ARTICLE | doi:10.20944/preprints201611.0057.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: multi-focus image, image fusion, region mosaic, contrast pyramid
Online: 10 November 2016 (07:34:22 CET)
This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.
ARTICLE | doi:10.20944/preprints202108.0481.v1
Subject: Social Sciences, Business And Administrative Sciences Keywords: CSR; Business ethis; Carroll's pyramid
Online: 25 August 2021 (10:37:44 CEST)
The business practices and the vast academic research on the field have shown that even in the years of the great economic recession, there CSR and Ethics in business environment are still on the spotlight. The current study is conducted in Greece and is a cross-sector analysis. The study aims to evaluate if the concept of Carroll’s pyramid (1991) is still applied nowadays. This model expresses the business priorities, admitting that the rational business prioritizes profit as the base of the pyramid, followed by a legal way of operating, followed by ethical and philanthropic responsibilities. However, the growing interest on CSR and ethical issues may set this model on question. Baden (2016) has developed a critical evaluation that showing that the priorities have been changed. We employ a similar methodology to evaluate these priorities within the Greek business environment. A sample of 950 participants has been employed. The sample is consisted of various business hierarchy employees, from various Greek cities. The results show that despite the bad economic situation of the country, the economic responsibilities are not ranked on highest position: legal and ethical responsibilities are prioritized against economic responsibilities. These results confirm the findings of Baden (2016) and contribute on the relevant literature considering the necessity of CSR and Ethical issues in business environment nowadays.
ARTICLE | doi:10.20944/preprints201811.0248.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: Complexity; Chebyshev Polynomials; Gear graph; Pyramid graphs.
Online: 9 November 2018 (15:17:44 CET)
In mathematics, one always aims to obtain new frameworks from specific ones. This also stratified to the regality of graphs, where one can produce numerous new graphs from a specific set of graphs. In this work we define some classes of pyramid graphs created by a gear graph and we derive straightforward formulas of the complexity of these graphs, using linear algebra matrix analysis techniques and employing knowledges of Chebyshev polynomials.
REVIEW | doi:10.20944/preprints202104.0025.v1
Subject: Medicine & Pharmacology, Nutrition Keywords: nutritional guidelines; food pyramid; mushrooms; viral diseases; african foods
Online: 1 April 2021 (14:10:48 CEST)
In Sub-Saharan Africa, despite poverty, chronic hunger and food insecurity, traditional eating has been related to positive health outcomes and sustainability. There is little health research on diet quality based on what African people consume. The defining characteristics of the traditional African cuisine are the richness in herbs and spices, fermented foods and beverages, and healthy and whole ingredients used. However, as countries in this region become more economically developed, there is a shift to “modern” occidental foods rich in saturated fats, sugar and sweeteners. As a result, there are increased incidences of previously unreported ailments due to unbalanced diet. The regular practice of infinite international aid to the region to curb food insecurity has been unsustainable, ineffective and with no end in sight. Local increase in production and productivity is imperative. Protein rich foods in dietary guidelines enhance only those of animal or plant sources while rich protein sources such of mushroom, has been absent in these charts. This article considers the valorisation of traditional African foods and the importance of establishing an African Food-Based Dietary Guidelines (AFBDGs), an unprecedented Food Pyramid, along with the added emphasis on the potential of African mushrooms, which may play a role in shielding Sub-Saharan Africans against the side-effects of a western stylish diet and promote health. It enhances the preventive role of mushrooms in viral diseases and other disorders.
ARTICLE | doi:10.20944/preprints202002.0125.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: image inpainting; image completion; attention; pyramid structure loss; deep learning
Online: 10 February 2020 (10:16:37 CET)
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.
ARTICLE | doi:10.20944/preprints202208.0329.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Synthetic Aperture Radar; Doppler frequencies; multi-chromatic analysis; micro-motion; Pyramid of Khnum-Khufu; sonic images
Online: 18 August 2022 (03:45:58 CEST)
A problem with synthetic aperture radar (SAR) is that, due to the poor penetrating action of electromagnetic waves inside solid bodies, the capability to observe inside distributed targets is precluded. Under these conditions, imaging action is provided only on the surface of distributed targets. The present work describes an imaging method based on the analysis of micro-movements on the Khnum-Khufu Pyramid, which are usually generated by background seismic waves. The results obtained prove to be very promising, as high-resolution full 3D tomographic imaging of the pyramid's interior and subsurface was achieved. Khnum-Khufu becomes transparent like a crystal when observed in the micro-movement domain. Based on this novelty, we have completely reconstructed internal objects, observing and measuring structures that have never been discovered before. The experimental results are estimated by processing series of SAR images from the second-generation Italian COSMO-SkyMed satellite system, demonstrating the effectiveness of the proposed method.
ARTICLE | doi:10.20944/preprints202108.0051.v1
Subject: Keywords: Systematized Literature Review; Base of the Pyramid; Shared Value creation; Micro-manufacturing factories; Business model
Online: 2 August 2021 (14:49:31 CEST)
Background: Shared value creation in Base of the Pyramid (BoP) communities is a crucial process towards building sustainable societies. BoP communities in developing countries represent more than four billion people who live on low incomes with limited access to basic products and services. Current or emerging technologies offer promising solutions for organisations pursuing manufacturing opportunities in BoP communities. This study seeks to explore the literature on how BoP communities may become active participants in sustainably manufacturing products using micro-manufacturing factories. The research question posed is: What are the core concepts that need to be taken into consideration for creating shared value through micro-manufacturing factories in BoP communities? Method: A Systematised Literature review (SLR) was completed following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) method for data selection criteria and analysis. The SLR is used to explore the state of literature with regards to creating manufacturing shared value in BoP communities with the objective to identify study gaps and to explore shared value creation concepts. Results: Literature indicates BoP initiatives that have pursued to engage BoP communities through various innovation strategies. The findings of the review is organised under three strategic pillars: Capability building strategy, Implementation process, and Growth strategy. The capability building strategy defines the users’ intention to create shared value in BoP communities with Micro-manufacturing factories (MMF). It is followed by the implementation process which guides the users to create manufacturing shared value in BoP communities. This is followed by a growth strategy to scale for impact.
Subject: Keywords: Single image deraining; Multi-layer Laplacian pyramid; Multi-scale feature extraction module; Channel attention module.
Online: 31 May 2021 (11:41:25 CEST)
Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches
COMMUNICATION | doi:10.20944/preprints202008.0197.v1
Subject: Social Sciences, Other Keywords: corporate sustainability strategy; corporate growth strategy; Maslow Pyramid; Organic Growth Theory; corporate social responsibility; corporate responsibility; sustainability
Online: 7 August 2020 (12:04:21 CEST)
This paper introduces a theory for the evolution of corporates in which the growth and sustainability strategies are developed simultaneously. Since the introduction of corporate sustainability, it has been an extra cost for risk mitigation and making ‘compensating’ positive impact. The world has reached a tipping point of volatility, mainly due to climate change but also by emergence of COVID-19, so that the applicability of existing corporate structures is under question and these poses high risk to the existence of our planet. On the other hand, the technology cost for sustainable investment has reached a parity in comparison with non-sustainable alternatives. Therefore, our proposed Organic Grow Theory introduces a step-by-step approach so that corporates can grow and be profitable without compromising the ability of future generations to meet their needs. It is concluded that, a new structure for corporate, called Founcorp, would be needed to direct corporates to evolve being a responsible legal entity.
REVIEW | doi:10.20944/preprints202105.0549.v1
Subject: Life Sciences, Biochemistry Keywords: COVID-19 pandemic; Africa; SARS-CoV-2 virus spread; lower COVID-19 disease burden; African populations; demographic pyramid; trained immunity; government measures
Online: 24 May 2021 (09:56:05 CEST)
COVID-19 differential spread and impacts across regions is a major focus for researchers and policy makers. Africa has attracted tremendous attention due to predictions of catastrophic impacts that have not yet materialized. Early in the pandemic, the seemingly low African case count was largely attributed to low testing and case reporting. However, there is also reason to consider that many African countries got out ahead of the virus early on. Factors explaining low spread include early government mandated lockdowns, community-wide actions, population distribution, social contacts, and ecology of human habitation. While recent data from seroprevalence studies posit more extensive circulation of the virus, continuing low COVID-19 burden may be explained by the demographic pyramid, prevalence of pre-existing conditions, trained immunity, genetics, and broader sociocultural dynamics. Though all these prongs contribute to the observed profile of COVID-19 in Africa, some provide stronger evidence than others. This review is important to expand what is known about the differential impacts of pandemics enhancing scientific understanding and gearing appropriate public health responses. Also, highlighting potential lessons the world may draw from Africa for global health on assumptions regarding deadly viral pandemics given its long experience with infectious diseases.
ARTICLE | doi:10.20944/preprints202109.0059.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: table detection; table recognition; cascade Mask R-CNN; atrous convolution; recursive feature pyramid networks; document image analysis; deep neural networks; computer vision, object detection.
Online: 3 September 2021 (11:05:10 CEST)
Table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursive Feature Pyramid network and Switchable Atrous Convolution in the existing backbone architecture. By utilizing a comparatively lightweight backbone of ResNet-50, this paper demonstrates that superior results are attainable without relying on pre and post-processing methods, heavier backbone networks (ResNet-101, ResNeXt-152), and memory-intensive deformable convolutions. We evaluate the proposed approach on five different publicly available table detection datasets. Our CasTabDetectoRS outperforms the previous state-of-the-art results on four datasets (ICDAR-19, TableBank, UNLV, and Marmot) and accomplishes comparable results on ICDAR-17 POD. Upon comparing with previous state-of-the-art results, we obtain a significant relative error reduction of 56.36%, 20%, 4.5%, and 3.5% on the datasets of ICDAR-19, TableBank, UNLV, and Marmot, respectively. Furthermore, this paper sets a new benchmark by performing exhaustive cross-datasets evaluations to exhibit the generalization capabilities of the proposed method.
ARTICLE | doi:10.20944/preprints202107.0277.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: Cervical cancer; Pap smear test; whole slide image (WSI); feature pyramid network (FPN); global context aware (GCA); region based convolutional neural networks (R-CNN); Region Proposal Network (RPN).
Online: 12 July 2021 (23:05:34 CEST)
Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.