bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Analysis of MIMO-NOMA Systems Based on Dynamic User Pairing Scheme
Authors: Deraz Shady A , Rizk Mohamed R M , Shaaban Shawky , Moussa Karim H 
Published: January 2020

Abstract
Multiple input multiple output (MIMO) structure can enhance the total capacity of the modern communication systems without using consuming excess power or bandwidth. Non-orthogonal multiple access (NOMA) configuration is a good nominee to accommodate with MIMO structure to fulfil the demands to extra user data rates and enriched spectral efficiencies. The dynamic uniform channel gain difference (DUCGD) user pairing technique plays an important role to maximize the capacity of all the paired and served users. The DUCGD is implemented by calculating the channel gain of every prospected user and sorting it in descending order to pair ones with the highest difference in channel gains to be functioned together. In this paper, the performance analysis of MIMO-NOMA systems by means of DUCGD is presented. The numerical analysis showed that, the increase of the number of users leads to an increase in the systems’ gain and the total achieved sum rates. This improvement is shown to be due to using DUCGD and an increased number of transmitting and receiving antennas, jointly.
Multi-class Image Classification Using Deep Learning Algorithm
Authors: Ezat W A , Dessouky M M , Ismail N A 
Published: January 2020

Abstract
Classifying images is a complex problem in the field of computer vision. The deep learning algorithm is a computerized model simulates the human brain functions and operations. Training the deep learning model is a costly process in machine resources and time. Investigating the performance of the deep learning algorithm is mostly needed. The convolutional neural network (CNN) is most commonly used to build a structure of the deep learning models. In this paper convolutional neural network (CNN) model pre-trained on Image-Net is used for classification of images of the PASCAL VOC 2007 data-set. The transfer learning approach is used to improve the performance of the deep learning CNN model where classification works fairly well with the smallest amount of computation time and fewer machine resources. The behavior of the Deep learning CNN model is studied and the performance has been measured. The obtained results are compared with the obtained test results from the Super-vector coding of local image descriptors method, SVM method, and Region Ranking SVM method, which tested with the PASCAL VOC 2007 data-set. The final results evaluate the deep learning algorithm as a state-of-the-art method for an image classification task.
Effect of phase noise on two-angle DoA estimation using planar arrays
Authors: Farahat A E , Hussein K F A 
Published: January 2020

Abstract
The effect of phase noise on the accuracy of the two-angle direction of arrival (DoA) is investigated. For this purpose, an efficient technique that depends on the correlation between the signals received by the 2D array and its steering matrix is proposed to estimate the DoA. A new model of the phase noise is proposed to get the consequent phase error in the received signals. The effect of the phase noise power and the array size on the accuracy of the estimated two-angle DoA is investigated. The average error in the estimation of the two-angles DoA is found to increase by increasing the power of the phase noise and by decreasing the array size. The numerical investigations are performed through electromagnetic simulation of a multiple plane waves incident on a 2D planar array of printed patch antennas.
Optimal Distributed Generation Allocation and Sizing Using Genetic and Ant Colony Algorithms
Authors: Zakaria Yousef Y. , Swief R. A. , El-Amary Noha H. , Ibrahim Amr M. 
Published: January 2020

Abstract
Distributed generators (DG) which installed into distribution network to face the increasing of load demand. DG can used to enhance power generation systems and improve distribution network efficiency. However, the distributed generators units’ implementation at not convenient position and sizing can lead to negative impacts such as a growing in power losses and invasion of system constraints. Because the rising of demand in energy. The appropriate placement and sizing of DG’s units is a credible solution to much problems in distribution system, for example power loss reduction and voltage regulation. The allocation of distributed generators into the distribution network can significantly impact in the transmission and distribution systems. Therefore, a method, which can identify an optimum DG location and size, is necessary. In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniques are proposed to find optimal sizing and location for distributed generation in electrical networks. The objective function of the work relies upon a linearized model to compute the active power losses as a function of power supplied from the generators. This strategy based on a strong coupling between active power and power flow taking into consideration the voltage angles. With the end goal to exhibit the adequacy of the proposed method, the proposed strategy is applied on IEEE 57-bus standard systems. Different maximum penetration level capacity of DG units and various possible places of DG units among several types of DG (active, reactive or active and reactive power) are considered. Results show that the optimization tools employing GA and ACO are effective in reducing active power losses by finding the optimal placement and sizing of DG units
Low Complexity Viterbi Decoder for RSCC Concatenated Codes
Authors: Samy Ramy , Mahran Ashraf 
Published: January 2020

Abstract
One of the primary obstacles to achieving reliable communication transmission for aerospace applications is the power constraints. Therefore, it is appealing to use powerful channel coding techniques with small complexity of decoding. The robust forward error correction scheme using Reed-Solomon as outer code concatenated with convolutional as inner code is an appealing scheme whose apps are commonly used in wireless and space transmissions. In this work we aim to reduce the complexity of the convolutional code decoding process as the number of computations and memory need increases exponentially with the code’s constraint length. We use the Adaptive Viterbi Algorithm to bring Viterbi decoder with small complexity. The proposed work yields approximately the same error performance as the conventional concatenated Reed-Solomon convolutional, while requiring a substantially smaller average number of computations for the convolutional code decoding process.
Detecting spam campaign in twitter with semantic similarity
Authors: Mostafa M , Abdelwahab A , Sayed H M 
Published: January 2020

Abstract
Twitter is a widespread supply for real-time news distribution between individuals. Furthermore, spammers could post any kinds of spam content to users, and a variant of incidents are committed on Twitter against users. These threats aren’t restricted to the social media platforms however they threaten the safety of Twitter users. Most of the researches use deep learning techniques to detect Twitter spammer activities. The traditional solutions check the behavior of each account or campaign of similar purpose accounts. The number of solutions concentrate on detecting spam campaign based on URL only and ignoring text in a tweet. In this paper, the lightweight framework is proposed to take tweet text into consideration for optimizing spam campaign detection methods based on deep learning techniques. The main contribution of this work summarized in two key points. First one is to summarize text of the tweets to cluster them. The second one is to find similar tweets based on Siamese Recurrent Network. Experimental results show the ability of the proposed technique to extract the right candidate campaign and classify them as spam or not with high recall and precision.
Towards Multi Robot Task Allocation and Navigation using Deep Reinforcement Learning
Authors: Elfakharany A , Yusof R , Ismail Z 
Published: January 2020

Abstract
Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP) as two separate steps each with its own set of algorithms in which the MRTA algorithm assigns each robot to a task and the MRPP algorithm guides each robot through the environment towards the assigned goal position while avoiding both static and dynamic obstacles. In this paper, we present a method that combines both steps by using a deep reinforcement learning model. The model consists of a decentralized sensor level policy which outputs the robot’s velocity to guide it through the environment towards the selected goal position and avoiding collisions. The model was trained in a simulation environment and all the robots are homogenous differential drive robots. The objective is to ensure that each robot reaches a unique goal position with the number of goal positions is equal to the number of robots. The results of training the policy in an environment is presented with both static and dynamic obstacles with four robots and four goal positions.
Waste Tire Carbon Adsorbent for Active Removal of Paracetamol in Aqueous Solution
Authors: Jusoh N. W. C. , Choo T.Y. , Masudi A. , Ali R. R. 
Published: January 2020

Abstract
Waste tyre carbon adsorbent was prepared via three different calcination temperatures (500°C, 700°C and 900°C) and activated by sodium hydroxide solution. The phase and crystallinity analysis of each adsorbent is determined using X-ray diffraction analysis. The study revealed that different crystallinity of prepared adsorbent were obtained when calcination temperature is varied. Next, the effect of calcination temperature was investigated on the removal of paracetamol in aqueous solution. The highest percentage removal (99.37%) was obtained when the waste tire carbon adsorbent is calcined at 900°C for paracetamol initial concentration of 10 mg/L at pH 3 and 120 mins of contact time. In this case, the result obtained can be contributed to the production of adsorbent using waste tire with suitable calcination temperature for the paracetamol removal in aqueous solution.
Discovering Hate Sentiment within Twitter Data through Aspect-Based Sentiment Analysis
Authors: Zainuddin Nurulhuda , Selamat Ali , Ibrahim Roliana 
Published: January 2020

Abstract
Aspect-based sentiment analysis is a vital issue in fine-grained sentiment evaluation, which intends to provide an automatic prediction of the sentiment polarity, given a particular aspect in its context. This paper presents an aspect-based sentiment analysis to find hate sentiment inside twitter data. Word embeddings have had prevalent utilisation in Natural Language Processing (NLP) applications because their vector representations have the ability to capture useful linguistic relationships and semantic properties between words with the help of deep neural networks. Word embeddings have often been used in machine learning models as feature input, which allows for the contextualisation of raw text data in machine learning techniques. The model has the ability to represent the relationship between the word embedding features and the aspects as feature representation within the suggested model. To assess the efficacy of the proposed method, extensive experiments were performed on the dataset of the researcher, as well as on widely utilised datasets. It was demonstrated by the experimental results that the proposed method was able to obtain impressive results among the three datasets.
Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska
Authors: Frost Gerald V , Loehman Rachel A , Saperstein Lisa B , Macander Matthew J , Nelson Peter R , Paradis David P , Natali Susan M 
Published: January 2020

Abstract
Alaska’s Yukon-Kuskokwim Delta (YKD) is one of the warmest parts of the Arctic tundra biome and tundra fires are common in its upland areas. Here, we combine field measurements, Landsat observations, and quantitative cover maps for tundra plant functional types (PFTs) to characterize multi-decadal succession and landscape change after fire in lichen-dominated upland tundra of the YKD, where extensive wildfires occurred in 1971–1972, 1985, 2006–2007, and 2015. Unburned tundra was characterized by abundant lichens, and low lichen cover was consistently associated with historical fire. While we observed some successional patterns that were consistent with earlier work in Alaskan tussock tundra, other patterns were not. In the landscape we studied, a large proportion of pre-fire moss cover and surface peat tended to survive fire, which favors survival of existing vascular plants and limits opportunities for seed recruitment. Although shrub cover was much higher in 1985 and 1971–1972 burns than in unburned tundra, tall shrubs (>0.5 m height) were rare and the PFT maps indicate high landscape-scale variability in the degree and persistence of shrub increase after fire. Fire has induced persistent changes in species composition and structure of upland tundra on the YKD, but the lichen-dominated fuels and thick surface peat appear to have limited the potential for severe fire and accompanying edaphic changes. Soil thaw depths were about 10 cm deeper in 2006–2007 burns than in unburned tundra, but were similar to unburned tundra in 1985 and 1971–1972 burns. Historically, repeat fire has been rare on the YKD, and the functional diversity of vegetation has recovered within several decades post-fire. Our findings provide a basis for predicting and monitoring post-fire tundra succession on the YKD and elsewhere.
Behavioral responses to spring snow conditions contribute to long-term shift in migration phenology in American robins
Authors: Oliver Ruth Y , Mahoney Peter J , Gurarie Eliezer , Krikun Nicole , Weeks Brian C , Hebblewhite Mark , Liston Glen , Boelman Natalie 
Published: January 2020

Abstract
Migratory birds have the capacity to shift their migration phenology in response to climatic change. Yet the mechanistic underpinning of changes in migratory timing remain poorly understood. We employed newly developed global positioning system (GPS) tracking devices and long-term dataset of migration passage timing to investigate how behavioral responses to environmental conditions relate to phenological shifts in American robins (Turdus migratorius) during spring migration to Arctic-boreal breeding grounds. We found that over the past quarter-century (1994–2018), robins have migrated ca. 5 d/decade earlier. Based on GPS data collected for 55 robins over three springs (2016–2018), we found the arrival timing and likelihood of stopovers, and timing of arrival to breeding grounds, were strongly influenced by dynamics in snow conditions along migratory paths. These findings suggest plasticity in migratory behavior may be an important mechanism for how long-distance migrants adjust their breeding phenology to keep pace with advancement of spring on breeding grounds.
When savannas recover from overgrazing, ecohydrological connectivity collapses
Authors: Basant Shishir , Wilcox Bradford P , Leite Pedro M , Morgan Cristine L 
Published: January 2020

Abstract
Grasslands and savannas across the globe have undergone a dramatic transition over the past century. Historical overgrazing has set in motion a cascade of events ranging from desertification in arid climates to woody plant encroachment (WPE) in semiarid and subhumid climates. In recent decades, grazing pressure on many of these landscapes has declined significantly, and where rainfall is sufficient (as in most semiarid and subhumid settings), herbaceous vegetation in intercanopy areas will recover. An important question is, how has this transition altered ecohydrological connectivity (overland flow and runoff–runon dynamics)? A woody-plant-encroached, subhumid savanna site in South Texas with a history of heavy grazing (but ungrazed since 1981) was used as a model landscape to address this question. Overland flow, soil moisture, and field saturated hydraulic conductivity (K
fs) were measured along a catena extending from the upland savanna-parkland areas to the downslope, more densely wooded areas. For comparison, K
fs and infiltrability were also measured at a moderately grazed upland site 14 km east of our study site, selected as a surrogate for past conditions at our site. In contrast to the prevailing hypothesis that the downslope areas (‘drainage-woodlands’) at our study site have continued to be supported by runoff generated from the upland areas, our measurements yielded no evidence for the redistribution of water from the uplands to the drainage areas under the current ungrazed conditions. Further, K
fs at the ungrazed study site was two orders of magnitude higher than that at the grazed site and infiltrability was twice as high at the ungrazed site than the grazed site. These findings, coupled with historical information from the site, strongly suggest that historical overgrazing amplified the runoff–runon process, resulting in significant subsidies of water from the uplands to the drainage areas. Then, with the relaxation of grazing pressure and subsequent landscape recovery, redistribution of water via surface runoff was relatively rare. We believe that our results are generalizable for savannas that have recovered from overgrazing. When these savannas are heavily grazed, ecohydrological connectivity is greatly increased; but if grazing pressure relaxes, ecohydrological connectivity will collapse. These changes in ecohydrological connectivity have important, but not always well understood, ecological consequences.
Species- and elevation-dependent productivity changes in East Asian temperate forests
Authors: Kim Moonil , Ham Bo-Young , Kraxner Florian , Shvidenko Anatoly , Schepaschenko Dmitry , Krasovskii Andrey , Park Taejin , Lee Woo-Kyun 
Published: January 2020

Abstract
The velocity and impact of climate change on forest appear to be site, environment, and tree species-specific. The primary objective of this research is to assess the changes in productivity of five major temperate tree species (Pinus densiflora, PD; Larix kaempferi, LK; Pinus koraiensis, PK; Quercus variabilis, QV; and Quercus mongolica, QM) in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODerate resolution Imaging Spectroradiometer (MODIS) net primary productivity as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation in primary productivity by tree species over the past four decades. Based on our result from NFI plots and increment core data sets, the productivity of PD, QV, and QM in LLF was relatively higher than those in HMF, while LK and PK in HMF were more productive than lowland ones. Our analysis of the increment core data revealed a contrasting pattern of long-term productivity changes between coniferous and oak tree species. While the productivity of oak tree species tended to increase after the 1990s, the productivity in coniferous forests tended to decrease. These differences across forest types and their altitudinal classes are also noticeable from the MODIS product. The results of our study can be used to develop climate-smart forest management strategies to ensure that the forests continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region.
Detection of type2 biomarkers for response in COPD
Authors: Yamaji Yoshikazu , Oishi Keiji , Hamada Kazuki , Ohteru Yuichi , Chikumoto Ayumi , Murakawa Keita , Matsuda Kazuki , Suetake Ryo , Murata Yoriyuki , Ito Kosuke , Asami-Noyama Maki , Edakuni Nobutaka , Hirano Tsunahiko , Matsunaga Kazuto 
Published: January 2020

Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous inflammatory lung disease. It is important to identify patients who would respond to anti-inflammatory treatment. This prospective study aims to determine how inflammatory biomarkers could be used to predict the potential effect of inhaled corticosteroids (ICS) in terms of symptoms and lung function. We evaluated the levels of blood eosinophils, exhaled nitric oxide fraction at a flow rate of 50 ml s−1 (FeNO), alveolar nitric oxide concentration (Calv), immunoglobulin E and atopy in 43 patients with symptomatic COPD and correlated these expression levels with the changes in the COPD Assessment Test (CAT) and lung function by 12 weeks of add-on therapy with ciclesonide 400 μg d−1 on bronchodilators. The mean changes in the CAT score and FEV1 were −1.4 points and +90 ml, respectively, with significant variation in the levels of change. The area under the receiver’s operating characteristic curve (AUC) for FeNO in predicting improvements in both the CAT score and FEV1 was 0.92. The AUC for Calv and blood eosinophils was 0.82 and 0.65. Two cutoffs were chosen, one corresponding to a high value of FeNO associated with certainty for response inclusion (FeNO = 35 ppb; sensitivity = 0.67, specificity = 0.94; positive predictive value = 0.80) and the other with certainty for response exclusion (FeNO = 20 ppb; sensitivity = 1.00, specificity = 0.58, negative predictive value = 1.00). Baseline FeNO values were significantly correlated with changes in FEV1 and CAT (all p < 0.0001). FeNO could be a valuable biomarker for identifying individuals who respond to steroid therapy among patients with symptomatic COPD in terms of symptoms and airflow limitation. The study was prospectively registered with the University Hospital Medical Information Network (UMIN) in Japan (protocol ID 000010711).
An Analysis of GaN-based Ultraviolet Photodetector
Authors: Fan Zitong 
Published: January 2020

Abstract
The GaN-based UV photodetectors have been a hot topic in semiconductor field. To present the existing research, this paper introduces the development of GaN-based UV photodetectors and the common types of detectors. The difficulties and new technologies of UV photodetector are also introduced. About the difficulties, the whole paper would focus on the substrate of the GaN. To solve this problem, this paper have introduced the PSS, an enhancement of sapphire substrate. Also this paper have put a hypothesis on the shape of PSS. The surface plasmon, which is not a common technology in photodetectors is also introduced. It is hoped that this paper will provide research background and direction for researchers who want to enter this field.