This study considers the role of leadership within the hospitality sector as a key tool in raising performance levels. Hospitality is unique in its service-based approach, which relies on employees to ensure effective service. Post-COVID-19 and Brexit, the hospitality sector has seen a shift in reliance towards a home workforce, and as such, retention has become an area of greater importance. This case study investigation adopted a qualitative approach to consider the perceptions of six managers within a UK-based luxury hotel. Semi-structured interviews were used to draw out their experience of approaches used to ensure effective delivery in their areas of responsibility. The research concludes that a shift in leadership approach (from autocratic to democratic) is a necessity to retain staff, particularly as the shift to a greater reliance on a home workforce due to COVID-19 and Brexit starts to impact the sector. There does, however, remain a need to be more autocratic in certain situations to ensure the quality of service. Subsequently, communication becomes critical in the building of relationships. The research considers leadership approaches from a managerial perspective and is based on individual perceptions. Traditionally, research has been conducted from an employee perspective.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

This research was conducted with the intention of investigating and analyzing the factors that influence the views that consumers have of advertising on social media platforms. The goal of this study is to look at the many ways that new media ads affect consumers’ purchasing behavior. An evaluation of the validity and reliability of the measures has been carried out with the assistance of confirmatory factor analysis. In addition, the quantitative research approach makes use of both simple random sampling and statistical sampling. The information was gathered via the use of a questionnaire that was issued to fans of new media. Using a Likert scale with five points, the questionnaire’s questions were evaluated to ensure that they were appropriately worded. The total sample size that is employed is 359. The purchase behavior of consumers of new media has been evaluated based on five variables, including the ability to attract attention, provide amusement, establish legitimacy, emphasize creative character qualities, and evoke emotional appeal. The objective of this study paper is to investigate the impact that advertisements broadcast via new media have on consumers’ decision-making processes regarding the acquisition of goods and services. The research’s findings show that when consumers are weighing their options for purchase, advertisements having the largest impact on their purchasing decisions in new media. With the goal of offering important insights into the new media advertising industry, the author seeks to link these results with pertinent ideas from the theoretical framework.

The study acknowledges empirical, conceptual, and policy-driven papers that address emotional assertiveness, assertive communication, and assertive training as means of improving employee performance in Chinese banking, which is a significant contributor to the Chinese economy. Most banking enterprises have suffered from poor performance and a lack of aggressiveness in operation. It can be used by both managers and employees to create a good interaction process and a favorable work environment, which can help elevate performances. The research employs a quantitative approach, utilizing a questionnaire survey and simple random sampling. The sample comprises 381 employees from the Chinese banking industry, with a response rate above 70%. The regression analysis confirms that emotional assertiveness, assertive training, and assertive communication significantly impact employee performance. In conclusion, this study contributes to academia and industries by addressing the importance of assertiveness in improving performance. The policy-driven evidence on the conceptual framework of HR literacy in emotional, training, communication, and job performance should be adopted and reviewed in the country’s existing management by objective policy and legal framework in resolving employee job performance and training that are still underutilized and have a great deal of potential to satisfy the employees and management needs by establishing and emerging nations.

This research investigates the effects of drying on some selected vegetables, which are Telfaria occidentalis, Amaranthu scruentus, Talinum triangulare, and Crussocephalum biafrae. These vegetables were collected fresh, sliced into smaller sizes of 0.5 cm, and dried in a convective dryer at varying temperatures of 60.0 °C, 70.0 °C and 80.0 °C respectively, for a regulated fan speed of 1.50 ms‒1, 3.00 ms‒1 and 6.00 ms‒1, and for a drying period of 6 hours. It was discovered that the drying rate for fresh samples was 4.560 gmin‒1 for Talinum triangulare, 4.390 gmin‒1for Amaranthu scruentus, 4.580 gmin‒1 for Talinum triangulare, and 4.640 gmin‒1 for Crussocephalum biafrae at different controlled fan speeds and regulated temperatures when the mass of the vegetable samples at each drying time was compared to the mass of the final samples dried for 6 hours. The samples are considered completely dried when the drying time reaches a certain point, as indicated by the drying rate and moisture contents tending to zero. According to drying kinetics, the rate of moisture loss was extremely high during the first two hours of drying and then steadily decreased during the remaining drying duration. The rate at which moisture was removed from the vegetable samples after the drying process at varying regulated temperatures was noted to be in this trend: 80.0 °C > 70.0 °C > 60.0 °C and 6.0 ms‒1 > 3.0 ms‒1 > 1.5 ms‒1 for regulated fan speed. It can be stated here that the moisture contents has significant effects on the drying rate of the samples of vegetables investigated because the drying rate decreases as the regulated temperatures increase and the moisture contents decrease. The present investigation is useful in the agricultural engineering and food engineering industries.

We present an innovative enthalpy method for determining the thermal properties of phase change materials (PCM). The enthalpy-temperature relation in the “mushy” zone is modelled by means of a fifth order Obreshkov polynomial with continuous first and second order derivatives at the zone boundaries. The partial differential equation (PDE) for the conduction of heat is rewritten so that the enthalpy variable is not explicitly present, rendering the equation nonlinear. The thermal conductivity of the PCM is assumed to be temperature dependent and is modelled by a fifth order Obreshkov polynomial as well. The method has been applied to lauric acid, a standard prototype. The latent heat and the conductivity coefficient, being the model parameters, were retrieved by fitting the measurements obtained through a simple experimental procedure. Therefore, our proposal may be profitably used for the study of materials intended for heat-storage applications.