The growing demand for complex structures, energy absorption, and mechanically strong materials has led to the exploration of innovative composites. This study focuses on the manufacture, characterization, and evaluation of PLA+ reinforced with woven glass fiber. Using Fused Filament Fabrication (FFF) 3D Printer technology, the effects of adding woven glass fiber were examined through a tensile test with Digital Image Correlation (DIC)-induced, flexural, Charpy impact resistance, Shore D hardness, Differential Scanning Calorimetry (DSC) thermal tester, and SEM morphological tests. Results showed that adding four layers of glass fiber significantly improved mechanical properties: tensile strength increased by 85% to 95.44 MPa, flexural strength by 13% to 91.51 MPa, and impact resistance by 450% to 15.12 kJ/m². However, a reduction in hardness and thermal resistance was noted due to chemical interactions. These findings suggest potential applications of PLA+ composites in high-strength products for vehicle bumpers in the automotive industry and shin pads in the sports industry.

As more schools incorporate 3D printing into their curriculum to stimulate the creativity of K-12 students with a learning-by-doing approach, it becomes crucial to understand how users work with 3D modeling tools and to evaluate integrated lesson plans in the STEAM (Science, Technology, Engineering, Arts and Math) educational framework. Our work consists of two stages: an investigation of usage patterns of modeling, and an evaluation of the usability of Qmodel Creator, in collaboration with Lanyu Primary and Junior High School, Sanchong High School, and Affiliated Experimental Elementary School of National Chengchi University. Participants operation logs, screen recordings, and finished work for respective 3D modeling software were recorded and analyzed. Two types of indicators have been developed. One is concerned with the quantification of learning behavior, including Effective Operating Period (EOP), Trial-and-Error Period (TEP), and Implementation Period (IP). The other has to do with the evaluation of learning outcome, i.e., the complexity of 3D models, including the Degree of Detail (DoD), shape (Cf), partition (Cp), and block-ratio (Cr) complexity. Based on the proposed features, we are able to identify the key factors that affect students’ learning experiences and performance in terms of learning patterns and model completeness. Through these indicators, instructors can gain better insights into student’s learning status of 3D modeling software.

The technology of generative adversarial networks (GAN) is constantly evolving, and synthesized images can no longer be accurately distinguished by the human eyes alone. GAN has been applied to the analysis of satellite images, mostly for the purpose of data augmentation. Recently, however, we have seen a twist in its usage. In information warfare, GAN has been used to create fake satellite images or modify the image content by putting fake bridges, buildings and clouds to mislead or conceal important intelligence. To address the increasing counterfeit cases in satellite images, the goal of this research is to develop algorithms that can classify fake remote sensing images robustly and efficiently. There exist many techniques to synthesize or manipulate the content of satellite images. In this paper, we focus on the case when the entire image is forged. Three satellite image synthesis methods, including ProGAN, cGAN and CycleGAN will be investigated. The effect of image pre-processing such as histogram equalization and bilateral filter will also be evaluated. Experiments show that satellite images generated by different GANs can be easily identified by individually trained models. The performance degraded when model trained with one type of GAN samples is employed to determine the originality of images synthesized with other types of GANs. Additionally, when histogram equalization is applied to the images, the detection model fails to distinguish its authenticity. A four-class universal classification model is proposed to address this issue. An overall accuracy of over 99% has been achieved even when pre-processing has been applied.

Modeling is a key application in 3D fabrication. Although numerous powerful 3D-modeling software packages exist, few people can freely build their desired model because of insufficient background knowledge in geometry and difficulties manipulating the complexities of the modeling interface; the learning curve is steep for most people. For this study, we chose a cubic model, a model assembled from small cubes, to reduce the learning curve of modeling. We proposed an intuitive modeling system designed for elementary school students. Users can sketch a rough 2D contour, and then the system enables them to generate the thickness and shape of a 3D cubic model.

As more schools incorporate 3D printing into their curriculum to stimulate the creativity of K-12 students with a learning-by-doing approach, it becomes crucial to understand how users work with 3D modeling tools. In this paper, we aim to develop model and usage-pattern-related features to quantize students' performance on 3D modeling operation. The dataset is gathered from the Affiliated Experimental Elementary School (AEES) of National Chengchi University. Participants' operation log and finished work for specific 3D modeling software are recorded and analyzed. In all our lesson plans, students are required to create structurally stable and printable 3D models. Three modeling software with different levels of difficulty have been introduced and tested. The collected data include screen recording, software operation log, experts evaluation, and interviews with students, which are employed for subsequent qualitative evaluation as well as quantitative analysis. With our proposed approach, we are able to identify the key factors affecting students' learning experience and performance in terms of model completeness and usage pattern. Through these indicators, instructors can understand student's learning status of 3D modeling software more comprehensively.