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Amidst the domestic labor shortage and worldwide pandemic in recent years, there has been an urgent need for a digital means that allows construction site workers, particularly site managers, to obtain information more efficiently in support of their daily managerial tasks. For workers who move around the site, traditional software applications that rely on a form-based interface and require multiple finger movements such as key hits and clicks can be inconvenient and reduce their willingness to use such applications. Conversational AI, also known as a chatbot, can improve the ease of use and usability of a system by providing an intuitive interface for user input. This study presents a demonstrative Natural Language Understanding (NLU) model and prototypes an AI-based chatbot for site managers to inquire about building component dimensions during their daily routines. Building Information Modeling (BIM) techniques are also applied to implement the answering module of the chatbot. The preliminary testing results show that the chatbot can successfully predict the intents and entities behind the inquiries raised by site managers with satisfactory accuracy for both intent prediction and the answer. These results provide site managers with alternative means to retrieve the information they need. [ABSTRACT FROM AUTHOR]

Both humans and non‐human animals exhibit sensitivity to the approximate number of items in a visual array, as indexed by their performance in numerosity discrimination tasks, and even neonates can detect changes in numerosity. These findings are often interpreted as evidence for an innate 'number sense'. However, recent simulation work has challenged this view by showing that human‐like sensitivity to numerosity can emerge in deep neural networks that build an internal model of the sensory data. This emergentist perspective posits a central role for experience in shaping our number sense and might explain why numerical acuity progressively increases over the course of development. Here we substantiate this hypothesis by introducing a progressive unsupervised deep learning algorithm, which allows us to model the development of numerical acuity through experience. We also investigate how the statistical distribution of numerical and non‐numerical features in natural environments affects the emergence of numerosity representations in the computational model. Our simulations show that deep networks can exhibit numerosity sensitivity prior to any training, as well as a progressive developmental refinement that is modulated by the statistical structure of the learning environment. To validate our simulations, we offer a refinement to the quantitative characterization of the developmental patterns observed in human children. Overall, our findings suggest that it may not be necessary to assume that animals are endowed with a dedicated system for processing numerosity, since domain‐general learning mechanisms can capture key characteristics others have attributed to an evolutionarily specialized number system. [ABSTRACT FROM AUTHOR]

Construction projects are usually designed by different professional teams, where design clashes may inevitably occur. With the clash detection tools provided by Building Information Modeling (BIM) software, these clashes can be discovered at an early stage. However, the number of clashes detected by BIM software is often huge. The literature states that the majority of those clashes are found to be irrelevant, i.e., harmless to the building and its construction. How to filter out these irrelevant clashes from the detection report is one of the issues to be resolved urgently in the construction industry. This study develops a method that automatically screens for irrelevant clashes by combining the two techniques of rule-based reasoning and supervised machine learning. First, we acquire experts' knowledge through interviews to compile rules for the preliminary classification of clash types. Subsequently, the results of the initial classification inferred by the rules are added into the training dataset to improve the predictive performance of the classifiers implemented by supervised machine learning. The average predictive performance obtained by using the hybrid method is up to 0.96, which has been improved from the traditional machine learning process only using individual or ensemble learning classifiers by 6%–17%. [ABSTRACT FROM AUTHOR]

Purpose: MITOsym, a new mathematical model of hepatocellular respiration and bioenergetics, has been developed in partnership with the DILIsym® model with the purpose of translating in vitro compound screening data into predictions of drug induced liver injury (DILI) risk for patients. The combined efforts of these two models should increase the efficiency of evaluating compounds in drug development in addition to enhancing patient care. Methods: MITOsym includes the basic, essential biochemical pathways associated with hepatocellular respiration and bioenergetics, including mitochondrial oxidative phosphorylation, electron transport chain activity, mitochondrial membrane potential, and glycolysis; also included are dynamic feedback signals based on perturbation of these pathways. The quantitative relationships included in MITOsym are based primarily on published data; additional new experiments were also performed in HepG2 cells to determine the effects on oxygen consumption rate as media glucose concentrations or oligomycin concentrations were varied. The effects of varying concentrations of FCCP on the mitochondrial proton gradient were also measured in HepG2 cells. Results: MITOsym simulates and recapitulates the reported dynamic changes to hepatocellular oxygen consumption rates, extracellular acidification rates, the mitochondrial proton gradient, and ATP concentrations in the presence of classic mitochondrial toxins such as rotenone, FCCP, and oligomycin. Conclusions: MITOsym can be used to simulate hepatocellular respiration and bioenergetics and provide mechanistic hypotheses to facilitate the translation of in vitro data collection to predictions of in vivo human hepatotoxicity risk for novel compounds. [ABSTRACT FROM AUTHOR]

Determination of Cortisol, T4, T3 and T-Uptake in Equine Plasma and Serum with Fluorescence-Polarization-Immunoassays (FPIA) The influence of duration and temperature of storage on hormone levels of whole blood, plasma and serum of horses was investigated. Using FPIAs Cortisol, T4 and T-uptake could be measured while the T3-FPIA did not work appropriately. Serum and Plasma stored under the same conditions did not show any difference in Cortisol, T4 and T-uptake values. In frozen heparinized plasma samples analysed on different days the Cortisol and T4 concentrations fluctuated markedly. The T-uptake values were rather stable. The smallest day by day changes of Cortisol and T4 in plasma were found when storing the samples at 20-22°C. At 4°C Cortisol decreased markedly while for T4 a clear trend was not found. The Cortisol concentration in whole blood stored at 4°C decreased more markedly than in samples stored at 20-22°C. T4 remained stable in whole blood samples stored at 20-22°C over 4 days while showing fluctuations in samples stored at 4°C. Zusammenfassung Es wurde der Einfluß von Lagerungsdauer und -temperatur auf die Konzentration von Hormo-nen in Vollblut-, Plasma- und Serumproben von Perden untersucht. Mittels FPIAs konnte Cortisol, T4 und T-uptake im Blutplasma und -serum bestimmt werden. Die für das Plasma und Serum ermittelten Werte stimmten dabei gut überein. Dagegen envies sich der T3-FPIA für Pferdeblutplasma als ungeeignet. In tiefgefrorenem Na-Heparinat-Plasma schwankten die Cortisol- und T4-Werte von Analyse-tag zu Analysetag erheblich. Die gemessenen T-uptake-Werte waren stabiler. Die geringsten Verände-rungen für Cortisol und T4, in bezug auf die am Tag der Blutentnahme gemessenen Werte, wurden bei 20-22°C aufbewahrtem Plasma gefunden. Dagegen sank die Cortisol-Konzentration mit zunehmen-der Lagerungsdauer in den bei 4°C gelagerten Plasmaproben deutlich. T4 verhielt sich uneinheitlich. Die Cortisol-Konzentration verringerte sich in bei 4°C gelagerten Vollblutproben, in Abhängig-keit von der Lagerungsdauer, starker als in den bei 20-22°C aufbewahrten. Der T4-Wert der Vollblutproben blieb bei 20-22°C über 4 Tage stabil, schwankte dagegen bei 4°C ohne einheitliche Tendenz. [ABSTRACT FROM AUTHOR]

The indoor space model is the foundation of most indoor location-based services (LBS). A complete indoor space model includes floor-level paths and non-level paths. The latter includes passages connecting different floors or elevations such as stairs, elevators, escalators, and ramps. Most related studies have merely discussed the modeling and generation of floor-level paths, while those considering non-level paths usually simplify the formation and generation of non-level paths, especially stairs, which play an important role in emergency evacuation and response. Although the algorithm proposed by i-GIT approach, which considers both floor-level and non-level paths, can automatically generate paths of straight stairs, it is not applicable to the spiral stairs and winder stairs that are common in town houses and other public buildings. This study proposes a novel approach to generate high-accuracy stair paths that can support straight, spiral, and winder stairs. To implement and verify the proposed algorithm, 54 straight and spiral stairs provided by Autodesk Revit's official website and three self-built winder stairs are used as test cases. The test results show that the algorithm can successfully produce the stair paths of most test cases (49/50), which comprehensively extends the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]