Miracle-dz/UniFAR-FTUs huggingface dataset:
Induced $\mathcal {L}_\infty$ Quantized Sampled-Data Control for T–S Fuzzy System With Bandwidth Constraint

This article proposes an induced <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> sampled-data control problem for the Takagi–Sugeno (T–S) fuzzy system with dynamic quantization, in which the communication channel is assumed to be bandwidth-constrained. First, according to the input delay approach, the dynamics of the considered closed-loop system is modeled by a delay system with constrained nonlinear term. Then, for the bandwidth-constrained communication channel, a constraint condition is introduced to ensure that the adjustable parameter of the dynamic quantization strategy is limited by a bounding region. The main purpose of the problem addressed is to design a controller, such that the closed-loop system guarantees the asymptotic stability with an induced <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> performance index. The sufficient conditions of stability with induced <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> control performance are given as the basis of controller design. Furthermore, the induced <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> sampled-data control scheme and the improved updating rule of dynamic parameter are proposed. Finally, an example shows the effectiveness of the the proposed controller design scheme.
Alzheimer Disease as a Vascular Disorder: Nosological Evidence

Background— The main stumbling block in the clinical management and in the search for a cure of Alzheimer disease (AD) is that the cause of this disorder has remained uncertain until now. Summary of Review— Evidence that sporadic (nongenetic) AD is primarily a vascular rather than a neurodegenerative disorder is reviewed. This conclusion is based on the following evidence: (1) epidemiological studies showing that practically all risk factors for AD reported thus far have a vascular component that reduces cerebral perfusion; (2) risk factor association between AD and vascular dementia (VaD); (3) improvement of cerebral perfusion obtained from most pharmacotherapy used to reduce the symptoms or progression of AD; (4) detection of regional cerebral hypoperfusion with the use of neuroimaging techniques to preclinically identify AD candidates; (5) presence of regional brain microvascular abnormalities before cognitive and neurodegenerative changes; (6) common overlap of clinical AD and VaD cognitive symptoms; (7) similarity of cerebrovascular lesions present in most AD and VaD patients; (8) presence of cerebral hypoperfusion preceding hypometabolism, cognitive decline, and neurodegeneration in AD; and (9) confirmation of the heterogeneous and multifactorial nature of AD, likely resulting from the diverse presence of vascular risk factors or indicators of vascular disease. Conclusions— Since the value of scientific evidence generally revolves around probability and chance, it is concluded that the data presented here pose a powerful argument in support of the proposal that AD should be classified as a vascular disorder. According to elementary statistics, the probability or chance that all these findings are due to an indirect pathological effect or to coincidental circumstances related to the disease process of AD seems highly unlikely. The collective data presented in this review strongly support the concept that sporadic AD is a vascular disorder. It is recommended that current clinical management of patients, treatment targets, research designs, and disease prevention efforts need to be critically reassessed and placed in perspective in light of these important findings.
The Effect of Different Nanomaterials Additions in Clay-Based Composites on Electromagnetic Transmission

In this study, clay composites were subjected to electromagnetic transmission testing at frequencies in the region of non-ionizing radiation. Specimens were made with partial substitution of clay with different admixtures by mass. Admixtures used were Fly Ash, four different particle sizes and phases of Titanium Dioxide (TiO2), Zinc Ferrite (ZnFe2O4), Maghemite (γ-Fe2O3) and Antimony Tin Oxide (ATO). The additives were thoroughly (chemically, structurally, morphologically) characterized. The replacement percentage was 5 wt. %. Electromagnetic transmission assessment included S21 transmission coefficient measurements for samples with different additives. The lowest transmission was reported for the clay specimens with ATO and Titanium Dioxide, especially at higher frequencies. A decrease in the transmission parameter with increasing specimen thickness was also confirmed.
Gb_ANR-47 Enhances the Resistance of Gossypium barbadense to Fusarium oxysporum f. sp. vasinfectum (FOV) by Regulating the Content of Proanthocyanidins

Anthocyanidin reductase (ANR) is an important regulator of flavonoid metabolism, and proanthocyanidins, the secondary metabolites of flavonoids, play an important role in the response of plants to pathogenic stress. Therefore, in this study, the expression analysis of the ANR gene family of Gossypium barbadense after inoculation with Fusarium oxysporum f. sp. vasinfectum (FOV) was performed at different time points. It was found that Gb_ANR-47 showed significant differences in the disease-resistant cultivar 06-146 and the susceptible cultivar Xinhai 14, as well as in the highest root expression. It was found that the expression of Gb_ANR-47 in the resistant cultivar was significantly higher than that in the susceptible cultivar by MeJA and SA, and different amounts of methyl jasmonate (MeJA) and salicylic acid (SA) response elements were found in the promoter region of Gb_ANR-47. After silencing GbANR-47 in 06-146 material by VIGS technology, its resistance to FOV decreased significantly. The disease severity index (DSI) was significantly increased, and the anthocyanin content was significantly decreased in silenced plants, compared to controls. Our findings suggest that GbANR-47 is a positive regulator of FOV resistance in Gossypium barbadense. The research results provide an important theoretical basis for in-depth analysis of the molecular mechanism of GbANR-47 and improving the anti-FOV of Gossypium barbadense.
Mean-Field Game Theory Based Optimal Caching Control in Mobile Edge Computing

Mobile edge computing (MEC) can use wireless access network (RAN) to provide users with nearby information technology (IT) services and cloud computing functions, which creates a high-performance and low latency service environment. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission. However, the time-varying and dynamic of the content requests may lead to the base station to cache the useless contents. In this paper, we study a distributed caching optimization problem in edge networks (ENs) with the spatio-temporal requirements. In the considered ENs, the cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the total cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original optimization problem is transformed into a mean field game (MFG). We propose a distributed caching iterative control algorithm that decouples the information interaction between the general SBS and others through the mean field distribution. In addition, we obtain the optimal edge caching control strategy, while the existence and uniqueness of the mean field equilibrium (MFE) can also be guaranteed. Simulation results demonstrate that our proposed caching control algorithm can average reduce 27.12% storage cost and achieve better performance than other existing schemes.
Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2logloss = 0.952) and realized (TSS = 0.959, R2logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.
Development and testing of a stifle function score in dogs

Objective The purpose of this study was to develop and test a quantitative stifle function score (SFS) in dogs with unilateral cranial cruciate ligament disease by combining clinical measures and functional tests. The objective of this study was to compare the proposed SFS to a symmetry index (SI) calculated from objective ground reaction forces (GRFs). We hypothesized that the SFS would have a strong correlation with SI. Methods Dogs with surgically and nonsurgically treated unilateral cranial cruciate ligament rupture and dogs with no known musculoskeletal problems were included in the study. Each dog was scored using the SFS and trotted across a force platform to obtain GRFs and calculate the SI, based on vertical GRFs. Fourteen items were included in the SFS: limb use at a walk, limb use at a trot, lameness at a walk, lameness at a trot, stair climbing, sit-to-stand, dancing, pain response, stifle effusion, thigh circumference/muscle atrophy, stifle extension, stifle flexion, and cranial drawer/tibial thrust, with each item scored based on previously determined criteria. A perfect SFS would receive a score of 100. Results Twenty-seven dogs were included in the study: twenty-one with unilateral cranial cruciate ligament disease and six control dogs. The mean SFS was 71.5 out of 100. To further characterize the association between SFS and SI the degree of gait asymmetry using SI was classified as <5%, 5.1–10%, 10.1–20%, 20.1–25%, and >25% difference between the pelvic limbs for all dogs. The mean SFS for each of the five categories were 97.8, 85.2, 65.4, 63, and 56.4, respectively. Correlation of SI and SFS was −0.863 (p < 0.0001). All of the individuals evaluated tests in the score were significantly correlated with SI except for pain response and stifle flexion. The SFS is in strong agreement with the SI, as confirmed by Bland–Altman analysis. Conclusion The SFS had a significant correlation and agreement with the SI calculated from GRFs. This SFS may be a useful, simple, and inexpensive tool to use in a clinical environment to monitor progression during the rehabilitation and recovery process following unilateral cranial cruciate ligament rupture.
The Impact of 51 Risk Factors on Life Expectancy in Canada: Findings from a New Risk Prediction Model Based on Data from the Global Burden of Disease Study

The aims of this study were (1) to develop a comprehensive risk-of-death and life expectancy (LE) model and (2) to provide data on the effects of multiple risk factors on LE. We used data for Canada from the Global Burden of Disease (GBD) Study. To create period life tables for males and females, we obtained age/sex-specific deaths rates for 270 diseases, population distributions for 51 risk factors, and relative risk functions for all disease-exposure pairs. We computed LE gains from eliminating each factor, LE values for different levels of exposure to each factor, and LE gains from simultaneous reductions in multiple risk factors at various ages. If all risk factors were eliminated, LE in Canada would increase by 6.26 years for males and 5.05 for females. The greatest benefit would come from eliminating smoking in males (2.45 years) and high blood pressure in females (1.42 years). For most risk factors, their dose-response relationships with LE were non-linear and depended on the presence of other factors. In individuals with high levels of risk, eliminating or reducing exposure to multiple factors could improve LE by several years, even at a relatively advanced age.
Social Media Engagement in Two Governmental Schemes during the COVID-19 Pandemic in Macao

Social media engagement is a vehicle for effective communication and engagement between governments and individuals, especially in crises such as the COVID-19 pandemic. Additionally, it can be used to communicate resilience measures and receive feedback. This research aims to investigate public social media engagement with resilience measures related to COVID-19 in Macao. We examined 1107 posts and 791 comments about the government’s face mask supply and consumption voucher schemes on Facebook. Using the Crisis Lifecycle model, we partitioned the data and analyzed the content and engagement of related posts, as well as the word semantics in user comments. Our findings show that social media engagement in these resilience measures is high and positive in the early stages of the pandemic, suggesting social media’s potential in mobilizing society, preserving social resilience, and serving as a two-way communication tool in public health emergencies.
Multi‐slot‐based dynamic clustering in cognitive radio network

The current work investigates the performance of multi‐slot‐based dynamic clustering in a cognitive radio network. For the spectrum sensing, every secondary user utilizes the multi‐slots of sensing time frame and combines the decisions of every multi‐slot, using the OR logic scheme, to get the sub‐local decision. The channel state information of the connections among the secondary users and access points present on the network's access points has been used to cluster the secondary users. To form the clusters, the secondary users with higher quality reporting channels for a specific access point have been chosen. Therefore, the clusters formed, in each iteration, with their respective access points, are dynamic in nature. Every secondary user in a cluster detects the availability of a primary user, makes a decision, and forwards it to its corresponding access point, via the reporting‐channel. Each access point employs the OR rule to merge the decisions of secondary users in the respective clusters, to make a local decision. Finally, the local decisions of all access points have been directed to fusion center, making the final decision about primary user's accessibility. The performance of multi‐slot‐based dynamic clustering has been studied relative to the different parameters, like detection probability, number of clusters, number of multi‐slots, secondary users, and access points. Further, the performance evaluation and comparisons of a multi‐slot‐based cognitive radio network with a single slot, cluster‐based cognitive radio network with non‐clustered, and multi‐slot‐based dynamic cluster with individual multi‐slots as well as dynamic clustering, have been done.
Optimal Model Predictive Controller for Grid-Connected Voltage Source Converters

Grid-connected power electronic converters are the fundamental building blocks of future power electronic-based power systems. Their fast dynamic performance and robustness are the key requirements for ensuring the reliable operation of such systems. This paper proposes a simple optimal model predictive controller (MPC) to control the active and the re-active current of the three-phase voltage source grid-connected converters. Better performance over other state-of-the-art controllers is evaluated based on quantitative criteria comprising the settling time, and the percentage of the overshoot. Finally, the experimental tests are provided to prove the applicability of the proposed approach.
Ideal P2Y12 Inhibitor in Acute Coronary Syndrome: A Review and Current Status

Dual antiplatelet therapy (DAPT) has remained the cornerstone for management of acute coronary syndrome (ACS) over the years. Clopidogrel has been the quintessential P2Y12 receptor (platelet receptor for Adenosine 5′ diphosphate) inhibitor for the past two decades. With the demonstration of unequivocal superior efficacy of prasugrel/ticagrelor over clopidogrel, guidelines now recommend these agents in priority over clopidogrel in current management of ACS. Cangrelor has revived the interest in injectable antiplatelet therapy too. Albeit the increased efficacy of these newer agents comes at the cost of increased bleeding and this becomes more of a concern when combined with aspirin. Which P2Y12i is superior over another has been intensely debated over last few years after the ISAR-REACT 5 study with inconclusive data. Three novel antiplatelet agents are already in the pipeline for ACS with all of them succeeding in phase II studies. The search for an ideal antiplatelet remains a need of the hour for optimal reduction of ischemic events in ACS.
Therapeutic oligonucleotides targeting liver disease

N fatty liver disease (NAFLD) has emerged as a leading cause of chronic liver disease in children. Liver biopsy remains the gold standard for diagnosis of NASH. A scoring system predictive of NASH based on these biomarkers would prove indispensable for avoiding the invasive liver biopsy. The estimated prevalence of pediatric NAFLD is 2.6%-9.6%. Studies estimate that the prevalence of NAFLD among overweight or obese children and adolescents is 24%-77%. Serum biomarkers to diagnose NASH may be divided into four categories: Markers of Inflammation, oxidative stress, apoptosis and fibrosis. TNF-α mRNA cutoff value of 100 ng/mL predicted NASH with a sensitivity of 66.7%, specificity 74.1%, Serum adiponectin, levels are lower in patients with NASH but no definite level for accurate prediction has been determined. Serum CK-18 is an independent predictor of NASH. A cut off value of 380 U/l provided a specificity of 94% and sensitivity of 91%. Ox-LDL and TBARS in serum were higher in biopsy-proven NASH patients. Apart from these a panel comprising of hyaluronic acid (HA), amino-terminal propeptide of type III collagen (PIIINP), and tissue inhibitor of metalloproteinase 1 (TIMP-1) is under evaluation as a predictor of fibrosis. Liver biopsy, considered as the gold standard to diagnose NASH is invasive and costly. Some of the Noninvasive Biomarkers have an established role in the diagnosis of NASH whereas the role of some needs further evaluation. Better noninvasive biomarkers or panels of biomarkers are urgently needed for diagnosis of NASH, providing risk information, and monitoring disease progression and treatment response.
Determination of the Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the Heart Rate Variability Using a Machine Learning Pipeline

Background: We are all aware of day-to-day healthy stress, but, when sustained for long periods, stress is believed to lead to serious physical and mental health issues. Materials and Methods: In this study, we investigated the potential effects of transcutaneous auricular vagus nerve stimulation (taVNS) on stress processing as reflected in the electrocardiogram (ECG)-derived biomarkers of stress adaptability. Stress reflecting biomarkers included a range of heart rate variability metrics: standard deviation of N-N intervals (SDNN), root mean squared of successive differences in heartbeat intervals (RMSSD), low-frequency component, high-frequency component and their ratio (LF, HF, and LF/HF). In addition, we created a machine learning model capable of distinguishing between the stimulated and nonstimulated conditions from the ECG-derive data from various subjects and states. The model consisted of a deep convolutional neural network, which was trained on R-R interval (RRI) data extracted from ECG and time traces of LF, HF, LF/HF, SDNN, and RMSSD. Results: Only LF/HF ratio demonstrated a statistically significant change in response to stimulation. Although the LF/HF ratio is expected to increase during exposure to stress, we have observed that stimulation during exposure to stress counteracts this increase or even reduces the LF/HF ratio. This could be an indication that the vagus nerve stimulation decreases the sympathetic activation during stress inducement. Our Machine Learning model achieved an accuracy of 70% with no significant variations across the three states (baseline, stress, and recovery). However, training an analogous neural network to identify the states (baseline, stress, and recovery) proved to be unsuccessful. Conclusion: Overall, in this study, we showed further evidence of the beneficial effect of taVNS on stress processing. Importantly we have also demonstrated the promising potential of ECG metrics as a biomarker for the development of closed-loop stimulation systems.
Plasma profile tomography for EAST based on integrated data analysis

In this study, a plasma profile reconstruction algorithm based on integrated data analysis (IDA) is proposed, which incorporates various diagnostics and can provide two-dimensional distributions of plasma current and electron density. The IDA algorithm based on Bayesian inference combines limited data from multiple diagnostics and builds models in a probabilistic manner, overcoming the limitations of models based on just external magnetic diagnostics and providing more accurate results. To reduce the probability of unreasonable solutions, two Gaussian priors are established: conditional autoregressive prior and squared exponential kernel function prior, which constrain the plasma current and electron density, respectively. Compared to the models based on only magnetic diagnostics, the IDA model improves the current distribution in the core and increases the accuracy of plasma profile reconstruction.