Ushbu maqolada boshlang‘ich sinf o‘quvchilarini darsga jalb qilishda muammoli ta’lim metodlarining o‘rni va samaradorligi tahlil qilingan. Tadqiqot davomida turli muammoli metodlar — muammoli savollar, aqliy hujum, rolli o‘yinlar va topshiriqlar orqali o‘quvchilarning darsga bo‘lgan faolligi va qiziqishi qanday oshishi ko‘rib chiqildi. Tajriba darslari asosida aniqlanishicha, muammoli metodlar o‘quvchilarda mustaqil fikrlash, savol berish va ijodiy yondashuvni shakllantiradi. Mazkur maqolada amaliy dars ishlanmasi, metodlar turlari va ularning qulayligi haqida konkret misollar keltirilib, boshlang‘ich ta’limda ushbu yondashuvni keng joriy etish bo‘yicha tavsiyalar berilgan.

The etiological structure and peculiarities of clinical and epidemiological manifestations of acute intestinal infections in hospitalised children are studied. A retrospective study of the case histories of 2479 children hospitalised with the clinic of acute intestinal infection was carried out. All patients underwent standard laboratory examination, including clinical, biochemical, instrumental diagnostic methods, bacteriological and molecular-biological studies to verify the causative agent. The epidemiological anamnesis of all children was clarified, and the frequency of background and concomitant diseases was studied. The diagnosis of intestinal infection was verified in 925 children (38%). Bacterial intestinal infections were detected in 610 (65 % of the transcripts).

Markaziy Osiyoda suv xavfsizligi tobora dolzarb masalaga aylanib bormoqda. Bunga iqlim o‘zgaruvchanligining kuchayishi, suvga bo‘lgan talabning ortishi hamda transchegaraviy daryolarni boshqarish bilan bog‘liq muammolar sabab bo‘lmoqda. Ushbu maqolada mintaqadagi transchegaraviy suv resurslarini boshqarish murakkabligi, milliy manfaatlar bilan mintaqaviy hamkorlik o‘rtasidagi o‘zaro bog‘liqlik asosida o‘rganiladi.

The food industry is a branch of the national economy that produces food products. The industry includes enterprises of meat and dairy, oil and fat, fish, flour and cereals, pasta, fruit and vegetable canning, dairy and cream, sugar, tea, confectionery, bakery, grape and champagne wines, alcohol, vodka, tobacco, brewing, thirst-quenching, soap and other industries.

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.