bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advances in Flexible and Wearable Energy‐Storage Textiles
Authors: Liu Zhuoxin , Mo Funian , Li Hongfei , Zhu Minshen , Wang Zifeng , Liang Guojin , Zhi Chunyi 
Published: July 2018

AbstractConsiderable attention has been drawn to flexible and wearable energy‐storage devices due to the blooming of portable and wearable electronics in recent years. However, huge challenges are yet to be addressed before the need of both high flexibility and high performance can be met. With many desired features for wearable applications, textiles have become a growing research frontier where scientific views from various fields collide, sparking new ideas. Here, recent research progress in energy‐storage textiles (ESTs), in which textiles are employed to enhance either electrochemical performance or flexibility and wearability, is summarized. The research of ESTs is mainly divided into three parts, with a focus on supercapacitors, lithium‐ion batteries (LIBs), and some other representative battery systems, respectively. Electrode preparation, cell assembly, device performance, and the remaining challenges are discussed in detail, aiming to provide insights for future development.
Automatic requirements extraction, analysis, and graph representation using an approach derived from computational linguistics
Authors: Mokammel Faisal , Coatanéa Eric , Coatanéa Joonas , Nenchev Vladislav , Blanco Eric , Pietola Matti 
Published: July 2018

AbstractThe quality of requirements is fundamental in engineering projects. Requirements are usually expressed partly or totally in a natural language (NL) format and come from different documents. Their qualities are difficult to analyze manually, especially when hundreds of thousands of them have to be considered. The assistance of software tools is becoming a necessity. In this article, the goal was to develop a set of metrics supported by NL processing (NLP) methods supporting different types of analysis of requirements and especially the dependencies between requirements. An NLP approach is used to extract requirements from text; to analyze their quality, links, similarities, and contradictions; and to cluster them automatically. The analysis framework includes different combinations of methods such as cosine similarity, singular value decomposition, and K‐means clustering. One objective is to assess the possible combinations and their impacts on detections to establish optimal metrics. Three case studies exemplify and support the validation of the work. Graphs are used to represent the automatically clustered requirements, as well as similarities and contradictions. A new contradiction analysis process that includes a rules‐based approach is proposed. Finally, the combined results are presented as graphs, which unveil the semantic relationships between requirements. Subsection 4.8 compares the results provided by the tool and the results obtained from experts. The proposed methodology and network presentation not only support the understanding of the semantics of the requirements but also help requirements engineers to review the interconnections and consistency of requirements systems and manage traceability. The approach is valuable during the early phases of projects when requirements are evolving dynamically and rapidly.
Geochemical properties and pollution assessment of heavy metals in the sediments of Daechung Lake, Korea
Authors: Yang Yun Mo , Chae Minhee , Cheon Seuk , Shim Moo Joon 
Published: July 2018

AbstractIn order to examine the forms, sources, and pollution of heavy metals—arsenic (As), aluminum (Al), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), and zinc (Zn)—in Daechung Lake, Korea, sediment samples were collected in November 2014. Daechung Lake was constructed to supply water for human consumption, agricultural use, and industrial use as well as to generate electric power. The lake is stratified in the summer and surrounded mostly by agricultural and mining areas. Our results indicate that the heavy metals (except As and Cd) displayed similar concentrations at all of the sampling stations. As and Cd were high in locations where fine sediments had built up. Based on the enrichment factor of the metals, the sediments collected from all of the sampling stations were highly polluted by As and Cd. Therefore, deposition of heavy metals in Daechung Lake is possibly controlled by grain size and anthropogenic activity, such as drainage from abandoned mines, agricultural activities, and/or the release of wastewater. The most dominant forms for all of the metals were oxide and silicate forms. This suggests that the sediments of Daechung Lake are not highly sulfidic. However, the sediment samples were collected after the collapse of seasonal stratification. Therefore, future studies should include elucidation of major sources for As and Cd and the collection of sediments during months of stratification.
An assets‐based approach to promoting girls' financial literacy, savings, and education
Authors: Clark Shelley , Paul Marianne , Aryeetey Richmond , Marquis Grace 
Published: July 2018

ABSTRACTPurposeThis study examined whether micro‐savings programs can improve young adolescent girls' financial knowledge, savings behaviors, and schooling outcomes in Ghana.MethodsWe evaluated the short‐ and medium‐term effects of a randomized control trial in which a sub‐sample of over 1400 girls living in the Eastern Region of Ghana received financial literacy training and a micro‐savings account.ResultsGirls in the intervention arm of the study initially exhibited higher levels of financial knowledge, planning, and savings, but some of these effects disappeared within two years. Nonetheless, girls with micro‐savings retained their greater knowledge of interest rates, had higher levels of savings, and were more likely to save for school. The effects on girls' educational enrollment was strongest in the second year.ConclusionsOur results suggest that even relatively young girls can manage micro‐savings accounts and that such programs, if sustained, can effectively build girls' financial and educational assets.
JC Virus Granule Cell Neuronopathy as AIDS-Presenting Illness
Authors: Grandjean Lapierre Simon , Dang Xin , Gilbert Danielle , Lauzier Sylvie , Koralnik Igor J. , LeBlanc Louiselle 
Published: July 2018

AbstractJC virus is the etiological agent of progressive multifocal leukoencephalopathy, a white matter demyelinating disease that mostly affects immunocompromised patients. JC virus can also infect neurons and meningeal cells and cause encephalitis, meningitis and granule cell neuronopathy. We report a patient with JC virus granule cell neuronopathy, without concomitant progressive multifocal leukoencephalopathy, presenting as inaugural acquired immune deficiency syndrome-related illness. This patient’s human immunodeficiency virus infection remained undiagnosed for several months after neurological symptoms onset. We review JC virus pathophysiology, clinical manifestations, treatment and prognosis, and emphasize the importance of considering human immunodeficiency virus infection and related opportunistic infections in the differential diagnosis of new-onset isolated cerebellar disease.
Experimental signal dissection and method sensitivity analyses reaffirm the potential of fossils and morphology in the resolution of the relationship of angiosperms and Gnetales
Authors: Coiro Mario , Chomicki Guillaume , Doyle James A. 
Published: July 2018

AbstractThe placement of angiosperms and Gnetales in seed plant phylogeny remains one of the most enigmatic problems in plant evolution, with morphological analyses (which have usually included fossils) and molecular analyses pointing to very distinct topologies. Almost all morphology-based phylogenies group angiosperms with Gnetales and certain extinct seed plant lineages, while most molecular phylogenies link Gnetales with conifers. In this study, we investigate the phylogenetic signal present in published seed plant morphological data sets. We use parsimony, Bayesian inference, and maximum-likelihood approaches, combined with a number of experiments with the data, to address the morphological–molecular conflict. First, we ask whether the lack of association of Gnetales with conifers in morphological analyses is due to an absence of signal or to the presence of competing signals, and second, we compare the performance of parsimony and model-based approaches with morphological data sets. Our results imply that the grouping of Gnetales and angiosperms is largely the result of long-branch attraction (LBA), consistent across a range of methodological approaches. Thus, there is a signal for the grouping of Gnetales with conifers in morphological matrices, but it was swamped by convergence between angiosperms and Gnetales, both situated on long branches. However, this effect becomes weaker in more recent analyses, as a result of addition and critical reassessment of characters. Even when a clade including angiosperms and Gnetales is still weakly supported by parsimony, model-based approaches favor a clade of Gnetales and conifers, presumably because they are more resistant to LBA. Inclusion of fossil taxa weakens rather than strengthens support for a relationship of angiosperms and Gnetales. Our analyses finally reconcile morphology with molecules in favoring a relationship of Gnetales to conifers, and show that morphology may therefore be useful in reconstructing other aspects of the phylogenetic history of the seed plants.
The Kinzers Formation (Pennsylvania, USA): the most diverse assemblage of Cambrian Stage 4 radiodonts
Authors: PATES STEPHEN , DALEY ALLISON C. 
Published: July 2018

AbstractRadiodonta, apex Cambrian predators such asAnomalocarishave been known from the Kinzers Formation (Cambrian Series 2, Stage 4 – Pennsylvania, USA) for nearly 100 years. Work over the last ten years, mainly on radiodont material from the Chengjiang (Cambrian Series 2, Stage 3 – Yunnan, China) and Burgess Shale (Miaolingian, Wuliuan – British Columbia, Canada), has greatly improved our knowledge of the diversity and disparity of radiodonts and their frontal appendages, including the description of new species, genera and families. Previous work identified two species of radiodonts from the Kinzers Formation:Anomalocaris pennsylvanicaResser, 1929 andAnomalocaris? cf.pennsylvanicabased on isolated frontal appendage material (Briggs, 1979). A restudy of Kinzers Formation material shows that only some of the specimens can be confirmed asAnomalocaris pennsylvanica, and a number of specimens previously attributed toAnomalocarisin fact belong to other more recently discovered radiodont generaAmplectobeluaandTamisiocaris. This reinterpretation makes the Kinzers Formation the most diverse Cambrian Stage 4 Burgess Shale Type Lagerstätten in terms of number of radiodont species. This assemblage includes the youngest knownTamisiocarisand the first from outside Greenland, the onlyAmplectobeluafrom Stage 4 and the oldest from Laurentia, two specimens tentatively assigned to the recently described Chengjiang genusLaminacaris, and the endemicAnomalocaris pennsylvanica. The identification of these new radiodont taxa increases the total known diversity of the Kinzers Formation to more than ten species, and so it should now be considered a Tier 2 Lagerstätte.
Design and implementation of a millirobot for swarm studies –mROBerTO
Authors: Kim Justin Y. , Kashino Zendai , Colaco Tyler , Nejat Goldie , Benhabib Beno 
Published: July 2018

SUMMARYThe use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. Themilli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2footprint.mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.Extensive experiments, some of which are presented herein, have illustrated the capability ofmROBerTOunits for use in implementing a variety of commonly proposed swarm algorithms.
Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends
Authors: Morsy S. , Dang T.N. , Kamel M.G. , Zayan A.H. , Makram O.M. , Elhady M. , Hirayama K. , Huy N.T. 
Published: July 2018

AbstractZika virus infection in humans has been linked to severe neurological sequels and foetal malformations. The rapidly evolving epidemics and serious complications made the frequent updates of Zika virus mandatory. Web search query has emerged as a low-cost real-time surveillance system to anticipate infectious diseases’ outbreaks. Hence, we developed a prediction model that could predict Zika-confirmed cases based on Zika search volume in Google Trends. We extracted weekly confirmed Zika cases of two epidemic countries, Brazil and Colombia. We got the weekly Zika search volume in the two countries from Google Trends. We used standard time-series regression (TSR) to predict the weekly confirmed Zika cases based on the Zika search volume (Zika query). The basis TSR model – using 1-week lag of Zika query and using 1-week lag of Zika cases as a control for autocorrelation – was the best for predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time in the prediction. Our results showed that we could use Google search queries to predict Zika cases 1 week earlier before the outbreak. These findings are important to help healthcare authorities evaluate the outbreak and take necessary precautions.
Is psychotherapy effective? A re-analysis of treatments for depression
Authors: Munder T. , Flückiger C. , Leichsenring F. , Abbass A. A. , Hilsenroth M. J. , Luyten P. , Rabung S. , Steinert C. , Wampold B. E. 
Published: July 2018

AbstractAimsThe aim of this study was to reanalyse the data from Cuijpers et al.'s (2018) meta-analysis, to examine Eysenck's claim that psychotherapy is not effective. Cuijpers et al., after correcting for bias, concluded that the effect of psychotherapy for depression was small (standardised mean difference, SMD, between 0.20 and 0.30), providing evidence that psychotherapy is not as effective as generally accepted.MethodsThe data for this study were the effect sizes included in Cuijpers et al. (2018). We removed outliers from the data set of effects, corrected for publication bias and segregated psychotherapy from other interventions. In our study, we considered wait-list (WL) controls as the most appropriate estimate of the natural history of depression without intervention.ResultsThe SMD for all interventions and for psychotherapy compared to WL controls was approximately 0.70, a value consistent with past estimates of the effectiveness of psychotherapy. Psychotherapy was also more effective than care-as-usual (SMD = 0.31) and other control groups (SMD = 0.43).ConclusionsThe re-analysis reveals that psychotherapy for adult patients diagnosed with depression is effective.
The politics of anticipation: the IPCC and the negative emissions technologies experience
Authors: Beck Silke , Mahony Martin 
Published: July 2018

Non-technical summaryIn the post-Paris political landscape, the relationship between science and politics is changing. We discuss what this means for the Intergovernmental Panel on Climate Change (IPCC), using recent controversies over negative emissions technologies (NETs) as a window into the fraught politics of producing policy-relevant pathways and scenarios. We suggest that pathways and scenarios have a ‘world-making’ power, potentially shaping the world in their own image and creating new political realities. Assessment bodies like the IPCC need to reflect on this power, and the implications of changing political contexts, in new ways.
Perceived Attainment of Developmental Tasks in Adolescents With and Without Mental Disorders
Authors: Pinquart Martin , Pfeiffer Jens P. , Becker Katja 
Published: July 2018

Abstract. The present study compared the perceived attainment of developmental tasks of 220 adolescents diagnosed with anxiety disorder, attention-deficit / hyperactivity disorder, conduct disorder, or depression with a matched sample of 220 peers without a mental illness. The clinical sample perceived lower success with solving personal, social, and socio-institutional developmental tasks compared with their healthy peers. Comparisons across diagnoses identified the largest differences between students with internalizing disorders (anxiety, depression) versus other disorders. We conclude that psychological interventions with adolescents with mental disorders should promote their attainment of age-typical developmental goals.
Cloud Optical Properties Over West Antarctica From Shortwave Spectroradiometer Measurements During AWARE
Authors: Wilson A. , Scott R. C. , Cadeddu M. P. , Ghate V. , Lubin D. 
Published: July 2018

AbstractA shortwave spectroradiometer was deployed on the West Antarctic Ice Sheet (WAIS) as part of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program ARM West Antarctic Radiation Experiment (AWARE). This instrument recorded 1‐min averages of downwelling hemispheric spectral irradiance covering the wavelength range 350–2,200 nm with spectral resolution 3 and 10 nm for wavelengths shorter and longer than 1,000 nm, respectively. Using simultaneous micropulse lidar data to identify the thermodynamic phase of stratiform clouds, a radiative transfer algorithm is used to retrieve optical depth and effective droplet (or particle) size for single‐phase liquid water and ice water clouds. The AWARE campaign on the WAIS first sampled typical climatological conditions between 7 December 2015 and 9 January 2016 and then a much warmer air mass with more moisture associated with a surface melt event between 10 and 17 January 2016. Before the melt event most liquid cloud effective droplet radii were consistent with pristine polar maritime clouds (mode radius 13.5 μm) but showed a second local maximum in the distribution (at 8 μm) consistent with colder, moisture‐limited conditions. Most ice clouds sampled occurred before the melt event (mode optical depth 4 and effective particle size 19 μm). During the melt event liquid water cloud optical depth nearly doubled (mode value increasing from 8 to 14). AWARE therefore sampled on the WAIS two cases relevant to climate model simulations: typical current climatological conditions, followed by warmer meteorology possibly consistent with future increasing surface melt scenarios.
Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)
Authors: Sakaguchi K. , Leung L. R. , Burleyson C. D. , Xiao H. , Wan H. 
Published: July 2018

AbstractThe Amazon forest plays an important role in the tropical circulation and global water and biogeochemical cycles. However, the common Amazon precipitation biases in global climate models undermine our ability to understand future changes in the Amazon and their global consequences. The Community Earth System Model exhibits a persistent wet season dry bias over the southwestern Amazon. The bias is also characterized by inaccurate diurnal timing and intensity of rainfall events. Analysis of a diverse set of simulations suggests that the bias originates from the land model and/or deep convection parameterization. A known land model bias in the partitioning of surface energy fluxes is found not to contribute significantly to the rainfall bias. Key model processes are identified to be the low‐level divergent circulation controlling horizontal moisture flow and the sensitivity of the modeled deep convection to the lower tropospheric moisture. The lower tropospheric moisture divergence dictates the spatiotemporal distributions of convective precipitation through parameterized entrainment mixing. While the entrainment effect improves the rainfall diurnal cycle, it suppresses convective precipitation under low‐level moisture divergence. Persistent moisture divergence is found over the southwestern Amazon and limits parameterized convective precipitation, particularly in the late afternoon, although uncertainty exists in the low‐level flow in the simulation and reanalyses. One way to sustain rainfall in the presence of low‐level divergence is a convective trigger based on both the moisture and temperature changes in the free troposphere, which is shown to help in reducing the dry bias.
Turbulence simultaneously stimulates small- and large-scale CO2 sequestration by chain-forming diatoms in the sea
Authors: Bergkvist Johanna , Klawonn Isabell , Whitehouse Martin J. , Lavik Gaute , Brüchert Volker , Ploug Helle 
Published: July 2018

AbstractChain-forming diatoms are key CO2-fixing organisms in the ocean. Under turbulent conditions they form fast-sinking aggregates that are exported from the upper sunlit ocean to the ocean interior. A decade-old paradigm states that primary production in chain-forming diatoms is stimulated by turbulence. Yet, direct measurements of cell-specific primary production in individual field populations of chain-forming diatoms are poorly documented. Here we measured cell-specific carbon, nitrate and ammonium assimilation in two field populations of chain-forming diatoms (Skeletonema and Chaetoceros) at low-nutrient concentrations under still conditions and turbulent shear using secondary ion mass spectrometry combined with stable isotopic tracers and compared our data with those predicted by mass transfer theory. Turbulent shear significantly increases cell-specific C assimilation compared to still conditions in the cells/chains that also form fast-sinking, aggregates rich in carbon and ammonium. Thus, turbulence simultaneously stimulates small-scale biological CO2 assimilation and large-scale biogeochemical C and N cycles in the ocean.