Energy Intensity Determination in Wood Processing Sawmills

Energy intensity is an important aspect to wood products producing sawmills in the State of West Virginia. This research aims to facilitate the accurate measurement of electrical energy intensity in sawmills by means of energy analysis and diagnostics using various data acquisition devices on electrical motors used in the manufacturing processes. Close to 90% of the electrical energy used in a typical sawmill is consumed by motors alone. The energy intensity determination is being accomplished by data collection with respect to electrical energy consumption parameters as well as production parameters. The electrical energy consumption was recorded on all the major motors in three sawmills for a period of one month. The recorded data were analyzed with respect to the production volume and the specific energy consumption for different size lumber of varying species was developed. The specific energy allocation for different size lumber was done based on the surface area cut to manufacture that lumber. The specific energy consumption of a particular size lumber has been compared with respect to different species. The specific energy consumption of different size lumber of the same species was developed. Sawmills can evaluate the impact of their production decisions on energy consumption using the results of this research. Energy consumption of different size and species was compared among three sawmills. Specific energy consumption of hardwood species in sawmill 1 for 4/4 lumber is varying from 124 kwh to 170 kWh per 1,000 board feet, where as in sawmill 2 it is varying from 79 kwh to 118 kWh and in sawmill 3 it is varying from 90 kwh to 145 kWh. Further, results of the energy assessment conducted in each sawmill would save on average 12% of energy consumption at current operation. Finally, productivity improvement that can be achieved by sawing high quality logs and using new saw blade technologies were discussed

Anton's syndrome due to cerebrovascular disease: a case report

<p>Abstract</p> <p>Introduction</p> <p>Anton's syndrome describes the condition in which patients deny their blindness despite objective evidence of visual loss, and moreover confabulate to support their stance. It is a rare extension of cortical blindness in which, in addition to the injury to the occipital cortex, other cortical centres are also affected, with patients typically behaving as if they were sighted.</p> <p>Case presentation</p> <p>We present a case report of an 83-year-old white woman with cortical blindness as a result of bilateral occipital lobe infarcts. Despite her obvious blindness, illustrated by her walking into objects, the patient expressed denial of visual loss and demonstrated confabulation in her accounts of her surroundings, consistent with a diagnosis of Anton's syndrome.</p> <p>Conclusions</p> <p>A suspicion of cortical blindness and Anton's syndrome should be considered in patients with atypical visual loss and evidence of occipital lobe injury. Cerebrovascular disease is the most common cause of Anton's syndrome, as in our patient. However, any condition that may result in cortical blindness can potentially lead to Anton's syndrome. Recovery of visual function will depend on the underlying aetiology, with cases due to occipital lobe infarction after cerebrovascular events being less likely to result in complete recovery. Management in these circumstances should accordingly focus on secondary prevention and rehabilitation.</p

Data Mining And Data Analysis Approach For GIS And Remote Sensing

The extraction of spatial examples and  attributes, spatial and non spatial information connections, and other information highlights covered up in the spatial database is spatial information mining. The need of propel strategies for extraction of information from spatial datasets has demonstrated the need in ascent of geographic learning revelation and spatial information mining as a dynamic research territory. There is a desperate prerequisite for profitable and intense procedures what's more, technique for mining significant learning from spatial datasets of high measurement and capricious measure. The paper features late work in information disclosure and spatial information mining. We assessed a few literary works in attributes of spatial information, regular strategies in spatial information mining, methods associated with spatial information mining and spatial affiliation control mining .The review close with different points of view toward the huge work done in spatial information mining and ongoing exploration work in spatial affiliation run mining

Cerebral misery perfusion due to carotid occlusive disease

Purpose Cerebral misery perfusion (CMP) is a condition where cerebral autoregulatory capacity is exhausted, and cerebral blood supply in insufficient to meet metabolic demand. We present an educational review of this important condition, which has a range of clinical manifestations. Method A non-systematic review of published literature was undertaken on CMP and major cerebral artery occlusive disease, using Pubmed and Sciencedirect. Findings Patients with CMP may present with strokes in watershed territories, collapses and transient ischaemic attacks or episodic movements associated with an orthostatic component. While positron emission tomography is the gold standard investigation for misery perfusion, advanced MRI is being increasingly used as an alternative investigation modality. The presence of CMP increases the risk of strokes. In addition to the devastating effect of stroke, there is accumulating evidence of impaired cognition and quality of life with carotid occlusive disease (COD) and misery perfusion. The evidence for revascularisation in the setting of complete carotid occlusion is weak. Medical management constitutes careful blood pressure management while addressing other vascular risk factors. Discussion The evidence for the management of patients with COD and CMP is discussed, together with recommendations based on our local experience. In this review, we focus on misery perfusion due to COD. Conclusion Patients with CMP and COD may present with a wide-ranging clinical phenotype and therefore to many specialties. Early identification of patients with misery perfusion may allow appropriate management and focus on strategies to maintain or improve cerebral blood flow, while avoiding potentially harmful treatment

DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification

Title from PDF of title page, viewed October 31, 2017Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 107-109)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Deep Learning technologies show promise for dramatic advances in fields such as image classification and speech recognition. Deep Learning (DL) is a class of Machine Learning algorithms that involves learning of multiple levels of features from data to build a model. One of the open questions in DL is whether up-to-date models can be built and provide dealing with dynamic and large volumes of new data created. This requires addressing how models can be consistently constructed and updated (incremental learning) in a scalable manner. Current research and practices of DL do not fully support these important features, such as distributed learning or incremental learning to an extent that is required. The objective of this thesis is to provide a solution to this problem by building a framework that is distributed and incremental in nature. In the DL-DI framework, a learning problem is composed of two stages: Local Learning and Global Learning. In the local learning stage, a learning problem is divided into several smaller problems. These smaller problems are solved using an optimized original solution for a better local performance. The learning outcomes from the local learning stage, such as predictions and activations, will feed into the global learning. A feed forward deep neural network is used in global learning. The presented framework focuses mainly on image classification problems, but this can be applied to several other learning problems. The proposed framework is implemented in TensorFlow, an open source machine learning library developed by Google, with the capability of building deep neural networks using parallel GPU computations. To support the effectiveness of the DL-DI framework, we have evaluated the DL-DI framework on image classification using Softmax Regression and Convolutional Neural Networks on MNIST, CIFAR10 datasets. The evaluation results have verified that the DL-DIS framework supports distributed incremental Deep Learning while achieving a reasonably high rate of prediction accuracy.Introduction -- Background and related work -- The DL-DI framework -- Results and evaluation -- Conclusion and future wor

Message Passing Algorithm for Different Problems Sum, Mean, Guide and Sorting in a Rooted Tree Network.

In this thesis, we give message passing algorithms in distributed environment for five different problems of a rooted tree having n nodes. In the first algorithm, every node has a value; the root calculates the sum of those values, and sends it to all the nodes in the network. In the second algorithm, the root computes the value of mean of values of all the nodes, and sends it to all nodes of the network. The third algorithm calculates the guide pairs. Guide pair of a node x is an ordered pair (pre_index(x), post_index(x)), where pre_index(x) and post_index(x) are the rank of x in the preorder and reverse postorder traversal of T. In the fourth algorithm, we compute the rank of all the nodes in the tree by considering the weight (value) present at every node. Finally, in the fifth algorithm, values present in the nodes are sorted in level order