Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Synthesis of Parallel-Connected Dielectric Filter Using Chain-Function Polynomial

Design and synthesis of parallel connected dielectric filters using chained function polynomials are presented in this paper. This filter will offer reduced sensitivity to fabrication tolerance while preserving its return loss response within the desired bandwidth in comparison to traditional Chebyshev filters. A novel transfer function FN according to chained is derived for fourth and sixth-order filters and the synthesis technique is presented. To demonstrate the feasibility of this approach, the circuit simulation based on parallel connected topology is carried out in ADS while the design and simulation of the fourth-order filter in dielectric technology in HFSS. Considerable sensitivity analysis is conducted to prove a better fabrication tolerance of the filter. In terms of implementation, this design technique will serve as a very useful mathematical tool for any filter design engineer.
Enhanced electrodynamic tether currents due to electron emission from a neutral gas discharge: Results from the TSS‐1R Mission

During the reflight of the first electrodynamic Tethered Satellite System (TSS‐1R) mission, the unplanned separation of the tether at the Orbiter end resulted in the highest tether current during the mission. In the moments just prior to the tether separation with 19.7 km of tether deployed and a generated electromotive force (EMF) of 3482 V, currents reaching approximately 0.97 A were shunted through the tether to the Orbiter electrical ground, which was in contact with the ionosphere primarily through its main engine surfaces. This current level was nearly twice as large as observed during any nominal operating period. As the failure point of the tether entered into the ambient plasma, the current increased to 1.1 A and maintained this level even after the break for approximately 75 s. The principal surprise in these results was that the broken end of the tether, with only a few short strands of copper wire, could support higher currents than the much larger Orbiter conducting surface areas. Analysis of possible current enhancement mechanisms revealed that only a gas‐enhanced electrical discharge, providing an electron emission source, was plausible. Ground plasma chamber tests confirmed this analysis. The TSS‐1R results thus represent the highest electron current emission from a neutral plasma source yet demonstrated in a space plasma. This is of interest for current collection processes in general and plasma contactor development in particular.
Investigation of Accuracy and Influence Factors of Predicting Lenticule Thickness in Small Incision Lenticule Extraction by Machine Learning Models

Small-incision lenticule extraction (SMILE) is a safe and effective surgical procedure for refractive correction. However, the nomogram from the VisuMax femtosecond laser system often overestimates the achieved lenticule thickness (LT), leading to inaccurate estimation of residual central corneal thickness in some patients. In order to improve the accuracy of predicting achieved LT, we used machine learning models to make predictions of LT and analyze the influencing factors of LT estimation in this study. We collected nine variables of 302 eyes and their LT results as input variables. The input variables included age, sex, mean K reading of anterior corneal surface, lenticule diameter, preoperative CCT, axial length, the eccentricity of the anterior corneal surface (E), diopter of spherical, and diopter of the cylinder. Multiple linear regression and several machine learning algorithms were employed in developing the models for predicting LT. According to the evaluation results, the Random Forest (RF) model achieved the highest performance in predicting the LT with an R2 of 0.95 and found the importance of CCT and E in predicting LT. To validate the effectiveness of the RF model, we selected additional 50 eyes for testing. Results showed that the nomogram overestimated LT by 19.59% on average, while the RF model underestimated LT by −0.15%. In conclusion, this study can provide efficient technical support for the accurate estimation of LT in SMILE.
Microbiota-Associated HAF-EVs Regulate Monocytes by Triggering or Inhibiting Inflammasome Activation

In pregnancy, human amniotic fluid extracellular vesicles (HAF-EVs) exert anti-inflammatory effects on T cells and on monocytes, supporting their immunoregulatory roles. The specific mechanisms are still not completely defined. The aim of this study was to investigate the ability of HAF-EVs, isolated from pregnant women who underwent amniocentesis and purified by gradient ultracentrifugation, to affect inflammasome activation in the human monocytes. Proteomic studies revealed that HAF-EV samples expressed several immunoregulatory molecules as well as small amounts of endotoxin. Surprisingly, metagenomic analysis shows the presence of specific bacterial strain variants associated with HAF-EVs as potential sources of the endotoxin. Remarkably, we showed that a single treatment of THP-1 cells with HAF-EVs triggered inflammasome activation, whereas the same treatment followed by LPS and ATP sensitization prevented inflammasome activation, a pathway resembling monocyte refractories. A bioinformatics analysis of microbiota-HAF-EVs functional pathways confirmed the presence of enzymes for endotoxin biosynthesis as well as others associated with immunoregulatory functions. Overall, these data suggest that HAF-EVs could serve as a source of the isolation of a specific microbiota during early pregnancy. Moreover, HAF-EVs could act as a novel system to balance immune training and tolerance by modulating the inflammasome in monocytes or other cells.
Multi-step interrupted-growth MBE technology for GaAs/AlGaAs (∼9.4 μm) room temperature operating quantum-cascade lasers

In order to adjust the highly controllable and optimum growth conditions, the multi-step interrupted-growth MBE processes were performed to deposit a series of GaAs/Al0.45Ga0. 55As QCL structures. The additional calibrations of MBE system were carried out during the designed growth interruptions. This solution was combined with a relatively low growth rate of active region layers, in order to suppress the negative effects of elemental flux instabilities. As a result, the fabricated QCL structures have yielded devices operating with peak optical power of ∼12 mW at room temperature. That is a better result than was obtained for comparable structures deposited with a growth rate kept constant, and with the only initial calibrations performed just before the epitaxy of the overall structure.
Using Domain-Specific Language for Describing Actor-Oriented Systems

The domain-specific language Strumok for describing actor-oriented systems is proposed. It is designed to eliminate the main drawbacks of the actor model: the absence of capabilities of composition of actors, the lack of mechanisms of shared inter-actor memory with parallel access, static typifying, and the absence of the interface of the processed messages. The language translates to source Java code compatible with Akka framework.
Analytical approximate solution for Initial post-buckling behavior of pipes in oil and gas wells

This paper presents analytical approximate solutions for the initial post-buckling deformation of the pipes in oil and gas wells. The governing differential equation with sinusoidal nonlinearity can be reduced to form a third-order-polynomial nonlinear equation, by coupling of the well-known Maclaurin series expansion and orthogonal Chebyshev polynomials. Analytical approximations to the resulting boundary condition problem are established by combining the Newton`s method with the method of harmonic balance. The linearization is performed prior to proceeding with harmonic balancing thus resulting in a set of linear algebraic equations instead of one of non-linear algebraic equations, unlike the classical method of harmonic balance. We are hence able to establish analytical approximate solutions. The approximate formulae for load along axis, and periodic solution are established for derivative of the helix angle at the end of the pipe. Illustrative examples are selected and compared to "reference" solution obtained by the shooting method to substantiate the accuracy and correctness of the approximate analytical approach.
Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.
Cryptographic quantum hashing

We present a version of quantum hash functions based on non-binary discrete functions. The proposed quantum procedure is ‘classical-quantum’, that is, it takes a classical bit string as an input and produces a quantum state. The resulting function has the property of a one-way function (pre-image resistance); in addition it has properties analogous to classical cryptographic hash second pre-image resistance and collision resistance. We also show that the proposed function can be naturally used in a quantum digital signature protocol.
NetAP-ML: Machine Learning-Assisted Adaptive Polling Technique for Virtualized IoT Devices

To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2–4) and threads (1–5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique.
Physics-informed Deep Learning for Flow Modelling and Aerodynamic Optimization

PDE-constrained optimization is an exceedingly difficult task due to the curse of dimensionality. This is particularly true in aerodynamics optimization as remeshing or deformation of existing meshes is often required. In this paper, we aim to overcome this challenge by proposing a simulation-based optimization framework that can handle various optimization problems efficiently. The proposed approach is able to simultaneous make predictions and perform optimizations. Specifically, the framework consists of a surrogate model to generate high-fidelity solutions and a gradient-based algorithm to address high-dimensional optimization problems. The starting point is to use physics-informed neural network (PINN) to construct surrogate models that output flow fields for airfoils of varied configurations. In this sense, PINN is computationally cheap as no labelled training data from a separate high-fidelity simulation is required. Thus, a trained surrogate model can efficiently generate flow fields for any intermediate optimization scenarios. More importantly, we design the architecture of surrogate models and include design variables as inputs to PINN. This mechanism enables neural network to extract geometric features of various input computational domains. A trained surrogate model can represent solutions on unseen design cases from both seen and unseen categories. In the optimization process, a quasi-Newton algorithm is used and further accelerated by automatic differentiation, a popular algorithm designed to efficiently compute the gradients of objective functions with respect to design parameters. An optimization examples aiming to achieve the maximal lift-to-drag ratio has been presented. The example is a single parameter optimization problem focusing on finding the optimal angle of attack. The proposed method is straightforward to implement and computationally efficient, providing a promising alternative for computationally intensive optimization problems.
Simulation of Radar Echoes from Ship-Ocean Scene Based on TDSBR Method

In this paper, an echo model based on the time-domain shooting and bouncing ray (TDSBR) is proposed to generate simulated radar echoes of maritime electrically large targets. The model uses the TDSBR method to calculate the composite scattering from the target and the sea surface in time domain directly, and combines with the “stop-go” method to deal with the motion of the radar and target. The simulation results show that the proposed model features good accuracy and high efficiency, which has important application in echo prediction and target detection of the missile-borne radar in the maritime environment.
Multi-modal Vehicle Trajectory Prediction via Attention-based Conditional Variational Autoencoder

Predicting the motion of surrounding vehicles is an essential ability for autonomous vehicles in real traffic, which can be beneficial to improving motion planning and decisionmaking. However, vehicle trajectory prediction has been a challenging task in autonomous driving, due to the uncertainty of driving intention and the interactions among multiple agents, etc. In this paper, we propose a novel encoder-decoder model via attention-based conditional variational autoencoder (CVAE), which is designed to predict multi-modal trajectories of vehicles on freeways according to different maneuvers. This model employs a multi-agent attention module for robustly capturing spatial interactions, and soft attention mechanism is used to learn temporal dependencies of trajectories. Besides, scheduled sampling is applied in the decoding process for improving the convergence of sequence prediction. We compare our approach with the prior methods on the NGSIM US-101 and I-80 datasets. The experimental results show that our model outperforms the existing methods in terms of the root mean square error (RMSE). Extensive experiments are performed to demonstrate the effectiveness of our proposed model.
The Role of Hydrogel in Cardiac Repair and Regeneration for Myocardial Infarction: Recent Advances and Future Perspectives

A myocardial infarction (MI) is the leading cause of morbidity and mortality, seriously threatens human health, and becomes a major health burden of our society. It is urgent to pursue effective therapeutic strategies for the regeneration and restore myocardial function after MI. This review discusses the role of hydrogel in cardiac repair and regeneration for MI. Hydrogel-based cardiac patches and injectable hydrogels are the most commonly used applications in cardiac regeneration medicine. With injectable hydrogels, bioactive compounds and cells can be delivered in situ, promoting in situ repair and regeneration, while hydrogel-based cardiac patches reduce myocardial wall stress, which passively inhibits ventricular expansion. Hydrogel-based cardiac patches work as mechanically supportive biomaterials. In cardiac regeneration medicine, clinical trials and commercial products are limited. Biomaterials, biochemistry, and biological actives, such as intelligent hydrogels and hydrogel-based exosome patches, which may serve as an effective treatment for MI in the future, are still under development. Further investigation of clinical feasibility is warranted. We can anticipate hydrogels having immense translational potential for cardiac regeneration in the near future.
A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%).
An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.
Research on underwater robot localization method based on the fusion of monocular camera and IMU

Underwater robots need to perform accurate positioning to complete their operations. However, the current commonly used underwater positioning methods are expensive, difficult to operate, and complex to recover. In response to the above problems, this paper combined the advantages of the Inertial Measurement Unit (IMU) with strong concealment and stability, low camera cost and high accuracy in a small range, and applied a positioning technology based on the fusion of monocular camera and inertial measurement unit to underwater robots. In this method, the IMU will obtain high-frequency internal motion information of the robot, while the camera can obtain rich visual information. Using the VINS-Mono framework, the image information collected by the monocular camera could be fused with the IMU data to construct a monocular vision/IMU’s tightly coupled nonlinear optimization model. Finally, the model was evaluated through experiments in the underwater environment, and it was proved that the model could meet the high-precision positioning requirements of underwater robots in a small range of shallow waters.
Cyanobacteria-Based Bioprocess for Cosmetic Products—Cyanobium sp. as a Novel Source of Bioactive Pigments

As a producer of pigments with known bioactive potential, cyanobacteria are a great source of active ingredients for cosmetics (i.e., carotenoids and phycobiliproteins). Multiple phases in the cyanobacteria-based bioprocess led to the obtention of these compounds. The marine Cyanobium sp. LEGE 06113 has been proposed as a promising source for pigments for cosmetic uses, and it has been optimized in the past few years in terms of production, extraction, and application of pigment extracts. This report aims at providing an overview of the cyanobacteria-based bioprocess, regarding optimization strategies, consolidating into a proposed bioprocess for this cyanobacterium. The optimization of Cyanobium sp. included strategies regarding its production (culture medium, light, temperature, pH and salinity) and extraction (successive solvent extraction and ohmic heating). After the optimization, the two pigment-rich extracts (carotenoids and phycobiliproteins) were assessed in terms of their cosmetic potential and compatibility as an ingredient. Finally, aiming a scale-up proposal, life cycle assessment (LCA) was used as tool for a sustainable process. Ultimately, the proposed process gives the possibility to obtain two stable cosmetic ingredients from the same biomass and applied as anti-agent agents, especially due to their high anti-hyaluronidase capacity. Moreover, there remain challenges and information regarding novel cosmetic ingredient regulations were also discussed.
A Critical Review of Snail Shell Material Modification for Applications in Wastewater Treatment

Sea material is becoming increasingly popular and widely used as an adsorbent in wastewater treatment. Snail shell, a low-cost and natural animal waste material, has been shown to have a high calcium content (>99%) and a large potential surface area for the development of sustainable adsorbents. This paper presents a novel synthesis of methods for using snail shell absorbent materials in the treatment of wastewater containing heavy metals, textile dyes, and other organic substances. Modified biochar made from snail shells has gained popularity in recent years due to its numerous benefits. This paper discusses and analyzes modification methods, including impregnating with supplements, combining other adsorbents, synthesis of hydroxyapatite, co-precipitation, and the sol–gel method. The analysis of factors influencing adsorption efficiency revealed that pH, contact time, temperature, initial concentration, and adsorbent dose all have a significant impact on the adsorption process. Future research directions are also discussed in this paper as a result of presenting challenges for current snail adsorbents.
Active control for a flexible aircraft wing in the presence of external disturbances

This paper investigates the problem of vibration reduction for a flexible aircraft wing with store. Active boundary control is developed to reduce the bending and twisting deflections of a flexible aircraft wing subjected to external disturbances. To handle the impact of the external disturbance, two extended state observers are designed under the active disturbance rejection control (ADRC) method. The developed control strategy guarantee the practical stability of flexible aircraft wing.