Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of relative permeability hysteresis on geological CO2 storage

Relative permeabilities are the key descriptors in classical formulations of multiphase flow in porous media. Experimental evidence and an analysis of pore‐scale physics demonstrate conclusively that relative permeabilities are not single functions of fluid saturations and that they display strong hysteresis effects. In this paper, we evaluate the relevance of relative permeability hysteresis when modeling geological CO2 sequestration processes. Here we concentrate on CO2 injection in saline aquifers. In this setting the CO2 is the nonwetting phase, and capillary trapping of the CO2 is an essential mechanism after the injection phase during the lateral and upward migration of the CO2 plume. We demonstrate the importance of accounting for CO2 trapping in the relative permeability model for predicting the distribution and mobility of CO2 in the formation. We conclude that modeling of relative permeability hysteresis is required to assess accurately the amount of CO2 that is immobilized by capillary trapping and therefore is not available to leak. We also demonstrate how the mechanism of capillary trapping can be exploited (e.g., by controlling the injection rate or alternating water and CO2 injection) to improve the overall effectiveness of the injection project.
Low Latency Radio Access in 3GPP Local Area Data Networks for V2X: Stochastic Optimization and Learning

The next generation vehicular applications substantially shifting the paradigm of human activity have been projected to empower intelligent transportation systems. Targeting at supporting vehicle-to-everything connections, conventional mobile network architectures mandatorily requiring data routing through the core network, however, induce unacceptable costs both in end-to-end latency and backhaul resource consumption. The technical merit of moving computation and storage resources along with mobile vehicles consequently renders the mobile edge computing (MEC) a promising remedy to relieve the burden at the core network. To practice MEC, 3GPP has launched the normative works of a new paradigm known as the local area data network (LADN). Through performing in-network cache to store popular information at LADNs, a vehicle locating within the service area of an LADN is able to access particular location-based wireless application and information. Avoiding data routing through the core network, LADNs, however, encounter two critical challenges in downlink radio access to induce additional latency issues: 1) resource starvation at fronthaul links and 2) discrimination of quality-of-service requirements of vehicles with distinct capabilities. To tackle these challenges, through formulating the Lyapunov function, a stochastic optimization maximizing the utilization of fronthaul resources while stabilizing the queue (and thus latency) of each vehicle is proposed to address the resource starvation. Subsequently, a reinforcement learning-based multiarmed bandit algorithm is further proposed to achieve optimum harmonization of feedback-based and feedbackless transmissions, so as to strikes the tradeoff among energy efficiency, latency, and reliability. The performance evaluation results full demonstrate the effectiveness of the proposed design, to serve urgent needs in the deployment of LADNs.
Fusing Location Data for Depression Prediction

Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While location information can be conveniently gathered by GPS, typical datasets suffer from significant periods of missing data due to various factors (e.g., phone power dynamics, limitations of GPS). A common approach is to remove the time periods with significant missing data before data analysis. In this paper, we develop an approach that fuses location data collected from two sources: GPS and WiFi association records, on smartphones, and evaluate its performance using a dataset collected from 79 college students. Our evaluation demonstrates that our data fusion approach leads to significantly more complete data. In addition, the features extracted from the more complete data present stronger correlation with self-report depression scores, and lead to depression prediction with much higher <inline-formula><tex-math notation="LaTeX">$F_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="yue-ieq1-2872569.gif"/></alternatives></inline-formula> scores (up to 0.76 compared to 0.5 before data fusion). We further investigate the scenario when including an additional data source, i.e., the data collected from a WiFi network infrastructure. Our results show that, while this additional data source leads to even more complete data, the resultant <inline-formula><tex-math notation="LaTeX">$F_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="yue-ieq2-2872569.gif"/></alternatives></inline-formula> scores are similar to those when only using the location data (i.e., GPS and WiFi association records) from the phones.
Efficient Scheduling and Collision Reduction in Hybrid Cognitive Radio Network using SBP

Nowadays, Cognitive Radio Networks have been considered, investigated and planned in an energetic manner. But maximum works are focused on separate areas like Allocation, Sensing and Sharing of Spectrum, etc. In this paper, a HCRN with the subsequent characteristics of priority based scheduling based on centralized switching delay aware network and distributed broadcasting protocol to decrease collision and enhance scheduling is proposed. For developing Hybrid Cognitive Radio Networks (HCRN) lot of space is still available. The proposed HCRN is simulated and network quality of service is estimated in terms of packet delivery ratio, control overhead, throughput and broadcast delay.
The middle/late Eocene transition in the Eastern Carpathians (Romania) based on foraminifera and calcareous nannofossil assemblages

Foraminiferal and calcareous nannofossil assemblages from the Eastern Carpathians (northern part of the Tarcău Nappe, Romania) were documented and correlated in order to reconstruct the palaeonvironmental settings and provide a biostratigraphic framework of the Plopu Formation. The benthic foraminiferal assemblages are dominated by flysch-type agglutinated taxa, suggesting a bathyal palaeodepth with frequent oscillations of the carbon compensation depth. The agglutinated foraminifera morphogroup analyses suggest different levels of organic matter influx and oxygenation. Both the foraminifera and calcareous nannofossil assemblages suggest a shift between the warm mid Eocene to the cooler late Eocene climate. Biostratigraphic data based on calcareous nannofossils (NP15–NP19/NNTe8–NNTe12 biozones) helped to establish the age of the formation. Four assemblages of benthic agglutinated foraminifera ( Psammosiphonella cylindrica – Nothia excelsa ; Paratrochamminoides spp. – Trochamminoides spp.; Karrerulina spp.– Reticulophragmium amplectens ; Spiroplectammina spectabilis ) correlated with calcareous nannofossil bioevents supported the placement of the mid to late Eocene transition within the Plopu Formation.
On blowing one's mind (19th-century style). A modest proposal.

ABSTRACT A few years ago I chanced upon a curious little book with the engaging, albeit long-winded, title of The Chemical Experimentalist; or, an attempt to allure by Experiment, and guide by Reasoning, the enquirer after Rational Amusement and Useful Knowledge, Towards the cultivation of the simple and sublime Science—Chemistry. This work is a remarkable hodge-podge of chemical experiments that one can perform at home, hints on cleaning silver and dying cloth, and even a section on the Philosophy of Nature wherein the author solemnly declares, inter alia, "that comets are male planets, and that their tails are their generative organs. " That a comet might possess a phallus and could thus presumably indulge in venery on a celestial scale is an intriguing enough notion, but to my mind the most entertaining item in the Chemical Experimentalist is the apparently serious suggestion that nitrous oxide could replace alcohol as a social stimulant.
Use of Task-Relevant Spoken Word Stimuli in an Auditory Brain-Computer Interface

Auditory brain-computer interfaces (aBCI) may be an effective solution for communication in cases of severely locked-in, late stage ALS (Lou Gehrig’s disease) and upper spinal cord injury patients who are otherwise not candidates for implanted electrodes. Feasibility of auditory BCI has been shown for both healthy participants, (Hill et al., 2004), and impaired populations (Sellers and Donchin, 2006). (Hill et al., 2014) found similar BCI performance in healthy participants and those with locked-in syndrome in a paradigm comparing words to pure tone stimuli. Additional BCI research has explored variations to augment P300 signals for use in speller paradigms, including more meaningful auditory stimuli (Klobassa et al., 2009; Furdea et al., 2009; Simon et al., 2014). It has been recognized in these studies that end users would much prefer natural sounds over a repeated tone stimulus. All of these systems required an association of sound with target stimuli, typically enforced by a visual support matrix. These systems would not be usable by the target end users of an auditory BCI. Attempts to remove the need for visual referencing by investigating a BCI system with serial presentation of spoken letter streams as stimuli (Hoehne and Tangermann, 2014) or spoken words (Ferracuti et al., 2013) has met with limited success but presents a potential high speed communication solutions. The present study highlights a method of using BCI task relevant spoken word stimuli to eliminate visually presented references. By utilizing spoken word stimuli, a BCI system could utilize a range of stimuli equivalent to the size of the users vocabulary and provide faster communication output than spelling systems. As a control, spoken word stimuli that have no task specific relevance are also tested. Stimuli audio-spatial cues have shown significant improve-
Crustal versus mantle sources of granitic magmas: a two-parameter model based on Nd isotopic studies

ABSTRACT Temporal and spatial variations in the Nd isotopic compositions of Tertiary caldera-forming rhyolite tuffs, and Cretaceous and Tertiary granites of the western U.S.A. are used as a basis for a model that accounts for the observed proportions of crustal versus mantle contributions to silicic magmas in terms of two parameters: the ambient crustal temperature and the rate of supply of basaltic magma from the mantle. The crustal contribution to silicic igneous rocks is measured in terms of the Neodymium Crustal Index (NCI). The relationships between crustal temperature, basalt supply and NCI are quantified using a model of a magma chamber undergoing continuous recharge, wall-rock assimilation and fractional crystallisation. From the model, a critical value of the ratio of basalt recharge-to-assimilation, (r/a)c, is deduced, which increases with decreasing crustal temperature. The r/a value must exceed (r/a)c to allow the volume of differentiated magma to increase, a prerequisite for developing large volumes of silicic magma. Strongly peraluminous (or S-type) magmas (NCI = 0·8–1), form under conditions of high crustal temperature and low basalt supply. Metaluminous or I-type granites form over a wide range of conditions (NCI = 0·1–1), generally where basalt supply is substantial. In individual long-lived volcanic centres, the large-volume high-silica ignimbrites are associated with the highest r/a and lowest NCI values, indicating that these magmas are typically differentiates of mantle-derived basaltic parents.
Bearing fault diagnosis based on improved ensemble learning and deep belief network

The collected bearing signals are easily interfered by strong ambient noise due to complex operating conditions. It’s a challenge to identify faults accurately and to reduce dependence on model hyper-parameters for intelligent diagnostic methods. This paper proposes an improved ensemble method based on deep belief network (DBN) for fault diagnosis of rolling bearings. Firstly, a series of DBNs with different hyper-parameters are constructed and trained. Secondly, the improved ensemble method is used to acquire the weights matrix for each DBN. Finally, each DBN votes together in accordance with its respective weight matrix to get the final diagnosis result. This method is applied to rolling bearing data from Case Western Reserve University. Experiments show that the effect of fault diagnosis is significantly improved because the feature learning ability of different DBNs is fully taken advantage of.
Satisfaction of Learning Management System Usage in a Blended Learning Environment among Undergraduate Students

Learning management system (LMS) helps higher learning institutions to manage their instructional resources and complement their traditional way of teaching. Although LMS survives via students’ and instructors’ use, its adoption is initiated by the students’ satisfaction. Thus, the objectives of this study are to examine the level of LMS satisfaction among university students in a blended learning program and to investigate the factors that may impact their satisfaction. A quantitative research approach via a questionnaire was used in the study. The subjects consisted of undergraduate students in a public university in Yemen. A total of 174 participants were surveyed using a questionnaire. Descriptive statistics and multiple linear regression were employed. The results show that the participants have a high level of satisfaction of LMS usage in a blended learning environment. Also the findings indicated that Instructor Attitude, Classmates Attitude, Course Quality, Management Support, and System Quality are significant predictors to students’ satisfaction of LMS. Therefore, this study has identified these significant factors in planning, designing and implementing a blended learning to promote and improve students’ learning satisfaction.
Analysis dictionary learning for classification

Data classification is the at the core of leading technologies today. With the explosion of data through mobility and growth of the Internet, analysis and classification of data is the immediate process after acquisition. Most of the information is hidden within the acquired data and needs to be extracted for further processing. Thus, feature extraction is an an important pre-processing task to classification. Till recently features were hand crafted which though accurate, were time consuming to generate and required human intervention. With the explosion of deep learning (since 2006), automatic feature generation has become the norm for most applications. Algorithms learn from the data and generate the required features adapted to various tasks, such as classification, reconstruction, denoising, sentiment analysis, data mining and the like. The most popular algorithms for automatic feature generation are Deep Belief Networks, Autoencoders and Convolutional Neural Networks(CNN) which also provide classification capabilities. All these algorithms and architectures derive motivation from the fact that human visual and audio cortex are compositional in nature and activate at various level of abstraction. Similarly, the aforementioned machines learn from raw data at multiple levels of abstraction with growing complexity. Such algorithms have proven to be very efficient and have also provided various benchmark tools and applications in the industry like Google Photos and Facebook’s deepFace. However, the major limitation to such tools are their enormous training times and humongous amount of data that is required to train them. In this thesis, a computationally simpler model for generating features through an Analysis Dictionary Learning approach is presented. In contrast with the synthesis dictionary learning approaches where the features are generated by solving an inverse problem via an iterative procedure, the analysis approach has the advantage of generating features from the data with minimal preprocessing by directly operating the data with the dictionary. The dictionary operates on the data and generates features, hence the framework is named as the analysis framework. Significant 1 improvement in test time feature generation is obtained as compared to other dictionary learning methods. Also, the learning procedure is computationally inexpensive and is flexible as any prior knowledge can easily be incorporated into the framework depending on the task the learnt features will be put to. To prove the versatility of the framework, the proposed approach is applied to various real world scenarios like digit recognition, speech recognition and Non Intrusive Load Monitoring (NILM). We have been able to establish state of the art results under varied conditions comparable to more complex deep learning techniques.
An Improved Bird Swarm Algorithm with Adaptive Characteristics

Abstract. Bird swarm algorithm (BSA) is a new heuristic intelligent algorithm, which has been successfully applied in many fields. In view of the shortcomings of bird swarm algorithm which is easy to fall into local optimum and premature convergence. I propose an improved bird swarm algorithm (IBSA). Firstly, the initial population is constructed by chaos optimization algorithm, so that the initial solution is uniformly distributed in the solution space, thus improving the diversity of the population. Secondly, by introducing inertial weights, nonlinear adjustment of cognitive and social coefficients can be trade-off bird local and global search ability. Finally, a disturbance strategy is added to the forging position of the birds. Thus, the diversity of population in the late iteration period is enhanced, and the ability to jump out of the local optimum is improved. Through the simulation experiments of several benchmark functions and compared with other intelligent algorithms, the results show that the improved bird swarm algorithm (IBSA) has better convergence speed and optimization precision, which proves its superiority.
Parallel Algorithms for Evaluating Centrality for Weighted Graphs

This paper discusses fast parallel algorithms for evaluating betweenness centrality in complex network analysis for weighted graphs. The previous studies on this topic mainly focused on unweighted graphs. Moreover, we will try to implement a shortest path algorithm which is the input of the parallel algorithm. These algorithms have been optimized to exploit properties typically observed in real-world large scale networks. The algorithm are implemented on real datasets such as the web graph, protein-interaction networks, movie-actor and citation networks, and report impressive parallel performance for evaluation of the computationally intensive centrality metrics on high-end shared memory symmetric multiprocessor and multithreaded architectures. For instance, we compute the exact betweenness centrality value for each vertex in a large US patent citation network (3 million patents, 16 million citations) in 42 minutes on 16 processors, utilizing 20GB RAM of the IBM p5 570. Current SNA packages on the other hand cannot handle graphs with more than hundred thousand edges.
A Method of Determining Needy Students Based on Deep Learning

For a long time, stipend has been send inaccurately due to China’s national conditions. Machine learning has been used to find impoverished students and achieved some results, but it was unsupervised and the accuracy was difficult to measure. In order to ensure the stipend being distributed fairly, a method based on deep learning is presented in this paper. During the experiment, accuracy rate was reached to 96%, through use the data came from school one-card and school library. The results show that this method can effectively improve the accuracy of grants, can save labor cost.
Design and Analysis of Rectangular Microstrip Patch Array Antenna for Fifth Generation of Mobile Technology

Introduction The development of the mobile phone device as a universal part of daily work and personal life presents the opportunity to examine how technology drivers are pushing for the integration of real life with mobile technology in future. In other words, mobile communication has become more popular in last few years due to fast revolution in mobile technology. Over the years, wireless telecommunications market has recognized as one of the most dynamic and fastest growing segments of the global telecommunications industry. In this regard, the world has witnessed four generations of mobile communication technology, with each new generation extending the capabilities and enhancing the end-user experience compared to the previous generation. From this point of view, global system of mobile communication has many developments starting from 1G passing by several modifications till the emerging of 4G. These versions
Face Recognition with Shape Features

Face recognition has become more significant in recent years. Facial landmarks, on the other hand, have been proven to be useful in face recognition. In this paper, we propose a method to identify frontal view faces using facial landmarks, and show that it is robust to 2D rotation, change of scale and change of position. We propose a face descriptor revealing the shape features, the Euclidean distance between landmarks and their mean. We evaluate the effectiveness of the representation by using it to perform face classification on our emotion dataset. We fit a multivariate normal distribution for each identity and construct a Naïve Bayes Classifier for classification. We show that our method performs well with a small classification error.
EXTRACTION OF GROUND SURFACE ALONG ROADWAY FROM MOBILE LIDAR DATA

Abstract. High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800 m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.