bluuebunny/crossref_metadata_2025_split huggingface dataset:
Activity Sensors to Evaluate the Effect of Sacubitril/Valsartan on Quality‐of‐Life in Heart Failure: rational and design of the AWAKE‐HF study
Authors: Khandwalla Raj M. , Birkeland Kade , Heywood J. Thomas , Steinhubl Steven , McCague Kevin , Fombu Emmanuel , Grant Daniel , Riebman Jerome B. , Owens Robert L. 
Published: October 2019

AbstractAimsLimited data are available regarding the ability of sacubitril/valsartan to provide clinically meaningful health‐related quality of life (HRQoL) improvements among individuals with heart failure (HF). Objective measurement of physical activity and sleep using actigraphy can provide insight into daily functioning and HRQoL.Methods and resultsWe designed an 18 week, multicenter, randomized, double‐blind, double‐dummy, parallel‐group study to objectively assess changes in function and HRQoL directly after initiating sacubitril/valsartan vs. enalapril in participants with HF in their home environments. A total of 136 outpatient, ambulatory participants with New York Heart Association Class II or III HF with reduced ejection fraction (HFrEF) will be included in the study. Patients will undergo a 2 week baseline observational phase (continuing current HF treatment); data from the second week of this phase will be the baseline value for comparison with those of subsequent periods. Patients will then enter an 8 week blinded‐treatment phase (randomly assigned 1:1 to sacubitril/valsartan or enalapril), followed by an 8 week open‐label extension phase (treatment with only sacubitril/valsartan). The primary efficacy endpoint is the change in mean activity counts during the most active 30 min of the participant's day between baseline and the final randomized treatment phase measurement. Secondary endpoints include the change in mean sleep activity during the randomized and open‐label phases; questionnaires will also assess HRQoL measures. Rather than analysing pooled actigraphy data, the researchers are considering each participant to be acting as his or her own control.ConclusionsThis will be the first study to assess the effects of sacubitril/valsartan on objective measures of sleep and activity in individuals with HFrEF within the context of their daily lives. Wearable accelerometer devices will be used to gain insight into how the medication affects physical activity and sleep.
Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis
Authors: Segar Matthew W. , Patel Kershaw V. , Ayers Colby , Basit Mujeeb , Tang W.H. Wilson , Willett Duwayne , Berry Jarett , Grodin Justin L. , Pandey Ambarish 
Published: October 2019

AbstractAimTo identify distinct phenotypic subgroups in a highly‐dimensional, mixed‐data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.Methods and resultsThe study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model‐based clustering analysis on 61 mixed‐data phenotypic variables. Phenogroup 1 had higher burden of co‐morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non‐cardiac co‐morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co‐morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all‐cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non‐echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase‐5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.ConclusionsMachine learning‐based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long‐term outcomes.
Effects of drying methods on the characteristics of rambutan (Nephelium lappaceum L.) seed fat: An optimisation approach
Authors: Jahurul M.H.A. , Ying L.N. , Amir H.M.S. , Azzatul F.S. , Sharifudin M.S. , Hasmadi M. , Lee J.S. , Mansoor A.H. , Jumardi R. , Matanjun P. , Firoz Khan M. , Zaidul I.S.M. 
Published: October 2019

The pre‐treatment of oilseeds prior to extraction process may affect oil yield and quality. The aim of this study was to investigate the effects of two drying methods on rambutan seed fat (RSF) yield and their oxidative stability, physicochemical properties, and crystal morphology. Response surface methodology (RSM) was used in the optimisation and investigation of the effects of three process conditions: seed weight (g), extraction time (min), and solvent volume (ml) on RSF yield. Under optimal conditions, a maximum RSF yield of 44.14% was obtained. The differences between RSF pre‐treated with oven‐drying and RSF with freeze‐drying methods in slip melting point (38.3°C to 39.7°C), free fatty acid (3.13 to 3.50 mg KOH/g fat), peroxide value (1.04 to 1.67 meq of O2/kg of fat), p‐anisidine value (1.10 to 1.56), and total oxidation value (4.21 to 5.67) were significant (p < 0.05). Both fats showed needle‐like shaped crystals. Our results provide useful information in the pre‐treatment of RSF, which has potential to be used as blending component with palm oil for cocoa butter equivalent formulation in chocolate and confectionery industries.
Role of computer software tools in industrial symbiotic networks and the examination of sociocultural factors
Authors: Fric Urška , Rončević Borut , Uršič Erika D. 
Published: October 2019

AbstractThis article represents some principal dilemmas and challenges that need to be undertaken when conducting future research on Industrial Symbiotic Networks (ISN) using existing and efficient Computer Software Tools (CS tools). Multidimensional networks in industrial symbiosis have involved two or more stakeholders in sharing services, utility, or by‐products/waste (material, energy, and water) in order to increase value and reduce financial and environmental costs. Multidimensional networks operate as dynamic complex networks, crossing the boundaries and are formed by natural, technical, social, and computer systems. For a better understanding, there is an outline and review of the benefits of using CS tools within managing industrial symbiotic networks.
Implications of municipal solid waste disposal methods in China on greenhouse gas emissions
Authors: Zhao Zeyu , Bian Rongxing , Zhao Fengbin , Chai Xiaoli 
Published: October 2019

AbstractWaste disposal sector is an important anthropogenic source of greenhouse gas (GHG) emissions; therefore, it is important to accurately estimate GHG budget of waste disposal activities and evaluate its contribution to global GHG emissions. Based on the GHG inventory guidelines provided by the Intergovernmental Panel on Climate Change, this study calculated GHGs from the municipal solid waste (MSW) disposal sector (including landfill, incineration, and compost) in China during the period 2002–2017. Overall GHG emission from the MSW disposal sector increased from 30,392.0 Gg CO2 in 2002 to 76,623.0 Gg in 2017. MSW landfill contributed most to GHG emissions as indicated by the emission share of landfill decreasing from a peak value of 93.5% in 2002 to 65.8% in 2017. GHG emissions from MSW incineration increased rapidly from less than 3.0% in 2002 to 32.8% in 2017. In 2017, East China contributed the most among the seven regions of China, while the Northwest contributed the least. Methane mitigation technologies such as landfill gas to energy projects and functional solid cover utilization are the primary measures for reducing GHG emissions from landfill; conversion of the treatment method from landfill to incineration is another effective measure; MSW sorting and source reduction of waste amounts prior to incineration were also found to be promising strategies.
A parametric study to simulate the non‐Newtonian turbulent flow in spiral tubes
Authors: Valizadeh Kamran , Farahbakhsh Soroush , Bateni Amir , Zargarian Amirhossein , Davarpanah Afshin , Alizadeh Araz , Zarei Mojtaba 
Published: October 2019

AbstractNon‐Newtonian fluids are considered to those types of fluids that do not follow Newton's law of viscosity where viscosity would change in either more solid or liquid. The objective of this study, a parametric simulation, was performed to investigate the considerable influence of non‐Newtonian fluids on different parameters on spiral tubes. Firstly, governing equations have derived by computational fluid dynamics methods to compare the laminar and turbulent flows. Then, the turbulent flow, the non‐Newtonian flow, power law flow, and cross models are simulated according to the boundary conditions. Consequently, for the Reynolds range of 600‐2500, increasing the Reynolds number decreases the friction coefficient. It is observed that in slow flow, there is no significant difference between the results of cross and power law models. The distribution of velocity profile has slight variation at the pipe outlet for Reynolds 9000 and 20 000. In other words, the flow is constant in developed region inside the spiral pipe. Moreover, the investigation of pressure drop inside the pipe revealed that regarding the increase in Reynolds number, the friction coefficient decreases. In spiral tubes, due to the presence of secondary currents, the friction coefficient is higher than the direct tube.
Dynamic analysis of concrete pipes under the coupled effects of traffic load and groundwater level fluctuations
Authors: Li Bin , Fang Hongyuan , Yang Kangjian , Tan Peiling , Wang Fuming 
Published: October 2019

AbstractFluctuations in the groundwater level make the working conditions of concrete pipes below the groundwater level more complex than those of concrete pipes above the groundwater level. This paper describes the application of a three‐dimensional (3‐D) refined finite element (FE) model to the analysis of a buried concrete pipe subjected to the coupled effects of traffic load and groundwater level fluctuations. First, the dynamic responses of the concrete pipes buried in dry soil were investigated with a full‐scale test and an FE model. The results show that the FE model predicted results similar to those observed in field pipes. Then, the dynamic responses based on the numerical results of pipes buried in dry soil under a traffic load and those of pipes buried in saturated soil under the coupled effects of a traffic load and groundwater level fluctuations were compared. Finally, a parametric analysis of the effects of the void ratio, permeability coefficient, and fluctuation period in the groundwater level in relation to the dynamic responses of the pipes was conducted. The results show that the mechanical behaviors of the pipes in dry soil were substantially different from those of pipes in saturated soil. Compared with pipes in dry soil under a traffic load, the stresses at the crown, springline, and invert of the bell under the coupled effects of the traffic load and groundwater level fluctuations were lowered by 44.8%, 52.0%, and 50.3%, respectively, while those of the spigot were lowered by 42.0%, 50.3%, and 49.6%, respectively. The maximum principal stress and the vertical displacement were proportional to the void ratio and the permeability coefficient, while their relationship to the groundwater level fluctuation period was less straightforward.
Detection of multipath routing with passive delay measurements: Hypothesis testing approaches
Authors: Pan Shengli , Wang Yan , Bai Ziming , Xiong Muzhou , Gu Lin 
Published: October 2019

AbstractMultipath routing, which is a common approach to achieve load balancing, will enable multiple active paths between two end‐hosts. However, it will cause out‐of‐order delivery of packets, which will not only severely debilitate the performances of TCP (Transmission Control Protocol) but also will make many single‐path–based applications work improperly. In this paper, we propose to utilize passive delay measurements between end‐hosts, to detect multipath routing with two different hypothesis testing approaches, ie, t‐Test and variance test. The motivation is that the obtained distributions of passive delays between end‐to‐end communication flows can be very similar to each other when they are routed by the identical end‐to‐end path, whereas distinct differences are observed among the ones of different end‐to‐end paths. Simulations based on NS2 validate the efficiency of both t‐Test and variance test and demonstrate the robustness of t‐Test against asynchronous measurements between different flows.
Health status diagnosis of distribution transformers based on big data mining
Authors: Wang Yue , Hu Linfang , Chen Jianwei , Ren Yueqing 
Published: October 2019

AbstractIn the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.
Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm
Authors: Siridhara A.L. , Ratnam D.V. 
Published: October 2019

AbstractResearch interest in Global Navigation Satellite System (GNSS) is increasing in the past few years due to its wide applicability in many application‐oriented environments, such as automobile navigation system and aircraft landing system. Global Positioning System (GPS) is one of the GNSSs having high precision navigation performance. Multipath is a dominant error source that occurs in several GPS applications, limiting the performance. To reduce the multipath effects of satellite signals, an effective multipath mitigation technique is required. This paper introduces the Taylor series–based bidirectional least mean square (Taylor‐BLMS) beamforming model for the multipath mitigation problems occurring in the GPS receiver. The performance metrics, such as antenna gain and bit error rate (BER), measure the performance of the proposed Taylor‐BLMS model against various comparative models. For the system with six sensors, the proposed Taylor‐BLMS model has the best performance with the values of 23.460 dB and 4.78E−05 for the antenna gain and the BER, respectively. For the system with 1 jammer, the proposed Taylor‐BLMS model has the antenna gain and the BER values of 22.2828 dB and 3.69E−05, respectively.
An effective handover management based on SINR and software‐defined network over urban vehicular ad hoc networks
Authors: Ahmed Adel A. 
Published: October 2019

AbstractUrban vehicular ad hoc network (UVANET) is a modality of high‐topology variation MANET where vehicles travel across constrained layout of the roads and tall buildings, which eventually results in shadowing effects. One of the critical challenges in UVANET is the handover process that happens when a vehicle changes its association with the roadside unit. Urban area suffers from high vehicular density at traffic signals, and vehicle slowdowns caused by speed limits, frequent link disconnection caused by signal attenuation due to obstacles. Thus, it is difficult for UVANET to disseminate the packets to their destinations located in a specified area. This paper presents an effective handover management that calculates the signal‐to‐interference‐plus‐noise ratio (SINR) to ultimately determine the optimal cluster head for seamless handover in UVANET. It also utilizes the software‐defined network to resolve the problem of handover failure in UVANET. The finding in the experimental results shows that the developed association clustering mechanism based on SINR significantly outperforms the baseline association mechanism. More importantly, it can resolve different types of handover problems that improved the quality of service in terms of throughput, handover latency, packet loss ratio, and handover failure ratio, when compared to the baseline handover mechanisms.
Passiflora edulis seed oil from west Cameroon: Chemical characterization and assessment of its hypolipidemic effect in high‐fat diet–induced rats
Authors: Ngakou Takam Prosper , Tonfack Djikeng Fabrice , Kuate Dieudonné , Nouemsi Kengne Anne Pascale , Doungué Tsafack Hermine , Makamwé Inelle , Oben Julius Enyong 
Published: October 2019

AbstractIn this study, the in vivo hypolipidemic effect of west Cameroonian Passiflora edulis variety seed oil (PE) was assessed in female and male Wistar rats. The chemical properties of the oil were evaluated through the determination of the peroxide, iodine, and thiobarbituric acid values, as well as its fatty acid composition using gas chromatography. Results showed that the oil extraction yield was 19.90% and its quality indexes were as follows: peroxide value = 2.10 ± 0.20 meq O2/kg; thiobarbituric acid value = 0.25 ± 0.00 ppm; and iodine value = 97.40 ± 0.45 g I2/100 g. Its fatty acid composition showed that it contains about 84.88% of unsaturated fatty acid, linoleic acid being the most represented (68.39%), followed by oleic acid (14.31%). The administration of this oil resulted in a significant reduction (p < .05) in the level of triglycerides, total cholesterol, and low‐density lipoprotein‐cholesterol in rats. The PE groups showed a significant increase (p < .05) in high‐density lipoprotein‐cholesterol compared with untreated male rats. A similar trend was observed with female rats for triglycerides, but lowest values were observed with olive oil at 1 ml. This study suggests that P. edulis seed oil is rich in linoleic acid, which might be responsible for its hypolipidemic effect comparable to that of olive oil.
Dynamic functioning of transient resting‐state coactivation networks in the Human Connectome Project
Authors: Janes Amy C. , Peechatka Alyssa L. , Frederick Blaise B. , Kaiser Roselinde H. 
Published: October 2019

AbstractResting‐state analyses evaluating large‐scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time‐varying or dynamic patterns of transient functional networks. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these “complex” DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex‐DMN states. Of note, dwell time was higher in “complex” DMN states, challenging the idea that the prototypical DMN, as a single unit, is the dominant resting‐state network as typically defined by static resting state methods. In comparing the two resting state scans, we also found high reliability in the spatial organization and dynamic activities of network states involving DMN or sensorimotor regions. Future work will determine whether these TNS defined by coactivation patterns are in other samples, and are linked to fundamental cognitive properties.
Functional network dynamics in a neurodevelopmental disorder of known genetic origin
Authors: Hawkins Erin , Akarca Danyal , Zhang Mengya , Brkić Diandra , Woolrich Mark , Baker Kate , Astle Duncan 
Published: October 2019

AbstractDynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9‐associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data‐driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting‐state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.