Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Power Adaption Methods on Fluctuating Two-Ray Fading Channels

In this letter, we consider a typical scenario where the transmitter employs different power adaption methods, including the optimal rate and power algorithm, optimal rate adaption, channel inversion and truncated channel inversion, to enhance the ergodic capacity (EC) with an average transmit power constraint over fluctuating two-way fading channels. In particular, we derive exact closed-form expressions for the EC under different power adaption methods, as well as corresponding asymptotic formulas for the EC valid in the high signal-to-noise ratio region. Finally, we compare the performance of the EC under different power adaption methods, and this also validates the accuracy of our derived expressions for the exact and asymptotic EC.
Traditional and Deep Learning Based Methods for Mammographic Image Analysis

Deep learning is becoming more and more mature in the field of machine learning, and therefore the application of deep learning in mammographic image analysis is considerably increasing. Deep learning is a multi-layer neural network based on big data, and the algorithms of deep learning can be divided into four categories: the model based on restricted Boltzmann machine (RBM), convolutional neural network (CNN), the model based on autoencoder (AE), and the model based on sparse coding (SC). In this paper, the development status and existing problems of traditional mammographic image analysis methods, covering abnormality detection, regions of interest (ROIs) segmentation, abnormality classification and computer-aided diagnosis (CAD), are first briefly reviewed. Then deep learning based methods for mammographic image segmentation, classification and CAD are reviewed and compared with the traditional methods in order to explore the advantages and disadvantages. Finally, considering the problems of deep learning in mammographic image analysis, and the limitations of the existing deep learning based methods, its development prospect is discussed. It is concluded that deep learning has great potential, and the related applications in computer-aided diagnosis will be more extensive.
Graph-Based Equivalence Concept Matching in Knowledge Organization System Integration: A Case Study on Thesaurus

Equivalence concept matching is one of major roadblocks towards successful Knowledge Organization System integration. This paper studies equivalence concept matching in the case of thesaurus which is a typical Knowledge Organization System. The related problems and the corresponding solutions are discussed on both effectiveness and efficiency: on the aspect of effectiveness, the reasons for matching errors and matching missing are analyzed, which includes the different preferred terms, term morphology, non-synonyms and homonymies; on the aspect of efficiency, the improvement measures are proposed for the generation of candidate concept matches and manual review. This solution can also be applied to other types of Knowledge Organization Systems. Empirical experiments have been conducted to evaluate the proposed solution on EI thesaurus and INSPEC thesaurus.
Survey on predicting property price

Prediction involves extracting the useful, understandable information from the raw data to forecast the unknown, whether the unknown is past, present or future. Thus, predictive analytics is a branch of data mining used to predict or forecast a trend or a behavior. Prediction is used in various fields like credit scoring, weather forecasting, healthcare, travel, financial sector and many more. This paper strives to explain the application of Predictive Analytics in the field of Real Estate. The paper aims at putting forward, the various predictive techniques used to forecast the property prices. The paper mentions different research work done in the real estate domain. The paper also describes in depth about the description of Neural Network and Decision Tree used to predict the sale price of the house.
Large Scale Huber Regression

Huber regression is a robust linear regression, which can learn appropriate model on the noisy data scenario. In the era of big data, the training data contains many normal data. In order to solve the Huber regression model in large-scale data, we transform the Huber regression problem into a global consensus problem and a sharing problem. Two different parallel solutions to the Huber regression problem are implemented based on Alternating Direction Method of Multipliers (ADMM) algorithm in Spark distributed computing framework. The experimental results show that the Huber regression can be solved quickly and maintain high precision by choosing appropriate parallelism.
A State-Space Dynamic Model for Photovoltaic Systems With Full Ancillary Services Support

Large-scale photovoltaic (PV) integration to the network necessitates accurate modeling of PV system dynamics under solar irradiance changes and disturbances in the power system. Most of the available PV dynamic models in the literature are scope-specific, neglecting some control functions and employing simplifications. In this paper, a complete dynamic model for two-stage PV systems is presented, given in entirely state-space form and explicit equations that takes into account all power circuit dynamics and modern control functions. This is a holistic approach that considers a full range of ancillary services required by modern grid codes, supports both balanced and unbalanced grid operation, and accounts for the discontinuous conduction mode of the dc/dc converter of the system. The proposed dynamic model is evaluated and compared to other approaches based on the literature, against scenarios of irradiance variation, voltage sags, and frequency distortion. Simulation results in MATLAB/Simulink indicate high accuracy at low computational cost and complexity.
High Power Medium Voltage PMSG based WECS using Three-Level Boost and Modular Multilevel Converters

This paper proposes a new topology for power conversion in high power medium voltage PMSG based wind energy conversion systems (WECS). The proposed topology consists of uncontrolled diode rectifier for converting the AC output of the PMSG to DC. This DC output level is stepped-up using a three level boost converter (TLBC). The proposed control system for the TLBC controls the DC-link overall voltage while also equalizing the two capacitor voltages of the DC-link. The boosted DC voltage from the three-level boost converter is inverted to AC output for grid integration using a modular multilevel converter (MMC) which also implements MPPT. The proposed high power medium voltage topology and control is validated by simulation for a 2.5MW/3400V PMSG based WECS.
Off‐grid DOA estimation for nested array using atomic norm minimisation

Nested array (NA) has received much attention for providing more degrees of freedom compared with the traditional uniform linear array. An off-grid compressed sensing (CS) method is proposed using atomic norm minimisation for NA to identify the unknown direction-of-arrival of multiple sources, which overcomes the drawback of grid-mismatch effect of the conventional CS algorithms such as orthogonal matching pursuit, and achieves better performance than the subspace methods. Simulation results verify the effectiveness of the proposed method.
Numerical Assessment of Machine Translation Quality by Method of Near Duplicates Analysis

The paper considers modern methods of near duplicates of textanalysisapplied for plagiarism detection, in order to use them in system of machine translation correction. The general concept of the translating machine with automatic correction of inadequacies revealed when comparing the original text and reverse machine translationis presented. Prospects of promotingof the given method are analyzed.
Strontium Peroxide-Loaded Composite Scaffolds Capable of Generating Oxygen and Modulating Behaviors of Osteoblasts and Osteoclasts

The reconstruction of bone defects remains challenging. The utilization of bone autografts, although quite promising, is limited by several drawbacks, especially substantial donor site complications. Recently, strontium (Sr), a bioactive trace element with excellent osteoinductive, osteoconductive, and pro-angiogenic properties, has emerged as a potential therapeutic agent for bone repair. Herein, a strontium peroxide (SrO2)-loaded poly(lactic-co-glycolic acid) (PLGA)-gelatin scaffold system was developed as an implantable bone substitute. Gelatin sponges serve as porous osteoconductive scaffolds, while PLGA not only reinforces the mechanical strength of the gelatin but also controls the rate of water infiltration. The encapsulated SrO2 can release Sr2+ in a sustained manner upon exposure to water, thus effectively stimulating the proliferation of osteoblasts and suppressing the formation of osteoclasts. Moreover, SrO2 can generate hydrogen peroxide and subsequent oxygen molecules to increase local oxygen tension, an essential niche factor for osteogenesis. Collectively, the developed SrO2-loaded composite scaffold shows promise as a multifunctional bioactive bone graft for bone tissue engineering.
A Gamma-Poisson Block Model for Community Detection in Directed Network

Detecting communities in networks is to find subgroups of nodes with similar characteristics, which is commonly defined as finding groups of nodes with dense connection in undirected networks. However, communities in directed networks can represent connectivity patterns because of asymmetric relations' which is difficult to capture using traditional algorithms. In this paper, a Gamma-Poisson block model is proposed for community detection in directed networks, which can model not only assortative communities but also communities with various connectivity patterns due to a block matrix. The model can also be extended to undirected networks if we set the block matrix symmetric, and for assortative community detection task if we set the block matrix diagonal. We develop an efficient Gibbs sampling algorithm for the inference work, which can scale to large sparse networks since links other than node pairs are considered during each iteration. We compare our model with several previous ones on a variety of real-world networks and the results demonstrate the advantages in our model.
An Attribute Reduction Algorithm Based on the Maximum Dependency and Minimum Redundancy of Attribute

The classical attribute reduction algorithms based on attribute dependence in rough set theory only select attributes which have a larger degree of dependence on decision attribute and don't consider attribute redundancy. This paper points out that only selecting condition attributes with a large degree of dependence on decision attribute is not enough, the redundancy between condition attributes should also be taken into account. In allusion to this matter, an algorithm based on the maximum dependency and minimum redundancy of attribute is presented. The results of experiments which are carried out on the UCI data sets suggest that the presented algorithm has gained favorable results.
Outage Constrained Robust Multigroup Multicast Beamforming for Multi-Beam Satellite Communication Systems

We investigate outage constrained robust multigroup multicast beamforming for multi-beam satellite communication systems with full frequency reuse. Based on a satellite downlink beam domain channel model with channel phase uncertainty taken into account, we first investigate robust multigroup multicast beamforming with the aim to maximize the worst-case outage signal-to-interference-plus-noise ratio under the outage and the per-beam power constraints. We then cast the outage constrained robust beamforming design into the convex optimization framework with some approximation techniques. Simulation results show that the proposed robust multigroup multicast beamformer can provide significant performance gains in terms of multicast rate and outage probability over the conventional approach.
Precise Crop Classification of Hyperspectral Images Using Multi-Branch Feature Fusion and Dilation-Based MLP

The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Convolutional Neural Networks (CNNs) are the premier model for hyperspectral image (HSI) classification for their outstanding locally contextual modeling capability, which facilitates spatial and spectral feature extraction. Nevertheless, the existing CNNs have a fixed shape and are limited to observing restricted receptive fields, constituting a simulation difficulty for modeling long-range dependencies. To tackle this challenge, this paper proposed two novel classification frameworks which are both built from multilayer perceptrons (MLPs). Firstly, we put forward a dilation-based MLP (DMLP) model, in which the dilated convolutional layer replaced the ordinary convolution of MLP, enlarging the receptive field without losing resolution and keeping the relative spatial position of pixels unchanged. Secondly, the paper proposes multi-branch residual blocks and DMLP concerning performance feature fusion after principal component analysis (PCA), called DMLPFFN, which makes full use of the multi-level feature information of the HSI. The proposed approaches are carried out on two widely used hyperspectral datasets: Salinas and KSC; and two practical crop hyperspectral datasets: WHU-Hi-LongKou and WHU-Hi-HanChuan. Experimental results show that the proposed methods outshine several state-of-the-art methods, outperforming CNN by 6.81%, 12.45%, 4.38% and 8.84%, and outperforming ResNet by 4.48%, 7.74%, 3.53% and 6.39% on the Salinas, KSC, WHU-Hi-LongKou and WHU-Hi-HanChuan datasets, respectively. As a result of this study, it was confirmed that the proposed methods offer remarkable performances for hyperspectral precise crop classification.
A Rapidly Convergent Projected Hestenes-Stiefel Conjugate Gradient Algorithm for Optimal Robust Controller of Bipedal Walking Robots

A rapidly convergent projected Hestenes-Stiefel conjugate gradient method is presented to help design an novel optimal robust controller of bipedal walking robots. The algorithm converges to a stable periodic gait based on an initial gait. In order to demonstrate the feasibility and effectiveness of the algorithm, we will conduct numerical simulations on the model of 5-link bipedal walking robot. Moreover, computer simulation results further demonstrate the superior performance of the projected Hestenes-Stiefel conjugate gradient algorithm compared with the conventional algorithm. Therefore, it is reasonable to infer that the projected Hestenes-Stiefel conjugate gradient approach can be used in real-time systems.
Sensitivity, advantages, limitations, and clinical utility of targeted next-generation sequencing panels for the diagnosis of selected lysosomal storage disorders

Abstract Lysosomal storage disorders (LSDs) constitute a heterogeneous group of approximately 50 genetic disorders. LSDs diagnosis is challenging due to variability in phenotype penetrance, similar clinical manifestations, and a high allelic heterogeneity. A powerful tool for the diagnosis of the disease could reduce the “diagnostic odyssey” for affected families, leading to an appropriate genetic counseling and a better outcome for current therapies, since enzyme replacement therapies have been approved in Brazil for Gaucher, Fabry, and Pompe diseases, and are under development for Niemann-Pick Type B. However, application of next-generation sequencing (NGS) technology in the clinical diagnostic setting requires a previous validation phase. Here, we assessed the application of this technology as a fast, accurate, and cost-effective method to determine genetic diagnosis in selected LSDs. We have designed two panels for testing simultaneously 11 genes known to harbor casual mutations of LSDs. A cohort of 58 patients was used to validate those two panels, and the clinical utility of these gene panels was tested in four novel cases. We report the assessment of a NGS approach as a new tool in the diagnosis of LSDs in our service.
Hotel Classification Based on Online Review Data

Online review is the real evaluation of the purchased on the internet, and play a leading role in the purchase of future customers. At present, the analysis of online reviews based on emotion. The methods of analysis have word-level, sentence-level, document-level. The using of the most widely is word2vec, sen2vec, text2vec, doc2vec based on neural network, deep learning. In the traditional method, that effect of context contact and semantic understanding is not good, and the way of the neural network and deep learning is relatively high in hardware, and the time is relatively long. This article mainly uses the traditional methods and the combination of the deep learning method, first to deal with comments according to the word, word frequency, part of speech analysis to find the candidate key, using the index and cluster analysis to merge candidate key, find out the keywords. According to the feature words and index, calculate the feature weights, obtain the value of weight at the standard weight and the Norm weight. According to compare the method of weight, we think the standard weight is relevant short text. We are using feature word labeled and classifying. This paper starts with the review (documents), analysis word, from word to sentences, to review (text), and to the hotel, finally, aim to the hotel classification. This paper will lay a foundation for the intelligent recommendation of hotels.
A Dual-Wideband Dual-Polarized Magneto-Electric Dipole Antenna With Dual Wide Beamwidths for 5G MIMO Microcell Applications

A dual polarized magneto-electric dipole antenna with dual wide beamwidths is proposed and investigated. Dual-layer U-shaped electric dipoles are cooperated with orthogonal <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>-shaped feeding line to obtain dual polarized and dual wideband features for 5G microcell applications. In addition, modified tetrahedral ground together with dual-layer three-element dipoles that are arranged as a triangle array is used to provide dual wide H-plane radiation characteristics. By doing so, a large coverage area and a reduced number of antennas can be achieved at the same time. In order to satisfy the stringent needs of high capacity and low mutual coupling, a three-dimensional (3D) MIMO system that consists of three proposed antenna elements is also designed. The antenna prototypes were fabricated and measured. Measured results show that an overlapped impedance bandwidth of 22.6% (VSWR < 2) with the stable gain of 6.9 ± 0.3 dBi were measured across the lower operating band. As for its corresponding upper operating band, it has exhibited overlapped impedance bandwidth of 19.6% (VSWR < 2) with the gain of 5.4 ± 0.7 dBi. The half-power beamwidths in the H-plane for both ports varies from 83° to 162°, and from 90° to 133°, respectively. Moreover, a low ECC of <inline-formula> <tex-math notation="LaTeX">$2\times 10^{-4}$ </tex-math></inline-formula> and the nearly identical MEG can be achieved by the MIMO system due to the 3D design. Hence, the proposed antenna is a desirable candidate for the future 5G microcell applications.
Search for Calling, Presence for Calling dan Makna Kerja Pada Karyawan Kehutanan

This study aims to determine the role of the two dimensions of calling, namely search for calling and presence of calling on the work meaning, and the difference in the level of meaningful work in the four categories of calling, namely calling diffusion, calling foreclosure, calling moratorium and calling achievement on Forestry employees. With purposive sampling, we obtained 130 employees who had worked for at least one year. The questionnaires used were the Calling and Vocation Questionnaire and the Comprehensive Meaningful Work Scale that had high-reliability scores. Using stepwise linear regression technique and ANOVA, we found that only the presence of calling has a significant role in the work meaning, and calling diffusion has the lowest level of work meaning compared to other categoriesPenelitian ini bertujuan untuk mengetahui peran dari kedua dimensi panggilan yaitu search for calling dan presence of calling terhadap makna kerja, sekaligus mengetahui perbedaan level makna kerja pada keempat kategori panggilan yaitu calling diffusion, calling foreclosure, calling moratorium dan calling achievement pada karyawan BUMN Kehutanan. Penarikan sampel penelitian menggunakan purposive sampling dan diperoleh partisipan sebanyak 130 karyawan, yang telah bekerja minimal 1 tahun. Kuesioner yang digunakan adalah Calling and Vocation Questionnaire dan Comprehensive Meaningful Work Scale yang memiliki nilai reliabilitas yang tinggi. Data dianalisis dengan regresi linier teknik stepwise untuk mengetahui peran terbesar dari kedua dimensi panggilan, dan ANOVA untuk mengetahui perbedaan level makna kerja pada keempat kategori panggilan. Hasil penelitian menunjukkan bahwa hanya presence of calling yang memiliki peran signifikan terhadap makna kerja, dan kategori calling diffusion memiliki level makna kerja yang paling rendah dibandingkan kategori lainnya.
Image-Sequence-Based Mobile Robot Localization

Considering the structural similarity of environments, an image-sequence-based mobile robot localization algorithm is proposed, which utilizes image sequences to represent different places in environments. To achieve localization of the mobile robot, the mobile robot is allowed to explore the environments in advance. The RGB image data stream acquired by the mobile robot is partitioned into different image sequences according to the moving distance of the mobile robot. Then, each of image sequences is sent into an LSTM (Long Short-Term Memory)network to learn the characteristics of each image sequence. The final cell states of the LSTM will be treated as the features of the image sequence. When the mobile robot travels in the environments once again, the newly acquired image sequence will be sent into this well-trained LSTM network to extract the features of the image sequence. By matching features among different image sequences, the mobile robot can recognize which place it may locate in. Finally, experimental comparisons with single-frame-based localization algorithms are conducted to verify the performance of the proposed algorithm from the localization accuracy.
Gut microbiota differs between treatment outcomes early after fecal microbiota transplantation against recurrent Clostridioides difficile infection

Abstarct In fecal microbiota transplantation (FMT) against recurrent Clostridioides difficile infection (CDI), clinical outcomes are usually determined after 8 weeks. We hypothesized that the intestinal microbiota changes earlier than this timepoint, and analyzed fecal samples obtained 1 week after treatment from 64 patients diagnosed with recurrent CDI and included in a randomized clinical trial, where the infection was treated with either vancomycin-preceded FMT (N = 24), vancomycin (N = 16) or fidaxomicin (N = 24). In comparison with non-responders, patients with sustained resolution after FMT had increased microbial alpha diversity, enrichment of Ruminococcaceae and Lachnospiraceae, depletion of Enterobacteriaceae, more pronounced donor microbiota engraftment, and resolution of gut microbiota dysbiosis. We found that a constructed index, based on markers for the identified genera Escherichia and Blautia, successfully predicted clinical outcomes at Week 8, which exemplifies a way to utilize clinically feasible methods to predict treatment failure. Microbiota changes were restricted to patients who received FMT rather than antibiotic monotherapy, indicating that FMT confers treatment response in a different way than antibiotics. We suggest that early identification of microbial community structures after FMT is of clinical value to predict response to the treatment.