CONTRIBUTION OF THE COOPERATION BETWEEN AGRICULTURE AND TOURISM FOR RURAL DEVELOPMENT

The main goal of rural development is to raise the economic, social and cultural levels of all individuals living in rural areas. Rural development, which is considered to be an important tool in the fight against poverty, increases the quality of life of rural population and improves the work and living conditions of the regions where these people live. With rural development, it is aimed to increase both agricultural and non-agricultural incomes. Undoubtedly, agriculture is the most important source of income for people living in rural areas. However, in recent years, those living in rural areas also generate significant incomes from non-agricultural activities. Rural tourism activities are an important source of income for people living in rural areas. Thanks to rural tourism, entrepreneurs are making investments in rural areas. Thus, income growth is experienced in rural areas and employment is increasing. Rural tourism also makes important contributions to the sustainability of the local culture. On the other hand, rural tourism is an alternative field of activity where farmers can make better use of their free time. Through rural tourism, rural-urban migration can be prevented. This is important for the sustainability of rural areas. The current study primarily discusses the concepts of rural development and rural tourism. The interaction between agriculture and tourism is examined and the benefits of rural tourism are uncovered. The results of the study have shown that rural tourism activities have positive effects on rural development

DEALING WITH CRITICAL INCIDENTS: EXPERIENCES OF TURKISH NOVICE EFL TEACHERS

Moments causing teachers to stop and think about their teaching are called critical incidents and reflecting on them can be a way of gaining insights into their practices and contexts. However, critical incidents are underexplored in the Turkish EFL context. Thus, this qualitative case study aimed to understand the types of critical incidents encountered by six Turkish novice EFL teachers who all graduated from the same English language teaching program, their ways of dealing with these, and how this affected them. Data obtained from reflective journals and a focus group interview were analysed thematically. The results revealed critical incidents related to multiple sources, mainly due to students’ behaviors. Moreover, teachers' strategies varied from addressing the student to acting as the authority. These critical incidents affect novice teachers in various ways, such as questioning their language teacher education and teaching competence, which were discussed in this study along with implications and directions for future studies

ResViT: Residual vision transformers for multi-modal medical image synthesis

Multi-modal imaging is a key healthcare technology that is often underutilized due to costs associated with multiple separate scans. This limitation yields the need for synthesis of unacquired modalities from the subset of available modalities. In recent years, generative adversarial network (GAN) models with superior depiction of structural details have been established as state-of-the-art in numerous medical image synthesis tasks. GANs are characteristically based on convolutional neural network (CNN) backbones that perform local processing with compact filters. This inductive bias in turn compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, to combine local precision of convolution operators with contextual sensitivity of vision transformers. ResViT employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine convolutional and transformer modules. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing methods in terms of qualitative observations and quantitative metrics

Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images

Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis is still challenging due to heterogeneity in size and location of pathology, as well as visual similarities and co-occurrence of separate pathology. Since disease-related regions often occupy a relatively small portion of diagnostic images, classification models based on traditional convolutional neural networks (CNNs) are adversely affected given their locality bias. While CNNs were previously augmented with attention maps or spatial masks to guide focus on potentially critical regions, learning localization guidance under heterogeneity in the spatial distribution of pathology is challenging. To improve multi-label classification performance, here we propose a novel method, HydraViT, that synergistically combines a transformer backbone with a multi-branch output module with learned weighting. The transformer backbone enhances sensitivity to long-range context in X-ray images, while using the self-attention mechanism to adaptively focus on task-critical regions. The multi-branch output module dedicates an independent branch to each disease label to attain robust learning across separate disease classes, along with an aggregated branch across labels to maintain sensitivity to co-occurrence relationships among pathology. Experiments demonstrate that, on average, HydraViT outperforms competing attention-guided methods by 1.2%, region-guided methods by 1.4%, and semantic-guided methods by 1.0% in multi-label classification performance