bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Statistical Testing of Quantum Random Number Generators
Authors: Martínez Aldo C. , Solis Aldo , Díaz Hernández Rojas Rafael , U'Ren Alfred B. , Hirsch Jorge G. , Pérez Castillo Isaac
Published: November 2018

Pseudo-random number generators are widely used in many branches of science, mainly in applications related to Monte Carlo methods, although they are deterministic in design and, therefore, unsuitable for tackling fundamental problems in security and cryptography. The natural laws of the microscopic realm provide a fairly simple method to generate non-deterministic sequences of random numbers, based on measurements of quantum states. In practice, however, the experimental devices on which quantum random number generators are based are often unable to pass some tests of randomness. In this review, we briefly discuss two such tests, point out the challenges that we have encountered in experimental implementations and finally present a fairly simple method that successfully generates non-deterministic maximally random sequences.
A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications?
Authors: Gkotsis Petros , Pugliese Emanuele , Vezzani Antonio
Published: November 2018

In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms’ capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets.
Magnetocaloric Effect in an Antidot: The Effect of the Aharonov-Bohm Flux and Antidot Radius
Authors: Negrete Oscar A. , Peña Francisco J. , Vargas Patricio
Published: November 2018

In this work, we report the magnetocaloric effect (MCE) for an electron interacting with an antidot, under the effect of an Aharonov-Bohm flux (AB-flux) subjected to a parabolic confinement potential. We use the Bogachek and Landman model, which additionally allows the study of quantum dots with Fock-Darwin energy levels for vanishing antidot radius and AB-flux. We find that AB-flux strongly controls the oscillatory behaviour of the MCE, thus acting as a control parameter for the cooling or heating of the magnetocaloric effect. We propose a way to detect AB-flux by measuring temperature differences.
Improving Entropy Estimates of Complex Network Topology for the Characterization of Coupling in Dynamical Systems
Authors: Craciunescu Teddy , Murari Andrea , Gelfusa Michela
Published: November 2018

A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems.
Introduction to Special Issue on Universal Design for Inclusive Pedagogy and a Future Research Agenda
Authors: Arendale David R.
Published: November 2018

This Special Issue seeks to address the needs of all postsecondary/tertiary students for a barrier-free learning environment to increase their academic achievement, engagement, learning mastery, and persistence to graduation. Universal Design for Inclusive Pedagogy (UDIP) is sensitive to diverse students and individual differences to promote access and equity. While our colleagues in elementary and secondary education have been addressing this issue for many years, postsecondary education is a newer field for this approach. The six articles in this issue break new ground with regards to expanding the boundaries of Universal Design (UD). Areas explored in this Special Issue are transformed curriculum, innovative teaching and learning practices, cross-national and cross-cultural student interactions, application of UD to academic pathways, and UDIP embedded into the institutional culture and policies. The central themes of the articles are increased access, equity, and social justice for all students.
Conversations on Critical Thinking: Can Critical Thinking Find Its Way Forward as the Skill Set and Mindset of the Century?
Authors: Sellars Maura , Fakirmohammad Razia , Bui Linh , Fishetti John , Niyozov Sarfaroz , Reynolds Ruth , Thapliyal Nisha , Liu-Smith Yu-Ling , Ali Nosheen
Published: November 2018

The capacity to successfully, positively engage with the cognitive capacities of critical thinking has become the benchmark of employability for many diverse industries across the globe and is considered critical for the development of informed, decisive global citizenship. Despite this, education systems in several countries have developed policies and practices that limit the opportunities for students to authentically participate in the discussions, debates, and evaluative thinking that serve to develop the skill set and mindset of critical thinkers. This writing examines the status of critical thinking in four different contexts across the globe as reflected in educational policies and academic experiences as a preface to investigating actual classroom practices and possible impacts the support of critical thinking skills may have on the potential development of the global citizens of the future. Each vignette reflects the contextualized difficulties that are presented by social and cultural concerns and traditions of making meaning. These stories of education also illustrate the various ways in which the skills and capacities of critical thinking are interpreted in different contexts and address the negative nuances with which thinking critically has become associated. Finally, a pedagogical model of teaching, which may support student development of the skill set of critical thinking within the boundaries of social and cultural mindsets, has been developed.
Improved Voltage Drop Compensation Method for Hybrid Fuel Cell Battery System
Authors: Eom Tae-Ho , Kang Jin-Wook , Kim Jintae , Shin Min-Ho , Lee Jung-Hyo , Won Chung-Yuen
Published: November 2018

In this paper, a voltage drop compensation method for hybrid hydrogen fuel cell battery system, with a hydrogen recirculation powering a forklift, is studied. During recirculating hydrogen fuel to recycle hydrogen that has not reacted enough at the system, impurities can be mixed with the hydrogen fuel. This leads to low hydrogen concentration and a drop in the output voltage of the fuel cell system. In excessive voltage drop, the fuel cell system can be shutdown. This paper proposes a voltage drop compensation method using an electrical control algorithm to prevent system shutdown by reducing voltage drop. Technically, voltage drop is typically caused by three kinds of factors: (1) The amount of pure hydrogen supply; (2) the temperature of fuel cell stacks; and (3) the current density to catalysts of the fuel cell. The proposed compensation method detects voltage drop caused by those factors, and generates compensation signals for a controller of a DC–DC converter connecting to the output of the fuel cell stack; thus, the voltage drop is reduced by decreasing output current. At the time, insufficient output current to a load is supplied from the batteries. In this paper, voltage drop caused by the abovementioned three factors is analyzed, and the operating principle of the proposed compensation method is specified. To verify this operation and the feasibility of the proposed method, experiments are conducted by applying it to a 10 kW hybrid fuel cell battery system for a forklift.
Illumination-Insensitive Skin Depth Estimation from a Light-Field Camera Based on CGANs toward Haptic Palpation
Authors: Ko Myeongseob , Kim Donghyun , Kim Mingi , Kim Kwangtaek
Published: November 2018

A depth estimation has been widely studied with the emergence of a Lytro camera. However, skin depth estimation using a Lytro camera is too sensitive to the influence of illumination due to its low image quality, and thus, when three-dimensional reconstruction is attempted, there are limitations in that either the skin texture information is not properly expressed or considerable numbers of errors occur in the reconstructed shape. To address these issues, we propose a method that enhances the texture information and generates robust images unsusceptible to illumination using a deep learning method, conditional generative adversarial networks (CGANs), in order to estimate the depth of the skin surface more accurately. Because it is difficult to estimate the depth of wrinkles with very few characteristics, we have built two cost volumes using the difference of the pixel intensity and gradient, in two ways. Furthermore, we demonstrated that our method could generate a skin depth map more precisely by preserving the skin texture effectively, as well as by reducing the noise of the final depth map through the final depth-refinement step (CGAN guidance image filtering) to converge into a haptic interface that is sensitive to the small surface noise.
Coordinated Multicast Precoding for Multi-Cell Massive MIMO Transmission Exploiting Statistical Channel State Information
Authors: You Li , Chen Xu , Wang Wenjin , Gao Xiqi
Published: November 2018

This paper considers coordinated multi-cell multicast precoding for massive multiple-input-multiple-output transmission where only statistical channel state information of all user terminals (UTs) in the coordinated network is known at the base stations (BSs). We adopt the sum of the achievable ergodic multicast rate as the design objective. We first show the optimal closed-form multicast signalling directions of each BS, which simplifies the coordinated multicast precoding problem into a coordinated beam domain power allocation problem. Via invoking the minorization-maximization framework, we then propose an iterative power allocation algorithm with guaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of the design objective to further reduce the optimization complexity via invoking the large-dimensional random matrix theory. Numerical results demonstrate the performance gain of the proposed coordinated approach over the conventional uncoordinated approach, especially for cell-edge UTs.
Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production
Authors: Elfarra Munir Ali , Kaya Mustafa
Published: November 2018

The common approach to wind energy feasibility studies is to use Weibull distribution for wind speed data to estimate the annual energy production (AEP). However, if the wind speed data has more than one mode in the probability density, the conventional distributions including Weibull fail to fit the wind speed data. This highly affects the technical and economic assessment of a wind energy project by causing crucial errors. This paper presents a novel way to define the probability density for wind speed data using splines. The splines are determined as a solution of constrained optimization problems. The constraints are the characteristics of probability density functions. The proposed method is implemented for different wind speed distributions including multimodal data and compared with Weibull, Weibull and Weibull and Beta Exponentiated Power Lindley (BEPL) distributions. It is also compared with two other nonparametric distributions. The results show that the spline-based probability density functions produce a minimum fitting error for all the analyzed cases. The AEP calculated based on this method is considered to have high fidelity, which will decrease the investment risk.
Optimal Voltage and Frequency Control of an Islanded Microgrid Using Grasshopper Optimization Algorithm
Authors: Jumani Touqeer Ahmed , Mustafa Mohd Wazir , Md Rasid Madihah , Mirjat Nayyar Hussain , Leghari Zohaib Hussain , Saeed M. Salman
Published: November 2018

Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).
Estimation of the Near Future Wind Power Potential in the Black Sea
Authors: Ganea Daniel , Mereuta Elena , Rusu Liliana
Published: November 2018

The main objective of the present study is to quantify the recent past and explore the near future wind power potential in the Black Sea basin, evaluating the possible changes. Furthermore, an analysis of the wind climate in the target area was also performed. The wind resources have been assessed using the wind fields provided by various databases. Thus, the wind power potential from the recent past was assessed based two different sources covering each one the 30-year period (1981–2010). The first source is the ERA-Interim atmospheric reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), while the second source represents the hindcast wind fields simulated by a Regional Climate Model (RCM) and provided by EURO-CORDEX databases. The estimation of the near future wind power potential was made based on wind fields simulated by the same RCM under future climate projections, considering two Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) and they cover also a 30-year time interval (2021–2050). Information in various reference points were analyzed in detail. Several conclusions resulted from the present work. Thus, as regards the mean wind power potential in winter season, in 51% of the locations a significant increase is projected in the near future (both scenarios). Besides providing a detailed description of the wind conditions from the recent past over the Black Sea basin considering two major sources, the novelty of the present work consists in the fact that it gives an estimation of the expected wind climate in the target area for the near future period and at the same time an evaluation of the climate change impacts on the wind speed and wind power potential.
Development and Validation of a Thermo-Economic Model for Design Optimisation and Off-Design Performance Evaluation of a Pure Solar Microturbine
Authors: Iaria Davide , Nipkey Homam , Al Zaili Jafar , Sayma Abdulnaser Ibrahim , Assadi Mohsen
Published: November 2018

The aim of this paper is to present a thermo-economic model of a microturbine for solar dish applications, which demonstrates the applicability and accuracy of the model for off-design performance evaluation and techno-economic optimisation purposes. The model is built using an object-oriented programming approach. Each component is represented using a class made of functions that perform a one-dimensional physical design, off-design performance analysis and the component cost evaluation. Compressor, recuperator, receiver and turbine models are presented and validated against experimental data available in literature, and each demonstrated good accuracy for a wide range of operating conditions. A 7-kWe microturbine and solar irradiation data available for Rome between 2004 and 2005 were considered as a case study, and the thermo-economic analysis of the plant was performed to estimate the levelised cost of electricity based on the annual performance of the plant. The overall energy produced by the plant is 10,682 kWh, the capital cost has been estimated to be EUR 27,051 and, consequently, the specific cost of the plant, defined as the ratio between the cost of components and output power in design condition, has been estimated to be around EUR 3980/kWe. Results from the levelised cost of electricity (LCOE) analysis demonstrate a levelised cost of electricity of EUR 22.81/kWh considering a plant lifetime of 25 years. The results of the present case study have been compared with the results from IPSEpro 7 where the same component characteristic maps and operational strategy were considered. This comparison was aimed to verify the component matching procedure adopted for the present model. A plant sizing optimisation was then performed to determine the plant size which minimises the levelised cost of electricity. The design space of the optimisation variable is limited to the values 0.07–0.16 kg/s. Results of the optimisation demonstrate a minimum LCOE of 21.5 [EUR/kWh] for a design point mass flow rate of about 0.11 kg/s. This corresponds to an overall cost of the plant of around EUR 32,600, with a dish diameter of 9.4 m and an annual electricity production of 13,700 [kWh].
Evaluating the Effect of Metal Bipolar Plate Coating on the Performance of Proton Exchange Membrane Fuel Cells
Authors: Ijaodola Oluwatosin , Ogungbemi Emmanuel , Khatib Fawwad Nisar. , Wilberforce Tabbi , Ramadan Mohamad , El Hassan Zaki , Thompson James , Olabi Abdul Ghani
Published: November 2018

Environmental concerns of greenhouse gases (GHG) effect from fossil commodities and the fast increase in global energy demand have created awareness on the need to replace fossil fuels with other sources of clean energy. PEM fuel cell (PEMFC) is a promising source of energy to replace fossil fuels. The commercialization of the cell depends on its price, weight and mechanical strength. Bipolar plates are among the main components of PEMFC which perform some significant functions in the fuel cell stack. Metal bipolar plate is considered by the research community as the future material for fuel cells. However, surface coating is required for metals to enhance its corrosion resistance, hydrophilicity and interfacial contact resistance (ICR) in PEM fuel cells. Open pore cellular metal foam (OPCMF) materials have been used to replace the conventional flow field channel in recent times due to its low electrical resistance, high specific area and high porosity; however, it endures the same corrosion problem as the metallic bipolar plate. This investigation offers an overview on different types of bipolar plates and techniques in coating metallic bipolar platse and open pore metal foam as flow field channel materials to improve the corrosion resistance which will eventually increase the efficiency of the fuel cell appreciably.
Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room
Authors: Charvátová Hana , Procházka Aleš , Zálešák Martin
Published: November 2018

This paper is devoted to modelling of temperature distribution and its time evolution in rooms with specific thermal insulation and heat transfer for different external conditions. The simulation results should help to design the room architecture and wall materials to reduce energy losses due to heating or cooling, and to increase the inside thermal comfort. For this purpose, a methodological procedure using real data processing in the COMSOL Multiphysics modelling environment and spatial visualization of temperature evolution is proposed. This paper describes a mathematical model for simulation of the temperature evolution inside a space with thermally insulated walls under selected outside conditions. Computer simulations are then used to assess the temperature distribution inside the room and the heat flow through the room walls. Results of the simulations are used for subsequent determination of the time needed for the desired decrease of air temperature inside the tested room during its cooling due to the low ambient temperature, which is related to the thermal stability of the building, specific heat capacity, and thickness of the thermal insulation. Under the studied conditions, the time to reach the temperature drops by 20 percent in a room with windows was from 1.4 to 1.8 times lower than that in the room without windows. The proposed methodology shows the flexibility of computer modelling in the design of insulated building systems. The mesh density testing was performed by comparing the air temperature evolution in the model of the selected mesh density and the model with its maximum value enabled by the size of computer memory. The maximum temperature deviation calculated for the mesh of the presented model was 0.57%.
The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load
Authors: Lu Yiqi , Li Yongpan , Xie Da , Wei Enwei , Bao Xianlu , Chen Huafeng , Zhong Xiancheng
Published: November 2018

To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.
Analyzing the Performance of Wave-Energy Generator Systems (SSG) for the Southern Coasts of Iran, in the Persian Gulf and Oman Sea
Authors: Khalifehei Kamran , Azizyan Gholamreza , Gualtieri Carlo
Published: November 2018

The Sea-wave Slot-cone Generator (SSG) wave-energy device is a type of electric energy converting structure that converts energy from sea waves, and which is designed and installed based on wave-overtopping in areas. Most of the previous studies have evaluated SSG systems based on hypothetical waves, considering the system geometry variations. However, it is important to consider the real wave conditions. This paper presents the results of a numerical study to investigate the performances of an SSG system in the context of the Persian Gulf and Oman Sea, where there is a strong need for renewable energies. The computational fluid dynamic (CFD) code Flow-3D was applied. First of all, available experimental data were applied to calibrate and evaluate the accuracy of the numerical model. Then, the real wave conditions on the coasts of the Persian Gulf and Oman Sea were imposed on the JONSWAP spectrum for the numerical modeling. Results of the study demonstrated that the hydraulic efficiency of the SSG system in the Persian Gulf and Oman Sea was low for wave heights lower than 0.5 m. The nominal efficiency of the system was relatively more than 60% for wave heights higher than 1 m; thus, the performance of the SSG system was suitably evaluated. Finally, the numerical results demonstrated that the most optimal conditions, with a nominal efficiency of 97%, were obtained for incident waves that had a height of 2 m and a period of 5.6 s. In this case, the hydraulic performance of the system was maximum.
A New Optimal Selection Method with Seasonal Flow and Irrigation Variability for Hydro Turbine Type and Size
Authors: Sakulphan Kiattisak , Bohez Erik L. J.
Published: November 2018

A micro hydropower plant of the run-of-river type is considered to be the most cost-effective investment in developing counties. This paper presents a novel methodology to improve flow estimation, without using the flow direction curve (FDC) method, to determine the turbine type and size to operate consistently. A higher precision is obtained through the use of seasonal flow occurrence data, irrigation variability, and fitting the best probability distribution function (PDF) using flow data. Flow data are grouped in classes based on the flow rate range. This method will need a larger dataset but it is reduced to a tractable amount by using the PDF. In the first part of the algorithm, the average flow of each range is used to select the turbine type. The second part of the algorithm determines the optimal size of the turbine type in a more accurate way, based on minimum and maximum flow rates in each class range instead of the average flow rate. A newly developed micro hydropower plant was installed and used for validation at Baan Khun Pae, Chiang Mai Province. It was found, over four years of observation from 2014–2018, that the plant capacity factor was 82%.
Springback Coefficient Research of API X60 Pipe with Dent Defect
Authors: Zhang Peng , Huang Yunfei , Wu Ying
Published: November 2018

Dent is a common form of defect on oil and gas pipeline. Some dents will undergo elastic or plastic recovery due to changes in internal pressure, also known as springback. To analyze the springback law of an API X60 pipeline with a dent defect, the secondary development technology of finite element software ABAQUS was used for parametric modeling of a dented pipeline. Using this model, the effects of various factors (wall thickness, internal pressure, indenter size, dent location, and dent depth) on the springback coefficient of a dented pipeline were analyzed. The significance of each factor was analyzed by combining an orthogonal experimental design with the Grey correlation degree. Finally, nonlinear regression analysis was used to obtain formulas for the springback coefficient as a function of the influential factors. The results show that the springback coefficient of the dented pipeline after pressurization was between 0.15 and 0.65, and the factor that had the largest effect on the springback coefficient was the dent location. The springback coefficient of the dented pipeline after de-pressurization was between 1.1 and 1.5, and the factor that had the largest effect on the springback coefficient was the internal pressure. The formulas that relate the springback coefficient and various influential factors can be used as a reference for estimating the springback of dented pipelines.
Torque Ripple Reduction for Switched Reluctance Motor with Optimized PWM Control Strategy
Authors: Cai Hui , Wang Hui , Li Mengqiu , Shen Shiqi , Feng Yaojing , Zheng Jian
Published: November 2018

The high current ripple and torque ripple are the main drawbacks of the switched reluctance motor (SRM) since the nonlinearity and double saliency, which limits its applications. In order to eliminate the current variation and torque ripple, an optimized pulse width modulation (PWM) control is presented in this paper. The voltage ratio duty is able to be predicted precisely according to the information of the motor running parameter. Based on torque sharing functions (TSFs), the current profile is pre-computed and four regions are defined according to the reference current profiles. The three modes, excitation, demagnetization and freewheeling, are flexibly chosen according to the characteristic of the current profile. It is indicated that it is better than that of conventional PWM modulation in terms of current ripple and the current tracing performance is improved without increasing the switching frequency or changing the hysteresis band. The current ripple is defined as the peak-to-peak value dividing the average value and it is reduced by 40%. A comparison in terms of the torque ripple and copper loss is also carried out: the torque ripple is significantly reduced via the proposed scheme under both magnetic linear and saturation conditions. The torque ripple and copper loss are reduced by about 70% and 12%, respectively. The validity and effectiveness of the proposed control strategy is verified by simulation and experimental results.
Multi-Objective Supervisory Control for DC/DC Converters in Advanced Aeronautic Applications
Authors: Cavallo Alberto , Canciello Giacomo , Guida Beniamino , Kulsangcharoen Ponggorn , Yeoh Seang Shen , Rashed Mohamed , Bozhko Serhiy
Published: November 2018

In this paper, an intelligent control strategy for DC/DC converters is proposed. The converter connects two DC busses, a high-voltage and a low-voltage bus. The control scheme is composed by a two-layer architecture, a low-level control based on the concept of sliding manifold, and high gain control, and a high-level control used to guarantee the achievement of various objectives. The proposed control strategies are based on solid mathematical arguments, with stability proofs for the non-linear case, and decision trees for parameter selection. The paper results are analyzed and discussed by using simulation at different detail levels in MATLAB/Stateflow/PowerSystem, and validated by experimental results, also considering MIL standard performance indices.
A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control
Authors: Song Shixin , Xiao Feng , Peng Silun , Song Chuanxue , Shao Yulong
Published: November 2018

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy.
A General Leaf Area Geometric Formula Exists for Plants—Evidence from the Simplified Gielis Equation
Authors: Shi Peijian , Ratkowsky David A. , Li Yang , Zhang Lifang , Lin Shuyan , Gielis Johan
Published: November 2018

Plant leaves exhibit diverse shapes that enable them to utilize a light resource maximally. If there were a general parametric model that could be used to calculate leaf area for different leaf shapes, it would help to elucidate the adaptive evolutional link among plants with the same or similar leaf shapes. We propose a simplified version of the original Gielis equation (SGE), which was developed to describe a variety of object shapes ranging from a droplet to an arbitrary polygon. We used this equation to fit the leaf profiles of 53 species (among which, 48 bamboo plants, 5 woody plants, and 10 geographical populations of a woody plant), totaling 3310 leaves. A third parameter (namely, the floating ratio c in leaf length) was introduced to account for the case when the theoretical leaf length deviates from the observed leaf length. For most datasets, the estimates of c were greater than zero but less than 10%, indicating that the leaf length predicted by the SGE was usually smaller than the actual length. However, the predicted leaf areas approximated their actual values after considering the floating ratios in leaf length. For most datasets, the mean percent errors of leaf areas were lower than 6%, except for a pooled dataset with 42 bamboo species. For the elliptical, lanceolate, linear, obovate, and ovate shapes, although the SGE did not fit the leaf edge perfectly, after adjusting the parameter c, there were small deviations of the predicted leaf areas from the actual values. This illustrates that leaves with different shapes might have similar functional features for photosynthesis, since the leaf areas can be described by the same equation. The anisotropy expressed as a difference in leaf shape for some plants might be an adaptive response to enable them to adapt to different habitats.
Exposure of Protected and Unprotected Forest to Plant Invasions in the Eastern United States
Authors: Riitters Kurt , Potter Kevin M. , Iannone Basil V. , Oswalt Christopher , Guo Qinfeng , Fei Songlin
Published: November 2018

Research Highlights: We demonstrate a macroscale framework combining an invasibility model with forest inventory data, and evaluate regional forest exposure to harmful invasive plants under different types of forest protection. Background and Objectives: Protected areas are a fundamental component of natural resource conservation. The exposure of protected forests to invasive plants can impede achievement of conservation goals, and the effectiveness of protection for limiting forest invasions is uncertain. We conducted a macroscale assessment of the exposure of protected and unprotected forests to harmful invasive plants in the eastern United States. Materials and Methods: Invasibility (the probability that a forest site has been invaded) was estimated for 82,506 inventory plots from site and landscape attributes. The invaded forest area was estimated by using the inventory sample design to scale up plot invasibility estimates to all forest area. We compared the invasibility and the invaded forest area of seven categories of protection with that of de facto protected (publicly owned) forest and unprotected forest in 13 ecological provinces. Results: We estimate approximately 51% of the total forest area has been exposed to harmful invasive plants, including 30% of the protected forest, 38% of the de facto protected forest, and 56% of the unprotected forest. Based on cumulative invasibility, the relative exposure of protection categories depended on the assumed invasibility threshold. Based on the invaded forest area, the five least-exposed protection categories were wilderness area (13% invaded), national park (18%), sustainable use (26%), nature reserve (31%), and de facto protected Federal land (36%). Of the total uninvaded forest area, only 15% was protected and 14% had de facto protection. Conclusions: Any protection is better than none, and public ownership alone is as effective as some types of formal protection. Since most of the remaining uninvaded forest area is unprotected, landscape-level management strategies will provide the most opportunities to conserve it.
Chinese Text Classification Model Based on Deep Learning
Authors: Li Yue , Wang Xutao , Xu Pengjian
Published: November 2018

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.
Acoustics of Bubble Arrays: Role Played by the Dipole Response of Bubbles
Authors: Leroy Valentin , Chastrette Nicolas , Thieury Margaux , Lombard Olivier , Tourin Arnaud
Published: November 2018

A model for acoustic transmission through a 2D square crystal of R-radius bubbles with a lattice constant L was previously proposed. Assuming a purely monopole response of the bubbles, this model offers a simple analytical expression of the transmission. However, it is not applicable when the bubbles are too close to each other (L/R < 5). This article proposes an extension of the model by including the dipole response of the bubbles. Comparisons with numerical and experimental results show that the new expression gives a good estimate of the concentration at which the monopole model is no longer valid, but fails at properly predicting the transmission.
Bread Enrichment with Oilseeds. A Review
Authors: De Lamo Beatriz , Gómez Manuel
Published: November 2018

The use of oilseeds in bakery products has gained popularity in recent years, both for their organoleptic and nutritional characteristics. The aim of this work is to provide an overview of the studies centered on the use of oilseeds (flaxseed, chia, sunflower, pumpkin, sesame and poppyseed) in breads and other bakery products. This review highlights the effect of oilseeds on the mechanical and physical properties of bread according to the enrichment level, origin and way of addition (whole, crushed, oil or mucilage). In general, the incorporation of oilseeds improves the nutritional profile of bakery products with and without gluten, and provides several health benefits. Mucilages of oilseeds can also act as a fat replacer thanks to their properties. The incorporation of oilseeds modifies the rheology of the doughs, the volume of the products and their texture, affecting their organoleptic characteristics and their acceptability. Nevertheless, these changes will depend on the type of seed used, as well as on the method of addition.
Binary Neutron Star and Short Gamma-Ray Burst Simulations in Light of GW170817
Authors: Nathanail Antonios
Published: November 2018

In the dawn of the multi-messenger era of gravitational wave astronomy, which was marked by the first ever coincident detection of gravitational waves and electromagnetic radiation, it is important to take a step back and consider our current established knowledge. Numerical simulations of binary neutron star mergers and simulations of short GRB jets must combine efforts to understand such complicated and phenomenologically rich explosions. We review the status of numerical relativity simulations with respect to any jet or magnetized outflow produced after merger. We compare what is known from such simulations with what is used and obtained from short GRB jet simulations propagating through the BNS ejecta. We then review the established facts on this topic, as well as discuss things that need to be revised and further clarified.
Hydrodynamic Shear-Induced Densification of Bacteriogenic Iron Oxides: Mechanisms and Implications
Authors: Edwards Brock A. , Ferris F. Grant
Published: November 2018

Bacterial–mineral aggregates are the products of a tight biogeochemical coupling between microbes and geological media and play an outsized role in governing the composition of natural waters through biogeochemical cycling and mineral formation and dissolution processes. The results of combined batch column settling experiments, volumetric analyses, and microscopic investigations demonstrate that composite bacteriogenic iron oxide aggregates are sensitive to densification in response to hydrodynamic shear, a physical fluid phenomenon that introduces significant alterations to aggregate size and structure, permeability, and settling and transport behaviour. After exposing aggregate suspensions to varying degrees of shear stress, final solids volume fractions decreased by as much as 75% from initial data, while aggregate bulk density saw increases from 999 kg·m–3 to as much as 1010 kg·m–3. Inverse modelling of time course data yielded estimates for settling rate constants and initial settling velocities that increased with shear stress application. As well as having implications for aqueous contaminant transport and potential bacterial bioenergetic strategies, these results suggest the preservation potential of microfossils formed from bacterial–mineral aggregates may be significantly reduced with shear-induced alterations, leading to a possible underrepresentation of these microfossils in the sedimentary record and a gap in our understanding of early life on Earth.
Editorial for Glacial and Geomorphological Cartography Special Issue
Authors: Pellitero Ondicol Ramón
Published: November 2018

Landforms are the most superficial part of the earth’s crust. [...]
Peculiarities of Nostalgia in Ayn Rand’s Novel Atlas Shrugged
Authors: Mirasova Kamila
Published: November 2018

Quite a number of Russian writers could not accept the October Revolution in 1917 and left the country. Their nostalgia for their motherland in emigration is a well-known fact. The Russian-born American writer Ayn Rand (1905–1982) was also driven out of Soviet Russia by a hatred for communism, yet her nostalgia is of a different kind. The purpose of this study is to describe the nature of Ayn Rand’s nostalgia. Discovering, on arrival in the U.S., a reality different from the image she bore in her mind, she did not start missing her homeland but continued longing for her ideal—19th century America. This ideal is fully reflected in her self-made philosophy known as “objectivism”, which underlies her novel Atlas Shrugged. Though philosophically substantiated, the ideal appears to be embodied in trivial myths of the American mass consciousness. The study highlights four of the most popular national myths in her novel. As a result, Rand’s literary works represent popular literature that are not within the mainstream of the Russian émigré literature of that period.
Farmers’ Knowledge, Perception and Practices in Apple Pest Management and Climate Change in the Fes-Meknes Region, Morocco
Authors: Moinina Alieu , Lahlali Rachid , MacLean Dustin , Boulif Mohammed
Published: November 2018

Apple production in the Fes-Meknes region of Morocco is highly affected by pests and adverse weather conditions. A survey of apple farmers’ knowledge, perceptions and practices (KPP) in pest management and climate change was conducted in spring 2018 in two major apple-producing provinces of the region. Each farmer reported three insect pests and two diseases on average affecting their orchards. Pest management was performed by a combination of cultural and chemical methods. All farmers used dormant chemical sprays. About 60% of the respondents adopted pest surveillance based on visual inspection and 41.9% chose their pesticides on the basis of the information received from pesticide sellers. An average of 20 treatments per year was applied in each orchard. Regression analysis showed that neither the age of apple trees nor the number of pesticide applications influenced yield. Adverse weather conditions affected all apple plantations and the most frequent problems perceived were frosts, hailstorms, hot winds and water shortage. Of the orchards reported, 51.3% were protected with anti-hail nets. In order to reduce the rate of pesticide applications, better information on integrated pest management is required. Introduction of organic farming is necessary as 40.5% of the farmers agreed to convert to this practice in the future provided that market facilitation is established.