Search Results (105)

The implementation of Digital Twins (DTs) in hospitality facilities represents a significant opportunity to optimize front-end services, enhancing guest experience and operational efficiency. This paper proposes an ontology-driven approach for DTs in hotel reception areas, focusing on integrating IoT devices, real-time data processing, and service optimization. By modeling interactions between guests, receptionists, and hotel management systems, DTs enhance resource allocation, predictive maintenance, and customer satisfaction. Simulations and historical data analysis enable forecasting demand fluctuations and optimizing check-in/check-out processes. This research provides a structured framework for DT applications in hospitality, validated through scenario-based simulations, showing significant improvements in check-in time and guest satisfaction. Validation was conducted through scenario-based simulations reflecting real-world operational challenges, such as guest surges, room assignment, and staff workload balancing. Metrics including check-in time, guest satisfaction index, task completion rates, and prediction accuracy were used to evaluate performance. Simulations were grounded in historical hotel data and modeled typical peak-period dynamics to ensure realism. Results demonstrated a 25–35% reduction in check-in time, a 20% improvement in staff efficiency, and significant enhancements in guest satisfaction, underscoring the practical value of the proposed framework in real hospitality settings. Full article

This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. Although iForest has received attention for its multivariate and ensemble-based nature, its performance under non-normal data distributions and varying bootstrap settings remains underexplored. To address this gap, a comprehensive simulation was performed across 18 scenarios involving log-normal, gamma, and t-distributions with different mean shift levels and bootstrap configurations. The results show that iForest substantially outperforms the conventional Hotelling’s T² control chart, especially in non-Gaussian settings and under small-to-medium process shifts. Enabling bootstrap resampling led to marginal improvements across classification metrics, including accuracy, precision, recall, F1-score, and average run length (ARL)₁. However, a key limitation of iForest was its reduced sensitivity to subtle process changes, such as a 1σ mean shift, highlighting an area for future enhancement. Full article

A real-time structural health monitoring (SHM) system of an airplane composite wing with adjustable damage is reported, where testing under realistic flight conditions is carried out in the controllable and repeatable environment of an industrial wind tunnel. An FBG-based sensing array monitors a debonded region, whose compromised structural strength is regained by a set of lockable fasteners. Damage tunability is achieved by loosening some of or all these fasteners. Real-time analysis of the data collected involves Principal Component Analysis, followed by Hotelling’s T-squared and Q measures. With previously set criteria, real-time data collection and processing software can declare the structural health status as normal or abnormal. During testing, the system using the Q measure successfully identified the initiation of the damage and its extent, while the T-squared one returned limited outcomes. Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

This paper proposes an improved random projection-based method for testing high-dimensional two-sample mean vectors and covariance matrices. For mean testing, the proposed approach incorporates training data to guide the construction of projection matrices toward the estimated mean difference, thereby substantially enhancing the power of the projected Hotelling’s $T^2$ statistic. We introduce three aggregation strategies—maximum, average, and percentile-based—to ensure stable performance across multiple projections. For covariance testing, the method employs data-driven projections aligned with the leading eigenvector of the sample covariance matrix to amplify the differences between matrices. Aggregation strategies—maximum-, average-, and percentile-based for the mean problem and minimum and average p-values for the covariance problem—are developed to further stabilize performance across repeated projections. An application to gene expression data is provided to illustrate the method. Extensive simulation studies show that the proposed method performs favorably compared to a recent state-of-the-art technique, particularly in detecting sparse signals, while maintaining control of the Type-I error rate. Full article

The comparison of covariance matrices is one of the most important assumptions in many multivariate hypothesis tests, such as Hotelling $T^2$ and MANOVA. The sample covariance matrix, however, is singular in high-dimensional data when the variable number (p) is greater than the sample size (n). Therefore, its determinant is zero, and its inverse cannot be calculated. Although many studies addressing this problem are discussed in the Introduction Section, they have not focused on outliers in datasets. In this study, we propose a test statistic that can be used on high-dimensional datasets without being affected by outliers. There is no distributional assumption because our proposed test is permutational. We investigate the performance of the proposed test based on simulation studies and real example data. In all cases, our proposed test demonstrates good type-1 error control, power, and robustness. Additionally, we have constructed an R function and added it to the “MVTests” package. Therefore, our proposed test can be performed easily on real datasets. Full article

Luxury beach hotels in tropical climates are large consumers of electricity, negatively impacting the environment and their profit margins. Energy efficiency and the incorporation of clean energy are among the main actions contributing to reducing this problem, but the implementation of this second solution is minimal among these types of hotels. A case study was conducted, and it was found that this is primarily due to a lack of space in their facilities. Solutions are proposed by implementing agrivoltaics farms in the areas adjacent to the destination studied. The project is technically, economically, and legally feasible, and the proposed agrivoltaics farms could supply nearly 580 million kWh annually, mitigating emissions of just over 390,000 tCO₂e/year and making Puerto Vallarta and Nuevo Vallarta a “Green Destination”, thus contributing to meeting international GHG mitigation targets. Full article

This paper presents the design of a fault detection system (FDD) based on principal component analysis (PCA) to detect faults in the transient state of distillation processes. The FDD system detects faults due to changes in calorific power and pressure leaks that can occur during the heating of the mixture to be distilled (transient), mainly affecting the quality of the distilled product and the safety of the process and operators. The proposed FDD system is based on PCA with a T2 Hotelling statistical approach, considering data from a real distillation pilot plant process. The FDD system is evaluated with two fault scenarios, performing power changes and pressure leaks in the pilot plant reboiler during the transient state. Finally, the results of the FDD system are analyzed using Accuracy, Precision, Recall, and Specificity metrics to validate its performance. Full article

The hospitality and tourism (H&T) sector, marked by intense employee–customer interactions, dynamic labor shifts, and high physical and emotional labor demands, faces chronic talent acquisition and retention. Therefore, tailored employer branding (EB) strategies that address the unique characteristics of the H&T sector are essential for improving the current situation. Despite the critical need for tailored solutions, a clear and unified approach for H&T companies to effectively manage their EB strategies, including the development of a compelling employee value proposition (EVP) that resonates with targeted professionals, has yet to be established. Following the PRISMA reporting guidelines, a systematic literature review of 30 peer-reviewed articles from the Scopus and Web of Science databases was conducted to synthesize existing research on EB practices in the H&T sector. The results reveal a fragmented literature that lacks a cohesive framework for categorizing and measuring EVP. The use of varied and inconsistent EVP models and scales across studies hampers comparative analysis and limits the development of generalizable insights. Furthermore, the review highlights a concentration of research within the hotel industry, leaving other important H&T industries, such as the restaurant and cruise industries, underexplored. This SLR emphasizes the urgent need for a unified approach to EB in H&T. Based on these results, promising research avenues are suggested to further advance EB research in H&T, along with managerial implications for enhancing talent attraction and retention in the sector. Full article

Researchers and companies are placing increasing importance on sustainability to fight climate change. This study analyzes the sustainability of hotel installation, photovoltaic (PV) solar panels, and the transition from traditional lighting to light-emitting diode (LED) bulbs. The results show that for the PV system, the NPV (net present value) varied between 3191 and 11,959 kTND/kW and that the installation of PV panels has a positive NPV of 100% in the case of a high market scenario and 79–84% in the case of a scenario with reduced market activity. Regarding energy optimization, the use of LED bulbs generates an NPV of 346 to 713 TND/bulb, depending on self-consumption and the cost of electricity. Ecological studies show that installing PV panels would reduce carbon dioxide emissions by 424 gCO2eq/kWh, or 61 tCO2eq/year. Furthermore, social evaluations have shown the importance of the use of renewable energy from an energy optimization point of view for the ecological transition. In conclusion, green investments improve the sustainability of hotels. However, to fully exploit this potential, a change in consumer attitudes is needed. Hotels must continue to promote their sustainability efforts while making their guests aware of the importance of making eco-friendly choices. Only a combined approach, involving both hoteliers and guests, will achieve a sustainable transition in the hotel sector. The objective of this article is therefore to examine the multidisciplinary interactions between photovoltaic solar energy and sustainable development by highlighting the inherent opportunities of this multidisciplinary approach for their success in the hospitality sector. Our methodological approach therefore combines a theoretical and a numerical study. These studies play a major role in energy transition projects due to their economic, environmental, technical, and technological contributions, which proves the importance of the multidisciplinary approach to address the energy transition in a holistic way. Full article

This research explores the effectiveness of Quality Management Systems (QMSs) certified under the ISO 9001 standard by applying Hotelling’s multivariate statistical test T². This research focuses on organizations in central Mexico, evaluating whether the adoption of the ISO 9001 standard generates the promised benefits (by the International Standardization Organization itself), such as process improvement, increased customer satisfaction, higher sales, and increased revenues. Using a comprehensive framework grounded in ISO 10014 and incorporating statistical tools, such as descriptive analysis, regression, simulation, and Hotelling’s T² test, this study examined performance differences across sectors and pinpointed the critical factors impacting QMS outcomes. The results demonstrated notable advantages, including average improvements exceeding 20% in anticipated benefits. Furthermore, the analysis underscored the importance of QMS maturity, process enhancement, and customer satisfaction as pivotal drivers of QMS success. Sector-specific patterns also emerged, revealing that public organizations prioritize process efficiency and customer satisfaction, whereas private entities emphasize sales and revenue growth. By employing multivariate techniques, this research offers valuable insights into the interconnected factors affecting QMS effectiveness and provides actionable recommendations for organizations to enhance their QMS performance. Full article

This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. In a concise multi-stage process, long-term climatic data and on-site technical documentation were analyzed to create a detailed BIM model. This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. The study also incorporated photovoltaic panels and solar collectors, reducing reliance on non-renewable energy sources. Results demonstrate the effectiveness of smart energy management, showcasing significant potential for scalability in similar building typologies. Future improvements include integrating a temporal evolution model, refining feature selection using advanced optimization techniques, and expanding validation across multiple case studies. This research highlights the transformative role of ML, IoT, and BIM in achieving sustainable, smart, and efficient building energy systems, offering a replicable framework for sustainable renovations in the hospitality sector. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with Hotelling’s T² Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-T² model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-T² model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-T² model is outperformed in 100% of the analyzed scenarios when compared to SAE-T² in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-T² methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE’s potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations. Full article

The tourism sector is a vital contributor to Thailand’s economy but also a significant source of greenhouse gas (GHG) emissions. This study aims to align Thailand’s Sustainable Tourism Goals (STGs), established in late 2023, with the nation’s GHG reduction targets. Adapted from the United Nations Sustainable Development Goals (SDGs), the STGs encompass 17 dimensions and 86 indicators but currently lack explicit quantitative targets. This research identifies key measurable criteria in energy (STGs 7, 11, 13), waste management (STG 12), and water management (STG 6), focusing on data from large hotels to assess their GHG emissions and reduction potential. The findings indicate that implementing STG measures could reduce emissions by 527,291 tCO₂eq, equivalent to 4.80% of the national GHG reduction target, through energy conservation, waste management, and water efficiency measures. Adjusted targets, including an 18.50% reduction in the energy sector (107 hotels), a 21.00% reduction in waste (121 hotels), and a 2.50% reduction in wastewater (14 hotels), could enable large hotels to achieve a reduction of 83,880 tCO₂eq, allowing them to fully meet their assigned reduction responsibilities. Furthermore, this would contribute 0.76% to the national target. This study demonstrates how integrating measurable components into the STG framework can enhance the tourism sector’s role in achieving national climate goals and promoting sustainable practices. Full article