Two approaches for synthesizing scalable residential energy consumption data

© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets

Prevalence of Vitamin B12 deficiency in patients of type 2 diabetes mellitus on metformin: A case control study from Pakistan

Introduction: Diabetes Mellitus is the most common endocrine disorder and metformin is the most commonly prescribed oral hypoglycemicagent. Metformin is well known to cause viamin B12 deficiency due to effect on calcium-dependent membrane action in the terminal ileum leading to malabsorption of vitamin B12. The purpose of this study is to determine prevalence and associations of Vitamin B12 deficiency in patients of type 2 diabetes mellitus treated with metformin. Methods: This case control study was carried out in department of medicine, Combined Military Hospital, Kharian from 1st Jan 2012 to 30 december 2012.We enrolled 114 outdoor patients of type 2 diabetes mellitus currently on metformin for atleast 12 months, by consecutive sampling, and 105 age and sex matched patients taken as control. Patients with vitamin B12 levels of less than 150 pg/ml were said to be B12 deficient. The results were analyzed on SPSS version 16.Results: Serum B12 levels were low in 35 patients (31%) on metformin as compared to only 9 patients (8.6%) among controls,(p value 0.002). Mean B12 levels were significantly low in metformin group 311 pg/ml (±194.4), p value 0.03. Dose of metformin had inverse correlation with B12 levels and the difference was statistically significant with pvalue < 0.001.Conclusion: Our study demonstrated significantly high prevalence of  vitamin B12 deficiency in patients treated with metformin with significant effect of dose and duration of metformin use on B12 levels. Physicians must recognize this important fact and screen diabetics on metformin therapy for underlying B12 deficiency

Ab-initio study of the bandgap engineering of Al(1-x)Ga(x)N for optoelectronic applications

A theoretical study of Al(1-x)Ga(x)N, based on full-potential linearized augmented plane wave method, is used to investigate the variations in the bandgap, optical properties and non-linear behavior of the compound with the variation of Ga concentration. It is found that the bandgap decreases with the increase of Ga in Al(1-x)Ga(x)N. A maximum value of 5.5 eV is determined for the bandgap of pure AlN which reaches to minimum value of 3.0 eV when Al is completely replaced by Ga. The static index of refraction and dielectric constant decreases with the increase in bandgap of the material, assigning a high index of refraction to pure GaN when compared to pure AlN. The refractive index drops below 1 for photon energies larger than 14 eV results group velocity of the incident radiation higher than the vacuum velocity of light. This astonishing result shows that at higher energies the optical properties of the material shifts from linear to non-linear. Furthermore, frequency dependent reflectivity and absorption coefficients show that peak value of the absorption coefficient and reflectivity shifts towards lower energy in the UV spectrum with the increase in Ga concentration. This comprehensive theoretical study of the optoelectronic properties of the alloys is presented for the first time which predicts that the material can be effectively used in the optical devices working in the visible and UV spectrum.Comment: 18 pages, 7 figure

Spectrum of Curvature Perturbation of Multi-field Inflation with Small-Field Potential

In this paper, we have studied the spectrum of curvature perturbation of multi-field inflation with general small-field potential. We assume that the isocurvature perturbation may be neglected, and by using the Sasaki-Stewart formalism, we found that the spectrum may be redder or bluer than of its corresponding single field. The result depends upon the values of fields and their effective masses at the horizon-crossing time. We discuss the relevant cases.Comment: 8 pages, no figure, to publish in JCA

Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach

This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches

A framework and mathematical modeling for the vehicular delay tolerant network routing

Vehicular ad hoc networks (VANETs) are getting growing interest as they are expected to play crucial role in making safer, smarter, and more efficient transportation networks. Due to unique characteristics such as sparse topology and intermittent connectivity, Delay Tolerant Network (DTN) routing in VANET becomes an inherent choice and is challenging. However, most of the existing DTN protocols do not accurately discover potential neighbors and, hence, appropriate intermediate nodes for packet transmission. Moreover, these protocols cause unnecessary overhead due to excessive beacon messages. To cope with these challenges, this paper presents a novel framework and an Adaptive Geographical DTN Routing (AGDR) for vehicular DTNs. AGDR exploits node position, current direction, speed, and the predicted direction to carefully select an appropriate intermediate node. Direction indicator light is employed to accurately predict the vehicle future direction so that the forwarding node can relay packets to the desired destination. Simulation experiments confirm the performance supremacy of AGDR compared to contemporary schemes in terms of packet delivery ratio, overhead, and end-to-end delay. Simulation results demonstrate that AGDR improves the packet delivery ratio (5-7%), reduces the overhead (1-5%), and decreases the delay (up to 0.02 ms). Therefore, AGDR improves route stability by reducing the frequency of route failures. © 2016 Mostofa Kamal Nasir et al

Towards a mood sensitive integrated development environment to enhance the performance of programmers

The aim of the research was to analyze the possibility of developing an Integrated Development Environment (IDE) that could improve a programmer‘s performance by considering their current mood. Various experiments were conducted to study this idea. However, the impact of moods on programmer performance was initially examined in the literature. Based on this, a Cognitive Programming Task Model (CPTM) was developed showing that various cognitive functions and programming activities are interrelated. A second model derived from the literature, the Cognitive Mood Model (CMM), suggested that moods are also interrelated with various cognitive functions. Combining these two models indirectly suggests a relation between moods and programming tasks, which was presented as the Mood Programming Model (MPM). As direct empirical support was lacking for this relation, two experiments were conducted to study the effect mood could have on performance in a debug task. Validated mood-inducing movie clips were used to induce specific moods along two-mood dimensions: valence and arousal. The first study was conducted online. The results showed that arousal is a significant factor when considering programmer performance whereas valence was found to have no significant effect. The second study was a continuation study to validate the findings from the first study within lab conditions. The results were not able to confirm the findings of the first experiment. The reasons for these findings were explained accordingly. As mood was found to have an effect on a programmer‘s coding and debugging performance, this factor might be considered when developing a support system. The next step in the research was therefore to consider mood measuring in a non-interruptive way. The next two experiments were based around the hypothesis that ―moods can be measured from the keyboard and mouse interaction of the computer user‖. In the first experiment an application was installed on participants‘ computers to record their key presses and mouse clicks in a log file. Their self reported moods in intervals of 20 minutes were also stored in the same file over an average period of eight days. Correlations between participants‘ self reported moods and their keyboard and mouse use revealed that it might be possible to measure moods of the some of the participants. The second experiment took place in the lab, where participants were asked to perform programming like tasks while listening to ii mood inducing background music. Their moods were measured with a Galvanic Skin Response (GSR) meter whereas key presses and mouse clicks again were recorded in log files. The correlations between GSR measurements and keyboard and mouse interaction validated the findings of the experiment in the field that it might be possible to measure the mood of some users from their computer use. Analyzing participants‘ personality traits showed dutifulness and self discipline as indicators that a person‘s mood correlates with his/her interaction behaviour. Considering that mood has an effect on programmer performance and that it might be possible to measure mood in a non-intrusive manner, the last question to focus on was whether a computer-generated intervention could change a programmer‘s mood and consequently improve their performance. In the final experiment programmers had to dry run algorithms for 16 minutes with the expectation that a level of boredom would set in. After this the video clip instructed them to participate in some physical exercises. Participants continued tracing algorithms for 8 minutes after the intervention. Results showed that the mood change after the intervention coincided with a programmers improved ability to provide the correct output of the algorithms. Together these findings lay the foundation for developing an IDE that can measure the programmer mood in a non-intrusive way and make effective interventions to improve programmer performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo