Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel operation of inverters by using Model Predictive Control in Islanded Microgrid

With the energy demand of the world increasing day-by-day, aging the present power system infrastructure and the increasing integration of renewable energy resources in quenching the load demand, the development of microgrids (MGs) is imminent. The performance analysis of AC MG and DC MG is made based on the concentration of the type of distributed energy resources (DER) in a development, availability of energy storage systems (ESS), use of converters and the economic/physical feasibility of connection and synchronization with the utility grid. The importance and demand of renewable resources has been rising in wake of the rising fuel costs and limited availability of conventional fuel sources demand the increased penetration of solar PV and wind energy into the grid. This can be implemented with the help of parallel inverter-based MG technology, which can not only help integrate the renewable energy into the main grid, reducing the load on fossil fuels, but also increase the reliability of electric power supply as MGs can operate in stand-alone mode as well, independent of the grid. This requires robust, fast and precise control system algorithms which can control the AC bus voltage and frequency even with variable loads associated with the MGs. Two different control algorithms, conventional droop control and Model Predictive Control (MPC) based droop control can be implemented for independent control of the MG. In MPC based operation, the voltage and frequencies are predicted and comparing it through a cost function with the reference value generated, the appropriate switching pattern of VSI is generated to produce the output voltage. In this paper, these two control methods have been implemented and their performance with active and reactive power sharing are compared.
Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices

Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition. Hand gestures are a form of non-verbal communication and can be used as a very important part of modern human–computer interaction systems. Currently, audio and video modalities are easily accessible by sensors of mobile devices. However, there is no out-of-the-box solution for automatic audio-visual speech and gesture recognition. This study introduces two deep neural network-based model architectures: one for AVSR and one for gesture recognition. The main novelty regarding audio-visual speech recognition lies in fine-tuning strategies for both visual and acoustic features and in the proposed end-to-end model, which considers three modality fusion approaches: prediction-level, feature-level, and model-level. The main novelty in gesture recognition lies in a unique set of spatio-temporal features, including those that consider lip articulation information. As there are no available datasets for the combined task, we evaluated our methods on two different large-scale corpora—LRW and AUTSL—and outperformed existing methods on both audio-visual speech recognition and gesture recognition tasks. We achieved AVSR accuracy for the LRW dataset equal to 98.76% and gesture recognition rate for the AUTSL dataset equal to 98.56%. The results obtained demonstrate not only the high performance of the proposed methodology, but also the fundamental possibility of recognizing audio-visual speech and gestures by sensors of mobile devices.
The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives

In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric component which are asymptotically normal and converge at parametric rate. However, smoothing can inflate the error in the normal approximation, so that refined approximations are of interest, especially in sample sizes that are not enormous. We show that a bootstrap distribution achieves a valid Edgeworth correction in case of density-weighted averaged derivative estimates of semiparametric index models. Approaches to bias-reduction are discussed. We also develop a higher order expansion, to show that the bootstrap achieves a further reduction in size distortion in case of two-sided testing. The finite sample performance of the methods is investigated by means of Monte Carlo simulations from a Tobit model.
Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis

Background Hemorrhagic transformation (HT) following reperfusion therapies for acute ischaemic stroke often predicts a poor prognosis. This systematic review and meta-analysis aims to identify risk factors for HT, and how these vary with hyperacute treatment [intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)]. Methods Electronic databases PubMed and EMBASE were used to search relevant studies. Pooled odds ratio (OR) with 95% confidence interval (CI) were estimated. Results A total of 120 studies were included. Atrial fibrillation and NIHSS score were common predictors for any intracerebral hemorrhage (ICH) after reperfusion therapies (both IVT and EVT), while a hyperdense artery sign (OR = 2.605, 95% CI 1.212–5.599, I2 = 0.0%) and number of thrombectomy passes (OR = 1.151, 95% CI 1.041–1.272, I2 = 54.3%) were predictors of any ICH after IVT and EVT, respectively. Common predictors for symptomatic ICH (sICH) after reperfusion therapies were age and serum glucose level. Atrial fibrillation (OR = 3.867, 95% CI 1.970–7.591, I2 = 29.1%), NIHSS score (OR = 1.082, 95% CI 1.060–1.105, I2 = 54.5%) and onset-to-treatment time (OR = 1.003, 95% CI 1.001–1.005, I2 = 0.0%) were predictors of sICH after IVT. Alberta Stroke Program Early CT score (ASPECTS) (OR = 0.686, 95% CI 0.565–0.833, I2 =77.6%) and number of thrombectomy passes (OR = 1.374, 95% CI 1.012–1.866, I2 = 86.4%) were predictors of sICH after EVT. Conclusion Several predictors of ICH were identified, which varied by treatment type. Studies based on larger and multi-center data sets should be prioritized to confirm the results. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=268927, identifier: CRD42021268927.
Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

Federated Learning (FL) presents a mechanism to allow decentralized training for machine learning (ML) models inherently enabling privacy preservation. The classical FL is implemented as a client-server system, which is known as Centralised Federated Learning (CFL). There are challenges inherent in CFL since all participants need to interact with a central server resulting in a potential communication bottleneck and a single point of failure. In addition, it is difficult to have a central server in some scenarios due to the implementation cost and complexity. This study aims to use Decentralized Federated learning (DFL) without a central server through one-hop neighbours. Such collaboration depends on the dynamics of communication networks, e.g., the topology of the network, the MAC protocol, and both large-scale and small-scale fading on links. In this paper, we employ stochastic geometry to model these dynamics explicitly, allowing us to quantify the performance of the DFL. The core objective is to achieve better classification without sacrificing privacy while accommodating for networking dynamics. In this paper, we are interested in how such topologies impact the performance of ML when deployed in practice. The proposed system is trained on a well-known MINST dataset for benchmarking, which contains labelled data samples of 60K images each with a size $28\times 28$ pixels, and 1000 random samples of this MNIST dataset are assigned for each participant’ device. The participants’ devices implement a CNN model as a classifier model. To evaluate the performance of the model, a number of participants are randomly selected from the network. Due to randomness in the communication process, these participants interact with the random number of nodes in the neighbourhood to exchange model parameters which are subsequently used to update the participants’ individual models. These participants connected successfully with a varying number of neighbours to exchange parameters and update their global models. The results show that the classification prediction system was able to achieve higher than 95% accuracy using the three different model optimizers in the training settings (i.e., SGD, ADAM, and RMSprop optimizers). Consequently, the DFL over mesh networking shows more flexibility in IoT systems, which reduces the communication cost and increases the convergence speed which can outperform CFL.
PAX2 and CAKUT Phenotypes: Report on Two New Variants and a Review of Mutations from the Leiden Open Variation Database

PAX2 is a transcription factor expressed during embryogenesis in the eye, ear, CNS, and genitourinary tract, and is one of the major regulators of kidney development. Mutations in this gene are associated with papillorenal syndrome (PAPRS), a genetic condition characterized by optic nerve dysplasia and renal hypo/dysplasia. In the last 28 years, many cohort studies and case reports highlighted PAX2’s involvement in a large spectrum of kidney malformations and diseases, with or without eye abnormalities, defining the phenotypes associated with PAX2 variants as “PAX2-related disorders”. Here, we reported two new sequence variations and reviewed PAX2 mutations annotated on the Leiden Open Variation Database 3.0. DNA was extracted from the peripheral blood of 53 pediatric patients with congenital abnormalities of the kidney and urinary tract (CAKUT). PAX2 gene-coding exonic and flanking intronic regions were sequenced with Sanger technology. Two unrelated patients and two twins carrying one known and two unknown PAX2 variations were observed. The frequency of PAX2-related disorders in this cohort was 5.8%, considering all CAKUT phenotypes (16.7% in the PAPRS phenotype and 2.5% in non-syndromic CAKUT). Although PAX2 mutations have a higher frequency in patients with PAPRS or non-syndromic renal hypoplasia, from the review of variants reported to date in LOVD3, PAX2-related disorders are detected in pediatric patients with other CAKUT phenotypes. In our study, only one patient had a CAKUT without an ocular phenotype, but his twin had both renal and ocular involvement, confirming the extreme inter- and intrafamilial phenotypic variability.
Modeling Wind Effects on Shallow Water Waves

AbstractA mechanism for the growth of waves by wind is included in a time-domain Boussinesq-type model. To facilitate direct analysis of the effect of wind on nonlinear wave–wave interactions over a flat bottom, a set of three harmonic evolution equations is derived from the time-domain model. These equations simulate the evolution of the principal components of three-wave (triad) nonlinear interactions, now including the effect of wind. A case of wave recurrence, in which energy is cycled between three harmonics, shows that a following wind can increase energy exchange to higher harmonics owing to nonlinearity, whereas an opposing wind suppresses this interaction. The time-domain model is then used to simulate wave propagation over a planar slope in the presence of wind. It is shown that wave growth is assisted by onshore winds and hindered by offshore winds. In addition, wave skewness and asymmetry, which quantify wave shape, are also similarly affected by wind direction. The results also show that the .
Neuroprotective Effect of Methanolic Ajwa Seed Extract on Lipopolysaccharide-Induced Memory Dysfunction and Neuroinflammation: In Vivo, Molecular Docking and Dynamics Studies

Islamic literature has indicated that daily consumption of Ajwa dates heals a variety of chronic diseases and disorders. The current research investigates the neuroprotective effect of methanolic Ajwa seed extract (MASE) on lipopolysaccharide (LPS)-induced cognitive deficits using multiple approaches. For animal studies, MASE (200 and 400 mg/kg, p.o.) was administrated for thirty consecutive days, and four doses of LPS (250 µg/kg, i.p.) were injected to induce neurotoxicity. Memory functions were evaluated using elevated plus-maze and novel object recognition tests. Acetylcholine (ACh) and neuroinflammatory markers (cyclooxygenase (COX)-2, tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-10, and transforming growth factor (TGF)-β1) were estimated in brain tissues. Studies of molecular docking and dynamics were conducted to provide insight into the molecular-level mechanisms. MASE administration resulted in a significant reversal of LPS-induced memory impairment in both maze models. Both doses of MASE elevated the ACh levels in an LPS-treated rat brain. In addition, the extract lowered COX-2 and proinflammatory cytokines (TNF-α and IL-6) while increasing anti-inflammatory cytokines (IL-10 and TGF-β1) in LPS-treated brain tissues. Molecular modeling results revealed that the compound’s ellagic acid, epicatechin, catechin, kaempferol, quercetin, and apigenin have the potential to act as a dual inhibitor of acetylcholinesterase (AChE) and COX-2 and can be responsible for the improvement of both cholinergic and inflammatory conditions, while the cinnamic acid, hesperidin, hesperetin, narengin, and rutin compounds are responsible only for the improvement of cholinergic transmission. The above compounds acted by interacting with the key residues Trp84, Asp72, Gly118, Ser200, Tyr334, and His440, which are responsible for the hydrolysis of ACh in AChE, while the COX-2 is inhibited by interacting with the residues (Val349, Leu352, Tyr355, Tyr385, Ala527, Ser530, and Leu531) of the hydrophobic channel. By promoting cholinergic activity and protecting neuroinflammation in the rat brain, MASE provides neuroprotection against LPS-induced cognitive deficits. Our preliminary findings will help with further drug discovery processes related to neuroinflammation-related neurodegeneration.
The Retrieval of Land Surface Pressure from MERIS Measurements in the Oxygen A Band

Abstract Measurements of the Medium-Resolution Imaging Spectrometer (MERIS) on the Environmental Satellite (Envisat) are used for the retrieval of surface pressure above land and ice surfaces. The algorithm is based on the exploitation of gaseous absorption in the oxygen A band at 762 nm. The strength of absorption is directly related to the average photon pathlength, which in clear-sky cases above bright surfaces is mainly determined by the surface pressure, with minor influences from scattering at aerosols. Sensitivity studies regarding the influences of aerosol optical thickness and scale height and the temperature profile on the measured radiances are presented. Additionally, the sensitivity of the retrieval to the accuracy of the spectral characterization of MERIS is quantified. The algorithm for the retrieval of surface pressure (SPFUB) is presented and validated against surface pressure maps constructed from ECMWF sea level pressure forecasts in combination with digital elevation model data. The ac.
Surface Wave Measurements from Subsurface Floats

AbstractPressure gradient measurements on a subsurface Lagrangian float are used to measure the spectrum of surface waves for 100 days of measurements at Ocean Weather Station Papa. Along Lagrangian trajectories of surface waves, the pressure is constant and the vertical pressure gradient fluctuations equal the Eulerian fluctuations at the mean float depth to second order in wave height. Measurement of the pressure difference between the top and the bottom of the float can thus be used to measure the waves. Corrections for the wave decay with depth, for the vertical motion of the float, for the finite sampling interval, and for the sampling noise (among others) are necessary to obtain accurate results. With these corrections, scalar spectra accurately match those from a nearby Waverider buoy for significant wave heights greater than about 3 m. For smaller wave heights, noise in the pressure measurements biases the float spectral measurements. Significant wave height is measured with an rms error of 0.37 m.
Research on Intelligent Traffic Safety Education System Based on Facial State Recognition

With the continuous development of the traffic industry, the continuing education of the drivers also urgently needs to be improved under the background of epidemic normalization. In order to improve the manager′s learning status of the online training of employees, we designed a traffic safety education system based on real-time facial recognition. First of all, high-precision face recognition is achieved through a lightweight face recognition network. Head posture is estimated based on the 3D rotation of the face. When the set threshold value is reached, the warning “Please drive carefully” pops up to ensure the learning effect of drivers and help managers avoid the loss and risk of traffic accidents caused by drivers′ weak safety awareness.
Investigation on Antioxidant Activity and Different Metabolites of Mulberry (Morus spp.) Leaves Depending on the Harvest Months by UPLC–Q-TOF-MS with Multivariate Tools

The changes in active components in mulberry leaves harvested in different months and their antioxidant activities were investigated. Ultra-high-performance liquid chromatography–tandem quadrupole time-of-flight mass spectrometry (UPLC–Q-TOF-MS) with multivariate statistical tools was used to investigate the chemical constituents in the extracts of mulberry leaves. The results indicated that mulberry leaves were rich in phenolic acids, flavonoids, organic acids, and fatty acid derivatives. In addition, 25 different compounds were identified in the different batches of mulberry leaves. The 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity was measured to evaluate the in vitro antioxidant activities of mulberry leaves. Among the four batches, batch A, harvested in December, exhibited the strongest DPPH radical-scavenging activity, while batch B, harvested in March, showed the weakest activity. This was related to the total phenolic content in the mulberry leaves of each batch. The optimal harvest time of mulberry leaves greatly influences the bioactivity and bioavailability of the plant.
Design of Controller for Brushless DC Motor Using a Hybrid Optimization Technique

Brushless DC motors (BLDCs) are utilized in a diverse range of businesses and other contexts. BLDC motors have an advantage over other types of motors as a result of their exceptional power density as well as the ease with which they can be controlled. Using a hybrid optimization strategy, this paper presents the design of a PI controller that can be used for BLDC motors. The controller is designed using the Particle Swarm Optimization (PSO) technique in conjunction with fuzzy logic. The performance of the proposed control scheme is validated by comparing it with the PI controller that was designed using three other well-known optimization techniques, i.e., bacterial forging PSO (BF-PSO), ant colony optimization (ACO), and simulation annealing techniques are some of these methods (SA). MATLAB was used to successfully implement BLDC motor speed control using the controllers that were designed. A comparative analysis of the designed controllers was carried out based on performance indices, sensitivity, complementary sensitivity functions, time response, frequency response, and the results of simulations. It has been observed through comparative research that PI controllers based on PSO-Fuzzy have better performance.
Effect of Dispersant on Disperse Dyeing in Silicone Waterless Dyeing System

Traditional water-based dyeing of polyester textiles usually generates burdensome processes and a great deal of wastewater, which can no longer meet the green and sustainable developments in the textile dyeing industry. In the silicone waterless dyeing system, polyester textiles can be dyed with disperse dye without water. However, the dyeing performance of polyester textiles is influenced by the dispersant. In this study, the relationship between the properties of dispersants and disperse dyeing performance was studied. When the amount of dispersant NNO (2-Naphthalenesulfonic acid) was 1.2%, the exhaustion of disperse red 177 and the final K/S value of the dyed fabric improved to 94.18% and 14.73, respectively. However, the exhaustion of disperse red 177 was reduced from 90.73% to 82.61%, and the final K/S value of the dyed fabric was decreased from 14.77 to 14.01 when the dosage of MF (Naphthalenesulfonic acid) was 1.2%. Compared with different dyeing systems, the final uptake of disperse red 177 was 93.81% and 94.18% in traditional water-based and silicone waterless dyeing systems and the K/S value of the dyed fabric was almost the same. The washing and rubbing fastness (wet and dry) of the dyed fabric were found to be at a level of 4 or 4–5, and the light fastness of the dyed fabric was 3–4. If only the dispersant was added in the silicone waterless dyeing system, there was no leveling problems on dyed samples. Moreover, the maximum absorption wavelength of disperse red 177 was not changed after adding the dispersant. With an increasing amount of dispersant NNO, the solubility of the dye in the silicone solvent decreased, but it increased with an increasing amount of dispersant MF. In the relationship between dye exhaustion and dye solubility in a silicone waterless dyeing system, the exhaustion of dye was linearly and inversely proportional to the dye solubility. A dispersant with better hydrophilicity can decrease the solubility of the dye in dyeing media, and the dyeing performance of dye is better. Compared with previous studies, the exhaustion of dye was consistent with the ClogP value (hydrophobic constant) of the dyeing accelerant. Therefore, a dispersant with high hydrophilicity can reduce the solubility of dye and improve the exhaustion of disperse dye in a silicone waterless dyeing system. Moreover, the color fastness of the dyed fabric did not change before and after adding the dispersant.
Evidence of Flavonoids on Disease Prevention

A growing body of evidence highlights the properties of flavonoids in natural foods for disease prevention. Due to their antioxidative, anti-inflammatory, and anti-carcinogenic activities, flavonoids have been revealed to benefit skeletal muscle, liver, pancreas, adipocytes, and neural cells. In this review, we introduced the basic classification, natural sources, and biochemical properties of flavonoids, then summarize the experimental results and underlying molecular mechanisms concerning the effects of flavonoid consumption on obesity, cancers, and neurogenerative diseases that greatly threaten public health. Especially, the dosage and duration of flavonoids intervening in these diseases are discussed, which might guide healthy dietary habits for people of different physical status.