Finite AG-groupoid with left identity and left zero

A groupoid G whose elements satisfy the left invertive law: (ab)c=(cb)a is known as Abel-Grassman's groupoid (AG-groupoid). It is a nonassociative algebraic structure midway between a groupoid and a commutative semigroup. In this note, we show that if G is a finite AG-groupoid with a left zero then, under certain conditions, G without the left zero element is a commutative group

Learning where to see : a novel attention model for automated immunohistochemical scoring

Estimatingover-amplification of human epidermal growth factor receptor2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the subimage patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model out performs other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images

An Overview of Prophylacticand Curative Approach for COVID-19 in Unani System of Medicine and Need of Development of the SOPs

COVID-19 also known as Novel Corona Virus Pneumonia, is a viral disease caused by novel corona virus. The infection is highly contagious in nature and spread from person to person through respiratory droplets. According to Unani System of Medicine, epidemiology and clinical features of COVID-19 like fever, cough, tiredness, sore throat, running nose, nasal congestion, difficulty in breathing, etc. are similar to Nazla-e-wabai (Epidemic Influenza) up to a great extent.On the basis of fundamental approach for living a healthy lifestyle and preventive measures during epidemic spread mentioned in classical  Unani texts, some standard operating procedures (SOPs)  are suggested for prophylactic and curative purpose in the management of COVID-19 pandemic

Decision Support Systems for Sustainable Logistics: A Review and Bibliometric Analysis

Purpose: Decision-making in logistics is an increasingly complex task for organizations as these involve decisions at strategic, tactical and operational levels coupled with the triple bottom line (TBL) of sustainability. Decision support systems (DSS) played a vital role in arguably solving the challenges associated with decision making in sustainable logistics. This review is a systematic attempt to explore the current state of the research in the domain of DSS for logistics while considering sustainability aspects. Design/methodology/approach: A systematic review approach using a set of relevant keywords with several exclusion criteria was adopted to identify literature related to DSS for sustainable logistics. A total of 40 papers were found from 1994 to 2015, which were then analysed along the dimensions of publishing trend, geographic distribution and collaboration, the most influential journals, affiliations and authors as well as the key themes of identified literature. The analysis was conducted by means of bibliometric and text mapping tools, namely BibExcel, gpsvisualizer, and VOSviewer. Findings: The bibliometric analysis showed that DSS for sustainable logistics is an emerging field; however, it is still evolving but at a slower pace. Furthermore, most of the contributing affiliations belong to the United States and the United Kingdom. The text mining and keyword analysis revealed key themes of identified papers. The inherent key themes were decision models and frameworks to address sustainable logistics issues covering transport, distribution and third party logistics. The most prominent sustainable logistics issue was carbon footprinting. Social impact has been given less attention in comparison to economic and environmental aspects. The literature has adequate room for proposing more effective solutions by considering various types of MCDA (multi-criteria decision analysis) methods and DSS configurations while simultaneously considering economic, environmental and social aspects of sustainable logistics. Moreover, the field has potential to include logistics from wide application areas including freight transport through road, rail, sea, air as well as inter-modal transport, port operations, material handling and warehousing. Originality/value: To the best of the authors’ knowledge, this is the first systematic review of DSS for sustainable logistics using bibliometric and text analysis. The key themes and research gaps identified in this paper will provide a reference point that will encourage and guide interested researchers for future study, thus aiding both theoretical and practical advancements in this discipline

Improving basic services for the bottom forty percent: lessons from Ethiopia

Ethiopia, like most developing countries, has opted to deliver services such as basic education, primary health care, agricultural extension advice, water, and rural roads through a highly decentralized system (Manor 1999; Treisman 2007). That choice is based on several decades of theoretical analysis examining how a decentralized government might respond better to diverse local needs and provide public goods more efficiently than a highly centralized government. Ethiopia primarily manages the delivery of basic services at the woreda (district) level. Those services are financed predominantly through intergovernmental fiscal transfers (IGFTs) from the federal to the regional and then the woreda administrations, although some woredas raise a small amount of revenue to support local services. Since 2006, development partners and the government have cofinanced block grants for decentralized services through the Promoting Basic Services (PBS) Program. Aside from funding the delivery of services, the program supports measures to improve the quality of services and local governments capacity to deliver them by strengthening accountability and citizen voice

Biosorption of lead(II) and chromium(VI) on groundnut hull: Equilibrium, kinetics and thermodynamics study

The biosorption of lead(II) and chromium(VI) on groundnut hull was investigated. Batch biosorption experiments were conducted to find the equilibrium time and biosorption capacity. Effect of parameters like pH, temperature and initial metal concentration was studied. The maximum biosorption capacity of lead(II) and chromium(VI) was found to be 31.54 \ub1 0.63 and 30.21 \ub1 0.74 mg g-1, respectively. The optimum pH for lead(II) and chromium(VI) removal was 5 \ub1 0.1 and 2 \ub1 0.1, respectively. The temperature change, in the range of 20 - 45\ubaC affected the biosorption capacity. The maximum removal of lead(II) was achieved at 20 \ub1 2\ubaC, where as maximum uptake of chromium(VI) was observed at 40 \ub1 2\ubaC. The biosorption data was fitted to the Langmuir and the Freundlich isotherm models. The Langmuir model showed better representation of data, with correlation coefficient greater than 0.98. The kinetics of biosorption followed the pseudo second order kinetics model. The thermodynamics parameters were evaluated from the experimental data

Persistent homology for fast tumor segmentation in whole slide histology images

Automated tumor segmentation in Hematoxylin & Eosin stained histology images is an essential step towards a computer-aided diagnosis system. In this work we propose a novel tumor segmentation approach for a histology whole-slide image (WSI) by exploring the degree of connectivity among nuclei using the novel idea of persistent homology profiles. Our approach is based on 3 steps: 1) selection of exemplar patches from the training dataset using convolutional neural networks (CNNs); 2) construction of persistent homology profiles based on topological features; 3) classification using variant of k-nearest neighbors (k-NN). Extensive experimental results favor our algorithm over a conventional CNN