This paper focuses on the selection and justification of deep learning models for emotion classification tasks. It provides a comprehensive analysis of various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Transformer models, assessing their performance in recognizing and classifying human emotions from multimodal data sources. The study examines the strengths and limitations of each model with respect to data type, training efficiency, computational complexity, and generalization capabilities. Furthermore, criteria for optimal model selection tailored to real-world emotion recognition applications are discussed. The findings contribute to enhancing the accuracy and robustness of emotion classification systems and offer valuable guidelines for researchers and practitioners developing advanced affective computing solutions.

The relationship between a speaker’s native language (L1) and their pronunciation in a second language (L2) has long been recognized as a critical area of study within second language acquisition. Pronunciation is not merely about producing sounds correctly; it encompasses various phonological elements such as stress, rhythm, intonation, and syllable structure — all of which are deeply shaped by the learner's first language. This paper aims to explore the extent to which L1 interferes with or supports the acquisition of accurate L2 pronunciation. It investigates both segmental (individual sounds) and suprasegmental (prosodic features) aspects of speech, presenting evidence from various language groups to illustrate common patterns of transfer. Moreover, the study discusses how phonological habits from the native language often lead to a foreign accent and reduced intelligibility in the second language, even among otherwise proficient speakers. Emphasis is placed on practical strategies and pedagogical approaches that can be used to address L1-induced difficulties, such as contrastive analysis, phonetic training, and the use of technological tools for self-monitoring and feedback. The paper concludes that although native language influence is a natural and often unavoidable aspect of second language learning, its impact on pronunciation can be significantly minimized through targeted instruction and increased learner awareness.

Ushbu maqolada hind xalqi og‘zaki va yozma adabiyoti javohirlaridan biri bo‘lmish “Kalila va Dimna” turkiy qo‘lyozmalarining o‘ziga xos bo‘lgan jihatlari va uning mazmuniy qurilishi tаdqiq etilgаn.

Ushbu maqolada fizika yo‘nalishidagi universitet talabalari uchun yarimo‘tkazgichli lazerlarni o‘qitishda axborot texnologiyalari va virtual laboratoriyalardan foydalanish masalasi nazariy jihatdan tahlil qilingan. Amaldagi ta’lim jarayonida mazkur texnologiyalarning yetarli darajada qo‘llanilmayotgani muammo sifatida ko‘rsatib o‘tilgan. Lazer hodisasining murakkab fizik tabiati va uni an’anaviy metodlar bilan tushuntirishdagi qiyinchiliklar yoritilgan. Shuningdek, zamonaviy axborot-kommunikatsiya texnologiyalari, xususan interaktiv dasturlar, 3D modellashtirish, virtual laboratoriyalar va multimedia vositalarining talabalarda mavzuni tushunishga bo’lgan ta’siri o’rganilgan.

Рассмотрены виды тестовых заданий по филологическим дисциплинам и методика их создания, дано определение теста, пояснено понятие предметной области. Тестовые задания разделены на виды, приведены примеры. Показано, что компетентностные знания, умения и навыки студентов можно оценить на основе тестовых образцов.