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This manuscript is devoted to constructing a novel iterative scheme and reckoning of fixed points for generalized contraction mappings in hyperbolic spaces. Also, we establish Δ\Delta and strong convergence results by the considered iteration under the class of mappings satisfying condition (E). Moreover, some qualitative results of the suggested iteration, like weak w2{w}^{2}-stability and data dependence results, are discussed. Furthermore, to test the efficiency and effectiveness of the proposed iteration, practical experiments are given. To support the theoretical results, illustrative examples are presented. Finally, our results improve and generalize several classical results in the literature of fixed point iterations.

In most of the medical treatments, intravenous catheterization is considered as a first crucial phase, in which health practitioners find the superficial vein to conduct blood sampling or medication procedures. In some patients these veins are hard to localize due to different physiological characteristics such as dark skin tone, scars, vein depth etc., which mostly results in multiple attempts for needle insertion. This causes pain, delayed treatment, bleeding, and even infections. To reduce these risks, an automated veins detection method is needed that can efficiently segment the veins and produce realistic results for cannulation purposes. For this purpose, many imaging modalities such as Photoacoustic, Trans-illumination, ultrasound, Near-Infrared etc. are used. Among these modalities Near-Infrared (NIR) imaging modality is considered most suitable due to its lower cost and non-ionizing nature. Over the past few years, subcutaneous veins localization using NIR have attracted increasing attention in the field of health care and biomedical engineering. Therefore, the proposed research work is based on NIR images for forearm subcutaneous veins segmentation. This paper presents a deep learning-based approach called Generative Adversarial Networks (GAN) for segmentation/localization of forearm veins. GANs have shown exciting results in the medical imaging field recently. These are used for unsupervised feature learning and image-to-image translation applications. These networks generate realistic results by learning data mapping from one state to another. Since GANs can produce state of the art results, therefore we have proposed a Pix2Pix GAN for segmentation of forearm veins. The proposed algorithm is trained and tested on forearm subcutaneous veins image dataset. The proposed model outperforms traditional approaches with the mean accuracy and sensitivity, values obtained are 0.971 and 0.862 respectively. The dice coefficient and Intersection over Union (IoU) score are respectively 0.962 and 0.936 which are better than the state-of-the-art methods.

The Belt and Road Initiative (BRI) significantly contributes to the world economy. However, the central part of the Belt and Road (B&R) is located in fragile ecological zones that are arid, semi-arid, or sub-humid. Using the entropy method, the economic-social-natural ecological niches and their coupling coordination during 2007–2019 along B&R’s 11 countries were explored along with regional differences and spatiotemporal characteristics. The economic-social-natural ecological niches were low, with a fluctuating upward trend. Additionally, the average annual growth rate of the synthesis ecological niche dramatically improved after the BRI. Further, the BRI facilitated inter-country trade and promoted the economic ecological niche. However, the BRI marginally affected the social ecological position, possibly because the social ecological niche was high pre-BRI. The natural ecological niche showed a negative growth after the BRI. Further, the coupling coordination of economic-social ecological niche and natural ecological niche showed an upward trend, transforming from severe discoordination to advanced coordination. Although BRI promoted advanced coordination, it did not affect internal categories. Policy recommendations for sustainable development in China-ASEAN Free Trade Area were provided. This study can assist policymakers to balance economic-social development and environmental protection.

Text detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.

Economic growth of emerging Asian economies like China and India has been a topic of interest for researchers. However, most of the existing studies have focused on economic growth trends of China and India. The aim of this paper is to identify the core sectors of both economies and analyse the impact of the Asian financial crisis of 1997 and the global financial crisis of 2008 on the performance of those sectors. We also intend to explore the impact of the aforementioned financial crises on the overall economic growth of both nations. Our review consists of five years’ average and critical analysis of the existing studies to identify the key sectors of economy and to analyse the impact of financial crises. The results indicate that industry and service sectors are the highest contributors in the GDP of China and India respectively. We also found heterogeneous impact of financial crises on the key sectors of both nations’ economy.