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.

This study aims to construct an integrative model for understanding the factors that shape Chinese tourists’ intentions to visit Thailand as a gastronomic tourism destination. In detail, we investigate the relationships among cognitive experiences, emotional experiences, cultural experiences, affective destination image, cognitive destination image, and the intention to visit Thailand for culinary experiences. Utilizing an online survey method to gather 562 Chinese tourists who have experienced Thai gastronomy, this study continues to use structural equation model to process data. The findings reveal that cognitive, emotional, and cultural experiences significantly influence tourists’ affective and cognitive destination images, positively impacting their intention to visit Thailand for its culinary offerings. The affective and cognitive destination images act as crucial mediators, intricately linking these experiences with travel intentions. This approach improves our understanding of the dynamics involved. It also provides practical insights for developing targeted marketing strategies.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.

In Côte d'Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R2 = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R2 = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.

Constructed wetlands have emerged as a sustainable alternative for decentralized wastewater treatment in developing countries which face challenges with urbanization and deteriorating infrastructure. This paper discusses the key factors affecting the implementation of constructed wetlands in developing countries. A case study research design was adopted, which focused on Bulawayo, Zimbabwe. A mixed-method approach was adopted for the study. Spatial analysis was conducted to identify potential sites for constructed wetlands in the city of Bulawayo. Semi structured interviews were conducted, with relevant stakeholders, such as town planners, civil engineers, NGO representatives, community leaders, and quantity surveyors. The findings reveal that political reforms, public acceptance, land availability, and funding are crucial for the successful implementation of constructed wetlands. Additionally, four sites were identified as the most favorable preliminary locations for these systems. The paper captures all the key factors relevant to the implementation of constructed wetlands (CWs) with a closer look at policy and the role it plays in the adoption of decentralized wastewater treatment systems. Formulating policy around the decentralized sanitation systems was considered imperative to the success of the systems whether in implementation or in operation. The paper adds to knowledge in the subject of sustainable wastewater treatment alternatives for developing countries. However, further research can be conducted with a different methodology to ascertain the applicability of the systems in developing urban cities considering other important aspects in the implementation of wastewater treatment systems.

Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.