bluuebunny/crossref_metadata_2025 huggingface dataset:
Singular Integral Solutions of Analytical Surface Wave Model with Internal Crack
Authors: Kang Sanggoo , Wu Yin Chao , Ham Suyun
Published: May 2020

In this study, singular integral solutions were studied to investigate scattering of Rayleigh waves by subsurface cracks. Defining a wave scattering model by objects, such as cracks, still can be quite a challenge. The model’s analytical solution uses five different numerical integration methods: (1) the Gauss–Legendre quadrature, (2) the Gauss–Chebyshev quadrature, (3) the Gauss–Jacobi quadrature, (4) the Gauss–Hermite quadrature and (5) the Gauss–Laguerre quadrature. The study also provides an efficient dynamic finite element analysis to demonstrate the viability of the wave scattering model with an optimized model configuration for wave separation. The obtained analytical solutions are verified with displacement variation curves from the computational simulation by defining the correlation of the results. A novel, verified model, is proposed to provide variations in the backward and forward scattered surface wave displacements calculated by different frequencies and geometrical crack parameters. The analytical model can be solved by the Gauss–Legendre quadrature method, which shows the significantly correlated displacement variation with the FE simulation result. Ultimately, the reliable analytic model can provide an efficient approach to solving the parametric relationship of wave scattering.
EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
Authors: Luo Ling , Xue Dingyu , Feng Xinglong
Published: May 2020

In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications.
A Physical Cure for Tuberculosis: Carlo Forlanini and the Invention of Therapeutic Pneumothorax
Authors: Mazzarello Paolo
Published: May 2020

Carlo Forlanini (1847–1918), a medical doctor professor at the universities of Turin and Pavia, was the inventor of artificial pneumothorax, a method that allowed a first significant victory in the long war of medicine against pulmonary tuberculosis. The article outlines a portrait of this important clinician and focuses on the therapeutic innovation he introduced for the treatment of this infectious disease.
Application of the Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence: A Case Study from Heze City, Shandong Province, China
Authors: Zhang Liangliang , Cheng Hua , Yao Zhishu , Wang Xiaojian
Published: May 2020

Taking into account the inadequacy of the Knothe time function model to predict the dynamic surface subsidence caused by underground mining, a new hypothesis is proposed, and the improved Knothe time function model is established. Theoretical analysis shows the improved model agrees well with surface subsidence dynamic change, velocity change, and acceleration change rules. Combined with field measured data, the probability integral method, dual-medium method, and least square method are adopted to determine the time influence parameter C and the model order n. Based on monitoring data from four monitoring stations in the Guotun coal mine subsidence basin strike main profile from Heze city, Shandong Province, China, the accuracies of the Knothe time function and improved model are compared and analyzed. Results show the improved model can accurately describe the dynamic surface subsidence process and subsidence velocity with mining time. The average relative standard error between the predicted and measured values is only 4.8%—far lower than the Knothe time function model is 23%, verifying the improved model’s accuracy and reliability.
Purification Efficiency for Treated Waste Water in Case of Joint Infiltration with Water Originating from Precipitation
Authors: Fichtner Thomas , Ibrahim Sharif Ibne , Hamann Frieder , Graeber Peter-Wolfgang
Published: May 2020

The decentralized treatment of wastewater and its on-site infiltration is common practice, especially in rural areas. However, uncertainties exist on the degradation potential of treated wastewater constituents mixed with additional infiltration of large quantities of water originating from precipitation. The intermixture of these waters is causing a reduction of residence times of the treated wastewater as well as an increased water saturation within the unsaturated soil zone. This can lead to a negative influence on the purification efficiency. Laboratory-scale 1D column experiments, accompanied by numerical simulations of water flow using the software code HYDRUS 1D, have been performed to evaluate the risks for a reduction of the degradation efficiency. Water content measurements and analysis of relevant organic substances in samples taken at different depths were the basis for evaluating the influence of the joint infiltration on the purification efficiency. The results highlight that a joint infiltration of treated wastewater and additional water originating from precipitation is not affecting the degradation efficiency for treated wastewater constituents. Degradation rates under these conditions were similar to the infiltration of treated wastewater alone. Timeframes with high water saturation were limited to the duration of the precipitation event.
Empirical Remarks on the Translational Equivariance of Convolutional Layers
Authors: Cheoi Kyung Joo , Choi Hyeonyeong , Ko Jaepil
Published: May 2020

In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance is more important than equivariance. In this paper, we investigate how vulnerable CNNs without pooling or augmentation are to translation in object recognition. For CNNs that are specialized in learning local textures but vulnerable to learning global geometric information, we propose a method to explicitly transform an image into a global feature image and then provide it as an input to neural networks. In our experiments on a modified MNIST dataset, we demonstrate that the recognition accuracy of a conventional baseline network significantly decreases from 98% to less than 60% even in the case of 2-pixel translation. We also demonstrate that the proposed method is far superior to the baseline network in terms of performance improvement.
Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
Authors: Weng Meng-Chia , Lin Chiou-Liang , Lee Cheng-Hsi
Published: May 2020

The heat treatment of oil-contaminated sites is widely carried out for the purposes of remediation. However, heat treatment changes the physical and chemical properties of soil. Before the soil can be reused as a construction material in civil engineering, such as in backfill or road base materials, the changes to its physical properties must be understood. Therefore, this study investigates the changes in the physical and chemical properties of oil-contaminated soil after heat treatment. In this investigation, experimental samples of soil with added oil from a refinery plant are used to investigate the removal rate of total petroleum hydrocarbons (TPHs) by thermal desorption and incineration. The physical properties of the soil, including water permeability and mechanical properties, are compared before and after heat treatment. The results of this study are as follows. (1) Particle size analysis reveals that heat treatment makes soil particles finer. (2) In the burning reduction test, heat treatment at 900 °C removes more than 90% of THP. (3) In the direct shear test, the friction angle (ϕ) increases with the removal rate. (4) In the hydraulic test, as the removal rate increases, the permeability coefficient increases after heat treatment.
A Dynamic Model for Continuous Lowering Analysis of Deep-Sea Equipment, Based on the Lumped-Mass Method
Authors: Gao Pan , Yan Keliang , Ni Mingchen , Fu Xuehua , Liu Zhihui
Published: May 2020

The installation of subsea equipment is a critical step in offshore oil and gas development. A dynamic model to evaluate the lowering process is proposed. The cable–payload system is discretized as a series of spring dampers with the lumped-mass method. For the first time, not only the lowering velocity but also the rope’s structural damping and the nonlinear loads, such as drag force and snap load, are considered. The lowering velocity of the cable is considered through a variable-domain technique. Snap loads are considered by setting the internal forces in the elements to be zero when the cable slacks. A series of simulations reveals that the lowering velocity has great effects on the dynamic force in the cable. However, the structural damping of the cable has little effect on the system response. The snap load may occur in the cable when subjected to rapid downward heave motion, and decreases with the lowered depth increasing. The cable stiffness affects the system’s resonance depth, but has little effect on the peak dynamic force. The present work should be a valuable reference for future subsea equipment installation analysis.
Combined Application of Pipe Roof Pre-SUPPORT and Curtain Grouting Pre-Reinforcement in Closely Spaced Large Span Triple Tunnels
Authors: Li Ran , Zhang Dingli , Wu Peng , Fang Qian , Li Ao , Cao Liqiang
Published: May 2020

The platform of the Badaling Great Wall Underground Station consisted of large span triple tunnels. The triple tunnels passed through several major faults. Based on preliminary in situ monitoring, the interaction behavior among triple tunnels was significantly severe, and the original supports failed to meet the safety requirement. Therefore, pipe roof pre-support (PRPS) and curtain grouting pre-reinforcement (CGPR) were used as the pre-construction techniques. By numerical investigation, the pipe diameter, the arrangement angle of PRPS, and the grouting thickness of CGPR were optimized as 108 mm, 150°, and 3 m, respectively. According to numerical results, PRPS predominantly bore loose rock and reduced mean crown settlement (MCS), while CGPR primarily improved the mechanical properties of rock and decreased the pillar plastic ratio (PPR). PRPS and CGPR had complementary advantages in time, space, and mechanical properties. PRPS could timely form a strong arch structure far ahead of the tunnel face, CGPR could effectively restrict the development of plastic zone. They mutually and actively formed a strong permanent ring in front of the tunnel face. After the field application of PRPS and CGPR, the mean surrounding rock pressure reduced by 33.4%, the MCS reduced by 58.7%, and no support damage was observed. The excavation safety was guaranteed.
Investigation of Factors Affecting Sensitivity Enhancement of an Optical Fiber Probe for Microstructure Measurement Using Oblique Incident Light
Authors: Murakami Hiroshi , Katsuki Akio , Sajima Takao , Uchiyama Kosuke , Yoshida Ichiro , Hamano Yasuo , Honda Hiroshi
Published: May 2020

Recently, the demand for accuracy measurement of microstructures has been remarkable. We have been developing a measurement system for microstructures using a stylus made of an optical fiber and prisms. In this measurement system, the oblique irradiation of the laser beam was employed to detect the displacement of the stylus shaft generated by contact between the stylus tip and the measured surface, and to expand the measurable area and depth. After these evaluations, we discovered that the sensitivity was enhanced with the increase in the incident angle of the laser beam on the stylus shaft. Therefore, in this study, we focus on the effect of the incident angle of light on the measurement sensitivity. First, the enhancement sensitivity is confirmed by using the basic experimental apparatus. Next, the relationship between the incident angle of the laser beam and the sensitivity is theoretically examined using the 3D ray-tracing method. Finally, the factor of the sensitivity enhancement is analyzed using the same method. As a result, it was confirmed that the sensitivity was enhanced with the increase in the incident angle of the laser beam on the stylus shaft.
Feature Selection for Improving Failure Detection in Hard Disk Drives Using a Genetic Algorithm and Significance Scores
Authors: Ahmad Wasim , Khan Sheraz Ali , Kim Cheol Hong , Kim Jong-Myon
Published: May 2020

Hard disk drives (HDD) are used for data storage in personal computing platforms as well as commercial datacenters. An abrupt failure of these devices may result in an irreversible loss of critical data. Most HDD use self-monitoring, analysis, and reporting technology (SMART), and record different performance parameters to assess their own health. However, not all SMART attributes are effective at detecting a failing HDD. In this paper, a two-tier approach is presented to select the most effective precursors for a failing HDD. In the first tier, a genetic algorithm (GA) is used to select a subset of SMART attributes that lead to easily distinguishable and well clustered feature vectors in the selected subset. The GA finds the optimal feature subset by evaluating only combinations of SMART attributes, while ignoring their individual fitness. A second tier is proposed to filter the features selected using the GA by evaluating each feature independently, using a significance score that measures the statistical contribution of a feature towards disk failures. The resultant subset of selected SMART attributes is used to train a generative classifier, the naïve Bayes classifier. The proposed method is tested on a SMART dataset from a commercial datacenter, and the results are compared with state-of-the-art methods, indicating that the proposed method has a better failure detection rate and a reasonable false alarm rate. It uses fewer SMART attributes, which reduces the required training time for the classifier and does not require tuning any parameters or thresholds.
Implementation of High Gas Barrier Laminated Films Based on Cellulose Nanocrystals for Food Flexible Packaging
Authors: Fotie Ghislain , Gazzotti Stefano , Ortenzi Marco Aldo , Piergiovanni Luciano
Published: May 2020

In this work, three types of cellulose nanocrystals (CNCs) were used: CNCSO3H extracted from wood pulp by sulfuric acid (H2SO4), CNCCOOH extracted from cotton linters by ammonium persulfate (APS) and CNCCOOR obtained by esterification of the previous two CNCCOOH and CNCSO3H. For a comparative assessment of gas barrier performance, plastic films such as PLA, PET, PE, PP, OPP and OPA were selected, coated with the three types of CNCs and finally laminated with a solvent-based polyurethanic adhesive. First, all dispersed CNCs were characterized by apparent hydrodynamic diameter and Z potential by means of dynamic light scattering (DLS) and electrophoretic light scattering (ELS) techniques, respectively, followed by the crystallinity index (XRD), thermogravimetric analysis (TGA) and evaluation of Fourier-transform infrared spectroscopy (FTIR), as well as the charges density. The surface chemistry of coated plastics (CNCs-P) was assessed by the Z potential through the electrokinetic technique (streaming potential method) and the optical contact angle (OCA). Lastly, laminated films (P-CNC-P) were evaluated by gas permeability measurements at 23 °C and 50–80% RH. It is worth noting that improvements between 90% and 100% of oxygen barrier were achieved after the lamination. This paper provides insights on the choice of cellulosic nanomaterials for the design and development of advanced and sustainable food packaging materials.
Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review
Authors: Ahmad Sumera , Miskon Suraya , Alkanhal Tawfeeq Abdullah , Tlili Iskander
Published: May 2020

Race towards industry 4.0 increases the hyper competition and puts pressure on organizations to integrate the advanced technologies. Business intelligence system (BIS) is one of the top prioritized technologies that attracted the significant attention of policy-makers and industry experts due to its ability to provide more informed and intelligent knowledge for decision-making processes. It is evident by literature that organizations and industries are prone to integrate the BIS at large scale, but more than 70% BIS projects fail to give the expected results. Hence, it is pertinent to explore the significant determinants that influence the BIS adoption and acceptance in organizations. Although previous literature did not have any comprehensive review relevant to the individual, technological, organizational, and environmental determinants. Therefore, the current study tries to narrow this gap by a systematic literature review (SLR) of 84 studies that were published during the period of 2011–2020. A total of 93 determinants are identified based on content analysis by using text mining techniques of Yoshikoder and human coding skills. The identified determinants are ranked according to their frequency of use. A theoretical framework has been developed with potential determinants and theories. The study results will enrich the recent BIS literature and improve the understanding of practitioners’ decision-making processes to leverage maximum value from the adoption of BIS.
Current Status and Developments of the Atomic Database on Rare-Earths at Mons University (DREAM)
Authors: Quinet Pascal , Palmeri Patrick
Published: May 2020

The main purpose of the Database on Rare Earths At Mons University (DREAM) is to provide the scientific community with updated spectroscopic parameters related to lanthanide atoms (Z = 57–71) in their lowest ionization stages. The radiative parameters (oscillator strengths and transitions probabilities) listed in the database have been obtained over the past 20 years by the Atomic Physics and Astrophysics group of Mons University, Belgium, thanks to a systematic and extensive use of the pseudo-relativistic Hartree-Fock (HFR) method modified for taking core-polarization and core-penetration effects into account. Most of these theoretical results have been validated by the good agreement obtained when comparing computed radiative lifetimes and accurate experimental values measured by the time-resolved laser-induced fluorescence technique. In the present paper, we report on the current status and developments of the database that gathers radiative parameters for more than 72,000 spectral lines in neutral, singly-, doubly-, and triply-ionized lanthanides.
The Role of the Histone Methyltransferase EZH2 in Liver Inflammation and Fibrosis in STAM NASH Mice
Authors: Lee Seul , Woo Dong-Cheol , Kang Jeeheon , Ra Moonjin , Kim Ki Hyun , Lee Seoung Rak , Choi Dong Kyu , Lee Heejin , Hong Ki Bum , Min Sang-Hyun , Lee Yongjun , Yu Ji Hoon
Published: May 2020

Non-alcoholic fatty liver disease (NAFLD) is a leading form of chronic liver disease, with few biomarkers and treatment options currently available. Non-alcoholic steatohepatitis (NASH), a progressive disease of NAFLD, may lead to fibrosis, cirrhosis, and hepatocellular carcinoma. Epigenetic modification can contribute to the progression of NAFLD causing non-alcoholic steatohepatitis (NASH), in which the exact role of epigenetics remains poorly understood. To identify potential therapeutics for NASH, we tested small-molecule inhibitors of the epigenetic target histone methyltransferase EZH2, Tazemetostat (EPZ-6438), and UNC1999 in STAM NASH mice. The results demonstrate that treatment with EZH2 inhibitors decreased serum TNF-alpha in NASH. In this study, we investigated that inhibition of EZH2 reduced mRNA expression of inflammatory cytokines and fibrosis markers in NASH mice. In conclusion, these results suggest that EZH2 may present a promising therapeutic target in the treatment of NASH.
Coronavirus Pandemic—Therapy and Vaccines
Authors: Lundstrom Kenneth
Published: May 2020

The current coronavirus COVID-19 pandemic, which originated in Wuhan, China, has raised significant social, psychological and economic concerns in addition to direct medical issues. The rapid spread of severe acute respiratory syndrome-coronavirus (SARS-CoV)-2 to almost every country on the globe and the failure to contain the infections have contributed to fear and panic worldwide. The lack of available and efficient antiviral drugs or vaccines has further worsened the situation. For these reasons, it cannot be overstated that an accelerated effort for the development of novel drugs and vaccines is needed. In this context, novel approaches in both gene therapy and vaccine development are essential. Previous experience from SARS- and MERS-coronavirus vaccine and drug development projects have targeted glycoprotein epitopes, monoclonal antibodies, angiotensin receptor blockers and gene silencing technologies, which may be useful for COVID-19 too. Moreover, existing antivirals used for other types of viral infections have been considered as urgent action is necessary. This review aims at providing a background of coronavirus genetics and biology, examples of therapeutic and vaccine strategies taken and potential innovative novel approaches in progress.
Synthesis of Magnetic Fe3O4/ZnWO4 and Fe3O4/ZnWO4/CeVO4 Nanoparticles: The Photocatalytic Effects on Organic Pollutants upon Irradiation with UV-Vis Light
Authors: Amin Marsooli Mohammad , Rahimi Nasrabadi Mehdi , Fasihi-Ramandi Mahdi , Adib Kourosh , Pourmasoud Saeid , Ahmadi Farhad , Eghbali Mohammad , Sobhani Nasab Ali , Tomczykowa Monika , Plonska-Brzezinska Marta E.
Published: May 2020

Magnetic Fe3O4/ZnWO4 and Fe3O4/ZnWO4/CeVO4 nanoparticles with different molar ratios of CeVO4 to other inorganic components were synthesized through co-precipitation with a sonochemical-assisted method. X-ray diffraction, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, ultraviolet-visible diffuse reflectance spectroscopy, vibrating sample magnetometry, and scanning electron microscopy (SEM) methods were used for the physico–chemical characterization of the obtained nanoparticles. As shown in the SEM images, the average sizes of the Fe3O4/ZnWO4 and Fe3O4/ZnWO4/CeVO4 nanoparticles that formed aggregates were approximately 50–70 nm and 80–100 nm, respectively. The photocatalytic performance of these nanoparticles was examined by measuring methylene blue degradation under visible light (assisted by H2O2). The sample with a mass ratio of 1:2:1 (Fe3O4/ZnWO4/CeVO4, S4) exhibited optimal photocatalytic performance, and thus this sample was subsequently used for the photodegradation of different organic pollutants upon irradiation with ultraviolet (UV) and visible light. Approximately 90% and 70% degradation of methyl violet and methylene blue, respectively, was observed after visible light irradiation. Additionally, the mechanism of the photocatalytic reaction was investigated by measuring ˙OH release under UV light in a system with terephthalic acid and by measuring the release of ˙O2−, ˙OH, and hole scavengers.
Pyrolysis of Polyethylene Terephthalate over Carbon-Supported Pd Catalyst
Authors: Park Chanyeong , Kim Soosan , Kwon Yeonghwan , Jeong Chaehyeon , Cho Yujin , Lee Chang-Gu , Jung Seungho , Choi Kwon-Young , Lee Jechan
Published: May 2020

Pyrolysis of polyethylene terephthalate (PET) produces polycyclic hydrocarbons and biphenyl derivatives that are harmful to human health and the environment. Therefore, a palladium metal catalyst (5 wt.% Pd loaded on activated carbon) was used to prevent the formation of harmful materials. When a Pd catalyst/PET ratio of 0.01 was applied in pyrolysis of PET, it did not show a meaningful difference in the generation of polycyclic hydrocarbons and biphenyl derivatives. However, when a Pd catalyst/PET ratio of 0.05 was used during pyrolysis, it prevented their formation and generation at experimental temperature ranges (400–700 °C). For example, the concentration of 2-naphthalenecarboxylic acid produced, which is a typical polycyclic hydrocarbon material, was reduced by 44%. In addition, the concentration of biphenyl-4-carboxylic acid, which is contained in biphenyl derivatives, was reduced by 79% compared to non-catalytic pyrolysis at 800 °C. This was because the ring-opening reaction and free radical mechanism caused by the Pd catalyst and thermal cracking were dominant during the pyrolysis of PET. Apart from these materials, amine compounds were generated as products of the pyrolysis of PET. Amine concentration showed a similar trend with polycyclic hydrocarbons and benzene derivatives. Based on these results, the total concentration of polycyclic hydrocarbons and biphenyl derivatives was compared; the results confirmed that the concentrations of all substances were reduced. This research suggests that a metal-supported catalyst will help create a more environmentally friendly and reliable method of industrial plastic waste disposal.
Generation of Charged Ti Nanoparticles and Their Deposition Behavior with a Substrate Bias during RF Magnetron Sputtering
Authors: Kwon Ji-Hye , Kim Du-Yun , Hwang Nong-Moon
Published: May 2020

This study is based on the film growth by non-classical crystallization, where charged nanoparticles (NPs) are the building block of film deposition. Extensive studies about the generation of charged NPs and their contribution to film deposition have been made in the chemical vapor deposition (CVD) process. However, only a few studies have been made in the physical vapor deposition (PVD) process. Here, the possibility for Ti films to grow by charged Ti NPs was studied during radio frequency (RF) sputtering using Ti target. After the generation of charged Ti NPs was confirmed, their influence on the film quality was investigated. Charged Ti NPs were captured on amorphous carbon membranes with the electric bias of −70 V, 0 V, +5 V, +15 V and +30 V and examined by transmission electron microscopy (TEM). The number density of the Ti NPs decreased with increasing positive bias, which showed that some of Ti NPs were positively charged and repelled by the positively biased TEM membrane. Ti films were deposited on Si substrates with the bias of −70 V, 0 V and +30 V and analyzed by TEM, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and X-ray reflectivity (XRR). The film deposited at −70 V had the highest thickness of 180 nm, calculated density of 4.974 g/cm3 and crystallinity, whereas the film deposited at +30 V had the lowest thickness of 92 nm, calculated density of 3.499 g/cm3 and crystallinity. This was attributed to the attraction of positively charged Ti NPs to the substrate at −70 V and to the landing of only small-sized neutral Ti NPs on the substrate at +30 V. These results indicate that the control of charged NPs is necessary to obtain a high quality thin film at room temperature.
Microstructural and Corrosion Properties of PEO Coated Zinc-Aluminized (ZA) Steel
Authors: Pezzato Luca , Settimi Alessio Giorgio , Cerchier Pietrogiovanni , Gennari Claudio , Dabalà Manuele , Brunelli Katya
Published: May 2020

Plasma Electrolytic Oxidation (PEO) is a surface treatment, similar to anodizing, that produces thick oxide films on the surface of metals. In the present work, PEO coatings were obtained on zinc-aluminized (ZA) carbon steel using a solution containing sodium silicate and potassium hydroxide as electrolyte, and working with high current densities and short treatment times in Direct Current (DC) mode. The thickness of the coating, as well as the surface morphology, were strongly influenced by the process parameters, with different dissolution grades of the ZA layer depending on the current density and treatment time. A compromise between thickness and porosity of the coating was found with low current density/long treatment time or high current density/short treatment time. The PEO layer was mainly composed of aluminum oxides and silicon compounds. The corrosion resistance increased remarkably in the samples with the PEO coating. These PEO coated samples are suitable for sealing treatments that further increase their corrosion properties or will be also an ideal substrate for commercial painting, assuring improved mechanical adhesion and protection even in the presence of damages.
Magnetic Nanostructures Embedded in III-Nitrides: Assembly and Performance
Authors: Navarro-Quezada Andrea
Published: May 2020

III-Nitride semiconductors are the materials of choice for state-of-the-art opto-electronic and high-power electronic applications. Through the incorporation of magnetic ions, like transition metals and rare-earths, III-Nitrides have further extended their applicability to spintronic devices. However, in most III-Nitrides the low solubility of the magnetic ions leads to the formation of secondary phases that are often responsible for the observed magnetic behavior of the layers. The present review summarizes the research dedicated to the understanding of the basic properties, from the fabrication to the performance, of III-Nitride-based phase-separated magnetic systems containing embedded magnetic nanostructures as suitable candidates for spintronics applications.
Generative Design by Using Exploration Approaches of Reinforcement Learning in Density-Based Structural Topology Optimization
Authors: Sun Hongbo , Ma Ling
Published: May 2020

A central challenge in generative design is the exploration of vast number of solutions. In this work, we extend two major density-based structural topology optimization (STO) methods based on four classes of exploration algorithms of reinforcement learning (RL) to STO problems, which approaches generative design in a new way. The four methods are: first, using ε -greedy policy to disturb the search direction; second, using upper confidence bound (UCB) to add a bonus on sensitivity; last, using Thompson sampling (TS) as well as information-directed sampling (IDS) to direct the search, where the posterior function of reward is fitted by Beta distribution or Gaussian distribution. Those combined methods are evaluated on some structure compliance minimization tasks from 2D to 3D, including the variable thickness design problem of an atmospheric diving suit (ADS). We show that all methods can generate various acceptable design options by varying one or two parameters simply, except that IDS fails to reach the convergence for complex structures due to the limitation of computation ability. We also show that both Beta distribution and Gaussian distribution work well to describe the posterior probability.
Left Supraclavicular Lymph Node Metastasis from Ovarian Cancer Associated with Papillary Thyroid Microcarcinoma, a Confusing Pathology-Essential Role of Functional Imaging
Authors: Piciu Doina , Meșter Alexandru , Căinap Calin , Bărbuș Elena , Morariu Dragos-Stefan , Piciu Andra
Published: May 2020

The revolution of imaging in medicine leads to new standards of care, mostly in specialties like oncology, neurology, or endocrinology. We present a review of the literature and a case report of a 62-year-old patient initially treated for a benign gynecologic pathology and followed-up for 7 years clinically, with serologic and with multiple imaging techniques. There is an actual growing use of highly sensitive functional imaging methods, like fluoro-deoxy-glucose (F18-FDG) positron emission tomography/computed tomography (PET/CT) in the evaluation of oncologic pathologies, staging, follow-up, and therapy response monitoring. This is the first case report described in the literature presenting the association of thyroid papillary microcarcinoma (MPTC) and supraclavicular metastasis of ovarian cancer. The study aims to underline the necessity of a complex and careful evaluation of each oncologic patient, due to the unexpected clinical presentation and rare association of diseases, sometimes leading to confusing management.
The Effects of Early Bispectral Index to Predict Poor Neurological Function in Cardiac Arrest Patients: A Systematic Review and Meta-Analysis
Authors: Chang Chun-Yu , Chen Chien-Sheng , Chien Yung-Jiun , Lin Po-Chen , Wu Meng-Yu
Published: May 2020

The diagnostic performance of the bispectral index (BIS) to early predict neurological outcomes in patients achieving return of spontaneous circulation (ROSC) after cardiac arrest (CA) remained unclear. We searched PubMed, EMBASE, Scopus and CENTRAL for relevant studies through October 2019. Methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analysis was performed using a linear mixed-effects model to the log-transformed data with a logistic distribution assumption. Bivariate meta-regression was performed to explore heterogeneity. In total, 13 studies with 999 CA adult patients were included. At the optimal threshold of 32, BIS obtained within 72 h of ROSC elicits a pooled sensitivity of 84.9% (95% confidence interval (CI), 71.1% to 92.7%), a pooled specificity of 85.9% (95% CI, 71.2% to 93.8%) and an area under the curve of 0.92. Moreover, a BIS cutoff < 12 yielded a pooled specificity of 95.0% (95% CI, 77.8% to 99.0%). In bivariate meta-regression, the timing of neurological outcome assessment, the adoption of targeted temperature management, and the administration of sedative agents or neuromuscular blocking agents (NMBA) were not identified as the potential source of heterogeneity. BIS retains good diagnostic performance during targeted temperature management (TTM) and in the presence of administrated sedative agents and NMBA. In conclusion, BIS can predict poor neurological outcomes early in patients with ROSC after CA with good diagnostic performance and should be incorporated into the neuroprognostication strategy algorithm.
Acute Renal Failure/Acute Kidney Injury (AKI) Associated with Endovascular Procedures
Authors: Krasinski Zbigniew , Krasińska Beata , Olszewska Marta , Pawlaczyk Krzysztof
Published: May 2020

AKI is one of the most common yet underdiagnosed postoperative complications that can occur after any type of surgery. Contrast-induced nephropathy (CIN) is still poorly defined and due to a wide range of confounding individual variables, its risk is difficult to determine. CIN mainly affects patients with underlying chronic kidney disease, diabetes, sepsis, heart failure, acute coronary syndrome and cardiogenic shock. Further research is necessary to better understand pathophysiology of contrast-induced AKI and consequent implementation of effective prevention and therapeutic strategies. Although many therapies have been tested to avoid CIN, the only potent preventative strategy involves aggressive fluid administration and reduction of contrast volume. Regardless of surgical technique—open or endovascular—perioperative AKI is associated with significant morbidity, mortality and cost. Endovascular procedures always require administration of a contrast media, which may cause acute tubular necrosis or renal vascular embolization leading to renal ischemia and as a consequence, contribute to increased number of post-operative AKIs.
Vitamin D-Binding Protein (Gc-Globulin) in Acute Liver Failure in Children
Authors: Grama Alina , Burac Lucia , Aldea Cornel Olimpiu , Bulata Bogdan , Delean Dan , Samasca Gabriel , Abrudan Carmen , Sirbe Claudia , Pop Tudor Lucian
Published: May 2020

This study aimed to analyse vitamin D-binding protein (Gc-globulin) serum levels in acute liver failure (ALF) in children in relation to disease outcomes and correlations with other known markers used to evaluate the severity of ALF. Our study included 34 children (mean age 4.87 ± 5.30 years) with ALF of different causes (metabolic, 26.47%; autoimmune, 23.53%; toxic, 20.59%; infection, 17.65%; unknown, 11.76%) and 30 children without any liver injury (mean age 6.11 ± 4.26 years). The outcome was poor in 14 patients (41.18%), including one child with liver transplantation (2.94%). Serum Gc-globulin levels were significantly lower in ALF patients compared to the control group (151.57 ± 171.8 mg/L vs. 498.63 ± 252.50 mg/L; p < 0.000001), with an optimum cut-off of 163.5 mg/L (Area Under the Curve, AUC, 0.8921; sensitivity, 76.50%; specificity, 100%). Levels were also lower in patients with poor outcomes compared to survivors (59.34 ± 33.73 mg/L vs. 216.12 ± 199.69 mg/L; p < 0.0001), with an optimum cut-off 115 mg/L (AUC, 0.7642; sensitivity, 100%; specificity, 50%). Gc-globulin serum levels were variable according to ALF aetiology, i.e., lower in metabolic, infectious, or unknown causes compared to autoimmune and toxic causes. Gc-globulin serum levels were decreased in children with ALF and lower in those with poor outcomes compared with survivors. Gc-globulin serum levels were correlated with other known parameters used to evaluate the severity of ALF and could help to identify patients at high risk for poor outcomes.
Reliable Long-Range Multi-Link Communication for Unmanned Search and Rescue Aircraft Systems in Beyond Visual Line of Sight Operation
Authors: Güldenring Johannes , Gorczak Philipp , Eckermann Fabian , Patchou Manuel , Tiemann Janis , Kurtz Fabian , Wietfeld Christian
Published: May 2020

With the increasing availability of unmanned aircraft systems, their usage for search and rescue is close at hand. Especially in the maritime context, aerial support can yield significant benefits. This article proposes and evaluates the concept of combining multiple cellular networks for highly reliable communication with those aircraft systems. The proposed approach is experimentally validated in several unprecedented large-scale experiments in the maritime context. It is found that in this scenario, conventional methods do not suffice for reliable connectivity to the aircraft with significantly varying overall availabilities between 68% and 97%. The underlying work, however, overcomes the limitations of single-link connectivity by providing availability of up to 99.8% in the analyzed scenarios. Therefore, the approach and the experimental data presented in this work yield a solid contribution to search and rescue drones. All results and flight recording data sets are published along with this article to enable future related work and studies, external reproduction, and validation of the underlying results and findings.
Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy
Authors: Pan Lizheng , Yin Zeming , She Shigang , Song Aiguo
Published: May 2020

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.
Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism
Authors: Friston Karl J. , Wiese Wanja , Hobson J. Allan
Published: May 2020

This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and ‘forces’ that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: (1) fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). (2) All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system—whose internal states can be distinguished from external states—must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.
Specifying the Unitary Evolution of a Qudit for a General Nonstationary Hamiltonian via the Generalized Gell-Mann Representation
Authors: Loubenets Elena R. , Käding Christian
Published: May 2020

Optimal realizations of quantum technology tasks lead to the necessity of a detailed analytical study of the behavior of a d-level quantum system (qudit) under a time-dependent Hamiltonian. In the present article, we introduce a new general formalism describing the unitary evolution of a qudit ( d ≥ 2 ) in terms of the Bloch-like vector space and specify how, in a general case, this formalism is related to finding time-dependent parameters in the exponential representation of the evolution operator under an arbitrary time-dependent Hamiltonian. Applying this new general formalism to a qubit case ( d = 2 ) , we specify the unitary evolution of a qubit via the evolution of a unit vector in R 4 , and this allows us to derive the precise analytical expression of the qubit unitary evolution operator for a wide class of nonstationary Hamiltonians. This new analytical expression includes the qubit solutions known in the literature only as particular cases.
Entropic Analysis of Votes Expressed in Italian Elections between 1948 and 2018
Authors: Marmani Stefano , Ficcadenti Valerio , Kaur Parmjit , Dhesi Gurjeet
Published: May 2020

In Italy, the elections occur often, indeed almost every year the citizens are involved in a democratic choice for deciding leaders of different administrative entities. Sometimes the citizens are called to vote for filling more than one office in more than one administrative body. This phenomenon has occurred 35 times after 1948; it creates the peculiar condition of having the same sample of people expressing decisions on political bases at the same time. Therefore, the Italian contemporaneous ballots constitute the occasion to measure coherence and chaos in the way of expressing political opinion. In this paper, we address all the Italian elections that occurred between 1948 and 2018. We collect the number of votes per party at each administrative level and we treat each election as a manifestation of a complex system. Then, we use the Shannon entropy and the Gini Index to study the degree of disorder manifested during different types of elections at the municipality level. A particular focus is devoted to the contemporaneous elections. Such cases implicate different disorder dynamics in the contemporaneous ballots, when different administrative level are involved. Furthermore, some features that characterize different entropic regimes have emerged.
Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation
Authors: Ding Fei , Wen Tao , Ren Suju , Bao Jianmin
Published: May 2020

The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.