bluuebunny/crossref_metadata_2025 huggingface dataset:
Fluorescent Guided Surgery in the Surgical Management of Glioma: The Dawn of a New Era
Authors: Fountas Kostas N.
Published: April 2020

A growing body of evidence supports the importance of marginal or even supramarginal resection in cases of high- but also of low-grade gliomas [...]
Circular Building Design: An Analysis of Barriers and Drivers for a Circular Building Sector
Authors: Kanters Jouri
Published: April 2020

Circular building design could significantly reduce the environmental impact of buildings and the pressure on natural resources. However, most buildings today are not designed according to the principles of the circular economy. Most literature has focused on either methods for quantifying the lifecycle analysis of buildings and materials, or on innovative circular building materials, but not much is known about the design process of circular buildings and how architects are dealing with translating the principles of the circular economy to the building sector. A series of semi-structured interviews with architects and consultants that have engaged in circular building design has been conducted to identify the barriers and drivers of the transformation towards a circular building sector. Interviews were analysed using qualitative coding analysis. The conservativeness of the building industry, the lack of political priority and the dependency throughout the building industry were found to be the main barriers, while a supportive client with a well-defined assignment and idea was considered to be the main driver. The contribution of this paper to key actors in the building sector is to identify the main barriers and drivers for a circular building sector.
Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Programmes on Incidence-Based Breast Cancer Mortality
Authors: Dibden Amanda , Offman Judith , Duffy Stephen W. , Gabe Rhian
Published: April 2020

In 2012, the Euroscreen project published a review of incidence-based mortality evaluations of breast cancer screening programmes. In this paper, we update this review to October 2019 and expand its scope from Europe to worldwide. We carried out a systematic review of incidence-based mortality studies of breast cancer screening programmes, and a meta-analysis of the estimated effects of both invitation to screening and attendance at screening, with adjustment for self-selection bias, on incidence-based mortality from breast cancer. We found 27 valid studies. The results of the meta-analysis showed a significant 22% reduction in breast cancer mortality with invitation to screening, with a relative risk of 0.78 (95% CI 0.75–0.82), and a significant 33% reduction with actual attendance at screening (RR 0.67, 95% CI 0.61–0.75). Breast cancer screening in the routine healthcare setting continues to confer a substantial reduction in mortality from breast cancer.
p53 and FBXW7: Sometimes Two Guardians Are Worse than One
Authors: Galindo-Moreno María , Giráldez Servando , Limón-Mortés M. Cristina , Belmonte-Fernández Alejandro , Sáez Carmen , Japón Miguel Á. , Tortolero Maria , Romero Francisco
Published: April 2020

Too much of a good thing can become a bad thing. An example is FBXW7, a well-known tumor suppressor that may also contribute to tumorigenesis. Here, we reflect on the results of three laboratories describing the role of FBXW7 in the degradation of p53 and the possible implications of this finding in tumor cell development. We also speculate about the function of FBXW7 as a key player in the cell fate after DNA damage and how this could be exploited in the treatment of cancer disease.
Efficient Oxidative Esterification of Furfural Using Au Nanoparticles Supported on Group 2 Alkaline Earth Metal Oxides
Authors: P. Ferraz Camila , Braga Adriano H. , Ghazzal Mohamed Nawfal , Zieliński Michał , Pietrowski Mariusz , Itabaiana Ivaldo , Dumeignil Franck , Rossi Liane M. , Wojcieszak Robert
Published: April 2020

Furfural (FF) is a strategic product for the development of highly valued chemicals from biomass. The oxidation product of FF, furoic acid (FA), is an important precursor for the synthesis of green esters, such as methyl furoate. Taking into account issues with the direct furfural oxidation, furfural derivatives, such as alkyl furoates, can be easily prepared via oxidative esterification. Here, Au nanoparticles that were immobilized on alkaline-earth metal oxide supports were studied for the oxidative esterification of furfural while using alcohol as both reactant and solvent. The formation of esters is favored by the presence of basic sites on catalyst surface, resulting in high selectivity, preventing the formation of the acetal as a by-product. The Au/MgO sample provided up to 95% methyl furoate (MF) yield, a fast reaction rate, and high performance for furfural:Au molar ratios between 50 and 300. Furthermore, this catalyst was stable during reuse, since both the selectivity and the activity were maintained after four cycles. Oxidative esterification products were achieved in the presence of other alcohols, leading to the formation of esters of up to C5 (isopentyl furoate) with high selectivity (>99%). Linear and branched esters were formed, but the long-chain linear alcohols resulted in higher yields, such as n-butyl furoate in 94% yield.
Improvement of Trisodium Citrate-Modified NiFe-Layered Double Hydroxide Nanosheets with Carbon Black for Oxygen Evolution Reaction
Authors: He Haitong , Gu Jun , Liu Xiaomeng , Yang Delong , Zhu Yong , Yao Rui , Fan Qi , Huang Runsheng
Published: April 2020

The pursuit of highly active and cost-effective catalysts toward oxygen evolution reaction (OER) is a crucial strategy to resolve the imminent energy crisis. NiFe layered double hydroxide (NiFe LDH) is acknowledged as one of the most promising OER electrocatalysts in alkaline electrolytes. Herein, we report a novel stepwise approach to synthesize NiFe LDHs materials merging with carbon black (CB) via trisodium citrate (TC), modifying toward OER. Benefiting from the inimitable wrapped structure, the decreased size of porous nanosheets and the superconductivity of CB substrate, NiFe LDHs/CB-TC presents excellent catalytic features with a comparative overpotential (236 mV at 10 mA cm−2) and an ultralow Tafel slope (31 mV dec−1), which are almost lower than those of advanced catalysts associated with expensive carbonaceous materials. Therefore, it is expected that such a high-activity and low-cost material can be a promising catalyst employed for the electrochemical energy storage and conversion systems.
Paving the Way toward Successful Multiple Myeloma Treatment: Chimeric Antigen Receptor T-Cell Therapy
Authors: Grywalska Ewelina , Sosnowska-Pasiarska Barbara , Smok-Kalwat Jolanta , Pasiarski Marcin , Niedźwiedzka-Rystwej Paulina , Roliński Jacek
Published: April 2020

Despite the significant progress of modern anticancer therapies, multiple myeloma (MM) is still incurable for the majority of patients. Following almost three decades of development, chimeric antigen receptor (CAR) T-cell therapy now has the opportunity to revolutionize the treatment landscape and meet the unmet clinical need. However, there are still several major hurdles to overcome. Here we discuss the recent advances of CAR T-cell therapy for MM with an emphasis on future directions and possible risks. Currently, CAR T-cell therapy for MM is at the first stage of clinical studies, and most studies have focused on CAR T cells targeting B cell maturation antigen (BCMA), but other antigens such as cluster of differentiation 138 (CD138, syndecan-1) are also being evaluated. Although this therapy is associated with side effects, such as cytokine release syndrome and neurotoxicity, and relapses have been observed, the benefit–risk balance and huge potential drive the ongoing clinical progress. To fulfill the promise of recent clinical trial success and maximize the potential of CAR T, future efforts should focus on the reduction of side effects, novel targeted antigens, combinatorial uses of different types of CAR T, and development of CAR T cells targeting more than one antigen.
FTO Intronic SNP Strongly Influences Human Neck Adipocyte Browning Determined by Tissue and PPARγ Specific Regulation: A Transcriptome Analysis
Authors: Tóth Beáta B. , Arianti Rini , Shaw Abhirup , Vámos Attila , Veréb Zoltán , Póliska Szilárd , Győry Ferenc , Bacso Zsolt , Fésüs László , Kristóf Endre
Published: April 2020

Brown adipocytes, abundant in deep-neck (DN) area in humans, are thermogenic with anti-obesity potential. FTO pro-obesity rs1421085 T-to-C single-nucleotide polymorphism (SNP) shifts differentiation program towards white adipocytes in subcutaneous fat. Human adipose-derived stromal cells were obtained from subcutaneous neck (SC) and DN fat of nine donors, of which 3-3 carried risk-free (T/T), heterozygous or obesity-risk (C/C) FTO genotypes. They were differentiated to white and brown (long-term Peroxisome proliferator-activated receptor gamma (PPARγ) stimulation) adipocytes; then, global RNA sequencing was performed and differentially expressed genes (DEGs) were compared. DN and SC progenitors had similar adipocyte differentiation potential but differed in DEGs. DN adipocytes displayed higher browning features according to ProFAT or BATLAS scores and characteristic DEG patterns revealing associated pathways which were highly expressed (thermogenesis, interferon, cytokine, and retinoic acid, with UCP1 and BMP4 as prominent network stabilizers) or downregulated (particularly extracellular matrix remodeling) compared to SC ones. Part of DEGs in either DN or SC browning was PPARγ-dependent. Presence of the FTO obesity-risk allele suppressed the expression of mitochondrial and thermogenesis genes with a striking resemblance between affected pathways and those appearing in ProFAT and BATLAS, underlining the importance of metabolic and mitochondrial pathways in thermogenesis. Among overlapping regulatory influences that determine browning and thermogenic potential of neck adipocytes, FTO genetic background has a thus far not recognized prominence.
Potential Role of Circulating Endoglin in Hypertension via the Upregulated Expression of BMP4
Authors: Gallardo-Vara Eunate , Gamella-Pozuelo Luis , Perez-Roque Lucía , Bartha José L. , Garcia-Palmero Irene , Casal J. Ignacio , López-Novoa José M. , Pericacho Miguel , Bernabeu Carmelo
Published: April 2020

Endoglin is a membrane glycoprotein primarily expressed by the vascular endothelium and involved in cardiovascular diseases. Upon the proteolytic processing of the membrane-bound protein, a circulating form of endoglin (soluble endoglin, sEng) can be released, and high levels of sEng have been observed in several endothelial-related pathological conditions, where it appears to contribute to endothelial dysfunction. Preeclampsia is a multisystem disorder of high prevalence in pregnant women characterized by the onset of high blood pressure and associated with increased levels of sEng. Although a pathogenic role for sEng involving hypertension has been reported in several animal models of preeclampsia, the exact molecular mechanisms implicated remain to be identified. To search for sEng-induced mediators of hypertension, we analyzed the protein secretome of human endothelial cells in the presence of sEng. We found that sEng induces the expression of BMP4 in endothelial cells, as evidenced by their proteomic signature, gene transcript levels, and BMP4 promoter activity. A mouse model of preeclampsia with high sEng plasma levels (sEng+) showed increased transcript levels of BMP4 in lungs, stomach, and duodenum, and increased circulating levels of BMP4, compared to those of control animals. In addition, after crossing female wild type with male sEng+ mice, hypertension appeared 18 days after mating, coinciding with the appearance of high plasma levels of BMP4. Also, serum levels of sEng and BMP4 were positively correlated in pregnant women with and without preeclampsia. Interestingly, sEng-induced arterial pressure elevation in sEng+ mice was abolished in the presence of the BMP4 inhibitor noggin, suggesting that BMP4 is a downstream mediator of sEng. These results provide a better understanding on the role of sEng in the physiopathology of preeclampsia and other cardiovascular diseases, where sEng levels are increased.
Hypoxia-Driven Immune Escape in the Tumor Microenvironment
Authors: Vito Alyssa , El-Sayes Nader , Mossman Karen
Published: April 2020

The tumor microenvironment is a complex ecosystem comprised of many different cell types, abnormal vasculature and immunosuppressive cytokines. The irregular growth kinetics with which tumors grow leads to increased oxygen consumption and, in turn, hypoxic conditions. Hypoxia has been associated with poor clinical outcome, increased tumor heterogeneity, emergence of resistant clones and evasion of immune detection. Additionally, hypoxia-driven cell death pathways have traditionally been thought of as tolerogenic processes. However, as researchers working in the field of immunotherapy continue to investigate and unveil new types of immunogenic cell death (ICD), it has become clear that, in some instances, hypoxia may actually induce ICD within a tumor. In this review, we will discuss hypoxia-driven immune escape that drives poor prognostic outcomes, the ability of hypoxia to induce ICD and potential therapeutic targets amongst hypoxia pathways.
Polarization Controlled Dual Functional Reflective Planar Metalens in Near Infrared Regime
Authors: Zhang Yuhui , Yang Bowei , Liu Zhiying , Fu Yuegang
Published: April 2020

The metalens has been a hotspot in scientific communications in recent years. The polarization-controlled functional metalens is appealing in metalens investigation. We propose a metalens with dual functions that are controlled by polarization states. In the first design, when applied with x- and y-polarized light, two focal spots with different focal lengths are acquired, respectively. The proposed metalens performs well when illuminated with adjacent wavelengths. In the second design, the reflected light is focused when applied with x-polarized light, and when applied with y-polarized light, the reflected light is split into two oblique paths. We believe that the results will provide a new method in light manipulation.
Dielectric Nanoparticles Coated upon Silver Hollow Nanosphere as an Integrated Design to Reinforce SERS Detection of Trace Ampicillin in Milk Solution
Authors: Lee Han , Yang Jia-Wei , Liao Jiunn-Der , Sitjar Jaya , Liu Bernard Haochih , Sivashanmugan Kundan , Fu Wei-En , Chen Guo Dung
Published: April 2020

Surface-enhanced Raman scattering (SERS) technique is competent to trace detection of target species, down to the single molecule level. The detection sensitivity is presumably degraded by the presence of non-specific binding molecules that occupy a SERS-active site (or hot spot) on the substrate surface. In this study, a silver hollow nano-sphere (Ag HNS) with cavity has been particularly designed, followed by depositing dielectric nanoparticles (Di NPs) upon Ag HNS. In the integrated nanostructures, Di NPs/Ag HNS were furthermore confirmed by cutting through the cross sections using the Focused Ion Beam (FIB) technique, which provides the Scanning Electron Microscope (SEM) with Energy-dispersive Spectroscope (EDS) mode for identifying the distribution of Di NPs upon Ag HNS. The results have indicated that Di NPs/Ag HNS exhibits small diameter of cavity, and among Di NPs in this study, Al2O3 with lower dielectric constant provides a much higher SERS enhancement factor (e.g., ~6.2 × 107). In this study, to detect trace amounts (e.g., 0.01 ppm) of Ampicillin in water or milk solution, Al2O3 NPs/Ag HNS was found to be more efficient and less influenced by non-specific binding molecules in milk. A substrate with integrated plasmonic and dielectric components was designed to increase the adsorption of target species and to repulse non-specific binding molecules from SERS-active sites.
Interdiffusion Behaviour of NiCoCrAlY Coating and N5 Single Crystal Superalloy
Authors: Cao Jiaxu , Liu Yingkun , Deng Chunming , Yang Kun , Li Feng
Published: April 2020

This paper aims at investigating the microstructure and phases evolution of single crystal superalloy/high temperature protective coating during high temperature static oxidation, and exploring the influence of element interdiffusion behaviour on microstructure and phase evolution of the single crystal superalloy substrate. A NiCoCrAlY high-temperature protective coating was deposited on the Ni-based single-crystal superalloy by low-pressure plasma spraying technology. The coated samples were subjected to static oxidation for 200 h at a constant temperature of 1100 °C. Scanning electron microscope, energy dispersive spectrometer and X-ray diffraction were used to characterise the microstructure and phase after interdiffusion between the coating and the substrate at high temperature. The results showed that a dense thermally grown oxide layer was formed on the surface of the NiCoCrAlY coating after oxidation for over 100 h. The only interdiffusion zone was formed after oxidation for 50 h, while both interdiffusion zone and secondary reaction zone could be observed after oxidation for over 100 h. The thickness of interdiffusion zone and secondary reaction zone is increased with the extension of oxidation time, and the grain growth of topological close-packed phase in the secondary reaction zone is found. Al, Cr and Co in the coating diffuse from the coating to the substrate, while Ni and refractory materials like Ta, Mo, Re and W diffuse from the coating to the substrate. The interdiffusion of coating and substrate leads to the instability of γ/γ′ phase in the substrate, which finally results in the formation of W, Re and Cr-rich needle-like topological close-packed phase in the substrate.
Optoelectronic Properties of Ti-doped SnO2 Thin Films Processed under Different Annealing Temperatures
Authors: Liu Chi-Fan , Kuo Chun-Hsien , Chen Tao-Hsing , Huang Yu-Sheng
Published: April 2020

Ti-doped SnO2 transparent conductive oxide (TCO) thin films are deposited on glass substrates using a radio frequency (RF) magnetron sputtering system and then are annealed at temperatures in the range of 200–500 °C for 30 min. The effects of the annealing temperature on the structural properties, surface roughness, electrical properties, and optical transmittance of the thin films are then systematically explored. The results show that a higher annealing temperature results in lower surface roughness and larger crystal size. Moreover, an annealing temperature of 300 °C leads to the minimum electrical resistivity of 5.65 × 10−3 Ω·cm. The mean optical transmittance increases with an increase in temperature and achieves a maximum value of 74.2% at an annealing temperature of 500 °C. Overall, the highest figure of merit (ΦTC) (3.99 × 10−4 Ω−1) is obtained at an annealing temperature of 500 °C.
Finite Element Analysis of Variable Viscosity Impact on MHD Flow and Heat Transfer of Nanofluid Using the Cattaneo–Christov Model
Authors: Ali Liaqat , Liu Xiaomin , Ali Bagh
Published: April 2020

In this mathematical study, magnetohydrodynamic, time-independent nanofluid flow over a stretching sheet by using the Cattaneo–Christov heat flux model is inspected. The impact of the thermal, solutal boundary and gravitational body forces with the effect of double stratification on the mass flow and heat transfer phenomena is also observed. The temperature-dependent viscosity impact on heat transfer through a moving sheet with capricious heat generation in nanofluids have studied, and the viscosity of the fluid is presumed to deviate as the inverse function of temperature. With the appropriate transformations, the system of partial differential equations is transformed into a system of nonlinear ordinary differential equations. By applying the variational finite element method, the transformed system of equations is solved. The properties of the several parameters for buoyancy, velocity, temperature, stratification, and Brownian motion parameters have examined. The enhancement in the concentration and thermal boundary layer thickness of the nanofluid sheet due to the increment in the viscosity parameter, also increased the temperature and concentration of nanoparticles. Moreover, the fluid temperature declined with the increasing values of thermal relaxation parameter. This displays that the Cattaneo–Christov heat flux model provides a better assessment of temperature distribution. Moreover, confirmation of the code and precision of the numerical method has inveterate with the valuation of the presented results with previous studies.
Microstructure Heredity of Inconel 718 Nickel-Based Superalloy during Preheating and Following Deformation
Authors: Wang Jianguo , Liu Dong , Ding Xiao , Wang Haiping , Wang Hai , Chen Jingqing , Yang Yanhui
Published: April 2020

Preheating and compression tests of Inconel 718 superalloy double cone specimens were carried out to investigate the microstructure heredity during hot working. Optical microscopy, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM) were used to characterize the microstructure evolution. The results show that intense microstructure heredity can be found at the temperature 960~990 °C. During the preheating process, δ phase precipitation or grain growth could increase the fraction of high angle grain boundary (HAGBs) and Σ3n boundaries. Otherwise, the generation or spread of annealing twin could increase the fraction of LAGBs, Volume fraction of recrystallized grains was evaluated at the whole hot working process. At the temperature of 960~990 °C, the volume fraction of recrystallized grains increases with effective strain increasing. At the super solution temperature of δ phase, the volume fraction of recrystallized grains decreases and then increases with the increase of the effective strain. The unimodal grain size distribution and fully recrystallized grains can be obtained at low strains at 960~990 °C. The twin boundary length fraction of deformed specimens is always lower than that of preheated ones. Discontinuous dynamic recrystallization (DDRX) was considered as the dominant nucleation mechanism, and continuous dynamic recrystallization (CDRX) was strengthened with the increasing grain size. Twin introduced deformation will be the main deformation mode for alloy 718 with larger grain.
A Review of Acoustic Metamaterials and Phononic Crystals
Authors: Liu Junyi , Guo Hanbei , Wang Ting
Published: April 2020

As a new kind of artificial material developed in recent decades, metamaterials exhibit novel performance and the promising application potentials in the field of practical engineering compared with the natural materials. Acoustic metamaterials and phononic crystals have some extraordinary physical properties, effective negative parameters, band gaps, negative refraction, etc., extending the acoustic properties of existing materials. The special physical properties have attracted the attention of researchers, and great progress has been made in engineering applications. This article summarizes the research on acoustic metamaterials and phononic crystals in recent decades, briefly introduces some representative studies, including equivalent acoustic parameters and extraordinary characteristics of metamaterials, explains acoustic metamaterial design methods, and summarizes the technical bottlenecks and application prospects.
RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization
Authors: Leong Wei , Arrabito Giuseppe , Prestopino Giuseppe
Published: April 2020

No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.
Gypsum Crystallization during Reverse Osmosis Desalination of Water with High Sulfate Content in Presence of a Novel Fluorescent-Tagged Polyacrylate
Authors: Oshchepkov Maxim , Golovesov Vladimir , Ryabova Anastasia , Redchuk Anatoly , Tkachenko Sergey , Pervov Alexei , Popov Konstantin
Published: April 2020

Gypsum scaling in reverse osmosis (RO) desalination process is studied in presence of a novel fluorescent 1,8-naphthalimide-tagged polyacrylate (PAA-F1) by fluorescent microscopy, scanning electron microscopy (SEM), dynamic light scattering (DLS) and a particle counter technique. A comparison of PAA-F1 with a previously reported fluorescent bisphosphonate HEDP-F revealed a better PAA-F1 efficacy, and a similar behavior of polyacrylate and bisphosphonate inhibitors under the same RO experimental conditions. Despite expectations, PAA-F1 does not interact with gypsum. For both reagents, it is found that scaling takes place in the bulk retentate phase via heterogeneous nucleation step. The background “nanodust” plays a key role as a gypsum nucleation center. Contrary to popular belief, an antiscalant interacts with “nanodust” particles, isolating them from calcium and sulfate ions sorption. Therefore, the number of gypsum nucleation centers is reduced, and in turn, the overall scaling rate is diminished. It is also shown that, the scale formation scenario changes from the bulk medium, in the beginning, to the sediment crystals growth on the membrane surface, at the end of the desalination process. It is demonstrated that the fluorescent-tagged antiscalants may become very powerful tools in membrane scaling inhibition studies.
Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression
Authors: Shamsah Sami M. Ibn , Owolabi Taoreed O.
Published: April 2020

The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.
Increased E2F1 mRNA and miR-17-5p Expression Is Correlated to Invasiveness and Proliferation of Pituitary Neuroendocrine Tumours
Authors: García-Martínez Araceli , López-Muñoz Beatriz , Fajardo Carmen , Cámara Rosa , Lamas Cristina , Silva-Ortega Sandra , Aranda Ignacio , Picó Antonio
Published: April 2020

miR-17-5p and E2F1 have been described as deregulated in cancer, but they have scarcely been studied in pituitary neuroendocrine tumours (PitNETs). This study evaluates the relationship of E2F1 and miR-17-5p with the invasiveness and proliferation of PitNETs. In this cross-sectional descriptive study, we evaluated the expression of E2F1, MYC, and miR-17-5p by quantitative real time PCR analysis in 60 PitNETs: 29 gonadotroph (GT), 15 functioning somatotroph (ST), and 16 corticotroph (CT) tumours, of which 8 were silent (sCT). The clinical data were collected from the Spanish Molecular Register of Pituitary Adenomas (REMAH) database. We defined invasiveness according to the Knosp classification and proliferation according to a molecular expression of Ki-67 ≥ 2.59. E2F1 was more expressed in invasive than in non-invasive tumours in the whole series (p = 0.004) and in STs (p = 0.01). In addition, it was overexpressed in the silent subtypes (GTs and sCTs; all macroadenomas) and normoexpressed in the functioning ones (fCTs and STs; some microadenomas). miR-17-5p was more expressed in proliferative than in non-proliferative tumours (p = 0.041) in the whole series but not by subtypes. Conclusions: Our study suggests that in PitNETs, E2F1 could be a good biomarker of invasiveness, and miR-17-5p of proliferation, helping the clinical management of these tumours.
Identifying Influencers in Social Networks
Authors: Huang Xinyu , Chen Dongming , Wang Dongqi , Ren Tao
Published: April 2020

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.
On a Class of Tensor Markov Fields
Authors: Hernández-Lemus Enrique
Published: April 2020

Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables. By extending the results of Dobruschin, Hammersley and Clifford to such tensor valued fields, we proved that tensor Markov fields are indeed Gibbs fields, whenever strictly positive probability measures are considered. Hence, there is a direct relationship with many results from theoretical statistical mechanics. We showed how this class of Markov fields it can be built based on a statistical dependency structures inferred on information theoretical grounds over empirical data. Thus, aside from purely theoretical interest, the Tensor Markov Fields described here may be useful for mathematical modeling and data analysis due to their intrinsic simplicity and generality.
The Role of Entropy in the Development of Economics
Authors: Jakimowicz Aleksander
Published: April 2020

The aim of this paper is to examine the role of thermodynamics, and in particular, entropy, for the development of economics within the last 150 years. The use of entropy has not only led to a significant increase in economic knowledge, but also to the emergence of such scientific disciplines as econophysics, complexity economics and quantum economics. Nowadays, an interesting phenomenon can be observed; namely, that rapid progress in economics is being made outside the mainstream. The first significant achievement was the emergence of entropy economics in the early 1970s, which introduced the second law of thermodynamics to considerations regarding production processes. In this way, not only was ecological economics born but also an entropy-based econometric approach developed. This paper shows that non-extensive cross-entropy econometrics is a valuable complement to traditional econometrics as it explains phenomena based on power-law probability distribution and enables econometric model estimation for non-ergodic ill-behaved (troublesome) inverse problems. Furthermore, the entropy economics has accelerated the emergence of modern econophysics and complexity economics. These new directions of research have led to many interesting discoveries that usually contradict the claims of conventional economics. Econophysics has questioned the efficient market hypothesis, while complexity economics has shown that markets and economies function best near the edge of chaos. Quantum economics has already appeared on the horizon, which recognizes money as a fundamental measurement device in the economy. The development of these sciences may indicate the need to reformulate all mainstream economics from its foundations.
Participation in an Online Course about Death and Dying: Exploring Enrolment Motivations and Learning Goals of Health Care Workers
Authors: Oh Claris , Miller-Lewis Lauren , Tieman Jennifer
Published: April 2020

The Dying2Learn massive open online course (MOOC) was a five-week course designed for the general community covering various topics related to death and dying, developed with a sociocultural focus that encouraged personal reflection and open discussion, rather than a medical or clinical focus. Yet, the majority of the participants identified as health care workers. Their motivations to enrolling in the course as well as their learning goals were examined. Responses to questions relating to their motivations for enrolment and learning goals were assigned codes and grouped into themes. We then made comparisons between the different demographic and occupational groups. The most commonly mentioned responses related to general interest in the topic of death and dying. HCWs were more likely to mention work-related motivations and improving communication skills than non-HCWs. We found that HCWs hoped to be better at talking about death and dying, which might indicate a possible gap in their formal education in this area.
Special Issue on Intelligent Electronic Devices
Authors: Meen Teen-Hang , Zhao Wenbing , Yang Cheng-Fu
Published: April 2020

The second IEEE International Conference on Knowledge Innovation and Invention 2019 (IEEE ICKII 2019) was held in Seoul, South Korea, 12–15 July 2019. This special issue “Intelligent Electronic Devices” selects 13 excellent papers form 260 papers presented in IEEE ICKII 2019 conference about the topics of Intelligent Electronic Devices. The main goals of this special issue are to encourage scientists to publish their experimental and theoretical results in as much detail as possible, and to discover new scientific knowledge relevant to the topics of electronics.
Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression
Authors: Srinivasan Kathiravan , Mahendran Nivedhitha , Vincent Durai Raj , Chang Chuan-Yu , Syed-Abdul Shabbir
Published: April 2020

Unipolar depression (UD), also referred to as clinical depression, appears to be a widespread mental disorder around the world. Further, this is a vital state related to a person’s health that influences his/her daily routine. Besides, this state also influences the person’s frame of mind, behavior, and several body functionalities like sleep, appetite, and also it can cause a scenario where a person could harm himself/herself or others. In several cases, it becomes an arduous task to detect UD, since, it is a state of comorbidity. For that reason, this research proposes a more convenient approach for the physicians to detect the state of clinical depression at an initial phase using an integrated multistage support vector machine model. Initially, the dataset is preprocessed using multiple imputation by chained equations (MICE) technique. Then, for selecting the appropriate features, the support vector machine-based recursive feature elimination (SVM RFE) is deployed. Subsequently, the integrated multistage support vector machine classifier is built by employing the bagging random sampling technique. Finally, the experimental outcomes indicate that the proposed integrated multistage support vector machine model surpasses methods such as logistic regression, multilayer perceptron, random forest, and bagging SVM (majority voting), in terms of overall performance.
A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services
Authors: Wan Xiangpeng , Ghazzai Hakim , Massoud Yehia
Published: April 2020

Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.
Photovoltaic Degradation Rate Affected by Different Weather Conditions: A Case Study Based on PV Systems in the UK and Australia
Authors: Dhimish Mahmoud , Alrashidi Abdullah
Published: April 2020

This article presents the analysis of degradation rate over 10 years (2008 to 2017) for six different photovoltaic (PV) sites located in the United Kingdom (mainly affected by cold weather conditions) and Australia (PV affected by hot weather conditions). The analysis of the degradation rate was carried out using the year-on-year (YOY) degradation technique. It was found that the degradation rate in the UK systems varies from −1.05% and −1.16%/year. Whereas a higher degradation ranging from −1.35% to −1.46%/year is observed for the PV systems installed in Australia. Additionally, it was found that in the Australian PV systems multiple faulty PV bypass diodes are present due to the rapid change in the ambient temperature and uneven solar irradiance levels influencing the PV modules. However, in cold weather conditions (such as in the Northern UK) none of the bypass diodes were damaged over the considered PV exposure period. Furthermore, the number of PV hot spots have also been observed, where it was found that in the UK-based PV systems the number of hot spotted PV modules are less than those found in the Australian systems. Finally, the analysis of the monthly performance ratio (PR) was calculated. It was found that the mean monthly PR is equal to 88.81% and 86.35% for PV systems installed in the UK and Australia, respectively.
Correlation in MIMO Antennas
Authors: Honma Naoki , Murata Kentaro
Published: April 2020

Multiple-input multiple-output (MIMO) antennas are commonly evaluated by using the correlation coefficient. However, the correlation has several different definitions and calculation methods, and this misleads the real performance of the MIMO antennas. This paper describes five definitions and their differences of the correlation, i.e., signal correlation, channel correlation, fading correlation, complex pattern correlation, and S-parameter-based correlation. The difference of these characteristics is theoretically and numerically discussed, and their relations are schematically presented. Furthermore, the relationship between the correlation coefficient and the antenna element spacing is numerically evaluated, and it is shown that correlation coefficient calculated only from the S-parameter yields the error greater than 10% when the efficiency of the antenna is lower than 97%.
A Parallel Timing Synchronization Structure in Real-Time High Transmission Capacity Wireless Communication Systems
Authors: Hao Xin , Lin Changxing , Wu Qiuyu
Published: April 2020

In the past few years, parallel digital signal processing (PDSP) architectures have been intensively studied to fulfill the growing demand of channel capacity in coherent optical communication systems. However, to our knowledge, real-time timing synchronization in such architectures is until now not implemented on a Field Programmable Gate Array (FPGA). In this article, a parallel timing synchronization architecture is proposed. In the architecture, a parallel First In First Out (FIFO) structure based on an index associated rearranging method, and a dual feedback loop based on the Gardner’s algorithm, are adopted. Taking advantages of the FIFO structure, 67% Look Up Table (LUT) is saved in comparison with earlier results, meanwhile the Numerically Controlled Oscillator (NCO) is efficiently improved to meet the FPGA timing requirements for real-time performance. MATLAB simulations are run to evaluate the Bit Error Rate (BER) deterioration of the architecture. The float- and fixed-point simulation results have shown that, The BER deteriorations are less than 0.5 dB and 1 dB, respectively. Further, the implementation of the architecture on a Xilinx XC7VX485T FPGA chip is achieved. A 20 giga bit per second (Gbps) 16 Quadrature Amplitude Modulation (16QAM) real-time system is achieved at the system clock of 159.524 MHz. This work opens a new pathway to improve the transmission capacity in real-time wireless communication systems.
Special Issue on “Advanced Technology Related to Radar Signal, Imaging, and Radar Cross-Section Measurement”
Authors: Kobayashi Hirokazu , Moriyama Toshifumi
Published: April 2020

A radar system is made of many elemental and hard/software technologies [...]
Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar
Authors: Zhu Mingzhe , Feng Zhenpeng , Zhou Xianda , Xiao Rui , Qi Yue , Zhang Xinliang
Published: April 2020

Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.
A Generalized Unit Commitment and Economic Dispatch Approach for Analysing the Polish Power System under High Renewable Penetration
Authors: Pluta Marcin , Wyrwa Artur , Suwała Wojciech , Zyśk Janusz , Raczyński Maciej , Tokarski Stanisław
Published: April 2020

The achievement of carbon neutrality requires a deep transformation of the Polish power sector. This paper analyses the impact of increased electricity generation from wind and solar technologies envisaged in the newest version of the Energy Policy of Poland until 2040 on the operation of dispatchable generators in 2030. The analysis was carried out using the Model of Economic Dispatch and Unit commitment for System Analysis (MEDUSA) model, which solves a mixed integer problem related to unit commitment and economic dispatch in electrical power production. At first, the model was validated based on the real operation data from 2018. Next, five scenarios were built to analyse the operation of the system in 2030. The overall result of the study is that the safest solution from the point of view of power system stability is to extend the decommissioning of coal units of 200 and 300 MW classes, to invest in renewable energy sources (RES) according to the energy policy, to build new gas power plants with the total capacity of ca. 4 GW, and to enforce Demand Side Management (DSM) programs for shifting the electrical load. The proposed framework for the optimization of power system planning helps to avoid wrong investment decisions that would have a negative impact on energy prices.
Establishment of a Two-Stage Turbocharging System Model and Analysis on Influence Rules of Key Parameters
Authors: Tian Wei , Du Defeng , Li Juntong , Han Zhiqiang , Yu Wenbin
Published: April 2020

This paper took a two-stage turbocharged heavy-duty six-cylinder diesel engine as the research object and established a two-stage turbocharging system matching model. The influence rules between the two-stage turbocharging key parameters were analyzed, while summarizing an optimization method of key parameters of a two-stage turbocharger. The constraint equations for the optimal distribution principle of the two-stage turbocharger’s pressure ratio and expansion ratio were proposed. The results show that when the pressure ratio constraint equation and expansion ratio constraint equation are satisfied, the diesel engine can achieve the target pressure ratio, while the total energy consumption of the turbocharger is the lowest.