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Bronxial astma bolalik davrida uchraydigan eng muhim surunkali respirator kasalliklardan biri bo‘lib, uning klinik kechishi simptomlarning davriy kuchayishi, jismoniy faollik va respirator infek…

Bronxial astma bolalik davrida uchraydigan eng muhim surunkali respirator kasalliklardan biri bo‘lib, uning klinik kechishi simptomlarning davriy kuchayishi, jismoniy faollik va respirator infeksiyalar bilan bog‘liq zo‘rayishlar hamda individual xavf omillarining mavjudligi bilan tavsiflanadi. Bolalarda kasallikni erta aniqlash, uning og‘irlik darajasini to‘g‘ri baholash va uzoq muddatli nazoratni ta’minlash pediatriyaning dolzarb masalalaridan biridir. Ushbu maqolada bolalarda bronxial astmaning zamonaviy diagnostik mezonlari, differensial diagnostikasi, kasallik nazoratini baholash, dori vositalari bilan davolashning asosiy tamoyillari, inhalyatsion terapiyaning o‘rni va profilaktik yondashuvlar tahlil qilindi. Zamonaviy xalqaro tavsiyalarda bolalar va o‘smirlarda astmani boshqarishda klinik simptomlarni baholash bilan bir qatorda o‘pka funksiyasini tekshirish, inhalyatsion kortikosteroidlar asosidagi nazorat qiluvchi terapiyadan oqilona foydalanish, xurujlarni o‘z vaqtida davolash va bemor hamda uning oilasini ta’lim bilan ta’minlash muhimligi ta’kidlanadi. Jahon sog‘liqni saqlash tashkilotining 2026-yilgi yangilangan tavsiyalarida bolalar va o‘smirlardagi astmani boshqarish bo‘yicha dalillarga asoslangan yondashuvlar kengaytirilgan. Dunyo Salomatligi Tashkiloti+1

Data collection platform for disassembled power tool images and metadata supporting computer vision tasks for life cycle assessments.

The integration of artificial intelligence (AI) technologies into healthcare represents a critical research frontier. This article examines the role and significance of AI in enhancing diagnostic accu…

The integration of artificial intelligence (AI) technologies into healthcare represents a critical research frontier. This article examines the role and significance of AI in enhancing diagnostic accuracy, treatment protocols, and patient management within healthcare systems. Through analysis of contemporary research, international perspectives, and practical applications, this study identifies key advantages of AI implementation. The findings demonstrate that AI technologies substantially improve diagnostic precision, enhance patient care quality, reduce treatment costs, and accelerate healthcare innovations. Specifically, machine learning algorithms enable early disease detection, predictive analytics support preventive medicine, and automated systems optimize clinical workflows. However, significant challenges remain, including data privacy concerns, ethical considerations, regulatory compliance, and the need for healthcare professional training. This paper proposes evidence-based recommendations for successful AI integration in clinical environments while maintaining patient safety and data security standards.

Ushbu maqolada yuz terisining biologik yoshini aniqlashning zamonaviy kosmetologiyadagi ahamiyati hamda teri holatini individual baholash imkoniyatlari yoritilgan. Xronologik va biologik yosh tushunch…

Ushbu maqolada yuz terisining biologik yoshini aniqlashning zamonaviy kosmetologiyadagi ahamiyati hamda teri holatini individual baholash imkoniyatlari yoritilgan. Xronologik va biologik yosh tushunchalarining farqlari, terining qarishiga ta’sir etuvchi ichki va tashqi omillar, jumladan, genetik moyillik, gormonal o‘zgarishlar, ultrabinafsha nurlanish, ekologik muhit, ovqatlanish, uyqu va stressning ta’siri tahlil qilingan. Shuningdek, korneometriya, kutometriya, sebumetriya, vizioskopiya, raqamli tasvirlash hamda sun’iy intellektga asoslangan teri tahlili kabi zamonaviy diagnostika usullarining imkoniyatlari ko‘rib chiqilgan. Maqolada diagnostika natijalariga asoslangan individual kosmetologik parvarish dasturlarini ishlab chiqish, terining namligi, elastikligi, mikrorelyefi va pigmentatsiyasi kabi ko‘rsatkichlarni kompleks baholashning ahamiyati asoslangan. Biologik yoshni muntazam monitoring qilish orqali kosmetologik parvarish samaradorligini ob’ektiv baholash va individual parvarish strategiyasini dinamik ravishda takomillashtirish imkoniyatlari ko‘rsatib berilgan.

Early detection of cellular anomalies remains challenging in resource-limited settings where conventional diagnostic infrastructure is unavailable. This work presents a unified electrochemical-dielect…

Early detection of cellular anomalies remains challenging in resource-limited settings where conventional diagnostic infrastructure is unavailable. This work presents a unified electrochemical-dielectric framework for multimodal (whole blood and buffer) label-free cellular screening that integrates Hydrogen Evolution Reaction (HER) voltammetry with dynamic ohmic drop compensation, Maxwell-Wagner dielectric spectroscopy with global spectral fitting, and machine learning with physics-informed regularization on an edge computing platform. We postulate that metabolic acidosis manifests as shifts in HER onset potential governed by Nernstian thermodynamics with explicit temperature dependence and kinetic/ohmic correction terms, while genomic anomalies, bacteria, and parasites alter complex permittivity through Hanai mixture dynamics with α and β dispersion differentiation. A 1D Convolutional Neural Network (CNN) with dual architecture and physics-informed regularization processes electrochemical signatures on an ESP32-S3 microcontroller, ensuring thermodynamic consistency during inference through explicit loss function formulation. Theoretical analysis indicates that populationlevel measurements (> 10Exp5 cells/mL) can achieve signal-to-noise ratios sufficient for anomaly flagging, though individual cell detection remains infeasible. We discuss implementation requirements, including dynamic iRΩ compensation via highfrequency pulse, external analog front-ends for impedance spectroscopy above 1 MHz, and uncertainty propagation analysis with error budgets for pH and dielectric parameters. This framework targets triage applications rather than definitive diagnosis, offering a portable screening tool for anomalies in multiple biological matrices. 

В статье рассматриваются современные направления применения технологий искусственного интеллекта в системе управления рисками таможенных органов. Актуальность исследования обусловлена увеличением объе…

В статье рассматриваются современные направления применения технологий искусственного интеллекта в системе управления рисками таможенных органов. Актуальность исследования обусловлена увеличением объемов международной торговли, ростом количества электронных таможенных деклараций и необходимостью повышения эффективности выявления потенциальных нарушений при одновременном сокращении времени проведения таможенных процедур. Особое внимание уделяется возможностям машинного обучения, интеллектуального анализа данных, автоматизированного профилирования участников внешнеэкономической деятельности и выявления аномальных операций. Рассматриваются основные преимущества использования искусственного интеллекта, включая повышение точности оценки рисков, автоматизацию обработки больших массивов информации и оптимизацию распределения ресурсов таможенных органов. Определены основные организационные, технологические и правовые проблемы внедрения интеллектуальных систем. Предложена концептуальная модель применения искусственного интеллекта в системе управления таможенными рисками, включающая сбор данных, их обработку, формирование риск-профиля, оценку вероятности нарушения и поддержку принятия решений должностным лицом. Сделан вывод о том, что искусственный интеллект способен стать важным инструментом совершенствования риск-ориентированного подхода в деятельности таможенных органов при условии обеспечения качества данных, прозрачности алгоритмов, информационной безопасности и сохранения человеческого контроля.

Python script used to detect and quantify Ca²⁺ influx events from GCaMP7 time-lapse imaging data in zebrafish embryos, as described in Akieda et al., "Endogenous cell competition kills pathogenic…

Python script used to detect and quantify Ca²⁺ influx events from GCaMP7 time-lapse imaging data in zebrafish embryos, as described in Akieda et al., "Endogenous cell competition kills pathogenic de novo mutant cells but declines under pH stress" (Nature Communications). Baseline fluorescence (F₀) is estimated by a rolling 10th-percentile method, and peaks in ΔF/F₀ traces are detected using scipy.signal.find_peaks.