bluuebunny/crossref_metadata_2025 huggingface dataset:
Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis
Authors: Ali Tariq , Masood Khalid , Irfan Muhammad , Draz Umar , Nagra Arfan Ali , Asif Muhammad , Alshehri Bandar M. , Glowacz Adam , Tadeusiewicz Ryszard , Mahnashi Mater H. , Yasin Sana
Published: December 2020

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.
A Compact Coprocessor for the Elliptic Curve Point Multiplication over Gaussian Integers
Authors: Safieh Malek , Thiers Johann-Philipp , Freudenberger Jürgen
Published: December 2020

This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
Grid Voltage Estimation Based on Integral Resonant Current Controller for LCL-Filtered Grid-Connected Inverter without AC Voltage Sensors
Authors: Tran Thuy Vi , Kim Kyeong-Hwa
Published: December 2020

A high reliability of a grid-connected inverter (GCI) system at reasonable cost is a critical requirement for maximizing renewable energy potential in the electrical energy market. Several grid voltage sensorless control approaches have been investigated not only to eliminate the vulnerability of faulty sensors but also to further reduce the GCI commercial price. In this paper, a frequency adaptive integral-resonant full-state feedback current control scheme with the facilitation of a full-state observer is adopted for a grid-connected inductive–capacitive–inductive (LCL) filtered inverter without sensing the grid voltages. The proposed scheme actively damps the filter resonance and ensures the robustness of the inverter system against unexpected severe grid conditions with low cost and simplified hardware construction. The synchronization of the inverter with the main grid is accomplished by the proposed current controller-based grid voltage estimator, in which the grid frequency and phase angle can be detected effectively. In addition, the actual grid voltages are precisely regenerated to ensure the stable performance of the full-state observer. A safe start-up procedure is also presented for the grid voltage sensorless control of the LCL-filtered inverter to avoid a critical overcurrent and long settling time during the start-up instant, offering a stable and reliable inverter system operation with low computational burden. The effectiveness and feasibility of the proposed voltage sensorless current control scheme are validated by the simulation and experimental results under non-ideal grid conditions such as the harmonic distortion, grid frequency variation, and sudden grid phase angle jump.
A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers
Authors: Hu Xiaoxuan , Sun Yanfei
Published: December 2020

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.
Optical Flow Filtering-Based Micro-Expression Recognition Method
Authors: Wu Junjie , Xu Jianfeng , Lin Deyu , Tu Min
Published: December 2020

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.
The Research of Relationship among Smile Developing Software, Internet Addiction, and Attachment Style
Authors: Lin Hao-Chiang Koong , Tsai Meng-Chun , Wu Kuang-Hsiang
Published: December 2020

By developing a software tool that helps students cultivate the habit of smiling, this study aims to enhance students’ interpersonal relationships and ability to interact with others and therefore effectively decrease their Internet addiction. The study participants were students from a vocational high school in Tainan, Taiwan. To begin with, it examined the choices of attachment styles and levels of Internet addiction among high school students enrolled in a practical skills program. The students used the software tool for fourteen consecutive days and completed their smile task, which was followed by a post-test questionnaire. The result shows that for interpersonal interactions, changes in the mean values for three types of attachment styles decrease (namely anxious–preoccupied, dismissive–avoidant, and fearful–avoidant styles). In particular, the dismissive–avoidant style was reported with the most prominent change of −1.267, and it was the only variable with a higher average value. This study also applied Bartholomew and Horowitz’s two-dimensional internal working model and found that the participants had demonstrated positive developments in their own self-internal modes and, in particular, others’ internal modes.
Polymorphic Memory: A Hybrid Approach for Utilizing On-Chip Memory in Manycore Systems
Authors: Lim Seung-Ho , Seok Hyunchul , Park Ki-Woong
Published: December 2020

The key challenges of manycore systems are the large amount of memory and high bandwidth required to run many applications. Three-dimesnional integrated on-chip memory is a promising candidate for addressing these challenges. The advent of on-chip memory has provided new opportunities to rethink traditional memory hierarchies and their management. In this study, we propose a polymorphic memory as a hybrid approach when using on-chip memory. In contrast to previous studies, we use the on-chip memory as both a main memory (called M1 memory) and a Dynamic Random Access Memory (DRAM) cache (called M2 cache). The main memory consists of M1 memory and a conventional DRAM memory called M2 memory. To achieve high performance when running many applications on this memory architecture, we propose management techniques for the main memory with M1 and M2 memories and for polymorphic memory with dynamic memory allocations for many applications in a manycore system. The first technique is to move frequently accessed pages to M1 memory via hardware monitoring in a memory controller. The second is M1 memory partitioning to mitigate contention problems among many processes. Finally, we propose a method to use M2 cache between a conventional last-level cache and M2 memory, and we determine the best cache size for improving the performance with polymorphic memory. The proposed schemes are evaluated with the SPEC CPU2006 benchmark, and the experimental results show that the proposed approaches can improve the performance under various workloads of the benchmark. The performance evaluation confirms that the average performance improvement of polymorphic memory is 21.7%, with 0.026 standard deviation for the normalized results, compared to the previous method of using on-chip memory as a last-level cache.
Influence of Clumps-Weighted Moorings on a Spar Buoy Offshore Wind Turbine
Authors: Bruschi Niccolo , Ferri Giulio , Marino Enzo , Borri Claudio
Published: December 2020

The spar buoy platform for offshore wind turbines is the most utilized type and the OC3 Hywind system design is largely used in research. This system is usually moored with three catenary cables with 120° between each other. Adding clump weights to the mooring lines has an influence on the platform response and on the mooring line tension. However, the optimal choice for their position and weight is still an open issue, especially considering the multitude of sea states the platform can be exposed to. In this study, therefore, an analysis on the influence of two such variables on the platform response and on the mooring line tension is presented. FAST by the National Renewable Energy Laboratory (NREL) is used to perform time domain simulations and Response Amplitude Operators are adopted as the main indicators of the clump weights effects. Results show that the clump weight mass is not as influential as the position, which turns out to be optimal, especially for the Surge degree of freedom, when closest to the platform.
FPAES: A Hybrid Approach for the Optimal Placement and Sizing of Reactive Compensation in Distribution Grids
Authors: José da Silva Diego , Antonio Belati Edmarcio , Werley Silva dos Angelos Eduardo
Published: December 2020

Reactive power compensation with Capacitor Banks (CBs) is one of the most successful approaches used in distribution systems, mainly due to their versatility, long-term acceptance in the power industry, and reduced costs. Most allocation methods, however, lack specific strategies to handle the limited discrete nature of CBs sizes seeking to improve the overall optimization and computational performance. We present an algorithm for the Optimal Placement of Capacitor Banks (OPCB) in distribution systems by means of a hybrid Flower Pollination Algorithm (FPA)–Exhaustive Search (ES) approach. The pollination process itself determines the sets of buses for placement, while CBs sizes and the final fitness values of each pollen are selected after a full-search is conducted in the sizing space. As the sizing phase works on the limited search space of predetermined discrete bank values, the computational effort to find the optimum CB capacity is greatly reduced. Tests were performed on distribution systems of 10, 34, and 85 buses with respect to the objective function, final losses, and voltage profile. The algorithm offers an excellent compromise between solution quality and computational effort, when compared to similar approaches.
Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training
Authors: Olivieri Carlo , de Paulis Francesco , Orlandi Antonio , Pisani Cosimo , Giannuzzi Giorgio , Salvati Roberto , Zaottini Roberto
Published: December 2020

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples.
Impact of Topographic Steps in the Wake and Power of a Wind Turbine: Part A—Statistics
Authors: Zhang Buen , Cheng Shyuan , Lu Fanghan , Zheng Yuan , Chamorro Leonardo P.
Published: December 2020

We experimentally explored the modulation of various forward- and backward-facing topographic steps on the wake and power output of a wind turbine model. The sharp surface changes located in the vicinity of the turbine tower consisted of steps Δz0/dT=−0.64, −0.42, −0.21, 0, 0.21, and 0.42, where Δz0 is the level difference between the upwind and downwind sides of the step and dT is the turbine diameter. Particle image velocimetry was used to obtain the wake statistics in the wake within the streamwise distance x/dT∈[2, 5] and vertical span z/dT∈[−0.7, 0.7], where the origin is set at the rotor hub. Complementary single-point hotwire measurements were obtained in the wake along the rotor axis every Δx/dT=1 within x/dT∈[1, 8]. Mean power output and its fluctuations were obtained for each of the six scenarios. The results indicate strong modulation of the steps in the wake statistics and some effect on the power output. Remarkably, the backward-facing steps induced a larger velocity deficit in the wake with respect to the base case with substantial wake deflection. In contrast, the forward-facing steps exhibited a much lower velocity deficit and negligible wake deflection. The mean flow and velocity gradients’ changes promoted distinct turbulence dynamics and, consequently, associated levels. In particular, turbulence intensity and kinematic Reynolds shear stress were enhanced and reduced with the backward- and forward-facing steps, respectively. It is worth pointing out the particular effect of the steps on the transport of the turbulence kinetic energy TKE. Ejections were predominant around the top tip, whereas sweeps dominated around the turbine hub height. The magnitude of these quantities was sensitive to the step height. In particular, a much weaker sweep occurred in the forward-facing steps; in addition, the flat terrain and the backward-facing step cases shared strong sweeps.
Numerical Modeling of Thermal Storage Performance of Encapsulated PCM Particles in an Unstructured Packed Bed
Authors: Mol Jeroen , Shahi Mina , Mahmoudi Amirhoushang
Published: December 2020

In this work, a numerical model to study phase transition in unstructured packed beds of phase change particles for latent heat thermal energy storage is presented and implemented into the open-source CFDEM® coupling program. The governing equations for both fluid and solid particles are discussed. The presented model is validated using experimental data. The validated model is used to study the effect of bed structure (e.g., using multiple sizes of particles and mixing of multiple configurations of particles) on the charging and discharging of PCM bed. It is found that using smaller particles leads to faster charging and discharging of the bed. A decrease of 26% was achieved by changing half the bed into particles with half the diameter. Furthermore, it is observed that placing small particles downstream has more effect on increased charging speed than placing them upstream when charging the bed fully. A decrease of 20% in charging time and a decrease of 13% in discharging time were observed in the configurations that were tested.
Power Performance Analysis According to the Configuration and Load Control Algorithm of Power Take-Off System for Oscillating Water Column Type Wave Energy Converters
Authors: Chan Roh , Kim Kil-Won , Park Ji-Yong , Park Se-Wan , Kim Kyong-Hwan , Kwak Sang-Shin
Published: December 2020

A power take-off (PTO) system for an oscillating water column (OWC) wave energy converter comprises a turbine-generator-power converter. In this study, only the topologies of the power converter that affect the load control algorithm are compared. A power converter for renewable energy is composed of a diode-dc/dc converter and a pulse-width modulation (PWM) converter operating at small and large capacities, respectively. However, selecting a power converter according to the capacity based on the characteristics of the wave energy converter, in which the input energy is highly fluctuating, can significantly reduce the power performance. Thus, to verify load control characteristics according to the topology of the power converter, the turbine-generator-power converter was incorporated in the modeling, and the power performance based on the power converter topology under various wave conditions was analyzed. Further, torque control to obtain the maximum power among load control algorithms was applied under irregular wave conditions, and the power performance and PTO system characteristics according to the torque coefficient were analyzed. The results of this study suggested an increase in the torque coefficient of the maximum power control for the operational stability of the OWC-WEC, and it was confirmed that the RPM characteristics of the PTO system were reduced.
Conceptual Design Development of Coal-to-Methanol Process with Carbon Capture and Utilization
Authors: Khalafalla Siddig , Zahid Umer , Abdul Jameel Abdul , Ahmed Usama , Alenazey Feraih , Lee Chul-Jin
Published: December 2020

Methanol is a clean fuel and an important feedstock for the petrochemical industry. Conventionally, the coal-to-methanol process generates a substantial amount of CO2 emissions with a low yield of methanol. In this study, we propose the conceptual design development of coal-to-methanol process using captured CO2 from the gasification plant by implying process intensification. The base case and three alternative designs have been developed using the Aspen Plus to analyze the process performance. The four designs have been compared in terms of their energy consumption, economics, methanol production rate, and carbon emissions while maintaining the gasifier operation conditions, sulfur content in the syngas, and stoichiometric number at the methanol synthesis reactor. Among the developed designs, the most feasible design requires an energy requirement and product cost of 21.9 GJ and $142.5 per ton of methanol product. The study also showed that the coal-to-methanol process assisted with external hydrogen from electrolysis plant for achieving higher production rates and low CO2 emissions is currently not economical due to the high H2 cost.
A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface
Authors: Abdallah Ramez , Natsheh Emad , Juaidi Adel , Samara Sufyan , Manzano-Agugliaro Francisco
Published: December 2020

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.
Exhaust Emissions and Energy Consumption Analysis of Conventional, Hybrid, and Electric Vehicles in Real Driving Cycles
Authors: Pielecha Jacek , Skobiej Kinga , Kurtyka Karolina
Published: December 2020

One of the environmental aims of the European Union is to achieve climate neutrality by 2050. According to European Parliament data, transport emissions accounted for about 25% of global carbon dioxide emissions in 2016, in which road transport had the largest share (approximately 72%). This phenomenon is particularly visible in urban agglomerations. The solution examples are the popularization of hybrid vehicles and the development of electromobility. The aim of this paper is an assessment of the energy consumption and exhaust emissions from passenger cars fitted with different powertrains in actual operation. For the tests, passenger cars with conventional engines of various emission classes were used as well as the latest hybrid vehicles and an electric car. It enabled a comparative assessment of the energy consumption under different traffic conditions, with particular emphasis on the urban phase and the entire RDE (Real Driving Emissions) test. The results were analyzed to identify changes in these environmental factors that have occurred with the technical advancement of vehicles. The lowest total energy consumption in real traffic conditions is characteristic of an electric vehicle; the plug-in hybrid vehicle with a gasoline engine is about 10% bigger, and the largest one is a combustion vehicle (30% bigger than an electric vehicle). These data may contribute to the classification of vehicles and identification of advantages of the latest developments in conventional, hybrid, and electric vehicles.
A Nonlinear Inverse Design Problem for a Pipe Type Heat Exchanger Equipped with Internal Z-Shape Lateral Fins and Ribs
Authors: Huang Cheng-Hung , Kuo Chih-Yang
Published: December 2020

A non-linear three-dimensional inverse shape design problem was investigated for a pipe type heat exchanger to estimate the design variables of continuous lateral ribs on internal Z-shape lateral fins for maximum thermal performance factor η. The design variables were considered as the positions, heights, and number of ribs while the physical properties of air were considered as a polynomial function of temperature; this makes the problem non-linear. The direct problem was solved using software package CFD-ACE+, and the Levenberg–Marquardt method (LMM) was utilized as the optimization tool because it has been proven to be a powerful algorithm for solving inverse problems. Z-shape lateral fins were found to be the best thermal performance among Z-shape, S-shape, and V-shape lateral fins. The objective of this study was to include continuous lateral ribs to Z-shape lateral fins to further improve η. Firstly, the numerical solutions of direct problem were solved using both polynomial and constant air properties and then compared with the corrected solutions to verify the necessity for using polynomial air properties. Then, four design cases, A, B, C and D, based on various design variables were conducted numerically, and the resultant η values were computed and compared. The results revealed that considering continuous lateral ribs on the surface of Z-shape lateral fins can indeed improve η value at the design working condition Re = 5000. η values of designs A, B and C were approximately 13% higher than that for Z-shape lateral fins, however, when the rib numbers were increased, i.e., design D, the value of η became only 11.5 % higher. This implies that more ribs will not guarantee higher η value.
Research on Estimation Method of Fuel Cell Health State Based on Lumped Parameter Model
Authors: Ren Xueshuang , Zhang Xin , Teng Teng , Li Congxin
Published: December 2020

The increasingly serious environmental pollution and the shortage of social energy have promoted the rapid development of fuel cell vehicles. The major factor which limits the commercialization of fuel cell vehicles is durability. Accurately estimating the state and parameters of a fuel cell is critical to extending the life of the fuel cell. To address this challenge, we extended a proton exchange membrane fuel cell (PEMFC) lumped parameter model and incorporated new algorithms that are essential to estimate the health of the fuel cell in a range-extended fuel cell car. The unscented Kalman filter (UKF) algorithm has been used to estimate the ohmic internal resistance of the fuel cell in real time. By using the unscented transformation (UT) method, the linearization of the nonlinear state equation is avoided, and the filtering accuracy is improved without increasing the complexity of the system. By comparing simulation and experimental results, the feasibility and accuracy of the algorithm in this paper are further verified. This method has high estimation accuracy and is suitable for an embedded system. The research of this method is an important basis for improving the control strategy of fuel cell vehicles. Reasonable use of fuel cells can extend battery life, and this method is of great significance to the commercialization of fuel cell vehicles.
Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter
Authors: Steczek Marcin , Jefimowski Włodzimierz , Szeląg Adam
Published: December 2020

In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.
A Study on the Prediction of the Temperature and Mass of Hydrogen Gas inside a Tank during Fast Filling Process
Authors: Li Ji-Qiang , Myoung No-Seuk , Kwon Jeong-Tae , Jang Seon-Jun , Lee Taeckhong
Published: December 2020

The hydrogen compression cycle system recycles hydrogen compressed by a compressor at high pressure and stores it in a high-pressure container. Thermal stress is generated due to increase in the pressure and temperature of hydrogen in the hydrogen storage tank during the fast filing process. For the sake of safety, it is of great practical significance to predict and control the temperature change in the tank. The hydrogen charging process in the storage tank of the hydrogen charging station was studied by experimentation and simulation. In this paper, a Computational Fluid Dynamics (CFD) model for non-adiabatic real filling of a 50 MPa hydrogen cylinder was presented. In addition, a shear stress transport (k-ω) model and real gas model were used in order to account for thermo-fluid dynamics during the filling of hydrogen storage tanks (50 MPa, 343 L). Compared to the simulation results with the experimental data carried out under the same conditions, the temperatures calculated from the simulated non-adiabatic condition results were lower (by 5.3%) than those from the theoretical adiabatic condition calculation. The theoretical calculation was based on the experimentally measured pressure value. The calculated simulation mass was 8.23% higher than the theoretical result. The results of this study will be very useful in future hydrogen energy research and hydrogen charging station developments.
A Review on Synthetic Ester Liquids for Transformer Applications
Authors: Rozga Pawel , Beroual Abderrahmane , Przybylek Piotr , Jaroszewski Maciej , Strzelecki Konrad
Published: December 2020

Synthetic esters have become more and more popular in last few decades, explaining the increasing number of units filled with this liquid year by year. They have been investigated under different aspects, both from the fundamental point of view and breakdown mechanisms, well as from the application point of view. However, their use in high voltage equipment is always a challenge and deeper knowledge of the various aspects that can be encountered in their exploitation is needed. The intent of this review paper is to present the recent research progress on synthetic ester liquid in relation to the selected issues, most important for ester development in the authors’ opinion. The described issues are the breakdown performance of synthetic esters, lightning impulse strength and pre-breakdown phenomena of synthetic esters, synthetic esters-based nanofluids, combined paper-synthetic ester based insulating systems, application of synthetic ester for retro-filling and drying of mineral oil-immersed transformers, DGA(dissolved gas analysis)-based diagnosis of synthetic esters filled transformers as well as static electrification of synthetic esters. The different sections are based both on the data available in the literature, but above all on the authors’ own experience from their research work on synthetic ester liquids for electrical application purposes.
A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems
Authors: Xie Longfei , Li Fengri , Zhang Lianjun , Widagdo Faris Rafi Almay , Dong Lihu
Published: December 2020

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.
Interannual Variation of Transpiration and Its Modeling of a Larch Plantation in Semiarid Northwest China
Authors: Wang Yanbing , Wang Yanhui , Li Zhenhua , Yu Pengtao , Han Xinsheng
Published: December 2020

Quantifying the variation of forest transpiration (T) is important not only for understanding the water and energy budget of forest ecosystems but also for the prediction, evaluation, and management of hydrological effects as well as many other ecosystem services of forests under the changes of climate, vegetation, and anthropological impacts. The accurate prediction of T, a key component of water used by forests, requires mechanism-based models describing the T response to environmental and canopy conditions. The daily T of a larch (Larix principis-rupprechtti) plantation was measured through monitoring the sap flow in the growing season (from May to September) of a dry year (2010), a normal year (2012), and a wet year (2014) at a shady slope in the semi-arid area of Liupan Mountains in northwest China. Meanwhile, the meteorological conditions, soil moisture, and forest canopy leaf area index (LAI) were monitored. To get a simple and easily applicable T model, the numerous influencing parameters were grouped into three factors: the atmospheric evapotranspiration demand indicated by the potential evapotranspiration (PET), the soil water supply ability indicated by the relative extractable soil water content (REW), and the vegetation transpiration capacity indicated by the forest canopy LAI. The T model was established as a continuous multiplication of the T response equations to individual factors, which were determined using the upper boundary lines of measured data. The effect of each factor on the T in a dry year (2010) or normal year (2012) was assessed by comparing the measured T in the baseline of the wet year (2014) and the model predicted T, which was calculated through inputting the actual data of the factor (i.e., PET) to be assessed in the dry or normal year and the measured data of other two factors (i.e., REW, LAI) in the baseline of the wet year. The results showed that the mean daily T was 0.92, 1.05, and 1.02 mm; and the maximum daily T was 1.78, 1.92, and 1.89 mm in 2010, 2012, and 2014, respectively. The T response follows a parabolic equation to PET, but a saturated exponential equation to REW and LAI. The T model parameters were calibrated using measured data in 2010 and 2012 (R2 = 0.89, Nash coefficient = 0.88) and validated using measured data in 2014 satisfactorily (R2 = 0.89, Nash coefficient = 0.79). It showed a T limitation in the dry year 2010 for all factors (18.5 mm by PET, 11.5 mm by REW, and 17.8 mm by LAI); while a promotion for PET (1.4 mm) and a limitation for REW (4.2 mm) and LAI (14.3 mm) in the normal year 2012. The daily T model established in this study can be helpful to assess the individual factor impact on T and improve the daily T prediction under changing environmental and canopy conditions.
A Bayesian Approach Based on Bayes Minimum Risk Decision for Reliability Assessment of Web Service Composition
Authors: Song Yang , Wang Yawen , Jin Dahai
Published: December 2020

Web service composition is the process of combining and reusing existing web services to create new business processes to satisfy specific user requirements. Reliability plays an important role in ensuring the quality of web service composition. However, owing to the flexibility and complexity of such architecture, sufficient estimation of reliability is difficult. In this paper, the authors propose a method to estimate the reliability of web service compositions based on Bayes reliability assessment by considering it to be a decision-making problem. This improves the testing efficiency and accuracy of such methods. To this end, the authors focus on fully utilizing prior information of web services to increase the accuracy of prior distributions, and construct a Markov model in terms of the reliabilities of the web composition and each web service to integrate the limited test data. The authors further propose a method of minimum risk (MMR) to calculate the initial values of hyperparameters satisfying the constraint of minimal risk of the wrong decision. Experiments demonstrate that the proposed method is capable of efficiently utilizing prior module-level failure information, comparing with the Bayesian Monte Carlo method (BMCM) and expert scoring method (ESM), when the number of failures increased from 0 to 5, reducing the required number of test cases from 19.8% to 28.9% and 6.1% to 14.1% separately, improving the reliability assessment of web service compositions, and reducing the expenses incurred by system-level reliability testing and demonstration.
Gels in Medicine and Surgery: Current Trends and Future Perspectives
Authors: Fiorillo Luca , Romano Giovanni Luca
Published: December 2020

Gel is a two-phase elastic colloidal material, consisting of a dispersed liquid incorporated in the solid phase [...]
An Open-Label, Head to Head Comparison Study between Prucalopride and Lactulose for Clozapine Induced Constipation in Patients with Treatment Resistant Schizophrenia
Authors: Damodaran Ishwary , Hui Koh Ong , Nordin Amer Siddiq Amer , Yee Anne , Gill Jesjeet Singh , Francis Benedict , Azhar Fatin Liyana , Sulaiman Ahmad Hatim
Published: December 2020

Clozapine remains as the gold standard for the management of treatment resistant schizophrenia. Nevertheless, mortality and morbidity associated with Clozapine is partly contributed by its adverse effect of constipation in view of its prominent anticholinergic properties. Despite the evidence that approximately 60% of patients on Clozapine will experience constipation, there is no proper guideline as to the best laxative in the treatment of Clozapine induced constipation. Hence this study was conducted to evaluate the efficacy and safety of Prucalopride and Lactulose in the treatment of Clozapine induced constipation. This was a four week, prospective, open-label head to head comparison study between Prucalopride and Lactulose in the treatment of Clozapine induced constipation. Male and female patients on Clozapine between the age of 18–60 with an established diagnosis of treatment resistant schizophrenia with ≤2 spontaneous complete bowel movement per week were recruited in this study. Eligible patients were assigned into two groups. Patients received Prucalopride 2 mg once daily or Lactulose 10 g once daily for four weeks. Efficacy was analyzed in 58 patients. The proportion of patient with ≥3 spontaneous complete bowel movement (SCBM) was higher in the Prucalopride 2 mg group, reaching significance at Week 4 with p-value of (p = 0.029). The proportion of patient with ≥3 SCBM at Week 1 was 71.4% in the Prucalopride 2 mg group and 60% in the Lactulose 10 g group. The proportion of patient with ≥3 SCBM at Week 4 was 85.7% in the Prucalopride 2 mg group and the proportion remained at 60% in the Lactulose 10 g group. The improvement in the dissatisfaction and treatment satisfaction subscales of the patient assessment of constipation—quality of life (PAC-QOL) were higher in the Prucalopride 2 mg group compared to the Lactulose 10 g group. The common adverse events associated with Prucalopride 2 mg were abdominal pain and loose stools which was transient and subsided within a few days. Over four weeks, in this population of patients with Clozapine induced constipation, Prucalopride 2 mg significantly improved the bowel movement and it was safe.
Effects of Water Stress on Vegetative Growth and ‘Merlot’ Grapevine Yield in a Semi-Arid Mediterranean Climate
Authors: Chacón-Vozmediano Juan L. , Martínez-Gascueña Jesús , García-Navarro Francisco J. , Jiménez-Ballesta Raimundo
Published: December 2020

Water stress is considered to be the most influential type of abiotic stress to which plants may be exposed. In grapevines (Vitis vinifera L.), it is a common practice to keep plants under water stress at different stages of the season with the aim of reducing yield and improving the composition of the fruit. The objective of this study was to evaluate foliar development and yield of ‘Merlot’ grapevines grown in the field when they are subjected to different levels of water stress in a semi-arid Mediterranean climate. Four treatments with different levels of water stress were applied during two phenological intervals (flowering-veraison and veraison-maturity) to 128 grapevines for a period of two consecutive years. The levels of water stress were none-light, light-moderate, moderate-intense, and intense-intense for the flowering-veraison and veraison-maturity intervals, respectively. The results revealed that the total leaf area, the exposed leaf area, and the yield all decreased as the degree of water stress increased. The weight of the berry was a decisive factor in determining yield. The least restrictive water regime treatment gave the heaviest berries and bunches and, as a result, the highest yields.
First Insights into Clinical and Resistance Features of Infections by Klebsiella pneumoniae among Oncological Patients from a Referral Center in Amazon Region, Brazil
Authors: Santos Anderson Lineu Siqueira dos , Rodrigues Yan Corrêa , Melo Marcos Vinícios Hino de , Silva dos Santos Pabllo Antonny , Oliveira Tatyellen Natasha da Costa , Sardinha Daniele Melo , Lima Luana Nepomuceno Gondim Costa , Brasiliense Danielle Murici , Lima Karla Valéria Batista
Published: December 2020

Klebsiella pneumoniae appears as one of the most prevalent pathogens among cancer patients. The present study investigates the clinical, epidemiological and microbiological aspects related to infections caused by K. pneumoniae in cancer patients treated at an oncology referral center in the state of Pará, Amazon region, Brazil. Between July 2017 to July 2019, an epidemiological, observational, cross-sectional study, with a descriptive and analytical approach was conducted, including patients with confirmed diagnosis of cancer who acquired infection by K. pneumoniae 72 h after hospital admission. K. pneumoniae isolates included in the study were obtained from different clinical materials (blood, urine, catheter tip and bladder catheter, orotracheal secretions, oncological and surgical wounds). Antimicrobial susceptibility testing and molecular detection of the carbapenemase-encoding genes were performed. A high prevalence of MDR K. pneumoniae isolates was observed, including two colistin-resistant isolates and seven isolates harboring blaKPC-1 gene. To conclude, our findings provide the firsts insights into the epidemiology and infection by K. pneumoniae in the state of Pará, Brazil, and may be useful on treatment guidance and establishment of strategies to control the spread of resistance strains of K. pneumoniae in the region.
Mental Health and Quality of Life of Women One Year after Maternal Near-Miss in Low and Middle-Income Countries: The Case of Zanzibar, Tanzania
Authors: Alemu Sisay , Herklots Tanneke , Almansa Josue , Mbarouk Shadya , Sulkers Esther , Stekelenburg Jelle , de Zeeuw Janine , Jacod Benoit , Biesma Regien
Published: December 2020

Women who experienced a maternal near-miss are at risk of mental health complications and lower quality of life, but long-term consequences are largely unknown. The aim of this study is to assess whether mental health symptoms and quality of life change over time and to examine associations with risk factors among post-partum women. In this cohort study, women with maternal near-miss were matched to women without or with mild complications at Mnazi Mmoja Hospital in Zanzibar. Depressive and post-traumatic stress disorder symptoms, and quality of life were measured at three, six, and twelve-months follow-up. A linear mixed-effects model was used for data analysis. Postpartum women in Zanzibar reported low levels of depressive and post-traumatic stress disorder symptoms. While depressive symptoms and quality of life trajectories were similar among women with and without maternal near-miss, differences for trajectories of post-traumatic stress disorder symptoms and physical quality of life were found. Social support, perinatal loss, and intercurrent illness were strongly associated with both depressive symptoms and quality of life in this group of Islamic women. These findings suggest that social support, embedded in the cultural context, should be considered in helping women cope with mental health issues in the aftermath of severe maternal complications.