Search results

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

This study aims to examine the association of employee resilience and agility with innovative performance and subjective well-being. Moreover, it tests job crafting as the underlying mechanism through which resilient and agile employees perform innovatively and experience higher subjective well-being.

The study used a survey-based research design and structural equation modeling technique to examine the proposed hypotheses. Data was collected from a sample of 380 employees working in the Indian information technology sector using survey questionnaires.

The results show that highly resilient and agile employees participate in job crafting that positively influences their innovative performance and subjective well-being. Job crafting fully mediates the association of resilience with work and well-being outcomes and partially mediates agility and outcomes.

There is a value in promoting the development of employee resilience and agility to foster ways in which employees can craft their jobs and, thus, maximize their innovative performance and subjective well-being.

This study makes an important contribution by underscoring the importance of personal resources (resilience and agility) as drivers of job crafting for higher innovative performance and subjective well-being.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The objective of this study was to develop wearable suit platforms with various anchoring structure designs with the intention of improving wearability and enhancing user satisfaction.

This study selected fabrics and materials for the suit platform through material performance tests. Two anchoring structure designs, 11-type and X-type are compared with regular clothing under control conditions. To evaluate the comfort level of the wearable suit platform, a satisfaction survey and electroencephalogram (EEG) measurements are conducted to triangulate the findings.

The 11-type exhibited higher values in comfort indicators such as α, θ, α/High-β and lower values in concentration or stress indicators such as β, ϒ, sensorimotor rhythm (SMR)+Mid-β/θ, and a spectral edge frequency of 95% compared to the X-type while walking. The 11-type offers greater comfort and satisfaction compared to the X-type when lifting based on the EEG measurements and the participants survey.

It is recommended to implement the 11-type when designing wearable suit platforms. These findings offer essential data on wearability, which can guide the development of soft wearable robots.

The potential growth in cryptocurrencies has raised serious ethical and religious issues leading to a new investment rethinking. This paper aims to identify the influence of religiosity on cryptocurrency acceptance through an extended technology acceptance model (TAM) model.

In the first phase, this research develops a conceptual model that extends the theory of the TAM by integrating the religiosity component. In the second phase, the proposed model is tested using search volume queries in daily frequencies from 01/01/2018 to 31/12/2022 and structural equation modeling (SEM).

The empirical results demonstrate a significant positive effect of religiosity on the intention to use cryptocurrency, the users' perceived usefulness (PU) and ease of use (PEOU). Besides, the authors note that PEOU positively influences the intention. Furthermore, religiosity indirectly affects the intention through the PEOU and positively impacts the intention through the PU. In the same way, PEOU has a considerable indirect effect on the intention through PU.

This study has practical and theoretical contributions by providing insights into the cryptocurrency acceptance factors. In other words, it contributes to the literature by extending TAM models. Practically, it helps managers determine factors affecting the intention to use cryptocurrencies. Therefore, they can adjust their industry according to the suitable characteristics for creating successful projects.

Identifying the effect of religiosity on cryptocurrency users' choices and decisions has a social added value as it provides an understanding of the evolution of psychological variants.

The findings emphasize the importance of integrating big data to analyze users' attitudes. Besides, most studies on cryptocurrency acceptance are investigated based on one kind of religion, such as Christianity or Islam. Nevertheless, this paper integrates the effect of five types of faith on the users' intentions.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

This research aims to investigate how manufacturing flexibility can address the challenges of an ever-changing and unpredictable business environment in Bangladesh’s labor-intensive ready-made garment (RMG) industry, which is underserved and situated in a developing country.

Using Partial Least Square Structural Equation Modeling, this study empirically evaluated the relationships between manufacturing flexibility, environmental uncertainty and firm performance. The analysis utilized 320 survey responses from potential RMG experts, representing 95 organizations.

The study achieved a decision-making model for implementing manufacturing flexibility in the RMG industry of Bangladesh with acceptable model fit criterion. The research pinpointed that workforce flexibility plays the maximum mediating among different types of manufacturing in coping with demand and supply uncertainty in the RMG sector.

The study made valuable contributions to theoretical and practical knowledge in the context of manufacturing flexibility in Bangladesh’s RMG and other underserved labor-intensive sectors in developing economies. It suggests that managers should shift from defensive and risky business strategies to more aggressive and proactive approaches by utilizing workforce flexibility resources adaptively to enhance manufacturing capabilities and align with dynamic market demand. Additionally, the study offers recommendations for future research to build upon its findings.

This study is unique in its approach because it presents a decision model for implementing manufacturing flexibility in a labor-intensive industry in a developing economy, specifically the RMG industry in Bangladesh, whereas previous research has primarily focused on high-tech industries in developed economies.

The proponents of cash waqf speak highly about its huge potential for mobilizing the third sector of the economy to fund the socio-economic development agenda. However, the under-collection issue has been characterizing the cash waqf movement globally. This study aims to examine how understanding the distinct cash waqf donating behavior across different generations has the potential to address the problem.

This study extends the theory of planned behavior by adding religiosity and knowledge variables into the standard model, using the partial least square structural equation modeling. A survey is conducted on 684 respondents representing the main provinces in Indonesia and four major generations (Baby Boomers [BB], Generations X, Y and Z).

Religiosity, Knowledge, Attitude, Subjective Norms and Perceived Behavioral Control directly or indirectly affect cash waqf intention. The effect is contingent on the characteristics of generations.

This study covers only the Indonesian case with limited coverage of the more heterogeneous provinces in the country. The sample distribution for BB can also be enlarged.

Cash waqf institutions (government and private) should apply the dynamic segmenting strategy, where the diversification of the promotion, marketing, awareness and approaches are contingent on the different characteristics of each generation.

To the best of the authors’ knowledge, this is the first study evaluating the intergenerational determinants of Intention toward cash waqf, particularly in Indonesia.

Drawing on the literature on brand–consumer relations in an attempt to apply the concept of brand love to a city tourism destination, this paper aims to explore the antecedents and consequences of city brand love.

Based on quantitative research and cluster sampling, study data was collected from 330 international tourists who visited six Iran cities. The research model is tested using partial least square structural equation modeling.

According to the results, city brand attractiveness, city brand identification and memorable city brand experience are the antecedents of city brand love. Moreover, the city brand attractiveness, both directly and indirectly through memorable experiences and identification with the city brand, is one of the antecedents of city brand love. Finally, the results suggest that word of mouth and revisit intention are major behavioral outcomes of city brand love.

By providing a validated conceptual model that traces the antecedents and consequences of city brand love, this study attempts to answer prior calls for examination from the viewpoint of city tourism scholars.

本研究基于定量研究和整群抽样, 研究数据来自访问伊朗六个城市的330名国际游客。研究采用偏最小二乘结构方程模型对研究模型进行了检验。

借鉴品牌-消费者关系的相关文献, 本研究试图将品牌爱的概念应用于一个城市旅游目的地, 探讨城市品牌喜爱的前因和后果。

结果表明, 城市品牌吸引力、城市品牌辨识度和令人难忘的城市品牌体验是城市品牌喜爱的前因。城市品牌吸引力通过城市品牌直接或间接通过难忘体验和认同产生, 是城市喜爱的前因。最后, 研究结果表明, 口碑和重访意愿是城市品牌喜爱的主要行为结果。

通过提供一个经过验证的概念模型, 追溯城市品牌喜爱的前因后果, 本研究试图从城市旅游学者的角度回答先前检验的呼吁。

A partir de una investigación cuantitativa y muestreo por conglomerados, los datos del estudio se recogieron de 330 turistas internacionales que visitaron seis ciudades iraníes. El modelo conceptual se analizó mediante un modelo de ecuaciones estructurales de mínimos cuadrados parciales (PLS).

Basándose en la literatura sobre las relaciones entre marca y consumidor para aplicar el concepto de amor de marca a una ciudad como destino turístico, la presente investigación analiza los antecedentes y las consecuencias del amor de marca de ciudad.

Conforme a los resultados, el atractivo de la marca ciudad, la identificación con la marca ciudad y la experiencia memorable con la marca ciudad son los antecedentes del amor a la marca ciudad. Adicionalmente, el atractivo de la marca ciudad, tanto directa como indirectamente a través de las experiencias memorables y la identificación con la marca ciudad, es uno de los antecedentes del amor por la marca ciudad. Finalmente, los resultados sugieren que la comunicación boca-oído y la intención de volver a visitar la ciudad son los principales resultados comportamentales del amor de marca de ciudad.

Al proporcionar un modelo conceptual validado que analiza los antecedentes y las consecuencias del amor de marca de ciudad, este estudio trata de responder a las llamadas para su estudio desde la óptica del turismo urbano.

To understand the experiences, expectations and suggestions of digital nomads towards intangible cultural heritage in the places they travel.

The qualitative research method was used and the research design is a phenomenological design. Multiple qualitative research design steps were followed in obtaining the research data. In this context, online interviews were conducted with a semi-structured interview form developed by the researchers, and the social media accounts of digital nomads were monitored. The sample consists of nine digital nomads who were interviewed online and five digital nomads whose videos and comments shared on social media were monitored. The inductive thematic analysis method was used to analyse the data.

It was found that digital nomads experience intangible cultural heritage elements, especially religious rituals and food culture, and actively participate in activities and rituals related to these elements as well as sharing them on their social media accounts. It was also concluded that the participants expected to work towards the protection of intangible cultural heritage elements through a number of activities such as awareness-raising and education, and that it is difficult to experience cultural heritage in societies that are not foreign-friendly.

The data were obtained from volunteer participants consisting of digital nomads and digital nomads who shared their experiences on social media. It is suggested that traditional food and religious rituals as intangible cultural heritage should be promoted and transferred by sharing the experiences these digital nomads on digital platforms.

Religious rituals and food cultures are prominent in the ICH experiences of digital nomads. Digital nomads who stay longer in their destination than tourists can play an intermediary role in promoting and transmitting this heritage. The social media and other digital platforms enable digital nomads to share their experiences of cultural heritage and create a global dialogue and understanding through these experiences.

The experiences and expectations of digital nomads towards ICH require a reassessment of traditional approaches to the preservation and transmission of cultural heritage. By experiencing different cultural heritages and sharing these experiences through digital platforms, digital nomads contribute to the reinterpretation and reproduction of cultural heritage. This process reveals that cultural heritage is not limited to a particular community or geography, but is rather a global phenomenon, and therefore needs to be addressed from a global perspective.

It is one of the pioneering studies on the experiences of digital nomads towards intangible cultural heritage.