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The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI's ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language. [ABSTRACT FROM AUTHOR]

Past construction research has focused primarily on the realization of tracking systems but much less on their effectiveness. Despite significant achievements, little is known about the relationship between the environmental site conditions and deployed sensors and their influence on system effectiveness. To reduce this knowledge gap, we investigate the effect of cement-based material on signal energy loss using electromagnetic simulation. We take conventional electromagnetic equations and derive equations that quantitatively describe the signal behavior influenced by cement-based material. Then, we conduct high frequency structure simulator (HFSS) simulations and use the experimental measurements for benchmark comparison with our analytical solution. Results suggest that our analytical method accurately quantifies attenuated signal loss. Also, we construct an attenuation spectrum of energy loss for cement-based material, which can serve as a reference for sensor deployment. Therefore, the method should enable quantification of the signal loss of sensors without actually deploying them or measuring signal data, which should lead to the more effective deployment of tracking sensors. Key words: radio signal strength indication (RSSI), electromagnetic simulation, indoor localization, mobile sensing, optimization, sensor deployment, tracking. Dans le passe, la recherche en construction a porte principalement sur la realisation de systemes de controle des signaux, mais beaucoup moins sur leur efficacite. Malgre des accomplissements importants, on en sait tres peu sur la relation entre les conditions environnementales de l'emplacement et les capteurs deployes et leur effet sur l'efficacite du systeme. Dans le but de reduire cet ecart de connaissances, nous examinons l'effet des materiaux a base de ciment sur la perte d'energie du signal, et ce, en utilisant la simulation electromagnetique. Nous prenons des equations electromagnetiques traditionnelles et derivons des equations qui decrivent quantitativement le comportement du signal influence par les materiaux a base de ciment. Ensuite, nous effectuons une simulation a l'aide d'un simulateur de structure a haute frequence (<< HFSS >>) et des mesures experimentales aux fins de comparaison de reference avec notre solution analytique. Les resultats suggerent que notre methode analytique quantifie precisement la perte de signal attenuee. Aussi, nous construisons un spectre d'attenuation de perte d'energie pour les materiaux a base de ciment, qui peut servir de reference pour le deploiement de capteurs. Donc, la methode devrait permettre la quantification de la perte de signal des capteurs sans pour autant les deployer ou mesurer des donnees de signal, ce qui devrait mener au deploiement plus efficace de capteurs de suivi. [Traduit par la Redaction] Mots-cles: indicateur de la force du signal recu (<< RSSI >>), simulation electromagnetique, localisation interieure, captage mobile, optimisation, deploiement de capteurs, suivi., Introduction Innovation in construction Capitalizing on technological advances in recent years, researchers have explored various methods that benefit the construction industry from design to operation and management. As a result, [...]

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains. [ABSTRACT FROM AUTHOR]