The National University of Mongolia (Mongolian: Монгол Улсын Их Сургууль, Mongol Ulsyn Ikh Surguuli, abbreviated NUM or MUIS) is a public university primarily located in Ulaanbaatar, Mongolia. Established in 1942, it is the oldest institution of higher learning in Mongolia, and originally named in honour of Khorloogiin Choibalsan as Choibalsan State University. It hosts 5 main faculties in Ulaanbaatar, two branches (in Uliastai, Zavkhan Province and Erdenet, Orkhon Province), and three academies of national importance (Mongol studies, economics, and sustainable development).After the establishment of the Mongolian People's Republic and its first modern secondary school in 1921, it was deemed necessary to establish an academic institution at a higher level. In 1942, the government established the National University of Mongolia as Mongolia's first university, with the first students graduating in 1946. During socialism, the University served as a training center for the party elite. Education was paid for and strictly controlled by the state. After democratization, it gradually changed into a more modern university. In 1995, it started to offer bachelors, masters, and doctoral programs.It holds a distinguished place in Mongolia's modern history, serving as both its first university as well as a parent to many of the country's premier universities. Many of the country's higher education institutions can trace their ancestry back to the National University of Mongolia's faculties and sub-institutes, including the University of Science and Technology, University of Life Sciences, University of Medical Sciences, and the University of the Humanities.As of 2018, there were over 18,000 students enrolled in various programs, mostly taught in Mongolian.
Game-based learning plays an increasingly vital role in engineering education. Driven by the recent surge in empirical studies demonstrating its effectiveness in this domain, this study aims to examine the structural framework, growth trajectory, and major contributors of the field through quantitative bibliometric analysis. That's the gap this paper addresses. We filtered the Web of Science database using PRISMA, then analysed 392 documents in VOS viewer. Publication numbers have grown steadily since 2010, though growth in recent years has been uneven. A co-authorship analysis put the United States, Spain, Germany, and the United Kingdom at the front of the pack. At the institutional level, European universities such as Universidad Politécnica de Madrid and Politecnico di Milano lead the way, and universities in Spanish-speaking countries (Spain, Mexico, Ecuador) stand out. Keyword co-occurrence analysis split the field’s intellectual structure into 5 thematic clusters. "Computer science" sits at the centre. Purely engineering terms like "mechanical engineering" and "engineering ethics," by contrast, sit out at the edges. Performance analysis backs this up: most of the top 10 journals are computer science conference proceedings. And an overlay map shows "engineering education" as one of the earliest core topics studied, while "game-based learning," "computer security," and "control (management)" have only recently picked up steam. By conducting this study, computer science specialists will be able to work more closely with engineering faculty to develop interactive game-based learning (GBL) environments and simulations, making abstract engineering theories clearer and more engaging for students.
BACKGROUND Solitary fibrous tumors (SFTs) are rare mesenchymal neoplasms most commonly originating in the pleura but may also arise in extrapleural sites, including the head and neck region. Subcutaneous SFTs in the chin area are exceptionally rare; only a few cases have been reported. CD34 and Bcl-2 are commonly used immunohistochemical markers in the initial diagnostic workup, and their co-expression is strongly suggestive of an SFT. However, CD34 negativity can be misleading and complicate diagnosis. Accurate identification of SFTs is potentially difficult because of histologic variability and overlap with other spindle cell tumors. CASE REPORT We encountered a rare subcutaneous SFT in the chin of a 22-year-old woman who presented with facial asymmetry and a tender mass. Imaging revealed a well-circumscribed, contrast-enhancing lesion in subcutaneous soft tissue over the anterior mandible. Histologically, the tumor lacked typical staghorn vasculature but showed spindle cell proliferation within a fibrous stroma. Immunohistochemistry demonstrated strong nuclear STAT6 positivity; expression of CD99, Bcl-2, and SMA; and focal H-caldesmon staining. CD34, S100, and ALK1 displayed negative staining. Based on these findings, the patient was diagnosed with an SFT. CONCLUSIONS This case highlights the importance of considering SFT in the differential diagnosis of spindle cell tumors in the head and neck region. It also underscores the critical role of immunohistochemistry, particularly STAT6 staining, in distinguishing SFT from histologic mimics. Vigilant follow-up remains essential, especially in atypical or CD34-negative cases, given their potential for aggressive behavior.
This study integrates Bandura's Social Cognitive Theory (SCT), Bronfenbrenner's Ecological Systems Theory (EST), and Ryan and Deci's Self-Determination Theory (SDT) to develop an integrated three-level institutional ecological model of teacher self-efficacy (TSE) in higher education. The model classifies TSE determinants into three levels. The institutional level includes perceived organizational support (POS), academic autonomy, KPI-based evaluation systems, and professional development opportunities. The professional level comprises technological pedagogical content knowledge (TPACK) and professional experience. The individual level includes prior achievement, reflective capacity, and psychological resilience.TSE is aligned with higher education's tripartite mission: teaching, research, and service. The model's novelty lies in its interaction mechanisms across three levels and the moderating role of reflective capacity identified from prior research.The proposed model offers a theoretical and methodological foundation for understanding KPI-based reforms in Mongolian higher education and supporting faculty development. Future research should empirically validate the model and develop measurement instruments.
Speaking of speech recognition within the English language, it is the process of recognizing oral speech and transcribing it into writing using exclusive algorithms. For the perishable skill of English language learning, use of innovative speech recognition technology using Advanced Speech Recognition Technologies MLP-LSTM is proposed in this paper to advance the existing online learning platforms. Previous research addresses the importance of NLP in English language learning but notes the challenges in effectively extracting and segmenting features from multimodal data. In order to overcome these problems, this paper incorporate the proposed MLP for feature extraction and LSTM for sequence learning. The utilization of MLP-LSTM provides not only a brilliant improvement of the capacity to transform spoken language and perceive it but also minimizes the Word Error Rate (WER) to 0.075. With this low WER, along with the total accuracy rate of 98.25 %, this paper focus on underlining how this system is more effective than traditional language learning tools. This paper has been implemented through Python Software. The given MLP-LSTM based speech recognition model lays the foundation for a highly complex yet accurate paced English language learning platform that will cater to the needs of the learners in the global scenario.
ABSTRACT In an era of rapid environmental change, accurately modeling aquatic ecosystems, particularly the lateral water flow through soil and permafrost, remains a pressing need. This study addresses this through the Water and Energy Transfer Process (WEP) model. The WEP model overcomes the limitations of previous models and plays a crucial role in estimating the lateral flow of groundwater in the basin. In this study, we use our new formula for calculating the lateral flow at the permafrost depth and the deep percolation formula to study the subsurface, over permafrost, and lateral water flows in the cold permafrost for 52 years (1970–2021). The model's application in Mongolia's Great Lakes basin, specifically the Khovd River-Khar-Us Lake basin, achieved Nash–Sutcliffe model efficiency (NSE) coefficients of 0.64–0.75. This suggests that the model is plausible and suitable for further research. Additionally, the model effectively captured soil temperature dynamics, with NSE coefficients ranging from 0.95 to 0.98 in the upper soil layer to 0.35–0.80 at a depth of 100 cm. These findings validate the model's ability to accurately account for lateral water flow above the permafrost layer in cold regions. Future work will extend these calculations to different conditions and basins.
