Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Modeling of RNAseq and Radiomics Data for Glioma Molecular Characterization and Prediction

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. In this study, we propose a novel radiogenomics-NB model for glioma grading and prediction. Our radiogenomics-NB model is developed based on differentially expressed RNAseq and selected radiomics/volumetric features which characterize tumor volume and sub-regions. The NB distribution is fitted to RNAseq counts data, and a log-linear regression model is assumed to link between the estimated NB mean and radiomics. Three radiogenomics-NB molecular mutation models (e.g., IDH mutation, 1p/19q codeletion, and ATRX mutation) are investigated. Additionally, we explore gender-specific effects on the radiogenomics-NB models. Finally, we compare the performance of the proposed three mutation prediction radiogenomics-NB models with different well-known methods in the literature: Negative Binomial Linear Discriminant Analysis (NBLDA), differentially expressed RNAseq with Random Forest (RF-genomics), radiomics and differentially expressed RNAseq with Random Forest (RF-radiogenomics), and Voom-based count transformation combined with the nearest shrinkage classifier (VoomNSC). Our analysis shows that the proposed radiogenomics-NB model significantly outperforms (ANOVA test, p < 0.05) for prediction of IDH and ATRX mutations and offers similar performance for prediction of 1p/19q codeletion, when compared to the competing models in the literature, respectively.
Toward a Nuanced and Contextualized Understanding of Undocumented College Students: Lessons from a California Survey

ABSTRACT Prior research has established that undocumented immigrant experiences are dynamic, reflecting the complex web of immigration-related policies that create legal vulnerability. As such, undocumented college students’ experiences must be situated in their current policy context. Drawing on descriptive analyses of a survey of 1,277 undocumented 4-year college students in California, we examine how undocumented students are faring in a relatively inclusive policy context. Results demonstrate the heterogeneity of undocumented student experiences and unpack the challenges they confront while also demonstrating the ways they thrive. We document how respondents are performing across a variety of academic, well-being, and civic and political engagement outcomes. We also show that undocumented students’ perceptions of legal vulnerability are complex and varied, taking into account family-level legal vulnerability and individual protections. Further, students perceive campuses as fairly welcoming spaces, with some differences arising across the two university systems. Ultimately, we argue that undocumented college students’ experiences merit more nuanced and contextualized analysis.
Local structure in lithium chloride solution: a Monte-Carlo simulation study

ABSTRACT Monte-Carlo simulations and the nearest neighbour approach have been employed in the present study to investigate the structural properties of aqueous solutions of LiCl salt at ambient conditions. The calculations are performed at increasing concentration of LiCl starting from c = 0.1 m up to 10 m. The analysis shows that increasing salt concentration results in a reduction of the tetrahedral arrangement of water in favour of a non-tetrahedral arrangement characterised by an O–O–O angle between the reference oxygen and two of its four closest oxygen neighbours at 60°. Moreover, the LiCl concentration has a considerable effect on the hydrogen-bonding structure of water. By correctly choosing the nearest neighbours, it is found that the deviation in water structure is due to the presence of chloride ion, not lithium ion, in the first solvation shell of water. It is also demonstrated that in concentrated LiCl solution Cl− ions can substitute the fourth water neighbour which leads to a tetrahedral network formed by water molecules and Cl− ions around a central water molecule. The analysis of the effect of concentration increase on the arrangement of the four closest water molecules neighbours from a reference lithium cation allows us to elucidate a structural maker role of Lithium ion.
Analytical Solution of Heat Conduction from Turbulence with an Isotropic Example

Aerodynamic heating due to turbulence significantly affects the operation of high-speed vehicles and the entrainment of fluid by turbulent plumes. In this paper, the heat generated and convected by fluid turbulence is examined by rearranging the Navier-Stokes equations into a single equation for the fluctuating dependent variables external to unsteady chaotic motions. This equation is similar to the nonhomogeneous heat equation where sources are terms resulting from this rearrangement. Mean and fluctuating quantities are introduced, and under certain circumstances, a heat equation for the fluctuating density results with corresponding mean and fluctuating source terms. The resultant equation is similar to Lighthill’s acoustic analogy and is a “heat analogy. ” A solution is obtained with the use of Green’s function as long as the observer is located outside the region of chaotic motion. Predictions for the power spectrum are shown for high Reynolds number isotropic turbulence. The power spectrum decays as the inverse of the wavenumber of the turbulent velocity fluctuations. The developed theory can easily be applied to other turbulent flows if the statistics of unsteady motion can be estimated.
Enzymatic Extraction and Characterization of Lipid Fraction Highly Rich in Omega-3 Fatty Acids from Mackerel (Scomber scombrus)

Fresh mackerel heads were submitted to enzymatic hydrolysis using Alcalase 2.4L (60°C, 2h). The centrifugation of the slurry displayed three lipid fractions. Chemical characteristics of these fractions showed significant differences for protein, lipid and ash contents. The rate of oil released after enzymatic extraction accounted for 19.20% and 21% for 3% E/S and 5% E/S respectively with no significant difference (p< 0.05). Lipids released after enzymatic processes, lipids from emulsified fraction displayed similar lipid classes level amounted to 95-98% for triglycerides (TG) and 2-5% for phospholipids (PL). As for heavy fraction, there were 42% of TG and 58 % of PL. Lipids released by enzymatic hydrolysis and lipids of emulsified fraction displayed similar content of PUFA and were compared to lipids extracted by solvent. The main PUFA were EPA (6.99 to 7.56%) and DHA (from 11.26 to 15.86%). The lipid of the heavy fraction, which contained the most abundant amount of the DHA accounted for 26.11% and a high amount of EPA (7.73%).
Learning-Based Balance Control of Wheel-Legged Robots

This letter studies the adaptive optimal control problem for a wheel-legged robot in the absence of an accurate dynamic model. A crucial strategy is to exploit recent advances in reinforcement learning (RL) and adaptive dynamic programming (ADP) to derive a learning-based solution to adaptive optimal control. It is shown that suboptimal controllers can be learned directly from input-state data collected along the trajectories of the robot. Rigorous proofs for the convergence of the novel data-driven value iteration (VI) algorithm and the stability of the closed-loop robot system are provided. Experiments are conducted to demonstrate the efficiency of the novel adaptive suboptimal controller derived from the data-driven VI algorithm in balancing the wheel-legged robot to the equilibrium.
Analysis of the Distance between adjacent Transmitting Coils in Segmented Dynamic Wireless Charging

Electric vehicle wireless charging, as an alternative to the conventional wired charging, is a hot research topic in recent years. Without a charging cable, electric vehicles can be continuously charged while driving, namely dynamic wireless charging. A common type of the dynamic wireless charging transmitter is a series of individual transmitting coils arranging along the charging route, which is called the segmented dynamic wireless charging. In this structure, the distance between adjacent transmitting coils is important in the system design due to its impact on the system cost and performance. In this paper, simulation analysis is carried out to evaluate the impact of the distance between adjacent transmitting coils. Using the parameters of an existing dynamic wireless charging system as a baseline, the influence of different distances between adjacent transmitting coils on the total mutual inductance and transferred power pulsation is demonstrated. These results are expected to provide the equivalent cost to evaluate the optimal distance between the adjacent transmitting coils, and some suggestions on the coefficients of the equation are also proposed.
Hybrid Precoding and Combining for Millimeter Wave/Sub-THz MIMO-OFDM Systems With Beam Squint Effects

In this paper, we consider the problem of hybrid precoding and combining for wideband millimeter wave (mmWave) and sub-terahertz (THz) MIMO-OFDM systems with beam squint effects. In the presence of the beam squint effect, the conventional spatially sparse precoding-based hybrid precoding method which approximates the optimal precoder/combiner with a set of array response vectors suffers severe performance degradation. To address this difficulty, we propose a new set of basis vectors to approximate the optimal precoder/combiner. Each new basis vector is devised to form a radiation pattern with a wide beam to cover the squinted beam directions caused by different frequencies. Simulation results show that the proposed method can effectively alleviate the beam squint effect and achieve a substantial performance improvement over the existing state-of-the-art hybrid precoding method.
Resource Allocation in D2D Enabled Vehicular Communications: A Robust Stackelberg Game Approach Based on Price-Penalty Mechanism

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments.
Power outage classification prediction method based on bagging ensemble learning

Power outage prediction can provide reference for power companies' power outage decision-making, reduce frequency of power outages on the user side, and improve reliability of low-voltage power supply. In this paper, Bagging Ensemble model is used to predict the power failure problem. First, paper construct spatial location matrix of different regions according to geographic location relationship of different regions, and construct spatial features through QR matrix decomposition. Then, bagging ensemble learning framework is used to randomly resample the data, train different base classifiers, and integrate classifiers according to the combination strategy. Finally, paper use integrated learning model to make predictions on the data. The common classification model evaluation index and AOC curve are used to measure performance of model. Experimental results show prediction model proposed in this paper has good performance for classification prediction of outage data.
Interference-Aware Antenna Synthesis for Enhanced Coverage in Intelligent Transportation System

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability.
Discriminative EdgeBoxes for Action Recognition

Due to the huge amount of online videos uploaded and viewed every day, there is an emerging need nowadays for action recognition techniques. These techniques need to consider the large variations in camera motion, viewpoint, cluttered background, etc. Moreover, they need to be unsupervised or weakly supervised to can absorb such amount of different actions. The goal of this paper is to introduce a new unsupervised technique for mining mid-level discriminative patches from videos. These patches are the most representative parts that can describe an action. To achieve this goal, we generalize a technique borrowed from 2D images to generate bounding boxes with high motion and appearance saliencies then apply iterative clustering/classification procedure on generated boxes. Then, we calculate discriminative score for each box and finally select top ranked boxes to train Exemplar-SVM on low-level features extracted inside selected boxes. We evaluate our approach on a challenging dataset namely YouTube and the experimental results demonstrate the effectiveness of our approach to achieve better average recognition accuracy than the state-of-the-art techniques.
The relationship between learning styles and students` chemistry achievement

This research aimed to determine the chemical content which is the most challenging for students, and also to study the differences in chemistry achievement among students who prefer different learning styles. The study was carried out on the sample of 265 second grade students (aged 15–16 years) from seven grammar schools in Vojvodina (Serbia). Two instruments were applied in the study to determine the learning styles: chemistry knowledge test and Learning Style Inventory (LSI version 3.1). According to the findings, students have difficulty learning the contents of the topics Chemical Equilibrium, Salt Hydrolysis and Oxidation-Reduction Reactions. The findings show that there is a significant relationship between achievements in chemistry and students’ learning styles. The findings obtained in this research represent a step towards improving chemistry education since they identified the topics that students find it the most difficult to learn.
Evaluation of anti-inflammatory and antibacterial activity of Pithecellobium dulce (Benth) extract

The use of herbal plants and their extracts have been utilized by all over the world for its various biological activities. The use of ethanolic Pithecellobium dulce extract has been analyzed for its anti-inflammatory and antibacterial activities. For this, Streptococcus pyrogenes, Staphylococcus aureus, Escherichia coli and Klebsiella pneumonia have been studied against the P. dulce extract. The result which shows the presence of many difference secondary metabolites such as alkaloids, flavonoids, glycosides, phenols, steroids, tannins, terpenoids and saponins have been detected. The anti-inflammatory responses such as percentage of inhibition of protein denaturation and HRBC membrane stabilization has showed an increase response when compared with the standard drug Aspirin of about 62.80 and 59.25% respectively. Also the antibacterial response showed a positive response when compared with the standard drug gentamycin at the lower concentration. Hence, the ethanolic extract of P. dulce can be used as a potential agent as anti-inflammatory and anti-bacterial compounds.
A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

The rise of social media platforms has significantly changed the way our world communicates, and part of those changes includes a rise in inappropriate behaviors, such as the use of aggressive and hateful language online. Detecting such content is crucial to filtering or blocking inappropriate content on the Web. However, due to the huge amount of data posted every day, automatic methods are essential for identifying this type of content. Seeking to address this issue, the Natural Language Processing community is increasingly involved in testing a wide range of techniques for hate speech detection. While achieving promising results, these techniques consider hate speech detection as the sole optimization objective, without involving other related tasks such as polarity and emotion classification that are strongly linked to offensive behavior. In this paper, we propose the first Multi-task approach that leverages the shared affective knowledge to detect hate speech in Spanish tweets, using a well-known Transformer-based model. Our results show that the combination of both polarity and emotional knowledge helps to detect hate speech more accurately across datasets.
Fast Beam Alignment for Millimeter Wave Time-Varying Channels Using Sparse Codes

In this paper, a novel beam alignment algorithm based on the sparse graph coding theory is proposed for millimeter wave (mmWave) time-varying channels. Firstly, a pilot design method is introduced to transform the mmWave time-varying beam alignment into a sparse-graph design and detection problem. Inspired by Low-Density-Parity-Check (LDPC) codes and fountain codes, a multi-stage sparse coding method is proposed for the design of the measurement matrix and the theoretical bound of the probability of success is derived to guide the design of the sparse-graph. A beam alignment algorithm is subsequently proposed to detect the beam index and estimate the carrier frequency offset (CFO). Then, the Carmeŕ-Rao Lower Bound (CRLB) is derived. Simulation results demonstrate that the proposed beam alignment algorithm achieves significant performance improvements over the conventional counterparts in both the noiseless and noise cases.
Physical-Layer Security in 6G Networks

The sixth generation (6G) of mobile network will be composed by different nodes, from macro-devices (satellite) to nano-devices (sensors inside the human body), providing a full connectivity fabric all around us. These heterogeneous nodes constitute an ultra dense network managing tons of information, often very sensitive. To trust the services provided by such network, security is a mandatory feature by design. In this scenario, physical-layer security (PLS) can act as a first line of defense, providing security even to low-resourced nodes in different environments. This paper discusses challenges, solutions and visions of PLS in beyond-5G networks.
Exploiting Cascaded Ensemble of Features for the Detection of Tuberculosis Using Chest Radiographs

Tuberculosis (TB) is a communicable disease that is one of the top 10 causes of death worldwide according to the World Health Organization. Hence, Early detection of Tuberculosis is an important task to save millions of lives from this life threatening disease. For diagnosing TB from chest X-Ray, different handcrafted features were utilized previously and they provided high accuracy even in a small dataset. However, at present, deep learning (DL) gains popularity in many computer vision tasks because of their better performance in comparison to the traditional manual feature extraction based machine learning approaches and Tuberculosis detection task is not an exception. Considering all these facts, a cascaded ensembling method is proposed that combines both the hand-engineered and the deep learning-based features for the Tuberculosis detection task. To make the proposed model more generalized, rotation-invariant augmentation techniques are introduced which is found very effective in this task. By using the proposed method, outstanding performance is achieved through extensive simulation on two benchmark datasets (99.7% and 98.4% accuracy on Shenzhen and Montgomery County datasets respectively) that verifies the effectiveness of the method.
Phenolics profiling of pomace extracts from different grape varieties cultivated in Argentina

Grape pomace can be considered as an excellent and inexpensive source of phenolic compounds with potential bioactive properties. Therefore, the key aim of this study was to carry out a comparative study of different Argentinean grape varieties for selecting the most convenient residue during the recovery of compounds with antioxidant activities that will be useful in emerging industrial applications. Phenolics, including 17 non-anthocyanins and 13 anthocyanins, isolated from grape pomace (GP) obtained from the varieties Malbec, Cabernet Sauvignon, Cabernet Franc, and Merlot, were characterised via assessing different chemical profiles of the studied samples. The maximum concentrations of the non-anthocyanins corresponded to the following flavanols: (−)-epicatechin (5518 μg g−1), (+)-catechin (5340 μg g−1), procyanidin B1 (1820 μg g−1), and syringic acid (6665 μg g−1). Malvidin-3-glucoside was the most abundant anthocyanin (32 056 μg g−1). The compound OH-tyrosol was first identified and quantified in the GPE of Cabernet Franc and Malbec. Moreover, stilbene trans-resveratrol was found at the levels as high as 328 μg g−1 GPE in Malbec, thus highlighting the potentiality of the variety as a source of compounds with nutraceutical value. A discussion about the relationship between the high levels of syringic acid and the anthocyanin malvidin-3-glucoside has also been presented. Based on the results, new knowledge for understanding the possible synergic effects between the contents of compounds of different families in the extracts was presented. This information can be helpful for the technological application of the extracts and to justify different beneficial effects achieved when these extracts are used in experimental models.