Miracle-dz/UniFAR-FTUs huggingface dataset:
Directional Analysis of Massive MIMO Channels at 11 GHz in Theater Environment

Massive multiple-input multiple-output (MIMO) is one of the key technologies of the fifth generation mobile communication system (5G). For development of massive MIMO systems, the directional properties of their wireless channel are of great importance. In this paper, directional analysis is presented based on a channel measurement campaign for massive MIMO communications in a theater environment. The measurements were conducted at 11 GHz with a bandwidth of 200 MHz. A 256-element virtual uniform rectangular array (URA) was used during the measurements, and the channel characteristics in angular domain are determined by applying the space-alternating generalized expectation-maximization algorithm (SAGE). According to the SAGE estimates, channel nonstationarity over the large-scale array is discussed. The dominant scatterers are identified, by directly relating the SAGE estimates to the physical objects in the measurement environment. Moreover, direction spread over the large-scale array at the Tx side is estimated, and channel performance on beamforming is evaluated. Corresponding analysis is given in detail. These results are useful for the design and application of the practical massive MIMO system in the future.
Influence of the interaction between different low‐ and mid‐level wind couplings and orography on the evolution of mesoscale convective systems in northwest China: A case study

A mesoscale convective systems (MCSs) event occurring in northwest China is investigated to discuss the influences of interaction between different couplings of low‐ and mid‐level winds and orography on the evolution of MCSs, using high‐resolution reanalysis data. The formation of a low‐level anticyclonic circulation related to the plateau high caused the sequential occurrence of reverse and perpendicular low‐ and mid‐level winds couplings and the stagnation and expansion of a high‐humidity zone originating from the mountains. In the case of reverse coupling of low‐ and mid‐level winds, mid‐level winds were constantly lifted by low‐level winds ascending the mountains, resulting in strong updrafts and relevant inertia–gravity waves occurring continuously in the upstream direction of mid‐level winds. The unstable stratification generated by the high‐humidity zone prevented the inertia–gravity waves from propagating into the updrafts, leading to the maintenance and connection of the updrafts. Meanwhile, a strong downdraft occurred and was maintained on the leeward side of the updrafts due to the coupling of the inertia–gravity waves associated with the uplift of mid‐level winds with the inertia–gravity wave triggered by low‐level winds ascending the mountains. Consequently, MCSs developed as they propagated into the updrafts, and perished abruptly when they reached the leeward‐side downdraft. In the case of perpendicular coupling of low‐ and mid‐level winds, a broad and strong low‐level updraft band formed and stayed on the windward side of the mountain ridge due to terrain‐induced uplift. Significant mid‐level updraft and an associated inertia–gravity wave were triggered on the side of the low‐level updraft band blown by mid‐level winds. The unstable stratification generated by the high‐humidity zone hindered the propagation of the gravity wave into the low‐level updraft band, leading to the maintenance of strength and continuity of the low‐level updraft band. Consequently, MCSs were maintained during their entire propagation along the low‐level updraft band.
Dietary Patterns Derived from Reduced Rank Regression Are Associated with the 5-Year Occurrence of Metabolic Syndrome: Aichi Workers’ Cohort Study

The aim of the present study was to derive dietary patterns to explain variation in a set of nutrient intakes or in the measurements of waist circumference (WC) and fasting blood glucose (FBG) using reduced rank regression (RRR) and to prospectively investigate these patterns in relation to the risk of developing metabolic syndrome (MetS) and its components during the follow-up. The study participants were comprised of 2944 government employees aged 30–59 years without MetS. RRR was applied with 38 food groups as predictors and with two sets of response variables. The first set included intake of putatively beneficial nutrients, and the first factor retained was named the Healthy Dietary Pattern (HDP). The second one included baseline WC and FBG, and the first factor was named the Unhealthy Dietary Pattern (UHDP). Multivariable Cox proportional hazard model was used to estimate hazard ratio and 95% confidence intervals with adjustments for age, sex, total energy consumption and other potential confounders. During the 5-year median follow-up, we ascertained 374 cases of MetS. The HDP score was inversely associated with the incidence of MetS (p-trend = 0.009) and hypertension (p-trend = 0.002) and marginally significantly associated with elevated triglyceride and decreased high-density lipoprotein cholesterol (p-trend = 0.08). The UHDP score was linearly positively associated with the incidence of MetS and all its components (all p-trend < 0.05). Both the HDP and UHDP predicted the development of MetS and its components.
User-Centric Power Saving in Self-Organizing Mobile Networks

Traffic growth in mobile networks is posing challenges not only from the point of view of the capacity demands, but also from the power consumption. At the same time, mobile networks are becoming increasingly complex, with heterogeneous tiers of Base Stations (BSs) and different technologies. Autonomous and self-adapting power saving schemes that take into account a large number of network metrics have been proposed to address these aspects. However, the performances of the applications running in the user terminals can be negatively affected by schemes that aim at switching off BSs to reduce the power consumed. In this paper, a power-saving algorithm that switches off BSs is proposed. The algorithm is evaluated taking the point of view of the users in the network, by means of the number of HARQ retransmissions experienced, as this directly affects the communication channel quality perceived by the application layer. To perform this evaluation, a detailed network and protocol model has been developed. Simulation results show gains for the power consumed, while also reducing retransmissions or at most ensuring only a minor increase ($\tilde{5}\%$).
Avaliação da autoestima de mulheres submetidas à cirurgia oncológica mamária

This cross-sectional observational study with a quantitative approach was developed with women who underwent breast cancer surgery in the period from 2007 to 2009. The aim was to characterize women who underwent such surgery and to analyze their self-esteem. A questionnaire was used to evaluate the self-esteem with the collection of sociodemographic and clinical data and the Rosenberg Scale. The majority of the women were aged between 41 and 50 years, were married, catholic, had low levels of schooling and received on average three times the minimum wage. Regarding the clinical aspects, the majority underwent conservative surgery performed on the left side and received complementary treatments, mainly endocrine therapy. Concerning self-esteem, 54.10% of the women had high self-esteem; Cronbach's alpha of 0.77. The women who had high self-esteem were married, had returned to work, underwent breast reconstruction and had received no complementary treatment. DESCRIPTORS: Self concept. Women's health. Nursing. Breast neoplasms.
Mining the Usage of Summary Oriented Features in Abstractive Summarization

In this paper, we propose a textual feature representation method used in seq2seq model to generate an abstractive summary for a Chinese natural language short text. Word embedding is the main feature applied in current seq2seq model while generating abstractive summary, which describes the relation of different words from the perspective of semantics. However, word embedding fails to capture some specific word information of topic, position, POS, and etc. These features are very important for summary generation. Therefore, based on using word embedding as main word characteristic representation, we propose to add extra summary oriented features to construct a more powerful denotation. We have studied 6 summary oriented word features, i.e. position, POS, NER, frequency, topic and hierarchical feature of hLDA. Experiments on the corpus of LCSTS have proven the validity of newly introduced summary oriented features in abstractive summarization.
Contrast Pattern-Based Classification for Bot Detection on Twitter

Detecting non-human activity in social networks has become an area of great interest for both industry and academia. In this context, obtaining a high detection accuracy is not the only desired quality; experts in the application domain would also like having an understandable model, with which one may explain a decision. An explanatory decision model may help experts to consider, for example, taking legal action against an account that has displayed offensive behavior, or forewarning an account holder about suspicious activity. In this paper, we shall use a pattern-based classification mechanism to social bot detection, specifically for Twitter. Furthermore, we shall introduce a new feature model for social bot detection, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis. From our experimental results, we shall see that our mechanism outperforms other, state-of-the-art classifiers, not based on patterns; and that our feature model yields better classification results than others reported on in the literature.
Deformable MRI-TRUS surface registration from statistical deformation models of the prostate

Transrectal ultrasound (TRUS) is considered the standard of care for imaging the prostate during biopsy and brachytherapy procedures. However, interpretation of TRUS images is challenging due to high specularity, making it difficult to recognize prostate boundaries. Image-guided brachytherapy and fusion-guided prostate biopsies require accurate non-rigid registration of magnetic resonance pre-operative image to intra-operative TRUS. State of the art techniques suggest semi-automated segmentation of the prostate on the TRUS images. However, due to the high variability, segmentation of the prostate is challenging. Segmentation errors could lead into poor localization of the biopsy target and can impact the registration of pre-operative images. In general, this kind of registration is challenging since the prostate anatomy undergoes motion due to TRUS probe pressure. In this paper, we propose a non-rigid surface registration approach for MR-TRUS fusion based on a statistical deformation model. Our method builds a statistical deformation model (SDM) of pre-operative to intra-operative deformations on a prostate dataset. In order to compute the fusion for an unseen MR-TRUS pair, the trained SDM is incorporated into the registration process to increase the fusion accuracy. The proposed approach is evaluated on a dataset of 23 patients with prostate cancer, for which the MRI-TRUS scans were available. We compared the proposed non-rigid SDM registration to non-rigid Iterative closest point (NICP) and rigid ICP approaches. Experiments demonstrate that the proposed SDM based method outperforms both NICP and ICP approaches, yielding a mean squared distance of 0.52 ± 0.26mm at the base, 0.45 ± 0.17mm mid-gland and 0.59 ± 0.13mm at the apex. These results show the advantage of integrating prior knowledge of deformation fields due to probe pressure for MR-TRUS fusion prostate interventions.
Front delineation and tracking with multiple underwater vehicles

This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short‐range Iver AUVs, Tethys‐class long‐range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.
Application of Fuzzy Equivalence Relation Kernel Clustering Algorithm to Car Evaluation

In this paper, the fuzzy equivalent relation kernel is constructed and represented via fuzzy information, and applied to car evaluation based on the kernel clustering algorithm. First, the fuzzy equivalent relation kernel clustering algorithm is introduced. Second, the fuzzy information in car evaluation problem is used to determine the membership functions to represent the fuzzy equivalent relation kernel. Finally, the clustering algorithm based on the fuzzy equivalent relation kernel is applied to the car evaluation. Numerical results indicate the validation of the proposed algorithm.
The Stability Analysis of T-S Fuzzy System with Constant Time-delay

To study the problems of stability analysis of non-linear system with time-delay, this paper focuses on the stability analysis of T-S fuzzy system with constant time-delay. By constructing a new type of Lyapunov-Krasovskii functional and employing integral inequality method based on Legendre orthogonal polynomials, a new stability criterion with less conservatism has proposed. Numerical example is given to illustrate the effectiveness of the proposed method.
Weighted Score Fusion Based LSTM Model for High-Speed Railway Propagation Scenario Identification

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assistance of railway long-term evolution (LTE) networks, we collected the channel impulse responses in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Ricean K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyper-parameters of the proposed model such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of feature selection, comparison of different feature fusion methods, and computational complexity. The evaluation results show that the proposed model has high identification accuracy but acceptable computational complexity.
A microRNA Signature for the Diagnosis of Statins Intolerance

Atherosclerotic cardiovascular diseases (ASCVD) are the leading cause of morbidity and mortality in Western societies. Statins are the first-choice therapy for dislipidemias and are considered the cornerstone of ASCVD. Statin-associated muscle symptoms are the main reason for dropout of this treatment. There is an urgent need to identify new biomarkers with discriminative precision for diagnosing intolerance to statins (SI) in patients. MicroRNAs (miRNAs) have emerged as evolutionarily conserved molecules that serve as reliable biomarkers and regulators of multiple cellular events in cardiovascular diseases. In the current study, we evaluated plasma miRNAs as potential biomarkers to discriminate between the SI vs. non-statin intolerant (NSI) population. It is a multicenter, prospective, case-control study. A total of 179 differentially expressed circulating miRNAs were screened in two cardiovascular risk patient cohorts (high and very high risk): (i) NSI (n = 10); (ii) SI (n = 10). Ten miRNAs were identified as being overexpressed in plasma and validated in the plasma of NSI (n = 45) and SI (n = 39). Let-7c-5p, let-7d-5p, let-7f-5p, miR-376a-3p and miR-376c-3p were overexpressed in the plasma of SI patients. The receiver operating characteristic curve analysis supported the discriminative potential of the diagnosis. We propose a three-miRNA predictive fingerprint (let-7f, miR-376a-3p and miR-376c-3p) and several clinical variables (non-HDLc and years of dyslipidemia) for SI discrimination; this model achieves sensitivity, specificity and area under the receiver operating characteristic curve (AUC) of 83.67%, 88.57 and 89.10, respectively. In clinical practice, this set of miRNAs combined with clinical variables may discriminate between SI vs. NSI subjects. This multiparametric model may arise as a potential diagnostic biomarker with clinical value.
WiFi Fingerprint Based Indoor Localization with Iterative Weighted KNN for WiFi AP Missing

In this paper, a real WiFi fingerprint-based indoor localization system is considered, where three primary components including the APP in smart phone, the server system and the embedded localization algorithm, have been designed. This paper proposes a dedicated data preprocessing algorithm to solve the singular-collection problem. Furthermore, the issue of WiFi access point (AP) missing is discussed and the theoretical analysis is presented under the condition of a two-AP scenario. Finally, because of unequal amount of location information contained in received signal strength (RSS) from different AP, the weighted RSS (WRSS) and the iterative weighted K nearest neighbor (IWKNN) algorithm are proposed for localization. Experimental results shows the proposed scheme achieves a competitive localization accuracy.
Effectiveness of Digital Simulation on Student Nurses’ Knowledge and Confidence:An Integrative Literature Review

Abstract Digital simulation is an emerging innovation with the potential to address the limitations to traditional clinical simulation. The pandemic has accelerated teaching on digital platforms, which is now embedded in pedagogic practice. This integrative review will evaluate the effectiveness of digital simulation on nursing students’ knowledge and/or confidence. The electronic database search carried out between October 3 and 7, 2021. We included primary research articles published in English language between January 2012 and October 2021. From 14,369 citations, 10 full-text documents were included in our final analysis and synthesis. Results suggest that digital simulation with realistic, immersive and interactive characters had a positive impact on students’ learning outcome in terms of knowledge acquisition and self-confidence. Therefore, combining digital simulation with face-to-face simulation will enhance learning.
Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks

Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.
Utilizing Consumer Health Posts to Identify Underlying Factors Associated with Patients' Attitudes towards Antidepressants

BACKGROUND: Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in a significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. Patients’ drug review posts in online healthcare communities may provide a significant insight into patients’ attitudes towards antidepressants and address the challenges of self-report methods such as patients’ recruitment. OBJECTIVE: The primary objective of this study is to use consumer health posts to identify factors affecting the patient’s attitude towards the drugs, which in turn, is a strong determinant of treatment non-adherence. METHODS: We randomly selected 892 patients’ drug reviews from a healthcare forum “askapatient.com”. We used the Framework Method to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. In the next step, four annotators coded the drug reviews at the sentence level using the analytical framework. To test the hypotheses, we first managed missing values using different imputation methods. Then, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude. RESULTS: Analysis of the sample showed that the drug reviews were posted from February 2001 to February 2016. The majority of the patients (76%; n= 680) were female. Approximately two-thirds of the patients (n=540) were less than 40 years of age. Testing the association between variables and attitude showed that “experience of adverse drug reactions (ADRs)” ( x=¿ 31.11, P= 2.91e-06), “perceived distress from ADRs” ( x=¿ 231.6, P= 2.2e-16), “drug effectiveness” ( x=¿ 548.52, P= 2.2e-16), “complaint about the lack of knowledge” ( x=¿ 59.36, P= 3.96e-12), “experience of withdrawal” ( x=¿ 55.63, P= 2.4e-11), and “duration of usage” (F-value= 43.66, P = 6.76e-11) were significantly associated with patients’ attitudes towards antidepressants. While variables “age” (F-value= 0.72, P = 0.4) and “gender” ( x=¿ 2.7, P= 0.21) were not associated with the patients’ attitudes. Moreover, modeling the relationship between variables and attitudes showed that “drug effectiveness” and “perceived distress from adverse drug reactions” were the two most significant factors affecting patients’ attitude toward antidepressants. CONCLUSIONS: Patients’ self-report experiences of medications in online healthcare communities can provide a unique insight for identifying underlying factors associated with patients’ perceptions and attitudes towards antidepressants. However, it cannot be used as a replacement for self-report methods due to the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.