bluuebunny/crossref_metadata_2025 huggingface dataset:
Paradoxical effects of acidosis on the noradrenaline-induced and neurogenic constriction of the rat tail artery at low temperatures
Authors: Yartsev Vladimir N.
Published: April 2021

Although vasodilatation evoked by acidosis at normal body temperature is well known, the reports regarding effect of acidosis on the reactivity of the isolated arteries at low temperatures are nonexistent. This study tested the hypothesis that the inhibitory effect of acidosis on the neurogenic vasoconstriction may be increased by cooling. Using wire myography, we recorded the neurogenic contraction of the rat tail artery segments to the electrical field stimulation in the absence and in the presence of 0.03–10.0 µmol/L noradrenaline. The experiments were conducted at 37 °C or 25 °C and pH 7.4 or 6.6 which was decreased by means of CO2. Noradrenaline at concentration of 0.03–0.1 µmol/L significantly potentiated the neurogenic vasoconstriction at 25 °C, and the potentiation was not inhibited by acidosis. Contrary to our hypothesis, acidosis at a low temperature did not affect the noradrenaline-induced tone and significantly increased the neurogenic contraction of the artery segments in the absence and presence of noradrenaline. These effects of acidosis were partly dependent on the endothelium and L-type Ca2+ channels activation. The phenomenon described for the first time might be of importance for the reduction in the heat loss by virtue of decrease in the subcutaneous blood flow at low ambient temperatures.
Atmospheric particulate pollution in South American megacities
Authors: La Colla Noelia S. , Botté Sandra E. , Marcovecchio Jorge E.
Published: April 2021

Air pollution is one of the major problems caused by urban growth, and both industrial and automobile emissions have been the main causes of air quality deterioration in cities since the beginning of the 20th century. Atmospheric pollution is the largest single environmental risk for health, causing about 7 million human deaths per year. On a global scale, developing countries are major contributors to air pollution due to their rising economies, with rapid industrial and population growth combined with poor emission controls. In South America, there are five megacities (Bogotá, Buenos Aires, Lima, Rio de Janeiro, and São Paulo) with over 10 million people potentially contributing to wide-ranging environmental consequences. Atmospheric particulate matter (APM) plays a leading role in the transport of trace metals and metalloids through the atmosphere and are chemical markers of air quality. The presence of these pollutants in APM has a detrimental effect on both air quality and human health. In this review, we provide an integrated assessment of hazardous metals and metalloids in the fine and coarse APM fractions, focusing on the South American megacities. We identified the current state of research for Ba, Cd, Cr, Cu, Mo, Ni, Pb, Pd, Pt, Rh, Sb, Sn, V, and Zn and summarized the findings in the 21st century. The findings of this review highlight that despite the phasing out of leaded gasoline, Pb continues to be a metal pollutant with one of the highest atmospheric emission rates, mainly due to vehicular pollution. The megacities from Brazil and Argentina were, by far, those with the highest number of studies performed; however, updated research is needed for the five megacities, including specific studies on fine and ultrafine particulate matter fractions as these pose serious human health issues. Urban agglomerations denoted sustained increases of most metals over time that is indicative of deteriorating air quality. Nickel and Cd concentrations in megacities from Argentina, Brazil, and Colombia, as well as Pb in one study from Colombia, were found to have exceeded international air quality guidelines.
Movement characteristics analysis of bi-directional friction linear micro-motor driven by a set of micro-actuator
Authors: Wang Zhenlu , Zhang Jiue , Ma Shuxia , Liu Guangqiao
Published: April 2021

In this paper, a bi-directional friction linear micro-motor (BFLM) driven by a set of micro-actuators was introduced and analyzed. The BFLM was fabricated using PolyMUMPs process. The impact dynamics model for the BFLM was established, and its motion equation was also derived that takes into account the friction and contact. The relative motion between the driving head and the slider, as well as forward or backward movement of the slider were analyzed in detail. The forward and backward movement displacement of the slider was measured, and the experimental results were in good agreement with theoretical predictions. The theoretical results show that the motion direction of the slider can be changed by controllable driving frequency to the BFLM. It was feasible to use a set of actuators to drive the slider in bi-directional motion.
Amplification of strong coupling in the simulation system of perovskite nanowire coated by the metal film
Authors: Ma Jun , Liang Kun , Wang Lulu , Yu Li
Published: April 2021

In this work, we report an amplified strong coupling phenomenon in a hybridized nanowire. The hybridized nanowire structure is composed of CH3NH3PbBr3 perovskite nanowire coated by SiO2 film and silver film. Due to the existence of silver film, Rabi splitting in hybrid structure can reach 303.2 meV, while the Rabi splitting in a pure perovskite nanowire is only 236.4 meV. Also, thicknesses of SiO2 film and Ag film can control the Rabi splitting of hybridized structure: (i) the thicker the Ag film, the greater the Rabi splitting; (ii) the thinner the SiO2 film, the greater the Rabi splitting. Finally, index of SiO2 film shows a linear relation with Rabi splitting.
Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks
Authors: Liu Zixi , Hu Zhengliang , Wang Longxiang , Zhou Tianshi , Chen Jintao , Zhu Zhenyu , Sui Hao , Zhu Hongna , Li Guangming
Published: April 2021

The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.
Measuring economies’ pivotability on the global value chain under the perspective of inter-country input–output network
Authors: Xing Lizhi , Han Yu , Wang Dawei
Published: April 2021

Under the dual background of trade disputes between China and the United States and the epidemic of 2019 novel coronavirus, the existing Global Value Chain (GVC) division and trading system are facing unprecedented impact. This paper reinforces the present studies on international trade by analyzing the fragments of GVC, which are made of numerous Inter-Country Input–Output (ICIO) relations. We first redefine the inter-country and inter-sector propagating process of intermediate goods, coming up with the concept of Strongest Relevance Path Length (SRPL) based on Revised Floyd–Warshall Algorithm (RFWA). Second, enlightened by betweenness centrality, we introduce Weighted Betweenness Centrality of Edge based on RFWA to measure the Value-Added Pivotability of Input–Output Relations, which brings forth pivotability at domestic, international, and global levels. The results show how much a given country can influence the world economic pattern by linking worldwide upstream and downstream industrial sectors, be it at home or abroad. Also, we can try to explain what is the cause of the phenomenon that the economic influence of nations is trading off and taking turns with all sorts of local or even global evens happening.
The asymptotic formula on average weighted path length for scale-free modular network
Authors: Niu Min , Shao Mengjun
Published: April 2021

In this paper, we discuss the average path length for a class of scale-free modular networks with deterministic growth. To facilitate the analysis, we define the sum of distances from all nodes to the nearest hub nodes and the nearest peripheral nodes. For the unweighted network, we find that whether the scale-free modular network is single-hub or multiple-hub, the average path length grows logarithmically with the increase of nodes number. For the weighted network, we deduce that when the network iteration [Formula: see text] tends to infinity, the average weighted shortest path length is bounded, and the result is independent of the connection method of network.
Scattered coupling between two wrinkled graphene nanoribbons analyzed by boundary element method
Authors: Wang Shuo , Hu Bin , Liu Weiguang , Liu Juan
Published: April 2021

In this paper, we propose a two-dimensional (2D) boundary element method (BEM) to explore the coupling properties of two distorted graphene nanoribbons (GNRs) with wrinkles. From the calculation results, it is found that the resonant wavelengths are redshifted compared with a single GNR, and the scattering intensity is greatly increased, due to the coupling between the two GNRs. In the non-degenerate structure, the coupling resonance modes of the two GNRs are the superposition of the resonance modes of every single GNR, and the resonant wavelength and scattering intensity of different modes change differently with the gap between the two GNRs and their heights. When the coupling effect becomes stronger, some modes will split. We explain the results through the charge and current distribution of the GNRs. This work will help to understand the coupling mechanism of surface plasmon resonances of GNRs.
A DARWINIAN DYNAMIC MODEL FOR THE EVOLUTION OF POST-REPRODUCTION SURVIVAL
Authors: CUSHING J. M. , STEFANKO KATHRYN
Published: April 2021

We derive and study a Darwinian dynamic model based on a low-dimensional discrete- time population model focused on two features: density-dependent fertility and a trade-off between inherent (density free) fertility and post-reproduction survival. Both features are assumed to be dependent on a phenotypic trait subject to natural selection. The model tracks the dynamics of the population coupled with that of the population mean trait. We study the stability properties of equilibria by means of bifurcation theory. Whether post-reproduction survival at equilibrium is low or high is shown, in this model, to depend significantly on the nature of the trait dependence of the density effects. An Allee effect can also play a significant role.
A DFT-BASED ANALYSIS OF ADSORPTION PERFORMANCE OF CU2+ AND ZN2+ ON DEFECTIVE GRAPHENE FOR THE APPLICATION OF POLLUTING METAL IONS TREATMENT
Authors: CHEN TAO , AN LIBAO , ZHANG YAN , JIA XIAOTONG
Published: April 2021

The density functional theory has been used to study the adsorption performance of polluting Cu[Formula: see text] and Zn[Formula: see text] ions on defective graphene. Compared to intrinsic graphene, the adsorption distance between defective graphene and Cu[Formula: see text]/Zn[Formula: see text] decreases greatly, and the adsorption energy and charge transfer amount increases significantly. The calculation of charge density demonstrates that clear hybridization happens between defective graphene and Cu[Formula: see text]/Zn[Formula: see text] ions, suggesting the formation of chemical adsorption. The frontier orbit analysis shows that defective graphene has greater electrical sensitivity after adsorbing Cu[Formula: see text]/Zn[Formula: see text] ions. Therefore, defective graphene could be a potential material for the treatment of contaminating heavy metal ions.
STUDY OF ACOUSTIC SOURCE EXCITED BY PULSED MAGNETIC FIELD
Authors: YANG YANJU , CHENG CHUNLEI , YANG WENYAO , LI JIE , CHENG ZHENGFU , ZHANG XIAOYU
Published: April 2021

In magnetoacoustic tomography with magnetic induction and magnetically mediated thermoacoustic imaging, tissues are exposed to an alternating field, generating magnetoacoustic and thermoacoustic effects in the tissues. This study aimed to investigate the relationship between magnetoacoustic and thermoacoustic effects in a low-conductivity object put in a Gauss-pulsed alternating magnetic field. First, the derivations of the magnetic flux density and electric field strength induced by a Gauss-pulsed current flowing through the coil based on the theory of electromagnetic field were examined. Second, the analytical solution of the magnetic field was studied by simulation. To validate the accuracy of the analytical solution, the analytical solution and the numerical simulation of the magnetic flux density were compared. It shows that the analytical solution coincides with the numerical simulation well. Then, based on the theoretical analysis of the acoustic source generation, numerical studies were conducted to simulate pressures excited by magnetoacoustic and thermoacoustic effects in low-conductivity objects similar to tissues in the Gauss-pulsed magnetic field. The thermoacoustic effect played a leading role in low-conductivity objects placed in the Gauss-pulsed magnetic field, and the magnetoacoustic effect could be ignored. This study provided the theoretical basis for further research on magnetoacoustic tomography with magnetic induction and magnetically mediated thermoacoustic imaging for pathological tissues.
Construction of an AI-Driven Risk Management Framework for Financial Service Firms Using the MRDM Approach
Authors: Hu Kuang-Hua , Chen Fu-Hsiang , Hsu Ming-Fu , Tzeng Gwo-Hshiung
Published: April 2021

The complex problem of risk factors has greatly increased globally due to the quick ever-changing digital era. The development of suitable techniques for facilitating the performance of risk management in the financial service domain is thus an urgent task, especially in today’s highly turbulent business environment. The development of such techniques involves many factors like the classical multiple criteria decision-making (MCDM) problem, but too many factors surrounding the users will confuse them and lead to improper judgments. To deal with this critical task, this study proposes a fusion multiple rule-based decision-making (MRDM) approach that integrates a rule-based technique [i.e., the fuzzy rough set theory (FRST) with particle swarm optimization (PSO)] into MCDM (i.e., DEMATEL, DANP, and modified-VIKOR) techniques that can help decision makers choose the optimal model necessary for achieving aspiration-level effects in a risk control strategy. The results indicate that the improvement priority, which runs in the order as (a) AI algorithm model, (c) AI regulatory and compliance, (d) AI conduct, and (b) AI technology based on the magnitude of the impact, can effectively improve the performance of AI-driven risk management for financial service firms.
Toward Automated Factchecking
Authors: Konstantinovskiy Lev , Price Oliver , Babakar Mevan , Zubiaga Arkaitz
Published: April 2021

In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This article is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics, and annotators than are previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.
multiPULPly
Authors: Eliahu Adi , Ronen Ronny , Gaillardon Pierre-Emmanuel , Kvatinsky Shahar
Published: April 2021

Computationally intensive neural network applications often need to run on resource-limited low-power devices. Numerous hardware accelerators have been developed to speed up the performance of neural network applications and reduce power consumption; however, most focus on data centers and full-fledged systems. Acceleration in ultra-low-power systems has been only partially addressed. In this article, we present multiPULPly, an accelerator that integrates memristive technologies within standard low-power CMOS technology, to accelerate multiplication in neural network inference on ultra-low-power systems. This accelerator was designated for PULP, an open-source microcontroller system that uses low-power RISC-V processors. Memristors were integrated into the accelerator to enable power consumption only when the memory is active, to continue the task with no context-restoring overhead, and to enable highly parallel analog multiplication. To reduce the energy consumption, we propose novel dataflows that handle common multiplication scenarios and are tailored for our architecture. The accelerator was tested on FPGA and achieved a peak energy efficiency of 19.5 TOPS/W, outperforming state-of-the-art accelerators by 1.5× to 4.5×.
RIDS
Authors: Gimenez Pierre-Francois , Roux Jonathan , Alata Eric , Auriol Guillaume , Kaaniche Mohamed , Nicomette Vincent
Published: April 2021

The expansion of the Internet-of-Things (IoT) market is visible in homes, factories, public places, and smart cities. While the massive deployment of connected devices offers opportunities to improve quality of life and to develop new services, the impact of such devices on the security of the users in a context where the level of malicious threat continues to increase is a major concern. One of the challenges is the heterogeneity and constant evolution of wireless technologies and protocols used. To overcome this problem, we propose RIDS, a
Radio Intrusion Detection System
that is based on the monitoring and profiling of radio communications at the physical layer level using autoencoder neural networks. RIDS is independent of the wireless protocols and modulation technologies used. Besides, it is designed to provide a threefold diagnosis of the detected anomalies: temporal (start and end date of the detected anomaly), frequential (main frequency of the anomaly), and spatial (location of the origin of the anomaly). To demonstrate the relevance and the efficiency of our approach, we collected a large dataset of radio-communications recorded with three different probes deployed in an experimental room. Multiple real-world attacks involving a wide variety of communication technologies are also injected to assess the detection and diagnosis efficiency. The results demonstrate the efficiency of RIDS in detecting and diagnosing anomalies that occurred in the 400–500 Mhz and 800–900 Mhz frequency bands. It is noteworthy that compromised devices and attacks using these communication bands are generally not easily covered by traditional solutions.
Dual-Embedding based Deep Latent Factor Models for Recommendation
Authors: Cheng Weiyu , Shen Yanyan , Huang Linpeng , Zhu Yanmin
Published: April 2021

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.
How to Prevent and Cope with Coincidence of Risks to the Global Food System
Authors: Fan Shenggen , Cho Emily EunYoung , Meng Ting , Rue Christopher
Published: April 2021

The global food system faces major risks and threats that can cause massive economic loss; dislocation of food supply chains; and welfare loss of producers, consumers, and other food system actors. The interrelated nature of the system has highlighted the complexity of risks. Climate change, extreme weather events, and degradation and depletion of natural resources, including water, arable, forestry, and pastural lands, loss of biodiversity, emerging diseases, trade chokepoints and disruptions, macroeconomic shocks, and conflicts, can each seriously disrupt the system. Coincidence of these risks can compound the effects on global food security and nutrition. Smallholder farmers, rural migrants, women, youth, children, low-income populations, and other disadvantaged groups are particularly vulnerable. The coronavirus disease 2019 (COVID-19) pandemic exemplifies a perfect storm of coincidental risks. This article reviews major risks that most significantly impact food systems and highlights the importance of prospects for coincidence of risks. We present pathways to de-risk food systems and a way forward to ensure healthy, sustainable, inclusive, and resilient food systems.
High-altitude cerebral edema: its own entity or end-stage acute mountain sickness?
Authors: Turner Rachel E. F. , Gatterer Hannes , Falla Marika , Lawley Justin S.
Published: April 2021

High-altitude cerebral edema (HACE) and acute mountain sickness (AMS) are neuropathologies associated with rapid exposure to hypoxia. However, speculation remains regarding the exact etiology of both HACE and AMS and whether they share a common mechanistic pathology. This review outlines the basic principles of HACE development, highlighting how edema could develop from 1) a progression from cytotoxic swelling to ionic edema or 2) permeation of the blood brain barrier (BBB) with or without ionic edema. Thereafter, discussion turns to the available neuroimaging literature in the context of cytotoxic, ionic, or vasogenic edema in both HACE and AMS. Although HACE is clearly caused by an increase in brain water of ionic and/or vasogenic origin, there is very little evidence that this type of edema is present when AMS develops. However, cerebral vasodilation, increased intracranial blood volume, and concomitant intracranial fluid shifts from the extracellular to the intracellular space, as interpreted from changes in diffusion indices within white matter, are observed consistently in persons acutely exposed to hypoxia and with AMS. Therefore, herein we explore the idea that intracellular swelling occurs alongside AMS, and is a critical precursor to extracellular ionic edema formation. We propose that this process produces a subtle modulation of the BBB, which either together with or independent of vasogenic edema provides a transvascular segue from the end-stage of AMS to HACE. Ultimately, this review seeks to shed light on the possible processes underlying HACE pathophysiology, and thus highlights potential avenues for future prevention and treatment.
The validity and reliability of ultra-short-term heart rate variability parameters and the influence of physiological covariates
Authors: Burma Joel S. , Graver Sarah , Miutz Lauren N. , Macaulay Alannah , Copeland Paige V. , Smirl Jonathan D.
Published: April 2021

A one size fits all methodological approach to quantify HRV metrics appears to be inappropriate, where study design considerations need to be conducted upon a variable-by-variable basis. The present results found 60 s (heart rate), 240 s (time-domain parameters), and 300 s (relative frequency-domain parameters) were required to obtain accurate and reproducible metrics. The lower validity/reliability of the ultra-short-term metrics was attributable to measurement error and/or confounding from extraneous physiological influences (i.e., respiratory and hemodynamic variables).
Impact of Lexical Features on Answer Detection Model in Discussion Forums
Authors: Khan Atif , Gul Muhammad Adnan , Alharbi Abdullah , Uddin M. Irfan , Ali Shaukat , Alouffi Bader
Published: April 2021

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high‐quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state‐of‐the‐art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.
Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets
Authors: Alsubari Saleh Nagi , Deshmukh Sachin N. , Al-Adhaileh Mosleh Hmoud , Alsaade Fawaz Waselalla , Aldhyani Theyazn H. H.
Published: April 2021

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.
Energy Investment Potential and Strategic Layout in Countries along the “Belt and Road” Based on Principal Component Analysis
Authors: Xu Xiao-Feng , Liu Min , Ma Li , Li Yang
Published: April 2021

It is important for energy enterprises to research on the investment potential of the energy markets in countries along the “Belt and Road,” which can help them optimize the regional investment structure, reduce investment risks, and conform to the development trend of “going global.” Therefore, we construct an investment potential assessment system of 29 indexes including five dimensions: politics, economy, society, energy, and cooperation and assess energy investment potential of 48 sample countries along the “Belt and Road” using principal component analysis to provide reference meanings for energy enterprises. The results show that the assessment results of investment potential are affected by a combination of multiple indexes. In addition, compared with Central Asia and South Asia, which have weak economic foundations and greater political and legal risks, the investment potential of Central and Eastern Europe and some emerging economies in Southeast Asia is higher.
Experimental Assessments of Treating Effect on Retaining Walls for Loess Slopes under Long‐Term Rainfall
Authors: Liu Guodong , Zhou Zhijun , Xu Shiqiang , Mi Wenjing
Published: April 2021

Failures of treated slope occurring in China are at a continually increasing rate, and the huge number of treated loess slopes is calling for a postevaluation; however, no mature technique is in place. Based on an actual loess slope in Shaanxi Province treated by retaining wall, indoor geotechnical and model tests were conducted, revealing the rainwater infiltration process and pressure variations behind the wall, and the processes were then adopted to perform the postevaluation of the treated slope. The results proposed that effectual measures hence needed to be taken so as to avert rainwater infiltrating along the wall face and back or flowing through the wall body, which can soften the soil of the slope bottom. Although the wet front was developed by the rainfall process, it cannot be used as the boundary between saturated and unsaturated areas. Despite the peculiarly large soil pressure upon the wall back at the top layer, the soil pressure increases to a large value and then decreases with the depth. The model test results and investigation results were used to conduct the postevaluation of the prototype slope, which formed a postevaluation frame relevant to other slope postevaluations.
Rifampicin-Associated Secondary Minimal Change Disease Presenting with Nephrotic Syndrome in a Pulmonary Tuberculosis Patient
Authors: Sathi Satyanand , Garg Anil Kumar , Singh Manoj Kumar , Saini Virendra Singh , Vohra Devinder
Published: April 2021

Various extraglomerular disease processes have been associated with drug-induced secondary minimal change disease (MCD). In a majority of cases, preferably, a hypersensitivity reaction appears to be involved, and in some cases, there is direct toxic effect over glomerular capillaries. There are several reports to demonstrate that rifampicin has been associated with various nephrotoxic adverse effects, but rifampicin-induced secondary minimal change disease (MCD) is very rare. Here, we report the case of a young adult male who presented with nephrotic proteinuria with bland urine sediment after one month of initiation of rifampicin treatment for pulmonary tuberculosis. The patient had no proteinuria before the start of antituberculosis treatment. Renal biopsy showed nonproliferative glomerulopathy and immunofluorescence did not show significant glomerular immune deposits. Electron microscopy showed diffuse effacement of visceral epithelial cell foot processes and did not show any presence of glomerular immune complexes and thickening of glomerular basement membrane, promoting the diagnosis of minimal change nephrotic syndrome. The patient got complete remission after discontinuation of rifampicin.
Impacts of Air-Sea Energy Transfer on Typhoon Modelling
Authors: Sun Difu , Song Junqiang , Leng Hongze , Ren Kaijun , Li Xiaoyong
Published: April 2021

The Coupled Ocean-Atmosphere-Wave-Sediment Transport model has been used to simulate Super Typhoon Yutu (2018). The impacts of four momentum transfer parameterization schemes (COARE, TY, OT, and DN) and three heat transfer parameterization schemes (COARE, GR, and ZK) on typhoon modelling have been studied by means of the track, intensity, and radial structure of typhoon. The results show that the track of Yutu is not sensitive to the choice of parameterization scheme, while the combinations of different parameterization schemes affect the intensity of Yutu. Among the four momentum flux parameterization schemes, three wave-state-based schemes (TY, OT, and DN) provide better intensity results than the wind-speed-based COARE scheme, but the differences between the three wave-state-based schemes are not obvious. Among the three heat flux parameterization schemes, the results of the GR scheme are slightly better than those of the COARE scheme, and both the GR and COARE schemes are significantly better than the ZK scheme, from which the intensity of Yutu is underpredicted obviously. The influence of the combination of different parameterization schemes on the intensity of the typhoon is also reflected in the radial structure of the typhoon, and the radial structure of the typhoon simulated by experiments with stronger typhoon intensity also develops faster. Differences of intensity between experiments are due mainly to the differences in sea surface heat flux, the enthalpy transferred from sea surface to the atmosphere has a significant impact on the bottom atmosphere wind field, and there is a strong correspondence between the distribution of enthalpy flux and the bottom wind field.
Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy
Authors: Zhang Bo , Liao Rongji
Published: April 2021

The application of machine learning touches all activities of human behavior such as computer network and routing packets in LAN. In the field of our research here, emphasis was placed on extracting weights that would affect the speed of the network′s response and finding the best path, such as the number of nodes in the path and the congestion on each path, in addition to the cache used for each node. Therefore, the use of these elements in building the neural network is worthy, as is the exploitation of the feed forwarding and the backpropagation in the neural network in order to reach the best prediction for the best path. The goal of the proposed neural network is to minimize the network time delay within the optimization of the packet paths being addressed in this study. The shortest path is considered as the key issue in routing algorithm that can be carried out with real time of path computations. Exploiting the gaps in previous studies, which are represented in the lack of training of the system and the inaccurate prediction as a result of not taking into consideration the hidden layers′ feedback, leads to great performance. This study aims to suggest an efficient algorithm that could help in selecting the shortest path to improve the existing methods using weights derived from packet ID and to change neural network iteration simultaneously. In this study, the design of the efficient neural network of appropriate output is discussed in detail including the principles of the network. The findings of the study revealed that exploiting the power of computational system to demonstrate computer simulation is really effective. It is also shown that the system achieved good results when training the neural network system to get 2.4% time delay with 5 nodes in local LAN. Besides, the results showed that the major features of the proposed model will be able to run in real time and are also adaptive to change with path topology.
Comparative Analysis of CNN and RNN for Voice Pathology Detection
Authors: Syed Sidra Abid , Rashid Munaf , Hussain Samreen , Zahid Hira
Published: April 2021

Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k‐fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10‐fold cross‐validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
Method of Calculating Secondary Porosity of Reef Limestone Reservoir by Casting Thin Section Calibrating Nuclear Magnetic

T

2

Spectrum
Authors: Zhao Bin , Li Zhaoping , Gao Chuqiao , Zhang Guoyi , Wu Jinbo , Gao Gang , Tang Yang
Published: April 2021

Secondary pores are the main reservoir space and transportation channel of oil and gas in reef limestone reservoir. At present, the main method of calculating secondary porosity is restricted by the morphological characteristics of porosity spectrum, regional artificial influence, and accuracy of calculation. We present a new method for calculating secondary porosity of reef limestone reservoir by the nuclear magnetic resonance

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spectrum which is calibrated by casting thin section. We begin with analyzing and determining the high correlation between the surface porosity of casting thin section and the total porosity. The objective is confirming the feasibility of the method of calculating secondary porosity by using thin-section information calibrate. Then, we use the surface porosity of thin section as the calibrating data and find the

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relaxation time corresponding to the best correlation between the secondary porosity and the secondary surface porosity of casting thin section, that is, the

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cutoff value of secondary porosity, through the Monte Carlo method. Finally, we calculate the secondary porosity by using the functional relationship between the secondary surface porosity and the surface porosity. The statistical analysis shows that the method of calculating secondary porosity effectively improves the calculation accuracy of secondary porosity. The secondary porosity calculation results have a high correlation with the reservoir productivity.
Topology‐Aware Bus Routing in Complex Networks of Very‐Large‐Scale Integration with Nonuniform Track Configurations and Obstacles
Authors: Zhu Ziran , Huang Zhipeng , Chen Jianli , Guo Longkun
Published: April 2021

As one of the most important routing problems in the complex network within a very‐large‐scale integration (VLSI) circuit, bus routing has become much more challenging when witnessing the advanced technology node enters the deep nanometer era because all bus bits need to be routed with the same routing topology in the context. In particular, the nonuniform routing track configuration and obstacles bring the largest difficulty for maintaining the same topology for all bus bits. In this paper, we first present a track handling technique to unify the nonuniform routing track configuration with obstacles. Then, we formulate the topology‐aware single bus routing as an unsplittable flow problem (UFP), which is integrated into a negotiation‐based global routing to determine the desired routing regions for each bus. A topology‐aware track assignment is also presented to allocate the tracks to each segment of buses under the guidance of the global routing result. Finally, a detailed routing scheme is proposed to connect the segments of each bus. We evaluate our routing result with the benchmark suite of the 2018 CAD Contest. Compared with the top‐3 state‐of‐the‐art methods, experimental results show that our proposed algorithm achieves the best overall score regarding specified time limitations.
Changes in the Heavy Metal Levels in Highway Landscaping and Protective Effect of Vegetative Materials
Authors: Vural Hüccet , Meral Alperen , Şenyiğit Doğan Sülem
Published: April 2021

Anthropogenic activities due to increasing population and traffic density are responsible for a great portion of highway pollution. The heavy metal accumulation in highway routes poses a risk both for agricultural areas and residential areas. The study investigated the changes in heavy metal accumulation along a 200 km long portion of the D300 highway passing through Elazığ, Bingöl, and Muş, cities located in the Eastern Anatolia region of Turkey. The heavy metal accumulation in 46 soil samples collected in 2018 and 2019 from 5 different land classes was analyzed using the ICP-MS device in an accredited laboratory. The analysis results were explained using different statistical methods depending on the standard, annual change, land class, and vegetation. Although the majority of the soil samples were within acceptable levels, the chromium (Cr) and nickel (Ni) levels of certain samples were above the standard levels. Considering the land classes, compared with other areas, residential areas (RA) contained higher levels of zinc (Zn); agricultural areas (AA) contained higher levels of chromium (Cr), cobalt (Co), nickel (Ni), zinc (Zn), cadmium (Cd), and lead (Pb); and unqualified areas (UA) contained higher levels of copper (Cu). Considering vegetation, the tree- and bush-covered soil samples contained lower amounts of Cr, Co, Ni, Cu, and Cd but higher levels of Zn and Pb compared with herbaceous or bare soil samples. A similar case also applies to the soil samples that were covered with Quercus sp., a natural plant cover on the route. The results and other similar studies have shown that there should be at least 15 m long ecological corridors (pollution-resistant tree-bush vegetation) between highway routes and both agricultural and residential areas.
Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics
Authors: Aldhyani Theyazn H. H. , Joshi Manish R. , AlMaaytah Shahab A. , Alqarni Ahmed Abdullah , Alsharif Nizar
Published: April 2021

According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can quicken response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression (SVMR), artificial neural network using particle swarm optimisation (ANNPSO), and our intelligent time series (INTS) to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza‐like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza‐like illness data was collected from January 4, 2009 (week 1), to December 27, 2015 (week 52), stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend (GFT), and autoregression with Google search data is also presented. The proposed model outperformed the existing models.