Magnitude and Temporal Trends in Avoidable Blindness in Children (ABC) in India.

The World Health Organization estimates that 19 million children are visually impaired, among whom, 1.4 million are blind. Childhood blindness is an excellent indicator of the state of child health and primary care services in a country. Childhood blindness is important not just due to the number of children blind but also because the number of years that the surviving child has to live with blindness (blind years lived). Childhood blindness is next only to adult cataract in terms of the number of blind person years lived. Under-five mortality rates have been used as a proxy measure to compute the prevalence of childhood blindness in low and middle income countries due to limitations of other methods of data collection. In India, it is estimated that there are 0.8 blind for 1000 children. Whole globe lesions, corneal scarring, retinal pathology and afflictions of the lens are important anatomical sites in children. Causes operating in childhood and hereditary causes are important in etiology of childhood blindness. In 38.2%-68.4% cases across the region, a specific cause of blindness could not be identified in South Asia. The proportion of blindness that can be prevented or treated (avoidable) in children is less than 50%. Therefore a comprehensive eye care system needs to be in place to cater to the needs of children with avoidable and those with incurable blindness. Early detection and prompt management are critical for success of programs targeting avoidable blindness in children

Analyzing the Value Stream Mapping to Achieve Lean Manufacturing via Line Balancing

Value stream mapping (VSM) visually depicts the flow of materials and information as a product passes through the manufacturing process; this information enables companies to meet customer demand by getting these materials and information for improvement at the right place and at the right time. This paper reviews the existing literature on VSM, describes the concepts and techniques of line balancing, and demonstrates through empirical study how the concepts and techniques of line balancing can help optimize VSM implementation to enhance business performance

The Most Influential Paper Gerard Salton Never Wrote

Gerard Salton is often credited with developing the vector space model (VSM) for information retrieval (IR). Citations to Salton give the impression that the VSM must have been articulated as an IR model sometime between 1970 and 1975. However, the VSM as it is understood today evolved over a longer time period than is usually acknowledged, and an articulation of the model and its assumptions did not appear in print until several years after those assumptions had been criticized and alternative models proposed. An often cited overview paper titled ???A Vector Space Model for Information Retrieval??? (alleged to have been published in 1975) does not exist, and citations to it represent a confusion of two 1975 articles, neither of which were overviews of the VSM as a model of information retrieval. Until the late 1970s, Salton did not present vector spaces as models of IR generally but rather as models of specifi c computations. Citations to the phantom paper refl ect an apparently widely held misconception that the operational features and explanatory devices now associated with the VSM must have been introduced at the same time it was fi rst proposed as an IR model.published or submitted for publicatio

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed