The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners.

The construction of this research was developed to reflect the way information and communication technologies (ICT) have transformed the tourist distribution channel. This phenomenon has caused, in the first place, the direct interaction between tourists and tourism operators and, second, the appearance of real virtual intermediation actors, a situation that disadvantages the activity of traditional travel agencies and causes immersions in reintermediation processes at risk of compromising its permanence in the market. On the other hand, in a sustainable tourism context, this work represents an opportunity for intermediation agencies in terms of a value management practice as they can develop sustainable promotion processes that promote, for example, the protection of the territory, the conservation of resources, and cultural rescue. Therefore, according to travel agencies, to directly influence the distribution chain, we verified this work to demonstrate the use of digital language as a benefit in the design of sustainable tourism products. Thus, from the methodological discipline of discourse analysis, we created sets of words with semantic content that were valued through the sentiment scales of the Facebook social media network. The results showed that digital promotion favors airline companies and hostels more than a sustainable tourism environment as such. Finally, from the study of probability and density equations, predictive models were used to configure linguistic icons in promoting sustainable tourism.

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement.

Spur gearboxes are an integral component in the operation of rotary machines. Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time‐frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub‐bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post‐hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy 96% and AUC 98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology's suitability for this type of analysis.

In the industry, gearboxes used for their efficiency in power transmission are critical components, making early failure detection essential. This work aims to determine the ranking of condition indicators (CIs) to extract information from the vibration signal in the frequency domain for a spur gearbox and to assess the accuracy of the classification model for the failure severity level. In laboratory conditions, the tooth breakage of a pinion with different severity levels was simulated in a spur gearbox. Four accelerometers were installed in the gearbox in a vertical position to obtain the vibration signal. First, information was extracted from the vibration signal through 25 CIs in the accelerometers. Using artificial intelligence, the ranking of 10 CIs was carried out. Subsequently, the random forest (RF) algorithm was used to determine the accuracy in classifying the failure severity level. Finally, an ANOVA test was conducted to determine if there were significant differences in classification accuracy for the four accelerometers. The results concluded that the CIs selected through the ranking are optimal for determining the failure severity level of tooth breakage in a spur gearbox and that the sensor placement affects the classification accuracy.

Purpose: This study aims to develop a structured methodology for selecting and ranking frequency-domain vibration features to assess the severity of failure in spur gearboxes, taking into account the effects of sensor position and inclination. Methods: Vibration signals were acquired from six accelerometers installed with varying spatial positions and inclinations on the gearbox housing. The signals were transformed into the frequency domain via Fast Fourier Transform (FFT), from which 25 condition indicators (CIs) were extracted. A two-step feature ranking strategy was implemented using effect size (Cohen’s d) and classification performance from Random Forest (RF) and K-Nearest Neighbors (KNN). Subsequently, Mahalanobis distance was employed to assess multivariate separation, and factorial ANOVA was used to evaluate the impact of sensor placement and inclination. Results: The Third Moment, Fourth Moment, and Central Frequency emerged as the most discriminative features across different fault types and severity levels. Mahalanobis distance increased with fault progression, confirming the effectiveness of the selected features for degradation tracking. Although sensor position and inclination introduced some variability, their influence on classification accuracy was limited. Conclusions: The proposed methodology enables interpretable and robust feature selection forcondition monitoring of gearboxes. It enhances diagnostic reliability while demonstrating resilience to sensor placement variability, making it suitable for practical industrial implementations.