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Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their influence on satisfaction and behavioural intentions.

The study focuses on the culinary experiences of 304 tourists dining at ethnic restaurants and uses causal relationship discovery modelling to analyse data.

Sensory factors are important in tourists’ culinary experiences with cleanliness, noise levels and room temperature at the top of the causal chain. Results also indicate the interplay between sensory and non-sensory factors to explain overall satisfaction, intention to return and intention to say positive things.

Using embodied cognition theory, the study offers novel insights into the role of senses in food tourism experiences at rural destinations.

美食是乡村旅游的主要吸引物之一。本研究的目的是调查游客在用餐体验中感官和非感官因素的相互作用, 以及这些因素如何影响游客的满意度和行为意愿。

本研究使用因果关系建模的方法来分析 304 名在某地方特色餐厅用餐的游客的问卷数据。

结果显示, 对于游客的用餐体验而言, 感官和非感官因素具备同等的重要性。此外, 结果发现, 游客感知到的噪音水平、适宜的室内温度及清洁度在与其他因素的相互作用中非常重要, 并能激发游客的满意度和重游意愿。

基于认知理论, 本研究为更好地理解感官因素和非感观因素在乡村旅游情境下的游客用餐体验中的作用提供了新的知识。

La comida es un elemento clave en las experiencias turísticas. Este estudio investiga la interacción de factores sensoriales y no sensoriales en las experiencias de turismo gastronómico y modela su influencia en la satisfacción y las intenciones de comportamiento.

El estudio se centra en las experiencias culinarias de 304 turistas que cenan en restaurantes étnicos y utiliza modelos de descubrimiento de relaciones causales para analizar los datos.

Los factores sensoriales son importantes en las experiencias culinarias de los turistas con la limpieza, los niveles de ruido y la temperatura ambiente en la parte superior de la cadena causal. Los resultados también indican la interacción entre factores sensoriales y no sensoriales para explicar la satisfacción general, la intención de regresar y la intención de decir cosas positivas.

Utilizando la teoría de la cognición incorporada, el estudio ofrece nuevos conocimientos sobre el papel de los sentidos en las experiencias de turismo gastronómico en destinos rurales.

Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/

This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable. The cloth absorbs sweat and then releases it, allowing the body to chill down. By capillary action, moisture is driven away from fabric pores or sucked out of yarns. Convectional air movement improves sweat drainage, which may aid in body temperature reduction. Clothing reduces the skin's ability to transport heat and moisture to the outside. Excessive moisture makes clothing stick to the skin, whereas excessive heat induces heat stress, making the user uncomfortable. Wet heat loss is significantly more difficult to understand than dry heat loss. The purpose of this study is to provided a good compilation of complete information on wet thermal comfort of textile and technological elements to be consider while constructing protective apparel.

This paper aims to critically review studies on the thermal comfort of textiles in wet conditions and assess the results to guide future research.

Several recent studies focused on wet textiles' impact on comfort. Moisture reduces the fabric's thermal insulation value while also altering its moisture characteristics. Moisture and heat conductivity were linked. Sweat and other factors impact fabric comfort. So, while evaluating a fabric's comfort, consider both external and inside moisture.

The systematic literature review in this research focuses on wet thermal comfort and technological elements to consider while constructing protective apparel.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

This paper aims to study the application of high-tolerance and flexible indigenous bacteria and fungi, along with the co-metabolism in recycled paper and cardboard mill (RPCM) wastewater treatment (WWT).

The molecular characterization of isolated indigenous bacteria and fungi was performed by 16S rRNA and 18S rRNA gene sequencing, respectively. Glucose was used as a cometabolic substrate to enhance the bioremediation process.

The highest removal efficiency was achieved for both chemical oxygen demand (COD) and color [78% COD and 45% color removal by Pseudomonas aeruginosa RW-2 (MZ603673), as well as approximately 70% COD and 48% color removal by Geotrichum candidum RW-4 (ON024394)]. The corresponding percentages were higher in comparison with the efficiency obtained from the oxidation ditch unit in the full-scale RPCM WWT plant.

Indigenous P. aeruginosa RW-2 and G. candidum RW-4 demonstrated effective capability in RPCM WWT despite the highly toxic and low biodegradable nature, especially with the assistance of glucose.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.