Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro angiogenesis in response to biomaterial properties for bone tissue engineering: a review of the state of the art

Abstract Bone tissue engineering (BTE) aims to improve the healing of bone fractures using scaffolds that mimic the native extracellular matrix. For successful bone regeneration, scaffolds should promote simultaneous bone tissue formation and blood vessel growth for nutrient and waste exchange. However, a significant challenge in regenerative medicine remains the development of grafts that can be vascularized successfully. Amongst other things, optimization of physicochemical conditions of scaffolds is key to achieving appropriate angiogenesis in the period immediately following implantation. Calcium phosphates and collagen scaffolds are two of the most widely studied biomaterials for BTE, due to their close resemblance to inorganic and organic components of bone, respectively, and their bioactivity, tunable biodegradability and the ability to produce tailored architectures. While various strategies exist to enhance vascularization of these scaffolds in vivo, further in vitro assessment is crucial to understand the relation between physicochemical properties of a biomaterial and its ability to induce angiogenesis. While mono-culture studies can provide evidence regarding cell–material interaction of a single cell type, a co-culture procedure is crucial for assessing the complex mechanisms involved in angiogenesis. A co-culture more closely resembles the natural tissue both physically and biologically by stimulating natural intercellular interactions and mimicking the organization of the in vivo environment. Nevertheless, a co-culture is a complex system requiring optimization of various parameters including cell types, cell ratio, culture medium and seeding logistics. Gaining fundamental knowledge of the mechanism behind the bioactivity of biomaterials and understanding the contribution of surface and architectural features to the vascularization of scaffolds, and the biological response in general, can provide an invaluable basis for future optimization studies. This review gives an overview of the available literature on scaffolds for BTE, and trends are extracted on the relationship between architectural features, biochemical properties, co-culture parameters and angiogenesis.
A Dynamic Effective Class Balanced Approach for Remote Sensing Imagery Semantic Segmentation of Imbalanced Data

The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation methods difficult to apply effectively, but the image segmentation methods based on deep learning are faced with the problem of extremely unbalanced data between categories. In order to solve this problem, first of all, according to the existing effective sample theory, the effective sample calculation method in the context of semantic segmentation is firstly proposed in the highly unbalanced dataset. Then, a dynamic weighting method based on the effective sample concept is proposed, which can be applied to the semantic segmentation of remote sensing images. Finally, the applicability of this method to different loss functions and different network structures is verified on the self-built Landsat8-OLI remote sensing image-based tri-classified forest fire burning area dataset and the LoveDA dataset, which is for land-cover semantic segmentation. It has been concluded that this weighting algorithm can enhance the minimal-class segmentation accuracy while ensuring that the overall segmentation performance in multi-class segmentation tasks is verified in two different semantic segmentation tasks, including the land use and land cover (LULC) and the forest fire burning area segmentation In addition, this proposed method significantly improves the recall of forest fire burning area segmentation by as much as about 30%, which is of great reference value for forest fire research based on remote sensing images.
Reliability Analysis and Fault Prediction of A/D Converter Based on Acceleration Model

The A/D converter with high-precision and reliable is the key core device to realize the acquisition of aircraft analog signal. By controlling the fusing state of the repair and adjustment fuse, the connection relationship between the benchmark debugging element, clock debugging element and ADC nuclear repair and adjustment capacitor can be accurately controlled. In the process of fracture of fuse wires at high temperature, if the spacing is too small and the stress is released, individual poly-crystalline fragments are able to contact each other, resulting in abnormal decline of fuse impedance, which will lead to error of data collection. Aiming at the early elimination of fuse fragmentation impedance anomalies, a defect elimination method based on Arrhenius temperature stress acceleration model is proposed. According to the Weibull failure probability distribution model, the number of fault components in the fault batch is predicted and confirmed by FIB test process, which improved the reliability of A/D converter.
Collisionless electrostatic shock generation using high-energy laser systems

Collisionless shock is ubiquitous in space and astrophysical plasmas, and is believed to be a source of cosmic rays. In this review article, historical achievements of three different types of collisionless shocks, i.e. magnetohydrodynamic, electromagnetic, and electrostatic (ES) shocks, in theory/simulation, observation in space and astrophysical plasmas, and laboratory experiments are shown. An overview is given on recent progress of collisionless ES shock experiments using high-energy laser systems for two schemes. One is an interaction between laser-produced high-density ablating plasma and a low-density ambient plasma, and the other is an interaction between laser-ablated counter-streaming plasmas using double-plane target. For the former scheme, detailed measurements of structures of collisionless ES shock and ion-acoustic soliton, and the transition from double-layer to collisionless ES shock are conducted by proton radiography. For the latter scheme, optical diagnostics are used to observe global structures of plasmas and shocks; collective laser Thomson scattering method is applied to clarify local plasma parameters, such as electron and ion temperatures, flow velocity, and electron density; and proton radiography is employed to measure a shock electric field. The measured large density-jump, steepening of self-emission profile, plasma parameters in the up- and down-stream regions of a shock, and the narrow width of the shock electric field compared with the ion-ion Coulomb mean-free-path reveal the evidence for the formation of collisionless ES shock. Graphical Abstract
Nano-ITLA Based on Thermo-Optically Tuned Multi-Channel Interference Widely Tunable Laser

A tunable light source with narrow linewidth, small size and low power consumption is necessary for high-speed digital coherent communication system. We have developed a monolithically integrated narrow linewidth widely tunable laser—multi-channel interference (MCI) laser, which realizes a quasi-continuous tuning range covering the C++ band. By packaging the laser chip, wavelength locker and thermoelectric cooler (TEC) into a transmitter optical subassembly (TOSA) box and designing a compact print circuit board (PCB), a nano-integrable tunable laser assembly (Nano-ITLA) based on the thermo-optically tuned MCI laser is demonstrated for the first time. The Nano-ITLA is 25.0 mm (L) × 15.6 mm (W) × 6.5 mm (H), and realizes 120 International Telecommunication Union (ITU)-grid switching over 48 nm and less than 3 W total power consumption. The side-mode suppression ratios (SMSRs) are higher than 46 dB, the fiber coupled output powers are greater than 16 dBm, and the Lorentzian linewidths are less than 150 kHz. High precision wavelength locking and temperature control system make the output frequency deviation of all channels from the ITU-grid less than ±0.5 GHz and the wavelength drift less than ±1 pm. The output power can be accurately controlled and stabilized at ±0.1 dB through a closed-loop feedback. The no-light shutdown function realizes an extinction ratio higher than 40 dB during wavelength switching. The Nano-ITLA demonstrates excellent application potential in the coherent optical communications.
Photoluminescence energy and linewidth in GaN/AlN stackings of quantum dot planes

We analyze the room temperature photoluminescence properties of several multilayer stackings of GaN/AlN quantum dots. We report drastic differences of emission energies and linewidths between continuous wave and time-resolvedphotoluminescence experiments. In continuous wave experiments, the screening of internal electric fields by accumulation of e-h pairs in quantum dot planes induces a blue-shift as well as an unexpected narrowing of the emission line, when the laser intensity is increased. Under intense, pulsed excitation, in time-resolvedphotoluminescence, a substantial blue-shift is induced, due to the partial cancelation of the quantum confined Stark effect. When the system is again free to relax, we observe a time-dependent red-shift of the line, which maintains a fairly constant width. We attribute the observed behavior of energies and linewidths to the intricate contributions of the in-plane distribution of dot sizes and of the depth-dependent decrease of the degree of excitation of the different planes. We support our interpretations by the use of a model based on a self-consistent solution of the Schrodinger and Poisson equations within the envelope function approximation.
Analysis of variations in annual energy production based on types of suction side erosion at the blade tip of a wind turbine using numerical simulation

ABSTRACT Wind turbine blades are prone to damage by airborne particulate matter; hence, periodic blade inspection is necessary for maintenance. However, quantitative data on wind turbine performance reduction due to erosion are lacking. Herein, erosion conditions were classified into four levels based on the erosion area in long-term operated wind turbine blades. Transient three-dimensional computational fluid dynamics analysis was performed by applying the suction side erosion shape to the tip airfoil of the National Renewable Energy Laboratory’s 5 MW blade. In the early stage, irregular erosion of the airfoil’s leading edge resulted in the development of a reverse pressure gradient inside the boundary layer and a larger recirculation area of the trailing edge. Thus, the lift coefficient of the airfoil decreased by up to 30%, whereas the drag coefficient increased by over 100%. The wind turbine output curve calculated using aeroelastic software showed that the worst erosion condition decreased output by 2.54%. Annual energy production had declined by up to 2% based on the annual average wind speed; therefore, immediately repairing erosion can minimize the loss of energy production.
Nanotechnology Promoting the Development of Products from the Biodiversity of the Asteraceae Family

Biodiversity is a hallmark of the Asteraceae family. Several species are known for their pharmacological potential. The search for new substances has permeated the chemistry of natural products for years. However, the development of a final product is still a challenge. Plant extracts have physicochemical characteristics that sometimes hinder administration, requiring a formulation. In this context, nanotechnology emerges as a tool to improve the pharmacokinetic parameters of several pharmacologically active substances. Nanoemulsions, liposomes, and nanoparticles are used to carry the active ingredients and thus improve therapeutic action, especially for substances with solubility and absorption problems. This paper aimed at compiling all the studies that used nanotechnology to develop formulations from species of the Asteraceae family from 2010 to 2021 in a literature review. The search showed that nanoemulsions are the most developed formulation associated with essential oils. The use of nanotechnology promoted an improvement in the pharmacokinetic parameters of active substances.
Extended integral equation formulation for scattering problems from a cylindrical scatterer

An extended integral equation is developed for electromagnetic scattering from a perfectly conducting cylinder and a dielectric cylinder. The conventional surface integral equations cannot yield unique solutions when the wavenumber of the electromagnetic wave is equal to an eigenwavenumber of the system. Several methods to overcome this difficulty have been presented, but each method includes some drawbacks. A numerical method is proposed in which the boundary element method is applied to the extended integral equations with the observation points lying on a closed surface inside the scatterer. It is shown that the extended integral equations have unique solutions for any given wavenumber. As examples, plane wave scattering from a perfectly conducting elliptic cylinder, a dielectric elliptic cylinder, and a dielectric rectangular cylinder is numerically analyzed. >
Wind profiler Doppler power spectrum segmentation using U-Net

Wind profiler radars can continually and effectively probe the atmosphere to obtain the Doppler power spectrum of ambient air motion. In addition to ambient air motion, the Doppler power spectrum used to get wind estimates may contain atmospheric and non-atmospheric disturbances. The wind estimations might be biased as a result of these disruptions. Accurate detection of ambient air motion, even in the presence of disturbances, is essential to reduce the impact of these biases. The Doppler power spectrum can be segmented using cutting-edge deep learning models to retrieve ambient air motion. In this work, we used one of the finest deep learning models, U-net, to segment the Doppler power spectrum. The proposed method’s performance evaluation shows promising results in segmenting and retrieving ambient air motion.
BAYESIAN MULTIVARIATE POISSON REGRESSION

The paper proposes a regression model for the multivariate Poisson distribution. So far inference in multivariate Poisson distributions has been prevented by the fact that computation of the probability mass function is difficult. Bayesian methods of inference are proposed which are organized around computational methods based on Gibbs sampling with data augmentation. The new methods are illustrated using artificial data as well as European forest damage data.
Malware Classification Based on GAF Visualization of Dynamic API Call Sequences

Due to the constant updates of malware and its variants and the continuous development of malware obfuscation techniques. Malware intrusions targeting Windows hosts are also on the rise. Traditional static analysis methods such as signature matching mechanisms have been difficult to adapt to the detection of new malware. Therefore, a novel visual detection method of malware is proposed for first-time to convert the Windows API call sequence with sequential nature into feature images based on the Gramian Angular Field (GAF) idea, and train a neural network to identify malware. The experimental results demonstrate the effectiveness of our proposed method. For the binary classification of malware, the GAF visualization image of the API call sequence is compared with its original sequence. After GAF visualization, the classification accuracy of the classic machine learning model MLP is improved by 9.64%, and the classification accuracy of the deep learning model CNN is improved by 4.82%. Furthermore, our experiments show that the proposed method is also feasible and effective for the multi-class classification of malware.
License plate detection optimization based on YOLO algorithm

In order to improve the recognition ability of license plates, this paper proposes an end-to-end license plate optimization recognition algorithm based on YOLOv3 algorithm, and proposes a method based on detection dewarping convolutional neural network (DU-CNN). Based on YOLOv3 model, the Darknet-31 network is proposed. This structure not only improves the extraction ability of the network but also speeds up the extraction speed. According to the characteristics of small license plate characters, a network prediction scale is added to improve the detection ability of license plate characters. Experimental results show that the proposed method has better recognition accuracy, outperforms some commercial systems in difficult data sets, and has better stability.
Novel Current Based Unit Protection Scheme for Transmission Line Connected to The Inverter Based DG

Addition of Photo Voltaic Based sources to the electrical grid has reduced the carbon emission from coal-based thermal generation and also the line losses as they are included in distribution system(DS). However, location and control schemes of DG modulate the fault current at the relay location. Further, an investigation on traditional current protection schemes with DGs reveals, a new protection scheme is essential for fault detection. The proposed method uses both ends current to compute the phase current ratio to detect fault with IBDGs. The threshold area is chosen considering the improvement of dependability, sensitivity, security, and reliability of relay. Simulation of a 100 kW PV array connected to a 25 kV, 60 Hz grid system in PSCAD/EMTDC reveals the proposed method has improved performance in detecting the internal fault.
Transfer Learning for Rice Leaf Disease Detection

To feed the world’s population of 7.9 billion people, preventing crop failure through early disease detection is essential. Various bacterial, viral, or fungal diseases affect the rice leaf and these diseases drastically lower rice yield. Therefore, identifying rice leaf diseases is essential to meeting the demand for rice from an extensive worldwide population. However, the ability to identify rice leaf disease is constrained by the image backgrounds and the circumstances under which the images were captured. When tested on independent rice leaf diseased data, the performance of deep learning models for automated detection of rice leaf diseases suffers substantially. This stusy examines the results of well-known and widely used transfer learning models to detect the rice leaf disease. This can be done in two ways: frozen layers and fine-tuning. It was observed that the results of the freeze layers, the DenseNet169, achieved a good testing accuracy of 99.66%, and when the results of the fine-tuned transfer learning models were examined, Xception performed well and achieved 99.99% of testing accuracy.
Delay Optimization of Power Internet of Things based on Edge-Cloud Collaboration

With the continuous development of the power Internet of Things and the emergence of multi service scenarios, real-time and high-speed process in power data services face more and more challenges. This paper presents an SDN based edge-cloud collaborative method for delay optimization. First, the delay optimization problem is formulated and transformed into a convex optimization problem further. Then a continuously variable coefficient is introduced and its optimal solution is found. Finally a valid task partition strategy that closet to the optimal solution can be found, and the system delay is minimized. Simulation results show that the reasonable loading of edge node tasks to the cloud can effectively reduce the total system delay of power business and alleviate network congestion. Compared with other conventional methods, this edge-cloud collaboration method not only has low system delay, but also makes full use of the control mechanism of SDN to coordinate distributed environment.
Using multilayer Neural Network to increase the Prediction Accuracy: application in the Taïba Ndiaye Power Plant

The Taïba plant is a wind power plant interconnected to the SENELEC network, and its production fills a gap in electricity consumption. Indeed, it represents 157 MW of installed capacity. Like all intermittent power plants, production depends on the environmental parameters of the area where it is located. Bad weather can cause instability in the electricity grid. It is necessary to use methods to forecast its production. This will facilitate the decision making on the amount of energy to be produced to meet the demand. In this sense, this paper aims to predict wind generation by dividing the prediction data into 80% for training our model and 20% for testing the prediction efficiency of the model in order to quantify the energy produced and to allow an optimal transition between intermittent and fossil energy sources. The proposed neural network model was combined with the Levenberg-Marquardt algorithm while varying the number of hidden layers to evaluate the impact of these layers on the prediction efficiency. The rapid convergence of this algorithm and the increase in hidden layers enhanced the prediction performances. The results obtained show that varying the number of hidden layers increases the performance of neural models applied to intermittent energy prediction. The proposed approach gives its best prediction accuracy of 94.57% for the 100 hidden layer network.
Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging

Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging Abdulla Ali Desmal Electromagnetic imaging is the problem of determining material properties from scattered fields measured away from the domain under investigation. Solving this inverse problem is a challenging task because (i) it is ill-posed due to the presence of (smoothing) integral operators used in the representation of scattered fields in terms of material properties, and scattered fields are obtained at a finite set of points through noisy measurements; and (ii) it is nonlinear simply due the fact that scattered fields are nonlinear functions of the material properties. The work described in this thesis tackles the ill-posedness of the electromagnetic imaging problem using sparsity-based regularization techniques, which assume that the scatterer(s) occupy only a small fraction of the investigation domain. More specifically, four novel imaging methods are formulated and implemented. (i) Sparsity-regularized Born iterative method iteratively linearizes the nonlinear inverse scattering problem and each linear problem is regularized using an improved iterative shrinkage algorithm enforcing the sparsity constraint. (ii) Sparsity-regularized nonlinear inexact Newton method calls for the solution of a linear system involving the Frechet derivative matrix of the forward scattering operator at every iteration step. For faster convergence, the solution of this matrix system is regularized under the sparsity constraint and preconditioned by leveling the matrix 4 singular values. (iii) Sparsity-regularized nonlinear Tikhonov method directly solves the nonlinear minimization problem using Landweber iterations, where a thresholding function is applied at every iteration step to enforce the sparsity constraint. (iv) This last scheme is accelerated using a projected steepest descent method when it is applied to three-dimensional investigation domains. Projection replaces the thresholding operation and enforces the sparsity constraint. Numerical experiments, which are carried out using synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.