Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Renal Denervation on Sympathetic Activation, Blood Pressure, and Glucose Metabolism in Patients with Resistant Hypertension

Increased central sympathetic drive is a hallmark of several important clinical conditions including essential hypertension, heart failure, chronic kidney disease, and insulin resistance. Afferent signaling from the kidneys has been identified as an important contributor to elevated central sympathetic drive and increased sympathetic outflow to the kidney and other organs is crucially involved in cardiovascular control. While the resultant effects on renal hemodynamic parameters, sodium and water retention, and renin release are particularly relevant for both acute and long term regulation of blood pressure, increased sympathetic outflow to other vascular beds may facilitate further adverse consequences of sustained sympathetic activation such as insulin resistance, which is commonly associated with hypertension. Recent clinical studies using catheter-based radiofrequency ablation technology to achieve functional renal denervation in patients with resistant hypertension have identified the renal nerves as therapeutic target and have helped to further expose the sympathetic link between hypertension and insulin resistance. Initial data from two clinical trials and several smaller mechanistic clinical studies indicate that this novel approach may indeed provide a safe and effective treatment alternative for resistant hypertension and some of its adverse consequences.
An Interfacing Scheme with Improved Bandwidth for Multi-Current-Output Sensors

The paper presents a simple interfacing electronics suitable for a sensor system, involving multiple current-output sensors. The proposed interface selects and processes the individual sensor nodes, and then efficiently estimates the sensor-current and/or their combination. The novel design of the interface, which comprises an improved current-sense circuit, a switching logic and multi-regiming signal conditioner, renders many meritorious features. The salient features include improved bandwidth, reduced effects of ADC resolution, as well as the ability to provide customized design as per user requirements. The methodology and performance evaluation of this interfacing scheme is described in the paper. The test results, using simulation as well as hardware-based tests on a four-sensor system, demonstrate the performance of the scheme. The maximum non-linearity for a range of (10 nA, 160 nA) is obtained as 0.184%. The improvement in bandwidth is about 9.9 kHz (factor of improvement = 100). The working of gain regimes is shown adequately for single as well as multiple current output sensing. This technique is useful for various instruments such as magnetic field sensors and photodiodes.
A passive method to estimate TCP round trip time from nonsender-side

TCP round trip time is an important subject of network measurement research. And it is widely utilized in congestion control and network tomography. Compared with active methods to measure round trip time, passive ones are superior for low cost and not injecting traffic into networks. However, all present passive measurement methods have deficiencies. Some of them are not accurate enough, some others are only applied in just one phase of TCP connection, so none of them can well satisfy the demand of network management. This paper presents a passive method named cross to estimate TCP round trip time from nonsender-side. With cross correlation function, it precisely finds the correspondence between the sequence of ACK packets and that of data packets, and then gets round trip time of TCP connection. The experiment upon PlanetLab shows that this method is able to precisely estimate TCP round trip time in any phase of TCP connection no matter the network is idle or congested.
DOA estimation for coherent sources in the presence of unknown correlated noise

A new direction of arrival (DOA) estimation method is proposed for coherent sources in the presence of spatially unknown correlated noise. The proposed method can fully eliminate the noise component by applying spatial smoothing technique to a new covariance matrix, and the new covariance matrix is the difference between the forward/backward spatial smoothing covariance and its rotation. The unknown correlated noise fields are supposed to be in a complex symmetric Toeplitz form, situations where this assumption is valid is common in sonar applications. Simulation results have shown that the proposed method can fully eliminate noise component and provide significant performance improvement over the conventional forward/backward spatial smoothing algorithm.
Transparency and awareness in a real-time groupware system

This article explores real-time groupware systems from the perspective of both the users and the designer. This exploration is carried out through the description of GroupDesign, a real-time multi-user drawing tool that we have developed. From the perspective of the users, we present a number of functionalities that we feel necessary in any real-time groupware system: Graphic & Audio Echo, Localization, Identification, Age, and History. From the perspective of the designer, we demonstrate the possibility of creating a multi-user application from a single-user one, and we introduce the notion of purely replicated architecture.
Road Segmentation for Remote Sensing Images Based on Global Feature Attention ResUnet

In current road segmentation methods for remote sensing images, high-level semantic features (such as roads and shadows) are often lost in the decoding process, resulting in a decrease in the accuracy of road segmentation. To solve this problem, we strengthened the correlation between shallow and deep features in a typical U-Net framework using two global attention mechanisms (channel and spatial) as follows. We first extract the features of the image in ResUnet's decoder and calculate channel attention on these features. we then map all features to shallow features and define spatial attention, which enhances the representation of deep spatial semantic information on the shallow feature. The feature maps processed by the global channel attention and global spatial attention are then combined with the ResUnet decoder for feature fusion and up-sampling, finally producing the road segmentation result. Experimental results show that our model effectively resolves the discontinuity problem in road segmentation and significantly improves the accuracy of road extraction from remote sensing images.
Rates of concussion are lower in National Football League games played at higher altitudes.

STUDY DESIGN Retrospective epidemiologic investigation. OBJECTIVE To investigate the relationship between altitude and concussion rate in the National Football League (NFL). Because of the physiologic responses that occur during acclimatization to altitude, it was hypothesized that games played on fields at a higher altitude would have reduced concussion rates compared to games played on fields at a lower altitude. BACKGROUND Recent research indicates that the elevation above sea level at which football games are played may be associated with the likelihood of a concussion in high school football athletes. METHODS Data on incident concussions and athlete exposures for the first 16 weeks of the NFL 2012 and 2013 regular seasons were obtained from publicly available web-based sources and used to calculate competition concussion rates for each NFL stadium. Concussion rates were analyzed in relation to game elevation. RESULTS During the first 16 weeks of the 2012 and 2013 NFL regular seasons, 300 concussions, involving 284 players, were reported (64.3 primary cases per 10,000 game exposures). The odds of a concussion were 30% lower when playing at a higher elevation (equal to or greater than 644 ft [196.3 m] above sea level) compared to a lower elevation (odds ratio = 0.70; 95% confidence interval: 0.53, 0.94). A multivariable generalized linear model controlling for season, week, and clustering of team at home and away confirmed these results, showing that the odds of at least 1 concussion were reduced by 32% in games played at higher elevation. CONCLUSION The results of this epidemiological investigation indicate that increased altitude was associated with a reduction in the odds of a sport-related concussion in NFL athletes. The reported relationship of concussion incidence and field elevation should be further investigated, and, if verified, further work will be needed to understand why that relationship exists. LEVEL OF EVIDENCE Prognosis, level 2c.
Calcium‐independent phospholipase A2 influences AMPA‐mediated toxicity of hippocampal slices by regulating the GluR1 subunit in synaptic membranes

We have recently documented that phosphorylation of the GluR1 subunit of α‐amino‐3‐hydroxy‐5‐methylisoxazole‐propionate (AMPA) glutamate receptors is influenced by calcium‐independent forms of phospholipase A2 (iPLA2) activity in the brain. Given the importance of GluR1 subunit phosphorylation in the control of AMPA receptor delivery to synaptic membranes, we tested the influence of iPLA2 activity on AMPA receptor distribution between neuronal compartments, using organotypic cultured hippocampal slices. In agreement with earlier reports, the iPLA2 inhibitor bromoenol lactone (BEL) markedly enhanced the phosphorylation of the GluR1 subunit at both Ser831 and Ser845 residues. GluR1 subunit phosphorylation levels were selectively increased by (R)‐BEL, an enantio‐selective inhibitor of iPLA2γ, but not by (S)‐BEL, an iPLA2β inhibitor. The iPLA2γ inhibitor R‐BEL also promoted the insertion of new GluR1 subunits into synaptic membranes and exacerbated AMPA‐mediated cell death in the CA1 region of the hippocampus. The latter effect was selectively abolished by IEM 1460 and philanthotoxin‐433, two antagonists specific for AMPA receptors lacking GluR2 subunits. These results provide evidence that iPLA2γ‐related regulation of AMPA receptor GluR1 subunit phosphorylation could represent an important mechanism modulating hippocampal cell death induced by AMPA receptor overstimulation. © 2007 Wiley‐Liss, Inc.
Study of the Thickness of the MF-4SK Membrane on the Electromechanical Parameters of IPMC Actuators

Micromechanical actuators based on ionic polymer-metal composites (IPMCs) are promising energy converters for microrobotics and medicine. In this work, we investigated the influence of the thickness variation of the MF-4SK membrane (90 and $290 \mu \mathrm{m}$) on the lifetime of the actuators, on the dependence of deformation amplitude on the amplitude and frequency of the input signal, and, accordingly, on the resonant frequency. Based on the results of the experiment, it was found that actuators based on a membrane with a thickness of $90 \mu \mathrm{m}$ are characterized by a shorter lifetime and deformation amplitude, and their resonant frequency is shifted to the short-wavelength side. Based on the results obtained, we can conclude that actuators based on thinner membranes are less effective as electromechanical converters.
THE IMPACT OF AN ACUTE REJECTION EPISODE ON LONG‐TERM RENAL ALLOGRAFT SURVIVAL (t1/2)1,2

An acute renal transplant rejection episode has been shown to be associated with decreased 1-year graft survival. The impact on long-term outcome is undefined. We studied the impact of an acute rejection episode on t1/2, the time it takes for 1/2 of the grafts functioning at 1 year to fail. Use of t1/2 avoids inclusion of early graft loss to acute rejection or complications of treatment. Since 1/1/86, a total of 653 patients have received a primary kidney transplant and had at least 1 year of function. Recipients were divided by the incidence and timing of rejection: no rejection; 1 rejection within the first year; >1 rejection, the first episode in the first year; and ≥ rejection, the first episode after the first year. A single rejection episode in the first year reduced t1/2 (45±11 years in those with no rejection vs. 25±8 years in those with 1 in the first year). Multiple rejections (t1/2=5±11 years) and a first rejection after the first year (t1/2=3±1 years) have a significant effect (P<.05). Both living and cadaver donor recipients with rejection had shortened t1/2. For those with >1 rejection, the first episode in the first year, and those with ≥1 rejection, the first episode after the first year, chronic rejection was the predominant cause of graft loss; noncompliance also played a role. We conclude that a single rejection episode shortens t1/2. Those with >1 rejection, the first episode within the first year, and those with ≥1 rejection, the first episode after the first year, are at high risk for late graft loss.
Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers

Exhausters are very crucial components in sinter plant of steel manufacturing industries. Problems in fan or motor side of the exhausters lead to machine stoppages or reduced speed, resulting in stoppage of the system and decreased sinter production. Common issues include imbalance, misalignments, looseness, eccentricity, and motor bearing failure. The proposed IoT-based system aims to continuously analyze vibration signals, predict issues, and prevent sudden stoppages and exhauster tripping, thereby enhancing overall plant efficiency. This study focuses on online monitoring and health prediction of industrial exhausters by selection of statistical features extracted from triaxial time-series vibration data by using multiple optimizers, followed by implementation of machine learning classifiers. By incorporating a novel feature selection process based on nature-inspired optimizers, this methodology aims to enhance the overall generalization performance of classification. The retrained models with cross-validation were evaluated with real-time industrial data, which shows 99.5% cross-validation accuracy and 95.83% real-time prediction accuracy. The proposed methodology holds great potential for detecting machine faults in industrial applications.
Load Balancing for Flow-Based Parallel Processing Systems in CMP Architecture

Load balancing is critical to the performance of parallel processing systems. It is more difficult for network systems such as NIDS and Web Servers, because they must preserve flow order. But traditional flow-based load balancing schemes of network parallel processing systems, such as LLF, cost much resource and introduce lots of communication overhead. With the rapid popularization of multi-core system, it is a good choice to apply NIDS in CMP architecture to achieve higher performance. Some companies have taken the first step. Their scheduling algorithm operates at a custom NIC based on FPGA technology. LLF algorithm can't be used in such environment because it needs much more memory than NIC owns. In this paper, we propose a scheduling scheme that remaps the new arrival flows when the system is unbalanced. To make effective adjustments we design a new triggering policy based on waiting lengths and their difference. Compared with LLF, our algorithm costs about 5% memory to get the same performance.
Incremental topo-metric SLAM using vision and robot odometry

We address the problem of simultaneous localization and mapping by combining visual loop-closure detection with metrical information given by the robot odometry. The proposed algorithm builds in real-time topo-metric maps of an unknown environment, with a monocular or omnidirectional camera and odometry gathered by motors encoders. A dedicated improved version of our previous work on purely appearance-based loop-closure detection [1] is used to extract potential loop-closure locations. Potential locations are then verified and classified using a new validation stage. The main contributions we bring are the generalization of the validation method for the use of monocular and omnidirectional camera with the removal of the camera calibration stage, the inclusion of an odometry-based evolution model in the Bayesian filter which improves accuracy and responsiveness, and the addition of a consistent metric position estimation. This new SLAM method does not require any calibration or learning stage (i.e. no a priori information about environment). It is therefore fully incremental and generates maps usable for global localization and planned navigation. This algorithm is moreover well suited for remote processing and can be used on toy robots with very small computational power.
Optimizing Federated Learning in Statistical Heterogeneity: A Fire Detection Use Case

Sensors serve as the vigilant eyes and ears of the modern world, seamlessly collecting and translating environmental cues into invaluable data. The collected data enables us to make precise predictions for related events, which relies on data-trained models. These data contain critical and sensitive information, which requires to gathered on central server for model training in traditional ML approach, raising the concern of data privacy. Therefore, preserving the data privacy during model training is crucial. Federated Learning (FL) emerges as a solution that provides privacy-preserving model training without sharing the data. However, it has been observed that statistical heterogeneity in FL leads to drifts among the local models, causing unsatisfactory FL performance. To address this, the work proposes using the ‘SARAH optimizer’ with the FedAvg algorithm to minimize the drift by reducing the variance of the local updates. The results of this proposed implementation have shown significant performance improvements and a better convergence rate. Additionally, the work provides a fire detection use case for the proposed approach.
Physiological Perspectives on Women Quitting Jobs

The complex subject of why women leave their employment has been investigated from a variety of perspectives, including social, economic, and psychological aspects. One of the most important, though occasionally overlooked, perspectives is the physiological one. By applying natural language processing NLP, polarity, and sentiment analysis to women's utterances about leaving their jobs, this research seeks to understand the physiological theories that underlie this behaviour. According to the specific example presented, one important aspect that contributes to this attitude is the thought of being passed up for leadership positions despite qualifications and dedication. As part of the data tokenization process, which is crucial to natural language processing, text is broken up into tokens, such as words or phrases. By calculating the frequency of each token after the text has been tokenized, you may identify the text's common themes or subjects. A further NLP technique known as sentiment analysis examines the text's underlying emotions and may beused to assess social media postings, client reviews, and other sorts of text data. Another essential element of sentiment analysis is polarity, which refers to the level of positive or negative sentiment in a text and may be represented as a polarity score provided by sentiment analysis algorithms. Overall, these techniques help computers understand the form and content of text data, allowing for more effective analysis. Positive sentiment analysis spans from 0.25 to 0.75, neutral opinion is represented by 0, and the negative sentiment analysis extends from -0.75 to - 0.25. The range of the emotion scores is from -1 to 1.
Implicit Bias-Term Effect of Bias-Term Free Feed-Forward Network Model can Improve Training Speed

Artificial neural networks consist of a large number of neurons. A neuron is a basic unit in which the input data are affine transformed and then nonlinearly transformed by an activation function. Under this premise, the classical universality theorem proves that any function can be approximated by only one intermediate layer. In this paper, it is clarified that the universality of the function approximation ability of the network is not lost even if the bias term is removed from the calculation formula in neurons by extending the intermediate layer to two layers when a specific type of activation function is used. In addition, we propose an activation function called SigLU, which is suitable for bias-term free feedforward model. The improvement of inference accuracy and stability is confirmed by the experimental results in image classification using MNIST and CIFAR10 dataset.
Indwelling versus Intermittent Urinary Catheterization following Total Joint Arthroplasty: A Systematic Review and Meta-Analysis

Objective The purpose of this study is to compare the rates of urinary tract infection (UTI) and postoperative urinary retention (POUR) in patients undergoing lower limb arthroplasty after either indwelling urinary catheterization or intermittent urinary catheterization. Methods We conducted a meta-analysis of relevant randomized controlled trials (RCT) to compare the rates of UTI and POUR in patients undergoing total joint arthroplasty after either indwelling urinary catheterization or intermittent urinary catheterization. A comprehensive search was carried out to identify RCTs. Study-specific risk ratios (RR) with 95% confidence intervals (CI) were pooled. Additionally, a meta-regression analysis, as well as a sensitivity analysis, was performed to evaluate the heterogeneity. Results Nine RCTs with 1771 patients were included in this meta-analysis. The results showed that there was no significant difference in the rate of UTIs between indwelling catheterization and intermittent catheterization groups (P>0.05). Moreover, indwelling catheterization reduced the risk of POUR, versus intermittent catheterization, in total joint surgery (P<0.01). Conclusions Based on the results of the meta-analysis, indwelling urinary catheterization, removed 24-48 h postoperatively, was superior to intermittent catheterization in preventing POUR. Furthermore, indwelling urinary catheterization with removal 24 to 48 hours postoperatively did not increase the risk of UTI. In patients with multiple risk factors for POUR undergoing total joint arthroplasty of lower limb, the preferred option should be indwelling urinary catheterization removed 24-48 h postoperatively. Level of Evidence Level I.
Customer Review Analysis of Indian Multiplex Chain Services

This research paper delves into the comprehensive analysis of customer reviews on Multiplex services, a prominent player in the entertainment industry. The services include theatre experience, the ambience and facilities, food and beverages experience, and online booking and ticketing. The paper aims to offer actionable recommendations for enhancing customer satisfaction and improving the overall service quality of Multiplex services. The paper provides analysis on different targeted services and identifies services for improvement. Data on multiplexes from various cities was extracted from different review platforms and consolidated. To extract the sentiments linked to each review provided by the customers we used NLP techniques. The paper shares which are the targeted services that are performing well and poorly in different cities and the sentiments linked to the customer base availing those services.