bluuebunny/crossref_metadata_2025 huggingface dataset:
Density, habitat use, and activity patterns of a vulnerable population of the Solitary Tinamou (Tinamus solitarius) in a Brazilian Atlantic Forest fragment
Authors: Ferreguetti Átilla Colombo , Pereira-Ribeiro Juliane , Tomas Walfrido Moraes , Bergallo Helena Godoy , Rocha Carlos Frederico Duarte
Published: October 2017

We present the first estimates of density of the Solitary Tinamou (Tinamus solitarius (Vieillot, 1819)) in the Reserva Natural Vale (RNV), one of the two largest remnants of the Atlantic Rainforest in the state of Espírito Santo, Brazil. We also modeled the spatial distribution and habitat use of the species using occupancy and detectability modeling. We used a line-transect survey to estimate density and abundance and camera traps to estimate habitat use and activity pattern. We conducted the survey within a 13 month period (April 2013 to May 2014). Estimated density was 9 ± 2 individuals/km2and estimated population size for the entire RNV was 2115 ± 470 individuals. Occupancy was best described by distance to forest edge, distance between trees, poaching intensity, and understory cover. Detectability was affected by distance between trees and poaching intensity. We conclude that in the RNV, the Near Threatened T. solitarius is a diurnal species preferring intact closed-canopy forest but tending to avoid areas near to forest edges or areas under comparatively high intensity of poaching. These results reinforce the importance of RNV as a resource source for T. solitarius reproduction at Brazilian Atlantic forest remnants.
A unified picture of cosmological entropy on apparent horizon in F(R, G) gravity
Authors: Keskin Ali İhsan , Acikgoz Irfan
Published: October 2017

In this study, the validity of the generalized second law of thermodynamics (GSLT) has been investigated in F(R, G) gravity. We consider that the boundary of the universe is surrounded by an apparent horizon in the spatially flat Friedmann–Robertson–Walker (FRW) universe, and we take into account the Hawking temperature on the horizons. The unified solutions of the field equations corresponding to gravity theory have been applied to the validity of the GSLT frame, and in this way, both the solutions have been verified and all the expansion history of the universe has been shown in a unified picture.
Coherent wave packet dynamics in a double-well potential in cavity
Authors: Zheng Li , Li Gang , Ding Ming-Song , Wang Yong-Liang , Zhang Yun-Cui
Published: October 2017

We investigate the coherent wave packet dynamics of a two-level atom trapped in a symmetric double-well potential in a near-resonance cavity. Prepared on one side of the double-well potential, the atom wave packet oscillates between the left and right wells, while recoil induced by the emitted photon from the atom entangles the atomic internal and external degrees of freedom. The collapse and revival of the tunneling occurs. Adjusting the width of the wave packets, one can modify the tunneling frequency and suppress the tunneling.
A NOVEL APPROACH TO DETECT EPILEPTIC SEIZURES USING A COMBINATION OF TUNABLE-Q WAVELET TRANSFORM AND FRACTAL DIMENSION
Authors: SHARMA MANISH , PACHORI RAM BILAS
Published: October 2017

The detection and quantification of seizures can be achieved through the analysis of nonstationary electroencephalogram (EEG) signals. The detection of these intractable seizures involving human beings is a challenging and difficult task. The analysis of EEG through human inspection is prone to errors and may lead to false conclusions. The computer-aided systems have been developed to assist neurophysiologists in the identification of seizure activities accurately. We propose a new machine learning and signal processing-based automated system that can detect epileptic episodes accurately. The proposed algorithm employs a promising time-frequency tool called tunable-Q wavelet transform (TQWT) to decompose EEG signals into various sub-bands (SBs). The fractal dimensions (FDs) of the SBs have been used as the discriminating features. The TQWT has many attractive features, such as tunable oscillatory attribute and time-invariance property, which are favorable for the analysis of nonstationary and transient signals. Fractal dimension is a nonlinear chaotic trait that has been proven to be very useful in the analysis and classifications of nonstationary signals including EEG. First, we decompose EEG signals into the desired SBs. Then, we compute FD for each SB. These FDs of the SBs have been applied to the least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel. We have used 10-fold cross-validation to ensure reliable performance and avoid the possible over-fitting of the model. In the proposed study, we investigate the following four popular classification tasks (CTs) related to different classes of EEG signals: (i) normal versus seizure (ii) seizure-free versus seizure (iii) nonseizure versus Seizure (iv) normal versus seizure-free. The proposed model surpassed existing models in the area under the receiver operating characteristics (ROC) curve, Matthew’s correlation coefficient (MCC), average classification accuracy (ACA), and average classification sensitivity (ACS). The proposed system attained perfect 100% ACS for all CTs considered in this study. The method achieved the highest classification accuracy as well as the largest area under ROC curve (AUC) for all classes. The salient feature of our proposed model is that, though many models exist in the literature, which gave high ACA, however, their performance has not been evaluated using MCC and AUC along with ACA simultaneously. The evaluation of the performance in terms of only ACA which may be misleading. Hence, the performance of the proposed model has been assessed not only in terms of ACA but also in terms AUC and MCC. Moreover, the performance of the model has been found to be almost equivalent to a perfect model, and the performance of the proposed model surpasses the existing models for the CTs investigated by us. Therefore, the proposed model is expected to assist clinicians in analyzing seizures accurately in less time without any error.
THE EFFECT OF DIABETES ON CARDIOVASCULAR SYSTEM
Authors: ADAM MUHAMMAD , TAN JEN HONG , NG EDDIE Y. K.
Published: October 2017

Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.
Cash Flow and Discount Rate Risk in the Investment Effect: A Downside Risk Approach
Authors: Yamani Ehab , Rakowski David
Published: October 2017

We examine whether sensitivities to cash flow and discount rate risk in down markets explain the investment effect, in which low-investment stocks earn higher expected returns than high-investment stocks. We show how productivity and financing constraints asymmetrically impact the systematic risk of low-investment and high-investment firms, conditional on market state. Our evidence is consistent with both productivity constraints and financing constraints as explanations for the investment effect, but, contrary to expectations, more when prices are rising than falling.
Tipping Toward Transformation: Progress, Patterns and Potential for Climate Change Adaptation in the Global South
Authors: Burch Sarah , Mitchell Carrie , Berbes-Blazquez Marta , Wandel Johanna
Published: October 2017

In response to observed and projected climate change impacts, major donors are funding an abundance of climate change research in the global South. The product of these funding schemes is often an abundance of cases with little attention paid to capturing the broader trends and patterns across cases. Furthermore, calls are increasingly being made for both adaptation and mitigation policies that are transformative: strategies that tackle the roots of vulnerability and high carbon development pathways to create a more fundamental shift towards sustainability. In this paper, we assess 54 cases of donor-funded adaptation research in the global South to paint a detailed picture of the types of adaptation options being proposed and implemented, their scope and the intended beneficiaries. We consider these data through the lens of transformation: to what extent do these cases illustrate adaptation actions that might push the social-ecological system over a tipping point towards a more desirable, sustainable state? Ultimately, we find that the adaptation options in these cases focus on educational or behavioral campaigns rather than deeper governance, legislative, or economic shifts. Similarly, the scale of action most often targets communities, rather than ecosystems, watershed, or regional/national scales. Even so, the emergence of resilience thinking in some projects, and the potential for a values shift triggered by these projects may sow the seeds of a longer-term transformation, if more attention is paid to synergies between development objectives and climate change actions.
Automated Vehicle Detection and Classification
Authors: Boukerche Azzedine , Siddiqui Abdul Jabbar , Mammeri Abdelhamid
Published: October 2017

Automated Vehicle Classification (AVC) based on vision sensors has received active attention from researchers, due to heightened security concerns in Intelligent Transportation Systems. In this work, we propose a categorization of AVC studies based on the granularity of classification, namely Vehicle Type Recognition, Vehicle Make Recognition, and Vehicle Make and Model Recognition. For each category of AVC systems, we present a comprehensive review and comparison of features extraction, global representation, and classification techniques. We also present the accuracy and speed-related performance metrics and discuss how they can be used to compare and evaluate different AVC works. The various datasets proposed over the years for AVC are also compared in light of the real-world challenges they represent, and those they do not. The major challenges involved in each category of AVC systems are presented, highlighting open problems in this area of research. Finally, we conclude by providing future directions of research in this area, paving the way toward efficient large-scale AVC systems. This survey shall help researchers interested in the area to analyze works completed so far in each category of AVC, focusing on techniques proposed for each module, and to chalk out strategies to enhance state-of-the-art technology.
DYNASCORE
Authors: Kritikakou Angeliki , Marty Thibaut , Roy Matthieu
Published: October 2017

In real-time mixed-critical systems, Worst-Case Execution Time (WCET) analysis is required to guarantee that timing constraints are respected—at least for high-criticality tasks. However, the WCET is pessimistic compared to the real execution time, especially for multicore platforms. As WCET computation considers the worst-case scenario, it means that whenever a high-criticality task accesses a shared resource in multicore platforms, it is considered that all cores use the same resource concurrently. This pessimism in WCET computation leads to a dramatic underutilization of the platform resources, or even failing to meet the timing constraints. In order to increase resource utilization while guaranteeing real-time guarantees for high-criticality tasks, previous works proposed a runtime control system to monitor and decide when the interferences from low-criticality tasks cannot be further tolerated. However, in the initial approaches, the points where the controller is executed were statically predefined. In this work, we propose a dynamic runtime control which adapts its observations to online temporal properties, further increasing the dynamism of the approach, and mitigating the unnecessary overhead implied by existing static approaches. Our dynamic adaptive approach allows one to control the ongoing execution of tasks based on runtime information, and further increases the gains in terms of resource utilization compared with static approaches.
Algorithm 986
Authors: Mehra Mani , Patel Kuldip Singh
Published: October 2017

A collection of Matlab routines that compute derivative approximations of arbitrary functions using high-order compact finite difference schemes is presented. Tenth-order accurate compact finite difference schemes for first and second derivative approximations and sixth-order accurate compact finite difference schemes for third and fourth derivative approximations are discussed for the functions with periodic boundary conditions. Fourier analysis of compact finite difference schemes is explained, and it is observed that compact finite difference schemes have better resolution characteristics when compared to classical finite difference schemes. Compact finite difference schemes for the functions with Dirichlet and Neumann boundary conditions are also discussed. Moreover, compact finite difference schemes for partial derivative approximations of functions in two variables are also given. For each case a Matlab routine is provided to compute the differentiation matrix and results are validated using the test functions.
Improving the Efficacy of Games for Change Using Personalization Models
Authors: Orji Rita , Mandryk Regan L. , Vassileva Julita
Published: October 2017

There has been a continuous increase in the design and application of computer games for purposes other than entertainment in recent years. Serious games—games that motivate behavior and retain attention in serious contexts—can change the attitudes, behaviors, and habits of players. These games for change have been shown to motivate behavior change, persuade people, and promote learning using various persuasive strategies. However, persuasive strategies that motivate one player may demotivate another. In this article, we show the importance of tailoring games for change in the context of a game designed to improve healthy eating habits. We tailored a custom-designed game by adapting only the persuasive strategies employed; the game mechanics themselves did not vary. Tailoring the game design to players’ personality type improved the effectiveness of the games in promoting positive attitudes, intention to change behavior, and self-efficacy. Furthermore, we show that the benefits of tailoring the game intervention are not explained by the improved player experience, but directly by the choice of persuasive strategy employed. Designers and researchers of games for change can use our results to improve the efficacy of their game-based interventions.
Autogen
Authors: Chowdhury Rezaul , Ganapathi Pramod , Tschudi Stephen , Tithi Jesmin Jahan , Bachmeier Charles , Leiserson Charles E. , Solar-Lezama Armando , Kuszmaul Bradley C. , Tang Yuan
Published: October 2017

We present A
utogen
—an algorithm that for a wide class of dynamic programming (DP) problems automatically discovers highly efficient cache-oblivious parallel recursive divide-and-conquer algorithms from inefficient iterative descriptions of DP recurrences. A
utogen
analyzes the set of DP table locations accessed by the iterative algorithm when run on a DP table of small size and automatically identifies a recursive access pattern and a corresponding provably correct recursive algorithm for solving the DP recurrence. We use A
utogen
to autodiscover efficient algorithms for several well-known problems. Our experimental results show that several autodiscovered algorithms significantly outperform parallel looping and tiled loop-based algorithms. Also, these algorithms are less sensitive to fluctuations of memory and bandwidth compared with their looping counterparts, and their running times and energy profiles remain relatively more stable. To the best of our knowledge, A
utogen
is the first algorithm that can automatically discover new nontrivial divide-and-conquer algorithms.
Evaluating the Use of Sound in Static Program Comprehension
Authors: Berman Lewis , Gallagher Keith , Kozaitis Suzanne
Published: October 2017

Comprehension of computer programs is daunting, due in part to clutter in the software developer's visual environment and the need for frequent visual context changes. Previous research has shown that nonspeech sound can be useful in understanding the runtime behavior of a program. We explore the viability and advantages of using nonspeech sound in an ecological framework to help understand the static structure of software. We describe a novel concept for auditory display of program elements in which sounds indicate characteristics and relationships among a Java program's classes, interfaces, and methods. An empirical study employing this concept was used to evaluate 24 sighted software professionals and students performing maintenance-oriented tasks using a 2×2 crossover. Viability is strong for differentiation and characterization of software entities, less so for identification. The results suggest that sonification can be advantageous under certain conditions, though they do not indicate the overall advantage of using sound in terms of task duration at a 5% level of significance. The results uncover other findings such as differences in comprehension strategy based on the available tool environment. The participants reported enthusiasm for the idea of software sonification, mitigated by lack of familiarity with the concept and the brittleness of the tool. Limitations of the present research include restriction to particular types of comprehension tasks, a single sound mapping, a single programming language, and limited training time, but the use of sound in program comprehension shows sufficient promise for continued research.
Visual Quality Assessment of 3D Models
Authors: Vanhoey Kenneth , Sauvage Basile , Kraemer Pierre , Lavoué Guillaume
Published: October 2017

Geometric modifications of three-dimensional (3D) digital models are commonplace for the purpose of efficient rendering or compact storage. Modifications imply visual distortions that are hard to measure numerically. They depend not only on the model itself but also on how the model is visualized. We hypothesize that the model’s light environment and the way it reflects incoming light strongly influences perceived quality. Hence, we conduct a perceptual study demonstrating that the same modifications can be masked, or conversely highlighted, by different light-matter interactions. Additionally, we propose a new metric that predicts the perceived distortion of 3D modifications for a known interaction. It operates in the space of 3D meshes with the object’s appearance, that is, the light emitted by its surface in any direction given a known incoming light. Despite its simplicity, this metric outperforms 3D mesh metrics and competes with sophisticated perceptual image-based metrics in terms of correlation to subjective measurements. Unlike image-based methods, it has the advantage of being computable prior to the costly rendering steps of image projection and rasterization of the scene for given camera parameters.
(In)Visibility in Disability and Assistive Technology
Authors: Faucett Heather A. , Ringland Kate E. , Cullen Amanda L. L. , Hayes Gillian R.
Published: October 2017

In this article, we present a meta-analysis of research examining visibility of disability. In interrogating the issue of visibility and invisibility in the design of assistive technologies, we open a discussion about how perceptions surrounding disability can be probed through an examination of visibility and how these tensions do, and perhaps should, influence assistive technology design and research.
Automatically Building VoIP Speech Parallel Corpora for Arabic Dialects
Authors: Almeman Khalid
Published: October 2017

This article discusses the process of automatically building Arabic multi-dialect speech corpora using Voice over Internet Protocol (VoIP). The Asterisk framework was adopted to act as the main connection between the parties, for which two virtual machines were created: a sender and a receiver. The sender makes a VoIP call to the receiver using the Asterisk framework, while the receiver records the call automatically, a process that is repeated for all the audio files involved in the corpora. In this work, more than 67,000 automatic calls were made between the sender and receiver machines, generating VoIP Arabic corpora for four Arabic dialects. The resulting corpora can be considered the first Arabic VoIP parallel speech corpora and will be made freely available to researchers in Arabic NLP and speech recognition research.
Experiencing EVA Park, a Multi-User Virtual World for People with Aphasia
Authors: Galliers Julia , Wilson Stephanie , Marshall Jane , Talbot Richard , Devane Niamh , Booth Tracey , Woolf Celia , Greenwood Helen
Published: October 2017

Virtual worlds are used in wide-ranging ways by many people with long-term health conditions, but their use by people with aphasia (PWA) has been limited. In contrast, this article reports the use of EVA Park, a multi-user virtual world designed for PWA to practice conversations, focusing on people's emotional, social, and conversational experiences. An analysis of observation and interview data collected from 20 people with aphasia who participated in a 5-week therapy intervention revealed key themes related to user experience. The themes offer a rich insight into aspects of the virtual world experience for PWA that go beyond therapeutic outcomes. They are as follows: affect (positive and negative); types of conversation, miscommunication, and misunderstanding; immersion in the virtual world; social presence and initiative and flow. Overall, the study showed that participants experienced positive emotional and social outcomes. We argue that this was achieved as a consequence of EVA Park being not only accessible but also a varied and entertaining environment within which PWA experienced both the realistic and the quirky while engaging with others and having fun.
An interview with David Ayman Shamma
Authors: Riegler Michael , Shamma David Ayman
Published: October 2017

About David Ayman Shamma:

I am a Principal Investigator and Senior Scientist at Centrum Wiskunde & Informatica (CWI) where I lead a team looking at Social Computing, Internet of Things (IoT), and fashion. Formerly, I was Director of Research at Yahoo Labs where I ran the HCI Research Group and I was the scientific liaison to Flickr (where I co-founded the Data-science group there). Broadly speaking, I design and prototype systems for multimedia-mediated communication, as well as, develops targeted methods and metrics for understanding how people communicate online in small environments and at web scale. Additionally, I create media art installations that have been reviewed by The New York Times, International Herald Tribune, and Chicago Magazine and exhibited internationally, including Second City Chicago, the Berkeley Art Museum, SIGGRAPH ETECH, Chicago Improv Festival, and Wired NextFest/NextMusic.

I have a Ph.D. in Computer Science from the Intelligent Information Laboratory at Northwestern University and a B.S./M.S. from the Institute for Human and Machine Cognition at The University of West Florida. Before Yahoo!, I was an instructor at the Medill School of Journalism; I have also taught courses in Computer Science and Studio Art departments. Prior to receiving my Ph.D., I was a visiting research scientist for the Center for Mars Exploration at NASA Ames Research Center.
MPEG column
Authors: Timmerer Christian
Published: October 2017

The original blog post can be found at the Bitmovin Techblog and has been updated here to focus on and highlight research aspects.
Excess ω-6 fatty acids influx in aging drives metabolic dysregulation, electrocardiographic alterations, and low-grade chronic inflammation
Authors: Kain Vasundhara , Ingle Kevin A. , Kachman Maureen , Baum Heidi , Shanmugam Gobinath , Rajasekaran Namakkal S. , Young Martin E. , Halade Ganesh V.
Published: October 2017

Maintaining a balance of ω-6 and ω-3 fatty acids is essential for cardiac health. Current ω-6 and ω-3 fatty acids in the American diet have shifted from the ideal ratio of 2:1 to almost 20:1; while there is a body of evidence that suggests the negative impact of such a shift in younger organisms, the underlying age-related metabolic signaling in response to the excess influx of ω-6 fatty acids is incompletely understood. In the present study, young (6 mo old) and aging (≥18 mo old) mice were fed for 2 mo with a ω-6-enriched diet. Excess intake of ω-6 enrichment decreased the total lean mass and increased nighttime carbohydrate utilization, with higher levels of cardiac cytokines indicating low-grade chronic inflammation. Dobutamine-induced stress tests displayed an increase in PR interval, a sign of an atrioventricular defect in ω-6-fed aging mice. Excess ω-6 fatty acid intake in aging mice showed decreased 12-lipoxygenase with a concomitant increase in 15-lipoxygenase levels, resulting in the generation of 15( S)-hydroxyeicosatetraenoic acid, whereas cyclooxygenase-1 and -2 generated prostaglandin E2, leukotriene B4, and thromboxane B2. Furthermore, excessive ω-6 fatty acids led to dysregulated nuclear erythroid 2-related factor 2/antioxidant-responsive element in aging mice. Moreover, ω-6 fatty acid-mediated changes were profound in aging mice with respect to the eicosanoid profile while minimal changes were observed in the size and shape of cardiomyocytes. These findings provide compelling evidence that surplus consumption of ω-6 fatty acids, coupled with insufficient intake of ω-3 fatty acids, is linked to abnormal changes in ECG. These manifestations contribute to functional deficiencies and expansion of the inflammatory mediator milieu during later stages of aging.

NEW & NOTEWORTHY Aging has a profound impact on the metabolism of fatty acids to maintain heart function. The excess influx of ω-6 fatty acids in aging perturbed electrocardiography with marked signs of inflammation and a dysregulated oxidative-redox balance. Thus, the quality and quantity of fatty acids determine the cardiac pathology and energy utilization in aging.
Exaggerated cardiovascular responses to treadmill running in rats with peripheral arterial insufficiency
Authors: Kuczmarski J. Matthew , Unrath Kellee , Thomas Gail D.
Published: October 2017

Patients with atherosclerotic peripheral artery disease have an augmented pressor response to treadmill walking, but the underlying mechanisms remain poorly understood and difficult to isolate because of the confounding presence of numerous cardiovascular risk factors. In the present study, we tested the hypothesis that a chronic deficit in muscle blood flow capacity would be sufficient to trigger an exaggerated pressor response to dynamic exercise. Sprague-Dawley rats (5 male and 5 female) were instrumented with radiotelemetry devices to measure the cardiovascular responses to treadmill running before and after bilateral femoral artery ligation, which has been previously shown to reduce the blood flow capacity of distal hindlimb muscles by >60%. Treadmill running evoked reproducible increases in mean arterial pressure (MAP) and heart rate (HR), which were significantly augmented 3 days after femoral artery ligation in both male rats [ΔMAP: +10 ± 1 (SE) vs. +18 ± 3 mmHg and ΔHR: +94 ± 12 vs. +148 ± 15 beats/min, P < 0.05] and female rats (ΔMAP: +16 ± 3 vs. +30 ± 5 mmHg and ΔHR: +128 ± 20 vs. +178 ± 19 beats/min, P < 0.05). Similar exaggerated MAP and HR responses were observed at repeated intervals between 3 and 65 days postligation. These findings indicate that a chronic deficit in muscle blood flow capacity is an important, persistent cause of the abnormal pressor and cardioaccelerator responses to dynamic exercise in both male and female rats with peripheral arterial insufficiency.

NEW & NOTEWORTHY Using radiotelemetry to assess cardiovascular effects of exercise, we showed that femoral artery obstruction in male and female rats is an important, persistent cause of exaggerated pressor and cardioaccelerator responses to treadmill running. This translational model reproduces the abnormal cardiovascular response to exercise seen in patients with peripheral artery disease.

Listen to this article’s corresponding podcast at http://ajpheart.podbean.com/e/treadmill-bp-in-simulated-peripheral-artery-disease/ .
Intranasal administration of recombinant progranulin inhibits bronchial smooth muscle hyperresponsiveness in mouse allergic asthma
Authors: Chiba Yoshihiko , Danno Shunta , Suto Rena , Suto Wataru , Yamane Yamato , Hanazaki Motohiko , Katayama Hiroshi , Sakai Hiroyasu
Published: October 2017

Progranulin (PGRN) is a growth factor with multiple biological functions and has been suggested as an endogenous inhibitor of Tumor necrosis factor-α (TNF-α)-mediated signaling. TNF-α is believed to be one of the important mediators of the pathogenesis of asthma, including airway hyperresponsiveness (AHR). In the present study, effects of recombinant PGRN on TNF-α-mediated signaling and antigen-induced hypercontractility were examined in bronchial smooth muscles (BSMs) both in vitro and in vivo. Cultured human BSM cells (hBSMCs) and male BALB/c mice were used. The mice were sensitized and repeatedly challenged with ovalbumin antigen. Animals also received intranasal administrations of recombinant PGRN into the airways 1 h before each antigen inhalation. In hBSMCs, PGRN inhibited both the degradation of IκB-α (an index of NF-κB activation) and the upregulation of RhoA (a contractile machinery-associated protein that contributes to the BSM hyperresponsiveness) induced by TNF-α, indicating that PGRN has an ability to inhibit TNF-α-mediated signaling also in the BSM cells. In BSMs of the repeatedly antigen-challenged mice, an augmented contractile responsiveness to acetylcholine with an upregulation of RhoA was observed: both the events were ameliorated by pretreatments with PGRN intranasally. Interestingly, a significant decrease in PGRN expression was found in the airways of the repeatedly antigen-challenged mice rather than those of control animals. In conclusion, exogenously applied PGRN into the airways ameliorated the antigen-induced BSM hyperresponsiveness, probably by blocking TNF-α-mediated response. Increasing PGRN levels might be a promising therapeutic for AHR in allergic asthma.
Brown adipocytes postnatally arise through both differentiation from progenitors and conversion from white adipocytes in Syrian hamster
Authors: Okamatsu-Ogura Yuko , Nio-Kobayashi Junko , Nagaya Kazuki , Tsubota Ayumi , Kimura Kazuhiro
Published: October 2017

To investigate the postnatal development of brown adipose tissue (BAT) in Syrian hamsters, we histologically examined interscapular fat tissue from 5–16-day-old pups, focusing on how brown adipocytes arise. Interscapular fat of 5-day-old hamsters mainly consisted of white adipocytes containing large unilocular lipid droplets, as observed in typical white adipose tissue (WAT). On day 7, clusters of small, proliferative nonadipocytes with a strong immunoreactivity for Ki67 appeared near the edge of the interscapular fat tissue. The area of the Ki67-positive regions expanded to ~50% of the total tissue area by day 10. The interscapular fat showed the typical BAT feature by day 16. A brown adipocyte-specific marker, uncoupling protein-1, was clearly detected on day 10 and thereafter, while not detected on day 7. During conversion of interscapular fat from WAT to BAT, unilocular adipocytes completely and rapidly disappeared without obvious apoptosis. Dual immunofluorescence staining for Ki67 and monocarboxylate transporter 1 (MCT1), another selective marker for brown adipocytes, revealed that most of the proliferating cells were of the brown adipocyte lineage. Electron microscopic examination showed that some of the white adipocytes contained small lipid droplets in addition to the large droplet and expressed MCT1 as do progenitor and mature brown adipocytes, implying a direct conversion from white to brown adipocytes. These results suggest that BAT of Syrian hamsters develops postnatally through two different pathways: the proliferation and differentiation of brown adipocyte progenitors and the conversion of unilocular adipocytes to multilocular brown adipocytes.

NEW & NOTEWORTHY Brown and white adipose tissues (BAT and WAT, respectively) are quite different in morphological features and function; however, the boundary between these tissues is obscure. In this study, we histologically evaluated the process of BAT development in Syrian hamsters, which shows postnatal conversion of WAT to BAT. Our results suggest that brown adipocytes arise through two different pathways: the proliferation and differentiation of brown adipocyte progenitors and the conversion from white adipocytes.
Study of Wind Turbine based Variable Reluctance Generator using Hybrid FEMM-MATLAB Modeling
Authors: Benamimour Tariq , Bentounsi Amar , Djeghloud Hind
Published: October 2017

Based on exhaustive review of the state of the art of the electric generators fitted to Wind Energy Conversion System (WECS), this study is focused on an innovative machine that is a Variable Reluctance Generator (VRG). Indeed, its simple and rugged structure (low cost), its high torque at low speed (gearless), its fault-tolerance (lowest maintenance), allow it to be a potential candidate for a small wind power application at variable wind speed. For better accuracy, a finite element model of a studied doubly salient VRG is developed using open source software FEMM to identify the electromagnetic characteristics such as linkage flux, torque or inductance versus rotor position and stator excitation. The obtained data are then transferred into  look-up tables of MATLAB/Simulink to perform various simulations. Performance of the proposed wind power system is analyzed for several parameters and results are discussed.
An Improved Detection and Classification Technique of Harmonic Signals in Power Distribution by Utilizing Spectrogram
Authors: Jopri M.H. , Abdullah A.R. , Manap M. , Yusoff M.R. , Sutikno T. , Habban M.F.
Published: October 2017

This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram.  The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
A Review on Fuzzy-GA based Controller for Power Flow Control in Grid Connected PV System
Authors: Thumu Raghu , Reddy K. Harinadha
Published: October 2017

Now-a-days Renewable Energy Sources became an alternative to meet the increasing load demand because they are environmental friendly and also available abundant in nature. Among the Renewable Energy Sources, the Photo Voltaic (PV) System is gaining more attention due abundant availability of solar energy. The Maximum Power Point Tracking Technique is used to extract maximum power from the Photo Voltaic (PV) Array. When there is a need to transfer bulk amount of power from PV Array to Power Grid, the power quality issues, especially the real and reactive power flow problems, are a major concern. In this paper a novel control technique was proposed to control the power flow and to deal with power quality issues that arise when PV Array is integrated with power grid. It consists of a Fuzzy-GA based Cascaded Controller fed Flexible AC Transmission System device, namely Unified Power Flow Controller, for effective control of real and reactive power flow in grid connected photovoltaic system. The output of the Fuzzy Logic Controller is a control vector which is fine tuned by using Genetic Algorithm approach.
A Survey on Block Matching Algorithms for Video Coding
Authors: Paramkusam Adapa Venkata , Arun Vuyyuru
Published: October 2017

Block matching algorithm (BMA) for motion estimation (ME) is the heart to many motion-compensated video-coding techniques/standards, such as ISO MPEG-1/2/4 and ITU-T H.261/262/263/264/265, to reduce the temporal redundancy between different frames. During the last three decades, hundreds of fast block matching algorithms have been proposed. The shape and size of search patterns in motion estimation will influence more on the searching speed and quality of performance. This article provides an overview of the famous block matching algorithms and compares their computational complexity and motion prediction quality.
Human Data Acquisition through Biometrics using LabVIEW
Authors: Rao K Nishanth , Reddy N. Vasudheva
Published: October 2017

Human Data Acquisition is an innovative work done based on fingerprints of a particular person. Using the fingerprints we can get each and every detail of any individual. Through this, the data acquired can be used in many applications such as Airport Security System, Voting System, and Employee login System, in finding the thieves etc. We in our project have implemented in Voting System. In this we use the components such as MyDAQ which is data acquisition device. The coding here is in done in a Graphical Programming language named LabVIEW where the execution of any program is done in a sequential way or step by step according to the data received.
MeMLO: Mobility-Enabled Multi-Level Optimization Sensor Network
Authors: Rajalakshmi M.C. , Prakash A.P. Gnana
Published: October 2017

The paper presents a technique called as Mobility-enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimi-zation attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational com-plexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.
Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem
Authors: A. Bewoor Laxmi , Chandra Prakash V. , U. Sapkal Sagar
Published: October 2017

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric.
Scalability Aware Energy Consumption and Dissipation Models for Wireless Sensor Networks
Authors: El Fissaoui Mohamed , Benkirane Said , Beni-Hssane Abderrahim , Saadi Mostafa
Published: October 2017

Most of Wireless Sensor Networks researches focus on reducing the amount of energy consumed by nodes and network to increase the network lifetime. Thus, several papers have been presented and published to optimize energy consumption in each area of WSNs, such as routing, localization, coverage, security, etc. To test and evaluate their propositions, authors apply an energy dissipation model; this model must be more realistic and suitable to give good results. In this paper we present a general preview on different sources of energy consumption in wireless sensor networks, and provide a comparative study between two energy models used in WSNs that offer an effective and an adequate tool for researchers.
Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction
Authors: Sudarma Made , Harsemadi I Gede
Published: October 2017

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music.  This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm.  In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process.  For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data.  The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious.  Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021.  Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.
Effect of Measurement Factors on Photovoltaic Cell Parameters Extracting
Authors: Chahid El Hadi , Oumhand Mohammed Idali , Feddaoui M’barek , Erritali Mohammed , Malaoui Abdessamad
Published: October 2017

In this paper, we study the influence of external factors on the measurement for the current–voltage (I-V) characteristic of the photovoltaic cell. These factors are the size of the number of measurements, the range of the cell generated voltage and the influence of measures step and mode combination of photovoltaic cells (parallel, serial, or hybrid). The main extracted parameters solar cell are the photocurrent Iph, the reverse diode saturation current I0, the ideality factor of diode n, the series resistance Rs and the shunt resistance Rsh. A method for finding these parameters, according to the single-diode model, was developed by Newton-Raphson’s method using Matlab. To assess the accuracy of this method, measured and calculated I–V characteristics were compared with provided data by the manufacturer at standard test condition (STC). The measurement results showed that these parameters are highly dependent on these four factors.
Optimized Active Learning for User’s Behavior Modelling based on Non-Intrusive Smartphone
Authors: Putri Ika Kusumaning , Liang Deron , Pramono Sholeh Hadi , Rahmadwati Rahmadwati
Published: October 2017

In order to protect the data in the smartphone, there is some protection mechanism that has been used. The current authentication uses PIN, password, and biometric-based method. These authentication methods are not sufficient due to convenience and security issue. Non-Intrusive authentication is more comfortable because it just collects user’s behavior to authenticate the user to the smartphone. Several non-intrusive authentication mechanisms were proposed but they do not care about the training sample that has a long data collection time. This paper propose a method to collect data more efficient using Optimized Active Learning. The Support Vector Machine (SVM) used to identify the effect of some small amount of training data. This proposed system has two main functionalities, to reduce the training data using optimized stop rule and maintain the Error Rate using modified model analysis to determine the training data that fit for each user. Finally, after we done the experiment, we conclude that our proposed system is better than Threshold-based Active Learning. The time required to collect the data can reduced to 41% from 17 to 10 minutes with the same Error Rate.
Review on Localization based Routing Protocols for Underwater Wireless Sensor Network
Authors: Ahmed Mukhtiar , Salleh Mazleena , Channa M.Ibrahim , Rohani Mohd Foad
Published: October 2017

Underwater Wireless Sensor Network (UWSN) can enable many scientific, military, safety, commercial and environmental applications. Majority of the network models has been introduced for the deployment of sensor nodes through routing schemes and methodologies along with different algorithms but still the design of routing protocol for underwater environment is a challenging issue due to distinctive characteristics of underwater medium. The majority of the issues are also needed to fulfill the appropriate approach for the underwater medium like limited bandwidth, high bit error rates, propagation delay, and 3D deployment. This paper focuses the comparative analysis of the localization based routing protocols for UWSN. This comparative analysis plays a significant attention to construct a reliable routing protocol, which provides the effectual discovery of the route between the source node and the sink node. In addition this comparative analysis also focuses the data packets forwarding mechanism, the deployment of sensor nodes and location based routing for UWSN in different conditions.
Control of an Autonomous Hybrid Microgrid as Energy Source for a Small Rural Village
Authors: Lampiao Americo Joaquim , Senjyu Tomonobu , Yona Atsushi
Published: October 2017

Nowadays, the exhaustion of electricity power in rural areas is becoming an important issue for many African Nations. Moreover, challenges include the high cost of extending the power grid to these locations, the economic health of the utilities and lack of revenue in impoverished villages. Numerous new initiatives are being implemented in the countries some of them co-financed by international organizations. In this paper, the hybrid microgrid is carried out as a feasible solution for a small rural village. A model of hybrid microgrid consisting of combination of photovoltaic (PV) panels and battery energy storage (BES) and a control system for managing the components of entire system to feed the village as local load is proposed. The control system must avoid the interruptions of power delivered to the consumers (village) and, therefore, good quality and reliability of system is required. The PI controllers are used to regulate the voltage and current using three-phase dq transformation, while the parameters are determined using Ziegler-Nichols tuning method. The effectiveness of the proposed method is verified by simulation results given by the Matlab/SimPowerSystems environment.

 

 