Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Constrained Stabilization Control Using Control Lyapunov and Control Barrier Function in the Presence of Measurement Noises

A lot of kinds of constraints must be considered to control real systems. In particular, state constraints are closely related to safety and stability of systems. Therefore, many researchers have proposed controllers to achieve good control performances without violating given state constraints. Recently, a new method to solve such constraint control problems has been proposed, which is called Control Lyapunov Function and Control Barrier Function based Quadratic Programs (CLF-CBF-QP). This method calculates an optimal control input by solving a quadratic problem under two kinds of constraints for output zeroing control and satisfying given constraints. It can achieve good control performances in the nominal case, however it is impossible to achieve good results in the presence of disturbances. This paper proposes a robust CLF-CBF -QP controller with Unscented Kalman Filter (UKF) which has an ability to attenuate effects of state disturbances and measurement noises.
Sparse Representation of Sensor Network Signals Based on the K-SVD Algorithm

The sparse basis of signals plays a key role in signals processing of wireless sensor networks (WSNs). However, the existing sparse bases, such as principal component analysis (PCA) and discrete cosine transform (DCT), do not support a good recovery effect in WSNs. In this paper, the general K-SVD (K-Means Singular Value Decomposition) is optimized and a new adaptive overcomplete dictionary (K-SVD-DCT) is constructed by extracting features of distributed WSN signals. First of all, we normalize the data and select the DCT matrix as the initial training dictionary D of the K-SVD algorithm, and then use the orthogonal matching pursuit (OMP) method to carry out sparse decomposition on signals, obtaining the sparse representation matrix. Then the dictionary atom is upgraded by iterating D. Eventually, K-SVD-DCT for sensor network signals' sparse representation is obtained after multiple iterations. We evaluate the performances of overcomplete dictionaries constructed by three initial training dictionaries. The experimental results show that the recovery errors of using the K-SVD-DCT are smaller than that of the PCA basis and are similar to that of the DCT basis. However, the successful recovery rate (8.0%) of the DCT basis is much lower than that of the K-SVD-DCT (82%).
Cognitive Impairment and Risk of All‐Cause and Cardiovascular Disease Mortality Over 20‐Year Follow‐up: Results From the BLSA

Background Cognitive impairment may increase the risk of all‐cause and cardiovascular disease (CVD) mortality. This study examined the association between cognitive function and risk of all‐cause and CVD mortality among the elderly in Beijing, China. Methods and Results A total of 1996 participants aged ≥55 years at baseline were enrolled from the BLSA (Beijing Longitudinal Study of Aging). Cognitive function was assessed using the Mini‐Mental State Examination (MMSE), and participants were categorized as: <18, 18 to 23, 24 to 27, and 28 to 30. Cox proportional hazard models were used to estimate the association. Hazard ratio (HR) and 95% confidence interval (CI) were reported. During a 20‐year follow‐up, 1122 (56.21%) participants died, 478 (42.60%) of whom died of CVD. Compared with MMSE scores of 28 to 30, participants with MMSE scores of <18 were independently associated with all‐cause mortality (hazard ratio, 2.14; 95% confidence interval, 1.59–2.87; P<0.001) and CVD mortality (hazard ratio, 4.52; 95% confidence interval, 2.80–7.30, P<0.001). Each 5‐point decrease in MMSE score was associated with a 34% increased risk of all‐cause mortality and a 56% increased risk of CVD mortality. This relationship remained statistically significant after using the competing risk model to consider non‐CVD death as a competing risk event. Conclusion Cognitive impairment measured by MMSE score was associated with elevated risk of all‐cause and CVD mortality among the elderly in Beijing, China.
Effects of Permaculture Practices on Soil Physicochemical Properties and Organic Matter Distribution in Aggregates: A Case Study of the Bec-Hellouin Farm (France)

The limitations of conventional agriculture have accelerated the need for a transition to an environmentally and economically sustainable agricultural model. In this regard, the role played by soil organic matter (SOM) is key. Here, we aimed to study the impact of permaculture and biointensive micro-gardening practices, characterized by intensive cultivation, the use of large and localized organic inputs and the non-use of mineral fertilizers and pesticides, on soil physico-chemical properties and SOM distribution in aggregate-size fractions. The physico-chemical properties of soils in permaculture farming implemented for seven years were compared with a soil under pasture. A soil experiencing conventional agriculture practices in similar geopedoclimatic conditions was simultaneously studied. Soils were separated into four aggregate-size fractions, into which organic carbon (OC) concentrations have been measured. The major soil physico-chemical properties were measured on the bulk soils. The concentrations of total OC and nitrogen (N) in bulk soils were higher under permaculture practices, due to significant inputs of manure and compost, resulting in higher concentrations of the bioavailable nutrients Ca, Mg, K and P. The permaculture practices sustained a level of macroaggregation similar to that of the pasture, and well above that of conventional practices. OC concentrations increased in the 250-2000 µm, 50-250 µm and occluded 50-250 µm fractions, but not in the <50 µm fraction compared to pasture. This can be explained by the too short duration of permaculture practices for allowing the decomposition of coarse particulate organic matter (POM) into fine sized SOM that is more associated with mineral particles in free clay and silt size fraction. The higher OC stocks in permaculture are therefore mainly governed by the coarse POM fraction. Our results show that permaculture/biointensive micro-gardening practices enhance SOM storage and modify the distribution of SOM in aggregate size fractions, while substantially improving nutrient bioavailability and suggests that permaculture practices strongly affect soil properties. The effects of permaculture practices on soil properties would certainly vary depending on geopedoclimatic context, justifying the need to implement the approach for other soil types. Finally, we recommend to compare biogeochemical budget and nutrients use efficiency of permaculture gardening with conventional horticulture gardening.
DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification

We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classification. Opposing to deep architectures which have been used for the task, the proposed CNN is much simpler However, the CNN’s convolutional layer is able to support convolutional kernels with different sizes, and therefore, capable of learning features at multiple temporal resolutions. In addition, the 1-max pooling strategy is employed at the pooling layer to better capture the shift-invariance property of EEG signals. We further propose a method to discriminatively learn a frequency-domain filter bank with a deep neural network (DNN) to preprocess the time-frequency image features. Our experiments show that the proposed 1-max pooling CNN performs comparably with the very deep CNNs in the literature on the Sleep- EDF dataset. Preprocessing the time-frequency image features with the learned filter bank before presenting them to the CNN leads to significant improvements on the classification accuracy, setting the state- of-the-art performance on the dataset.
SIV/SHIV-Zika co-infection does not alter disease pathogenesis in adult non-pregnant rhesus macaque model

Due to the large geographical overlap of populations exposed to Zika virus (ZIKV) and human immunodeficiency virus (HIV), understanding the disease pathogenesis of co-infection is urgently needed. This warrants the development of an animal model for HIV-ZIKV co-infection. In this study, we used adult non-pregnant macaques that were chronically infected with simian immunodeficiency virus/chimeric simian human immunodeficiency virus (SIV/SHIV) and then inoculated with ZIKV. Plasma viral loads of both SIV/SHIV and ZIKV co-infected animals revealed no significant changes as compared to animals that were infected with ZIKV alone or as compared to SIV/SHIV infected animals prior to ZIKV inoculation. ZIKV tissue clearance of co-infected animals was similar to animals that were infected with ZIKV alone. Furthermore, in co-infected macaques, there was no statistically significant difference in plasma cytokines/chemokines levels as compared to prior to ZIKV inoculation. Collectively, these findings suggest that co-infection may not alter disease pathogenesis, thus warranting larger HIV-ZIKV epidemiological studies in order to validate these findings.
Atypical Parkinsonism‐Dystonia Syndrome Caused by a Novel DJ1 Mutation

We describe a sporadic case of atypical parkinsonism‐dystonia of subacute onset at the age of 16 years in a male from a consanguineous family. He showed marked orofacial dystonia, levodopa‐induced dyskinesia, and a stereotyped bilateral eye‐pressing movement disorder. We combined Sanger sequencing of candidate genes, homozygosity mapping, and whole‐exome sequencing. A homozygous mutation was identified disrupting a splice site in exon 5 of the DJ1 (PARK7) gene. Clinical details and a video are provided. DJ1 mutations are a rare cause of atypical complex parkinsonism. Exome sequencing is efficacious in identifying the causal gene variant.
The Vehicular Social Network (VSN)-Based Sharing of Downloaded Geo Data Using the Credit-Based Clustering Scheme

This paper proposed a clustering scheme named as the credit-based clustering (CBC) scheme for point of interests’ (POIs’) geo data sharing in vehicular social network (VSN). In the proposed CBC scheme, a number of vehicles that belong to the same VSN can form a cluster to download POIs’ geo data when they are approaching to a new set of POIs. One vehicle is selected as the cluster head to download POIs’ geo data using its cellular network and shares the downloaded POIs’ geo data to its cluster members using IEEE 802.11p network. This paper 1) uses GPS to get vehicles’ locations to calculate the timing of triggering the clustering process, 2) proposes a clustering method to organize a group of vehicles that belong to the same VSN and are proximate with each other for a while during their touring to become a cluster, and 3) deploys a credit scheme to evaluate the credit that the cluster head can get and each cluster member needs to pay to encourage all vehicles to share POIs’ geo data. The simulation results show that the proposed scheme can achieve the goal of fairness, the higher successful ratio of the complete sharing of downloaded POIs’ geo data and the better receiving efficiency.
Wound Pathogens: Investigating Antimicrobial Activity of Commercial Essential Oil Combinations against Reference Strains

This study aimed to determine the antimicrobial activity of 247 essential oil combinations against the reference strains of wound pathogens. Essential oil combinations were investigated for antimicrobial activity against five pathogens. The minimum inhibitory concentration (MIC) assay was used and the fractional inhibitory concentration index (ΣFIC) calculated to determine interactions between selected oils. Twenty‐six combinations displayed broad‐spectrum antimicrobial activity against all five reference strains and several displayed synergy against more than one pathogen. The combination of Santalum austrocaledonicum (sandalwood) with Commiphora myrrha (myrrh) displayed noteworthy antimicrobial activity against all five reference strains and synergy against four (MIC values 0.03–1.00 mg/ml and ΣFIC values 0.19–1.00 mg/ml) pathogens. No antagonism was observed. Santalum spp. and Vetiveria zizanioides essential oils contributed the most to antimicrobial activity in combination. Essential oil combinations are presented as a viable option in wound therapy.
Galla Rhois water extract inhibits lung metastasis by inducing AMPK-mediated apoptosis and suppressing metastatic properties of colorectal cancer cells

Galla Rhois is a commonly used medicine in East Asia for the treatment of several diseases. However, the effects of Galla Rhois on the metastasis of colorectal cancer (CRC) and the underlying molecular mechanisms have not been studied. We investigated the anti-metastatic properties of Galla Rhois water extract (GRWE) on metastatic CRC cells. The effect of GRWE on the viability of colon 26 (CT26) cells was evaluated using WST-8 assay. Annexin V assay and western blot analysis were performed to elucidate the underlying molecular mechanisms involved in apoptosis. GRWE suppressed viability of CT26 cells by inducing apoptosis through the cleavage of caspase-3 and PARP, downregulation of caspase-8, caspase-9, Bcl-2 and Bcl-xL, and upregulation of Bax. Metastatic phenotypes such as epithelial-mesenchymal transition (EMT), migration, and invasion of CRC cells were investigated by real-time reverse transcription polymerase chain reaction, wound healing assay, and matrigel invasion assay, respectively. Non-cytotoxic concentrations of GRWE inhibited EMT in CRC cells by regulating the expression of EMT markers. GRWE attenuated cell migration and invasion through the inhibition of matrix metalloproteinase (MMP)-2 and MMP-9 activity. Moreover, GRWE suppressed colorectal lung metastasis in vivo, suggestive of its potential application for the treatment of colorectal metastasis.
Cystatin C for Risk Stratification in Patients After an Acute Coronary Syndrome

Background Cystatin C (Cys‐C) is a marker of renal function that has shown prognostic value for cardiovascular risk stratification across different patient populations. The incremental value of Cys‐C beyond established cardiac and renal biomarkers remains incompletely explored. Methods and Results SOLID‐TIMI 52 (Stabilization of Plaques Using Darapladib‐Thrombolysis in Myocardial Infarction 52; http://www.clinicaltrials.gov, NCT01000727) randomized patients ≤30 days post–acute coronary syndrome were treated with darapladib or placebo. The association between Cys‐C and long‐term risk (median follow‐up 2.5 years) was assessed in 4965 individuals with adjustments made for clinical variables and other risk markers (eg, estimated glomerular filtration rate, high‐sensitivity troponin I, brain‐type natriuretic peptide, and fibroblast growth factor‐23). The prespecified outcome of interest was cardiovascular death (CVD) or heart failure hospitalization. Cys‐C was strongly correlated with creatinine (r=0.60) and estimated glomerular filtration rate (r=−0.68), moderately correlated with fibroblast growth factor‐23 (r=0.39), and weakly correlated with brain‐type natriuretic peptide (r=0.28) and high‐sensitivity troponin I (r=0.06) (all P<0.0001). After multivariate adjustment, increasing concentration of Cys‐C (per SD of log‐transformed Cys‐C) was significantly associated with a 28% higher hazard of CVD or heart failure hospitalization (hazard ratio [HR] 1.28, 95% confidence interval [CI] 1.12‐1.46, P<0.001), including CVD (HR 1.24, 95% CI 1.04‐1.47, P=0.01) and heart failure hospitalization (HR 1.42, 95% CI 1.19‐1.69, P<0.001). Cys‐C was also associated with a higher hazard of CVD, myocardial infarction, or stroke (HR 1.15, 95% CI 1.04‐1.28, P<0.01), including myocardial infarction (HR 1.17, 95% CI 1.02‐1.33, P=0.02). The addition of Cys‐C to a fully adjusted model without estimated glomerular filtration rate improved the C‐statistic from 0.80 to 0.81 (P=0.03) for CVD or heart failure hospitalization. In contrast, the addition of estimated glomerular filtration rate to a fully adjusted model without Cys‐C failed to improve model discrimination (P=0.17). Conclusions Cys‐C is associated with the risk of adverse outcomes in patients after acute coronary syndrome. This relationship is independent of established and novel biomarkers of the cardiorenal axis.
A Randomised Ensemble Learning Approach for Multiclass Motor Imagery Classification Using Error Correcting Output Coding

Common Spectral Pattern (CSP) algorithm remains predominant for feature extraction from multichannel EEG motor imagery data. However, multiclass classification of from this featureset has been a challenging job. Different approaches have been proposed to be applied on featureset of different EEG subbands to achieve significant classification accuracy. Ensemble learning is very effective in this context to achieve higher accuracy in Brain-Computer Interface (BCI) domain. In this study, we have proposed enhanced classification algorithms to achieve higher classification accuracies. The methods were evaluated against the motor imagery data from Dataset 2a of the publicly available BCI Competition IV (2008). This dataset consists of 22 channels EEG data of 9 subjects for four different movements. A tree based ensemble approach for supervised classification, Extra-Trees algorithm, has been proposed in this paper and also evaluated for its efficacy on this dataset to classify between left hand and right hand movement imaginations. Moreover, this classifier has its inherent capability to select optimum features. Furthermore, in this paper an extension of the binary classification into multiclass domain is also implemented with error correcting output codes (ECOC) approach using the same dataset. Subject-specific frequency bands $\alpha$ (8-12Hz) and $\beta$ (12-30Hz) along with $HG$ (70-100Hz) were considered to extract CSP features. We have achieved an individual peak accuracy of 98ȥ and 84ȥ in binary class and multiclass classification respectively. Furthermore, the results yielded a mean kappa value of 0.58 across all the subjects. This kappa value is higher than of the winner of competition and also from the most of the other approaches applied in this dataset.
Development of a Lightweight Flexible Construction Work Assist Suit Using Pneumatic Rubber Artificial Muscles

We developed an assist suit with lightweight, flexible artificial muscles of pneumatic rubber for reducing muscle load in the lumbar region. We designed two assist forces to control the artificial muscles with pulse width modulation based on the measured EMG of the spinal column muscle and estimated the torque of the hip joint. The experimental results confirmed the developed work assist suit could unload muscle activity during bending and stretching exercises. We also proposed to use an EMG measurement device at the wearer’s temple to control the assist timing and confirmed the feasibility of detecting the intention of the wearer.
tDCS modulates behavioral performance and the neural oscillatory dynamics serving visual selective attention

Transcranial direct‐current stimulation (tDCS) is a noninvasive method for modulating human brain activity. Although there are several hypotheses about the net effects of tDCS on brain function, the field's understanding remains incomplete and this is especially true for neural oscillatory activity during cognitive task performance. In this study, we examined whether different polarities of occipital tDCS differentially alter flanker task performance and the underlying neural dynamics. To this end, 48 healthy adults underwent 20 min of anodal, cathodal, or sham occipital tDCS, and then completed a visual flanker task during high‐density magnetoencephalography (MEG). The resulting oscillatory responses were imaged in the time‐frequency domain using beamforming, and the effects of tDCS on task‐related oscillations and spontaneous neural activity were assessed. The results indicated that anodal tDCS of the occipital cortices inhibited flanker task performance as measured by reaction time, elevated spontaneous activity in the theta (4–7 Hz) and alpha (9–14 Hz) bands in prefrontal and occipital cortices, respectively, and reduced task‐related theta oscillatory activity in prefrontal cortices during task performance. Cathodal tDCS of the occipital cortices did not significantly affect behavior or any of these neuronal parameters in any brain region. Lastly, the power of theta oscillations in the prefrontal cortices was inversely correlated with reaction time. In conclusion, anodal tDCS modulated task‐related oscillations and spontaneous activity across multiple cortical areas, both near the electrode and in distant sites that were putatively connected to the targeted regions.
A Soft Pneumatic Actuator as a Haptic Wearable Device for Upper Limb Amputees: Toward a Soft Robotic Liner

Object manipulation and fluid, goal-directed, movements require sensory information for effective execution. Amputees lose this intrinsic feedback when controlling their artificial limbs and must instead rely on visual information to compensate. Here, we describe an application for providing touch and kinesthetic information to amputees. We report on a soft robotic pneumatic actuator approach that can be incorporated into a prosthetic limb as the silicone suction socket liner itself. This approach alleviates many problems inherent to rigid tactors such as poor trim lines from external mounting, electromyography (EMG) signal contamination, and loss of limb fixation suction due to holes in the liner to pass touch and vibration to the residual limb. We analyzed two soft materials and different chamber geometries to generate a prototype. We characterized the static and dynamic properties of this prototype during operation obtaining a maximum force of 12.5 N at 70 kPa, free displacement of 4.5 mm at 50 kPa, and a bandwidth near 70 Hz. We presented an analytical model that fits well with the experimental data and provided a comparison between this soft pneumatic actuator and other rigid tactor devices. The results of testing the prototype in able-bodied participants and one amputee individual demonstrated that this soft pneumatic actuator achieved good performance at frequencies of 5 and 70 Hz at 60 kPa. In sequential days of training with the prototype participants reported perceptions of wrist flexion/extension and demonstrated learned associations between tapping and hand closing.
A Formula Embedding Approach to Math Information Retrieval

Intricate math formulae, which majorly constitute the content of scientific documents, add to the complexity of scientific document retrieval. Although modifications in conventional indexing and search mechanisms have eased the complexity and exhibited notable performance, the formula embedding approach to scientific document retrieval sounds equally appealing and promising. Formula Embedding Module of the proposed system uses a Bit Position Information Table to transform math formulae, contained inside scientific documents, into binary formulae vectors. Each set bit of a formula vector designates presence of aspecific mathematical entity. Mathematical user query is transformed into query vector, in similar fashion, and the corresponding relevant documents are retrieved. Relevance of a search result is characterized by extent of similarity between the indexed formula vector and the query vector. Promising performance, under moderately constrained situation, substantiates competence of the proposed approach.