Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting User Clustering and Fixed Power Allocation for Multi-Antenna UAV-Assisted IoT Systems

Internet of Things (IoT) systems cooperative with unmanned aerial vehicles (UAVs) have been put into use for more than ten years, from transportation to military surveillance, and they have been shown to be worthy of inclusion in the next wireless protocols. Therefore, this paper studies user clustering and the fixed power allocation approach by placing multi-antenna UAV-mounted relays for extended coverage areas and achieving improved performance for IoT devices. In particular, the system enables UAV-mounted relays with multiple antennas together with non-orthogonal multiple access (NOMA) to provide a potential way to enhance transmission reliability. We presented two cases of multi-antenna UAVs such as maximum ratio transmission and the best selection to highlight the benefits of the antenna-selections approach with low-cost design. In addition, the base station managed its IoT devices in practical scenarios with and without direct links. For two cases, we derive closed-form expressions of outage probability (OP) and closed-form approximation ergodic capacity (EC) generated for both devices in the main scenario. The outage and ergodic capacity performances in some scenarios are compared to confirm the benefits of the considered system. The number of antennas was found to have a crucial impact on the performances. The simulation results show that the OP for both users strongly decreases when the signal-to-noise ratio (SNR), number of antennas, and fading severity factor of Nakagami-m fading increase. The proposed scheme outperforms the orthogonal multiple access (OMA) scheme in outage performance for two users. The analytical results match Monte Carlo simulations to confirm the exactness of the derived expressions.
Brief communication: global temporal trends in the efficacy of clarithromycin-based regimens for the treatment of Helicobacter pylori infection

Background: Helicobacter pylori eradication rates achieved with clarithromycin-based triple therapies are declining due to antibiotic resistance, but data regarding temporal changes in efficacy with these eradication therapies are scarce. Objective: To evaluate the efficacy of clarithromycin-based triple eradication regimens over time. Design: A comprehensive literature review and time-trend analysis. Data sources and methods: Bibliographies of recently published systematic literature reviews were searched and supplemented with a targeted literature review conducted using Medline and Embase databases and ProQuest from conception to May 2021. Studies reporting H. pylori eradication rates of clarithromycin-based triple therapies were included and temporal trends were estimated using a random-effects model. Results: Eradication rates for triple therapies containing proton pump inhibitors (PPIs), clarithromycin, and amoxicillin showed a significant decline over the past 23 years (p = 0.0315). However, this decline was not significant when eradication rates achieved with vonoprazan-based triple therapy were included (p = 0.3910). Conclusion: Vonoprazan-based triple therapy partially mitigated the decline in eradication rates seen with PPI-based triple therapy, likely due to more powerful acid suppression of vonoprazan.
Conductor Losses in Radiofrequency Coils for Magnetic Resonance below 3T: Estimation Methods and Minimization Strategies

The design of optimized radiofrequency (RF) coils is a fundamental task for maximizing the signal-to-noise ratio (SNR) in Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) applications. An efficient coil should be designed by minimizing the coil noise with respect to the sample noise, since coil conductor resistance affects data quality by reducing the SNR, especially for coils tuned to a low frequency. Such conductor losses strongly depend on the frequency (due to the skin effect) and on the conductor cross-sectional shape (strip or wire). This paper reviews the different methods for estimating conductor losses in RF coils for MRI/MRS applications, comprising analytical formulations, theoretical/experimental hybrid approaches and full-wave simulations. Moreover, the different strategies for minimizing such losses, including the use of Litz wire, cooled and superconducting coils, are described. Finally, recent emerging technologies in RF coil design are briefly reviewed.
Relationships of Body Mass Index, Relative Fat Mass Index, and Waist Circumference with Serum Concentrations of Parameters of Chronic Inflammation

(1) Background: Obesity in the perimenopausal period is associated with hormonal changes, lifestyle, and environment. In obesity, elevated levels of IL-6 and TNF-α and reduced levels of adiponectin are observed, and the associated chronic inflammation favors the development of cardiometabolic diseases. Therefore, the aim of our study was to assess the relationship between selected measures of obesity (BMI, WC, RFM, VAI, WHtR) and parameters of chronic inflammation (CRP, TNF-α, IL-6) in perimenopausal women. (2) Methods: The study involved 172 perimenopausal women. The methods used in this study were diagnostic surveys, anthropometric measurements, blood pressure measurements, and venous blood sampling. (3) Results: Preliminary multivariate linear regression analysis showed that CRP moderately positively correlated with IL-6 (β = 0.25; p = 0.001) and weakly negatively correlated with adiponectin (β = −0.23; p = 0.002). Similar associations were noted in preliminary multivariate linear regression analysis adjusted for age, menopausal status, and smoking status. Preliminary multivariate linear regression analysis also showed that BMI positively correlated with IL-6 (β = 0.16; p = 0.033). VAI weakly positively correlated with CRP (β = 0.25; p = 0.001) and negatively correlated with adiponectin (β = −0.43; p = 0.000). (4) BMI, WC, RFM, VAI, and WHtR are clearly related to selected parameters of chronic inflammation. Our study suggests that each of the anthropometric variables provides distinct information on metabolic processes associated with inflammatory parameters.
Function and solution structure of the Arabidopsis thaliana RALF8 peptide

We report the recombinant preparation from Escherichia coli cells of samples of two closely related, small, secreted cysteine‐rich plant peptides: rapid alkalinization factor 1 (RALF1) and rapid alkalinization factor 8 (RALF8). Purified samples of the native sequence of RALF8 exhibited well‐resolved nuclear magnetic resonance (NMR) spectra and also biological activity through interaction with a plant receptor kinase, cytoplasmic calcium mobilization, and in vivo root growth suppression. By contrast, RALF1 could only be isolated from inclusion bodies as a construct containing an N‐terminal His‐tag; its poorly resolved NMR spectrum was indicative of aggregation. We prepared samples of the RALF8 peptide labeled with 15N and 13C for NMR analysis and obtained near complete 1H, 13C, and 15N NMR assignments; determined the disulfide pairing of its four cysteine residues; and examined its solution structure. RALF8 is mostly disordered except for the two loops spanned by each of its two disulfide bridges.
UFO-Net: A Linear Attention-Based Network for Point Cloud Classification

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.
Some Design Considerations in Passive Indoor Positioning Systems

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user’s privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user’s privacy in a busy office environment.
Associating Functional Neural Connectivity and Specific Aspects of Sensorimotor Control in Chronic Stroke

Hand sensorimotor deficits often result from stroke, limiting the ability to perform daily living activities. Sensorimotor deficits are heterogeneous among stroke survivors. Previous work suggests a cause of hand deficits is altered neural connectivity. However, the relationships between neural connectivity and specific aspects of sensorimotor control have seldom been explored. Understanding these relationships is important for developing personalized rehabilitation strategies to improve individual patients’ specific sensorimotor deficits and, thus, rehabilitation outcomes. Here, we investigated the hypothesis that specific aspects of sensorimotor control will be associated with distinct neural connectivity in chronic stroke survivors. Twelve chronic stroke survivors performed a paretic hand grip-and-relax task while EEG was collected. Four aspects of hand sensorimotor grip control were extracted, including reaction time, relaxation time, force magnitude control, and force direction control. EEG source connectivity in the bilateral sensorimotor regions was calculated in α and β frequency bands during grip preparation and execution. Each of the four hand grip measures was significantly associated with a distinct connectivity measure. These results support further investigations into functional neural connectivity signatures that explain various aspects of sensorimotor control, to assist the development of personalized rehabilitation that targets the specific brain networks responsible for the individuals’ distinct sensorimotor deficits.
Improved methods for estimating abundance and related demographic parameters from mark‐resight data

Over the past decade, there has been much methodological development for the estimation of abundance and related demographic parameters using mark‐resight data. Often viewed as a less‐invasive and less‐expensive alternative to conventional mark recapture, mark‐resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark‐resight models currently implemented in the popular capture‐recapture freeware Program MARK. We propose a composite likelihood solution based on a zero‐inflated Poisson log‐normal model and find the performance of this new estimator to be superior in terms of bias and confidence interval coverage. Under Pollock's robust design, we also extend the models to accommodate individual‐level random effects across sampling occasions as a potentially more realistic alternative to models that assume independence. As a motivating example, we revisit a previous analysis of mark‐resight data for the New Zealand Robin (Petroica australis) and compare inferences from the proposed estimators. For the all‐too‐common situation where encounter rates are low, individual detection heterogeneity is non‐negligible, and the number of marked individuals is unknown, we recommend practitioners use the zero‐inflated Poisson log‐normal mark‐resight estimator as now implemented in Program MARK.
Li2100deplMoO4 Scintillating Bolometers for Rare-Event Search Experiments

We report on the development of scintillating bolometers based on lithium molybdate crystals that contain molybdenum that has depleted into the double-β active isotope 100Mo (Li2100deplMoO4). We used two Li2100deplMoO4 cubic samples, each of which consisted of 45-millimeter sides and had a mass of 0.28 kg; these samples were produced following the purification and crystallization protocols developed for double-β search experiments with 100Mo-enriched Li2MoO4 crystals. Bolometric Ge detectors were utilized to register the scintillation photons that were emitted by the Li2100deplMoO4 crystal scintillators. The measurements were performed in the CROSS cryogenic set-up at the Canfranc Underground Laboratory (Spain). We observed that the Li2100deplMoO4 scintillating bolometers were characterized by an excellent spectrometric performance (∼3–6 keV of FWHM at 0.24–2.6 MeV γs), moderate scintillation signal (∼0.3–0.6 keV/MeV scintillation-to-heat energy ratio, depending on the light collection conditions), and high radiopurity (228Th and 226Ra activities are below a few µBq/kg), which is comparable with the best reported results of low-temperature detectors that are based on Li2MoO4 using natural or 100Mo-enriched molybdenum content. The prospects of Li2100deplMoO4 bolometers for use in rare-event search experiments are briefly discussed.
Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

In order to improve the stability and economy of 4WID-4WIS (four-wheel independent drive—four-wheel independent steering) electric vehicles in trajectory tracking, this paper proposes a trajectory tracking coordinated control strategy considering energy consumption economy. First, a hierarchical chassis coordinated control architecture is designed, which includes target planning layer, and coordinated control layer. Then, the trajectory tracking control is decoupled based on the decentralized control structure. Expert PID and Model Predictive Control (MPC) are employed to realize longitudinal velocity tracking and lateral path tracking, respectively, which calculate generalized forces and moments. In addition, with the objective of optimal overall efficiency, the optimal torque distribution for each wheel is achieved using the Mutant Particle Swarm Optimization (MPSO) algorithm. Additionally, the modified Ackermann theory is used to distribute wheel angles. Finally, the control strategy is simulated and verified using Simulink. Comparing the control results of the average distribution strategy and the wheel load distribution strategy, it can be concluded that the proposed coordinated control not only provides good trajectory tracking but also greatly improves the overall efficiency of the motor operating points, which enhances the energy economy and realizes the multi-objective coordinated control of the chassis.
Non-Contact Assessment of Swallowing Dysfunction Using Smartphone Captured Skin Displacements

Early and accurate dysphagia diagnosis is essential for reducing the risk of associated co-morbidities and mortalities. Barriers to current evaluation methods may alter the effectiveness of identifying at-risk patients. This preliminary study evaluates the feasibility of using iPhone X-captured videos of swallowing as a non-contact dysphagia screening tool. Video recordings of the anterior and lateral necks were captured simultaneously with videofluoroscopy in dysphagic patients. Videos were analyzed using an image registration algorithm (phase-based Savitzky–Golay gradient correlation (P-SG-GC)) to determine skin displacements over hyolaryngeal regions. Biomechanical swallowing parameters of hyolaryngeal displacement and velocity were also measured. Swallowing safety and efficiency were assessed by the Penetration Aspiration Scale (PAS), Residue Severity Ratings (RSR), and the Normalized Residue Ratio Scale (NRRS). Anterior hyoid excursion and horizontal skin displacements were strongly correlated with swallows of a 20 mL bolus (rs = 0.67). Skin displacements of the neck were moderately to very strongly correlated with scores on the PAS (rs = 0.80), NRRS (rs = 0.41–0.62), and RSR (rs = 0.33). This is the first study to utilize smartphone technology and image registration methods to produce skin displacements indicating post-swallow residual and penetration-aspiration. Enhancing screening methods provides a greater chance of detecting dysphagia, reducing the risk of negative health impacts.
The First Direct Observation of Double-Beta Decay

The possibility of double-beta (ββ) decay was recognized in the mid 1930s, and by 1939 something of its potential to reveal fundamental properties of the neutrino was known as well. But experimental observation of the phenomenon was out of reach. Despite numerous attempts over the next 30 years, and strong suggestions of its existence from geochemical experiments, ββ decay had not been observed to occur in the laboratory. Our group at UC Irvine took up the search in the early 1970s, first with a cloud chamber and then with a time-projection chamber. The following narrative traces a discovery process that unfolded over the ensuing 15 years—a sequence of setbacks and false starts punctuated by occasional victories and, ultimately, by a definitive laboratory observation of two-neutrino ββ decay, in August 1987.
Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
Multi-relational learning via hierarchical nonparametric Bayesian collective matrix factorization

Relational learning addresses problems where the data come from multiple sources and are linked together through complex relational networks. Two important goals are pattern discovery (e.g. by (co)-clustering) and predicting unknown values of a relation, given a set of entities and observed relations among entities. In the presence of multiple relations, combining information from different but related relations can lead to better insights and improved prediction. For this purpose, we propose a nonparametric hierarchical Bayesian model that improves on existing collaborative factorization models and frames a large number of relational learning problems. The proposed model naturally incorporates (co)-clustering and prediction analysis in a single unified framework, and allows for the estimation of entire missing row or column vectors. We develop an efficient Gibbs algorithm and a hybrid Gibbs using Newton's method to enable fast computation in high dimensions. We demonstrate the value of our framework on simulated experiments and on two real-world problems: discovering kinship systems and predicting the authors of certain articles based on article–word co-occurrence features.
Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.
Analytic inhomogeneous electron liquid and its density for model spin-compensated two-electron atomic ions with Coulomb confinement: an exact nonrelativistic Hamiltonian

In earlier work, Howard and March (HM) proposed an analytic ground-state electron density ρ( r ) starting from the s-wave model of the He atom. Subsequently Ancarani has constructed by numerical methods a variational approach for this s-wave model with a lower energy than the HM result. This clearly means that the HM ρ( r ) is not the ground-state electron density of the He s-wave model. Therefore, we derive here an exact nonrelativistic Hamiltonian, with strong radial correlation plus Coulomb confinement, for which the HM ρ( r ) is indeed the ground-state electron density.