bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions
Authors: Bello-Cerezo Raquel , Bianconi Francesco , Di Maria Francesco , Napoletano Paolo , Smeraldi Fabrizio
Published: February 2019

Convolutional Neural Networks (CNN) have brought spectacular improvements in several fields of machine vision including object, scene and face recognition. Nonetheless, the impact of this new paradigm on the classification of fine-grained images—such as colour textures—is still controversial. In this work, we evaluate the effectiveness of traditional, hand-crafted descriptors against off-the-shelf CNN-based features for the classification of different types of colour textures under a range of imaging conditions. The study covers 68 image descriptors (35 hand-crafted and 33 CNN-based) and 46 compilations of 23 colour texture datasets divided into 10 experimental conditions. On average, the results indicate a marked superiority of deep networks, particularly with non-stationary textures and in the presence of multiple changes in the acquisition conditions. By contrast, hand-crafted descriptors were better at discriminating stationary textures under steady imaging conditions and proved more robust than CNN-based features to image rotation.
Intelligent Recommender System for Big Data Applications Based on the Random Neural Network
Authors: Serrano Will
Published: February 2019

Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.
Synthesis of Optimized Molecularly Imprinted Polymers for the Isolation and Detection of Antidepressants via HPLC
Authors: Hudson Alexander D. , Solà Ricard , Ueta Jorge T. , Battell William , Jamieson Oliver , Dunbar Thomas , Maciá Beatriz , Peeters Marloes
Published: February 2019

Antidepressants such as amitryptiline and fluoxetine are on the list of modern essential medicines of the World Health Organization. However, there are growing concerns regarding the ecological impact of these pharmaceuticals, leading to a great need to improve current wastewater treatment procedures. In this contribution, we will report on the use of molecularly imprinted polymers (MIPs) for the extraction of antidepressants in water samples. MIPs were developed for fluoxetine and duloxetine, antidepressants belonging to the class of selective serotonin reuptake inhibitors (SSRIs). The binding capacity of these microparticles was evaluated using ultraviolet–visible (UV–Vis) spectroscopy. A new high-performance liquid chromatography (HPLC) procedure coupled to UV detection was developed, which enabled the study of mixtures of fluoxetine and duloxetine with other nitrogen-containing compounds. These results indicate that it is possible to selectively extract SSRIs from complex samples. Therefore, these versatile polymers are a promising analytical tool for the clean-up of water samples, which will benefit aquatic life and reduce the ecological impact of pharmaceuticals.
Ironic or Overcompensation Effects of Motor Behaviour: An Examination of a Tennis Serving Task Under Pressure
Authors: Gorgulu Recep
Published: February 2019

With specific regard to the hypothesized effects of anxiety on performance in motor behaviour, the rival predictions emanating from the Wegner’s “ironic processes theory” and the “implicit overcompensation hypothesis” are largely indiscriminate. Specifically, Wegner’s theory predicts that self-instructions not to perform in a certain manner would lead to the very behaviour the individual seeks to avoid under pressure. On the other hand, the implicit overcompensation hypothesis predicts that avoidant instructions would produce the opposite outcome to that intended by the performer under pressure. The present novel study directly compared these predictions using a tennis serving task under manipulated instructions. The sample comprised 32 (20 men, 12 women; Mage = 20.81, SD = 2.20) experienced tennis players who performed a tennis serving task. Participants’ levels of cognitive anxiety, somatic anxiety and self-confidence were measured by using Mental Readiness Form-3. A 2 (anxiety: low, high) × 3 (serving zone: target zone, non-target ironic error zone, non-target non-ironic error zone) repeated measures of ANOVA revealed a significant anxiety × serving zone interaction F(2, 62) = 32.27, p < 0.001 which provides specific support for the Wegner’s ironic processes of mental control theory rather than implicit overcompensation hypothesis. More specifically, Bonferroni-corrected follow-up paired samples t-tests revealed that when instructed not to serve in a specific direction, anxious performers did so a significantly greater number of times (t31 = −5.15, p < 0.001). The present research demonstrates that ironic performance errors are a meaningful and robust potential concern for performers who are required to perform under pressure.
Protein Phosphatases—A Touchy Enemy in the Battle Against Glioblastomas: A Review
Authors: Tomiyama Arata , Kobayashi Tatsuya , Mori Kentaro , Ichimura Koichi
Published: February 2019

Glioblastoma (GBM) is the most common malignant tumor arising from brain parenchyma. Although many efforts have been made to develop therapies for GBM, the prognosis still remains poor, mainly because of the difficulty in total resection of the tumor mass from brain tissue and the resistance of the residual tumor against standard chemoradiotherapy. Therefore, novel adjuvant therapies are urgently needed. Recent genome-wide analyses of GBM cases have clarified molecular signaling mechanisms underlying GBM biology. However, results of clinical trials targeting phosphorylation-mediated signaling have been unsatisfactory to date. Protein phosphatases are enzymes that antagonize phosphorylation signaling by dephosphorylating phosphorylated signaling molecules. Recently, the critical roles of phosphatases in the regulation of oncogenic signaling in malignant tumor cells have been reported, and tumorigenic roles of deregulated phosphatases have been demonstrated in GBM. However, a detailed mechanism underlying phosphatase-mediated signaling transduction in the regulation of GBM has not been elucidated, and such information is necessary to apply phosphatases as a therapeutic target for GBM. This review highlights and summarizes the phosphatases that have crucial roles in the regulation of oncogenic signaling in GBM cells.
KCNMA1 Expression Is Downregulated in Colorectal Cancer via Epigenetic Mechanisms
Authors: Basile Maria Sofia , Fagone Paolo , Mangano Katia , Mammana Santa , Magro Gaetano , Salvatorelli Lucia , Li Destri Giovanni , La Greca Gaetano , Nicoletti Ferdinando , Puleo Stefano , Pesce Antonio
Published: February 2019

KCNMA1 is a gene located at 10q22 that encodes the pore-forming α-subunit of the large-conductance Ca2+-activated K+ channel. KCNMA1 is down-regulated in gastric carcinoma tumors, through hypermethylation of its promoter. In the present study, we have evaluated the expression levels of KCNMA1 both in a mouse model of Colorectal Cancer (CRC) and in human CRC samples. Additionally, epigenetic mechanisms of KCNMA1 gene regulation were investigated. We observed a significant down-regulation of KCNMA1 both in a human and mouse model of CRC. No differences in KCNMA1 levels were, however, observed at different TNM stages. We also wanted to determine whether the modulation in KCNMA1 was dependent on epigenetic mechanisms. A statistically significant inverse correlation between KCNMA1 expression and mir-17-5p levels was observed in patients with CRC. Furthermore, in the tumor samples, we found a significant hypermethylation of the promoter, in the loci cg24113782 and cg25655799, compared to healthy tissue. Overall, our data suggest the possible use of KCNMA1 as a therapeutic target in the early stages of CRC.
Application of Cell-Free Protein Synthesis for Faster Biocatalyst Development
Authors: Rolf Jascha , Rosenthal Katrin , Lütz Stephan
Published: February 2019

Cell-free protein synthesis (CFPS) has become an established tool for rapid protein synthesis in order to accelerate the discovery of new enzymes and the development of proteins with improved characteristics. Over the past years, progress in CFPS system preparation has been made towards simplification, and many applications have been developed with regard to tailor-made solutions for specific purposes. In this review, various preparation methods of CFPS systems are compared and the significance of individual supplements is assessed. The recent applications of CFPS are summarized and the potential for biocatalyst development discussed. One of the central features is the high-throughput synthesis of protein variants, which enables sophisticated approaches for rapid prototyping of enzymes. These applications demonstrate the contribution of CFPS to enhance enzyme functionalities and the complementation to in vivo protein synthesis. However, there are different issues to be addressed, such as the low predictability of CFPS performance and transferability to in vivo protein synthesis. Nevertheless, the usage of CFPS for high-throughput enzyme screening has been proven to be an efficient method to discover novel biocatalysts and improved enzyme variants.
Optimal Inspired Fraction of Oxygen in the Delivery Room for Preterm Infants
Authors: Lara-Cantón Inmaculada , Solaz Alvaro , Parra-Llorca Anna , García-Robles Ana , Vento Máximo
Published: February 2019

Postnatal adaptation of preterm infants entails a series of difficulties among which the immaturity of the respiratory system is the most vital. To overcome respiratory insufficiency, caregivers attending in the delivery room use positive pressure ventilation and oxygen. A body of evidence in relation of oxygen management in the delivery room has been accumulated in recent years; however, the optimal initial inspired fraction of oxygen, the time to achieve specific oxygen saturation targets, and oxygen titration have not been yet clearly established. The aim of this review is to update the reader by critically analyzing the most relevant literature.
Electronic Cigarettes and Youth in the United States: A Call to Action (at the Local, National and Global Levels)
Authors: Jenssen Brian , Boykan Rachel
Published: February 2019

E-cigarettes have emerged and soared in popularity in the past ten years, making them the most common tobacco product used among youth in the United States (US). In this review, we discuss what the Surgeon General has called a public health “epidemic”—the precipitous increase in youth use of e-cigarettes and the health consequences of this behavior. Further, we review tobacco control policy efforts (e.g., Tobacco 21, banning flavors, advertising restrictions, and clean indoor air laws)—efforts proven to be critical in reducing cigarette smoking and smoking-related disease and death among US children and adults—including their potential and challenges regarding managing and mitigating the emergence of e-cigarettes. Finally, we close with a discussion of the efforts of transnational tobacco companies to rebrand themselves using e-cigarettes and other new products.
Tridoped TiO2 Composite Films for Improved Photocatalytic Activities
Authors: Zhao Li-Zhu , Han Ke , Li Fang , Yao Ming-Ming
Published: February 2019

The Fe/B/F tridoped TiO2-ZnO composite films attached to glass substrates were prepared via a simple sol–gel method. We appraised all samples’ photocatalytic activities by the degradation of methyl green and formaldehyde solutions. The samples were characterized by photoluminescence (PL) spectra, UV-Vis diffraction reflectance absorption spectra (DRS), X-ray diffraction (XRD), differential thermal analysis-thermogravimetry (DTA-TG), field emission scanning electron microscopy (FE-SEM) equipped with energy-dispersive spectroscopy (EDS), and Brunner–Emmet–Teller (BET) measurements. According to the results of DRS and PL spectroscopy, the multi-modification could not only enhance visible light absorption intensity, but also decrease the recombination rate of photo-generated electron-hole pairs. XRD results revealed that the sample was mainly in anatase crystal type. FE-SEM results shown that the sample had fewer particle aggregates and almost no cracks. The specific surface area of the Fe/B/F tridoped TiO2-ZnO was 104.9 m2·g−1, while that of the pure TiO2 was 84.0 m2·g−1. Compared to pure TiO2 and TiO2-ZnO, the Fe/B/F tridoped TiO2-ZnO composite film had the highest photocatalytic activity due to their synergies.
Research Status and Prospect of Friction Stir Processing Technology
Authors: Li Kan , Liu Xuemei , Zhao Yi
Published: February 2019

Friction stir processing (FSP) is a novel solid-phase processing technique that is derived from friction stir welding (FSW). The microstructure of the base metal can be modified with the friction heat and stir function during processing. It can be used to fabricate surface composites and in situ composites by adding reinforced particles into the metal matrix via FSP. Friction stir processing can significantly improve the hardness, wear resistance, ductility, etc., while preventing defects caused by material melting. It is an ideal material processing technology and has good prospects in the field of superplastic materials and for the preparation of metal matrix composites. This paper reviews research developments into the principle, process, and applications of FSP technology as well as its future research directions and development prospects.
Ångström-Scale, Atomically Thin 2D Materials for Corrosion Mitigation and Passivation
Authors: Tanjil Md Rubayat-E , Jeong Yunjo , Yin Zhewen , Panaccione Wyatt , Wang Michael Cai
Published: February 2019

Metal deterioration via corrosion is a ubiquitous and persistent problem. Ångström-scale, atomically thin 2D materials are promising candidates for effective, robust, and economical corrosion passivation coatings due to their ultimate thinness and excellent mechanical and electrical properties. This review focuses on elucidating the mechanism of 2D materials in corrosion mitigation and passivation related to their physicochemical properties and variations, such as defects, out-of-plane deformations, interfacial states, temporal and thickness variations, etc. In addition, this review discusses recent progress and developments of 2D material coatings for corrosion mitigation and passivation as well as the significant challenges to overcome in the future.
Choosing the Method of Crystallization to Obtain Optimal Results
Authors: Govada Lata , Chayen Naomi E.
Published: February 2019

Anyone who has ever attempted to crystallise a protein or other biological macromolecule has encountered at least one, if not all of the following scenarios: No crystals at all, tiny low quality crystals; phase separation; amorphous precipitate and the most frustrating; large, beautiful crystals that do not diffract at all. In this paper we review a number of simple ways to overcome such problems, which have worked well in our hands and in other laboratories. It brings together information that has been dispersed in various publications and lectures over the years and includes further information that has not been previously published.
Genome Analysis of the Marine Bacterium Labrenzia sp. Strain 011, a Potential Protective Agent of Mollusks
Authors: Amiri Moghaddam Jamshid , Dávila-Céspedes Antonio , Alanjary Mohammad , Blom Jochen , König Gabriele M. , Schäberle Till F.
Published: February 2019

The marine bacterium Labrenzia sp. strain 011 was isolated from the coastal sediment of Kronsgaard, Germany. The Labrenzia species are suggested to be protective agents of mollusks. Labrenzia sp. strain 011 produces specialized metabolites, which showed activity against a range of microorganisms, thereunder strong inhibitory effects against Pseudoroseovarius crassostreae DSM 16,950 (genus Roseovarius), the causative agent of oyster disease. The genome of Labrenzia sp. strain 011 was sequenced and assembled into 65 contigs, has a size of 5.1 Mbp, and a G+C content of 61.6%. A comparative genome analysis defined Labrenzia sp. strain 011 as a distinct new species within the genus Labrenzia, whereby 44% of the genome was contributed to the Labrenzia core genome. The genomic data provided here is expected to contribute to a deeper understanding of the mollusk-protective role of Labrenzia spp.
The Ordering of Shannon Entropies for the Multivariate Distributions and Distributions of Eigenvalues
Authors: Tsai Ming-Tien , Hsu Feng-Ju , Tsai Chia-Hsuan
Published: February 2019

In this paper, we prove the Shannon entropy inequalities for the multivariate distributions via the notion of convex ordering of two multivariate distributions. We further characterize the multivariate totally positive of order 2 ( M T P 2 ) property of the distribution functions of eigenvalues of both central Wishart and central MANOVA models, and of both noncentral Wishart and noncentral MANOVA models under the general population covariance matrix set-up. These results can be directly applied to both the comparisons of two Shannon entropy measures and the power monotonicity problem for the MANOVA problem.
Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network
Authors: Zhao Yiming , Bai Lin , Lyu Yecheng , Huang Xinming
Published: February 2019

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset.
Simulation Model for Blockchain Systems Using Queuing Theory
Authors: Memon Raheel Ahmed , Li Jian Ping , Ahmed Junaid
Published: February 2019

In recent years, blockchains have obtained so much attention from researchers, engineers, and institutions; and the implementation of blockchains has started to revive a large number of applications ranging from e-finance, e-healthcare, smart home, Internet of Things, social security, logistics and so forth. In the literature on blockchains, it is found that most articles focused on their engineering implementation, while little attention has been devoted to the exploration of theoretical aspects of the system; however, the existing work is limited to model the mining process only. In this paper, a queuing theory-based model is proposed for understanding the working and theoretical aspects of the blockchain. We validate our proposed model using the actual statistics of two popular cryptocurrencies, Bitcoin and Ethereum, by running simulations for two months of transactions. The obtained performance measures parameters such as the Number of Transactions per block, Mining Time of Each Block, System Throughput, Memorypool count, Waiting Time in Memorypool, Number of Unconfirmed Transactions in the Whole System, Total Number of Transactions, and Number of Generated Blocks; these values are compared with actual statistics. It was found that the results gained from our proposed model are in good agreement with actual statistics. Although the simulation in this paper presents the modeling of blockchain-based cryptocurrencies only, the proposed model can be used to represent a wide range of blockchain-based systems.
Energy Selective Filter with Power-Dependent Transmission Effectiveness in Waveguide
Authors: Wu Zhaofeng , Lin Mingtuan , Zhang Jihong , Liu Jibin
Published: February 2019

A novel Energy Selective Filter (ESF) mounted in waveguide is presented based on nonlinear element, which is sensitive to the power intensity of incident wave achieving frequency selection as well as energy selection. The proposed ESF consists of three parts, the middle circuit board with diode loaded, the upper and bottom ground metallic patches. The mechanism of the ESF is analyzed through equivalent circuit model and its performance is investigated numerically and experimentally. According to the waveguide measurement, a shielding effectiveness of 13 dB is achieved in case of high power input and the insertions loss is less than 0.3 dB across the whole frequency range when low power signal inputs. It is a pluggable component in waveguide that could reflect high power signals of specific frequency to protect the following electronic equipment.
Gallium Nitride Normally-Off Vertical Field-Effect Transistor Featuring an Additional Back Current Blocking Layer Structure
Authors: Huang Huolin , Li Feiyu , Sun Zhonghao , Sun Nan , Zhang Feng , Cao Yaqing , Zhang Hui , Tao Pengcheng
Published: February 2019

A gallium nitride (GaN) semiconductor vertical field-effect transistor (VFET) has several attractive advantages such as high power density capability and small device size. Currently, some of the main issues hindering its development include the realization of normally off operation and the improvement of high breakdown voltage (BV) characteristics. In this work, a trenched-gate scheme is employed to realize the normally off VFET. Meanwhile, an additional back current blocking layer (BCBL) is proposed and inserted into the GaN normally off VFET to improve the device performance. The electrical characteristics of the proposed device (called BCBL-VFET) are investigated systematically and the structural parameters are optimized through theoretical calculations and TCAD simulations. We demonstrate that the BCBL-VFET exhibits a normally off operation with a large positive threshold voltage of 3.5 V and an obviously increased BV of 1800 V owing to the uniform electric field distribution achieved around the gate region. However, the device only shows a small degradation of on-resistance (RON). The proposed scheme provides a useful reference for engineers in device fabrication work and will be promising for the applications of power electronics.
Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach
Authors: Diez Carlos , Palanca Javier , Sanchez-Anguix Victor , Heras Stella , Giret Adriana , Julián Vicente
Published: February 2019

This work proposes a persuasion model based on argumentation theory and users’ characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.
Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid
Authors: Saponara Sergio , Mihet-Popa Lucian
Published: February 2019

The special issue “Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid” on MDPI Energies presents 20 accepted papers, with authors from North and South America, Asia, Europe and Africa, related to the emerging trends in energy storage and power conversion electronic circuits and systems, with a specific focus on transportation electrification and on the evolution of the electric grid to a smart grid. An extensive exploitation of renewable energy sources is foreseen for smart grid as well as a close integration with the energy storage and recharging systems of the electrified transportation era. Innovations at both algorithmic and hardware (i.e., power converters, electric drives, electronic control units (ECU), energy storage modules and charging stations) levels are proposed.
Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis
Authors: Shihavuddin ASM , Chen Xiao , Fedorov Vladimir , Nymark Christensen Anders , Andre Brogaard Riis Nicolai , Branner Kim , Bjorholm Dahl Anders , Reinhold Paulsen Rasmus
Published: February 2019

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.
A Lifecycle Analysis of the Corporate Average Fuel Economy Standards in Japan
Authors: Kaneko Mitsuki
Published: February 2019

This study estimated the corporate average fuel economy (CAFE) and CAFE targets of Japan’s domestic automobile manufacturers and evaluated whether manufactures have achieved these estimated CAFE targets. Furthermore, an analysis framework was proposed for estimating what impact the introduction of the CAFE standards in Japan will have on motor vehicle-derived lifecycle CO2 emissions. As a result, the following was found: (1) Automobile manufacturers can maximize their sales under the constraints of the CAFE standards, but vehicle sales plans based on sales maximization will lower their CAFE standard scores. (2) Economically optimal automobile manufacturer behavior—striving to achieve CAFE standards while maximizing sales—will increase the manufacturers’ overall carbon footprint and actually worsen the environment.
A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges
Authors: Haes Alhelou Hassan , Hamedani-Golshan Mohamad Esmail , Njenda Takawira Cuthbert , Siano Pierluigi
Published: February 2019

Power systems are the most complex systems and have great importance in modern life. They have direct impacts on the modernization, economic, political and social aspects. To operate such systems in a stable mode, several control and protection techniques are required. However, modern systems are equipped with several protection schemes with the aim of avoiding the unpredicted events and power outages, power systems are still encountering emergency and mal-operation situations. The most severe emergencies put the whole or at least a part of the system in danger. If the emergency is not well managed, the power system is likely to have cascading failures that might lead to a blackout. Due to the consequences, many countries around the world have research and expert teams who work to avoid blackouts on their systems. In this paper, a comprehensive review on the major blackouts and cascading events that have occurred in the last decade are introduced. A particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world and it is also due to the ready availability of data for the past events. The paper also highlights the root causes of different blackouts around the globe. Furthermore, blackout and cascading analysis methods and the consequences of blackouts are surveyed. Moreover, the challenges in the existing protective schemes and research gaps in the topic of power system blackout and cascading events are marked out. Research directions and issues to be considered in future power system blackout studies are also proposed.
Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response
Authors: Zhou Xiao , Shi Jing , Tang Yuejin , Li Yuanyuan , Li Shujian , Gong Kang
Published: February 2019

The improvement of intelligent appliances provides the basis for the demand response (DR) of residential loads. Thermostatically controlled loads (TCLs) are one of the most important DR resources and are characterized by a large load and a high degree of control. Due to its distribution characteristic, the aggregation of TCLs and their control are key issues in implementing the load control for the DR. In this study, we focus on air conditioning loads as an example of TCLs and propose a simple and transferable aggregate model by establishing a virtual house model, which accurately captures the aggregate flexibility. The deviation of the aggregate model is analyzed for the model evaluation. An air conditioning DR control scheme is proposed based on the aggregate model; it has the advantage of simple implementation and convenient control for the individual units. Simulations are performed in Gridlab-D to evaluate the accuracy and effectiveness of the proposed model and control method.
Statistical Learning for Service Quality Estimation in Broadband PLC AMI
Authors: Kim Dong Sik , Chung Beom Jin , Chung Young Mo
Published: February 2019

In this paper, we propose a method to estimate communication performance for the advanced metering infrastructure that employs the power line communication (PLC) technology. Using bit-per-symbol signals from the PLC network management system, we estimate a PLC model quality in terms of packet success rate based on statistical learning. We also verify the accuracy of the estimations by comparing them with measured communication test results at test sites. Finally, from the packet success rate estimate, the qualities of services, such as meter readings and time-of-use pricing data downloading under several metering protocol sequences, are investigated through a mathematical analysis, and numerical results are provided.
Integrated Optimization Design of Combined Cooling, Heating, and Power System Coupled with Solar and Biomass Energy
Authors: Zhang Lizhi , Li Fan , Sun Bo , Zhang Chenghui
Published: February 2019

The combined cooling, heating, and power (CCHP) systems coupled with solar energy and biomass energy can meet the needs of island or rural decentralized and small-scale integrated energy use, which have become increasingly popular in recent years. This study presents a renewable energy sources integrated combined cooling, heating, and power (RES-CCHP) system, driven by a biogas fueled internal combustion engine (ICE) and photovoltaic (PV) panels, which is different from the traditional natural gas CCHP system. Owing to the solar energy volatility and the constraint of biomass gas production, the traditional optimization design method is no longer applicable. To improve the energetic, economic and environmental performances of the system, an integrated design method with renewable energy capacity, power equipment capacity and key operating parameters as optimization variables is proposed. In addition, a case study of a small farm in Jinan, China, is conducted to verify the feasibility of the proposed RES–CCHP system structure and the corresponding optimal operation strategy. The results illustrate that the implementation of the optimal design is energy-efficient, economical and environmentally-friendly. The values of primary energy saving ratio, annual total cost saving rate and carbon emission reduction ratio are 20.94%, 11.73% and 40.79%, respectively. Finally, the influence of the volatility of renewable energy sources on the optimization method is analyzed, which shows that the RES–CCHP system and the method proposed are robust.
Sensitivity Analysis of Different Parameters on the Performance of a CHP Internal Combustion Engine System Fed by a Biomass Waste Gasifier
Authors: Villarini Mauro , Marcantonio Vera , Colantoni Andrea , Bocci Enrico
Published: February 2019

The present paper presents a study of biomass waste to energy conversion using gasification and internal combustion engine for power generation. The biomass waste analyzed is the most produced on Italian soil, chosen for suitable properties in the gasification process. Good quality syngas with up to 16.1% CO–4.3% CH4–23.1% H2 can be produced. The syngas lower heating value may vary from 1.86 MJ/ Nm3 to 4.5 MJ/Nm3 in the gasification with air and from 5.2 MJ/ Nm3 to 7.5 MJ/Nm3 in the gasification with steam. The cold gas efficiency may vary from 16% to 41% in the gasification with air and from 37% to 60% in the gasification with steam, depending on the different biomass waste utilized in the process and the different operating conditions. Based on the sensitivity studies carried out in the paper and paying attention to the cold gas efficiency and to the LHV, we have selected the best configuration process for the best syngas composition to feed the internal combustion engine. The influence of syngas fuel properties on the engine is studied through the electrical efficiency and the cogeneration efficiency.
A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction
Authors: Chen Zhen , Han Xiaoyan , Fan Chengwei , Zheng Tianwen , Mei Shengwei
Published: February 2019

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this problem. In the first stage, the relevance between features and classes is measured by normalized mutual information (NMI), and the features are ranked based on the NMI values. Then, a predefined number of top-ranked features are selected to form the strongly relevant feature subset, and the remaining features are described as the weakly relevant feature subset, which can be utilized as the prior knowledge for the next stage. In the second stage, the binary particle swarm optimization is adopted as the search algorithm for feature selection, and a new particle encoding method that considers both population diversity and prior knowledge is presented. In addition, taking the imbalanced characteristics of TSSP into consideration, an improved fitness function for TSSP feature selection is proposed. The effectiveness of the proposed method is corroborated on the Northeast Power Coordinating Council (NPCC) 140-bus system.
Torreya jackii (Taxaceae): A Special Species that is Genetically Admixed, Morphologically Distinct, and Geographically Sympatric with Parent Species
Authors: Wang Yu-Jin , Xiao Kun , Kou Yi-Xuan
Published: February 2019

Torreya jackii Chun is an endangered species (Taxaceae) confined to a few localities in China. However, the species status of T. jackii within Torreya Arn. has not been clearly elucidated under a phylogenetic context. In this study, phylogenetic analyses based on the nuclear internal transcribed spacer (ITS) and amplified fragment length polymorphism (AFLP) indicated that T. jackii is closely related with a sympatric species T. grandis Fort. ex Lindl. that is present due to cultivation. However, analysis based on the concatenated sequences of seven chloroplast loci resolved T. jackii as the first branch within the genus. Given their overlapping distribution and synchronous blooming, we suggest that the plastid-nuclear incongruence was derived from the dilution of the nuclear genome of T. jackii by T. grandis via pollen-mediated introgression hybridization when the two species met due to cultivation. Introgressive hybridization is fairly common in plants but few cases have been recognized as independent species. Our study highlights the complexity of protecting endangered species and the need for caution to prevent the unreasonable expansion of economic crops into the distribution ranges of their wild relatives.
Genome-Wide Identification and Analysis of High-Copy-Number LTR Retrotransposons in Asian Pears
Authors: Jiang Shuang , Wang Xiaoqing , Shi Chunhui , Luo Jun
Published: February 2019

A large proportion of the genome of ‘Suli’ pear (Pyrus pyrifolia) contains long terminal repeat retrotransposons (LTR-RTs), which suggests that LTR-RTs have played important roles in the evolution of Pyrus. Further analysis of retrotransposons, particularly of high-copy-number LTR-RTs in different species, will provide new insights into the evolutionary history of Pyrus. A total of 4912 putative LTR-RTs classified into 198 subfamilies were identified in the ‘Suli’ pear genome. Six Asian pear accessions, including cultivars and wild species, were resequenced. The comparison of copy number for each LTR-RT subfamily was evaluated in Pyrus accessions, and data showed up to four-fold differences for some subfamilies. This contrast suggests different fates for retrotransposon families in the evolution of Pyrus. Fourteen high-copy-number subfamilies were identified in Asian pears, and more than 50% of the LTR-RTs in the genomes of all Pyrus accessions were from these 14 identified LTR-RT subfamilies. Their average insertion time was 3.42 million years ago, which suggests that these subfamilies were recently inserted into the genome. Many homologous and specific retrotransposon insertion sites were identified in oriental and occidental pears, suggesting that the duplication of retrotransposons has occurred throughout almost the entire origin and evolution of Pyrus species. The LTR-RTs show high heterogeneity, and their copy numbers vary in different Pyrus species. Thus, our findings suggest that LTR-RTs are an important source of genetic variation among Pyrus species.
Inferring Drug-Protein–Side Effect Relationships from Biomedical Text
Authors: Song Min , Baek Seung Han , Heo Go Eun , Lee Jeong-Hoon
Published: February 2019

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms.
From Politics to Pope: An Account of the Group Aesthetic
Authors: Breton Rob
Published: February 2019

This paper discusses the study of Chartist and working-class literatures, noting that the pronounced development of aesthetic criticism in these areas uncomfortably corresponds with the rejection of “aesthetics” in other fields. Chartist, working-class, and laboring-class scholars have broken free from monolithically sociological or political readings that only a generation ago too often dismissed artistic endeavors as, at best, merely a re-accenting of the mainstream. Current studies focus on the aesthetic innovations that emerged out of working-class entanglements with mainstream counterparts. The paper argues that the rejection of “aesthetics” generally fails to recognize marginalized and group aesthetics (including the critical work done on marginalized and group aesthetics) and specifically what it meant for a political cohort—the Chartists are my example—to think aesthetically.
The Importance of Knowledge of Provenance for the Provenance of Knowledge: The Case of Traditional Costumes Collections in Greece
Authors: Papathoma Eleni
Published: February 2019

In Greece there are several collections of “traditional costumes”, i.e., garments with a strongly local character, which were in use up to the early 20th century. “Traditional costumes” are directly linked to Modern Greek folk culture: they were formed in its context and they are its most typical and obvious image. They have often been used as a national icon and are popular with people of all ages, who admire them and, on occasions of national celebrations and dance festivals, take pleasure and pride in dressing in replicas of them. Since they have stopped being worn many decades ago, the existing collections are a major source for their study, and they are respectfully referred to by scholars, the public and makers of replicas. The provenance of these collections—the criteria used, the persons involved, the purposes served, etc.—deeply affects the extent and the nature of our knowledge on the objects included in them. Given the inadequate information usually recorded on provenance issues, a thorough search of all potential sources with an aim to put together ‘biographies’ of collections will help to understand the collecting context of the objects and their respective historical and interpretational value.
DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non-Atopic Asthma
Authors: Jeong Ayoung , Imboden Medea , Ghantous Akram , Novoloaca Alexei , Carsin Anne-Elie , Kogevinas Manolis , Schindler Christian , Lovison Gianfranco , Herceg Zdenko , Cuenin Cyrille , Vermeulen Roel , Jarvis Deborah , Amaral André F. S. , Kronenberg Florian , Vineis Paolo , Probst-Hensch Nicole
Published: February 2019

A high body mass (BMI) index has repeatedly been associated with non-atopic asthma, but the biological mechanism linking obesity to asthma is still poorly understood. We aimed to test the hypothesis that inflammation and/or innate immunity plays a role in the obesity-asthma link. DNA methylome was measured in blood samples of 61 non-atopic participants with asthma and 146 non-atopic participants without asthma (non-smokers for at least 10 years) taking part in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) study. Modification by DNA methylation of the association of BMI or BMI change over 10 years with adult-onset asthma was examined at each CpG site and differentially methylated region. Pathway enrichment tests were conducted for genes in a priori curated inflammatory pathways and the NLRP3-IL1B-IL17 axis. The latter was chosen on the basis of previous work in mice. Inflammatory pathways including glucocorticoid/PPAR signaling (p = 0.0023), MAPK signaling (p = 0.013), NF-κB signaling (p = 0.031), and PI3K/AKT signaling (p = 0.031) were enriched for the effect modification of BMI, while NLRP3-IL1B-IL17 axis was enriched for the effect modification of BMI change over 10 years (p = 0.046). DNA methylation measured in peripheral blood is consistent with inflammation as a link between BMI and adult-onset asthma and with the NLRP3-IL1B-IL17 axis as a link between BMI change over 10 years and adult-onset asthma in non-atopic participants.