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As a new type of spinning machine, the jet spinning machine absorbs the carding system of the rotating cup spinning series and the nozzle part of the jet spinning. This paper aims to intends to introduce the double carding structure currently studied by the rotating cup spinning into the jet spinning machine, and analyze the influence of the nozzle characteristic number on the flow field in the double carding structure to verify the advantages of the double carding structure.

The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field. The influence of the nozzle characteristic number on the flow pattern in the four models is compared. The advantages and disadvantages of a conventional single comb and a double comb with a bypass channel on the longer side of the transport channel as an additional air supply channel are also evaluated.

At present, the double comb technology of rotary cup spinning is being studied at home and abroad to improve the spinning quality and improve the difficult problem of mixed yarn with large difference in processing fiber properties. At present, the jet spinning machine combines the advantages of rotary cup spinning and jet spinning, absorbing the comb system of rotary cup spinning series and the nozzle part of jet spinning. Therefore, it is found that the introduction of the double-split structure into the wool jet spinning has research value to improve the spinning quality.

The purpose of this paper is to refer to the previous research on the double comb structure in rotary spinning, and to apply the double comb structure in the new jet spinning machine to improve the spinning quality. The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field.

The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos.

Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models.

The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data.

The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models.

The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images.

By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high.

This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.

Absorptive capacity, technological collaboration and servitization are analyzed to establish ways to overcome the balance between products and services in manufacturing companies. A fresh perspective is introduced by presenting a framework for innovation strategy, moving beyond product-based R&D.

The hypotheses are tested using data on Spanish firms in the high-tech chemical and pharmaceutical industries through ordinary least squares regression analysis. The sample consists of 112 manufacturing firms included in the Spanish Survey of Business Strategies.

The results show that absorptive capacity facilitates servitization and that technological collaboration moderates the relationship between absorptive capacity and servitization. The synergies between absorptive capacity and technological collaboration for servitization are recognized from the perspective of open innovation as a way of resolving the trade-off between products and services.

Future research should introduce more sources of collaboration by broadening the value chain perspective. Other approaches to innovation may also be considered, including relationships to process innovation.

The results can provide meaningful guidance for companies to determine the key opportunities of servitization driven by absorptive capacity, and the best ways to leverage open innovation and collaboration strategies to exploit such approaches.

This research enriches theories on servitization, open innovation and innovative behavior. Open innovation strategy should be linked to greater servitization activity and should support an open service strategy. This approach is crucial for building innovation capabilities through technological collaboration.