Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks

In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Flow Boiling of Water in a Rectangular Metallic Microchannel

Copyright © 2020 The Author(s). The article presents the experimental results of flow boiling of water in single rectangular microchannels. Three rectangular copper microchannels having the same hydraulic diameter (0.56mm) and length (62mm) but different aspect ratios (width/height, 0.5, 2.56, and 4.94) were investigated using de-ionized water as the working fluid. The experiments were conducted over the experimental range of mass flux 200–800 kg/(m2s), heat flux 4–1350 kW/m2 and inlet subcooling of ~14 K. The results showed that the channel with smaller aspect ratio exhibited better heat transfer performance up to certain heat fluxes ( 480–500 kW/m2), whilst the effect of channel aspect ratio became insignificant for higher heat fluxes. The flow boiling patterns were observed and the main flow regimes were bubbly, slug, churn, and annular flow. Flow reversal was also observed that caused a periodic flow in the two microchannels having smaller aspect ratio. A comparison of the experimental results with widely used macro and micro-scale heat transfer correlations is presented. The macro-scale correlations failed to predict the experimental data while some micro-scale correlations could predict the data reasonably well.Turkish Higher Research CouncilTurkish Higher Research Counci

Encephalitis, acute renal failure, and acute hepatitis triggered by a viral infection in an immunocompetent young adult: a case report

<p>Abstract</p> <p>Introduction</p> <p>Cytomegalovirus generally causes self-limited, mild and asymptomatic infections in immunocompetent patients. An aggressive course in immunocompetent healthy patients is unusual.</p> <p>Case presentation</p> <p>We report the case of an immunocompetent 16-year-old Egyptian boy with encephalitis, acute renal failure, and acute hepatitis triggered by viral infection with a complete recovery following antiviral treatment.</p> <p>Conclusion</p> <p>We believe that this case adds to the understanding of the molecular biology, clinical presentation and increasing index of suspicion of many viral infections.</p

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Genome-Wide Analysis of Structural Variants in Parkinson Disease

OBJECTIVE: Identification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD. METHODS: We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data. RESULTS: We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2 kb intronic deletion within the gene LRRN4. INTERPRETATION: We identified three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. ANN NEUROL 202

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Cyclin A and cyclin D1 as significant prognostic markers in colorectal cancer patients

BACKGROUND: Colorectal cancer is a common cancer all over the world. Aberrations in the cell cycle checkpoints have been shown to be of prognostic significance in colorectal cancer. METHODS: The expression of cyclin D1, cyclin A, histone H3 and Ki-67 was examined in 60 colorectal cancer cases for co-regulation and impact on overall survival using immunohistochemistry, southern blot and in situ hybridization techniques. Immunoreactivity was evaluated semi quantitatively by determining the staining index of the studied proteins. RESULTS: There was a significant correlation between cyclin D1 gene amplification and protein overexpression (concordance = 63.6%) and between Ki-67 and the other studied proteins. The staining index for Ki-67, cyclin A and D1 was higher in large, poorly differentiated tumors. The staining index of cyclin D1 was significantly higher in cases with deeply invasive tumors and nodal metastasis. Overexpression of cyclin A and D1 and amplification of cyclin D1 were associated with reduced overall survival. Multivariate analysis shows that cyclin D1 and A are two independent prognostic factors in colorectal cancer patients. CONCLUSIONS: Loss of cell cycle checkpoints control is common in colorectal cancer. Cyclin A and D1 are superior independent indicators of poor prognosis in colorectal cancer patients. Therefore, they may help in predicting the clinical outcome of those patients on an individual basis and could be considered important therapeutic targets