A New Approach for Mining Correlated Frequent Subgraphs

Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.

Garnet Chemical Zoning Based Thermobarometry: Method Evaluation and Applications in the Menderes Massif, Western Turkey

The garnet chemical zoning method (GZM) is a reliable thermodynamic approach for forward modeling pressure-temperature (P-T) paths using observed garnet and bulk rock compositions. However, intracrystalline diffusion is known to compromise the integrity of GZM modeled garnet-growth P-T paths. For this reason, extracting reliable metamorphic estimates from garnet-bearing schists in the Central Menderes Massif (CMM), western Turkey, has been difficult. To evaluate the impact of diffusion on GZM, we simulate garnet growth and diffusion for an average metapelite using the program Theria_G. Modeled garnet compositions from four simulations are used to estimate P-T conditions and paths by GZM, which are compared against Theria_G specified P-T-t trajectories. Factors influencing results are heating/cooling rate, grain size, and peak T. At a maximum T of 610 °C, both undiffused and diffused garnet compositions returned estimates comparable to prescribed conditions regardless of heating/cooling rate. Diffused profiles from simulations reaching a maximum T of 670 °C also reproduced prescribed P-T paths if tectonism occurred at high heating/cooling rates (50 °C/my). From these insights and additional Theria_G simulation-derived observations for CMM garnets, we deduce that metamorphism in the region exceeded 650 °C and achieved a maximum burial P between 8–10 kbar prior to Cenozoic exhumation.

Playful Metaphors for Narrative-Driven E-Learning

A team of e-learning specialists was assigned the mission to design and develop seven short e-learning modules for young learners on popular topics with a prime focus on social-emotional learning. However, these courses had to be produced on a limited budget, hosted in a Moodle platform, and be delivered for free in asynchronous only mode. Hence, a playful method of meaningful storyfication was applied in e-learning to captivate attention and spark interest. In each module, a fictional story or metaphorical challenge with playful elements was introduced where learners were invited to assist the story’s protagonists or become the heroes themselves by engaging with content. As the role of teachers is vital for the adoption of technology-based solutions in education, a mixed method evaluation was undertaken to assess the effectiveness of the method. Forty-two educators reviewed the courses, answered a questionnaire and participated in interviews. Results indicated that participants highly appreciated the narrative aspects, visual, and auditory elements rendering the e-learning courses effective for the target audience. This article can assist educators, distance education designers and developers to adopt a playful mindset and learn how to integrate practically storytelling elements into their classroom-based and online teaching.

Improvement of Automatic Measurement Evaluation System for Subway Structures by Adjacent Excavation

This study evaluated the structural stability of subway structures based on adjacent excavations by comparing automatically measured and numerically analyzed data. The reliability of the automated measurement methodology was evaluated by first applying probability statistical analysis to the measured results and then comparing these results with the numerically analyzed results. An improvement in the calculation method evaluation system, including the method of processing and analysis of the automatically measured data of subway structures through the average value of probability density, was proposed. As a result of the field measurement and numerical analysis, the measured results of tunnel displacement and track deformation exhibited some differences. However, it was determined that the construction stage and location where the maximum values of the tunnel displacement and track deformation occurred had similarities.

A Novel Method for Extracting Time Series Information of Deformation Area of a Single Landslide Based on Improved U-Net Neural Network

This paper proposed an improved U-Net fully convolutional neural network to automatically extract a single landslide deformation information under time series based on the physical model experiments. This method extracts time series information for three different landslide deformation ranges. Compared to U-Net and mainstream superpixel method, evaluation indicators of DSC, VOE and RVD verify the high recognition accuracy and strong robustness of our method.

Pelatihan dan Pendampingan Mata Pelajaran Bahasa Inggris bagi Siswa-Siswi Sekolah Dasar Negeri Leyangan Terdampak Pandemi COVID-19

Since the study from home system was implemented, several obstacles were found, especially for students at Elementary School of Leyangan in RT 004 RW 002 Dusun Krajan Desa Leyangan Kecamatan Ungaran Timur, Kabupaten Semarang. The students were difficult to learn the subjects by themselves, expecially for English subject. The main obstacles of study from home were lack of teacher’s direct explanation and lack of parents’ attention and mentoring. Based on this explanation, students needed face-to-face training and monitoring, especially the 'Self-Introduction' in English subjects. Through the USM’s Students Community Services Back to Village program, student focused on community service for face-to-face training and mentoring to improve the English skills for students at Elementary School of Leyangan. The community services method used was directly explanation and speaking practice for the given example sentences. The participants were required ro listen, rewrite and practice to speak for the given subjects. The mentoring was held in June 2021 with the COVID-19 health protocol face-to-face method. Evaluation results of the speaking pratice showed increment in students' knowledge and skill of 'Self-Introduction' subject and better pronunciation.