bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Functional Performance of Paving Materials Based on the Driving Wheel Pavement Analyzer
Authors: Cai Xu , Wang Duanyi , Yu Jiangmiao
Published: April 2020

There is still a lack of suitable methods for evaluating pavement functional performance according to the characteristics of real-world environment and traffic. This study developed an acceleration test method based on the Driving Wheel Pavement Analyser (DWPA) to evaluate the anti-sliding properties, anti-stripping properties, and tire–pavement noise of AC-13, SMA-13, and OGFC-13 asphalt mixtures, and MS-1, MS-2, and MS-3 micro-surfacing materials. The results indicate that the OGFC-13 mixture exhibited the largest texture depth, and the SMA-13 mixture exhibited the largest British pendulum number (BPN) at the end of the test. The MS-3 material had the best anti-sliding performance among the micro-surfacing materials. Coarse gradation improved the anti-stripping performance of the micro-surfacing materials. The tire–pavement noise for all materials increased with the increase of wheel repetitions. The OGFC-13 mixture and MS-3 micro-surfacing material exhibited the best and worse noise reduction performance, respectively. The new measurement method for evaluating the pavement surface functional performance was proved to be efficient.
Asymptotic Performance Analysis of Maximum Likelihood Algorithm for Direction-of-Arrival Estimation: Explicit Expression of Estimation Error and Mean Square Error
Authors: Paik Ji Woong , Lee Kyu-Ho , Lee Joon-Ho
Published: April 2020

This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results.
UV-B Radiation Largely Promoted the Transformation of Primary Metabolites to Phenols in Astragalus mongholicus Seedlings
Authors: Liu Yang , Liu Jia , Abozeid Ann , Wu Ke-Xin , Guo Xiao-Rui , Mu Li-Qiang , Tang Zhong-Hua
Published: April 2020

Ultraviolet-B (UV-B) radiation (280–320 nm) may induce photobiological stress in plants, activate the plant defense system, and induce changes of metabolites. In our previous work, we found that between the two Astragalus varieties prescribed by the Chinese Pharmacopoeia, Astragalus mongholicus has better tolerance to UV-B. Thus, it is necessary to study the metabolic strategy of Astragalus under UV-B radiation further. In the present study, we used untargeted gas chromatography-mass spectrometry (GC-MS) and targeted liquid chromatography-mass spectrometry (LC-MS techniques) to investigate the profiles of primary and secondary metabolic. The profiles revealed the metabolic response of Astragalus to UV-B radiation. We then used real-time polymerase chain reaction (RT-PCR) to obtain the transcription level of relevant genes under UV-B radiation (UV-B supplemented in the field, λmax = 313 nm, 30 W, lamp-leaf distance = 60 cm, 40 min·day−1), which annotated the responsive mechanism of phenolic metabolism in roots. Our results indicated that supplemental UV-B radiation induced a stronger shift from carbon assimilation to carbon accumulation. The flux through the phenylpropanoids pathway increased due to the mobilization of carbon reserves. The response of metabolism was observed to be significantly tissue-specific upon the UV-B radiation treatment. Among phenolic compounds, C6C1 carbon compounds (phenolic acids in leaves) and C6C3C6 carbon compounds (flavones in leaves and isoflavones in roots) increased at the expense of C6C3 carbon compounds. Verification experiments show that the response of phenolics in roots to UV-B is activated by upregulation of relevant genes rather than phenylalanine. Overall, this study reveals the tissues-specific alteration and mechanism of primary and secondary metabolic strategy in response to UV-B radiation.
Octominin Inhibits LPS-Induced Chemokine and Pro-inflammatory Cytokine Secretion from RAW 264.7 Macrophages via Blocking TLRs/NF-κB Signal Transduction
Authors: Sanjeewa K. K. Asanka , Nagahawatta D. P. , Yang Hye-Won , Oh Jae Young , Jayawardena Thilina U. , Jeon You-Jin , De Zoysa Mahanama , Whang Ilson , Ryu Bomi
Published: April 2020

Inflammation is a well-organized innate immune response that plays an important role during the pathogen attacks and mechanical injuries. The Toll-like receptors (TLR)/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is a major signal transduction pathway observed in RAW 264.7 macrophages during the inflammatory responses. Here, we investigated the anti-inflammatory effects of Octominin; a bio-active peptide developed from Octopus minor in RAW 264.7 macrophages in vitro. Octominin was found to inhibit lipopolysaccharides (LPS)-stimulated transcriptional activation of NF-κB in RAW 264.7 cells and dose-dependently decreased the mRNA expression levels of TLR4. Specifically, in silico docking results demonstrated that Octominin has a potential to inhibit TLR4 mediated inflammatory responses via blocking formation of TLR4/MD-2/LPS complex. We also demonstrated that Octominin could significantly inhibit LPS-induced secretion of pro-inflammatory cytokine (interleukin-β; IL-1β, IL-6, and tumor necrosis factor-α) and chemokines (CCL3, CCL4, CCL5, and CXCL10) from RAW 264.7 cells. Additionally, Octominin repressed the LPS-induced pro-inflammatory mediators including nitric oxide (NO), prostaglandin E2, inducible NO synthase, and cyclooxygenase 2 in macrophages. These results suggest that Octominin is a potential inhibitor of TLRs/NF-κB signal transduction pathway and is a potential candidate for the treatment of inflammatory diseases.
A Novel NAD-RNA Decapping Pathway Discovered by Synthetic Light-Up NAD-RNAs
Authors: Abele Florian , Höfer Katharina , Bernhard Patrick , Grawenhoff Julia , Seidel Maximilian , Krause André , Kopf Sara , Schröter Martin , Jäschke Andres
Published: April 2020

The complexity of the transcriptome is governed by the intricate interplay of transcription, RNA processing, translocation, and decay. In eukaryotes, the removal of the 5’-RNA cap is essential for the initiation of RNA degradation. In addition to the canonical 5’-N7-methyl guanosine cap in eukaryotes, the ubiquitous redox cofactor nicotinamide adenine dinucleotide (NAD) was identified as a new 5’-RNA cap structure in prokaryotic and eukaryotic organisms. So far, two classes of NAD-RNA decapping enzymes have been identified, namely Nudix enzymes that liberate nicotinamide mononucleotide (NMN) and DXO-enzymes that remove the entire NAD cap. Herein, we introduce 8-(furan-2-yl)-substituted NAD-capped-RNA (FurNAD-RNA) as a new research tool for the identification and characterization of novel NAD-RNA decapping enzymes. These compounds are found to be suitable for various enzymatic reactions that result in the release of a fluorescence quencher, either nicotinamide (NAM) or nicotinamide mononucleotide (NMN), from the RNA which causes a fluorescence turn-on. FurNAD-RNAs allow for real-time quantification of decapping activity, parallelization, high-throughput screening and identification of novel decapping enzymes in vitro. Using FurNAD-RNAs, we discovered that the eukaryotic glycohydrolase CD38 processes NAD-capped RNA in vitro into ADP-ribose-modified-RNA and nicotinamide and therefore might act as a decapping enzyme in vivo. The existence of multiple pathways suggests that the decapping of NAD-RNA is an important and regulated process in eukaryotes.
SUMOylation Protects FASN Against Proteasomal Degradation in Breast Cancer Cells Treated with Grape Leaf Extract
Authors: Floris Andrea , Mazarei Michael , Yang Xi , Robinson Aaron , Zhou Jennifer , Barberis Antonio , D’hallewin Guy , Azara Emanuela , Spissu Ylenia , Iglesias-Ara Ainhoa , Orrù Sandro , Tomasi Maria
Published: April 2020

Existing therapeutic strategies for breast cancer are limited by tumor recurrence and drug-resistance. Antioxidant plant-derived compounds such as flavonoids reduce adverse outcomes and have been identified as a potential source of antineoplastic agent with less undesirable side effects. Here, we describe the novel regulation of fatty-acid synthase (FASN), the key enzyme in de novo fatty-acid synthesis, whereby Vitis vinifera L. cv Vermentino leaf hydroalcoholic extract lowers its protein stability that is regulated by small ubiquitin-like modifier (SUMO)ylation. The phenolic compounds characterization was performed by liquid chromatography–mass spectrometry (LC–MS), whereas mass spectrometry (LC–MS/MS), Western blotting/co-immunoprecipitation (Co-IP) and RT-PCR, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), clonogenicity assays, and FACS analysis were used to measure the expression of targets and tumorigenicity. Vermentino extract exhibits antitumorigenic effects, and we went on to determine that FASN and ubiquitin-conjugating enzyme 9 (UBC9), the sole E2 enzyme required for SUMOylation, were significantly reduced. Moreover, FASN was found SUMOylated in human breast cancer tissues and cell lines, and lack of SUMOylation caused by SUMO2 silencing reduced FASN protein stability. These results suggest that SUMOylation protects FASN against proteasomal degradation and may exert oncogenic activity through alteration of lipid metabolism, whereas Vermentino extract inhibits these effects which supports the additional validation of the therapeutic value of this compound in breast cancer.
Handle the Autism Spectrum Condition during Coronavirus (COVID-19) Stay at Home Period: Ten Tips for Helping Parents and Caregivers of Young Children
Authors: Narzisi Antonio
Published: April 2020

COVID-19 has become pandemic [...]
Therapy of Sporadic and NF2-Related Vestibular Schwannoma
Authors: Yao Longping , Alahmari Mohammed , Temel Yasin , Hovinga Koos
Published: April 2020

Vestibular schwannoma (VS) is a benign primary brain tumor that occurs sporadic or as part of a genetic syndrome. The most common cause is the mutation of the NF2 tumor suppressor gene that is involved in the production of the protein merlin. Merlin plays a role in cell growth and cell adhesion. In patients with NF2, the VSs arise bilaterally and coincide with other brain tumors. In sporadic VS, the tumor is typically unilateral and does not coincide in combination with other tumors. MRI is the standard imaging technique and can be used to assess the size and aspect of the tumor as well as the progression of disease. The preferred management of large VS in both VS types is surgery with or without adjuvant radiation. The management for the medium- or small-sized VS includes wait and scan, radiotherapy and/or surgery. This choice depends on the preference of the patient and institutional protocols. The outcomes of surgical and radiotherapy treatments are improving due to progress in surgical equipment/approaches, advances in radiation delivery techniques and dose optimizations protocols. The main purpose of the management of VS is preserving function as long as possible in combination with tumor control.
Active, Selective, and Recyclable Zr(SO4)2/SiO2 and Zr(SO4)2/Activated Carbon Solid Acid Catalysts for Esterification of Malic Acid to Dimethyl Malate
Authors: Yu Pei , Chen Can , Li Guangci , Wang Zhong , Li Xuebing
Published: April 2020

The esterification of malic acid using traditional homogenous catalysts suffers from the difficulty in reuse of the catalyst and undesirable side reactions. In this work, Zr(SO4)2/SiO2 and Zr(SO4)2/activated carbon (AC) as solid acid catalysts were prepared for malic acid esterification with methanol. The conversion of malic acid over these two catalysts is comparable to that over H2SO4 and unsupported Zr(SO4)2∙4H2O catalysts; however; a 99% selectivity of dimethyl malate can be realized on these two supported catalysts, which is much higher than that of conventional H2SO4 (75%) and unsupported Zr(SO4)2∙4H2O (80%) catalysts, highlighting the critical role of AC and SiO2 supports in tuning the selectivity. We suggest that the surface hydroxyls of AC or lattice O2− ions from SiO2 donate electrons to Zr4+ in Zr(SO4)2/AC and Zr(SO4)2/SiO2 catalysts, which results in the increase in electron density on Zr4+. The enhanced electron density on Zr4+ reduces the degree of H delocalization from crystal water and then decreases the Brønsted acid strength. Consequently, the reduced Brønsted acid strength of Zr(SO4)2/AC and Zr(SO4)2/SiO2 catalysts suppresses the intermolecular dehydration side reaction. In addition, these two supported catalysts can be easily separated from the reaction system by simple filtration with almost no loss of activity.
Bioinspired Superhydrophobic Surface Constructed from Hydrophilic Building Blocks: A Case Study of Core–Shell Polypyrrole-Coated Copper Nanoneedles
Authors: Liu Yang , Wang Ben , Wang Yuanfeng , Chen Jiareng , Cui Bin , Yin Pengfei , Chen Jianming , Zhang Xinyu , Zhang Li , Xin John H.
Published: April 2020

Hydrophilic polypyrrole-coated copper nanoneedles (PPy-CuNDs) were synthesized and utilized to construct a superhydrophobic surface on a polyethylene terephthalate fabric (PET) by using the spray-coating technique. The morphology of the as-synthesized PPy-CuNDs can be facilely tuned by changing the concentration of the reducing agent: hydrazine monohydrate. The CuNDs with well-defined nanocrystalline structures and nanoscale thick, rough PPy coating layers were formed simultaneously in one pot. The PPy-CuNDs self-assembled into an entangled, stacking nanocarpet on the surface of the PET fabric, and they eventually formed a reentrant surface texture similar to that of chrysanthemum leaves. The PPy-CuND-PET surface initially showed good superhydrophobic properties, but a fast transition from the superhydrophobic state to the highly adhesive state was observed. The underlying mechanism of this transition and its potential applications were proposed in the context.
Surface and Microstructure Analysis of CoCrPt Film on RuCoCrX (X = Ti, Re) Intermediate Layers
Authors: Tsai Jai-Lin , Chen Jyun-You , Dai Cheng , Hsu Ting-Wei , Weng Shi-Min
Published: April 2020

The microstructural and magnetic properties of perpendicular anisotropic CoCrPt films deposited on Ru or RuCoCrX (X = Ti, Re) intermediate layers were studied. The c-axis of CoCrPt grains were promoted by (0002) textured RuCoCr, and RuCoCrX (X = Ti, Re) intermediate layers due to smaller lattice misfit as compared to Ru. The narrower rocking width (Δθ50 = 3.76°) in RuCoCrRe intermediate layer and CoCrPt shows higher out of plane coercivity (Hc = 6.2 kOe), magnetic anisotropy constant (Ku = 6.2 × 106 erg/cm3) and nucleation field (HN = −2.8kOe) as compared to the Ru intermediate layer (Hc = 5.4 kOe, Ku = 5.9 × 106 erg/cm3, HN = −1.6 kOe). The partial intergranular exchange decoupling of CoCrPt grains was observed. The grain boundaries oxides were formed by the residual oxygen in targets and sputtering processes. The minor Cr2O3, CoO, TiO2, ReO3 oxides were investigated by surface analysis. Due to the minor oxides and Cr segregation at grains boundaries, the CoCrPt films present high coercivity. Samples CoCrPt/RuCoCr and CoCrPt/RuCoCrTi present a minimum at 45° but the values are much higher than the ideal Stoner-Wohlfarth theoretical value 0.5 which could be due to fewer natural oxides for magnetic grains separation. In sample CoCrPt/RuCoCrRe, there is an increase of intergranular interaction as indicated by the large asymmetry and the shift of the minimum at lower angles.
What Is the Effect of Using Mobile Augmented Reality in K12 Inquiry-Based Learning?
Authors: Pedaste Margus , Mitt Geidi , Jürivete Teele
Published: April 2020

A systematic review of the potential of implementing augmented reality (AR) in inquiry-based learning was conducted. We considered the purposes, potential advantages, application characteristics and the effects of using AR in inquiry-based learning. The findings reveal that AR, in the context of inquiry-based learning, is mostly implemented successfully to achieve cognitive and, less often, motivational and emotional learning goals. The AR solutions have mainly been applied in the Conceptualization phase and less in the Investigation phase. The affordances of AR in the Orientation, Conclusion and Discussion phases need to be applied in further studies.
A Through-Life Cost Analysis Model to Support Investment Decision-Making in Concentrated Solar Power Projects
Authors: Shafiee Mahmood , Alghamdi Adel , Sansom Chris , Hart Phil , Encinas-Oropesa Adriana
Published: April 2020

This research paper aims to propose a through-life cost analysis model for estimating the profitability of renewable concentrated solar power (CSP) technologies. The financial outputs of the model include net present value (NPV) and benefit-cost ratio (BCR) of the project, internal rate of return (IRR) and discounted payback period (DPBP) of the investment, and levelized cost of energy (LCoE) from the CSP technology. The meteorological data for a specific location in the city of Tucson in Arizona is collected from a network of automated weather stations, and the NREL System Advisor Model (SAM) is applied to simulate hourly energy output of the CSP plant. An Excel spreadsheet tool is designed to calculate, in a bottom-up approach, the financial metrics required for approval of CSP projects. The model is tested on a 50 MW parabolic trough CSP plant and the results show an annual energy production of 456,351,232 kWh, NPV of over $64 million and LCoE of 0.16 $/kWh. Finally, a sensitivity analysis is performed to identify the factors which have the most significant effect on the economic performance of CSP technologies. The proposed model can provide valuable guidance to support the strategic planning and investment decision-making in CSP projects.
The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling
Authors: Bokde Neeraj , Feijóo Andrés , Al-Ansari Nadhir , Tao Siyu , Yaseen Zaher Mundher
Published: April 2020

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.
Numerical and Experimental Study of an Asymmetric CPC-PVT Solar Collector
Authors: Nasseriyan Pouriya , Afzali Gorouh Hossein , Gomes João , Cabral Diogo , Salmanzadeh Mazyar , Lehmann Tiffany , Hayati Abolfazl
Published: April 2020

Photovoltaic (PV) panels and thermal collectors are commonly known as mature technologies to capture solar energy. The efficiency of PV cells decreases as operating cell temperature increases. Photovoltaic Thermal Collectors (PVT) offer a way to mitigate this performance reduction by coupling solar cells with a thermal absorber that can actively remove the excess heat from the solar cells to the Heat Transfer Fluid (HTF). In order for PVT collectors to effectively counter the negative effects of increased operating cell temperature, it is fundamental to have an adequate heat transfer from the cells to the HTF. This paper analyzes the operating temperature of the cells in a low concentrating PVT solar collector, by means of both experimental and Computational Fluid Dynamics (CFD) simulation results on the Solarus asymmetric Compound Parabolic Concentrator (CPC) PowerCollector (PC). The PC solar collector features a Compound Parabolic Concentrator (CPC) reflector geometry called the Maximum Reflector Concentration (MaReCo) geometry. This collector is suited for applications such as Domestic Hot Water (DHW). An experimental setup was installed in the outdoor testing laboratory at Gävle University (Sweden) with the ability to measure ambient, cell and HTF temperature, flow rate and solar radiation. The experimental results were validated by means of an in-house developed CFD model. Based on the validated model, the effect of collector tilt angle, HTF, insulation (on the back side of the reflector), receiver material and front glass on the collector performance were considered. The impact of tilt angle is more pronounced on the thermal production than the electrical one. Furthermore, the HTF recirculation with an average temperature of 35.1 °C and 2.2 L/min flow rate showed that the electrical yield can increase by 25%. On the other hand, by using insulation, the thermal yield increases up to 3% when working at a temperature of 23 °C above ambient.
Regulation, Innovation, and Systems Integration: Evidence from the EU
Authors: Cambini Carlo , Congiu Raffaele , Soroush Golnoush
Published: April 2020

Energy systems integration (ESI) provides a holistic view of the electricity, gas, and heat sectors, which allows the identification and delivery of system solutions that lead to an overall cost efficiency while granting the reliability of the energy system. In this paper, we search for evidence of investments in ESI in the EU to assess whether policymakers are incentivizing its adoption adequately. To do so, we examine how innovation is being fostered in the energy sector in six EU countries by looking at the incentives provided by each country’s regulatory system. We look for evidence on investments in ESI-enabling technologies or ESI projects. We find a variety of approaches towards incentivizing innovation, which range from regulation-driven to government-driven ones. Preferences for different technologies emerge on a per-country basis. Nevertheless, what appears as most striking is the low level of investments throughout the six countries, both for ESI-enabling technologies and ESI projects. Although ESI’s role in the EU’s green transition has been recognized, there is still a need for technological and policy solutions to foster its adoption.
Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting
Authors: McCandless Tyler , Jiménez Pedro Angel
Published: April 2020

In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predicting cloud cover. Further developments to enhance the cloud mask estimations for improved short-term solar irradiance and power forecasting with the MAD-WRF NWP model are discussed.
Isomerization of n-C5/C6 Bioparaffins to Gasoline Components with High Octane Number
Authors: Hancsók Jenő , Kasza Tamás , Visnyei Olivér
Published: April 2020

The thermal and catalytic conversion processes of alternative feedstocks (e.g., waste and biomass) to different engine fuels can result in the formation of a significant amount of light hydrocarbons as by-products in the boiling range of gasoline. The properties of these C5/C6 hydrocarbons need to be improved due to many reasons, e.g., their benzene content, and/or poor oxidation stability (high olefin content) and low octane number (<60). The aim of the research work was to increase the octane number of benzene containing C5/C6 bioparaffin fractions by catalytic isomerization. These by-products were obtained from special hydrocracking of waste cooking oil to hydrocarbons in the boiling range of aviation turbine fuels (JET fuels)/diesel fuels. Experiments were carried out in a reactor system containing down-flow tubular reactors over Pt/Al2O3/Cl and Pt/H-Mordenite/Al2O3 catalysts at 115–145 °C and 230–270 °C, respectively. Based on the results obtained at different process parameter combinations, it was concluded that the hydrogenation of benzene was complete over both catalysts, and the liquid yields were higher (ca. 98% > ca. 93 %) in the case of Pt/Al2O3/Cl. In addition, the octane number was also enhanced (ca. 32 > ca. 27 unit) in the products compared to the feedstock. This was because a higher isoparaffin content can be obtained at a lower operating temperature. Moreover, cracking side reactions take place to a lesser extent. The utilization of these isomerized bio-origin light fractions can contribute to the competitiveness of second-generation biofuels.
Optical Radiation from an Electric Arc at Different Frequencies
Authors: Nagi Łukasz , Kozioł Michał , Zygarlicki Jarosław
Published: April 2020

The article presents research on the electric arc generated by AC current at different frequencies. The measurement procedure and system are described. Optical spectra of the generated arc in the air were recorded using a spectrophotometer. Optical spectra for five frequencies were obtained. The article also presents the energy balance of the components of the registered spectrum. The visible changes in the spectra that depend on the frequency of the AC current generating the electric arc can be significant to the diagnostics of gas insulators. The research presented in the article can be used in multiple areas of technology where an electric arc is used. The influence of the frequency of the current supplying the electric arc on the electromagnetic radiation spectrum in the area of light radiation emitted by the electric arc allows for the construction of systems that can shape the desired characteristics of the electric arc.
Understanding the Role of Optimized Land Use/Land Cover Components in Mitigating Summertime Intra-Surface Urban Heat Island Effect: A Study on Downtown Shanghai, China
Authors: Guo Yan-jun , Han Jie-jie , Zhao Xi , Dai Xiao-yan , Zhang Hao
Published: April 2020

In this study, 167 land parcels of downtown Shanghai, China, were used to investigate the relationship between parcel-level land use/land cover (LULC) components and associated summertime intra-surface urban heat island (SUHI) effect, and further analyze the potential of mitigating summertime intra-SUHI effect through the optimized LULC components, by integrating a thermal sharpening method combining the Landsat-8 thermal band 10 data and high-resolution Quickbird image, statistical analysis, and nonlinear programming with constraints. The results show the remarkable variations in intra-surface urban heat island (SUHI) effect, which was measured with the mean parcel-level blackbody sensible heat flux in kW per ha (Mean_pc_BBF). Through measuring the relative importance of each specific predictor in terms of their contributions to changing Mean_pc_BBF, the influence of parcel-level LULC components on excess surface flux of heat energy to the atmosphere was estimated using the partial least square regression (PLSR) model. Analysis of the present and optimized parcel-level LULC components and their contribution to the associated Mean_pc_BBF were comparable between land parcels with varying sizes. Furthermore, focusing on the gap between the present and ideally optimized area proportions of parcel-level LULC components towards minimizing the Mean_pc_BBF, the uncertainties arising from the datasets and methods, as well as the implications for sustainable land development and mitigating the UHI effect were discussed.
Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation
Authors: Liu Fang , Ma Jie , Su Weixing , Chen Hanning , He Maowei
Published: April 2020

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness.
Factorial Decomposition of the Energy Footprint of the Shaoxing Textile Industry
Authors: Wang Xiaopeng , Chen Xiang , Cheng Yiman , Zhou Luyao , Li Yi , Yang Yongliang
Published: April 2020

To present great environmental pressure from energy consumption during textile production, this paper calculates the energy footprint (EFP) of Shaoxing’s textile industry, from 2005 to 2018. Moreover, this study analyzes the relationship between Shaoxing’s textile industry energy consumption and economic development by using decoupling theory. Furthermore, the Logarithmic Mean Divisia Index decomposition method was employed to investigate the main factors that affect the EFP of Shaoxing’s textile industry. Research results show the following: (1) The growth rate of the total output value of Shaoxing’s textile industry was greater than the growth rate of the EFP, from 2005 to 2007. Thus, the decoupling state showed a weak decoupling, and EFP intensity decreased. (2) The EFP and economic growth were mainly based on the strong decoupling of Shaoxing’s textile industry from 2008 to 2015 (except for 2011), and EFP intensity declined further. (3) Economic recession in the textile industry was severe in Shaoxing, from 2016 to 2018, and the EFP also showed a downward trend. The state of decoupling appeared as a recessive decoupling (2016) and a weak negative decoupling (2017 and 2018), and EFP intensity first increased and then decreased. (4) The total effect of the factors affecting the EFP of the textile industry in Shaoxing demonstrated a pulling trend, and industrial scale played a significant role in driving the EFP. The energy consumption intensity effect contributed the largest restraint. This paper fills in the gaps in the environmental regulation means and methods of pillar industrial clusters in specific regions.
A Novel Hybrid Model for Cu–Al2O3/H2O Nanofluid Flow and Heat Transfer in Convergent/Divergent Channels
Authors: Khan Umar , Adnan  , Ahmed Naveed , Mohyud-Din Syed Tauseef , Baleanu Dumitru , Khan Ilyas , Nisar Kottakkaran Sooppy
Published: April 2020

In the present study, our aim is to present a novel model for the flow of hybrid nanofluids in oblique channels. Copper and aluminum oxide have been used to obtain a novel hybrid nanofluid. The equations that govern the flow of hybrid nanofluids have been transformed to a set of nonlinear equations with the implementation of self-similar variables. The resulting system is treated numerically by using coupled shooting and Runge–Kutta (R-K) scheme. The behavior of velocity and temperature is examined by altering the flow parameters. The cases for narrowing (convergent) and opening (divergent) channels are discussed, and the influence of various parameters on Nusselt number is also presented. To indicate the reliability of the study, a comparison is made that confirms the accuracy of the study presented.
Regional Flood Frequency Analysis Using an Artificial Neural Network Model
Authors: Kordrostami Sasan , Alim Mohammad A , Karim Fazlul , Rahman Ataur
Published: April 2020

This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad.
A New Application of Social Impact in Social Media for Overcoming Fake News in Health
Authors: Pulido Cristina , Ruiz-Eugenio Laura , Redondo-Sama Gisela , Villarejo-Carballido Beatriz
Published: April 2020

One of the challenges today is to face fake news (false information) in health due to its potential impact on people’s lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.
The Code of the Street Fights Back! Significant Associations with Arrest, Delinquency, and Violence Withstand Psychological Confounds
Authors: Burgason Kyle A. , DeLisi Matt , Heirigs Mark H. , Kusow Abdi , Erickson Jacob H. , Vaughn Michael G.
Published: April 2020

Since Anderson’s now classic, Code of the Street: Decency, Violence, and the Moral Life of the Inner City, an increasing number of researchers have found a significant association between the code of the street and antisocial behavior. Less researched, however, is the relationship between the code of the street and cognate psychological factors. Building on the hypothesis that the code of the street is simply a reflection of elements of the population who exhibit antisocial traits, our aim in this study is to empirically test whether the observed association between the code of the street and antisocial behavior can withstand psychological confounds among a sample of institutionalized juvenile delinquents. Negative binomial regression models show that the code of the street remained a significant predictor of antisocial behavior despite the specification of psychopathy and temperamental traits and other controls. Moreover, as theorized, differential effects were found for African American delinquents compared to non-African American delinquents. We discuss theoretical and practical implications.
Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis
Authors: Battineni Gopi , Sagaro Getu Gamo , Chinatalapudi Nalini , Amenta Francesco
Published: April 2020

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
Determination of the Environmental Factors that Affect the Growth and Survival of Greek Fir Seedlings
Authors: Detsis Vassilis , Efthimiou Georgios , Theodoropoulou Olga , Siorokou Stavroula
Published: April 2020

Forests in the montane-Mediterranean zone have only recently began to be affected by wildfires, therefore the knowledge necessary for restoration projects is missing. The aim of the study was to assess the effects of factors related to seedling attributes, weather conditions and site suitability on seedling performance. The characterisation of sites was based on bedrock and soil clay content as well as pre-fire vegetation. Apical growth and survival of seedlings was monitored for four years in Parnitha National Park. The parameters of a linear mixed model were estimated using annual apical growth of seedlings surviving in the end of the study as the dependent variable and type of site, rainfall, initial seedling height and age as explanatory ones. A quantile regression model using all the data available was estimated for each year of study, taking into account only initial height and site type as well as a logistic regression model of survival. The findings indicate that the growth of Greek fir seedlings depends on May rainfall mediated by soil clay content, which in turn depends on bedrock, which is consistent with the “inverse texture hypothesis”. Sites with low soil clay content were always more beneficial for survival, which was stronger affected by summer–autumn rainfall. In both contexts, drought stress due to soil clay content fades with increasing age. Sites that were not fir dominated prior to fire proved unsuitable also for planting fir seedlings. A minor part of the observed variability could be associated with the initial height of seedlings, especially for seedlings showing high rates of apical growth.
Formation Mechanism of MgO Containing Inclusions in the Molten Steel Refined in MgO Refractory Crucibles
Authors: Li Yiyan , Yang Wen , Zhang Lifeng
Published: April 2020

In order to study the influence of MgO refractory on inclusions during the steelmaking process, laboratory quasi-equilibrium experiments were performed in MgO crucibles at 1873 K. It was found that there were no MgO-containing inclusions formed when ultra-low Al ultra-low C steel was used. When the contents of dissolved Al and C in steel changed, different composition ranges led to different types of inclusions. It was shown as well that for both low Al high C steel and high Al low C steel, the order of average MgO content in inclusions from large to small was: MgO-10% slag addition > MgO-free slag addition > no slag addition. Based on the results, a simple formation mechanism of MgO-containing inclusions in the steel with different compositions in MgO crucibles was proposed. The results could provide guidance for the reduction of MgO-containing inclusions in the industrial production of steels.
Compound Layer Design for Deep Nitrided Gearings
Authors: Hoja Stefanie , Steinbacher Matthias , Zoch Hans-Werner
Published: April 2020

Deep nitriding is used to obtain a nitriding hardness depth beyond 0.6 mm. The long nitriding processes, which are necessary to reach the high nitriding hardness depths, mostly have a negative influence on the hardness and strength of the nitrided layer as well as on the bulk material. The compound layer often is considered less, because in most practical cases, it is removed mechanically after nitriding, to avoid spalling in service. However, in former investigations, it was shown, that thick and compact compound layers have the potential for high flank load capacity of gears. The investigations focus on the simultaneous formation of a high nitriding depth and a thick and compact compound layer. Beside the preservation of the strength, a challenge is to control the porosity of the compound layer, which should be as low as possible. The investigations were carried out using the common nitriding and heat treatable mild steel 31CrMoV9, which is often used for gear applications. The article gives an insight on the development of multistage nitriding processes studied by short- and long-term experiments aiming for a specific compound layer build-up with low porosity and high strength of the nitride layer and core material.
Layout Transposition for Non-Visual Navigation of Web Pages by Tactile Feedback on Mobile Devices
Authors: Maurel Fabrice , Dias Gaël , Safi Waseem , Routoure Jean-Marc , Beust Pierre
Published: April 2020

In this paper, we present the results of an empirical study that aims to evaluate the performance of sighted and blind people to discriminate web page structures using vibrotactile feedback. The proposed visuo-tactile substitution system is based on a portable and economical solution that can be used in noisy and public environments. It converts the visual structures of web pages into tactile landscapes that can be explored on any mobile touchscreen device. The light contrasts overflown by the fingers are dynamically captured, sent to a micro-controller, translated into vibrating patterns that vary in intensity, frequency and temperature, and then reproduced by our actuators on the skin at the location defined by the user. The performance of the proposed system is measured in terms of perception of frequency and intensity thresholds and qualitative understanding of the shapes displayed.
Special Issue: Microbial Degradation of Xenobiotics
Authors: Nagata Yuji
Published: April 2020

Xenobiotics are released into the environment by human activities, and they often cause problems such as environmental pollution, since most such compounds cannot be readily degraded, and have harmful effects on human beings and the natural ecosystem [...]
Complexes of Ectoine with the Anionic Surfactants as Active Ingredients of Cleansing Cosmetics with Reduced Irritating Potential
Authors: Bujak Tomasz , Zagórska-Dziok Martyna , Nizioł-Łukaszewska Zofia
Published: April 2020

For many years, an increasing number of diagnosed atopy and skin problems have been observed. For people affected by the problem of atopy, the selection of skin care products, including cosmetics, is extremely important. Cleansing cosmetics, due to their ability to cause skin irritations and disturb the hydrolipidic barrier, can increase problems with atopic skin. New solutions to reduce the effects of these products on the skin are very important. In this work, the effect of ectoine on the properties of anionic surfactants was analyzed. Based on model systems, analysis of the effect of ectoine on the irritating effect of four anionic surfactants and their ability to solubilize model sebum was performed. Antioxidant activity was also evaluated, and cytotoxic studies were performed on cell cultures. It was shown that the addition of ectoine to the anionic surfactant solutions improves its safety of use. After introducing ectoine to the surfactant solution, a decrease of irritant potential (about 20%) and a decrease in the ability to solubilize of model sebum (about 10–20%) was noted. Addition of ectoine to surfactant solutions also reduced their cytotoxicity by up to 60%. The obtained results indicate that ectoine may be a modern ingredient that improves the safety of cleansing cosmetics.
Amyloids: Regulators of Metal Homeostasis in the Synapse
Authors: Kawahara Masahiro , Kato-Negishi Midori , Tanaka Ken-ichiro
Published: April 2020

Conformational changes in amyloidogenic proteins, such as β-amyloid protein, prion proteins, and α-synuclein, play a critical role in the pathogenesis of numerous neurodegenerative diseases, including Alzheimer’s disease, prion disease, and Lewy body disease. The disease-associated proteins possess several common characteristics, including the ability to form amyloid oligomers with β-pleated sheet structure, as well as cytotoxicity, although they differ in amino acid sequence. Interestingly, these amyloidogenic proteins all possess the ability to bind trace metals, can regulate metal homeostasis, and are co-localized at the synapse, where metals are abundantly present. In this review, we discuss the physiological roles of these amyloidogenic proteins in metal homeostasis, and we propose hypothetical models of their pathogenetic role in the neurodegenerative process as the loss of normal metal regulatory functions of amyloidogenic proteins. Notably, these amyloidogenic proteins have the capacity to form Ca2+-permeable pores in membranes, suggestive of a toxic gain of function. Therefore, we focus on their potential role in the disruption of Ca2+ homeostasis in amyloid-associated neurodegenerative diseases.