A CASE STUDY ON AD STRATEGIES OF SAMSUNG GALAXY AND NOKIA LUMIA
Today the first thing that we look for before moving out of our house is our cell phone. This gadget has made possible for us to keep in touch with the entire globe irrespective of where we are. The first ever mobile phone which was commercially available was DynaTAC 8000x but that was the era when not many people could afford it. Contrary to this, today is the time where every nine out of ten people have their personal cell phones. This to a certain limit was made possible by Reliance. It came out with affordable CDMA cell phones which even the lower class population of India could afford. Although these companies have created a benchmark for themselves, they have not really been the leading brands of the industry in terms of variation, style, preference, promotion etc. To a certain extent, it has been noticed that Nokia has been the preference of the people due to its variety, style and longitivity. Nokia has been the leading smart phone brand in the industry for several years. For long it has maintained its credibility, keeping itself connected with the clients. The loyalty that its customers had for the company was strong enough for them to not go in search for other brands. Samsung, on the contrary wasnt doing very well in the market. Its designs and applications were constantly failing. Therefore the fact that Samsung is now the leading brand in our country has a lot aspects attached to it. There have been uncompromising strategies put behind the success of Samsung. From a very nominal smart phone company, it has risen to the most popular smart phone company in India. Its has been noticed that with the incoming of new handsets and softwares, there has been a change even in its promotional activities. Nokia, which in the past was loaded with new launches like the N series or the E series, suddenly came to a halt. This was when the Samsung started coming up with its touch screen Corby phones. Therefore this study will be executed to mark the difference in the marketing strategies of Samsung at its stage of rival, also comparing it to the parallel Nokia Advertisements. -
Complete analysis of beam analyzing powers in d + ? ? ? n + p at near threshold energies
Focusing attention on the photon spin in d ( ? ? , n ) p at near threshold energies of interest to Big Bang Nucleosynthesis, a complete analysis of beam analyzing powers in d ( ? ? , n ) p reaction is carried out. A complete analysis of the reaction needs not only measurements using one state of linear polarization of photon but also measurements using another state of linear polarization inclined to the first at ?/4 and the two states of circular polarization of the photon. A discussion on the complete characterization of the states of photon polarization is presented. The beam analyzing powers with respect to photon polarization are discussed theoretically, using model independent irreducible tensor formalism. 2022 IOP Publishing Ltd. -
Complete analysis of differential cross section in 7 Li + ? ? ? 6 Li + n at astrophysical energies
We have carried out complete analysis of differential cross section in 7 Li + ? ? 6 Li + n using model-independent theoretical formalism. A complete analysis of the reaction involves measurements of not only one state of linear polarization of the photon but also another state of linear polarization inclined to the first at 45? and two states of circular polarization of the photon. An analytical study of the differential cross section including all the photon polarization states is carried out at near-threshold energies of interest to Big Bang Nucleosynthesis. 2024 IOP Publishing Ltd -
Complex and Multifaceted Nature of Cryptocurrency Markets: A Study to Understand its Time-Varying Volatility Dynamics
Decentralised Finance (DeFi) provides a new way to perform complex financial transactions by exploiting blockchain's ability to maintain a decentralised ledger of transactions without being constrained by centralised systems or human intermediaries. DeFi provides alternative financial instruments that might lessen portfolio risk, especially given the erratic state of the financial markets today. This study analyses the association between the year of the coin in which it was introduced and the market capitalisation of the respective companies. Furthermore, the study also tries to understand the volatility associated with cryptocurrencies using EGARCH & GJR-GARCH models. The results reveal that market capitalisation is not similar for all three stages of the age of cryptocurrency. Also, negative news tends to impact Bitcoin more than positive news, and the volatility is persistent and long-lasting. Ethereum, BNB & Solana see more volatility from absolute past shocks; however, Tether exhibits low but persistent volatility as a stablecoin. 2024, Creative Publishing House. All rights reserved. -
Complicated Grief during COVID-19: An International Perspective
Cultures across the globe have evolved time-tested rituals to honor those who die and offer solace and support to survivors with the goal of helping them to accept the reality of the death, cope with the feelings of loss, adjust to life without the deceased, and find ways to maintain a connection to the memory of the deceased. The COVID-19 pandemic has disrupted these rituals and brought significant changes to the way we mourn. Specifically, public health responses to COVID-19 such as social distancing or isolation, delays or cancellations of traditional religious and cultural rituals, and shifts from in-person to online ceremonies have disrupted rituals and thus made it more difficult to access support and complete the psychological tasks typically associated with bereavement. This paper conceptualizes the common bereavement tasks including emotion-focused coping, maintaining a connection to the deceased, disengagement and reframing death and loss, and problemfocused coping. It provides examples of how the COVID-19 pandemic has altered mourning rituals across several cultures and religions and contributed to prolonged grief disorder as defined by the ICD-11 that includes depressive symptoms and post-traumatic stress. Early evidence suggested that the suddenness of loss, the social isolation, and the lack of social support often associated with COVID-19-related death are salient risk factors for complicated grief. As a consequence, psychological assessments, grief counseling, and mental health support are needed by families of patients who died from COVID-19. These services must be essential components of any comprehensive public health response to the pandemic. 2022 Hogrefe Publishing. -
Components of the diffuse ultraviolet radiation at high latitudes
We have used data from the Galaxy Evolution Explorer to study the different components of the diffuse ultraviolet background in the region between the Galactic latitudes 70?-80?. We find an offset at zero dust column density (E(B ? V) = 0) of 240 18 photon units in the FUV (1539 and 394 37 photon units in the NUV (2316 . This is approximately half of the total observed radiation with the remainder divided between an extragalactic component of 114 18 photon units in the FUV and 194 37 photon units in the NUV and starlight scattered by Galactic dust at high latitudes. The optical constants of the dust grains were found to be a = 0.4 0.1 and g = 0.8 0.1 (FUV) and a = 0.4 0.1 and g = 0.5 0.1 (NUV). We cannot differentiate between a Galactic or extragalactic origin for the zero-offset but can affirm that it is not from any known source. 2019 Oxford University Press. All rights reserved. -
Composites based on natural polymers
Polymers are composed of macromolecules of high molecular weight formed by the repeated union of small molecules known as monomers. Polymer materials over other materials such as metals and ceramics, are of light weight and hence extensively used. The use of renewable raw materials can provide a substantial activity for a sustainable society and environment. Natural polymer composites can provide an alternative to increasingly scarce raw materials from plants and animals. 2017 Nova Science Publishers, Inc. -
Compositionally Homogeneous Soft Wrinkles on Elastomeric Substrates: Novel Fabrication Method, Water Collection from Fog, and Triboelectric Charge Generation
Functionality and stimuli-response of natural and artificial elastomeric materials depend significantly on the morphology of their surfaces. Structural transformability and tunable responsiveness of wrinkles on elastomeric materials can enable numerous applications in flexible electronics, optics, and adhesives. Currently existing fabrication techniques rely on sophisticated instrumentation, complex experimental setups, and expensive reagents. These methods are limited in terms of mechanical robustness of the wrinkles produced. Here, a simple, inexpensive, scalable, and reproducible strategy, making use of buckling instability for the creation of soft surface wrinkles on polydimethylsiloxane (PDMS), is presented. PDMS with lower elastic modulus is spin-coated onto a mechanically stretched film of PDMS with a higher elastic modulus. Thermal curing followed by the release of prestrain resulted in the formation of wrinkles in the top layer of the PDMS. The hydrophobic soft surface wrinkles with compositional homogeneity exhibit efficient fog water collection and triboelectric charge generation useful for the preparation of triboelectric nanogenerator devices. Furthermore, the substrates show high mechanical stability and mechanoresponsive optical behaviors. The simplicity and general applicability of the method presented here is expected to establish a promising pathway toward the formation of soft wrinkles in other elastomeric systems also, facilitating important applications in various fields. 2022 Wiley-VCH GmbH. -
Composting for a sustainable future: Turning waste into nutrient-rich soil
As the worldwide waste management problem has risen, composting has gained popularity. It turns organic trash into nutrient-rich soil for agriculture, landscaping, and environmental rehabilitation. This chapter on composting, "Composting for a Sustainable Future: Turning Waste into Nutrient-Rich Soil," is comprehensive. It covers decomposition science, composting methods, materials, and procedures. Composting's agricultural, landscaping, and environmental remediation benefits are also covered. The chapter also examines composting's role in climate change mitigation, its obstacles, and remedies. Decomposition can help people, businesses, and communities live more sustainably. It urges decomposition and trash reduction and provides information about tools to start composting, a simple yet efficient solution to worldwide waste management. 2024, IGI Global. All rights reserved. -
Compounds and methods for the treatment of non-alcoholic steatohepatitis /
Patent Number: WO2019111225, Applicant: AVALIV THERAPEUTICS.
Compounds and compositions are provided having the structure of Formula (I) or a pharmaceutically acceptable salt, tautomer, or stereoisomer thereof, wherein T, T', U, U', V, W, R1, R2, R3', n, o, o', o'', and o''' are as defined herein. Suchcompounds are useful for treating liver diseases and abnormal liver conditions, including non-alcoholic steatohepatitis via inhibition of the lysosomal enzyme cathepsin D. -
Comprehending algorithmic bias and strategies for fostering trust in artificial intelligence
Fairness is threatened by algorithm bias, systematic and unfair disparities in machine learning results. Amazon's AI-driven hiring tool favoured men. AI promised data-driven, impartial decision-making, but it has revealed sector-wide prejudice, perpetuating systematic imbalances. The algorithm's bias is data and design. Biassed historical data and feature selection and pre-processing can bias algorithms. Development is harmed by human biases. Algorithm prejudice impacts money, education, employment, and crime. Diverse and representative data collection, understanding complicated "black box" algorithms, and legal and ethical considerations are needed to address this bias. Despite these issues, algorithm bias elimination techniques are emerging. This chapter uses secondary data to study algorithm bias. Algorithm bias is defined, its origins, its prevalence in data, examples, and issues are discussed. The chapter also tackles bias reduction and elimination to make AI a more reliable and impartial decision-maker. 2024, IGI Global. All rights reserved. -
Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods
This research leveraged machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to predict breast cancer from CT and MRI scans. A dataset comprising 2345 instances of malignant and benign cases was meticulously curated, with 80% allocated for training and 20% for testing. The experimental results revealed the DNN as the top-performing model, exhibiting remarkable accuracy (95.2%), precision (94.8%), recall (95.6%), and F1 score (95.2%). The ANN also demonstrated strong performance, achieving an accuracy of 93.6% with balanced precision and recall scores. In contrast, the SVM, while respectable, fell slightly behind the machine learning models in terms of overall accuracy and performance. Detailed confusion matrices further elucidated the models capabilities and limitations, providing valuable insights into their diagnostic prowess. These findings hold great promise for breast cancer diagnosis, offering a non-invasive and highly accurate means of early detection. Such a tool has the potential to enhance patient care, reduce the strain on healthcare systems, and alleviate patient anxiety. The success of this research highlights the transformative impact of advanced machine learning in medical imaging and diagnosis, signaling a path toward more efficient and effective healthcare solutions. Further research and clinical validation are essential to translate these promising results into practical applications that can positively impact patients and healthcare providers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comprehensive Data Analysis of Anticorrosion, Antifouling Agents, and the Efficiency of Corrosion Inhibitors in CO2 Pipelines
This study explores the various methods that are being proposed for their anticorrosion and antifouling capabilities and also reviews the unique properties that make them suitable for such applications. Special attention has also been given to the problem of corrosion in CO2 pipelines, considering the corrosion inhibitors currently being used and performing statistical analysis about if and how various factors such as temperature, flow velocity, pH, and CO2 pressure affect the rate of corrosion of the CO2 pipelines. Tests including ANOVA, correlation, and graph analyses were conducted to explore their relationships, and suitable conclusions were drawn for the data collected. 2024 Scrivener Publishing LLC. -
Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the models robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the models predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The systems sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. The Author(s) 2024.