Benchmarking Utility Clean Energy Deployment: 2016

Benchmarking Utility Clean Energy Deployment: 2016 provides a window into how the global transition toward clean energy is playing out in the U.S. electric power sector. Specifically, it reveals the extent to which 30 of the largest U.S. investor-owned electric utility holding companies are increasingly deploying clean energy resources to meet customer needs.Benchmarking these companies provides an opportunity for transparent reporting and analysis of important industry trends. It fills a knowledge gap by offering utilities, regulators, investors, policymakers and other stakeholders consistent and comparable information on which to base their decisions. And it provides perspective on which utilities are best positioned in a shifting policy landscape, including likely implementation of the U.S. EPA's Clean Power Plan aimed at reducing carbon pollution from power plants

Mechanical and Systems Biology of Cancer

Mechanics and biochemical signaling are both often deregulated in cancer, leading to cancer cell phenotypes that exhibit increased invasiveness, proliferation, and survival. The dynamics and interactions of cytoskeletal components control basic mechanical properties, such as cell tension, stiffness, and engagement with the extracellular environment, which can lead to extracellular matrix remodeling. Intracellular mechanics can alter signaling and transcription factors, impacting cell decision making. Additionally, signaling from soluble and mechanical factors in the extracellular environment, such as substrate stiffness and ligand density, can modulate cytoskeletal dynamics. Computational models closely integrated with experimental support, incorporating cancer-specific parameters, can provide quantitative assessments and serve as predictive tools toward dissecting the feedback between signaling and mechanics and across multiple scales and domains in tumor progression.Comment: 18 pages, 3 figure

Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used

Graph-to-Graph Translations To Augment Abstract Meaning Representation Tense And Aspect

HonorsCognitive ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/169387/1/bakalm.pd

Anxiety Treatments in Older Adults

Purpose and Significance: This research study focused on the topic of anxiety management in older adults. Previous research has noted the prevalence of anxiety in the older adult population. It has also established previous success with nonpharmacological treatment in decreasing anxiety levels. The purpose of the study was to determine the amount of knowledge possessed by older adults about different anxiety treatment methods. The significance of the study was to determine whether older adults are being educated properly on how to effectively employ nonpharmacological techniques to manage anxiety. Theoretical Framework: Imogene King’s conceptual system, including the personal, interpersonal, and social systems, provided the foundation for this research study (Husband, 1988). In assessing the knowledge and effectiveness of specific anxiety treatments, health care providers can ensure that anxiety is managed adequately and safely encompassing all three systems emphasized by King. Method: This study was a quantitative research study that incorporated a simple descriptive research design. Data was collected using a 28-question tool accessed through the Survey Monkey website. The survey was prefaced with a cover letter explaining inclusion criteria that included: informed consent, an age of 55 years or older with recognizable feelings of anxiety at least once a month for the last year. Data collection was voluntary and anonymous. Data was analyzed using SAS Version 9.4 software. Measures of central tendencies, relative frequencies and the chi-square test were utilized in analysis. Findings: Fifty-nine surveys were completed and used for data analysis. The sample was composed of 59% females, 37% male, and 4% who preferred not to identify with a specific gender. The mean age range of participants was 60-64. Of the five treatments surveyed, 93.2% of participants report using distraction techniques to treat anxiety. However, therapy is used least often, with only 30.5% of participants who report using this treatment method. Although distraction was the most commonly used treatment, as needed anxiety medication was the treatment that was found to be the most effective, with 47.4% reporting effective anxiety reduction with this treatment. Exercise was found to be the next most effective anxiety treatment, with 41.5% reporting that it is moderately or very effective. Knowledge of therapy and as needed medication were reported the highest while less than half of the participants reported knowledge of exercise to treat anxiety. Using the chi-square test it was noted that despite anxiety level, as needed medications were reported the most effective treatment. However, those with minimal or mild anxiety also reported a high effectiveness level with exercise (63%) and therapy (66.7%) as well. Conclusion: The results of this study provide insight in the most effective ways to treat anxiety in the older adult population. Supported by prior research, results indicate exercise is the most effective nonpharmacological treatment. The results of this study can be translated to patient teaching through emphasis on exercise, discussion, and distraction as first line nonpharmacological treatments for minimal to mild anxiety levels

PFPS: Priority-First Packet Scheduler for IEEE 802.15.4 Heterogeneous Wireless Sensor Networks

This paper presents priority-first packet scheduling approach for heterogeneous traffic flows in low data rate heterogeneous wireless sensor networks (HWSNs). A delay sensitive or emergency event occurrence demands the data delivery on the priority basis over regular monitoring sensing applications. In addition, handling sudden multi-event data and achieving their reliability requirements distinctly becomes the challenge and necessity in the critical situations. To address this problem, this paper presents distributed approach of managing data transmission for simultaneous traffic flows over multi-hop topology, which reduces the load of a sink node; and helps to make a life of the network prolong. For this reason, heterogeneous traffic flows algorithm (CHTF) algorithm classifies the each incoming packets either from source nodes or downstream hop node based on the packet priority and stores them into the respective queues. The PFPS-EDF and PFPS-FCFS algorithms present scheduling for each data packets using priority weight. Furthermore, reporting rate is timely updated based on the queue level considering their fairness index and processing rate. The reported work in this paper is validated in ns2 (ns2.32 allinone) simulator by putting the network into each distinct cases for validation of presented work and real time TestBed. The protocol evaluation presents that the distributed queue-based PFPS scheduling mechanism works efficiently using CSMA/CA MAC protocol of the IEEE 802.15.4 sensor networks

A Deterministic Eviction Model for Removing Redundancies in Video Corpus

The traditional storage approaches are being challenged by huge data volumes. In multimedia content, every file does not necessarily get tagged as an exact duplicate; rather they are prone to editing and resulting in similar copies of the same file. This paper proposes the similarity-based deduplication approach to evict similar duplicates from the archive storage, which compares the samples of binary hashes to identify the duplicates. This eviction is done by initially dividing the query video into dynamic key frames based on the video length. Binary hash codes of these frames are then compared with existing key frames to identify the differences. The similarity score is determined based on these differences, which decides the eradication strategy of duplicate copy. Duplicate elimination goes through two levels, namely removal of exact duplicates and similar duplicates. The proposed approach has shortened the comparison window by comparing only the candidate hash codes based on the dynamic key frames and aims the accurate lossless duplicate removals. The presented work is executed and tested on the produced synthetic video dataset. Results show the reduction in redundant data and increase in the storage space. Binary hashes and similarity scores contributed to achieving good deduplication ratio and overall performance

A Review of Image Super Resolution using Deep Learning

The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging

Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

The most expressive way of communication for individuals who have trouble speaking or hearing is sign language. Normal people are unable to comprehend sign language. As a result, communication barriers are put up. Majority of people are right-handed. Statistics say that, an average population of left-handed person in the world is about 10%, where they use left hand as their dominating hand. In case of hand written text recognition, if the text is written by left-handed or right-handed person, then there would not be any problem in recognition neither for human and nor for computer. But same thing is not true for sign language and its detection using computer. When the detection is performed using computer vision and if it falls into the category of detection by appearance, then it might not detect correctly. In machine and deep learning, if the model is trained using just one dominating hand, let’s say right hand, then the predictions can go wrong if same sign is performed by left-handed person. This paper addresses this issue. It takes into account the signs performed by any type of signer: left-handed, right-handed or ambidexter. In proposed work is on Indian Sign Language (ISL). Two models are trained: Model I, is trained on one dominating hand and Model II, is trained on both the hands. Model II gives correct predictions regardless of any type of signer. It recognizes alphabets and numbers in ISL. We used the concept of Key points and Transfer Learning techniques for implementation. Using this approach, models get trained quickly and we could achieve validation accuracy of 99%