bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pyrolysis of bamboo over Ce/Fe composite metal oxide catalyst to enhance the production of hydrocarbons and ketonic hydrocarbon precursors
Authors: Du Haoran , Zhong Zhaoping , Zhang Bo , Fu Jimin , Li Zhaoying 
Published: December 2020

AbstractTo obtain high‐value bio‐oil, pure ceria (CeO2) and a series of Ce/Fe composite metal oxides were synthesized via precipitation method and were used to enhance hydrocarbons and ketones production during catalytic pyrolysis of bamboo sawdust. The characterization results were comprehensively analyzed and revealed that doping CeO2 with Fe promoted the formation of a solid solution structure, which further increased the surface area and number of oxygen vacancies of the catalyst for deoxygenation. Experimental consequences demonstrated that, compared to non‐catalytic trial, the catalytic pyrolysis over CeO2 generated lower amounts of acids and aldehydes, and enhanced the conversion of large oxygenates to monofunctional hydrocarbon precursors via decarboxylation, deoxidation, and ketonization. The concentrations of hydrocarbons and ketones obtained over Ce/Fe catalysts were significantly higher than those obtained over CeO2, and that was attributed to the higher surface area and oxygen storage capacity of Ce/Fe catalysts. Particularly, the composite catalyst with the Ce/Fe molar ratio of 4 (Ce0.8Fe0.2) presented the most optimal deoxidation capacity in this study. The relative concentration of hydrocarbons generated over Ce0.8Fe0.2 was the highest, and monocyclic aromatics and short‐chain aliphatic hydrocarbons accounted for 47.13% and 29.72%, respectively, of the total hydrocarbons. Simultaneously, the amount of ketones, the main hydrocarbon precursors, obtained over Ce0.8Fe0.2 was significantly higher than that obtained over CeO2, and the fraction of linear and cyclic ketones of the total ketones increased from 45.96% for the non‐catalytic pyrolysis to 97.57%. This further confirmed that the mesoporous Ce/Fe composite catalysts efficiently catalyzed the aldol condensation and ketonization reactions.
Pyrolysis of Miscanthus and characterization of value‐added bio‐oil and biochar products
Authors: Singh Arshdeep , Nanda Sonil , Guayaquil‐Sosa Jesus Fabricio , Berruti Franco 
Published: December 2020

AbstractMiscanthus, an invasive crop, has recently gained attention as an emerging energy crop because of certain traits like fast growth, high yield, ability to grow in marginal land, and resistance to extreme weather conditions. In this work, Miscanthus was selected as the feedstock for fast pyrolysis in a mechanically fluidized bed reactor at variable temperatures (400°C, 450°C, and 500°C) and vapour residence times (1.4, 2.7, and 5.2 seconds). Fast pyrolysis performed at 450°C with 1.4 seconds of vapour residence time gave the highest yield of bio‐oil (>50 wt%). Biochar obtained at different pyrolysis temperatures was activated at 900°C for 1.5 hours under CO2 atmosphere to enhance its value as a potential adsorption agent for pollutants. Several physicochemical characterization techniques were used to study the bio‐oils, biochars, and activated biochars obtained from the pyrolysis of Miscanthus. The absorption of methylene blue as a model dye was done to evaluate the performance of activated biochar vs the biochar precursors. Both pyrolysis and physical activation complemented each other as new technologies for energy extraction and material synthesis from Miscanthus.
Rational Design of Bimetallic Au/Cu Nanostructure: An Efficient Catalyst for Methanol Oxidation
Authors: Biswas Rathindranath , Singh Simranjeet , Ahmed Imtiaz , Patil Ranjit A. , Ma Yuan‐Ron , Haldar Krishna Kanta 
Published: December 2020

AbstractWe show the design of noble Au/Cu bimetallic nanostructures by a gentle solution process. Transmission electron microscopy (TEM), X‐ray diffraction (XRD), and X‐ray photoelectron spectroscopy (XPS) analysis confirm the presence of both Au and Cu in the Au/Cu bimetallic nanostructures. The Au/Cu catalyst was explored as an efficient electrocatalyst for methanol oxidation with a current density of 5.71 mA/cm2 at 0.46 V.
Illumination correction via support vector regression based on improved whale optimization algorithm
Authors: Wang Chao , Zhu Zefei , Chen Sheng , Yang Junyi 
Published: December 2020

AbstractVariations in illumination lead to serious errors in the evaluation of chromatic aberration. We propose an illumination correction model based on the opposition‐based learning improved whale optimization algorithm for support vector regression optimization, named "OBL‐IWOA‐SVR." Because the initial population quality of the whale optimization algorithm has a significant impact on the solution speed and accuracy, the Opposition‐based learning strategy is adopted in this article to mix the original population and its opposite individuals and select the best as the new population, replacing the random initialization to generate a more suitable initial population. This increases the diversity of the population and thus overcomes the impact of the quality of the initial population on the algorithm performance. Secondly, the proposed algorithm adopts the adaptive weight and the convergence factor based on the variation of the cosine law to balance the algorithm's global exploration ability and local development ability and enhance the convergence accuracy. Finally, the algorithm utilizes good global searching ability to optimize the penalty factor and nuclear parameters and obtains the optimal support vector machine parameter combination to construct the illumination correction model OBL‐IWOA‐SVR accurately and robustly. Experimental results show that the illumination correction model proposed in this article is found superior to other models in significance analysis: the root mean square error of the proposed model is 0.0173, the smallest of all illumination correction models. Furthermore, the model exhibits good stability and high illumination estimation accuracy.
Dyed fabric illumination estimation with regularized random vector function link network
Authors: Zhou Zhiyu , Liu Dexin , Guo Jiushen , Zhang Jianxin , Zhu Zefei , Wang Chao 
Published: December 2020

AbstractThe unstable light source in the printing and dyeing environment would cause the change of fabric surface color and lead to serious color difference evaluation error. To solve this problem, a dyed fabric illumination estimation with regularized random vector functional link network (RRVFL) was proposed in this study. First, the Gray‐Edge framework is used to extract the color features of the sample images collected from the actual scene, and the extracted color features and the illumination information of the sample images constitute a data set. Then, considering the ill‐conditioned solution of the output weight of the traditional random vector functional link network (RVFL), regularization is proposed to solve this problem. Thus, we constitute a highly robust RRVFL dyed fabric illumination model. By analyzing the parameters that affect the precision of RRVFL model, the optimal parameters of RRVFL can be selected. Finally, the traditional RVFL, extreme learning machine (ELM), back‐propagation (BP), regularized extreme learning machine (RELM), support vector regression (SVR), and RRVFL estimation algorithm proposed in this study were compared and analyzed using the measurement standards of angle error, colorimetric error, and T‐test, respectively. The experimental results show that RRVFL has the best predictive results and the most stable performance compared with the traditional algorithm. RRVFL is 0.00036, 2.8050, 3.3518, 4.1669, and 2.9289 less than RVFL, ELM, BP, RELM, and SVR, respectively, in terms of average angle error. RRVFL is 0.0131, 0.0763, 0.0232, 0.0241, and 0.0221 lower than RVFL, ELM, BP, RELM, and SVR, respectively, in the average colorimetric error.
Three‐Color Imaging Enables Simultaneous Screening of Multiple RNA Targets on Small Molecule Microarrays
Authors: Jordan Deondre , Yang Mo , Schneekloth John S. 
Published: December 2020

AbstractSmall molecule microarray (SMM) technology has become a powerful tool used in high‐throughput screening for target‐based drug discovery. One area in which SMMs have found use is the identification of small molecule ligands for RNA. RNAs with unique secondary or tertiary three‐dimensional structures are considered to be attractive targets for small molecules. Complex RNA structures can form hydrophobic pockets suitable for small molecule binding, representing an opportunity for developing novel therapeutics. Our lab has previously taken a target‐based approach, screening a single target against many small molecules on an SMM platform. Here, we report a screening protocol for SMMs to investigate multiple RNAs simultaneously using multi‐color imaging. By introducing a mixture containing different fluorophore‐labeled RNAs, the fluorescence signal of each binding event can be observed simultaneously. Thus, the specificity of a hit compound binding to one RNA target over other highly abundant RNAs (such as tRNA or rRNA) can be easily evaluated. © 2020 Wiley Periodicals LLC.Basic Protocol: RNA screening on SMMs by multi‐color imagingSupport Protocol 1: Preparation of SMM slidesSupport Protocol 2: Fluorophore labeling of RNA through maleimide chemistry
Visualizing RNA Cytidine Acetyltransferase Activity by Northern Blotting
Authors: Bryson Keri M. , Thalalla‐Gamage Supuni , Meier Jordan L. 
Published: December 2020

AbstractCytidine acetyltransferases are an emerging class of nucleic‐acid‐modifying enzymes responsible for the establishment of N4‐acetylcytidine (ac4C) in RNA. In contrast to histone acetyltransferases, whose activity is commonly studied by western blotting, relatively few methods exist for quickly assessing the activity of cytidine acetyltransferases from a biological sample of interest or the distribution of ac4C across different RNA species. In this protocol, we describe a method for analysis of cellular cytidine acetyltransferase activity using dot‐ and immuno‐northern‐blotting‐based detection. © 2020 Wiley Periodicals LLC.Basic Protocol 1: Detection of N4‐Acetylcytidine in RNA by dot blottingBasic Protocol 2: Visualizing N4‐Acetylcytidine Distribution in RNA by northern blotting
Open OnDemand: State of the platform, project, and the future
Authors: Chalker Alan , Franz Eric , Rodgers Morgan , Dockendorf Trey , Johnson Doug , Sajdak Doris , White Joseph P. , Plessinger Benjamin D. , Zia Mohammad , Gallo Steven M. , Settlage Robert E. , Hudak David E. 
Published: December 2020

SummaryHigh performance computing (HPC) has led to remarkable advances in science and engineering and has become an indispensable tool for research. Unfortunately, HPC use and adoption by many researchers is often hindered by the complex way these resources are accessed. Indeed, while the web has become the dominant access mechanism for remote computing services in virtually every computing area, HPC is a notable exception. Open OnDemand is an open source project negating this trend by providing web‐based access to HPC resources (
https://openondemand.org). This article describes the challenges to adoption and other lessons learned over the 3‐year project that may be relevant to other science gateway projects. We end with a description of future plans the project team has during the Open OnDemand 2.0 project including specific developments in machine learning and GPU monitoring.
Cu2ZnSnS4 Thin Film Fabricated by the Calcinated Nanocrystals
Authors: Chen Jin , Sun Yu , Ju Jiaqi , Wang Fengchao , Jin Yan , Zhang Canyun , Kong Jinfang , Peng Xiaogai , Wang Chenfei , Dong Hengxing , Chen Qinmiao , Dou Xiaoming 
Published: December 2020

AbstractIn the present work, Cu2ZnSnS4 (CZTS) thin film is successfully prepared by the calcinated nanocrystals with screen‐printing approach. The dependences of the obtained samples’ properties on the annealing parameters of temperature and time are well studied via X‐ray diffraction, Raman, field emission scanning electron microscopy, ultraviolet‐visible‐infrared (UV‐vis‐IR) spectroscopy, energy‐dispersive X‐ray spectroscopy, and X‐ray photoelectron spectroscopy treatments. The results suggested that the best performances of prepared sample can be obtained at 600 °C for 20 min. The obtained sample features a kesterite CZTS phase, a surface morphology consisting of microns nanoparticles, a Cu‐rich and Zn‐rich chemical composition, and an ideal band gap of 1.4 eV. Additionally, the impurity phase of copper sulfide resulting from Cu‐rich composition is needed to further improve in the next work.
Analysis of security and reliability in cognitive radio networks over Rayleigh channels
Authors: Sun Xiyuan , Xie Zhibin , Wang Yajun , Weng Zhihui , Zhang Zhenkai 
Published: December 2020

SUMMARYIncreasing the transmitting power will improve the system performance, but it will also increase the potential intercepted risk. Thus, the research of security‐reliability trade‐off (SRT) is valuable. In this paper, we investigate the physical layer security in cognitive radio network (CRN), which consists of a source node, a destination node, an eavesdropper, and multiple relays. The security and reliability are measured by intercept probability (IP) and outage probability (OP), respectively. Different from the previous researches, a more actual scenario is studied for the CRN in the Rayleigh fading environment; that is, the eavesdropper can eavesdrop on the secondary source (SS) and the relays. Besides, we consider the probability of false alarm and missing alarm and study their impact on the OP and IP for direct transmission (DT) and relay selective transmission (RST). The closed‐form expressions of OP and IP show that the false alarm affects the OP and the missing alarm has influence on the OP and IP. Furthermore, we accumulate the sum of the OPs for the RST in the two time slots and compare them with the DT, when we explore the OP. In addition, since the signals may be both intercepted in the two stages of RST, we analyze the IPs in the two time slots respectively and compare them with the DT separately. The simulation results show that the RST scheme can greatly improve the security and reliability by increasing the number of relays compared with the DT scheme.
Neuronal Hippo signaling: From development to diseases
Authors: Sahu Manas Ranjan , Mondal Amal Chandra 
Published: December 2020

AbstractHippo signaling pathway is a highly conserved and familiar tissue growth regulator, primarily dealing with cell survival, cell proliferation, and apoptosis. The Yes‐associated protein (YAP) is the key transcriptional effector molecule, which is under negative regulation of the Hippo pathway. Wealth of studies have identified crucial roles of Hippo/YAP signaling pathway during the process of development, including the development of neuronal system. We provide here, an overview of the contributions of this signaling pathway at multiple stages of neuronal development including, proliferation of neural stem cells (NSCs), migration of NSCs toward their destined niche, maintaining NSCs in the quiescent state, differentiation of NSCs into neurons, neuritogenesis, synaptogenesis, brain development, and in neuronal apoptosis. Hyperactivation of the neuronal Hippo pathway can also lead to a variety of devastating neurodegenerative diseases. Instances of aberrant Hippo pathway leading to neurodegenerative diseases along with the approaches utilizing this pathway as molecular targets for therapeutics has been highlighted in this review. Recent evidences suggesting neuronal repair and regenerative potential of this pathway has also been pointed out, that will shed light on a novel aspect of Hippo pathway in regenerative medicine. Our review provides a better understanding of the significance of Hippo pathway in the journey of neuronal system from development to diseases as a whole.
Comparison of environmental inference approaches for ecometric analyses: Using hypsodonty to estimate precipitation
Authors: Short Rachel A. , Pinson Katherine , Lawing A. Michelle 
Published: December 2020

Abstract

Ecometrics is the study of community‐level functional trait–environment relationships. We use ecometric analyses to estimate paleoenvironment and to investigate community‐level functional changes through time.

We evaluate four methods that have been used or have the potential to be used in ecometric analyses for estimating paleoenvironment to determine whether there have been systematic differences in paleoenvironmental estimation due to choice of the estimation method. Specifically, we evaluated linear regression, polynomial regression, nearest neighbor, and maximum‐likelihood methods to explore the predictive ability of the relationship for a well‐known ecometric dataset of mammalian herbivore hypsodonty metrics (molar tooth crown to root height ratio) and annual precipitation. Each method was applied to 43 Pleistocene fossil sites and compared to annual precipitation from global climate models. Sites were categorized as glacial or interglacial, and paleoprecipitation estimates were compared to the appropriate model.

Estimation methods produce results that are highly correlated with log precipitation and estimates from the other methods (p < 0.001). Differences between estimated precipitation and observed precipitation are not significantly different across the four methods, but maximum likelihood produces the most accurate estimates of precipitation. When applied to paleontological sites, paleoprecipitation estimates align more closely with glacial global climate models than with interglacial models regardless of the age of the site.

Each method has constraints that are important to consider when designing ecometric analyses to avoid misinterpretations when ecometric relationships are applied to the paleontological record. We show interglacial fauna estimates of paleoprecipitation more closely match glacial global climate models. This is likely because of the anthropogenic effects on community reassembly in the Holocene.
Autopolyploidy‐driven range expansion of a temperate‐originated plant to pan‐tropic under global change
Authors: Cheng Jiliang , Li Jun , Zhang Zheng , Lu Huan , Chen Guoqi , Yao Beibei , Dong Yingxue , Ma Ling , Yuan Xiaoxiao , Xu Jingxuan , Zhang Ying , Dai Weimin , Yang Xianghong , Xue Lifang , Zhang Yu , Zhang Chaobin , Mauricio Rodney , Peng Gary , Hu Shuijin , Valverde Bernal E. , Song Xiaoling , Li Yi , Stift Marc , Qiang Sheng 
Published: December 2020

AbstractAngiosperms are believed to have emerged initially in the tropics and expanded their distribution range poleward through diverse mechanisms, for example polyploidization‐driven cold tolerance evolution. Reversed expansion from temperate to pan‐tropic climates through a polyploidization‐driven shift in heat tolerance remains largely unknown. Here, we found autopolyploidy in relation to the global expansion of Solidago canadensis from its temperate‐climate native range in North American to hot‐summer climate in an introduced range. Our cytogeographical study of 2,062 accessions from 471 locations worldwide demonstrates that ploidy levels correlate negatively with latitude and positively with average temperature. An isotherm‐dependent shift of the climate niches at the threshold of 20°–24°C between geo‐cytotypes can be attributed mainly to autopolyploidy‐driven differentiation of heat tolerance; only polyploids and not diploids are able to complete sexual reproduction, germinate, and grow in the hot‐summer climate of low latitudes. Ploidy‐dependent fertility appears to play a key role in the hot‐summer introduced range in the northern hemisphere through both pre‐adaptation and rapid post‐introduction adaptive evolution of delayed flowering and improved heat tolerance during embryo development. The MaxEnt model predicts continued expansion of this plant species under global change. These results provide new insights into the mechanisms governing autopolyploidy‐driven backward range expansion of plant species from temperate origins.
Messenger RNA typing of environmental RNA (eRNA): A case study on zebrafish tank water with perspectives for the future development of eRNA analysis on aquatic vertebrates
Authors: Tsuri Kenji , Ikeda Shizuya , Hirohara Takaya , Shimada Yasuhito , Minamoto Toshifumi , Yamanaka Hiroki 
Published: December 2020

AbstractAs an alternative/supplement to conventional biodiversity survey methods, environmental DNA (eDNA) analysis has developed rapidly during the past decade, and is widely used for the biomonitoring of wildlife. However, the interpretation of eDNA results has been limited to the presence/absence or biomass/abundance of the target species because of uncertainty regarding the dynamics of eDNA in natural environments. This limitation may be ameliorated by targeting environmental RNA (eRNA). RNA is more prone to degrade than DNA and the pattern of messenger RNA (mRNA) expression changes depending on physiological conditions, meaning that the presence or concentration of mRNA could reflect the organism's presence with higher temporal resolution and provide information beyond simple presence/absence. Technical developments in the detection of eRNA focusing on mRNA with these distinct features could permit the advanced usage of genetic materials in water. In advancing this technique, we initiated this study asking that if we can detect elevated levels of eRNA whose genes are specific to a tissue source (e.g., gills or skin), then could not we infer the tissue origin of the genetic material detected. To this end, we developed gene‐specific primer sets for the target genes with biased expression in the gills, skin, and intestine, and conducted reverse transcription–polymerase chain reaction on zebrafish breeding tank water samples, obtaining positive results for all assays. The result of our experiment confirmed that the specific target tissues can be the source of genetic materials detected in water and with that we offer a proof of concept for eRNA analysis targeting specific mRNAs of aquatic vertebrates. In this commentary, we provide information on the experimental steps used for mRNA typing of eRNA from zebrafish as well as the limitations and challenges of this technique and the prospect of mRNA typing of eRNA in the future.
Energy efficient resource allocation for uplink hybrid power domain sparse code nonorthogonal multiple access heterogeneous networks with statistical channel estimation
Authors: Chege Simon , Walingo Tom 
Published: December 2020

AbstractModern 5G heterogeneous networks (HetNets) require hybrid multiple access technology for optimal performance. The feasibility of a hybrid power domain sparse code nonorthogonal multiple access (PD‐SCMA) that integrates both power domain nonorthogonal multiple access (PD‐NOMA) and sparse code multiple access (SCMA) for an uplink hierarchical HetNet system is demonstrated. Hybrid schemes namely: Successive Codebook Ordering Assignment (SCOA) for codebook assignment (CA), opportunistic MUE‐SUE pairing (OMSP) for user pairing (UP), and a QoS‐aware power allocation (QAPA) for power allocation (PA) are developed. The SCOA algorithm is based on channel quality ordering metric, OMSP algorithm is based on channel quality diversity and pairing interference metric while the QAPA algorithm features a QoS awareness metric. A joint energy efficiency (EE) resource allocation (JEERA) algorithm that iteratively performs CA, UP, and PA for small cell user equipment (SUE) and the macro user equipment (MUE) to limit interference, improve spectral and energy efficiency is presented. The problem is formulated as a mixed integer nonconvex system EE resource allocation optimization for the small cells under QoS constraints of minimum sum‐rate, interference temperature, maximum power, and SCMA structure for a hybrid low complexity joint SIC‐Log‐MPA receiver. A modified near‐optimal dual decomposition analytical methodology featuring Dinkelbach fractional transformations is utilized to assess the system's performance on an imperfect wireless channel. Through numerical results, the proposed schemes are shown to improve the EE of the small cells in comparison with the prevalent schemes.