Communication through images can be considered the earliest form of non-verbal communication used by humankind. Modern scholars of rock and cave paintings continue to study and interpret these works, showing that they are not merely pictures but carry deep communicative meaning. Before writing emerged, images served as a primary tool for sharing ideas. From ancient times through the end of the 19th century, literacy rates were low, so visual communication remained widely used until recently. Even at the end of the 20th century and the beginning of the 21st, short-form informational imagery continued to play an important role in communication. Comics, which developed in the 1950s in European countries such as France, as well as the United States, and Japan, evolved not only as entertainment but also as an influential form of modern visual culture that combines fine art and literature, which has had a significant impact on social psychology. The purpose of this research is to examine the unique style of modern comics and identify current development trends. To do so, the study applies art analysis, comparative image studies, and semiotics.
Teacher stress has increasingly emerged as a significant challenge affecting both the efficiency of educational systems and the psychological well-being of teachers worldwide. However, empirical studies on teacher stress in Mongolia remain scarce, particularly regarding the development of psychometrically sound measurement tools. This study aimed to transculturally validate the Teacher Stress Inventory (TSI) in the Mongolian educational context and identify the major sources of teacher stress among schoolteachers. This quantitative study involved Mongolian schoolteachers from various educational settings. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to examine the factorial validity and reliability of the Mongolian version of the TSI. The findings provided empirical evidence supporting the reliability and construct validity of the instrument. A six-factor structure was identified, accounting for 68.14% of the total variance, with acceptable to excellent reliability across the scales. Both EFA and CFA confirmed the validity and adequacy of the Mongolian TSI structure, and the resulting six-factor model demonstrated acceptable fit indices. Compared with the original ten-factor structure, the Mongolian version revealed highly interrelated stress dimensions, particularly in relation to job-related stressors, time management, discipline and motivation, and physiological and behavioral reactions to stress. In addition, the study identified the major sources of teacher stress as work-related issues and professional expectation issues. The findings contribute to the limited literature on teacher stress in Mongolia and provide a valuable foundation for designing intervention strategies aimed at improving teachers’ welfare and psychological well-being.
Background: Tandem leucine-rich repeats (LRRs) are typically classified into eleven types; however, several variant motifs have also been reported. Here, we identified new LRR variants that exhibit dual characteristics of two distinct types. We investigated how the dual characteristics influence the structure and function of LRRs. Methods: We conducted sequence similarity searches using the protein database and analyzed sequence features. We also characterized the structural features of these LRR variant motifs using solved structures and AlphaFold models and investigated their potential biological functions through domain analysis. Results: Of the identified 3222 proteins, approximately 60% originate from the bacterial PVC superphylum. The variants were classified into two groups: one defined by the consensus sequence LxxLxLxx(C/T)xzI TDxxLxx(L/F)xx(L/C)xx, and the other by LxxLxLxxCxxI TDxxLxxLxxLP (where “z” denotes a deletion). The LRRs highly similar to the variants are occasionally observed in solved structures and comprise three types of super-secondary structures (SSSs): β-strand–α-helix adjoining a 3(10)-helix–β-strand, β-strand–3(10)-helix–β-strand, and β-strand–3(10)-helix adjoining an α-helix–β-strand. The AlphaFold models adopt these SSSs and, in addition, include the SSS of the β–α–β motif. Functional annotation identified kinase and F-box domains in a subset of these LRR proteins. Conclusions: The coexistence of these four SSSs and the high frequency of the first SSS appear to reflect the dual characteristics of the LRR variants. The LRR variant-containing proteins suggest potential roles in bacterial immunity and ubiquitination. The present findings expand the structural diversity of LRR proteins and provide new insights into their functional roles.
Multi Agent Cognitive Automation framework attempts to develop a scalable and explainable multi-agent automation system that can turn out to be robust in difficult industrial environments that operate under uncertainty. Existing distributed automation and multi-agent reinforcement learning algorithms are more effective in promoting coordination and optimality but are still feeble in the instances of reward instability, slow convergence, communication bottlenecks and interpretability in changing environments. In order to address these gaps, this paper suggests a Cognitive Reinforced Multi-Agent Automation Framework (CRMAF) that integrates hybrid reinforcement learning, cognitive belief reasoning, distributed knowledge graphs, cooperative reward maximization, and explainable decision layer. The model is tested on the Smart Manufacturing Multi-Agent Control Dataset that models industrial processes of several autonomous agents. CRMAF, which was introduced in PyTorch 2.0 and trained on simulated interaction, has a classification accuracy of 93.7 percent with large power consumption and response time benefits, making it useful to industrial automation engineers and smart manufacturing researchers.
Abstract In recent years, the integration of digital sensor technologies into physics laboratory instruction has created new opportunities to improve measurement accuracy and experimental efficiency. This paper presents a DrDAQ sensor-based experimental setup for determining gravitational acceleration ( g ) using a free-fall object. The system employs a light sensor to record high-resolution temporal and positional data, enabling precise determination of motion parameters. The experimental results show that the DrDAQ sensor provides approximately 1 ms temporal resolution and approximately 1 mm spatial resolution, allowing measurements to be performed with high repeatability. The measured value of gravitational acceleration, g = 9.755 ± 0.009 m s − 2 , differs from the standard value ( 9.807 m s − 2 ) by approximately 0.5%, demonstrating the accuracy and reliability of the system. Compared to conventional methods, such as manual timing with a stopwatch or pendulum-based approaches, the sensor-based system reduces human error and enables rapid repeated measurements within a short time frame. The experiment was implemented in a real laboratory setting within a physics teacher education program at the Mongolian National University of Education, where students interacted directly with the sensor system. Qualitative findings from observations and student interviews indicate that the use of the sensor simplifies data collection, supports independent experimental work, and makes data analysis more accessible. These results demonstrate that integrating low-cost digital sensor systems, such as DrDAQ, as didactic tools into physics laboratories is effective in improving measurement accuracy, reliability, and practical feasibility.
