Human resources (HR) analytics is garnering increasing interest each year and is set to play a pivotal role in the development of human resources. In the present era, numerous companies are harnessing the power of analytics to gain a competitive advantage by comprehending all the vital aspects of their workforce by enhancing employee retention through leveraging HR analytics to inform strategic HR choices. Many companies are now incorporating analytical tools into their HR function as a fact-based approach to develop relevant strategies and make informed decisions in managing their workforce more effectively. However, HR faces several challenges in implementing data analytics. Talent management commonly utilizes data analytics to enhance employee engagement, including retention rates, recruitment, job satisfaction, and happiness. This paper discusses the adoption of HR data analytics to enhance employee retention in the workplace. This study delves into the significance of HR data analytics in the realm of employee retention, aiming to assess the efficacy of data-driven decisions. A thorough examination of scholarly publications was undertaken, encompassing both indexed and non-indexed papers sourced from reputable electronic databases to gain insights into the present understanding of HR analytics and its influence on employee retention. The discussion uncovers that HR analytics has a noteworthy impact on improving employee retention in the workplace.

Metal organic framework is a class of hybrid network of supramolecular solid materials comprised of a large number of inorganic and organic linkers all bounded to metal ions in a well-organized fashion. This type of compounds possess a greater surface area with an advantage of changing pore sizes, diversified and beautiful structure which withdrew an intense interest in this field. In the present review articles, the structural aspects, classification, methods of synthesis, various factors affecting the synthesis and stability, properties and applications have been discussed. Recent advances in the field and new directions to explore the future scope and applications of MOFs have been incorporated in this article to provide current status of the field.

The activities and characteristics of heritage, cultural, and creative tourism are notably distinct despite the fact that they are frequently confused and misunderstood. Moreover, these types of tourism have been significantly affected by the COVID-19 pandemic. This review article aims to explore the characteristics of three types of tourism, both pre- and post-pandemic, and seeks to propose sustainable solutions with new opportunities for the tourism industry. The article adopts a PRISMA flow diagram and VOSviewer to perform a systematic literature review, ultimately selecting 179 articles from the Scopus, ScienceDirect, and Google Scholar databases and grouping them into five clusters: 1) heritage, cultural, and creative tourism; 2) co-creation; 3) creative city; 4) sustainability; and 5) technology and innovation. Consequently, this review article proposes a final framework presenting five related clusters suggesting sustainable solutions for creative tourism. It may aid the tourism industries in their transition to creative tourism, which is more sustainable and broadly focused while ensuring safety and enhancing income for local communities in the post-pandemic period.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.