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Purpose: This study aims to identify communication speech acts and transaction terms in online stores (Olshop) during live streaming on Facebook amid the COVID-19 pandemic and to understand communication patterns between sellers and buyers when shopping on Facebook live streaming in Indonesia.

The Need for Research: This research is motivated by the skill gap arising from increasing buying and selling transactions through live streaming on Facebook. Cultural and demographic shifts, along with the widespread availability of modern technologies and marketing 2.0 have resulted in the global population adopting social media at rates far beyond our use of the Internet, making a compelling case by example and analogy that social media has the potential to level the playing field and is effective in reaching their target market.

Methodology: The type of research is descriptive-qualitative using corpus data instruments. The data collection technique in this study was carried out by reading and observing the data and listening to speeches about buying and selling women’s equipment from various online stores on Facebook. Then select and sort the data designated as forms, strategies, and functions of speech acts in buying and selling transactions during live streaming on Facebook. The data analysis technique has three steps: (1) reducing the amount of data; (2) presenting the data; and (3) concluding.

Findings: The results show that there are four types of speech acts between sellers and buyers in the live-streaming online shop on Facebook, namely, assertive, directive, expressive, and commissive speech acts.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA).

Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined.

The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem.

This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors’ knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results.

Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.

This paper aims to examine the thermal transmission and entropy generation of hybrid nanofluid filled containers with solid body inside. The solid body is seen as being both isothermal and capable of producing heat. A time-dependent non-linear partial differential equation is used to represent the transfer of heat through a solid body. The current study’s objective is to investigate the key properties of nanoparticles, external forces and particular attention paid to the impact of hybrid nanoparticles on entropy formation. This investigation is useful for researchers studying in the area of cavity flows to know features of the flow structures and nature of hybrid nanofluid characteristics. In addition, a detailed entropy generation analysis has been performed to highlight possible regimes with minimal entropy generation rates. Hybrid nanofluid has been proven to have useful qualities, making it an attractive coolant for an electrical device. The findings would help scientists and engineers better understand how to analyse convective heat transmission and how to forecast better heat transfer rates in cutting-edge technological systems used in industries such as heat transportation, power generation, chemical production and passive cooling systems for electronic devices.

Thermal transmission and entropy generation of hybrid nanofluid are analysed within the enclosure. The domain of interest is a square chamber of size L, including a square solid block. The solid body is considered to be isothermal and generating heat. The flow driven by temperature gradient in the cavity is two-dimensional. The governing equations, formulated in dimensionless primitive variables with corresponding initial and boundary conditions, are worked out by using the finite volume technique with the SIMPLE algorithm on a uniformly staggered mesh. QUICK and central difference schemes were used to handle convective and diffusive elements. In-house code is developed using FORTRAN programming to visualize the isotherms, streamlines, heatlines and entropy contours, which are handled by Tecplot software. The influence of nanoparticles volume fraction, heat generation factor, external magnetic forces and an irreversibility ratio on energy transport and flow patterns is examined.

The results show that the hybrid nanoparticles concentration augments the thermal transmission and the entropy production increases also while the augmentation of temperature difference results in a diminution of entropy production. Finally, magnetic force has the significant impact on heat transfer, isotherms, streamlines and entropy. It has been observed that the external magnetic force plays a good role in thermal regulations.

Hybrid nanofluid is a desirable coolant for an electrical device. Various nanoparticles and their combinations can be analysed. Ferro-copper hybrid nanofluid considered with the help of prevailing literature review. The research would benefit scientists and engineers by improving their comprehension of how to analyses convective heat transmission and forecast more accurate heat transfer rates in various fields.

Due to its helpful characteristics, ferrous-copper hybrid nanofluid is a desirable coolant for an electrical device. The research would benefit scientists and engineers by improving their comprehension of how to analyse convective heat transmission and forecast more accurate heat transfer rates in cutting-edge technological systems used in sectors like thermal transportation, cooling systems for electronic devices, etc.

Entropy generation is used for an evaluation of the system’s performance, which is an indicator of optimal design. Hence, in recent times, it does a good engineering sense to draw attention to irreversibility under magnetic force, and it has an indispensable impact on investigation of electronic devices.

An efficient numerical technique has been developed to solve this problem. The originality of this work is to analyse convective energy transport and entropy generation in a chamber with internal block, which is capable of maintaining heat and producing heat. Effects of irreversibility ratio are scrutinized for the first time. Analysis of convective heat transfer and entropy production in an enclosure with internal isothermal/heat generating blocks gives the way to predict enhanced heat transfer rate and avoid the failure of advanced technical systems in industrial sectors.

This paper aims to study the effect of concentric hot circular cylinder inside egg-cavity porous-copper nanofluid on natural convection phenomena.

The finite element method–based Galerkin approach is applied to solve numerically the set of governing equations with appropriate boundary conditions.

The effects of different range parameters, such as Darcy number (10–3 = Da = 10–1), Rayleigh number (103 = Ra = 106), nanoparticle volume fraction (0 = ϑ = 0.06) and eccentricity (−0.3 = e = 0.1) on the fluid flow represent by stream function and heat transfer represent by temperature distribution, local and average Nusselt numbers.

A comparison between oval shape and concentric circular concentric cylinder was investigated.

In the current numerical study, heat transfer by natural convection was identified inside the new design of egg-shaped cavity as a result of the presence of a circular inside it supported by a porous medium filled with a nanofluid. After reviewing previous studies and considering the importance of heat transfer by free convection inside tubes for many applications, to the best of the authors’ knowledge, the current work is the first study that deals with a study and comparison between the common shape (concentric circular tubes) and the new shape (egg-shaped cavity).

Cross-border e-commerce live streaming morphs into an epidemic shopping scenario, yet there is a paucity of research on impulse purchasing in this context. The purpose of this study is to empirically investigate the adoption motivation of cross-border e-commerce live streaming and its influence mechanism on intrinsic response and purchase impulse and to highlight the mediating role of browsing behavior.

Based on the use and gratification lens, a new conceptualization model is established to captivate the theoretical relationships between perceived stimuli, individual attitudes, browsing behavior and impulsive purchases. A questionnaire survey was used to collect cross-sectional data from 427 Malaysian consumers and the estimated framework was validated through AMOS-structural equation modeling technique.

The findings confirm that perceived interactivity, perceived information usefulness and perceived enjoyment significantly influenced positive attitudes toward live-streaming, which in turn induced impulsive purchases; however, perceived affective gratification did not stimulate positive attitudes. Consumers’ utilitarian browsing had a stronger effect on impulse purchases than hedonic browsing and utilitarian browsing behavior mediated the relationship between positive attitudes and impulse buying; however, hedonic browsing had neither a direct nor a mediating effect on impulsive purchases.

This research enhances the literature on the impact of cross-border e-commerce live streaming, an emerging technology, on consumer behavior and offers managerial implications for e-commerce practitioners to gain insights into consumer impulse purchasing behavior.

The findings revamp conventional knowledge and provide new angles for understanding the formation mechanisms of impulse purchases, motivations for virtual media use and browsing behavior mediating effects in the context of live streaming.

This study aims to investigate the influence of wall curvature in a semicircular thermal annular system on magneto-nanofluidic flow, heat transfer and entropy generation. The analysis is conducted under constant cooling surface and fluid volume constraints.

The mathematical equations describing the thermo-fluid flow in the semicircular system are solved using the finite element technique. Four different heating wall configurations are considered, varying the undulation numbers of the heated wall. Parametric variations of bottom wall undulation (f), buoyancy force characterized by the Rayleigh number (Ra), magnetic field strength represented by the Hartmann number (Ha) and inclination of the magnetic field (γ) on the overall thermal performance are studied extensively.

This study reveals that the fluid circulation strength is maximum in the case of a flat bottom wall. The analysis shows that the bottom wall contour and other control parameters significantly influence fluid flow, entropy production and heat transfer. The modified heated wall with a single undulation exhibits the highest entropy production and thermal convection, leading to a heat transfer enhancement of up to 21.85% compared to a flat bottom. The magnetic field intensity and orientation have a significant effect on heat transfer and irreversibility production.

Further research can explore a wider range of parameter values, alternative heating wall profiles and boundary conditions to expand the understanding of magneto-nanofluidic flow in semicircular thermal systems.

This study introduces a constraint-based analysis of magneto-nanofluidic thermal behavior in a complex semicircular thermal system, providing insights into the impact of wall curvature on heat transfer performance. The findings contribute to the design and optimization of thermal systems in various applications.

The disposition effect remains one of the most significant investor behavior puzzles. This study aims to consolidate the knowledge, explore current dynamics, elicit trends and offer future research directions to demystify the disposition effect.

This study applies the hybrid review method. It first used bibliometric analysis (212 documents), followed by content analysis (54 articles) to analyze the breadth and depth of literature on the disposition effect.

This study presents performance analysis and science mapping. It identifies five main research streams: evidence, implications and mitigation techniques; theoretical explanations; investor biases and hedonic framing; attributes, beliefs and preferences; and implications for asset pricing and market efficiency. This study further offers future research directions for disposition effect research.

This study deploys sequential bibliometric and content analysis. A meta-analysis of quantitative articles could provide specific insights regarding the disposition effect. Besides, this study is based on Scopus-indexed journals only.

This study benefits investors and portfolio managers as they learn effective ways to guard against the disposition effect. Policymakers may tweak tax laws to incentivize long-term holding, and regulators can run investor education campaigns to minimize the disposition effect’s consequences effectively.

To the best of the authors’ knowledge, this is probably the first hybrid review of high-quality, contemporary articles on the disposition effect that offers science mapping, research streams, future research directions and a succinct summary of theories, contexts, characteristics and methods deployed in the field of research.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.