bluuebunny/crossref_metadata_2025 huggingface dataset:
Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
Authors: Alur Varun Chandra , Raju Varshita , Vastrad Basavaraj , Vastrad Chanabasayya
Published: April 2019

Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C), VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.
Evidence of Clinical Pathology Abnormalities in People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) from an Analytic Cross-Sectional Study
Authors: Nacul Luis , de Barros Barbara , Kingdon Caroline C. , Cliff Jacqueline M. , Clark Taane G. , Mudie Kathleen , Dockrell Hazel M. , Lacerda Eliana M.
Published: April 2019

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease presenting with extreme fatigue, post-exertional malaise, and other symptoms. In the absence of a diagnostic biomarker, ME/CFS is diagnosed clinically, although laboratory tests are routinely used to exclude alternative diagnoses. In this analytical cross-sectional study, we aimed to explore potential haematological and biochemical markers for ME/CFS, and disease severity. We reviewed laboratory test results from 272 people with ME/CFS and 136 healthy controls participating in the UK ME/CFS Biobank (UKMEB). After corrections for multiple comparisons, most results were within the normal range, but people with severe ME/CFS presented with lower median values (p < 0.001) of serum creatine kinase (CK; median = 54 U/L), compared to healthy controls (HCs; median = 101.5 U/L) and non-severe ME/CFS (median = 84 U/L). The differences in CK concentrations persisted after adjusting for sex, age, body mass index, muscle mass, disease duration, and activity levels (odds ratio (OR) for being a severe case = 0.05 (95% confidence interval (CI) = 0.02–0.15) compared to controls, and OR = 0.16 (95% CI = 0.07–0.40), compared to mild cases). This is the first report that serum CK concentrations are markedly reduced in severe ME/CFS, and these results suggest that serum CK merits further investigation as a biomarker for severe ME/CFS.
Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications
Authors: Cuesta-Frau David , Murillo-Escobar Juan Pablo , Orrego Diana Alexandra , Delgado-Trejos Edilson
Published: April 2019

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.
Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample
Authors: Lin Lin , Wang Bin , Qi Jiajin , Wang Da , Huang Nantian
Published: April 2019

To improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered. Furthermore, commonly used multi-classifiers could misidentify the type or severity of faults without using normal samples as training samples. Therefore, a novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample. First, the bearing vibration signals are decomposed into numerous intrinsic mode functions using empirical wavelet transform. Then, 284 features including multiple entropy are extracted from the original signal and intrinsic mode functions to construct the initial feature set. Lastly, a hybrid classifier based on one-class support vector machine trained by normal samples and a random forest trained by imbalanced fault data without some specific severities is set up to accurately identify the mechanical state and specific fault type of the bearings. The experimental results show that the proposed method can significantly improve the classification accuracy compared with traditional methods in different diagnostic target.
Exergy Analyses of Low-Temperature District Heating Systems With Different Sanitary Hot-Water Boosters
Authors: Poredoš Primož , Kitanovski Andrej , Poredoš Alojz
Published: April 2019

This paper presents an exergy-efficiency analysis of low-temperature district heating systems (DHSs) with different sanitary hot-water (SHW) boosters. The required temperature of the sanitary hot water (SHW) was set to 50 °C. The main objective of this study was to compare the exergy efficiencies of a DHS without a booster to DHSs with three different types of boosters, i.e., electric-, gas-boiler- and heat-pump-based, during the winter and summer seasons. To achieve this, we developed a generalized model for the calculation of the exergy efficiency of a DHS with or without the booster. The results show that during the winter season, for a very low relative share of SHW production, the DHS without the booster exhibits favorable exergy efficiencies compared to the DHSs with boosters. By increasing this share, an intersection point above 45 °C for the supply temperatures, at which the higher exergy efficiency of a DHS with a booster prevails, can be identified. In the summer season the results show that a DHS without a booster at a supply temperature above 70 °C achieves lower exergy efficiencies compared to DHSs with boosters at supply temperatures above 40 °C. The results also show that ultra-low supply and return temperatures should be avoided for the DHSs with boosters, due to higher rates of entropy generation.
Educational Robotics in the Stage of Secondary Education: Empirical Study on Motivation and STEM Skills
Authors: Arís Nuria , Orcos Lara
Published: April 2019

Educational robotics (ER) is increasingly present in secondary education classrooms and has acquired greater projection, especially with the appearance of championships, such as FIRST® LEGO® League. These competitions are based on a globalizing focus of the different areas of the curriculum, therefore, we consider that it directly links with the achievement of STEAM (science, technology, engineering, arts, and mathematics) skills. We present a research study that provides objective data based on the opinions of teachers and students that participated in this championship during the course 2017/2018 about its impact in the learning process. To this end, Spanish students and teachers answered questionnaires to collect their perceptions and assessments just after their participation. The results obtained allow us to conclude that both teachers and students believe this project promotes interest and scientific curiosity, as well as social skills through teamwork.
Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption
Authors: Tang Fengyi , Wu Wei , Liu Jian , Wang Huimei , Xian Ming
Published: April 2019

The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not collude. Once they collude, the server could decrypt and get data of all learning participants. Moreover, since the private keys of all learning participants are the same, a learning participant must connect to the server via a distinct TLS/SSL secure channel to avoid leaking data to other learning participants. To fix these problems, we propose a privacy-preserving distributed deep learning scheme with the following improvements: (1) no information is leaked to the server even if any learning participant colludes with the server; (2) learning participants do not need different secure channels to communicate with the server; and (3) the deep learning model accuracy is higher. We achieve them by introducing a key transform server and using homomorphic re-encryption in asynchronous stochastic gradient descent applied to deep learning. We show that our scheme adds tolerable communication cost to the deep learning system, but achieves more security properties. The computational cost of learning participants is similar. Overall, our scheme is a more secure and more accurate deep learning scheme for distributed learning participants.
A Semi-Floating Gate Memory with Tensile Stress for Enhanced Performance
Authors: Yuan Ying , Jiang Shuye , Sun Bingqi , Chen Lin , Zhu Hao , Sun Qingqing , Zhang David Wei
Published: April 2019

With the continuous scaling down of devices, traditional one-transistor one-capacitor dynamic random access memory (1T-1C DRAM) has encountered great challenges originated from the large-volume capacitor and high leakage current. A semi-floating gate transistor has been proposed as a capacitor-less memory with ultrafast speed and silicon-compatible technology. In this work, a U-shaped semi-floating gate memory with strain technology has been demonstrated through TCAD simulation. Ultra-high operation speed on a timescale of 5 ns at low operation voltages (≤ 2.0 V) has been obtained. And the tensile stress induced in its channel region by using contact etch stop layer (Si3N4 capper layer) was found to significantly improve the drain current by 12.07%. Furthermore, this device demonstrated a favorable retention performance with a retention time over 1 s, and its immunity to disturbance from bit-line has also been investigated that could maintain data under the continuous worst writing disturbance operation over 10 ms.
Inverse Scattering Analysis from Measurement Data of Total Electric and Magnetic Fields by Means of Cylindrical-Wave Expansion
Authors: Tsuburaya Tomonori , Meng Zhiqi , Takenaka Takashi
Published: April 2019

Microwave tomography is an effective technique to estimate material distribution, where inverse scattering analysis is performed on the assumption that accurate information on the incident field is known for a measurement curve as well as in the target region. In reality, however, the information may often be unobtainable due to multiple scattering between the transmitting antenna and the target object, or existence of unwanted waves and obstacles. In this paper, a method to extract information on incident fields from measured total field data is proposed. The validity of the proposed method is verified on 2D TMz problems, where a cylindrical, a square, and an L-shape homogeneous object are employed as a target object. Furthermore, it is shown that the method is available even when there are unwanted obstacles outside the measurement curve.
Robust Stable Control Design for AC Power Supply Applications
Authors: Chang En-Chih , Yang Sung-Chi , Wu Rong-Ching
Published: April 2019

This paper applies modified feedback technology to carry out the exact steady-state and fast transient in a high-performance alternating current (AC) power supply. The presented scheme displays the virtues of a finite-time convergence control (FTCC) and a discrete grey prediction model (DGPM). The FTCC, derived from a terminal sliding-mode (TSM) design principle, can produce the finite system-state convergence time and evade the singularity. It is noteworthy that the chattering/steady-state error around the FTCC may occur because of the overestimated or underestimated uncertainty bound. The DGPM with the bound estimate ability is integrated into the FTCC to cope with internal parameter variations and external load disturbances. The less chattering and steady-state error can be obtained, providing more robust performance in the AC power supply. The combination of the FTCC and the DGPM extends the standard TSM design for the purpose of faster singularity-free convergence, as well as introducing the grey modeling method in the case of a more exact uncertainty estimate. The modified control technology has a high-precision tracking performance and a fast convergent speed. Simulated and experimental results point out that the modified control technology can effectuate low total harmonic distortion (THD) and fast dynamic response in the presence of rectifier loads and abrupt step load changes.
Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid
Authors: Yang Fan , Qiu Robert C. , Ling Zenan , He Xing , Yang Haosen
Published: April 2019

Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event via the high-resolution phasor measurement unit (PMU) data, such that proper actions can be taken before any sporadic fault escalates to cascading blackouts. Under the framework of random matrix theory, the proposed approach maps the raw data into a high-dimensional space with two parts: (1) factors (spikes, mapping faults); (2) residuals (a bulk, mapping white/non-Gaussian noises or normal fluctuations). As for the factors, we employ their number as a spatial indicator to estimate the number of constituent components in a multiple event. Simultaneously, the autoregressive rate of the noises is utilized to measure the variation of the temporal correlation of the residuals for tracking the system movement. Taking the spatial-temporal correlation into account, this approach allows for detection, decomposition and temporal localization of multiple events. Case studies based on simulated data and real 34-PMU data verify the effectiveness of the proposed approach.
Electricity Generation in India: Present State, Future Outlook and Policy Implications
Authors: Tiewsoh Lari Shanlang , Jirásek Jakub , Sivek Martin
Published: April 2019

India is one of the fastest developing countries in the world. To sustain this growth, energy and electricity demands will increase. In 2015, of the 1337 TWh produced, 916 TWh were from fossil fuels. We prepared several models of electricity demand from 2015 to 2030, based on publicly available datasets and trends. Models were tested on data from previous years and adjusted accordingly. From several scenarios, we decided to introduce two possibilities, i.e., a scenario using high energy savings in all sectors, and a scenario counting on a high industrial growth not supported by an equal increase of electricity savings. For both cases we prepared models for extreme situations: (1) where coal- and lignite-based power plants are preferred after slow-down of a renewable energy boom, and (2) with high utilization of renewable energy supported by natural gas and nuclear energy. With GDP and population increasing at the same rate as in previous years, the unambiguous result in all scenarios is a 2 to 3-fold increase of the electricity demand by 2030. On the electricity production side, all scenarios stress the role of coal, renewables and nuclear sources. Both energy and climate policies should be prepared for such a development in advance.
A Robust Assessment Model of the Solar Electrical-Thermal Energy Comprehensive Accommodation Capability in a District Integrated Energy System
Authors: Wei Wei , Jia Haoyue , Mu Yunfei , Wu Jianzhong , Jia Hongjie
Published: April 2019

As effective utilization of solar resources is a significant way to address the imbalance between energy supply and demand. Therefore, reasonably assessing the accommodation capability of solar energy is important. A two-stage robust evaluation model is proposed for the solar electricity-thermal energy comprehensive accommodation capability in a district integrated energy system. The accommodation capability index is constructed based on the second law of thermodynamics. A robust optimization model was adopted to deal with the uncertainty of solar irradiance. In the solution procedure, the non-convex non-linear power flow model is transformed into a second-order cone model to effectively fit the proposed two-stage robust evaluation model. Finally, a case study verifies the effectiveness of the proposed model and the solution method. The influence of irradiance fluctuation range, gas boiler, and energy storage is discussed in detail.
Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme
Authors: Tao Hai , Ebtehaj Isa , Bonakdari Hossein , Heddam Salim , Voyant Cyril , Al-Ansari Nadhir , Deo Ravinesh , Yaseen Zaher Mundher
Published: April 2019

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.
Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System
Authors: Lopes Francis M. , Conceição Ricardo , Silva Hugo G. , Fasquelle Thomas , Salgado Rui , Canhoto Paulo , Collares-Pereira Manuel
Published: April 2019

Short-term forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS) and the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used in the simulation of a solar power tower, through the System Advisor Model (SAM). Recent results demonstrated that DNI forecasts have been enhanced, having the potential to be a suitable tool for plant operators that allows achieving higher energy efficiency in the management of concentrating solar power (CSP) plants, particularly during periods of direct solar radiation intermittency. The main objective of this work was to assert the predictive value of the IFS forecasts, regarding operation outputs from a simulated central receiver system. Considering a 365-day period, the present results showed an hourly correlation of ≈0.78 between the electric energy injected into the grid based on forecasted and measured data, while a higher correlation was found for the daily values (≈0.89). Operational strategies based on the forecasted results were proposed for plant operators regarding the three different weather scenarios. Although there were still deviations due to the cloud and aerosol representation, the IFS forecasts showed a high potential to be used for supporting informed energy dispatch decisions in the operation of central receiver units.
Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis
Authors: Baghli Mehdi , Delpha Claude , Diallo Demba , Hallouche Abdelhamid , Mba David , Wang Tianzhen
Published: April 2019

This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.
The Co-Movement and Asymmetry between Energy and Grain Prices: Evidence from the Crude Oil and Corn Markets
Authors: Chen Zhan-Ming , Wang Liyuan , Zhang Xiao-Bing , Zheng Xinye
Published: April 2019

This paper investigates the co-movement and asymmetric interactions between energy and grain prices, based on the evidence from the crude oil and corn markets, the most important energy and grain markets, respectively. Time series analysis indicates that there is a consistent trend between the crude oil price and corn price with a significant positive correlation coefficient 0.7471 during the sampling period, from January 2008 to February 2016. In addition, we find that there is a long-run equilibrium relationship between the two commodities’ prices. Moreover, while linear Granger causality tests suggest that there is a two-way Granger causality relationship between the price changes in the two markets, non-linear Granger causality tests suggest that there is only a one-way causality relationship from corn to oil price. However, both linear and non-linear Granger causality tests indicate the asymmetry of causality relationship between the two markets (the price change in corn market can more significantly Granger cause the change in crude oil market). Further analysis suggests that the contribution of the corn market to price discovery in a large commodity market is larger than that of the crude oil market.
Active Pressure Ripple Control in Axial Piston Pumps through High-Frequency Swash Plate Oscillations—A Theoretical Analysis
Authors: Casoli Paolo , Pastori Mirko , Scolari Fabio , Rundo Massimo
Published: April 2019

Pressure ripple has always been a major drawback in hydraulic circuits, since it is the main source of the overall noise emitted by the pump. This article presents a theoretical analysis on the active control of the pressure ripple in an axial piston pump by properly moving the swash plate. The reduction of the pressure oscillations is studied in an active way and for this purpose a mathematical model of the whole pump has been developed, focusing on both the fluid dynamic aspects and the component dynamics. An experimental activity has been performed in order to validate the pump mathematical model. The results of the simulation, compared with the experimental data, highlight a suitable capability of the model to predict both the dynamics of the swash plate and the delivery pressure ripple. The validated model has been used for implementing an active control of the pressure ripple with the aim of properly modifying the machine displacement at high frequency in order to vary the instantaneous delivery flow rate and, consequently, the outlet pressure. The control strategy is grounded on moving the swash plate for modifying the motion law of the pistons through a servo valve integrated into the displacement control system. The simulations results have demonstrated that acting on the pump displacement control is possible to considerably reduce the amplitude of the pressure oscillations.
Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in Turbo-Generator Rotor Windings
Authors: Wu Yucai , Ma Guanhua
Published: April 2019

Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil method. Compared with dynamic eccentricity faults, the characteristics of the variations in the main magnetic field after a turn-to-turn short circuit in rotor windings were analyzed and the unique characteristics were extracted. We propose that the degree of a turn-to-turn short circuit can be judged by the difference in the induction voltage of the double U-shaped detection coils mounted on the stator core. Here, the faulty slot position was determined by the local convex point formed by the difference in the induced voltage. Numerical simulation was used here to determine the induced voltage characteristics in the double U-shaped coils caused by the turn-to-turn short circuit fault. We analyzed the dynamic eccentricity fault as well as combined the fault of a turn-to-turn short circuit and dynamic eccentricity. Finally, we demonstrate the positive anti-interference performance associated with this fault detection method. This new online detection method is satisfactory in terms of sensitivity, speed, and positioning, and overall performance is superior to the traditional online detection methods.
Numerical Investigation of the Wind and Thermal Conditions in Sky Gardens in High-Rise Buildings
Authors: Mohammadi Murtaza , Calautit John Kaiser
Published: April 2019

High-rise buildings are known to be highly energy intensive, adding stress on already stressed resources. Alternatively, designers are looking at passive strategies and investing in architectural elements, such as sky gardens, which could improve the performance of buildings. Sky gardens are green areas located in a building which are exposed to the outdoors. They could provide multifaceted improvements in buildings by introducing environmental benefits to occupants and altering microclimate. This study aims to determine the wind comfort and thermal condition in sky gardens in high-rise buildings using numerical modelling. Different geometrical configurations of sky gardens were simulated and analysed. Based on the initial results, the study reveals that sky gardens can generate high wind velocities of the order ~10 m/s when located on a high-rise building. The addition of features such as trees and other architectural elements, which can act as a buffer, can help attenuate the high wind speeds and creating habitable spaces. The reduction varies 50%–80%, depending on the location and spatial domain of the sky garden. Furthermore, the study also investigated the reduction in air temperature due to the addition of trees, which can further reduce temperature in hot weather.
Study of Sensitive Parameters on the Sensor Performance of a Compression-Type Piezoelectric Accelerometer Based on the Meta-Model
Authors: Lee Gyoung-Ja , Hwang Won-Ju , Park Jin-Ju , Lee Min-Ku
Published: April 2019

Through a numerical analytical approach based on piezoelectric analysis and meta-modeling, this study investigated the effect of the component design of an accelerometer sensor on sensitivity and resonance frequency. The results of the study confirmed that the resonance frequency obtained from the piezoelectric analysis was almost the same as the experimental value of the resonance frequency obtained from the fabricated sensing module and proved the validity of the piezoelectric analysis using a finite element method. Moreover, the results of examining the influence of the component design on the resonance frequency and electrical potential suggested that the diameter and height of the head (seismic mass) had the greatest influence. As the diameter and height of the head increased, the sensitivity increased, but the resonance frequency decreased, which indicates that it is necessary to select an appropriate mass to optimize the sensor performance. In addition, the increase in tail height and epoxy thickness had a positive effect on both the resonance frequency and electric potential, and the base diameter had a negative effect on both of them.
Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model
Authors: Chen Dapeng , Jing Zhaoxia , Tan Huijuan
Published: April 2019

Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.
A Decentralized Architecture Based on Cooperative Dynamic Agents for Online Voltage Regulation in Smart Grids
Authors: Andreotti Amedeo , Petrillo Alberto , Santini Stefania , Vaccaro Alfredo , Villacci Domenico
Published: April 2019

The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, based on both local and global computing, could be deployed for online voltage regulation in active power networks, the identification of the most effective approach, which is influenced by the available computing resources, and the required control performance, is still an open problem. To face this issue, in this paper, the mathematical backbone, the expected performance, and the architectural requirements of a novel decentralized control paradigm based on dynamic agents are analyzed. Detailed simulation results obtained in a realistic case study are presented and discussed to prove the effectiveness and the robustness of the proposed method.
The Role of Tourism Impacts on Cultural Ecosystem Services
Authors: Taff B. Derrick , Benfield Jacob , Miller Zachary D. , D’Antonio Ashley , Schwartz Forrest
Published: April 2019

Parks and protected areas are recognized for the important ecosystem services, or benefits, they provide society. One emerging but understudied component is the cultural ecosystem services that parks and protected areas provide. These cultural ecosystem services include a variety of benefits, such as cultural heritage, spiritual value, recreation opportunities, and human health and well-being. However, many of these services can only be provided if people visit these parks and protected areas through tourism opportunities. However, with this tourism use comes a variety of inevitable resource impacts. This current research connects potential impacts from tourism in parks and protected areas to the health and well-being aspect of cultural ecosystem services. We used an MTurk sample to record affective responses across a range of resource conditions. Results demonstrate that as tourism-related ecological impacts increased, positive affect decreased. Decreases in positive affect were more severe for park and protected area scenes featuring informal and/or undesignated social trails when compared to scenes with increasing levels of trampling/vegetation loss. Collectively, the results show that managing tourism in parks and protected areas in a manner that reduces impact is essential to providing beneficial cultural ecosystem services related to human health and well-being.
National Forest Parks in China: Origin, Evolution, and Sustainable Development
Authors: Chen Ziru , Fu Weicong , Konijnendijk van den Bosch Cecil C. , Pan Hui , Huang Shuping , Zhu Zhipeng , Qiao Yuxuan , Wang Nannan , Dong Jianwen
Published: April 2019

The concept of National Forest Park (NFP) is mainly used in mainland China. Originating in 1982, NFP embodies a “top-down” concept and associated program launched by the Chinese government. It is aimed at promoting forest-based tourism and economic development under the premise of protecting forest resources. After 30 years of development, NFPs have made great achievements in protecting specific forest resources, promoting forest-based tourists, promoting regional economic development, and they have gained popularity worldwide. However, due to the fast pace of NFP expansion, lack of predictable planning and innovative thinking, and ineffective governance, some problems like overexploitation, scenic pollution, monotonous development patterns, and ecological degradation associated with NFP constrain its sustainable development. In order to solve these problems effectively, a holistic review of the status of NFPs in China is needed. To help meet this need, the origin, evolution, and current status of NFPs in China were analyzed. The presented research also included retrospective analyses of challenges and opportunities for NFPs sustainable development in China. Results show that from 1982 to 2015, the number of NFPs grew dramatically, and this development occurred in four phases. In addition, NFP development has been unbalanced in regional distribution. When analyzing the evolution of NFPs, the main issues to date have included failure to implement Master Plans in practice, unclear supervisory responsibilities, ambiguous classification, unbalanced distribution, destruction of natural resource and ecosystems, insufficient cultural protection, weak awareness of nature education, lack of resource statistics, monotonous planning, and weak marketing. Study findings can contribute to promoting the sustainable future development of NFPs and support the forest-based tourism industry.
Simultaneous Bioconversion of Gelatinized Starchy Waste from the Rice Noodle Manufacturing Process to Lactic Acid and Maltose-Forming α-Amylase by Lactobacillus plantarum S21, Using a Low-Cost Medium
Authors: Unban Kridsada , Kanpiengjai Apinun , Khatthongngam Nuttapong , Saenjum Chalermpong , Khanongnuch Chartchai
Published: April 2019

A direct bioconversion of gelatinized starchy waste (GSW) to lactic acid by amylolytic lactic acid bacterium Lactobacillus plantarum S21 was investigated. Corn steep liquor (CSL) was selected as the most suitable low-cost nitrogen source for replacing yeast extract, beef extract, and peptone in De Man, Rogosa and Sharpe (MRS) medium. Plackett–Burman design results indicated that GSW and CSL were the two most nutrients that significantly influence lactic acid production, among eight medium components, including GSW, CSL, K2HPO4, CH3COONa, (NH4)2HC6H5O7, MgSO4, MnSO4, and Tween 80. A new low-cost medium containing only GSW (134.4 g/L) and CSL (187.7 g/L) was achieved as omitting other six components from the optimized medium had no effect on lactic acid yield. Batch fermentation at 37 °C both in 1 L and 10 L jar fermenters showed non-significantly different productivity. A by-product, maltose-forming α-amylase, was successfully achieved up to 96% recovery yield using an ultrafiltration unit equipped with a 50 kDa cut-off membrane. Crude lactic acid exhibited the additional benefit of antimicrobial activity against food and feed pathogens Salmonella enterica serovar Typhimurium TISTR 292, Vibrio cholerae TH-001, and also E. coli ATCC 25922. This study presents a promising bioprocess for the simultaneous production of lactic acid, and a value-added food enzyme, using only two industrial wastes, GSW and CSL, as the medium components.
A Review of Machine Learning and IoT in Smart Transportation
Authors: Zantalis Fotios , Koulouras Grigorios , Karabetsos Sotiris , Kandris Dionisis
Published: April 2019

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.
Corn and Rice Starch-Based Bio-Plastics as Alternative Packaging Materials
Authors: Marichelvam M. K. , Jawaid Mohammad , Asim Mohammad
Published: April 2019

Due to the negative environmental impacts of synthetic plastics, the development of biodegradable plastics for both industrial and commercial applications is essential today. Researchers have developed various starch-based composites for different applications. The present work investigates the corn and rice starch-based bioplastics for packaging applications. Various samples of bioplastics are produced, with different compositions of corn and rice starch, glycerol, citric acid, and gelatin. The tensile properties were improved after adding rice starch. However, water absorption and water solubility were reduced. On the basis of these results, the best sample was analyzed for thickness testing, biodegradability properties, SEM, hydrophilicity, thermogravimetric analysis, and sealing properties of bioplastic. The results show the suitability of rice and corn-based thermoplastic starch for packaging applications.
Fire Temperature Based on the Time and Resistance of Buildings—Predicting the Adoption of Fire Safety Measures
Authors: Santarpia Luciano , Bologna Simone , Ciancio Virgilio , Golasi Iacopo , Salata Ferdinando
Published: April 2019

During a fire in enclosed spaces, having structures with a good level of resistance is very important. The post flashover fire time interval, during which rescue squads operate, is important to verify if the structure can still resist fire for an acceptable time interval. This can be determined through the REI value. Hence, the way the fire develops must be examined together with the trend of the temperature that might guarantee that the structure will resist the heat flux released during the combustion. This article examines and compares, through a case study, the most important methods for analysis of the fire risk: the prescriptive approach and the simplified performance-based approach. The performance-based method (more suitable for the actual planning demand with respect to the more cautious prescriptive approach) is affected by the parameters influencing its development. The goal of this article is to provide a graph (based on parameters like the type of building, opening factor “O” and fire load “q”) that might be used by designers and architects to carry out the planning phase and adopt fire prevention systems before dealing with the assessments required by the engineering field for the fire risk analysis.
In Vitro Gene Delivery in Retinal Pigment Epithelium Cells by Plasmid DNA-Wrapped Gold Nanoparticles
Authors: Trigueros Sònia , B. Domènech Elena , Toulis Vasileios , Marfany Gemma
Published: April 2019

Many rare diseases course with affectation of neurosensory organs. Among them, the neuroepithelial retina is very vulnerable due to constant light/oxidative stress, but it is also the most accessible and amenable to gene manipulation. Currently, gene addition therapies targeting retinal tissue (either photoreceptors or the retinal pigment epithelium), as a therapy for inherited retinal dystrophies, use adeno-associated virus (AAV)-based approaches. However, efficiency and safety of therapeutic strategies are relevant issues that are not always resolved in virus-based gene delivery and alternative methodologies should be explored. Based on our experience, we are currently assessing the novel physical properties at the nanoscale of inorganic gold nanoparticles for delivering genes to the retinal pigment epithelium (RPE) as a safe and efficient alternative approach. In this work, we present our preliminary results using DNA-wrapped gold nanoparticles (DNA-gold NPs) for successful in vitro gene delivery on human retinal pigment epithelium cell cultures, as a proof-of-principle to assess its feasibility for retina in vivo gene delivery. Our results show faster expression of a reporter gene in cells transfected with DNA-gold NPs compared to DNA-liposome complexes. Furthermore, we show that the DNA-gold NPs follow different uptake, internalization and intracellular vesicle trafficking routes compared to pristine NPs.
Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population
Authors: Lee Jungjae , Kang Ji-Hoon , Kim Jun-Mo
Published: April 2019

Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits.
‘Daring, Unusual Things’: Bertolt Brecht’s Photo-Epigrams as Poetic Inventions
Authors: Alizadeh Ali
Published: April 2019

This essay explores the aesthetics of Bertolt Brecht’s compositions of poetry with photography in the so-called photo-epigrams of his 1955 book War Primer. The photo-epigrams have mostly been viewed and appreciated as interventions in photography; but in this essay I aim to show their novelty and efficacy as poetic inventions. To do so, I draw on Karl Marx’s and Walter Benjamin’s views apropos the decline of poetry under modern, industrial capitalism to argue that Brecht, in his photo-epigrams, is responding to—and attempting to counter—a specific problem at the heart of modern poetry: the crisis in perceptibility and accessibility. By coupling poems with photographs—in unique and uniquely politicised ways—Brecht provides a resonant critique of the deadly ideologies of the ruling classes engaged in World War II, as well as a method for addressing the decline in the readability of poetry in the modern era.