Search results

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

This study conducted a review following the guidelines of Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Searches were conducted in databases PubMed, Scopus, Web of Science, Cochrane Library, cumulative index to nursing and allied health literature, PsycINFO, China national knowledge infrastructure Wanfang, VIP Database for Chinese Technical Periodicals and SinoMed from inception to February 12, 2023.

A total of 14 studies (describing 14 serious games) for improving the mental health of adolescents with chronic diseases were included. Of all the included games, 12 were not described as adopting any theoretical framework or model. The main diseases applicable to serious games are cancer, type 1 diabetes and autism spectrum disorder. For interventional studies, more than half of the study types were feasibility or pilot trials. Furthermore, the dosage of serious games also differs in each experiment. For the game elements, most game elements were in the category “reward and punishment features” (n = 50) and last was “social features” (n = 4).

Adolescence is a critical period in a person’s physical and mental development throughout life. Diagnosed with chronic diseases during this period will cause great trauma to the adolescents and their families. Serious game interventions have been developed and applied to promote the psychological health field of healthy adolescents. To the best of the authors’ knowledge, this study is the first to scope review the serious game of promoting mental health in the population of adolescents with chronically ill. At the same time, the current study also extracted and qualitatively analyzed the elements of the serious game.

In recent years, fresh produce (fresh vegetables and fruit) has been circulated widely via e-commerce in Chinese large cities in the form of fresh produce portfolios (FPPs). The purpose of this paper is to analyze the preferences of Chinese consumers for specific FPP attributes.

A choice experiment approach was used to explore consumer preferences. The authors conducted a means-end-chains evaluation to select the attributes for the choice experiment. The authors used a fractional factorial design and finally obtained 18 choice scenarios. The authors collected 166 effective consumer questionnaires in Beijing.

The authors found that among the four attributes considered, certification and the diversity of the FPP had significant effects on the willingness to pay (WTP) among consumers. Residents had heterogeneous preferences for FPP diversity and certification, but certification was the major concern when considering fresh produce in the FPP. With regard to the WTP for attributes in the portfolio, the WTP values for “green” and “organic” attributes were high, but the WTP for the diversity of FPPs was low.

This study is the first attempt to explore the preferences of Chinese consumers regarding the attributes of FPP in an e-commerce environment.

With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

The purpose of this paper is to improve the efficiency of on-orbit operations through the top-level task design based on DoDAF. Based on the existing upper stage rocket technology, orbit transfer vehicles (OTVs) have developed rapidly in recent years. However, the lack of decision guidance based on overall task analysis requires integrating top-level analysis and bottom-level execution to achieve the smooth development of full-process tasks.

Using the Department of Defense Architecture Framework (DoDAF) as a reference, this paper performs the top-level mission analysis modeling of the on-orbit rendezvous and capture of the OTV. Moreover, the typical operational view products are obtained, and the cooperative relations among the mission requirements, the system requirements, and the functional requirements are also analyzed.

The results show that the attitude of the OTV changes violently during the maneuver and rendezvous phases. In addition, the view products can be optimized based on the results.

The proposed DoDAF-based on-orbit task integration analysis method achieves the effective fusion of high-level analysis and bottom-level execution of OTV on-orbit rendezvous and capture task through the top-level task modeling, operation view generation and task relationship analysis. According to the requirements and constraints of the on-orbit rendezvous and capture task, the control instructions of the vehicle are efficiently generated under the DoDAF framework.

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

The purpose of this paper is to study the influence of different rolling deformation parameters on the morphology, microstructure and mechanical properties of Inconel 718 superalloy in hybrid plasma arc and micro-rolling (HPAMR) additive manufacturing.

In this paper, different deformation strains are designed, which are as-deposited, 15% and 30%. Two straight walls are fabricated by HPAMR for each kind of deformation. One wall underwent post-deposition heat treatment, and the other wall is treated without heat treatment. These samples are further investigated to evaluate the effects of deformation on the morphology, microstructure and mechanical properties.

As compared to as-deposited samples, the morphology can be significantly improved, the generation of defects and microporosity inside the alloy can be suppressed, and finer equiaxed crystals can be obtained with deformation of 30%. With heat treatment and 30% deformation, the Laves phase at the grain boundary is completely disappearing, more γ” and γ' strengthening phase is precipitated in the crystal and the size of the strengthening phase is smaller. Mechanical properties have been significantly improved.

HPAMR technology is used to successfully manufacture Inconel 718 superalloy aero-engine casing.

Compared with plasma arc additive manufacturing, HPAMR technology adds a rolling process, which can effectively improve the morphology of walls, refine internal grains, eliminate defects and microporosity, increase precipitation of strengthening phase and improve mechanical properties. It provides an optional manufacturing method for the integrated manufacturing of Inconel 718 parts.