Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Biometric Recognition and Diagnosis Network With Residual Learning for Arrhythmia Screening Using Electrocardiogram Recordings

Arrhythmia is one of the most persistent chronic heart diseases in the elderly and is associated with high morbidity and mortality such as stroke, cardiac failure, and coronary artery diseases. It is significant for patients with arrhythmias to automatically detect and classify arrhythmia heartbeats using electrocardiogram (ECG) signals. In this paper, we develop three robust deep convolutional neural network (DCNN) models, including a plain-CNN network and two MSF(multi-scale fusion)-CNN architectures (A and B), to aid in better feature extraction for the detection of arrhythmia and thus significantly improve the performance metrics. The proposed models are trained and tested with a public MIT-BIH arrhythmia database on five types of signals. Six groups of ablation experiments are conducted to analyze the performance of the models. The accuracy, sensitivity, and specificity obtained from MSF-CNN architecture A are higher than those from the plain-CNN model, demonstrating that the different parallel group convolution blocks (<inline-formula> <tex-math notation="LaTeX">$1\times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1\times 5$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1\times 7$ </tex-math></inline-formula>) dramatically improve a model’s performance. Additionally, the best model MSF-CNN architecture B achieves an average accuracy, sensitivity, and specificity of 98.00%, 96.17%, and 96.38%, respectively. This illustrates the method with residual learning and concatenation group convolution blocks has a profound effect on the feature learning of the model. The results of ablation experiments show that our proposed biometric recognition and diagnosis network with residual learning (MSF-CNN B) achieves a rapid and reliable diagnosis approach on ECG signal classification, which has the potential for introduction into clinical practice as an excellent tool for aiding cardiologists in reading ECG heartbeat signals.
Nitrogen Metabolic Characteristics in Rice Genotypes with Different Nitrogen Harvest Index(NHI)

Nitrogen harvest index(NHI),defined as ratio of nitrogen accumulation in economic organs to whole plant at harvest,is an important index for evaluating nitrogen utilization efficiency,but less information is available on its physiological mechanisms and regulation methods in rice cultivation.Pot experiment was conducted using three rice genotypes including 4434(low NHI),Dianrui 302(medium NHI),and Yuchi 231(high NHI) to elucidate nitrogen metabolism characteristics in leaves,panicle internodes and grains during grain filling and their relationships to NHI.The results showed that change patterns of grain yield,harvest index and nitrogen accumulation in grains were consistent with that of NHI among three rice genotypes,and all the parameters were the highest in Yuchi 231.Highly significant differences were observed in nitrogen translocation amount after anthesis in plants with the order of 4434 Dianrui 302 Yuchi 231,while rate of N translocation and contribution rate of translocated N to grain had no significant difference among three genotypes.At maturity,the contents of total nitrogen,protein nitrogen and non-protein nitrogen in leaf-stems and grains exhibited the order of 4434Dianrui 302 Yuchi 231 with significant genotypic differences for the parameters but non-protein nitrogen.Protein nitrogen accumulation in leaf-stems of Yuchi 231 was significantly lower than those of 4434 and Dianrui 302,while that was significantly higher in grains,suggesting higher nitrogen accumulation in grains was a key characteristics of the rice genotype with high NHI.Moreover,the activities of glutamine synthetase(GS) and glutamate synthase(GOGAT) in leaves of Yuchi 231 were highly significantly higher than those of 4434 and Dianrui302 at filling,which would be in favor of the formation of free amino acids in leaves and its translocation to grain,and resulted in higher bleeding intensity and free amino acid content in panicle internodes and providing the substrate for nitrogen accumulation in grains.Meantime,activities of GS and GOGAT in grains were higher in Yuchi 231 than in 4434 and Dianrui 302 which accelerated more proteins synthesis in grains.Stepwise regression indicated that free amino acid content in bleedings of panicle internode was a main physiological index involving in nitrogen assimilation related to NHI.The above results suggest that higher post-anthesis nitrogen translocation amount, protein nitrogen accumulation in grains and free amino acid content in bleedings of panicle internode are more beneficial indicators for enhancing rice NHI in nitrogen management and genetic improvement.
Learning a Phenotypic-Attribute Attentional Brain Connectivity Embedding for ADHD Classification using rs-fMRI

Automated diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) from brain’s functional imaging has gained more interest due to its high prevalence rates among children. While phenotypic information, such as age and gender, is known to be important in diagnosing ADHD and critically affects the representation derived from fMRI brain images, limited studies have integrated phenotypic information when learning discriminative embedding from brain imaging for such an automatic classification task. In this work, we propose to integrate age and gender attributes through attention mechanism that is jointly optimized when learning a brain connectivity embedding using convolutional variational autoencoder derived from resting state functional magnetic resonance imaging (rs-fMRI) data. Our proposed framework achieves a state-of-the-art average of 86.22% accuracy in ADHD vs. typical develop control (TDC) binary classification task evaluated across five public ADHD-200 competition datasets. Furthermore, our analysis points out that there are insufficient linked connections to the brain region of precuneus in the ADHD group.
Neural Respiratory Drive Estimation in Respiratory sEMG with Cardiac Arrhythmias

Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively).
Leaf nutrient status, growth and flower yield in rose as influenced by organic and inorganic sources

A field experiment was conducted to partially substitute the chemical fertilizers by organic manures and bio-fertilizers in rose cv. Gruss-an-Teplitz. Treatments consisted of T0 (control), T1 (full dose of NPK @ 50,40, 30 g/m2 by chemical fertilizers), T2 (4 kg FYM + remaining required dose of NPK by chemical fertilizers), T3 (T2 + Azotobacter), T4 (T2 + PSB), T5 (2 kg poultry manure + remaining required dose of NPK bychemical fertilizers), T6 (T5 + Azotobacter), and T7 (T5+PSB), T3 treatment significantly increased leaf N concentration; whereas maximum P and K content was recorded with T4, Application of full dose of NPK supplied by chemical fertilizers (T1) significantly increased leaf area index, whereas T3 treatment resulted in maximum plant height. T1 treatment also significantly increased individual flower weight during winter and summer seasons and number of flowers and yield of flowers/ha, whereas number of flowers and yield were at par with T3 (4 kg FYM + remaining required dose of NPK by chemical Fertilizers + Azotobacter) treatment.
Symmetrizability for Myopic AVCs

Myopic arbitrarily varying channels (AVCs) are point-to-point communication models in which a channel state is controlled by a malicious adversary (a jammer) who receives side-information about the transmitted codeword via a side- channel (wiretapping) and wishes to maximize the probability of error. Compared to standard "oblivious" AVCs, myopic AVCs can potentially use the side information to launch a more effective attack, lowering the capacity of the channel. In this paper, we define a novel property, myopic symmetrizability, and prove it is a sufficient condition for the capacity of any myopic AVC to be zero. We also study the sufficiently myopic setting, in which, roughly speaking, the jammer’s side information reveals less information on the codeword transmitted than eventually available at the receiver. In this scenario we show that myopic symmetrizability is also a necessary condition for the capacity to equal zero, by providing a novel code construction using non-i.i. d. codebooks. A key technical lemma, interesting in its own right, is an argument showing that for any positive-rate code (whether for myopic AVCs or not) one can identify a corresponding distribution PX,X' that is a convex combination of product distributions, and such that a constant fraction of pairs of codewords have an empirical distribution approximately equaling PX,X'.
A Generic Cycle Life Model for Lithium-Ion Batteries Based on Fatigue Theory and Equivalent Cycle Counting

This paper proposes a cycle life model for lithium-ion batteries. The main objective of this work is to facilitate the electrical simulation of lithium-ion battery aging (due to cycling), and its impact on battery capacity and internal resistance. Most of the reported cycle life models are either: a) physics based, with parameters difficult to retrieve or b) semi-empirical, where the parameter identification process requires large amount of experimental data, huge manpower and test duration lasting from months to years. Moreover, these models tend to be valid only for the underlying battery and not for other battery types. This makes the simulation of lithium-ion batteries cycling effects, difficult to achieve, expensive and time consuming. The model proposed in this paper is based on simple physical equations from fatigue theory and equivalent cycle counting. The parameter identification process is straightforward and requires only few data from battery datasheets and limited (or short duration) cycling experiments. The proposed model is generic and able to represent the impact of common cycle life factors such as: depth-of-discharge (DoD), temperature and C-rate. The model is validated using two lithium-ion battery types (LFP-LiFePO<inline-formula><tex-math notation="LaTeX">$_{4}$</tex-math></inline-formula> and NMC-LiNiMnCoO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>) and simulation results are close to reality with an error within <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 1.5% compared to experimental results.
In Silico Assessment of Genetic Variation in PITX2 Reveals the Molecular Mechanisms of Calcium-Mediated Cellular Triggered Activity in Atrial Fibrillation*

Genome-wide association studies have identified genetic variants including rs13143308T in the homeobox gene Pitx2 associated with atrial fibrillation (AF) populations. However, the molecular mechanisms leading to AF due to the rs13143308T variant are poorly understood. Therefore, this study aims to investigate the effects of this variant-induced alteration in calcium handling on properties of Ca2+-transients (CaT) and spontaneous calcium-release events (SCaEs). Based on recent experimental data on variants-induced alterations in ryanodine receptor channels (RyR) and sarcoplasmic reticulum (SR) calcium ATPase 2a (SERCA2a), we incorporated modifications to calcium handling into a previously published model of the human atrial cardiomyocyte with a spatial representation of calcium wave propagation. We identified that the rs13143308T variant has a higher incidence of spontaneous membrane depolarizations and amplitude of CaT than atrial myocytes without this variant. We showed a higher density of SCaEs and content of SR Ca2+ in atrial myocytes with the rs13143308T risk variant. Further computational analysis revealed that these calcium-mediated triggered activities were mainly linked to the gain of SERCA2a function but not the RyR2 dysfunction. Taken together, our model provides a powerful tool for assessing the impact of genetic variants in Pitx2, and these simulated results enhance our understanding of the molecular mechanisms underlying Pitx2-induced AF.
Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm’s optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system’s service quality. The theoretical analysis was consistent with the experimental results.
Quantifying the Peak Amplitude Distributions of Electromyogram in Bicep Brachii muscle after Stroke

The objective of this study was to quantify the differences in surface electromyogram (EMG) signal characteristics between affected and contralateral arm muscles of hemispheric stroke survivors. EMG signals were recorded from the biceps brachii muscles using single differential electrodes. Four chronic stroke subjects performed isometric elbow flexions at sub-maximal voluntary contraction levels on both the affected and contralateral limbs. The force generated on the contralateral side was matched to the force generated on the affected side. We observed different types of EMG activation on the affected side compared to the contralateral side.Specifically, two subjects showed lower RMS EMG activity on the affected side whereas two subjects showed greater EMG activity on the affected side compared to the contralateral side. Analysis of the peak amplitudes of the EMG activity showed greater number of peaks in the EMG on affected side compared to the contralateral side in all subjects. The histogram of the peak amplitudes showed greater number of smaller peak amplitudes in subjects with lower EMG activity on the affected side suggesting a reliance on smaller motor units. Our combined EMG signal analysis techniques on one set of recorded signals provides insight regarding potential mechanisms of weakness.Clinical Relevance— Decoding neural information from surface EMG signals without decomposition into individual motor units could provide clinicians with quick insight about disease progress and potential treatment.
Development of A Learning-Based Terrain Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs*

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand torque assistance to wheelchair users. Although the available power can reduce the physical load of wheelchair propulsion, it may also cause maneuverability and controllability issues. Commercially-available PAPAW controllers are insensitive to environmental changes, leading to inefficient and/or unsafe wheelchair movements. In this regard, adaptive velocity/torque control strategies could be employed to improve safety and stability. To investigate this objective, we propose a context-aware sensory framework to recognize terrain conditions. In this paper, we present a learning-based terrain classification framework for PAPAWs. Study participants performed various maneuvers consisting of common daily-life wheelchair propulsion routines on different indoor and outdoor terrains. Relevant features from wheelchair frame-mounted gyroscope and accelerometer measurements were extracted and used to train and test the proposed classifiers. Our findings revealed that a one-stage multi-label classification framework has a higher accuracy performance compared to a two-stage classification pipeline with an indoor-outdoor classification in the first stage. We also found that, on average, outdoor terrains can be classified with higher accuracy (90%) compared to indoor terrains (65%). This framework can be used for real-time terrain classification applications and provide the required information for an adaptive velocity/torque controller design.
Impact of Environmental Parameters on SNR and RSS in LoRaWAN

Long Range Wide Area Networks (LoRaWAN) are widely used in Internet of Things (IoT) applications, as they target key requirements of IoT such as secure bi-directional communication, mobility and localization services. LoRaWAN uses Long Range (LoRa) physical layer and defines Media Access Control layer and general system architecture. One of the main advantage of the LoRa is flexible configuration, which means that it is possible to increase/decrease the bit rate while decreasing/increasing the range. This feature can be very useful in case when propagation conditions change rapidly and frequently, because it allows to configure spreading factor according to the channel conditions. In this paper, we conducted a study of environmental parameters impact in LoRaWAN. We presented design and realization of the testbed for analysis of the impact of temperature and relative humidity changes on Received Signal Strength (RSS) and Signal to Noise Ratio (SNR) with manual change of data rate/spreading factor. Our results showed that increase of temperature and relative humidity lead to noticeable degradation of network performances in terms of RSS and SNR. Moreover, we observed that snowing lead to high fluctuations in SNR and RSS when Adaptive Data Rate (ADR) is disabled.
Fuzzy Analytical Queries: A New Approach to Flexible Fuzzy Queries

A new approach to the use of fuzzy terms in analytic functions which constitute a standard part of the SQL syntax is proposed. The motivation is that though extensions of the SQL queries including linguistic (fuzzy) terms have been widely used as they have made it possible to better and more directly represent complex requirements of a human user searching a relational database, notably for generating linguistic summaries, there has been little attention paid so far to the use of analytic functions in this context. Analytic functions can provide for an attractive way of analysing data in a relational database and may be possibly adopted as a tool to generate even more sophisticated linguistic summaries. In this paper we study a basic structure of standard (crisp) analytic functions use and point out some opportunities to extend it using linguistic terms. A starting point for our discussion is the mechanism of grouping rows in SQL as it is one of the most important components of the application of analytic functions.
Design of Photonic Filter Using Metamaterial Based Defected Ternary Structure Under Normal Incidence

Ripple factor of ternary photonic bandpass filter is analytically computed around 1.55 μm under normal incidence of electromagnetic wave. Parallel nanorod is considered as the metamaterial for the simulation purpose with air gap, and transfer matrix technique is adopted for the investigation. Improved noise rejection characteristics are obtained compared to the published results for positive index materials. Different structural parameters are varied to observe the bandwidth as well as noise variation for optimized performance. The structure is taken as defected one, where the density of point defect is within the limit of fabrication, so that the simulated findings can be taken as realistic.
Estimating Fetal Age by Fetal Maternal Heart Rate Coupling Parameters

Beat-by-beat maternal and fetal heart couplings were reported to be evident throughout the fetal development. However, it is still unknown whether maternal-fetal heartbeat coupling parameters are associated with fetal development, and the potential interrelationships. Therefore, this study aims to investigate the associations of coupling parameters with fetal gestational age by multivariate regression models. Ten min abdominal lead-based maternal and fetal ECG signals were collected from 16 healthy pregnant women with healthy singleton pregnancies (19-32 weeks). Maternal and Fetal Heart Rate Variability (MHRV and FHRV) values as well as maternal-fetal heart rate coupling (strength, measured by A) parameters at various coupling ratios (associated with different Maternal:Fetal heartbeat ratios of 1:2, 1:3, 2:3, 2:4, 3:4, and 3:5) were calculated. Based on those features stepwise multivariate regression models were constructed by validating against the gold standard gestational age identified by crown-rump length from doppler echocardiogram. Among all models, the best model (Root Mean Square Error, RMSE=1.92) was found to be significantly (p<0.05) associated with mean fetal heart rate, mean maternal heart rate, standard deviation of maternal heart rate, λ[1:3], λ[2:3], λ[2:4]. Correlation coefficients and Bland Altman plots were constructed to statistically validate the results. The model developed based on coupling parameters only, showed the second-best performance (RMSE=2.50). Therefore, combining maternal and fetal heart rate variability parameters with maternal-fetal heart rate coupling values (rather than considering FHRV or MHRV parameters only) is found to be better associated with fetal development.Clinical relevance— This is a brief additional statement on why this might be of interest to practicing clinicians. Example: This establishes the anesthetic efficacy of 10% intraosseous injections with epinephrine to positively influence cardiovascular function.