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The purpose of this paper is to explore the possibility of an enhanced continuous liquid interface production (CLIP) with a porous track-etched membrane as the oxygen-permeable window, which is prepared by irradiating polyethylene terephthalate membranes with accelerated heavy ions.

Experimental approaches are carried out to characterize printing parameters of resins with different photo-initiator concentrations by a photo-polymerization matrix, to experimentally observe and theoretically fit the oxygen inhibition layer thickness during printing under conditions of pure oxygen and air, respectively, and to demonstrate the enhanced CLIP processes by using pure oxygen and air, respectively.

Owing to the high permeability of track-etched membrane, CLIP process is demonstrated with printing speed up to 800 mm/h in the condition of pure oxygen, which matches well with the theoretically predicted maximum printing speed at difference light expose. Making a trade-off between printing speed and surface quality, maximum printing speed of 470 mm/h is also obtained even using air. As the oxygen inhibition layer created by air is thinner than that by pure oxygen, maximum speed cannot be simply increased by intensifying the light exposure as the case with pure oxygen.

CLIP process is capable of building objects continuously instead of the traditional layer-by-layer manner, which enables tens of times improvement in printing speed. This work presents an enhanced CLIP process by first using a porous track-etched membrane to serve as the oxygen permeable window, in which a record printing speed up to 800 mm/h using pure oxygen is demonstrated. Owing to the high permeability of track-etched membrane, continuous process at a speed of 470 mm/h is also achieved even using air instead of pure oxygen, which is of significance for a compact robust high-speed 3D printer.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

Due to the political, economic and infrastructure barriers and risks that international entrepreneurs (IEs) face when researching an emerging economy's agrifood sector, this research aims to identify the major barriers, analyse their relationships, quantify their importance, classify and rank them. Thus, the IEs will gain a better understanding and vision of their decision-making processes in this era.

To do this, the authors first created a list of barriers to entry for IEs into Iran's rising economy's agrifood industry. Following that, a multi-layer decision-making approach was developed and implemented to accomplish the research objectives. The first stage utilized a hybrid of interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to depict the level-based conceptual model and classification of the IEs’ obstacles to entry into the agrifood sector. Following that, a hybrid decision-making trial and evaluation laboratory (DEMATEL), and analytic network process (ANP) called DANP was utilized to present a causal relationship between the barriers, identify their causes and effects, and also quantify the relevance of each barrier.

After employing the multi-layer decision-making approach, the results demonstrated that fundamental limitations, including infrastructure and technology limitations, are the most critical barriers alongside policy factors encompassing governmental support and access to global or regional economy/market. According to the results, innovation and economic sustainability of the agrifood supply chain also matter. All of these critical barriers are intertwined and should be planned and solved simultaneously. Furthermore, based on DANP results, the sustainability pillars (economy, environment, society), besides the low efficiency of the agrifood sector in Iran, should be investigated further for future policy makings.

A hybrid multi-layer decision-making approach has been used for analysing the barriers of investment in the agrifood sector of the emerging economy of Iran for the international entrepreneurs. Moreover, the authors provide implications and insights for IEs and officials for decision-making in the future.