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Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing.

The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure.

It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates.

Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.

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.

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.

The purpose of this paper is to subject the potential of Internet of Things (IoT) technology for libraries in terms of the possible scope and usage forms of this technology in public and academic library services.

Based on analysis of the subject literature, the main areas of IoT applications in commercial institutions were identified, then an analysis of Library and Information Science English-language literature from the years 2010 to 2015 was conducted in order to create a profile of modern library services. The range of activities of commercial and non-commercial institutions were compared to ascertain if areas in which commercial entities using or planning to use IoT could also be an inspiration for libraries. In this way, a theoretical model of IoT use in library activities was developed.

The research showed that IoT technology might have the potential to be used in library services and other activities, similar to how it is implemented in the commercial sector.

The aim of the paper is to determine the possible, not the actual, scope and forms of using this technology in public and academic libraries’ services.

The results can be widely used in libraries as an inspiration for the use of IoT technology in modern library services.

The use of new technologies in libraries can help to improve the image of these institutions in the eyes of users, especially the younger generation.

The use of IoT in libraries is a new issue that has not been studied much yet. The issue of using the potential of this technology for the needs of libraries has, in recent years, been raised at international conferences, become a subject of interest to librarian associations, and been widely discussed in the blogosphere, thus proving that this topic is important to practitioners. It is difficult, however, to find any scientific, comprehensive studies of this topic.

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.

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.