ALBERT

Description: (1) Background: This study aims to apply drama therapy to a counseling group to address the mental health problems of college students in Taiwan due to the increasingly serious psychological problems that have happened in recent times. Based on the healing factors in drama therapy, we applied such therapy activities to four counseling groups composed of 12 high-risk students from Taiwan. (2) Methods: “Questionnaire-based assessment, participant self-assessment and participant attitude assessment” methods were used to evaluate the six mental health indicators of the participants in the evaluation of drama therapy’s effect and the groups’ pre-test and post-test (the first group and the last group). The six indicators were self-awareness, self-expression, interpersonal and communication skills, self-cognitive reconstruction ability, social role ability, and decision-making ability. Data were collected and assessed for the frequencies and percentages of each indicator item. Sets of paired-samples t-tests, independent t-tests, and two-way repeated measures ANOVAs were employed to evaluate the different designs. (3) Results: The results revealed that drama therapy could deliver significantly positive effects for and improve the six mental health indicators of the participants. Males’ self-awareness and decision-making actions were more positively affected than females. (4) Conclusions: The study helps to provide a path of establishing the mental health module of drama therapy in the education sector in Taiwan.

Description: In order to compare the properties of alkali-activated carbon steel slag (CSS) and stainless steel slag (SSS), the effects of sodium hydroxide/sodium silicate solution mass ratio (NH/NS), liquid/solid ratio and blast furnace slag (BFS) dosage on the compressive strength, hydration products and hydration degree of CSS and SSS were studied. Furthermore, a combination of X-ray diffraction (XRD), thermo-gravimetric analysis coupled with differential thermal analysis (TGA-DTA), Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscope-energy dispersive spectrometer (SEM-EDS) were used to characterize the morphology and structure of alkali-activated CSS-BFS and SSS-BFS cementitious materials. As the results revealed, the primary hydrate of alkali-activated CSS and SSS is C-(A)-S-H with Q2 [SiO4] units, which has a low Ca/Si ratio and includes inert phases like a CaO-FeO-MnO-MgO solid solution (RO) in CSS while cuspidine, magnesiochromite etc. in SSS. More active C3S and β-C2S promote the alkali activation of CSS, whereas the less active γ-C2S hinders the depolymerization of SSS. The incorporation of BFS does not change the hydrate, whose seed effect is helpful for accelerating the depolymerization and polycondensation of CSS and SSS, especially for SSS, and makes the hydrate increase significantly. Owing to the high SiO2 and Al2O3 contents of SSS, the C-(A)-S-H chain length is increased, thus facilitating the polycondensation effect. In this study, the optimal NH/NS of CSS and SSS is NH/NS= 1:2, and the optimal liquid/solid ratio is 0.29. Compared to CSS–BFS, the C-(A)-S-H gel produced by SSS–BFS has lower Ca/Si and Al/Si ratios. Unlike CSS, pure SSS is inappropriate as an alkali-activated precursor and needs to be co-activated with BFS.

Description: Smoke detection technology based on computer vision is a popular research direction in fire detection. This technology is widely used in outdoor fire detection fields (e.g., forest fire detection). Smoke detection is often based on features such as color, shape, texture, and motion to distinguish between smoke and non-smoke objects. However, the salience and robustness of these features are insufficiently strong, resulting in low smoke detection performance under complex environment. Deep learning technology has improved smoke detection performance to a certain degree, but extracting smoke detail features is difficult when the number of network layers is small. With no effective use of smoke motion characteristics, indicators such as false alarm rate are high in video smoke detection. To enhance the detection performance of smoke objects in videos, this paper proposes a concept of change-cumulative image by converting the YUV color space of multi-frame video images into a change-cumulative image, which can represent the motion and color-change characteristics of smoke. Then, a fusion deep network is designed, which increases the depth of the VGG16 network by arranging two convolutional layers after each of its convolutional layer. The VGG16 and Resnet50 (Deep residual network) network models are also arranged using the fusion deep network to improve feature expression ability while increasing the depth of the whole network. Doing so can help extract additional discriminating characteristics of smoke. Experimental results show that by using the change-cumulative image as the input image of the deep network model, smoke detection performance is superior to the classic RGB input image; the smoke detection performance of the fusion deep network model is better than that of the single VGG16 and Resnet50 network models; the smoke detection accuracy, false positive rate, and false alarm rate of this method are better than those of the current popular methods of video smoke detection.