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Mazkur maqolada boshlang‘ich sinf o‘quvchilarida muvaffaqiyatga erishishga intilish motivining shakllanishi va uning shaxs rivojlanishidagi ahamiyati yoritilgan. O‘quvchining bilim o…

Mazkur maqolada boshlang‘ich sinf o‘quvchilarida muvaffaqiyatga erishishga intilish motivining shakllanishi va uning shaxs rivojlanishidagi ahamiyati yoritilgan. O‘quvchining bilim olishga bo‘lgan qiziqishi, o‘z imkoniyatlariga ishonchi, mustaqil faoliyat yuritishi, maqsad qo‘yishi hamda qiyinchiliklarni yengib o‘tishga intilishi muvaffaqiyat motivining muhim tarkibiy qismlari sifatida tahlil qilinadi. Shuningdek, 1–4-sinf “Tarbiya” darslarida mazkur motivni rivojlantirishga xizmat qiluvchi pedagogik usul va vositalar, o‘qituvchining roli hamda amaliy tavsiyalar bayon etilgan.

Mixed-methods research (MMR) refers to an integrative research approach that combines quantitative and qualitative methods within a single study to both understand and explain a phenomenon. It involve…

Mixed-methods research (MMR) refers to an integrative research approach that combines quantitative and qualitative methods within a single study to both understand and explain a phenomenon. It involves collecting, analyzing, and integrating both numerical (quantitative) and textual or observational (qualitative) data to leverage their complementary strengths. Three main philosophical worldviews commonly underpin the practice of MMR: (a) pragmatism, (b) critical realism, and (c) dialectical pluralism. In addition, the MMR literature highlights several key rationales for employing MMR, including cross-referencing, supplementarity, and expansion. Depending on their research objectives, scholars can adopt either parallel or sequential MMR designs to effectively integrate quantitative and qualitative approaches. Translation & Interpreting Studies (TIS) draws on a diverse range of theories, methods, and techniques to explore issues related to translation and interpreting (T&I). Given its inherently interdisciplinary nature, MMR aligns well with TIS. The development of MMR in TIS can be characterized by three distinct periods: (1) emerging discussions on methodological integration in the 2000s, (2) advocacy for and analysis of MMR practice in TIS in the 2010s, and (3) diversified application of MMR across various realms of TIS in the 2020s. Despite its growing adoption, TIS researchers face several challenges in the design, implementation, and reporting of MMR. These challenges are primarily related to ensuring and maintaining the quality and rigor of MMR-based TIS. To advance MMR in TIS, researchers should undertake evidence-based methodological reviews of MMR applications in TIS and implement rigorously designed MMR studies to explore a wide range of research areas, such as machine translation post-editing, translation reception, T&I quality assessment, and meta-research on TIS.

The growing need for intelligent systems, which can operate in heterogeneous areas, indicates the limitations of traditional transfer learning methods, especially with regard to processing high-dimens…

The growing need for intelligent systems, which can operate in heterogeneous areas, indicates the limitations of traditional transfer learning methods, especially with regard to processing high-dimensional data and operating in real-time conditions. This paper presents a new cross-domain transfer learning system based on Quantum Neural Networks (QNNs) to be used in multi-task applications with real-time adaptation, to facilitate scalable and efficient knowledge transfer across domains, including, but not limited to, healthcare, IoT, and cybersecurity. The suggested architecture is based on a hybrid quantum-classical architecture, where parameterized quantum circuits are used to represent the features, domain-invariant learning and multi-task optimization strategies are applied, and a reinforcement learning-based module is used to provide continuous real-time adaptation by using streaming data. Experimental findings show that the model has 94.8% accuracy, which is significantly higher than the current classical and transformer-based methods, and that the adaptation latency of the model is 95 ms, and the transfer efficiency is more than 10 times higher, which means that generalization and robustness are higher. The results prove that quantum-enhanced transfer learning is a powerful and scalable solution in next-generation intelligent systems, with significant practical impact in real-time systems, such as smart healthcare, cybersecurity, and IoT systems.

Abstract: Maternal immunisation is a core public health strategy that boosts immunity in pregnant women and their babies. Despite the importance of vaccination in this demographic, uptake remains inco…

Abstract: Maternal immunisation is a core public health strategy that boosts immunity in pregnant women and their babies. Despite the importance of vaccination in this demographic, uptake remains inconsistent and is influenced by individual, professional, and organisational factors. This scoping review aimed to map evidence on how nurses and midwives can address uptake barriers and support facilitators of maternal vaccination within primary care and community-based settings. It aligned with the Joanna Briggs Institute (JBI) methodology. It reported findings in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses Extension for Scoping Reviews (PRISMA-ScR). We obtained relevant literature published between 2019 and August 2026 using PubMed/MEDLINE and citation searching.We supplemented this with database-specific searches in CINAHL and Scopus. Eligible studies were empirical peer-reviewed articles written in the English language published between January 2019 and August 2026. Sources for the scoping review had to be relevant, specifically addressing vaccination during pregnancy and including a nursing or midwifery contribution. After applying the eligibility criteria, the review deemed sixteen studies suitable for the evidence map. The evidence leaned more toward high-income settings and midwifery-led or midwifery-involved care. The review identified common barriers to maternal immunisation, including limited staff training, safety concerns and misinformation, time constraints, fragmented immunisation records, inconsistent recommendations, and difficulty accessing vaccines. The review reported promising vaccine uptake and feasibility for integrated antenatal vaccination and multi-component approaches.Although communication Training was associated with improved clinicians’ confidence; its association with vaccination uptake was inconsistent. Overall, the scoping review suggests that nurse- and midwife-involved maternal immunization interventions are operationally promising, although evidence on causality remains limited.

As the utilization of Cloud Computing is growing rapidly it encounters more challenging and evolving cybersecurity threats and zero-day attacks. In order to detect intrusion detection more Intelligent…

As the utilization of Cloud Computing is growing rapidly it encounters more challenging and evolving cybersecurity threats and zero-day attacks. In order to detect intrusion detection more Intelligent and adaptive deep learning models are crucial. With the goal to identify anomalies in cloud network traffic, this study compares three sequential learning architectures the conventional LSTM, its bidirectional extension (Bi LSTM), and Projected LSTM Classifier (PLC) and proposes a novel Transformer Encoder with Adversarial Hardening (Transformer+AT). Two popular datasets, CICIDS2018 and CIC-IoT2023, were used for the experiments. During preprocessing, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to concentrate on the extreme class imbalance that can be prevalent in real-world cloud data. Amid the baselines models PLC has acheieved an accuracy of 96% on CICIDS2018 but has significantly dropped to 82% on CIC-IoT2023. Show cases a overview gap which is addressed by the proposed model. The proposed Transformer+AT achieve 96.05% on CICIDS2018 and 96.1% on CIC-IoT2023 shows significant stable performance among the two datasets different from PLC as it has dropped from 96% to 82% on CIC-IoT2023. Decisively Transformer+AT shows outstanding adversarial robustness by 0.06% drop of accuracy under FGSM attack on CICIDS2018 and 8.39% on CIC-IoT2023 when compared to the 20-25% drop for baseline models. These findings disclose that when Transformer architectures with SMOTE and adversarial hardening are combined, they improve generalization and robustness among varied environments of Cloud and IoT.

Same code, trained weights and result tables as v1.0.0, with Zenodo metadata (.zenodo.json) added. This release is archived on Zenodo with a DOI.

В статье рассматриваются теоретические и практические аспекты формирования «зеленого» бухгалтерского учета как самостоятельного направления бухгалтерского учета, ориентированного на отражение экологич…

В статье рассматриваются теоретические и практические аспекты формирования «зеленого» бухгалтерского учета как самостоятельного направления бухгалтерского учета, ориентированного на отражение экологических затрат, обязательств и рисков хозяйствующих субъектов. Актуальность исследования обусловлена переходом Республики Узбекистан к модели «зеленой» экономики, а также необходимостью сближения национальной практики бухгалтерского учета с международными стандартами отчетности в области устойчивого развития (IFRS S1/S2, GRI). На основе анализа нормативно-правовой базы, международного опыта и практики отдельных отраслей выявлены основные препятствия для внедрения экологического учета в Узбекистане. Предложены авторская классификация экологических затрат и рекомендации по совершенствованию методики их учета с учетом адаптации к национальному плану счетов.

Ushbu maqolada tarbiya jarayonining mazmun-mohiyati, tarbiyaning umumiy metodlari, shakllari va vositalari haqida ma’lumot beriladi. Shuningdek, ta’lim-tarbiya jarayonida tarbiya metodlari…

Ushbu maqolada tarbiya jarayonining mazmun-mohiyati, tarbiyaning umumiy metodlari, shakllari va vositalari haqida ma’lumot beriladi. Shuningdek, ta’lim-tarbiya jarayonida tarbiya metodlarini to‘g‘ri tanlash, ulardan samarali foydalanish hamda o‘quvchilarda ma’naviy-axloqiy, estetik, jismoniy, mehnat va fuqarolik sifatlarini shakllantirish masalalari yoritiladi.

Properly diagnosing nutrient deficiencies in the crop is essential in order to maximize fertilizer use, improve agricultural productivity and to sustain farming. Most of these methods used for soil nu…

Properly diagnosing nutrient deficiencies in the crop is essential in order to maximize fertilizer use, improve agricultural productivity and to sustain farming. Most of these methods used for soil nutrient assessment (primarily chemical laboratory-based) are more time consuming, expensive, and can't be conducted real-time. This paper proposes a new method named MorpBioFusion-YOLO that addresses these problems using the morphological and color-based deep learning in RGB images obtained by the field. To achieve this, we have proposed a novel method, called MorpBioFusion-YOLO, which at the same time tackles the problem of autonomous classification of nutrient deficiency, based on the deep learning in both morphological and color components from RGB images captured in the field. The suggested framework includes feature extraction at multiple scales, adaptive morphological representation learning using colour-sensitized spatial analysis functions to find nutrient stress pattern of Boron, Calcium, Iron, Magnesium, Manganese, Zinc, Potassium, Sulphur deficiency and Healthy leaf conditions. From a technical perspective, it predominantly uses an adapted detection backbone composed of YOLO and allows for a path aggregation system that performs detailed fusion operations in the various features, notwithstanding varying illumination, orientation and background conditions across the entire image. Augmentation strategies and texture preserving transformations are employed during training to increase the ability of generalization. Our proposed model performance on the banana nutrient deficiency plants with labels yields high overall mean Average Precision (mAP@0.5): 81.7% with the extreme accuracy in identifying Healthy and Sulphur classes 0.995 and good performance in other classes as Boron deficiency, Potassium deficiency and Manganese deficiency. Moreover, the presented framework is able to provide effective real-time inference performance, ideal for edge-supported smart farming applications. Experimental studies showed that morphological structures of leaves, venation characteristics, chlorosis pattern and variation in the level of pigmentation are reliable traits for the estimation of nutrient stress. The proposed BioFusion-YOLO framework is intelligent agricultural solution for realizing early nutrient diagnosis and precision crop management that is effective, low in cost and scalable.

Background: Nigeria’s federal structure is under intense strain from two ideologically opposed violent non-state actors: the separatist Indigenous People of Biafra (IPOB) in the South-East and t…

Background: Nigeria’s federal structure is under intense strain from two ideologically opposed violent non-state actors: the separatist Indigenous People of Biafra (IPOB) in the South-East and the jihadist Islamic State West Africa Province (ISWAP) in the North-East. Although their doctrinal foundations differ, both movements contest the territorial integrity and institutional legitimacy of the Nigerian state. Research Gap: Despite extensive literature on insecurity in Nigeria, three gaps remain. First, existing studies examine IPOB and ISWAP insurgencies as distinct security threats, with little comparative analysis of their implications for Nigerian federalism. Second, while federalism is widely discussed in Nigeria, its connection to contemporary dual insurgencies remains under-theorized. Third, there is limited empirical research on how two ideologically divergent violent non-state actors one separatist and one jihadist jointly challenge the legitimacy and sustainability of the Nigerian federation between 2015 and 2024. This study seeks to fill this gap through a comparative analysis. Objective: The broad objective of this study is to comparatively examine the effects of separatist and insurgencies on Nigerian federalism, using IPOB and ISWAP as case studies, between 2015 and 2025. Method: A qualitative documentary analysis was used in the study. The study adopts a Structural-Functionalist framework to demonstrate how security dysfunctions within constituent units trigger adaptive, centralizing responses from the federal center Results: The findings indicate that despite differences in ideology, operational tactics, and geographic location, both insurgencies generate similar structural consequences. These include the centralization of security powers at the federal level, weakening of state and local autonomy, sharp decline in state revenues, and the militarization of everyday governance. Conclusion: The study concludes that asymmetric security crises act as catalysts for federal decay. While IPOB and ISWAP pursue divergent ideological goals, their shared structural effects like  hyper-centralization of security, erosion of subnational autonomy, fiscal contraction, and militarization of civil life  reveals a deeper federal malaise. Paradoxically, the federal government's security-led response has not strengthened federalism but has instead institutionalized a centralized security state. Unique Contribution: The unique contribution of this research lies in its comparative federalism framework. By juxtaposing a separatist insurgency (IPOB) and a jihadist insurgency (ISWAP) within a single analytical lens, the study demonstrates that ideologically divergent threats produce structurally convergent effects on federalism.. Recommendation: The study recommends, among others, multi-level policing as a structural response to hyper-centralization, fiscal federalism reform to address fiscal contraction in constituent states and institutionalization of structured political dialogue to restore equality of belonging.