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This study aims to explore the innovative capabilities of building information modeling (BIM) in construction projects. The objectives are to construct an inclusive conceptual framework of BIM-enabled construction innovation, identify the status and trends of innovation-related research in BIM publications, synthesize research pertaining to BIM-enabled construction innovation and discover core research requirements in the related body of knowledge.

This study proposes a comprehensive theoretical framework, named innovation pyramid, comprising context, actor, artifact, process, structure and innovative task for exploring and analyzing the innovative capability of BIM in construction projects. Accordingly, mixed methods were used to perform a systematic review of research on the topic of BIM-enabled construction innovation.

The findings reveal that BIM innovation-related articles have predominantly considered BIM as an innovation from a technological standpoint, while the innovative capabilities of BIM have remained under-researched with a fragmented research focus. Fertile grounds for research have emerged and call for research pertaining to entities of “structure” and “innovative task” as well as interaction, interrelations and mutually adjusting effect among the entities.

The framework proposed may be useful for subsequent research design and for assisting project management practitioners in the use of BIM to achieve innovation more efficiently.

This research provides an insight into the innovative capabilities of BIM based on the BIM–collaboration–construction innovation logic chain. It contributes to the body of knowledge by devising an inclusive conceptual framework of BIM-enabled construction innovation, synthesizing the state of the art and exposing the research needs in this area.

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor.

A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints.

The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller.

This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

From January 2021, the potential flow of Chinese household non-mortgage loans, including business loans and short-term consumption loans to the residential real estate market, has attracted the attention of the regulatory authorities. This study aims to examine the effects of household non-mortgage loans on the Chinese residential real estate market.

Based on a monthly data set between July 2011 and December 2019, this study adopts a cointegration analysis.

This study finds that household non-mortgage loans do play a significant role in driving residential real estate prices in China.

While many studies have examined the Chinese real estate market and its linkage with the financial system and the economy, this study is the first of its kind in the academic literature that exclusively focusses on the role of non-mortgage loans in real estate prices, and makes an original contribution.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This study aims to illustrate the impact-assessment procedure of low-carbon tourism (LCT) program implemented in a world heritage city and to develop specific indicators toward sustainability.

The impact-assessment framework was indicator-based and designed for creating sustainable tourism (ST) in a case study. A set of indicators in various dimensions was developed and applied, referring to the UNWTO guideline. A mixed method of primary and secondary data collected from different sources included document review, site observation, key informant interview, questionnaire survey and focus-group discussions. Assessment of actual/observed impacts was proceeded based on the data collected from tourists and stakeholders, especially on tourist behaviors and resource consumptions.

The implementation of LCT program in a world heritage city provided impacts in different dimensions and characters. The observed activities were majorly tourism activities in accommodations and recreational places. The indicator initiation is the first development toward sustainability in a case of tourism study in a city destination. Indicators were developed with participation from key stakeholders and covered sustainability and carbon-emission dimensions. Impact-assessment results show a positive theme in less carbon emission, enhanced local income distribution and community capacity. However, the negative impacts include increased amounts of resource consumption and waste generation in visiting sites. The impact matrix works as the map for decision-makers to maximize benefits and manage the cons of the LCT program toward ST principles.

Research methodology, procedure and results on impact assessment with holistic perspectives imply academic contribution and practical benefits for decision-makers regarding ST development. The number of samples and enterprises was limited because of the program implementation period.

The research illustrates the impact-assessment process for an implemented city-based LCT program toward ST, where stakeholder participation was also functioning. A list of indicators was specially designed and can be practically applied for other LCT programs in city destinations. Applying a sustainability impact-assessment framework to the program can provide a clear presentation on how to develop ST.

This study’s purpose is to analyze the international mobile marketing (IMK) in order to stage the importance of this tool in the internationalization of companies. Our understanding of mobile marketing is constantly evolving, due to its high business penetration in a world globalized by technologies.

A review of the relevant literature on IMK, companies and customers is undertaken to understand the link between them. The paper begins by explaining the coronavirus disease 2019 is accelerating the change of the rules of the game in traditional and online commerce around the world. Furthermore, this study uses secondary data from organisation for economic co-operation and development (OECD), Sensor Tower, mobile marketing association (MMA), App Annie, among others, to support research results.

The results have shown that IMK has opened a melting pot of opportunities for companies and consumers in this period of pandemic; the potential of this tool is being redefined, in order to identify, anticipate and satisfy customers requirement profitably and efficiently. This study aims to provide an assessment of new concept of IMK and how this tool has to be integrated into the firm’s digital marketing strategies.

The study contributes to make better future decisions in the international digital expansion of companies by company executives and marketing experts. This paper provides a comprehensive framework intended to guide research efforts focusing on digital marketing as well as to aid practitioners in their quest to achieve IMK success.

The purpose of this paper is to examine the use of fused deposition modelling (FDM) models and finite element analysis (FEA) related to dysplastic hip orthopaedic surgery.

The study involved the use of Mimics and Abaqus softwares. Mimics was used to process the CT scan patient data to STL format before producing FDM models which were for before and after surgery. FEA was done to study the two different type of implant biomaterials used in dysplastic hip surgery.

The use of FDM pre models for preplanning of dysplastic hip surgery by orthopaedic surgeons and viewing of the surgery outcome via FDM post models. Different implant biomaterials used gave different results in reduction of stresses that were achieved.

This is original work involving patients in hospital, which got ethical approval and was funded by a university grant. The paper describes a new kind of research in the university.

The purpose of this study is to explore how the unprecedented rise in the economic policy uncertainty influence Indian banking sector stability. The unprecedented rise in the economic policy uncertainty during the recent pandemic has garnered the attention of policymakers to investigate its consequences on different sectors of the economy.

In this quest, the present study uses system generalized method of moments and other econometric tools to examine the influence of economic policy uncertainty on the Indian banking sector, covering the time frame from 2000 to 2022. In addition, the current study also investigates the mediating role of regulation and supervision in the nexus of economic policy uncertainty and the Indian banking sector stability.

The empirical outcome reveals that economic policy uncertainty negatively influences banking stability. However, when economic policy uncertainty interacts with stringent banking regulations, private monitoring and supervisions, it assists in diversifying the negative impact of economic policy uncertainty on the Indian banking sector stability.

To the best of the author’s knowledge, the study is an original work and provides robust estimates that will assist policymakers in understanding the influence of policy uncertainty on the banking stability. Moreover, the study also helps in understanding the role of supervision and regulation in mitigating the negative consequences of policy uncertainty on the banking stability.

This paper aims to critically review the isolation and chemistry of plant pigments.

A literature survey from 1974 to 2022 was carried out and studied thoroughly. The authors reviewed literature in various areas such as isolation methods and catalytic properties of pigments.

With vast growing research in the field of catalytic activities of various pigments like chlorophyll, anthocyanin and flavonoids, there is still scope for further research for the pigments such as Lycopene, carotenoids and xanthophyll as there has not been any significant work in this area.

Plant pigments may be used as an ecofriendly catalyst for chemical reactions.

One can get the direction of pigment research.

Plant pigments are natural and ecofriendly catalyst which can reduce the pollution.

This is an original work. This paper precisely depicts the advantages as well as disadvantages of the isolation techniques of pigments. This study also presents the chemistry of plant pigments.

Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.

For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.

Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).

The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.

The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.