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This study delves into the impact of mindfulness on the retention intention of technology employees, with a particular focus on the mediating variables of affective commitment (AC) and organizational identification (OI). The primary aim is to gain a comprehensive understanding of the underlying mechanisms through which mindfulness influences the retention intention of technology employees.

The research employed a survey approach with self-administered questionnaires and structural equation modeling. The collected data were analyzed using Statistical Product and Service Solutions (SPSS) 24 and Analysis of Moment Structure (AMOS) 28. Multiple mediation analyses was conducted through AMOS to examine the mediating effects of OI and AC.

The association between mindfulness and retention intention among technology employees showed an overall positive correlation. Additionally, AC and OI were positively correlated with retention intention. In the impact of employee mindfulness (EM) on retention intention, all indirect effects were found to be significant.

To the best of the authors' knowledge, this study is the first to investigate the relationship between EM and retention intention, as well as the associations of AC and OI with them, extending the application of mindfulness in management and offering insights for talent retention among company decision-makers.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

One of the most significant changes in monetary economics at the beginning of the twenty-first century has been the virtual disappearance of what was once a dominant focus, the role of money in monetary policy, and parallelly, the disappearance of the liquidity preference-money supply (LM) curve. Economists used to consider monetary policy with the help of the LM curve as part of the analytical framework which captures the demand for money. However, the workhorse model of modern monetary theory and policy, the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework, only comprises the dynamic investment-savings (IS) curve, the New Keynesian (NK) Phillips curve, and a monetary policy rule. The monetary policy rule is generally known as the Taylor rule. It relates the nominal interest rate to the output-gaps and inflation-gaps, but typically not to either the quantity or the growth rate of money. This change in the modern monetary model reflects how the central banks make monetary policy now. This study provides a detailed discussion on the role of money in monetary policy formulation in the context of the NK and the New Monetarist perspectives. The pros and cons of abandonment of money or the LM curve from monetary policy models have been discussed in detail.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

This paper aims to understand the magnetohydrodynamics (MHD) mechanism in the molten pool under different modes of magnetic field. The comparison focuses on the Lorenz force excitation and its effect on the melt flow and solidification parameters, intending to obtain practical references for the design of magnetic field-assisted laser directed energy deposition (L-DED) equipment.

A three-dimensional transient multi-physical model, coupled with MHD and thermodynamic, was established. The dimension and microstructure of the molten pool under a 0T magnetic field was used as a benchmark for accuracy verification. The interaction between the melt flow and the Lorenz force is compared under a static magnetic field in the X-, Y- and Z-directions, and also an oscillating and alternating magnetic field.

The numerical results indicate that the chaotic fluctuation of melt flow trends to stable under the magnetostatic field, while a periodically oscillating melt flow could be obtained by applying a nonstatic magnetic field. The Y and Z directional applied magnetostatic field shows the effective damping effect, while the two nonstatic magnetic fields discussed in this paper have almost the same effect on melt flow. Since the heat transfer inside the molten pool is dominated by convection, the application of a magnetic field has a limited effect on the temperature gradient and solidification rate at the solidification interface due to the convection mode of melt flow is still Marangoni convection.

This work provided a deeper understanding of the interaction mechanism between the magnetic field and melt flow inside the molten pool, and provided practical references for magnetic field-assisted L-DED equipment design.

The purpose of this paper is to design and construct an amplitude shift keying (ASK) modulator, which, using the digital binary modulating signal, controls a floating memristor emulator (MR) internally without the need for additional control circuits to achieve the ASK modulated wave.

A binary digital unipolar signal to be modulated is converted by a pre-processor circuit into a suitable bipolar modulating direct current (DC) signal for the control of the MR state, using current conveyors the carrier signal’s amplitude is varied with the change in the memristance of the floating MR. A high pass filter is then used to remove the DC control signal (modulating signal) leaving only the modulated carrier signal.

The results from the experiment and simulation are in agreement showed that the MR can be switched between two states and that a change in the carrier signals amplitude can be achieved by using an MR. Thus, showing that the circuit behavior is in line with the proposed theory and validating the said theory.

In this paper, the binary signal to be modulated is modified into a suitable control signal for the MR, thus the MR relies on the internal operation of the modulator circuit for the control of its memristance. An ASK modulation can then be achieved using a floating memristor without the need for additional circuits or signals to control its memristance.

The discourse traverses the intricate landscape of the metaverse, exploring its evolution, intricacies, and the symbiotic integration of artificial intelligence (AI). The metaverse, a virtual realm where individuals interact with digital entities, burgeons from a decades-old concept to a contemporary reality, captivating attention with its immersive potential. The union of AI and the metaverse heralds unprecedented possibilities and challenges. It fuels personalized recommendations, realistic avatars, intelligent Non-Playable Character (NPCs), and predictive analytics. However, concerns loom, spanning addiction, privacy, and security, as users immerse themselves in virtual realms, potentially neglecting real-world responsibilities and sharing sensitive information has been discussed in this chapter. The narrative further delves into the metaverse's anatomy, delineating its infrastructure, hardware, software, content creation, and commerce. The integration of AI into metaverse security epitomizes a confluence of innovation and growth. Balancing the potential benefits and risks, stakeholders embark on a journey toward a secure, immersive digital realm. The discourse advocates for proactive and responsible AI usage, encompassing transparency, accountability, and trustworthiness. Regulatory frameworks and standards emerge as essential guardrails, protecting user privacy and forestalling AI misuse.