bluuebunny/crossref_metadata_2025 huggingface dataset:
Reinforced Concrete Slab Optimization with Simulated Annealing
Authors: Stochino Flavio , Lopez Gayarre Fernando
Published: August 2019

Flat slabs have several advantages such as a reduced and simpler formwork, versatility, and easier space partitioning, thus making them an economical and efficient structural system. When producing structural components in series, every detail can lead to significant cost differences. In these cases, structural optimization is of paramount relevance. This paper reports on the structural optimization of reinforced concrete slabs, presenting the case of a rectangular slab with two clamped adjacent edges and two simply supported edges. Using the yield lines method and the principle of virtual work, a cost function can be formulated and optimized using simulated annealing (SA). Thus, the optimal distribution of reinforcing bars and slab thickness can be found considering the flexural ultimate limit state and market materials costs. The optimum result was defined by the orthotropic coefficient k = 8, anisotropic coefficient g = 1.4, and slab thickness H = 11.8 cm. A sensitivity analysis of the solution was developed considering different material costs.
Stochastic Natural Vibration Analyses of Free-Form Shells
Authors: San Bingbing , Ma Yunlong , Xiao Zhi , Feng Dongming , Yin Liwei
Published: August 2019

This work investigates the natural vibration characteristics of free-form shells when considering the influence of uncertainties, including initial geometric imperfection, shell thickness deviation, and elastic modulus deviation. Herein, free-form shell models are generated while using a self-coded optimization algorithm. The Latin hypercube sampling (LHS) method is used to draw the samplings of uncertainties with respect to their stochastic probability models. ANSYS finite element (FE) software is adopted to analyze the natural vibration characteristics and compute the natural frequencies. The mean values, standard deviations, and cumulative distributions functions (CDFs) of the first three natural frequencies are obtained. The partial correlation coefficient is adopted to rank the significances of uncertainty factors. The study reveals that, for the free-form shells that were investigated in this study, the natural frequencies is a random quantity with a normal distribution; elastic modulus deviation imposes the greatest effect on natural frequencies; shell thickness ranks the second; geometrical imperfection ranks the last, with a much lower weight than the other two factors, which illustrates that the shape of the studied free-form shells is robust in term of natural vibration characteristics; when the supported edges are fixed during the shape optimization, the stochastic characteristics do not significantly change during the shape optimization process.
Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype
Authors: Youssef Ali , Colon Jeroen , Mantzios Konstantinos , Gkiata Paraskevi , Mayor Tiago , Flouris Andreas , De Bruyne Guido , Aerts Jean-Marie
Published: August 2019

Bicyclists can be subjected to crashes, which can cause injuries over the whole body, especially the head. Head injuries can be prevented by wearing bicycle helmets; however, bicycle helmets are frequently not worn due to a variety of reasons. One of the most common complaints about wearing bicycle helmets relates to thermal discomfort. So far, insufficient attention has been given to the thermal performance of helmets. This paper aimed to introduce and develop an adaptive model for the online monitoring of head thermal comfort based on easily measured variables, which can be measured continuously using impeded sensors in the helmet. During the course of this work, 22 participants in total were subjected to different levels of environmental conditions (air temperature, air velocity, mechanical work and helmet thermal resistance) to develop a general model to predict head thermal comfort. A reduced-order general linear regression model with three input variables, namely, temperature difference between ambient temperature and average under-helmet temperature, cyclist’s heart rate and the interaction between ambient temperature and helmet thermal resistance, was the most suitable to predict the cyclist’s head thermal comfort and showed maximum mean absolute percentage error (MAPE) of 8.4%. Based on the selected model variables, a smart helmet prototype (SmartHelmet) was developed using impeded sensing technology, which was used to validate the developed general model. Finally, we introduced a framework of calculation for an adaptive personalised model to predict head thermal comfort based on streaming data from the SmartHelmet prototype.
Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt
Authors: Nguyen Hoang-Long , Le Thanh-Hai , Pham Cao-Thang , Le Tien-Thinh , Ho Lanh Si , Le Vuong Minh , Pham Binh Thai , Ly Hai-Bang
Published: August 2019

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.
Comprehensive Prediction and Discriminant Model for Rockburst Intensity Based on Improved Variable Fuzzy Sets Approach
Authors: Wang Hong , Nie Lei , Xu Yan , Lv Yan , He Yuanyuan , Du Chao , Zhang Tao , Wang Yuzheng
Published: August 2019

Rockburst intensity prediction is one of the basic works of underground engineering disaster prevention and mitigation. Considering the dynamic variability and fuzziness in rockburst intensity prediction, variable fuzzy sets (VFS) are selected for evaluation and prediction. Here, there are two problems in the application of traditional VFS: (i) the relative membership degree (RMD) calculation process is complex and time-consuming, and the RMD matrix of all indexes can be only obtained by using the RMD function repeatedly; (ii) unreasonable weights of indicators have great impact on the synthetic relative membership degree (SRMD), so it is difficult to guarantee the correctness of the final prediction result. In view of the above problem, this paper established three simplified feature relationship expressions of RMD based on VFS principle and used the SRMD function to establish a BP neural network model to optimize SRMD. The improved VFS method is more efficient and the prediction results are more stable and reliable than the traditional VFS method. The main advantages are as follows: (1) the improved VFS method has higher computational efficiency; (2) the improved VFS method can verify the correctness of RMD at all times; (3) the improved VFS method has higher prediction accuracy; and (4) the improved VFS method has higher fault tolerance and practicability.
Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
Authors: Zhou Aiguo , Li Zhenyu , Shen Yong
Published: August 2019

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.
Experimental Study on the Removal of Real Exhaust Pollutants from a Diesel Engine by Activated Carbon
Authors: Wang Zongyu , Kuang Hailang , Zhang Jifeng , Chu Lilin , Ji Yulong
Published: August 2019

So far, most of the experimental researchers studying the removal of diesel exhaust pollutants have done so with simulated exhaust gas, which cannot demonstrate the ability of catalysts accurately. Because activated carbon (AC) has low price, no secondary pollution, good adsorption performance, and certain catalytic activity, it has good application prospects in the field of marine exhaust pollutant removal. In this paper, the removal of particulate matter (PM), carbon monoxide (CO), and NOx from real exhaust gas by AC was studied. The results show that PM removal efficiency reached up to 77%, while the pressure drop increased with running time. AC may degenerate to some extent with the increase of temperature, resulting in a negative removal efficiency of CO. The denitration efficiency of AC was up to 34.5% without urea, and further increased to 44.8% after spraying urea, still a distance from industrial applications. In the future, it may be necessary to install a fan to compensate the reactor or to backwash the reactor to avoid excessive pressure drop. The thermal stability of the AC also needs to be improved. To increase the denitration performance, it may be helpful to modify the AC or impregnate other metal oxides.
Variable Chromosome Genetic Algorithm for Structure Learning in Neural Networks to Imitate Human Brain
Authors: Park Kang-moon , Shin Donghoon , Chi Sung-do
Published: August 2019

This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.
Design of Interactions for Handheld Augmented Reality Devices Using Wearable Smart Textiles: Findings from a User Elicitation Study
Authors: Nanjappan Vijayakumar , Shi Rongkai , Liang Hai-Ning , Xiao Haoru , Lau Kim King-Tong , Hasan Khalad
Published: August 2019

Advanced developments in handheld devices’ interactive 3D graphics capabilities, processing power, and cloud computing have provided great potential for handheld augmented reality (HAR) applications, which allow users to access digital information anytime, anywhere. Nevertheless, existing interaction methods are still confined to the touch display, device camera, and built-in sensors of these handheld devices, which suffer from obtrusive interactions with AR content. Wearable fabric-based interfaces promote subtle, natural, and eyes-free interactions which are needed when performing interactions in dynamic environments. Prior studies explored the possibilities of using fabric-based wearable interfaces for head-mounted AR display (HMD) devices. The interface metaphors of HMD AR devices are inadequate for handheld AR devices as a typical HAR application require users to use only one hand to perform interactions. In this paper, we aim to investigate the use of a fabric-based wearable device as an alternative interface option for performing interactions with HAR applications. We elicited user-preferred gestures which are socially acceptable and comfortable to use for HAR devices. We also derived an interaction vocabulary of the wrist and thumb-to-index touch gestures, and present broader design guidelines for fabric-based wearable interfaces for handheld augmented reality applications.
On Sharing an FIB Table in Named Data Networking
Authors: Mun Ju Hyoung , Lim Hyesook
Published: August 2019

As a new networking paradigm, Named Data Networking (NDN) technology focuses on contents, and content names are used as identifiers for forwarding and routing, as opposed to IP addresses in the current Internet. NDN routers forward packets by looking up a Forwarding Information Base (FIB), each entry of which has a name prefix and output faces. An FIB should have the information to forward Interest packets for any contents. Hence, the size of an FIB would be excessively large in NDN routers, and the traffic for building an FIB would be significant. In order to reduce the traffic associated with building an FIB table and memory requirement for storing an FIB table, this paper proposes a new efficient method which combines the routing of network connectivity and the building of a forwarding engine using Bloom filters. We propose to share the summary of an FIB using a Bloom filter rather than to advertise each name prefix. The forwarding engine of the proposed scheme is a combination of Bloom filters, and hence the memory requirement of the forwarding can be much smaller than the regular FIB. Simulation results using ndnSIM under real network topologies show that the proposed method can achieve nearly the same performance as the conventional link state algorithm with 6–8% of the traffic for distributing the connectivity information and 5–9% of the memory consumption.
Essential Oil Compositions and Antifungal Activity of Sunflower (Helianthus) Species Growing in North Alabama
Authors: Lawson Sims K. , Sharp Layla G. , Powers Chelsea N. , McFeeters Robert L. , Satyal Prabodh , Setzer William N.
Published: August 2019

Helianthus species are North American members of the Asteraceae, several of which have been used as traditional medicines by Native Americans. The aerial parts of two cultivars of Helianthus annuus, “Chianti” and “Mammoth”, and wild-growing H. strumosus, were collected from locations in north Alabama. The essential oils were obtained by hydrodistillation and analyzed by gas chromatography—mass spectrometry. The Helianthus essential oils were dominated by monoterpene hydrocarbons, in particular α-pinene (50.65%, 48.91%, and 58.65%, respectively), sabinene (6.81%, 17.01%, and 1.91%, respectively), β-pinene (5.79%, 3.27%, and 4.52%, respectively), and limonene (7.2%, 7.1%, and 3.8%, respectively). The essential oils were screened against three opportunistic pathogenic fungal species, Aspergillus niger, Candida albicans, and Cryptococcus neoformans. The most sensitive fungus was C. neoformans with minimum inhibitory concentration (MIC) values of 78, 156, and 78 μg/mL, respectively.
Seismic Vulnerability Assessment of Hybrid Mold Transformer Based on Dynamic Analyses
Authors: Dinh Ngoc Hieu , Kim Joo-Young , Lee Seung-Jae , Choi Kyoung-Kyu
Published: August 2019

In the present study, the seismic vulnerability of a hybrid mold transformer was investigated using a dynamic analytical approach incorporating the experimental results of shaking table tests. The analytical model consisted of linear springs and plastic beam elements, and it has six degrees of freedom simulating the hybrid mold transformer. The dynamic characteristics of the analytical model were determined based on the shaking table tests. The reliability of the analytical model was verified by comparing the test results and analytical results. In order to assess the seismic vulnerability, three critical damage states observed during the shaking table tests were investigated by incorporating the three performance levels specified in ASCE 41-17. Comprehensive dynamic analyses were performed with a set of twenty earthquakes in consideration of the variation of the uncertain parameters (such as the effective stiffness and coil mass) of the mold transformer. Based on the analytical results, fragility curves were established to predict the specified exceedance probability of the mold transformer according to the performance levels.
Effect of Pore Characteristics in Polyvinylidene Fluoride/Fumed Silica Membranes on Mass Flux in Solar-Assisted Evaporation Applications
Authors: Bahman Mona , AlNahyan Maryam , Mustafa Ibrahim , AlMarzooqi Faisal
Published: August 2019

Although important, very little has been demonstrated in the literature to experimentally demonstrate the effects of porosities and pore size on the evaporation flux in polymeric membranes. Additionally, we suspect that a batch-mode setup, i.e., stagnant water, could cause a build-up of heat in the system, influencing the evaporation mass-flux mechanism, and jeopardizing the ability to attain a real correlation between evaporation and effects of pore characteristics. Herein, we fabricate polyvinylidene fluoride membranes containing variable amounts of a Fumed Silica additive to achieve membranes with variable properties, and we investigate the change in the performance of the solar-assisted thin-film evaporation utilizing an in-house built continuous flow evaporation setup (to avoid heat build-up effects in the bulk of the water and demonstrate a continuous flow system). Our membrane design approach had two important advantages: (1) the achievement of similar heat transfer and solar absorbance properties and (2) the achievement of variable pore sizes and volume porosities. We show that the mass flux increased as the mean pore size decreased, indicating that the mode of mass transfer occurred due to the thin-film region of the meniscus from the small fluid velocities near the interface, and we attribute the results to the increase in the capillary pumping effects through the mesoporous channels as they get thinner.
Active Vibration Control of Rib Stiffened Plate by Using Decentralized Velocity Feedback Controllers with Inertial Actuators
Authors: Ma Xiyue , Wang Lei , Xu Jian
Published: August 2019

Active control of low frequency vibration and sound radiation from a rib stiffened plate has great practical significance as this structure is widely applied in engineering, such as aircraft or ship fuselage shells. This paper presents an investigation on the performance of active vibration control of the rib stiffened plate by using decentralized velocity feedback controllers with inertial actuators. A simple modeling approach in frequency domain is proposed in this research to calculate the control performance. The theoretical model of vibrating response of the ribbed plate and the velocity feedback controllers is first established. Then, as an important part, the influences of the control gain and the number of the decentralized unit on the control performance are investigated. Results obtained demonstrate that—similar to that of the unribbed plate case—appropriately choosing the number of the unit and their feedback gains can achieve good control results. Too many units or very high feedback gains will not bring further noise reduction.
Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
Authors: Gao Mingming , Li Jianjing , Hong Feng , Long Dongteng
Published: August 2019

Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46–13.46%.
Integration of Reversible Heat Pumps in Trigeneration Systems for Low-Temperature Renewable District Heating and Cooling Microgrids
Authors: Urbanucci Luca , Testi Daniele , Bruno Joan Carles
Published: August 2019

District heating and cooling networks based on trigeneration systems and renewable energy technologies are widely acknowledged as an energy efficient and environmentally benign solution. These energy systems generally include back-up units, namely fossil-fuel boilers and electric chillers, to enhance system flexibility and cover peak energy demand. On the other hand, 4th generation district heating networks are characterized by low-temperature water distribution to improve energy and exergy efficiencies. Moreover, reversible heat pumps are a versatile technology, capable of providing both heating and cooling, alternately. In this paper, the integration of reversible heat pumps as single back-up units in hybrid renewable trigeneration systems serving low-energy micro-district heating and cooling networks is investigated. A detailed modeling of the system is provided, considering part-load and ambient condition effects on the performance of the units. Size and annual operation of the proposed system are optimized in a case study, namely a large office building located in Pisa (Italy), by means of a genetic algorithm-based procedure. A comparison with the conventional trigeneration system is performed in terms of economic and environmental perspectives. Results show that the integration of reversible heat pumps is an economically viable solution capable of reducing by 7% the equivalent annual cost, increasing the installed power of renewables up to 23%, and lowering by 11% carbon dioxide emissions, compared to the energy system with conventional back-up units.
Online Knowledge Learning Model Based on Gravitational Field Theory
Authors: Ji Wenfei , Jiang Tonghai , Wang Meng , Tang Xinyu , Chen Guang , Yang Shan
Published: August 2019

Currently, low-dimensional embedded representation learning models are the mainstream approach in knowledge representation research, due to ease of calculation and ability to utilize the spatial relationship between knowledge areas, which benefit from static knowledge learning. However, these models cannot update and learn knowledge online. Although using update strategies to update the knowledge base has been proposed by some scholars, this still requires retraining of knowledge and does not use the previous learning parameters and models. TransOnLine, an online knowledge learning method based on the theory of gravitational field, inspired by the fact that the forces acting on two objects in a gravitational field are only related to the distances between objects, rebalances the knowledge space caused by new knowledge through dynamic programming via introducing the spatial energy function and energy transfer function to solve the above problems. TransOnLine can reuse the parameters and models of previous learning. Experiments show that the performance of the TransOnLine method is close to state-of-the-art methods, and it is suitable for online learning and updating a relational-intensive knowledge base.
Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study
Authors: Belmonte Lidia María , Morales Rafael , Fernández-Caballero Antonio
Published: August 2019

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.
Analysis of Severe Elevated Thunderstorms over Frontal Surfaces Using DCIN and DCAPE
Authors: Market Patrick , Grempler Kevin , Sumrall Paula , Henson Chasity
Published: August 2019

A 10-year study of elevated severe thunderstorms was performed using The National Centers for Environmental Information Storm Events Database. A total of 80 elevated thunderstorm cases were identified, verified, and divided into “Prolific” and “Marginal” classes. These severe cases occurred at least 80 km away from, and on the cold side of, a surface boundary. The downdraft convective available potential energy (DCAPE), downdraft convective inhibition (DCIN), and their ratio are tools to help estimate the potential for a downdraft to penetrate through the depth of a stable surface layer. The hypothesis is that as the DCIN/DCAPE ratio decreases, there exists enhanced possibility of severe surface winds. Using the initial fields from the Rapid Refresh numerical weather prediction model, datasets of DCIN, DCAPE, and their ratio were created. Mann-Whitney U tests on the Prolific versus Marginal case sets were undertaken to determine if the DCAPE and DCIN values come from different populations for the two different case sets. Results show that the Prolific cases have values of DCIN closer to zero, suggesting the downdraft is able to penetrate to the surface causing severe winds. Thus, comparing DCIN and DCAPE is a viable tool in determining if downdrafts will reach the surface from elevated thunderstorms.
Long Exposure to a Diet Supplemented with Antioxidant and Anti-Inflammatory Probiotics Improves Sperm Quality and Progeny Survival in the Zebrafish Model
Authors: Valcarce David G. , Riesco Marta F. , Martínez-Vázquez Juan M. , Robles Vanesa
Published: August 2019

The aim of the present experiment is to study the effects of oral ingestion of a mixture of two probiotic bacteria on sperm quality and progenies. Three homogeneous groups of juvenile zebrafish were created. Once having reached adulthood (3 months postfertilization; mpf), each group received different feeding regimens: a standard diet (control), a maltodextrin-supplemented diet (vehicle control), or a probiotic-supplemented diet (a mixture (1:1) of Lactobacillus rhamnosus CECT8361 and Bifidobacterium longum CECT7347). The feeding regime lasted 4.5 months. Growth parameters (weight and length) were determined at 3, 5, and 7.5 mpf. Sperm motility was evaluated using computer-assisted sperm analysis at 5 and 7.5 mpf. Progeny survival, hatching rate, and malformation rate were also evaluated. Results showed that probiotic-supplemented diet improved growth parameters compared with the standard diet. The highest percentage of motile spermatozoa was reported in the probiotic-fed group. Concomitantly, the percentage of fast sperm subpopulation was significantly lower in samples derived from control males. Furthermore, there was a significant difference in progeny survival between the probiotic-fed group and the control group at three developmental times (24 hours postfertilization (hpf), 5 days postfertilization (dpf) and 7 dpf). In conclusion, in zebrafish, prolonged ingestion of a mixture of Lactobacillus rhamnosus CECT8361 and Bifidobacterium longum CECT7347 has positive effects on growth, sperm quality, and progeny survival.
Gelatin/Hyaluronic Acid Content in Hydrogels Obtained through Blue Light-Induced Gelation Affects Hydrogel Properties and Adipose Stem Cell Behaviors
Authors: Sakai Shinji , Ohi Hiromi , Taya Masahito
Published: August 2019

Composite hydrogels of hyaluronic acid and gelatin attract great attention in biomedical fields. In particular, the composite hydrogels obtained through processes that are mild for cells are useful in tissue engineering. In this study, hyaluronic acid/gelatin composite hydrogels obtained through a blue light-induced gelation that is mild for mammalian cells were studied for the effect of the content of each polymer in the precursor solution on gelation, properties of resultant hydrogels, and behaviors of human adipose stem cells laden in the hydrogels. Control of the content enabled gelation in less than 20 s, and also enabled hydrogels to be obtained with 0.5–1.2 kPa Young’s modulus. Human adipose stem cells were more elongated in hydrogels with a higher rather than lower content of hyaluronic acid. Stem cell marker genes, Nanog, Oct4, and Sox2, were expressed more in the cells in the composite hydrogels with a higher content of hyaluronic acid compared with those in the hydrogel composed of gelatin alone and on tissue culture dishes. These results are useful for designing conditions for using gelatin/hyaluronic acid composite hydrogels obtained through blue light-induced gelation suitable for tissue engineering applications.
Tribology Performance of Surface Texturing Plunger
Authors: Wei Songbo , Shang Hongfei , Liao Chenglong , Huang Junyuan , Shi Bairu
Published: August 2019

Plunger pumps are widely used in oil pumping units around the world. The water content of the wellbore is increasing along with the development progress, so the lubricating capacity of the well fluids between the plunger and barrel is decreasing correspondingly. Commonly, the substrate material of the plunger and barrel are stainless steel, and the plunger surface is usually covered with nickel-based coating. Therefore, the performance of the plunger and barrel has been affected due to poor lubrication and eccentric wear. Non-smooth surfaces have been proven to improve the tribology performance in many cases. A surface texturing plunger covered with specific dimples has been prepared by using laser surface texturing technology. The morphology of the surface texturing plunger was characterized and analyzed. The tribology performance of surface texturing plunger samples was tested using standard friction and wear test machines with oil and water lubrication, respectively. The results indicated that surface texturing could effectively reduce the coefficient of friction, and the wear resistance of the surface textured samples has been improved to some extent.
Effectiveness of a Motor Intervention Program on Motivation and Learning of English Vocabulary in Preschoolers: A Pilot Study
Authors: Padial-Ruz Rosario , García-Molina Raquel , Puga-González Esther
Published: August 2019

(1) Background: Linking physical activity to the teaching of curricular contents provides numerous motivational and emotional benefits which improve academic performance and lead to the improvement and creation of healthy habits from an early age. (2) Method: The objective of the study is to analyze the effectiveness of a 5-week intervention program based on the use of a combined methodology of physical activity and gestures on motivation and vocabulary learning in English. The sample of children was aged from 4 to 7 years and was recruited from three children’s centers in Tegucigalpa, Honduras. A quasi-experimentalstudy was carried out using a pretest–posttest design in a sample (n = 88). (3) Results: Statistically significant results were obtained in the learning of words through the combined methodology of gestures and motor activity, compared to the traditional methodology used in the control group. (4) Conclusions: The main conclusions are that motor and expressive activities at an early age can be an effective motivational resource that promotes an increase in children’s physical activity time in the classroom. Further, it improves academic performance, producing a more effective learning of the vocabulary of a second language.
Gifted and Talented Services for EFL Learners in China: A Step-by-Step Guide to Propensity Score Matching Analysis in R
Authors: Tang Shifang , Tong Fuhui , Lu Xiuhong
Published: August 2019

We sought to quantify the effectiveness of a gifted and talented (GT) program, as was provided to university students who demonstrated a talent for learning English as a foreign language (EFL) in China. To do so, we used propensity score matching (PSM) techniques to analyze data collected from a tier-1 university where an English talent (ET) program was provided. Specifically, we provided (a) a step-by-step guide of PSM analysis using the R analytical package, (b) the codes for PSM analysis and visualization, and (c) the final analysis of baseline equivalence and treatment effect based on the matching sample. Collectively, the results of descriptive statistics, visualization, and baseline equivalence indicate that PSM is an effective matching technique for generating an unbiased counterfactual analysis. Moreover, the ET program yields a statistically significant, positive effect on ET students’ English language proficiency.
A New Approach to Measuring Tax Effort
Authors: Dalamagas Basil , Palaios Panagiotis , Tantos Stefanos
Published: August 2019

This paper attempts to extend the theoretical and empirical methodology employed in previous literature, by proposing a utility maximization process to estimate the optimal tax revenue from a sample of 30 countries. It is shown that an optimal tax system is defined solely by two crucial determining factors: The productive capacity of the country (GDP) and consumers’ preferences (consumption spending). All the other variables can be disregarded, as macroeconomic determinants (GDP, consumption) tend to capture the impact of all the remaining factors on tax revenue. It is also shown that our utility maximization method generates tax-effort indices which do not differ significantly from those of IMF and World Bank studies. The actual tax burden for most of the sample countries is shown to be below its optimal level. As expected, the tax-effort performance of each of the sample countries appears to be affected by the variety of approaches employed throughout the text.
A 0.94 μW 611 KHz In-Situ Logic Operation in Embedded DRAM Memory Arrays in 90 nm CMOS
Authors: Hwang Myeong-Eun , Kwon Sungoh
Published: August 2019

Conventional computers based on the Von Neumann architecture conduct computation with repeated data movements between their separate processing and memory units, where each movement takes time and energy. Unlike this approach, we experimentally study memory that can perform computation as well as store data within a generic memory array in a non-Von Neumann architecture way. Memory array can innately perform NOR operation that is functionally complete and thus realize any Boolean functions like inversion (NOT), disjunction (OR) and conjunction (AND) operations. With theoretical exploration of memory array performing Boolean computation along with storing data, we demonstrate another potential of memory array with a test chip fabricated in a 90 nm logic process. Measurement results confirm valid in-situ memory logic operations in a 32-kbit memory system that successfully operates down to 135 mV consuming 130 nW at 750 Hz, reducing power and data traffic between the units by five orders of magnitude at the sacrifice of performance.
Ransomware Detection System for Android Applications
Authors: Alsoghyer Samah , Almomani Iman
Published: August 2019

Android ransomware is one of the most threatening attacks nowadays. Ransomware in general encrypts or locks the files on the victim’s device and requests a payment in order to recover them. The available technologies are not enough as new ransomwares employ a combination of techniques to evade anti-virus detection. Moreover, the literature counts only a few studies that have proposed static and/or dynamic approaches to detect Android ransomware in particular. Additionally, there are plenty of open-source malware datasets; however, the research community is still lacking ransomware datasets. In this paper, the state-of-the-art of Android ransomware detection approaches were investigated. A deep comparative analysis was conducted which shed the key differences among the existing solutions. An application programming interface (API)-based ransomware detection system (API-RDS) was proposed to provide a static analysis paradigm for detecting Android ransomware apps. API-RDS focuses on examining API packages’ calls as leading indicator of ransomware activity to discriminate ransomware with high accuracy before it harms the user’s device. API packages’ calls of both benign and ransomware apps were thoroughly analyzed and compared. Significant API packages with corresponding methods were identified. The experimental results show that API-RDS outperformed other recent related approaches. API-RDS achieved 97% accuracy while reducing the complexity of the classification model by 26% due to features reduction. Moreover, this research designed a proactive mechanism based on a high quality unique ransomware dataset without duplicated samples. 2959 ransomware samples were collected, tested and reduced by almost 83% due to samples duplication. This research also contributes to constructing an up-to-date, unique dataset that covers the majority of existing Android ransomware families and recent clean apps that could be used as a labeled reference for research community.
Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks
Authors: Grigoras Gheorghe , Neagu Bogdan-Constantin
Published: August 2019

In this paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in three-phase networks with the voltage level below 0.4 kV (low voltage—LV) is presented. In the first stage, the input data regarding the hourly active and reactive powers of the consumers and producers are introduced. The powers are loaded from the database of the smart metering system (SMS) for the consumers and producers integrated in this system or files containing the characteristic load profiles established by the Distribution Network Operator for the consumers, which have installed the conventional meters non-integrated in the SMS. In the second stage, a function, which is based on the work with the structure vectors, was implemented to easily identify the configuration of analysed networks. In the third stage, an improved version of a forward/backward sweep-based algorithm was proposed to quickly calculate the power/energy losses to three-phase LV distribution networks in a balanced and unbalanced regime. A real LV rural distribution network from a pilot zone belonging to a Distribution Network Operator from Romania was used to confirm the accuracy of the proposed algorithm. The comparison with the results obtained using the DigSilent PowerFactory Simulation Package certified the performance of the algorithm, with the mean absolute percentage error (MAPE) being 0.94%.
Simultaneous Inertia Contribution and Optimal Grid Utilization with Wind Turbines
Authors: Jauch Clemens , Gloe Arne
Published: August 2019

This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilization and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilization and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants.
The Influence of Colorants, Flavorants and Product Identity on Perceptions of Naturalness
Authors: Murley Tyler , Chambers Edgar
Published: August 2019

Natural foods are important to consumers, yet frustrating to producers due to the lack of a formal definition of “natural”. Previous work has studied how consumers define naturalness and how they rate the naturalness of various products, but there is a gap in knowledge relating to how color and flavor additives impact perceptions. The objective of this study was to understand how colorants and flavorants on ingredient statements affect perceptions of naturalness. An online survey was launched in the United States, United Kingdom, and Australia to determine how consumers perceive products with ingredient statements containing different combinations of artificial and natural colors and flavors when shown with and without the product identity. Results showed that consumers look at the whole product primarily to make decisions about naturalness, but also consider other factors. Products derived from plants and products with natural colors and flavors were perceived to be the most natural. Artificial flavors may be more acceptable than artificial colors due to negative health perceptions and labeling rules associated with colors. Additionally, factors like ingredient familiarity and processing likely influence consumers when making decisions about product naturalness. Males, Millennials, and educated participants have higher naturalness scores than other participants in their respective demographics.
Effective Analysis of Interactive Effects with Non-Normal Data Using the Aligned Rank Transform, ARTool and SAS® University Edition
Authors: Durner Edward
Published: August 2019

Most statistical techniques commonly used in horticultural research are parametric tests that are valid only for normal data with homogeneous variances. While parametric tests are robust when the data ‘slightly’ deviate from normality, a significant departure from normality leads to reduced power and the probability of a type I error increases. Transformations often used to normalize non-normal data can be time consuming, cumbersome and confusing and common non-parametric tests are not appropriate for evaluating interactive effects common in horticultural research. The aligned rank transformation allows non-parametric testing for interactions and main effects using standard ANOVA techniques. This has not been widely adapted due to its rigorous mathematical nature, however, a downloadable (ARTool) is now available, which performs the math needed for the transformation. This study provides step-by-step instructions for integrating ARTool with the free edition of SAS (SAS University Edition) in an easily employed method for testing normality, transforming data with aligned ranks, and analysing data using standard ANOVAs.
Calibration and Validation of the Cosmic Ray Neutron Rover for Soil Water Mapping within Two South African Land Classes
Authors: Vather Thigesh , Everson Colin , Franz Trenton E.
Published: August 2019

Knowledge of soil water at a range of spatial scales would further our understanding of the dynamic variable and its influence on numerous hydrological applications. Cosmic ray neutron technology currently consists of the Cosmic Ray Neutron Sensor (CRNS) and the Cosmic Ray Neutron Rover (CRNR). The CRNR is an innovative tool to map surface soil water across the land surface. This research assessed the calibration and validation of the CRNR at two survey sites (hygrophilous grassland and pine forest) within the Vasi area with an area of 72 and 56 ha, respectively. The assessment of the calibrations showed that consistent calibration values (N0) were obtained for both survey sites. The hygrophilous grassland site had an average N0 value of 133.441 counts per minute (cpm) and an average error of 2.034 cpm. The pine site had an average N0 value of 132.668 cpm and an average error of 0.375 cpm between surveys. The validation of CRNR soil water estimates with interpolated hydro-sense soil water estimates showed that the CRNR can provide spatial estimates of soil water across the landscape. The hydro-sense and CRNR soil water estimates had a R2 of 0.439 at the hygrophilous grassland site and 0.793 at the pine site.