The Mountain Gazelle Optimizer (MGO) is a recent nature-inspired metaheuristic that has attracted growing interest due to its simplicity, flexibility, and competitive search performance. This paper presents a systematic review of 64 peer-reviewed studies published between 2022 and 2025, covering algorithmic foundations, methodological enhancements, hybrid frameworks, and application-specific adaptations of the mountain gazelle optimizer. Methodological studies mainly focus on improving convergence behavior, population diversity, and robustness through adaptive parameter control, chaotic and opposition-based strategies, and neighborhood search mechanisms. Hybrid variants integrating machine learning models, control systems, mathematical programming, and system-level frameworks further extend its capability to solve nonlinear and constrained optimization problems. Application-oriented research demonstrates the effectiveness of the optimizer in renewable energy systems, power system optimization, machine learning, internet of things and communication networks, engineering design, and construction management. Reported results consistently show that this optimizer and its variants perform competitively compared with established metaheuristic algorithms. Key challenges and future research directions are discussed to support further development and real-world adoption of the mountain gazelle optimizer-based optimization approaches.
Endophytic fungi are microorganisms that live within plant tissues without causing disease symptoms, playing a crucial role in enhancing plant resistance against pathogens and insect pests while simultaneously promoting plant growth through various mechanisms. This systematic review analyzes 47 research articles published between 2020 and 2025, examining the role of endophytic fungi. The literature search was conducted in Scopus and PubMed databases using relevant keywords, initially yielding 2,730 records that were subsequently filtered based on four inclusion criteria: (i) Studies must examine interactions between endophytic fungi and plants; (ii) studies must include plant defense mechanisms induced by endophytic fungi; (iii) studies must demonstrate the fungi’s entomopathogenic capabilities; and (iv) studies must show the fungi’s ability to enhance plant growth. The analysis revealed that Beauveria bassiana was the most extensively studied endophytic fungus species (59.15
This paper investigates the use of large language models (LLMs) as evaluators in multidimensional machine translation (MT) assessment, focusing on the English–Indonesian language pair. Building on established evaluation frameworks, we adopt an MQM-aligned rubric that assesses translation quality along morphosyntactic, semantic, and pragmatic dimensions. Three LLM-based translation systems (Qwen 3 (0.6B), LLaMA 3.2 (3B), and Gemma 3 (1B)) are evaluated using both expert human judgments and an LLM-based evaluator (GPT–5), allowing for a detailed comparison of alignment, bias, and consistency between human and AI-based assessments. In addition, a classroom calibration study is conducted to examine how rubric-guided evaluation supports alignment among novice evaluators. The results indicate that GPT–5 exhibits strong agreement with human evaluators in terms of relative quality ranking, while systematic differences in absolute scoring highlight calibration challenges. Overall, this study provides insights into the role of LLMs as reference-free evaluators for MT and illustrates how multidimensional rubrics can support both research-oriented evaluation and pedagogical applications in a mid-resource language setting.
Large Language Models such as ChatGPT, Gemini, and recent open-source systems have shown strong performance in natural language tasks. However, their reliability and pedagogical suitability in low-resource educational settings—especially in high-stakes multiple-choice exams that require explicit reasoning—remain underexplored. This paper benchmarks six LLMs—ChatGPT (GPT-4), Gemini 1.5 Pro, Microsoft Copilot, DeepSeek-V2 (16B), Qwen 3 (14B), and Phi-2 (2.7B)—on Turkish and Indonesian exam questions. The evaluation uses 2,000 items in total: 1,000 Turkish graduate admission questions and 1,000 Indonesian vocational questions. We assess (i) answer accuracy, (ii) justification quality (coherence, option comparison, reasoning depth, and Bloom-level alignment), and (iii) temporal consistency under repeated prompting. Expert-written rationales are used as a reference for human alignment. Quantitative analysis combines multilingual semantic similarity, lexical overlap, and coherence deviation measures, supported by SHAP-based feature attribution and UMAP visualization to examine explanation patterns across models. The results show clear variation in both correctness and justification behavior. GPT-4 produces the most consistently high-quality and pedagogically aligned explanations, while Gemini and Qwen 3 achieve competitive but less stable performance across cognitive categories. DeepSeek-V2 performs strongly on several reasoning metrics but shows lower temporal stability, whereas Copilot and Phi-2 are weaker on higher-order reasoning. These findings have direct implications for educational equity, student achievement, and quality education in low-resource contexts, particularly where AI-assisted assessment tools may influence access to fair and reliable academic evaluation. The results also highlight risks such as fluent but incorrect explanations and instability across runs, which should be carefully considered when deploying LLMs in formal assessment environments.
This study aims to describe the concepts and methods of learning foreign languages from Fiki Naki (FN) in the digital age. This research is qualitative descriptive research. The data collection method used in this study is Grounded Theory. Data collection used Library research instruments or Visual Material Review. The research library used eight YouTube videos of interviews with FN and FN's answers to several questions from his fans and used other media sources. The data analysis uses four stages: Codes, Sub-Categories, Categories, and Theory. This study concludes that the FN learning model is suitable for adaptation to foreign language learning to use a foreign language in simple conversation with a native speaker of the target language, not for academic purposes. The concept of learning FN consists of two: psychological and non-psychological. Psychological learning concepts include Learning by fun, high determination and consistency, confidence in using Language, and Self-Regulated Learning. The concepts of non-psychological Learning include Autodidact, intensive Learning, direct language practice, imaginary conversation with oneself and practice it with native speakers, mastery of vocabulary and phrases with keyword strategies, and effective use of online media. At the same time, the learning method that FN uses is a communicative method with several learning techniques, including a focus on listening and speaking skills, using Mnemonic techniques in vocabulary mastery, and conjugation mastery techniques. The novelty of this research is the description of FN's foreign language learning concepts and methods in self-taught foreign language learning. The researchers recommend analyzing and implementing FN's concepts and learning methods in learning Arabic as a foreign language.
