bluuebunny/crossref_metadata_2025 huggingface dataset:
Discovery of Quark-Gluon Plasma: Strangeness Diaries
Authors: Rafelski Johann
Published: January 2020

We look from a theoretical perspective at the new phase of matter, quark-gluon plasma (QGP), the new form of nuclear matter created at high temperature and pressure. Here I retrace the path to QGP discovery and its exploration in terms of strangeness production and strange particle signatures. We will see the theoretical arguments that have been advanced to create interest in this determining signature of QGP. We explore the procedure used by several experimental groups making strangeness production an important tool in the search and discovery of this primordial state of matter present in the Universe before matter in its present form was formed. We close by looking at both the ongoing research that increases the reach of this observable to LHC energy scale pp collisions, and propose an interpretation of these unexpected results.
Numerical study of bioconvection flow of nanofluids using non-Fourier’s heat flux and non-Fick’s mass flux theory
Authors: Aneja Madhu , Sharma Sapna
Published: January 2020

The characteristics of buoyancy-driven convection of nanofluid stream containing motile gyrotactic micro-organisms over a continuous heated surface are explored. The benefits of including micro-organisms to the suspension incorporate micro-scale mixing and foreseen enhanced stability of nanofluid. For heat transfer and mass transfer processes, non-Fourier’s heat flux theory and non-Fick’s mass flux theory are employed. This theory is actively under investigation to resolve some drawbacks of the famous Fourier’s Law and Fick’s Law. The modified parameters in conventional laws are thermal and solutal relaxation times, respectively. The governing equations are remodeled using appropriate similarity transformations into a system of coupled ordinary differential equations. Finite Element Methodology is used to obtain the solution of nonlinear coupled differential equations. The governing equations are associated with dimensionless parameters like [Formula: see text]. The influence of these parameters is analyzed graphically on velocity, temperature profile, concentration profile and density of micro-organisms. The computational results obtained reveal that the temperature profile and concentration profile have an inverse relationship with thermal relaxation and solutal relaxation time, respectively. Furthermore, the velocity increases with increasing values of the Richardson number, while a reverse pattern is observed for bioconvection Rayleigh number and Buoyancy ratio parameter.
International Relations with Chinese Characteristics
Authors: Pettman Ralph
Published: January 2020

International relations, as currently construed, are multi-dimensional. They are also Euro-American, which means modern-day China had no hand in making them. It was obliged to adapt to the state-centered, marketeering, nationalistic realities with which it was confronted when it became independent. And adapt it did. It also, however, revised these realities by adopting its own approach. Its leaders first repudiated China’s traditional experiences, while reworking its world ones to promote their own ends. Later, however, they began to express admiration for the values and vision of their own culture and civilization. They began to articulate policies, like the Belt and Road Initiative, that were not only representative of Euro-American principles, such as international cooperation and free trade, but also representative of non-Euro-American principles, such as the so-called “tribute system”. The latter characterized China’s foreign policy approach for millennia. It still arguably demonstrates China’s willingness not only to accept — while reforming — those Euro-American practices imposed upon it, but also to repudiate — by revolutionizing — those very same practices.
Perspective—Challenges in Developing Wearable Electrochemical Sensors for Longitudinal Health Monitoring
Authors: Fan Ruolan , Andrew Trisha L.
Published: January 2020

Wearable electrochemical sensors have the potential to overcome the problem of infrequent clinical visits that leads to transient events of potential diagnostic importance being unduly overlooked. The promise of real-time, personalized health care has driven multidisciplinary work on fabricating various forms of wearable sensors. Although remarkable advances in device form factor and integrated circuit design have been achieved, notable hurdles, such as shelf life, reuseability, flex and sweat resistance, and longitudinal performance, remain unaddressed. This perspective seeks to summarize major advances in current wearable electrochemical sensors and to highlight the most pressing challenges that will benefit from collective research endeavors.
Fundamental Aspects of Contamination during the Hydrogen Evolution/Oxidation Reaction in Alkaline Media
Authors: Weber D. J. , Dosche C. , Oezaslan M.
Published: January 2020

This work discusses the experimental pitfalls and challenges for activity benchmarking of hydrogen evolution/oxidation reactions (HER/HOR) for Pt-based catalysts in alkaline media. For identifying the source of impurities, we systematically analyzed our electrochemical set-up like cell material, hydrogen gas, and electrolyte solutions. Commonly used 0.1 M electrolyte solutions like LiOH, NaOH, and KOH were tested. We could mainly eliminate the glass cell and hydrogen gas as a main source of contamination for poly-Pt during HER/HOR in alkaline media. Long-term chronoamperometric experiments in different electrolyte solutions were applied to accumulate the impurities at the electrode surface. X-ray photoelectron spectroscopy (XPS) data enable clear identification of the observed contamination after HER/HOR. Thereby, we showed that traces of metal cations in the electrolyte solution can be electro-deposited on the electrode surface during HER and leads to strong catalyst poisoning. We have developed an electrochemical purification step to reduce these impurities stemmed from the electrolyte solution. The use of purified electrolyte solutions provides reliable and reproducible studies for HER/HOR activity benchmarking of Pt-based catalysts which is essential to underderstand HER/HOR kinetics and mechanism in alkaline media.
Study on Mechanical Properties of Submunition’s Ribbon Straightening Section
Authors: Qin Dong-ze , Zhang Wei , Zhang Shu-Yun , Guo Tian-Ji , Pei Shui-Yuan , Zhang Jin-Shi
Published: January 2020

The humanitarian damage caused by the unexploded submunitions is one of the hot issues of concern to the international community at present. A portion of the submunition that did not explode was caused by a break at the connection between the ribbon riveting and the fuze. According to the physical structure of the submunition and the trajectory into which it was ejected, we analyzed the forces of the submunition in flight, deduced the related mathematical models, and clarify the key elements of the mechanics. In this paper, the commercial simulation software was used to calculate the mechanical properties of the ribbon. And the variation regularity between drop velocity and straightening force of ribbon are revealed. And the response characteristics of different material ribbon with different sizes of riveting holes and riveting joints under tensile action were simulated. The simulation results show that, in the trajectory environment with 30 m/s~55 m/s typical stream speed, the tensile force of the ribbon is less than 300 N, and the application concentration of the connecting parts of the riveting joint and the ribbon will not cause the failure of the kevlar ribbon, but it will cause the failure of the nylon ribbon. In order to verify the variation of the tension of kevlar ribbons in different trajectory environments, we designed the experimental scheme of tension test of the ribbon straightening section of submunition and conducted experiments. Experimental results and numerical simulation results revealed the same law. This paper provides effective technical support for solving the problem of unexploded submunitions.
Effects of Acupuncture on Hospitalized Patients with Urinary Retention
Authors: Chen Suhui , Sun Hua , Xu Hong , Zhang Yamin , Wang Huanyuan
Published: January 2020

Objective. The aim of this study was to investigate the effects of acupuncture on urinary retention and provide treatment suggestions. Methods. A total of 113 hospitalized patients with urinary retention were included in this study. The GV20, CV6, CV4, CV3, ST28, SP6, and SP9 points were selected as the main acupoints. Acupuncture therapy was conducted for 30 minutes per session. The total number of treatment sessions was determined by the symptoms and the length of hospital stay. Bladder postvoid residual urine volume (PVR) was measured pretreatment and posttreatment by ultrasonic. Efficacy defined as spontaneous urination and a residual urine volume <50 mL was measured. Results. The median number of acupuncture treatment sessions was 3 (range, 1–12 times). Acupuncture treatment significantly reduced the PVR (545.1 ± 23.9 mL vs 67.4 ± 10.7 mL; p < 0.001). Among the 113 patients, 99 (87.6%) patients were cured and 8 (7.1%) patients were improved of their urinary retention. The remaining 6 (5.3%) patients’ urinary retention did not improve. The effective rate was 94.7%. There was significant difference in the efficacy rate between patients with one urinary catheterization and with two or more. Acupuncture treatment was not associated with side effects. Conclusion. Acupuncture is an effective and safe treatment option for urinary retention. Early application of acupuncture treatment should be considered in clinic, and repeated urinary catheter insertion and removal should be avoided. Our study suggests that a randomized controlled study with a large sample size to verify the efficacy of acupuncture for the treatment of urinary retention is warranted.
Prognostic Value of Functional Assessment of Cancer Therapy‐General (FACT‐G) in Advanced Non‐Small‐Cell Lung Cancer Treated with Korean Medicine
Authors: Jeon Hyeonjin , Eo Wankyu , Shim Bumsang , Kim Sehyun , Lee Sookyung
Published: January 2020

Objectives. The impact of health‐related quality of life (HRQoL) on survival has been investigated in patients with various cancers. Here, we evaluated the prognostic value of HRQoL using the Functional Assessment of Cancer Therapy‐General (FACT‐G) in advanced non‐small‐cell lung cancer (NSCLC) patients treated with Korean medicine. Methods. A retrospective review of medical records and FACT‐G scores of patients with advanced NSCLC who received treatment with Korean medicine was conducted. The reliability of the FACT‐G was determined using Cronbach’s alpha and calculating floor‐and‐ceiling effects. Correlations between FACT‐G scores were estimated using Pearson’s correlation analysis. Overall survival was calculated using the Kaplan–Meier method, and the prognostic impact of FACT‐G scores and patients’ characteristics was evaluated with Cox proportional hazards regression. Results. Of the 165 enrolled patients, 115 (70%) had extrathoracic metastasis and 139 (84%) had undergone prior anticancer treatment. The median overall survival was 10.1 months. The mean FACT‐G score was 65.0, and Cronbach’s alpha for the FACT‐G was 0.917. Age ≥65 years, male sex, smoking history, squamous‐cell carcinoma, Eastern Cooperative Oncology Group Performance Status (ECOG‐PS) ≥2, and presence of extrathoracic metastasis were associated with an increased risk of mortality. High FACT‐G total scores, physical well‐being (PWB), emotional well‐being, and functional well‐being were associated with prolonged survival. After adjusting for age, sex, smoking history, ECOG‐PS, histological type, and presence of extrathoracic metastasis, a high FACT‐G total score (hazard ratio (HR): 0.99, p = 0.032) and high PWB score (HR: 0.94, p < 0.001) were associated with prolonged survival as independent prognostic factors in patients with advanced NSCLC. Conclusion. The FACT‐G total score and PWB score as HRQoL measurements were significant prognostic factors for survival in advanced NSCLC patients treated with Korean medicine. This finding implies that the FACT‐G can be used in clinical practice as a predictor of survival in patients with advanced NSCLC.
Laparoscopic Resection of a Splenic Artery Aneurism with Spleen Preservation
Authors: Correia de Sá Tiago , Soares Carlos , Queirós Jacinta , Rocha Teresa Mónica , Oliveira Manuel
Published: January 2020

Introduction. Splenic artery aneurisms (SAA) are the third most common aneurysms, with reported incidences up to 10.4%. There is a higher prevalence in women, and most are incidental findings on imaging studies. Symptomatic or SAA larger than 20 mm and aneurysms in pregnant or in women of childbearing age are indications for surgery, because of the increased risk of rupture. Treatment options include endovascular, laparoscopic, and open surgical approaches. Presentation of Case. A 50-year-old female patient with nonspecific abdominal pain performed a computed tomography scan and angiography, which revealed a 24×20×19 mm SAA. After a multidisciplinary discussion, selective laparoscopic excision of the aneurysm was performed, with spleen preservation. Perioperative course was uneventful, and the patient remained asymptomatic. Discussion. A multidisciplinary discussion is of major importance in guaranteeing the optimal treatment for any given visceral aneurysm. Not all SAA are amenable to endovascular treatment, and laparoscopic surgery has mostly replaced open procedures. Laparoscopic ligation and resection of the SAA with splenic preservation offers permanent treatment, with a low morbidity and short hospital stay. Conclusion. Selective laparoscopic aneurysm resection is a safe and effective approach, with good short- and long-term results, allowing permanent treatment of SAA while maintaining splenic function.
Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient
Authors: Liu Runqiao , Wei Minxiang , Sang Nan , Wei Jianwei
Published: January 2020

Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.
Color Tuning in the Luminescence of Two Oligomers Derived from N,N′-(Naphthalenediyl)bis-phenylimine Containing Oligomers
Authors: Panunzi B. , Diana R. , Caruso U.
Published: January 2020

Two oligomers with different molecular weight were produced. Fluorescence properties were examined both in solutions and in the solid state. In the solid state, molecular weight turned out to be an important factor influencing the aggregation structures and therefore the emission of the compounds, in colour and intensity. The neat compounds emit two different colours, blue and yellow, with increasing the molecular weight, whereas both compounds are blue emitters in solution and dispersed in host polymers. Dissolved in a LC host polymer and in a PVK matrix, they displayed noteworthy photoluminescence quantum yields.
Anchor Stress and Deformation of the Bolted Joint under Shearing
Authors: Lin Hang , Zhu Youyan , Yang Jianyu , Wen Zhijie
Published: January 2020

Bolts are widely used in rock mass engineering, wherein the bolt support improves the safety and stability of the rock mass. To reveal the mechanical behavior of the bolt and failure mechanism of the bolted joint in the shearing process, a direct shear test was conducted by changing the state of grouting, number of bolt, and inclination angle of the bolt. The change in the axial force of the anchor in the shearing process was evaluated by conducting a strain gauge test, and the mechanical behavior of the bolt under the external force was studied. The results showed that under the same normal stress, the yield displacement of the bolt decreased and the stiffness of the joint gradually increased with increased number of bolts. At the same number of bolts, their yield displacement increased with increased normal stress. Analysis further revealed that grouting on the joint improved the force condition of the bolt, increased the yield displacement of the bolt, and coordinated the deformation of the grouting body and bolt, thereby improving the shear strength of the joint. Lastly, when the anchor angles differed, the axial pulling resistance of the anchor changed, and the yield displacement of the anchor with 45° inclination was <90°. The yield displacement of the bolt showed that the supporting effect of the bolt with a 45° inclination was better than that of the bolt with a 90° inclination.
Bus Priority Signal Control Considering Delays of Passengers and Pedestrians of Adjacent Intersections
Authors: Li Jiali , Liu Yugang , Yang Hongtai , Chen Bin
Published: January 2020

In this paper, a bus priority signal control (BPSC) method based on delays of passengers and pedestrians at adjacent intersections, is proposed. The influences of BPSC on passenger and pedestrian delay at adjacent intersections under the condition of coordinated control of green waves are studied. The implementation of BPSC at intersections not only reduces the delay of bus passengers, social vehicle passengers and pedestrians, but also improves the traffic flow of priority buses and social vehicles at downstream intersections. This study takes the green phase extension as an example of the active BPSC strategy, and analyzes three cases of priority vehicles reaching downstream intersection. Firstly, passenger and pedestrian delays at adjacent intersections are calculated under different traffic situations. Secondly, models with the goal of maximizing the reduced total delays are established. Thirdly, three algorithms are used to solve the problem to obtain the optimal signal timing adjustment scheme at upstream intersections. Ultimately, the result shows that the BPSC can effectively reduce pedestrian delays at intersections, protect the rights and interests of pedestrians, reduce the delays of priority vehicles, and maximize the reduced total delay.
Existence and Multiplicity of Positive Solutions for Kirchhoff-Type Equations with the Critical Sobolev Exponent
Authors: Zhou Junjun , Hu Xiangyun , Xiao Tiaojie
Published: January 2020

In this paper, we consider the following Kirchhoff-type problems involving critical exponent −a+b∫Ω∇u2dxΔu+Vxu=μu2∗−1+λgx,u, x∈Ωu>0, x∈Ωu=0, x∈∂Ω. The existence and multiplicity of positive solutions for Kirchhoff-type equations with a nonlinearity in the critical growth are studied under some suitable assumptions on Vx and gx,u. By using the mountain pass theorem and Brézis–Lieb lemma, the existence and multiplicity of positive solutions are obtained.
Optimizing Transmit Sequence and Instrumental Variables Receiver for Dual-Function Complexity System
Authors: Yao Yu , Zhao Junhui , Wu Lenan
Published: January 2020

This correspondence deals with the joint cognitive design of transmit coded sequences and instrumental variables (IV) receive filter to enhance the performance of a dual-function radar-communication (DFRC) system in the presence of clutter disturbance. The IV receiver can reject clutter more efficiently than the match filter. The signal-to-clutter-and-noise ratio (SCNR) of the IV filter output is viewed as the performance index of the complexity system. We focus on phase only sequences, sharing both a continuous and a discrete phase code and develop optimization algorithms to achieve reasonable pairs of transmit coded sequences and IV receiver that fine approximate the behavior of the optimum SCNR. All iterations involve the solution of NP-hard quadratic fractional problems. The relaxation plus randomization technique is used to find an approximate solution. The complexity, corresponding to the operation of the proposed algorithms, depends on the number of acceptable iterations along with on and the complexity involved in all iterations. Simulation results are offered to evaluate the performance generated by the proposed scheme.
Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals
Authors: Lu Yun , Wang Mingjiang , Wu Wanqing , Zhang Qiquan , Han Yufei , Kausar Tasleem , Chen Shixiong , Liu Ming , Wang Bo
Published: January 2020

Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.
Macroscopic Fundamental Diagram Based Discrete Transportation Network Design
Authors: Hu Guojing , Lu Weike , Wang Feng , Whalin Robert W.
Published: January 2020

The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and k-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results.
A Doubly Constrained TV Algorithm for Image Reconstruction
Authors: Qiao Zhiwei , Redler Gage , Epel Boris , Halpern Howard
Published: January 2020

Purpose. The total variation (TV) minimization algorithm is an effective image reconstruction algorithm capable of accurately reconstructing images from sparse and/or noisy data. The TV model consists of two terms: a data fidelity term and a TV regularization term. Two constrained TV models, data divergence-constrained TV minimization (DDcTV) and TV-constrained data divergence minimization (TVcDM), have been successfully applied to computed tomography (CT) and electron paramagnetic resonance imaging (EPRI). In this work, we propose a new constrained TV model, a doubly constrained TV (dcTV) model, which has the potential to further improve the reconstruction accuracy for the two terms which are both of constraint forms. Methods. We perform an inverse crime study to validate the model and its Chambolle-Pock (CP) solver and characterize the performance of the dcTV-CP algorithm in the context of CT. To demonstrate the superiority of the dcTV model, we compare the convergence rate and the reconstruction accuracy with the DDcTV and TVcDM models via simulated data. Results and Conclusions. The performance-characterizing study shows that the dcTV-CP algorithm is an accurate and convergent algorithm, with the model parameters impacting the reconstruction accuracy and the algorithm parameters impacting the convergence path and rate. The comparison studies show that the dcTV-CP algorithm has a relatively fast convergence rate and can achieve higher reconstruction accuracy from sparse projections or noisy projections relative to the other two single-constrained TV models. The knowledge and insights gained in the work may be utilized in the application of the new model in other imaging modalities including divergence-beam CT, magnetic resonance imaging (MRI), positron emission tomography (PET), and EPRI.
Study on Failure Modes and Energy Evolution of Coal-Rock Combination under Cyclic Loading
Authors: Song Shilin , Liu Xuesheng , Tan Yunliang , Fan Deyuan , Ma Qing , Wang Honglei
Published: January 2020

The loading modes and roof lithology have a significant influence on the mechanical properties of coal seams. To reveal the failure modes and energy evolution law of underground coal during the mining process, conventional uniaxial and uniaxial cyclic loading tests were carried out on three types of samples: coal, rock, and coal-rock combinations. The results show that the samples mainly behave with three failure modes (shear slip, tensile splitting, and fracture), and all the coal sections in the coal-rock combinations fail, whereas most rock sections remain intact. The compressive strength of the coal-rock combination is higher than coal and much smaller than rock. Compared with the conventional uniaxial loading condition, both the maximum deformation before failure and Young’s modulus under the cyclic loading condition are greater, and the latter increases quadratically with the cycle index. The energy densities are also calculated, and their variations are analysed in detail. The results show that with increasing cycle index, both the elastic energy stored in the sample and the dissipated energy increase in a quadratic function, and the failure process becomes more intense. This research reveals the failure modes, deformation characteristics, and energy evolution of the coal-rock combination under different loading conditions, which can provide strong support for controlling underground surrounding rocks of the coal face and roadway in coalmines.
Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism
Authors: Qin RuoXi , Wang Zhenzhen , Jiang LingYun , Qiao Kai , Hai Jinjin , Chen Jian , Xu Junling , Shi Dapeng , Yan Bin
Published: January 2020

Lung cancer ranks among the most common types of cancer. Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. Due to restrictions caused by single modality images of dataset as well as the lack of approaches that allow for a reliable extraction of fine-grained features from different imaging modalities, research regarding the automated diagnosis of lung cancer based on noninvasive clinical images requires further study. In this paper, we present a deep learning architecture that combines the fine-grained feature from PET and CT images that allow for the noninvasive diagnosis of lung cancer. The multidimensional (regarding the channel as well as spatial dimensions) attention mechanism is used to effectively reduce feature noise when extracting fine-grained features from each imaging modality. We conduct a comparative analysis of the two aspects of feature fusion and attention mechanism through quantitative evaluation metrics and the visualization of deep learning process. In our experiments, we obtained an area under the ROC curve of 0.92 (balanced accuracy = 0.72) and a more focused network attention which shows the effective extraction of the fine-grained feature from each imaging modality.
Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM
Authors: Zhang Gang , Liu Hongchi , Li Pingli , Li Meng , He Qiang , Chao Hailiang , Zhang Jiangbin , Hou Jinwang
Published: January 2020

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.
Biological Differentiation of Dampness‐Heat Syndromes in Chronic Hepatitis B: From Comparative MicroRNA Microarray Profiling to Biomarker Identification
Authors: Wen Li , Jiang Cen , Wan Ting-Jun , Wang Dong , Yan Di , Li Gui-Yu , Su Yue , Liu Xi-Yang , Rong Li-Jun , Ye Hua , Li Bai-Xue , Feng Quan-Sheng
Published: January 2020

Increasing interest is aroused by traditional Chinese medicine (TCM) treatment of chronic hepatitis B (CHB) based on specific TCM syndrome. As the most common CHB syndromes, spleen‐stomach dampness‐heat (SSDH) syndrome and liver‐gallbladder dampness‐heat (LGDH) syndrome are still apt to be confused in TCM diagnosis, greatly hindering the stable exertion of TCM effectiveness. It is urgently needed to provide objective and biological evidences for differentiation and identification of the two significant syndromes. In this study, microRNA (miRNA) microarray analyses coupled with bioinformatics were employed for comparative miRNA profiling of SSDH and LGDH patients. It was found that the two syndromes had both the same and different significantly differentially expressed miRNAs (SDE‐miRNAs). Commonness and specificity were also both found between their SDE‐miRNA‐based bioinformatics analyses, including Hierarchical Clustering, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and miRNA‐GO/pathway networks. Furthermore, syndrome‐specific SDE‐miRNAs were identified as the potential biomarkers, including hsa‐miR‐1273g‐3p and hsa‐miR‐4419b for SSDH as well as hsa‐miR‐129‐1‐3p and hsa‐miR‐129‐2‐3p for LGDH. All these laid biological and clinical bases for classification and diagnosis of the two significant CHB dampness‐heat syndromes including SSDH and LGDH, providing more opportunities for better application of TCM efficacy and superiority in CHB treatment.
Effect of Peat Intervention on Pain and Gait in Patients with Knee Osteoarthritis: A Prospective, Double‐Blind, Randomized, Controlled Study
Authors: Kim Myeongkyu , Lee Kyu Hoon , Han Seung Hoon , Lee Sung Jae , Kim Choong-Gon , Choi Jae Ho , Hwang Sun Hee , Park Si-Bog
Published: January 2020

Background/Purpose. Peat is a plant material that is decayed in anaerobic and acidic conditions. Peat is known for its pain relief effect on degenerative and rheumatologic diseases for hundreds of years. This research aims to see effects of peat intervention on knee osteoarthritis patients’ pain, biomarkers, and parameters about gait. Methods. Knee osteoarthritis patients with Visual Analog Scale (VAS) of 3 or more and Kellgren–Lawrence osteoarthritis grades 1 to 3 were included. Patients with history of intraarticular injection treatment were excluded. Forty‐one participants were randomly allocated to the peat intervention group (n = 22) or the hot‐pack‐only control group (n = 19). Peat and hot pack were applied to both knees of each group of patients. Each intervention session lasted 20 minutes, and eight sessions were completed over five days. VAS, serum cartilage oligomeric matrix protein (COMP), and gait parameters were evaluated before and after the whole interventions. Results. VAS in the peat group decreased from 6.000 to 3.409 after intervention (p < 0.001) and also decreased in the control group from 5.737 to 4.421 (p < 0.001). VAS score reduction between two periods was greater in the peat group than that in the control group (p < 0.001). There was no significant difference in the serum COMP level in either intergroup or intragroup analysis. In gait analysis, the gait velocity of the peat group increased from 0.781 m/s to 0.873 m/s after intervention (p = 0.002), while it decreased in the control group. The knee varus/valgus range of motion during gaits was reduced from 11.455° to 8.439° after intervention in the peat group (p = 0.006). Conclusions. This study showed that peat can be considered as a therapeutic option for pain relief of knee osteoarthritis patients. The reduction in knee joint varus/valgus range of motion and the increase in gait velocity after peat intervention were also identified through this research, which is the first to analyze the effects of peat on gait.
Study of the Effect of TiO2 Layer on the Adsorption and Photocatalytic Activity of TiO2-MoS2 Heterostructures under Visible-Infrared Light
Authors: Cruz-González N. , Calzadilla O. , Roque J. , Chalé-Lara F. , Olarte J. K. , Meléndez-Lira M. , Zapta-Torres M.
Published: January 2020

In the last decade, the urgent need to environmental protection has promoted the development of new materials with potential applications to remediate air and polluted water. In this work, the effect of the TiO2 thin layer over MoS2 material in photocatalytic activity is reported. We prepared different heterostructures, using a combination of electrospinning, solvothermal, and spin-coating techniques. The properties of the samples were analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), X-ray diffraction (XRD), nitrogen adsorption-desorption isotherms, UV-Vis diffuse reflectance spectroscopy (UV-Vis-DRS), and X-ray photoelectron spectroscopy (XPS). The adsorption and photocatalytic activity were evaluated by discoloration of rhodamine B solution. The TiO2-MoS2/TiO2 heterostructure presented three optical absorption edges at 1.3 eV, 2.28 eV, and 3.23 eV. The high adsorption capacity of MoS2 was eliminated with the addition of TiO2 thin film. The samples show high photocatalytic activity in the visible-IR light spectrum.
Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect
Authors: Li Tenglong , Hui Fei , Liu Ce , Zhao Xiangmo , Khattak Asad J.
Published: January 2020

The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused on traditional traffic flow. To this end, this paper investigates the change in the traffic jerk effect between inactive and active vehicle-to-vehicle (V2V) communications based on field experimental data. Data mining results show that the correlation between the jerk effect and the driving behavior increases by 50.6% on average when V2V messages are received. In light of the data analysis results, a new car-following model is proposed to explore the jerk effect in a connected environment. The model parameters are calibrated, and the results show that the standard deviation between the new model simulation data and the observed data decreases by 38.2% compared to that of the full velocity difference (FVD) model. Linear and nonlinear analyses of the calibrated model are then carried out to evaluate the connected traffic flow stability. Finally, the theoretical analysis is verified by simulation experiments. Both the theoretical and simulation results show that the headway amplitude and velocity fluctuations are reduced when considering the jerk effect in a connected environment, and the traffic flow stability is improved.
Structural Modal Analysis and Optimization of SUV Door Based on Response Surface Method
Authors: Chen Hao , Lu Chihua , Liu Zhien , Shen Cunrui , Sun Yi , Sun Menglei
Published: January 2020

Sensitivity analysis and response surface methods were employed to optimize the structural modal of SUV doors. A finite element numerical simulation model was established and was calibrated by restraint modal tests. To screen out highly sensitive panels, a sensitivity analysis for the thickness of door panels was proposed based on the fifth-order modal frequency of the door. Data points were obtained by a faced central composite design with the design variables from the thickness of the highly sensitive panels, and a second-order explicit response surface function of the fifth-order modal frequency of the vehicle door was established. An optimization model was established according to the response surface method. The final results demonstrate that the modal-frequency matching of the door and body in white was optimized after changing the thicknesses, with a 5.74% material reduction.
Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models
Authors: Yang Xiaoxue , Zou Yajie , Tang Jinjun , Liang Jian , Ijaz Muhammad
Published: January 2020

Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.
Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD)
Authors: Fan Houming , Wu Jiaxin , Li Xin , Jiang Xiaodan
Published: January 2020

This study proposes a three-index flow-based mixed integer formulation to solve a two-echelon location routing problem with simultaneous pickup and delivery. In this formulation, pickup and delivery demands can be addressed using the same vehicle in each echelon of the network to reduce costs and increase logistics efficiency. We solve such NP-hard problem by developing a multistart hybrid heuristic with path relinking (MHH-PR) which is composed of local search and a variable neighbourhood descent algorithm. In the algorithm, three constructive heuristics are applied to generate diversified initial solutions, and path relinking is introduced for intensification and postoptimisation. Results indicate that MHH-PR can reduce the gap between the near optimal and global optimal solutions by 1%-2%. The proposed algorithm significantly improves computational efficiency by reducing the computational time of more than 10 min for existing cases involving 20 nodes to less than 10 s.
Hospitalization after Adolescent and Young Adult (AYA) Cancer: A Population-Based Study in Utah
Authors: Anderson Chelsea , Kaddas Heydon K. , Ou Judy Y. , Ramsay Joemy M. , Trogdon Justin G. , Kirchhoff Anne C. , Nichols Hazel B.
Published: January 2020

Background:

Adolescents and young adults (AYA, age 15–39 years) with cancer may be at elevated risk for late morbidity following their cancer treatment, but few studies have quantified the excess burden of severe disease in this population. Using population-based data from Utah, we examined the risk of inpatient hospitalizations among AYA cancer survivors compared with their siblings and the general population.

Methods:

Survivors of AYA cancer who were ≥2 years from diagnosis and diagnosed from 1994 to 2015 (N = 6,330), their siblings (N = 12,924), and an age- and sex-matched comparison cohort (N = 18,171) were identified using the Utah Population Database (UPDB). Hospitalizations from 1996 to 2017 were identified from statewide discharge records in the UPDB. We estimated multivariable-adjusted hazard ratios (HR) for first hospitalization and rate ratios (RR) for total hospitalizations for survivors relative to the matched comparison cohort and siblings.

Results:

Overall, the risk of a first hospitalization was higher among AYA cancer survivors than the matched population-based cohort [HR = 1.93; 95% confidence interval (CI), 1.81–2.06]. Risk was most elevated for survivors of leukemia (HR = 4.76), central nervous system tumors (HR = 3.45), colorectal cancers (HR = 2.83), non-Hodgkin lymphoma (HR = 2.76), and breast cancer (HR = 2.37). The rate of total hospitalizations was also increased among survivors relative to the comparison cohort (RR = 2.05; 95% CI, 1.95–2.14). Patterns were generally similar in analyses comparing survivors to their siblings.

Conclusions:

AYA cancer survivors have a higher burden of inpatient hospitalization than their siblings and the general population.

Impact:

Results indicate the importance of long-term, risk-based follow-up care to prevent and treat severe morbidities after cancer treatment.
Activation of the Nitric Oxide Pathway and Acute Myocardial Infarction Complicated by Acute Kidney Injury
Authors: Parenica Jiri , Kala Petr , Mebazaa Alexandre , Littnerova Simona , Benesova Klara , Tomandl Josef , Goldbergová Pavkova Monika , Jarkovský Jiří , Spinar Jindrich , Tomandlova Marie , Dastych Milan , Ince Can , Helanova Katerina , Tesak Martin , Helan Martin , Lokaj Petr , Legrand Matthieu ,   GREAT Network
Published: January 2020

Background/Aims: The pathophysiology of acute kidney injury (AKI) in ST-elevation myocardial infarction (STEMI) patients remains poorly explored. The involvement of the nitric oxide (NO) pathway has been demonstrated in experimental ischemic AKI. The aim of this study was to assess the predictive value of circulating biomarkers of the NO pathway for AKI in STEMI patients. Methods: Four hundred and twenty-seven STEMI patients treated with primary percutaneous coronary intervention were included. The primary end point was AKI. Biomarkers of the NO pathway (plasma superoxide dismutase [SOD], uric acid, nitrite/nitrate [NOx], neopterin) as well as cardiac biomarkers (B-type natriuretic peptide [BNP] and troponin) were sampled 12 h after admission. The predictive value of circulating biomarkers was evaluated in addition to the multivariate clinical model. Results: AKI developed in 8.9% of patients. The 3-month mortality was significantly higher in patients with AKI (34.2 vs. 4.1%; p < 0.001). SOD, uric acid, NOx, neopterin, BNP and troponin were significantly associated with the development of AKI (area under curve [AUC]-receiver operating curve [ROC] ranging between 0.70 and 0.81). In multivariate analysis cardiogenic shock, neopterin, NOx and troponin were independent predictors of AKI. AUC-ROC of the association of multibiomarkers and clinical model was 0.90 and outperformed the predictive value of the clinical model alone. OR of NOx ≥45 µmol/L was 8.0 (95% CI 3.1–20.6) for AKI. Conclusion: Biomarkers of the NO pathway are associated with the development of AKI in STEMI patients. These results provide insights into the pathophysiology of AKI and may serve at developing preventing strategies for AKI targeting this pathway.