Research Paper Research Articles

Determinants of approach behavior for ChatGPT and their configurational influence in the hospitality and tourism sector: a cumulative prospect theory

Purpose This research paper aims to explore the concerns and determinants of travelers’ behavior toward ChatGPT in the hospitality and tourism context. It also examines the weight of risk factors versus that of motivation and innovation characteristics influencing travelers’ approach behaviors toward ChatGPT. Design/methodology/approach A cumulative prospect theory was used to determine travelers’ responses to ChatGPT. This study, using a fuzzy-set qualitative approach, explored risk, motivation and innovation factors as determinants of approach behaviors for ChatGPT. Findings Findings revealed that risk, motivation and innovation factors were the key triggers of approach behaviors for ChatGPT. An intricate combination effect of the perceived risk, motivation and innovation characteristics was found, and the necessary predictors were determined. Practical implications The findings of this study will expand our current knowledge and offer practical insights for the development of ChatGPT in the hospitality and tourism sector. Originality/value This study makes a significant contribution to the existing literature by providing a nuanced understanding of the intricate interplay between the various factors that shape customer behavior in the context of technology adoption in hospitality and tourism studies.

Creative webs: decoding the creativity potential of Twitter followers

IntroductionPrevious studies have associated creativity to one’s social networks. This study builds on this framework and examines the premise that one’s social fabric is a catalyst for creativity, specifically probing the dynamics between online social connections and creative expressions in the realm of Esports. Therefore, this research paper examined a correlation between people’s creativity and their Effective Network Size (non-redundant ties) on Twitter, to see if potentially non-redundant information is related to creativity.MethodsCreativity score was defined as the propensity of a participant to utilize specific terms relating to Esports in its emerging stages, prior to its peak popularity as evidenced by Google Trends. Effective Network Size was analyzed based on the social ties of participants on Twitter (N = 50,000).ResultsThe findings indicate that the higher the Effective Network Size score, the higher the creativity score. Furthermore, geographically dispersed social networks moderated the relationship between Effective Network Size and creativity. For people with more dispersed social networks, ENS was more constructive for creativity.DiscussionThese findings are discussed in the broader context of the relevancy of online social networking sites for creativity.

Enhancing students’ L2 writing skills online: a case study of an introductory English literature course for ESL students

Abstract This research paper is based on a case study of an introductory English literature course that is being offered at a community college in Hong Kong, China. The 2020–2021 cohort was unique as it had the entire course online using Zoom without any face-to-face or hybrid teaching, which was considered the “new normal” of higher education under the pandemic. This study employs narrative inquiry (Barkhuizen, G. (2014). Revisiting narrative frames: An instrument for investigating language teaching and learning. System, 47, 12–27; Clandinin, D. J. (2016). Engaging in narrative inquiry. Routledge), semi-structured interviews, and analysis of student essays to demonstrate that online essay consultations and collaborative writing are useful for enhancing the essay writing skills of second language learners. The paper specifically reports and highlights how individual Zoom essay consultations, together with collaborative writing on Padlet, scaffold and enhance students’ essay writing and critical thinking skills. Essentially, the paper argues that online technologies expand the dimension of literature education and empower it as a form of content-based ESL instruction for long-term use.

CRYPTOCURRENCY “THE PERFECT ALTERNATIVE SOLUTION TO HIGH-RISK/HIGH-RETURN INVESTMENTS”

In recent years, there has been a notable increase in the number of financial institutions incorporating cryptocurrencies into their investment portfolios. Cryptocurrencies represent a groundbreaking addition to asset managers' repertoire, being the first purely digital assets to be embraced in this manner. While they share some similarities with traditional assets, cryptocurrencies possess unique characteristics that set them apart, making their behavior as an asset class a subject of ongoing exploration. Given the evolving nature of cryptocurrency trading, it is crucial to consolidate existing research findings on the subject. This paper aims to provide a comprehensive overview of cryptocurrency trading research by synthesizing insights from 146 research papers. Topics covered include cryptocurrency trading systems, market bubbles, volatility and return predictions, portfolio construction, technical trading strategies, and more. Through an analysis of datasets, research trends, and distribution among research topics and technologies, this paper sheds light on the current landscape of cryptocurrency trading research. It also identifies promising opportunities for further exploration in this dynamic and rapidly evolving field.

Exploring the Enablers of Organisational Inclusion: An Interpretive Structural Modelling Approach

This research paper aims to identify and structure the enablers that promote organisational inclusivity, fostering a workplace that values diversity and inclusion. Using Interpretive Structural Modelling (ISM), the study identifies the following four clusters of enablers: autonomous, linkage, dependent and driving. Furthermore, the research findings suggest that inclusive leadership acts as a key driving factor for promoting inclusivity in the organisation. The study offers practical implications for human resource managers to develop targeted strategies to create an inclusive culture and enhance overall organisational performance by recognising and prioritising the identified enablers.

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

The growing need for faster and more effective data handling and data transfer capabilities of a network require energy-efficient and more powerful smart sensor devices. These devices have become increasingly necessary in wireless sensor networks (WSN). The architecture or topology of WSN can greatly impact the organizational efficacy and connectivity among the sensor nodes employed within the given area. It is foremost important to implement a robust network topology which must be capable of ensuring continuous and reliable communication within the whole network. This research paper presents an effective approach based on Ant colony scheme to optimize mesh network topology ant colony optimization (ACO) is used to place the sensor nodes optimally in the given area of the whole network. The proposed research carries out comprehensive performance evaluation of the network under various QoS parameters such as bandwidth, throughput, delay, residual energy and routing load. These parameters shows the effectiveness or adaptability of the network with different and dynamically changing communication requirements suggested by this topology scheme. The network based on the proposed method has been simulated several times and the achieved simulation patterns have been analyzed under the mentioned QoS constraints.

Exploring accelerated expansion in the universe: A study of [formula omitted] gravity with parameterized EoS and cosmological constraints

The study conducted in this research paper utilizes the f(Q,T) gravity, where Q represents non-metricity and T represents the trace of the energy–momentum tensor, to investigate the accelerated expansion of the universe. To complete the study, an effective EoS with one parameter α, is parameterized as ωeff=−3α(1+z)3+3. The linear version of f(Q,T)=−Q+σT is also considered, where σ is a constant. By constraining the model with six BAO points, 57 Hubble points, and 1048 Pantheon sample datasets, the parameters α and σ are determined to best match the data. The cosmological parameters and energy conditions for the model are derived and examined. The results show that the model is in good agreement with observations, and can serve as a valuable starting point for analyzing FLRW models in the f(Q,T) theory of gravity.

Application of UV LEDs to inactivate antibiotic resistant bacteria: Kinetics, efficiencies, and reactivations

Unregulated antibiotic use has led to the proliferation of antibiotic-resistant bacteria (ARB) in aquatic environments. Ultraviolet light-emitting diodes (UV LEDs) have evolved as an innovative technology for inactivating microorganisms offering several advantages over traditional mercury lamps. This research concentrated on utilizing UV LEDs with three distinct wavelengths (265 nm, 275 nm, and 285 nm) to inactivate E. coli DH10β encoding the ampicillin-resistant blaTEM-1 gene in its plasmid. Non-linear models, such as Geeraerd's and Weibull, provided more accurate characterization of the inactivation profiles than the traditional log-linear model due to the incorporation of both biological mechanisms and a deterministic approach within non-linear models. The inactivation rates of ARB were higher than antibiotic-sensitive bacteria (ASB) when subjected to UV LEDs. The highest inactivation rates were observed when all microorganisms were exposed to 265 nm. Photoreactivation emerged as the primary mechanism responsible for repairing DNA damage induced by UV LEDs. 285 nm showed the highest reactivation efficiencies for ARB under different fluences. At higher fluences, both 265 and 275 nm displayed similar effectiveness in suppressing reactivation, while at lower fluences, 275 nm exhibited better efficacies in controlling the reactivation. Therefore, the inhibition of reactivation was influenced by the extent of damage incurred by both DNA and enzymes. In nutrient-poor media (0.9 % NaCl), ASB did not exhibit any reactivation potential. However, the addition of Luria-Bertani (LB) broth promoted the reactivation of ASB. Lower fluence rate was more beneficial at 265 nm whereas higher fluence rates were more effective for longer wavelengths. The inactivation of ARB was enhanced by dissolved organic carbon (DOC) at low fluences. However, the removal of ARB was reduced due to the presence of DOC at higher fluences. The highest energy demand for ARB inactivation was reported at 285 nm. Environmental implicationThe excessive and unregulated utilization of antibiotics has emerged as a significant issue for public health. This paper presents a comprehensive analysis of the effectiveness of UV LEDs, an emerging technology, in the inactivation of antibiotic-resistant bacteria (ARB). This research paper explores the kinetics of UV LEDs with different wavelengths to inactivate ARB along with the reactivation efficiencies. This research work also explores the impact and relevant mechanisms of the impact of dissolved organic carbon (DOC) on the inactivation of ARB by UV LEDs.

In-Depth Financial Statement Review of IDBI Federal Life Insurance in New Delhi: A Comprehensive Analysis

The research paper delves into the financial statement analysis of IDBI Federal Life Insurance, aiming to conduct a thorough examination of its financial standing. It will encompass an extensive review of the company's financial statements, key ratios, capital structure, profitability, liquidity, and solvency. Moreover, the study will compare the company's financial performance with that of its peers in the life insurance sector to pinpoint strengths and weaknesses. Recommendations for improvement will be proposed based on these findings. Additionally, the research will evaluate the influence of external factors like economic conditions, regulatory changes, and market trends on the company's financial performance. The methodology involves collecting and analyzing financial data, performing ratio analysis, and drawing conclusions to provide actionable insights. Ultimately, the research aims to offer valuable insights into IDBI Federal Life Insurance's financial health and performance, guiding strategies for sustainable growth and success.

Efficacy of clear aligner therapy over conventional fixed appliances in controlling orthodontic movement: A systematic review

OBJECTIVE: The purpose of the current systematic review was to answer the clinical research question “Is Clear Aligner Therapy (CAT) effective in controlling the orthodontic movement?” by bringing together the most up-to-date information about the available evidence for CAT. METHODOLOGY: On January 1, 2023, a search was conducted in PubMed, ERIC, Embase, and CINHAL for any research papers published in the previous 10 years that provided an overview of the PICO questions. Both the titles and abstracts of the selected studies were evaluated independently by two different authors, and if there was any disagreement between the two review authors, a third reviewer was brought in to settle it. RESULTS: Among included studies, three were retrospective non-randomized and two studies were prospective randomized clinical trials. Various authors reported better outcome for fixed orthodontic appliances than for clear aligner treatment (CAT) in relation to mandibular incisor proclination. The mean objective grading system score was better for braces (17) than for CAT (12) with no clinically significant difference, while staging had a significant impact on treatment efficacy. CONCLUSION: The results of this study suggest that clear aligners may be an effective alternative to traditional braces, but more research is needed to confirm these findings and determine the optimal size of future prospective studies evaluating this treatment.

A Review on Soft Exoskeletons for Hand Rehabilitation

Background: How to enhance the quality of life for the elderly has emerged as a key issue in many nations due to the ageing population. Stroke is the most prevalent disease among the elderly; specifically, the hand dysfunction caused by stroke is also a powerful obstacle to the daily life of the elderly. Soft Exoskeletons for Hand Rehabilitation (SEHRs) have become a major trend for the future due to the increasing demand for hand rehabilitation. Objective: To provide a reference for readers in this field by introducing the most recent research developments in the field of SEHR, including their classification and properties. Methods: By reviewing different types of hand rehabilitation exoskeleton research papers and patents, the advantages and disadvantages, differences, and applications of various SEHRs were summarized. Results: According to the driving mode and realizing the function of SEHRs, the structure characteristics of SEHRs are analyzed and compared. The key problems and future development trends of SEHRs were expounded. Conclusion: According to the driving method, the research shows that SEHRs can be divided into Air-Driven Soft Exoskeletons for Hand Rehabilitation (ADSEHRs), Motor-Driven Soft Exoskeletons for Hand Rehabilitation (MDSEHRs), and Hybrid-Driven Soft Exoskeletons for Hand Rehabilitation (HDSEHRs). Future research is required to further optimize the flexibility and adaptability of soft exoskeletons, improve their accuracy and sensitivity, and enhance human-machine interaction with the human hand.

The Influence of Gold, Black Gold, and Digital Gold on Stock Market Portfolios

This research paper aims to study the influence of Bitcoin (BTC), Crude Palm Oil (CPO), Crude Oil (WTI), the Dollar Rate (USD), and Gold (XAU), on Stock Market Portfolios (KLSE). Time series data for this investigation was obtained from Yahoo Finance and Investing.com. Furthermore, the Unit Root Test conducted found that the variables are stationary after 1st differencing. Moreover, since there was no Cointegration discovered between the variables, VAR was performed to determine the long-run causality effect, while the Wald Test was conducted to examine the short-run causality effect. The findings revealed that none of the variables contained a long-run relationship, whereas Bitcoin (BTC), Crude Palm Oil (CPO), and Gold (XAU) contained a short-run relationship with the independent variable (KLSE).

LINEAR ALGEBRA AS THE MATHEMATICAL FOUNDATION OF ARTIFICIAL INTELLIGENCE: CONCEPTS, APPLICATIONS, AND FUTURE PROSPECTS

Linear algebra, a fundamental tool in the field of mathematics, plays an essential part in the mathematical foundation of the groundbreaking arena of Artificial Intelligence (AI). This mathematics branch offers immense possibilities for capturing complex relationships within datasets through matrices and vectors. These mathematical tools facilitate the concise representation of large volumes of data, contributing to efficient data analysis between them, bringing immense value to AI algorithms and models. This research paper investigates the pivotal role of linear algebra as the fundamental mathematical framework underpinning artificial intelligence. This paper also explores the foundational concepts of linear algebra and its direct applications in various AI algorithms, demonstrating how matrices, vectors, and transformations provide the essential mathematical scaffolding for the development and optimization of intelligent systems.

Anomaly Detection in Blockchain Using Machine Learning

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

The Next Frontier of Security: Homomorphic Encryption in Action

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Estimating the influence of research and development expenditures on the income of companies revenue in the field of engineering of innovative developments under the conditions of changing project management methodology

Estimating the influence of research and development expenditures on the income of companies revenue in the field of engineering of innovative developments under the conditions of changing project management methodology

Specifically formulated ketogenic, low carbohydrate, and carnivore diets can prevent migraine: a perspective.

This article presents a hypothesis explaining the cause of migraines, suggesting that electrolyte imbalance, specifically a lack of sufficient sodium in the extracellular space of sensory neurons, leads to failed action potentials. The author argues that migraines are triggered when sodium channels fail to initiate action potentials, preventing communication between neurons. The article discusses the evolutionary perspective of the migraine brain, stating that migraineurs have a hypersensitive brain with more sensory neuronal connections, making them more reactive to environmental stimuli and in need of more minerals for the increased sensory neuronal communication. Since glucose is often used to reduce serum hypernatremia, it follows that a high carbohydrate diet reduces sodium availability for use in the brain, causing an electrolyte imbalance. Low carbohydrate diets, such as ketogenic, low carb-high fat (LCHF), and carnivore (all animal products), can be beneficial for migraineurs by reducing/eliminating carbohydrate intake, thereby increasing sodium availability. In support, many research papers and some anecdotal evidences are referred to. The article concludes by proposing lifestyle modifications, such as dietary changes and sodium intake management. These will provide migraineurs with a long-term healthy metabolic foundation helping them to maintain strong nutritional adherence and with that aiding continued proper neuronal functioning and migraine free life.

Evolution of COVID-19 Impact on Russian Stock Market: Panic Effect

Over the past few years, many research papers have referred to stock market volatility in relation to investor attention and sentiment and our article adds to the current literature on financial market reactions to the economic consequences of COVID-19. An event such as an outbreak of an infectious disease causes a negative change in investor sentiment, which strongly influences their investment decisions and, consequently, stock market prices. The subject of the study is the mutual influence of stock market characteristics and market sentiment, during a COVID-19 pandemic crisis. The purpose of the study is to provide empirical support for the hypothesis of indirect impact of uncertainty and panic under the COVID-19 pandemic on the dynamics of the stock market in Russia. The World Health Organization and experts forecast that the world will face more than one crisis related to the spread of infectious diseases in the future, so understanding the mechanisms of mutual influence of sentiment and financial markets remains relevant. In this study, we take a novel approach to deriving an indicator for panic that has not been used before. We perform econometric modeling using a Vector Autoregressive Model (VAR) and a Vector Error Correction Model (VECM), which allows us to describe in the model not only the long-term equilibrium but also the dynamics towards it. As a result, we got consistent and efficient estimates of the long-term and short-term effects of panic and mortality rates on the volatility of the RTS stock index and found that the market reaction to COVID-19 changed as the pandemic spread: the effects of uncertainty and panic, while having a significant impact at the beginning of the crisis, faded away. The conclusions obtained in the analysis of the Russian stock market dynamics coincide with those obtained by other authors in their analysis of markets in other countries over a similar period.

On the Controllability of Coupled Nonlocal Partial Integrodifferential Equations Using Fractional Power Operators

In this research paper, we investigate the controllability in the α-norm of a coupled system of integrodifferential equations with state-dependent nonlocal conditions in generalized Banach spaces. We establish sufficient conditions for the system’s controllability using resolvent operator theory introduced by Grimmer, fractional power operators, and fixed-point theorems associated with generalized measures of noncompactness for condensing operators in vector Banach spaces. Finally, we present an application example to validate the proposed methodology in this research.

대학의 비교과교육과정 개발⋅운영 모형 연구

Objectives The purpose of this study is to help universities systemically develop and operate extracurricular curriculum that can reflect their own characteristics, through research on a development and management model for extracurricular curriculum activities. Methods The steps of research were progressed with three stages: research planning, literature review and the model development. Based on the Taba’s(1962) curriculum development model, the research planning stage planned the extracurricular curriculum development and operation model of the university. The literature research stage collected and analyzed the extracurricular, extracurricular activities, extracurricular curriculum which were related to the pilot study, curriculum development model, model research, curriculum development the research paper and also were related to the books and research papers. The model development stage based on the curriculum model and the curriculum development model which are the foundation of curriculum brought out the implication and the model was confirmed after a feasibility study by a expert of curriculum. Results The management and development model for extracurricular curriculums consists of four stages: 1. Drafting a proposal and planning of university extracurricular curriculums; 2. crafting detailed plans for each extracurricular curriculum activity; 3. executing and operating the extracurricular curriculum activities; and 4. evaluating and assessing university extracurricular curriculums. Each stage is comprised of five different sub-categories, such as ① subject, ② period, ③ foundation, ④ method, and ⑤ model. Conclusions The reason for the establishment and operation of the extracurricular curriculum is to instill students with the university’s core values and help them become competent members of society and the university. Therefore, the university extracurricular curriculum must be developed and operated according to the stages and procedures as in the required and elective curriculum.