Historic buildings and urban streetscapes face increasing threats from climate change, development, and aging infrastructure, creating a pressing need for accurate and scalable documentation methods. This review assesses the combined use of photogrammetry and unmanned aerial vehicle (UAV) technologies in preserving built cultural heritage. We systematically analyze the end-to-end workflow, from the sophisticated processing of imagery into highly detailed and accurate 3D models in photogrammetry software via data acquisition using diverse UAV platforms and sensor payloads. Through case studies, including the mapping of ancient Maya sites in the Yucatán Peninsula and the conservation of the Notre Dame Cathedral, the review highlights the accuracy, efficiency, and accessibility offered by this technological synergy, underscoring its significance for heritage conservation, research, and the development of digital twins. Furthermore, it explores how these advancements foster public engagement and virtual accessibility, enabling immersive experiences and enriched educational opportunities. The paper also critically assesses the inherent technical, ethical, and legal challenges associated with this methodology, offering a balanced perspective on its application. By synthesizing the current knowledge, this review proposes future research trajectories and advocates for best practices, aiming to guide heritage professionals in leveraging photogrammetry and UAVs for the effective documentation and safeguarding of global cultural heritage.

The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.

Wireless Sensor Networks (WSN) can produce decisions that are unreliable due to the large inherent uncertainties in the areas which they are deployed. It is vital for the applications where WSN's are deployed that accurate decisions can be made from the data produced. Fault detection is a vital pursuit, however it is a challenging task. In this paper we present a fuzzy logic data fusion approach to fault detection within a Wireless Sensor Network using a Statistical Process Control and a clustered covariance method. Through the use of a fuzzy logic data fusion approach we have introduced a novel technique into this area to reduce uncertainty and false-positives within the fault detection process.

The integration of large language models (LLMs) into remote healthcare has the potential to revolutionize medication management by enhancing communication, improving medication adherence, and supporting clinical decision-making. This study aims to explore the role of LLMs in remote medication management, focusing on their impact. This paper comprehensively reviews the existing literature, medical LLM cases, and the commercial applications of LLMs in remote healthcare. It also addresses technical, ethical, and regulatory challenges related to the use of artificial intelligence (AI) in this context. The review methodology includes analyzing studies on LLM applications, comparing their impact, and identifying gaps for future research and development. The review reveals that LLMs have shown significant potential in remote medication management by improving communication between patients and providers, enhancing medication adherence monitoring, and supporting clinical decision-making in medication management. Compared to traditional reminder systems, AI reminder systems have a 14% higher rate in improving adherence rates in pilot studies. However, there are notable challenges, including data privacy concerns, system integration issues, and the ethical dilemmas of AI-driven decisions such as bias and transparency. Overall, this review offers a comprehensive analysis of LLMs in remote medication management, identifying both their transformative potential and the key challenges to be addressed. It provides insights for healthcare providers, policymakers, and researchers on optimizing the use of AI in medication management.

Advances in digital interactive technologies have created a range of innovative products and services for the well-being of society. Extended Reality (XR) technology has shown enormous potential in the educational, commercial, and medical fields. Considering the potential of XR, we analyzed the use of XR technology for the improvement of web service user experiences. The paper discusses Web Extended Reality (XR) and its current circumstances. The study also discusses the advantages of web componentization and the Page Builder System, which is a famous framework for web componentization. Furthermore, the study analyzes the characteristics of XR. The research designs the Web XR User Interface framework with XR characteristics and componentization design. A 3D UI framework is proposed for providing an immersive, explorative, and readable user experience. The framework covers three aspects, including main content, a scrollbar, and navigation. In order to evaluate the performance efficiency of the framework, a proof-of-concept prototype was developed to examine the concept. We conducted a user study with 60 participants to evaluate the reading performance. Compared with the traditional web layout, the experimental results showed that the framework can improve the user experience in static websites. The paper indicates possible future views based on the experimental study.