Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization

It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios.
Hydrogen bubble visualization of viscoelastic flow in a model of the carotid artery bifurcation

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Three-dimensional evaluation of social smile symmetry.

OBJECTIVE To evaluate the social smile symmetry using three-dimensional (3D) stereophotogrammetric images. MATERIALS AND METHODS The study was conducted with 3D facial images of 30 individuals (age range 13-25 years). The rest position was considered as the reference image and the social smile image was aligned on this image using the best-fit alignment method. The spatial differences between the same points established on both images using 3D analyses were determined for right and left points in X, Y, and Z planes. RESULTS The highest difference related to spatial distance in right and left points was -0.56 mm (95% confidence interval [CI], -1.19, 0.06 mm) between right and left commissure (Com) points. The difference was not significant, and the Bland-Altman upper and lower limits were -3.85 mm and 2.71 mm, respectively. The highest difference for the transversal plane was found in Com points, similarly to the spatial distance (mean: 0.50 mm, 95% CI, -2.62, 1.02 mm). The differences between the changes in the left and right points in the Y and Z plane were not significant (P > .05). CONCLUSIONS The social smile was observed to show asymmetry in varying amounts in the different directions. Asymmetry increases in some cases, specifically for the corners of the mouth.
Discovery of peptide ligands through docking and virtual screening at nicotinic acetylcholine receptor homology models

Significance Predicting how conotoxins bind to nicotinic acetylcholine receptors (nAChRs) is hard. Not only are these venom-derived peptides large, but the structures of many nAChRs are unknown. In response, we developed an ensemble-docking algorithm named ToxDock. We used ToxDock to reliably dock the conotoxin α-GID to a homology model of the α4β2 nAChR, a main nAChR in the brain and target for nicotine addiction therapeutics. A virtual screen with ToxDock identified four α-GID analogs and, based on experimental evidence, correctly predicted their activity at the α4β2 nAChR in all cases. More screening showed that two of these analogs have substantially reduced antagonism at the human α7 nAChR, a key step in optimizing α-GID into a tool for studying brain nAChRs. Venom peptide toxins such as conotoxins play a critical role in the characterization of nicotinic acetylcholine receptor (nAChR) structure and function and have potential as nervous system therapeutics as well. However, the lack of solved structures of conotoxins bound to nAChRs and the large size of these peptides are barriers to their computational docking and design. We addressed these challenges in the context of the α4β2 nAChR, a widespread ligand-gated ion channel in the brain and a target for nicotine addiction therapy, and the 19-residue conotoxin α-GID that antagonizes it. We developed a docking algorithm, ToxDock, which used ensemble-docking and extensive conformational sampling to dock α-GID and its analogs to an α4β2 nAChR homology model. Experimental testing demonstrated that a virtual screen with ToxDock correctly identified three bioactive α-GID mutants (α-GID[A10V], α-GID[V13I], and α-GID[V13Y]) and one inactive variant (α-GID[A10Q]). Two mutants, α-GID[A10V] and α-GID[V13Y], had substantially reduced potency at the human α7 nAChR relative to α-GID, a desirable feature for α-GID analogs. The general usefulness of the docking algorithm was highlighted by redocking of peptide toxins to two ion channels and a binding protein in which the peptide toxins successfully reverted back to near-native crystallographic poses after being perturbed. Our results demonstrate that ToxDock can overcome two fundamental challenges of docking large toxin peptides to ion channel homology models, as exemplified by the α-GID:α4β2 nAChR complex, and is extendable to other toxin peptides and ion channels. ToxDock is freely available at rosie.rosettacommons. org/tox_dock.
Automatic determination of topological contacts in the presence of sensing uncertainties

A method for obtaining sufficient topological contact information from the current position/orientation data for the objects and geometric models involved, taking into account sensing uncertainties, is introduced. The information obtained can serve as a basis for further and more accurate extraction of the topological contact data using additional sensing means, such as vision and force/moment sensing. The aim is to achieve automatic recognition of unknown contact situations in spite of uncertainties. The method is discussed in detail for contacts between 2-D polygons and can be extended to 3-D polyhedra. The key idea is to discretize the continuous region of uncertain configurations of an object so that the topological contacts formed at those finite and discrete configurations constitute all possible contact situations taking into account uncertainties. To accomplish this, some general geometric constraints on the objects, which are easy to satisfy, are imposed. <<ETX>>
Tanezumab in the treatment of chronic musculoskeletal conditions

ABSTRACT Introduction: The management of pain associated with chronic musculoskeletal conditions represents a significant challenge for the clinician. There remains a need for novel medications that have a significant analgesic benefit and are also safe and well tolerated. Both pre-clinical and clinical data have provided evidence of the role of nerve growth factor (NGF) in a multitude of pain eliciting conditions. Therefore, the development of monoclonal antibodies to NGF for chronic painful musculoskeletal conditions has generated interest. Areas covered: This manuscript is a review that examines both the pharmacological properties and clinical studies of tanezumab, the most widely studied antibody to NGF, for management of osteoarthritis (OA) and low back pain. In addition, the safety and tolerability profile and development history of tanezumab are also discussed. Expert opinion: Most studies provide strong support for the ability of tanezumab to provide clinically meaningful pain relief in individuals with these conditions, with longer-term studies suggesting durability of effect. The adverse event profile appears favorable, assuming the risk mitigation strategies are effective at reducing the incidence of joint-related side effects. Further data are being collected to define the optimal dose and dosing strategy in both OA and chronic low back pain.
Interactions of proteins in aqueous ammonium-sulfate solutions:Mixtures of lysozyme and ovalbumin

We present a three-dimensional, time-dependent simulation of a laboratory-scale rod-stabilized premixed turbulent V-flame. The simulations are performed using an adaptive time-dependent low Mach number model with detailed chemical kinetics and a mixture model for differential species diffusion. The algorithm is based on a second-order projection formulation and does not require an explicit sub grid model for turbulence or turbulence chemistry interaction. Adaptive mesh refinement is used to dynamically resolve the flame and turbulent structures. Here, we briefly discuss the numerical procedure and present detailed comparisons with experimental measurements showing that the computation is able to accurately capture the basic flame morphology and associated mean velocity field. Finally, we discuss key issues that arise in performing these types of simulations and the implications of these issues for using computation to form a bridge between turbulent flame experiments and basic combustion chemistry.
Do Social Media Privacy Policies Matter? Evaluating the Effects of Familiarity and Privacy Seals on Cognitive Processing

News stories of security breaches and government surveillance have made Internet users more concerned about their privacy, translating perhaps to greater scrutiny of privacy policies of social media platforms and online application providers. The purpose of the research was to examine whether individuals unquestioningly accept the privacy policies of social media platforms and the extent to which individual information processing influences users' agreement. The Heuristic-Systematic Model (HSM) provided the theoretical framework for an experimental study that compared privacy policies from familiar and unfamiliar social media platforms that also varied in the presence of TRUSTe authentication signals. The results implicate heuristic processing where individuals, rather than examine the content of a policy, blindly comply in agreement. The heuristic effect was most pronounced when individuals were familiar with the social media platform. Surprisingly, the presence of a TRUSTe seal reduced decision confidence, and rather than stimulate heuristic processing, caused a more detailed assessment of the policy content.
Global prevalence and incidence of traumatic spinal cord injury

Background Spinal cord injury (SCI) is a traumatic event that impacts a patient’s physical, psychological, and social well-being and places substantial financial burden on health care systems. To determine the true impact of SCI, this systematic review aims to summarize literature reporting on either the incidence or prevalence of SCI. Methods A systematic search was conducted using PubMed, MEDLINE, MEDLINE in process, EMBASE, Cochrane Controlled Trial Register, and Cochrane Database of Systematic Reviews to identify relevant literature published through June 2013. We sought studies that provided regional, provincial/state, or national data on the incidence of SCI or reported estimates of disease prevalence. The level of evidence of each study was rated using a scale that evaluated study design, methodology, sampling bias, and precision of estimates. Results The initial search yielded 5,874 articles, 48 of which met the inclusion criteria. Forty-four studies estimated the incidence of SCI and nine reported the prevalence, with five discussing both. Of the incidence studies, 14 provided figures at a regional, ten at a state or provincial level and 21 at a national level. The prevalence of SCI was highest in the United States of America (906 per million) and lowest in the Rhone-Alpes region, France (250 per million) and Helsinki, Finland (280 per million). With respect to states and provinces in North America, the crude annual incidence of SCI was highest in Alaska (83 per million) and Mississippi (77 per million) and lowest in Alabama (29.4 per million), despite a large percentage of violence injuries (21.2%). Annual incidences were above 50 per million in the Hualien County in Taiwan (56.1 per million), the central Portugal region (58 per million), and Olmsted County in Minnesota (54.8 per million) and were lower than 20 per million in Taipei, Taiwan (14.6 per million), the Rhone-Alpes region in France (12.7 per million), Aragon, Spain (12.1 per million), Southeast Turkey (16.9 per million), and Stockholm, Sweden (19.5 per million). The highest national incidence was 49.1 per million in New Zealand, and the lowest incidences were in Fiji (10.0 per million) and Spain (8.0 per million). The majority of studies showed a high male-to-female ratio and an age of peak incidence of younger than 30 years old. Traffic accidents were typically the most common cause of SCI, followed by falls in the elderly population. Conclusion This review demonstrates that the incidence, prevalence, and causation of SCI differs between developing and developed countries and suggests that management and preventative strategies need to be tailored to regional trends. The rising aging population in westernized countries also indicates that traumatic SCI secondary to falls may become an increasing public health challenge and that incidence among the elderly may rise with increasing life expectancy.
Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population

Variants in HNF1A encoding hepatocyte nuclear factor 1α (HNF-1A) are associated with maturity-onset diabetes of the young form 3 (MODY 3) and type 2 diabetes. We investigated whether functional classification of HNF1A rare coding variants can inform models of diabetes risk prediction in the general population by analyzing the effect of 27 HNF1A variants identified in well-phenotyped populations (n = 4,115). Bioinformatics tools classified 11 variants as likely pathogenic and showed no association with diabetes risk (combined minor allele frequency [MAF] 0.22%; odds ratio [OR] 2.02; 95% CI 0.73–5.60; P = 0.18). However, a different set of 11 variants that reduced HNF-1A transcriptional activity to <60% of normal (wild-type) activity was strongly associated with diabetes in the general population (combined MAF 0.22%; OR 5.04; 95% CI 1.99–12.80; P = 0.0007). Our functional investigations indicate that 0.44% of the population carry HNF1A variants that result in a substantially increased risk for developing diabetes. These results suggest that functional characterization of variants within MODY genes may overcome the limitations of bioinformatics tools for the purposes of presymptomatic diabetes risk prediction in the general population.
29 Stereo Vision Based SLAM Issues and Solutions

1Simultaneous Localization and Mapping (SLAM) has been one of the active research areas in robotic research community for the past few years. When a robot is placed in an unknown environment a SLAM solution attempts to build a perfect map of the environment while localising the robot with respect to this map simultaneously. Traditionally SLAM utilised endogenous sensor data in the process. Successful SLAM implementations using laser (Guivant and Nebot, 2002), sonar and radar (Clark and Dissanayake, 1999) can be found in the literature, which prove the possibility of using SLAM for extended periods of time in indoor and outdoor environments with well bounded results. Recent extensions to the general SLAM problem has looked in to the possibility of using 3dimensional features and the use of alternative sensors to traditionally used lasers and radars. Cameras are competitive alternatives owing to the low cost and rich information content they provide. Despite the recent developments in camera sensors and computing, there are still formidable challenges to be resolved before successful vision based SLAM implementations are realised in realistic scenarios. Monocular camera based SLAM is widely researched (Davison et al., 2004; Kwok et al., 2005), however, binocular camera based SLAM is mostly overlooked. Some of the noted stereo implementations can be found in (Davison and Murray, 2002) and recently in (Jung, 2004). Lack of enthusiasm for research in this direction could possibly be attributed to the misconception that range and bearing information provided by the stereo vision system is directly utilizable providing a simplistic solution to SLAM which is academically less appealing or the apparent success in single camera SLAM implementations. However, after rigorous analysis and sensor modelling, we found that the standard extended Kalman filter (EKF) based SLAM with small base line stereo vision systems can easily become inconsistent (Herath et al., 2006a) . This chapter attempts to provide readers with an understanding of the SLAM problem and its solutions in the context of stereo vision. The chapter introduces the Extended Kalman Filter as applied to the generic SLAM Problem. Then, while identifying the prevailing issues inherent in solutions to the SLAM problem in stereo vision context, our solutions are presented with simulated and experimental evaluations. Several components of the stereo
Smart Homes predicting the Multi-Domain Symptoms of Alzheimer ’ s Disease

As members of an increasingly aging society, one of our major priorities is the development of tools to early detect age-related disorders such as Alzheimer’s Disease (AD). The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to detect the multimodal symptoms of AD. After gathering longitudinal smart home data of 29 older adults for an average of > 2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. AD symptoms were assessed every six months by means of self-reported mobility, memory/cognition and mood tests. Using these data, we created regression models to predict AD symptoms as measured by the tests and a feature selection analysis was performed. Classification models were built to detect reliable absolute change in the scores predicting AD and SmoteBOOST algorithm was used to overcome class imbalance where needed. Results show that all mobility, cognition/memory and depression symptoms are predictable by the activity-aware smart home data, as well as a reliable change in mobility and visuospatial skills related to cognition. Results also suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work must focus on improving the sensitivity of the presented models by collecting more longitudinal data and by focusing on class-imbalance suitable algorithms and in in-depth feature selection. The results presented herein contribute significantly towards the development of an early AD detection system based on smart home technology.
The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

As successfully treated individuals with Human Immunodeficiency Virus (HIV)-infected age, cognitive and health challenges of normal aging ensue, burdened by HIV, treatment side effects, and high prevalence comorbidities, notably, Alcohol Use Disorders (AUD) and Hepatitis C virus (HCV) infection. In 2013, people over 55 years old accounted for 26% of the estimated number of people living with HIV (~1.2 million). The aging brain is increasingly vulnerable to endogenous and exogenous insult which, coupled with HIV infection and comorbid risk factors, can lead to additive or synergistic effects on cognitive and motor function. This paper reviews the literature on neuropsychological and in vivo Magnetic Resonance Imaging (MRI) evaluation of the aging HIV brain, while also considering the effects of comorbidity for AUD and HCV.
A perspective on single cell behavior during infection

ABSTRACT The interface between immune cells and intracellular bacterial pathogens produces complex, diverse interactions. During individual encounters, highly adaptable and dynamic host cells and bacteria vary in their responses, thereby contributing to well-documented heterogeneous outcomes of infection. The challenge now is to break down the multidimensionality of these interactions into informative readouts of population physiology and predictors of responses to infection. We recently reported one approach to this challenge that couples single cell RNA-seq analysis with fluorescent markers to characterize infection phenotype.1 We detected bacterial subpopulations that elicit profoundly different host responses to infection in the specific host cells that they infect. Here we describe how heterogeneity might be maintained in populations of host and pathogens and discuss the advantages that heterogeneity confers to bacteria during infection and to host cells for eradicating a pathogen. We propose that single cell studies will allow the unraveling of host-pathogen biology and lead to an understanding of how the sum of individual encounters leads to the infection outcome of a whole organism.
Second-Order Occlusion-Aware Volumetric Radiance Caching

We present a second-order gradient analysis of light transport in participating media and use this to develop an improved radiance caching algorithm for volumetric light transport. We adaptively sample and interpolate radiance from sparse points in the medium using a second-order Hessian-based error metric to determine when interpolation is appropriate. We derive our metric from each point’s incoming light field, computed by using a proxy triangulation-based representation of the radiance reflected by the surrounding medium and geometry. We use this representation to efficiently compute the first- and second-order derivatives of the radiance at the cache points while accounting for occlusion changes. We also propose a self-contained 2D model for light transport in media and use it to validate and analyze our approach, demonstrating that our method outperforms previous radiance caching algorithms both in terms of accurate derivative estimates and final radiance extrapolation. We generalize these findings to practical 3D scenarios, where we show improved results while reducing computation time by up to 30% compared to previous work.
An ADMM-Based Coordination and Control Strategy for PV and Storage to Dispatch Stochastic Prosumers: Theory and Experimental Validation

This paper describes a two-layer control and coordination framework for distributed energy resources. The lower layer is a real-time model predictive control (MPC) executed at 10 s resolution to achieve fine tuning of a given energy setpoint. The upper layer is a slower MPC coordination mechanism based on distributed optimization, and solved with the alternating direction method of multipliers (ADMM) at 5 minutes resolution. It is needed to coordinate the power flow among the controllable resources such that enough power is available in real-time to achieve a pre-established energy trajectory in the long term. Although the formulation is generic, it is developed for the case of a battery system and a curtailable PV facility to dispatch stochastic prosumption according to a trajectory at 5 minutes resolution established the day before the operation. The proposed method is experimentally validated in a real-life setup to dispatch the operation of a building with rooftop PV generation (i.e., 101 kW average load, 350 kW peak demand, 82 kW peak PV generation) by controlling a 560 kWh/720 kVA battery and a 13 k W peak curtailable PV facility.