Miracle-dz/UniFAR-FTUs huggingface dataset:
Tagetnoic acid, a new lipoxygenase inhibitor peroxy fatty acid from Tagetes minuta growing in Saudi Arabia

Abstract A new peroxy fatty acid, tagetnoic acid (5) [4-((3S,6S)-6-((3E,8E)-octadeca-3,8-dien-1-yl)-3,6-dihydro-1,2-dioxin-3-yl)butanoic acid] and four known metabolites: ecliptal (5-formyl-α-terthiophene) (1), 5-(4-hydroxybut-1-ynyl)-2,2′-bithiophene (2), 22,23-dihydrospinasterone (3), and stigmasterol (4) were separated from the n-hexane fraction of the aerial parts of Tagetes minuta L. (Asteraceae). Their chemical structures were verified using IR, UV, 2D and 1D NMR, and HRMS. Compounds 3–5 displayed potent lipoxygenase inhibitory potential with IC50s 2.26, 1.83, and 1.17 μM, respectively compared to indomethacin (IC50 0.89 μM). Moreover, molecular docking study revealed that the potent activity of 5 is due to H-bonding and hydrophobic interaction. The results of this study suggested that Tagetes minuta dietary consumption would be useful for the individuals at risk of acute and chronic inflammatory disorders. Graphical Abstract
Noise Removal Based on Artificial Intelligence Synergy Methods for Surveillance Scenarios

Surveillance footage pose particular difficulties in terms of low-quality either in resolution due to high compression or sensor inconsistency. Latter ones could generate several types of noises such as salt and pepper noises. In this article we apply and evaluate an innovative method for salt and pepper noise removal on surveillance images. The method combines synergy of neural networks, evolutive and statistical methods. The Pulse Coupled Neural Network (PCNN) is used to detect the pixels affected by noise whose parameters have been optimized using a Genetic Algorithm. The pixels that are affected by noise are corrected using a Gaussian Kernel. The proposed method proves to be high efficient o n detecting a nd removing s alt a nd pepper noise from images without affecting surrounding clean pixels. The method has been tested on images from real-world CCTV cameras. The examples from Scotland Yard database show how the filtered images with o ur proposed method retain fine details of the scene even if the noise level was high.
Imbalanced Learning for Fault Diagnosis Problem of Rotating Machinery Based on Generative Adversarial Networks

Learning from imbalanced datasets has become a significant and challenging problem and gained much attention in machine learning and many aspects. Accumulated historical data collected from the working process of industrial machines are often imbalanced, which means the number of recorded observations in some classes are significantly scarcer than other classes. There are already various different methods addressing this problem, among which most strategies focus on generating similar data for the minority classes. Synthetic minority oversampling technique (SMOTE) is an important and valid algorithm, which generates synthetic samples along the line segment, and a great many of variations have been proposed and proved effective. Generative adversarial networks (GANs) were originally invented to generate images from an adversarial training process. While SMOTE approaches are based on local information, GAN methods learn from the overall class distribution. In this paper, we proposed a deep convolutional GAN (DC GAN) model to simulate the original distribution from minority classes and generate new data to solve the imbalance problem. To verify the effectiveness of proposed DCGAN model, we compare the performance against multiple different resampling algorithms. The results show that the quality of generated data using DCGAN is significantly better than other oversampling method and therefore the classification accuracy is superior for fault diagnosis.
Sliding mode fault tolerant for nonlinear systems with external disturbance

This paper focus on the output sliding mode fault tolerant control (OSMFTC) technique for a class of nonlinear system with actuator fault and external disturbance. Precisely, fist, an unknown input observer (UIO) is designed through a descriptor system, which is consist of state and actuator fault. Then, the OSMFTC scheme is constructed to guarantee the stability of the system. Furthermore, the $H_{\infty}$ performance is introduced for the feasibility and robustness of the method. The effectiveness of the proposed method is verified by a numerical example.
Development of Shoreline Extraction Method Based on Spatial Pattern Analysis of Satellite SAR Images

The extensive monitoring of shorelines is becoming important for investigating the impact of coastal erosion. Satellite synthetic aperture radar (SAR) images can cover wide areas independently of weather or time. The recent development of high-resolution satellite SAR images has made observations more detailed. Shoreline extraction using high-resolution images, however, is challenging because of the influence of speckle, crest lines, patterns in sandy beaches, etc. We develop a shoreline extraction method based on the spatial pattern analysis of satellite SAR images. The proposed method consists of image decomposition, smoothing, sea and land area segmentation, and shoreline refinement. The image decomposition step, in which the image is decomposed into its texture and outline components, is based on morphological component analysis. In the image decomposition step, a learning process involving spatial patterns is introduced. The outline images are smoothed using a non-local means filter, and then the images are segmented into sea and land areas using the graph cuts’ technique. The boundary between these two areas can be regarded as the shoreline. Finally, the snakes algorithm is applied to refine the position accuracy. The proposed method is applied to the satellite SAR images of coasts in Japan. The method can successfully extract the shorelines. Through experiments, the performance of the proposed method is confirmed.
A new multi-layer AiP solution based on organic substrate

This work proposes a low-cost Antenna-in-Packaging (AiP) solution based on organic substrate for 80GHz radar application. Redistribution layer (RDL) technology is used to attach antenna to RFIC for high-precision interconnection and system level assembly. The RFIC is embedded in organic substrate. Within this single package, the antenna element, antenne feedline and I/Os of low-speed and high-speed signals are integrated in a vertical three layer structure. The antenna system is compose of 5 patches, driven by coaxial line, which operate at 80GHz with gain of 7.79dB, bandwidth of 3.4% and S11 of -21dB. The overall footprint of the proposed AiP is 5 mm × 4 mm × 0.45 mm and the whole design can be brought into 80GHz radar applications and other mmW AiP applications.
Linking Issue Tracker with Q&A Sites for Knowledge Sharing across Communities

Collaborative development communities and knowledge sharing communities are highly correlated and mutually complementary. The knowledge sharing between these two types of open source communities can be very beneficial to both of them. However, it is a great challenge to automate this process. Current studies mainly focus on knowledge acquisition in one type of community, and few of them have tackle this problem efficiently. In this paper we take Android Issue Tracker and Stack Overflow as a case to study the mutual knowledge sharing between them. We propose an automatic approach by integrating semantic similarity with temporal locality between Android issues and Stack Overflow posts based on the internal citation-graph to reveal the potential associations between them. Our approach explores the internal citations in communities for closely related posts or issues clustering, exploits the rich semantics in fine-grained information of issues and posts for associations building, and leverages the temporal correlations between issues and posts in-depth for associations ranking. Extensive experiments show that the precision of our approach reaches 62.51 percent for top 10 recommendations when recommending Stack Overflow posts to Android issues, and 66.83 percent in reverse.
A Selective Active Contour Model Based on Fractional Order Differentiation

A novel active contour model is proposed for image segmentation, which based on fractional order differentiation and the selective segmentation model. The energy functional for the proposed model consists of three term: fractional order fitting term, selective segmentation term and penalty term. Firstly, by constructing a fractional order fitting term, the novel model can protect texture and lower frequency features of images. So it can extract more image details compared with the local binary fitting energy model (LBF). Secondly, due to the combination with the selective segmentation model, the proposed model is able to selective segment images with intensity inhomogeneity and has desirable performance for images with noise. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a penalizing energy. Finally, experimental results for both synthetic and real image show desirable performance of our method.
Comparing Approaches for Evolving High-Level Robot Control Based on Behaviour Repertoires

Evolutionary robotics approaches have traditionally been focused on monolithic controllers. Recent studies on the evolution of hierarchical control have, however, yielded promising results. Hierarchical approaches typically rely on a repertoire of behaviour primitives (which themselves can be the result of an evolutionary process), and an evolved top-level arbitrator that continually executes primitives from the repertoire to solve a given task. In this paper, we compare different controller architectures for the evolution of top-level arbitrators. We propose two new methods, one based on neural networks and another based on decision trees induced by genetic programming. We compare the new approaches with existing ones, namely neural network regressors and non-hierarchical control, in a challenging simulated maze navigation task that requires a broad diversity of primitives. Based on empirical results, we draw a number of conclusions regarding the strengths and limitations of each of the studied approaches.
Inserting Co-Evolution Information from Contact Maps into a Multiobjective Genetic Algorithm for Protein Structure Prediction

Protein structure prediction (PSP) can be described as a multiobjective optimization (MO) problem since the energy function involves potentially conflicting terms to be simultaneously optimized. During the last three CASP editions (10th, 11th, and 12th), promising results were achieved with the introduction of co-evolution information, in the form of residues contact maps, in methodologies for PSP. In this paper, a residue-residue contact map potential is introduced into the evaluation function of the GAPF program, and it is optimized using a MO strategy. The Aggregation Tree (AT) method is applied to group in separated objectives the energetic potentials that compose the GAPF's evaluation function. The results are compared with those obtained from two consolidated PSP methods, QUARK and MEAMT.
Significance of Bottom-Up Attributes in Video Saliency Detection without Cognitive Bias

Saliency in an image or video is the region of interest that stands out relative to its neighbors and consequently attracts more human attention. To determine the salient areas within a scene, visual importance and distinctiveness of the regions must be measured. A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, we investigated the bottom-up features including color, texture, and motion in video sequences for both one-by-one and combined scenarios to provide a ranking system stating the most dominant circumstances for each feature individually and in combination with other features as well. In this work, we only considered the individual features and various visual saliency attributes investigated under conditions in which we had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgements and decision-making actions. Since computers do not typically have this ability, we tried to minimize this bias in the design of our experiment. First, we modelled our test data as 2D images and videos in a virtual environment to avoid any cognitive bias. Then, we performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed ranking system of salient visual attention stimuli was achieved using an eye tracking procedure. This work provides a benchmark to specify the most salient stimulus with comprehensive information.
A Study on Reactive Power Sharing and Voltage Variation in an Inverter Dominated Islanded Microgrid

The concept of the micro grid is still growing with regard to co-ordination strategies and the development of proper controllers to enhance the reliability aspects under different operating modes. In this context,due to the growing interest of renewable energy sources droop based inverter dominated micro grids are one of the fastest emerging technologies. This research paper aims to study reactive power sharing and voltage variation under different scenarios in an inverter dominated islanded micro grid. In this regard, non-linear equations that describe the model have been derived and an averaged model is used in the derivation of the mathematical model resulting in simplified equations which are used to model large signal equivalent circuits in PLECS software. In addition, the impact of load perturbations, filter parameters, line impedances and droop variations are analyzed
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer-aided diagnosis applications (e.g., detection and segmentation) requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve the fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality’s feature across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions (lungs, mediastinum, and tumors) with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion (fused inputs (FSs), multi-branch (MB) techniques, and multi-channel (MC) techniques) and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, ${p} < {0.05}$ ) than the fusion baselines (FS: 99.00%, MB: 99.08%, and TC: 98.92%) and a significantly higher Dice score (63.85%) than the recent PET-CT tumor segmentation methods.
Path Planning for the Marsupial double-UAVs System in Air-ground Collaborative Application

This paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of an unmanned ground vehicle(UGV) and two flying robots. The UGV, as a carrier, is able to deploy and retrieve the flying robots while each of the latter usually takes off and lands on the carrier. This work is motivated by cooperative search and reconnaissance missions in field of heterogeneous robot system. A sequence of tasks, whose positions are known as prior knowledge, are expected to be accessed by the flying robots in a given order. Owing to their limited duration in the air, the aerial robots, usually unmanned aerial vehicles (UAVs), should return to the UGV timely and land for its energy-saving and recharge. This paper mainly contributes on the mathematical model for path planning of the special marsupial system. Many real constraints including the maximum velocity of the UAV and the UGV, the maximum hovering time and so on are taken into account in the model. The objective function is constructed by minimizing the time for completing the whole mission. In order to clarify the novel method, a single task point model first is proposed and then extended to multi tasks one. Finally, particle swarm optimization (PSO) method is used to solve the path planning problem. Several simulations verify the feasibility and effectiveness of the proposed method.
Linearity Enhancement using Bulk-Degeneration for Source Degenerated OTAs

A modified bulk degeneration technique for source-degenerated OTAs is proposed in this paper. The approach employs drain voltage division and cross-coupling the resultant voltage to the bulk terminal of differential pair which reduces third harmonic distortion (HD3). Simulations are carried on 0.18 $\mu {\rm m}$ process with 1.8 V power supply which shows that (HD3) is decreased by 14 dB (for frequencies of 100 KHz) as compared to previously reported bulk degenerated transconductors. An added advantage is the variation of DC transconductance using the resistance ratio of the voltage divider circuit which offers the tunability in the transconductance.
SSDP+: A Diverse and More Informative Subgroup Discovery Approach for High Dimensional Data

This paper presents an evolutionary approach for mining diverse and more informative subgroups focused on high dimensional data sets. Subgroup Discovery (SD) is an important tool for knowledge discovery that aims to identify sets of features that distinguish a target group from the others (e.g. successful from unsuccessful treatments). At the same time, to extract information from high dimensional data sets becomes more natural. One of the first and most efficient SD heuristics focused on high dimensional data is the SSDP. However, this model deals superficially with diverse/redundancy in top-k subgroups, which can result in poor information for users. This work presents SSDP+, an extension of the SSDP model to provide diversity in a way that explore the relation between subgroups order to 2enerate a more informative set of patterns.
High-Isolation Array Antenna Integration for Single-Chip Millimeter-Wave FMCW Radar

By using the second bandgap in mushroom electromagnetic bandgap (EBG) structures, a high isolation can be achieved between transmitting (TX) and receiving (RX) microstrip array antennas integrated with a millimeter-wave single-chip frequency-modulated continuous-wave (FMCW) radar. These EBG structures are much easier to manufacture at millimeter waves as compared to the first bandgap mushroom EBG structures. In addition to improving isolation, the EBG structures are also used to reduce radiation pattern ripples caused by edge diffractions due to surface waves. Experimental validation is performed on antenna level and on the radar system level. For the system-level validation, the antenna arrays have been integrated with a single-chip mm-wave FMCW radar using the bond-wire interconnect technology. Measurement results in the 57–64 GHz band show the TX–RX isolation of 40 dB, which is 15–20 dB better than the isolation between TX and RX arrays without EBG structures. In addition, the radiation pattern ripple caused by edge diffractions is reduced below 3 dB.
The Spatiotemporal Deposition of Lysophosphatidylcholine Within Starch Granules of Maize Endosperm and its Relationships to the Expression of Genes Involved in Endoplasmic Reticulum–Amyloplast Lipid Trafficking and Galactolipid Synthesis

The presence of lipids within starch granules is specific to cereal endosperm starches. These starch lipids are composed of lysophospholipids, especially lysophosphatidylcholine (LysoPC) and free fatty acids that strongly impact the assembly and properties of cereal starches. However, the molecular mechanisms associated with this specific lipid routing have never been investigated. In this study, matrix-assisted laser desorption ionization mass spectrometry imaging revealed decreasing gradients in starch LysoPC concentrations from the periphery to the center of developing maize endosperms. This spatiotemporal deposition of starch LysoPC was similar to that previously observed for endoplasmic reticulum (ER)-synthesized storage proteins, i.e. zeins, suggesting that LysoPC might originate in the ER, as already reported for chloroplasts. Furthermore, a decrease of the palmitate concentration of amyloplast galactolipids was observed during endosperm development, correlated with the preferential trapping of palmitoyl-LysoPC by starch carbohydrates, suggesting a link between LysoPC and galactolipid synthesis. Using microarray, the homologous genes of the Arabidopsis ER-chloroplast lipid trafficking and galactolipid synthesis pathways were also expressed in maize endosperm. These strong similarities suggest that the encoded enzymes and transporters are adapted to managing the differences between chloroplast and amyloplast lipid homeostasis. Altogether, our results led us to propose a model where ER-amyloplast lipid trafficking directs the LysoPC towards one of two routes, the first towards the stroma and starch granules and the other towards galactolipid synthesis.
Piezoelectric Biomaterials for Sensors and Actuators

Recent advances in materials, manufacturing, biotechnology, and microelectromechanical systems (MEMS) have fostered many exciting biosensors and bioactuators that are based on biocompatible piezoelectric materials. These biodevices can be safely integrated with biological systems for applications such as sensing biological forces, stimulating tissue growth and healing, as well as diagnosing medical problems. Herein, the principles, applications, future opportunities, and challenges of piezoelectric biomaterials for medical uses are reviewed thoroughly. Modern piezoelectric biosensors/bioactuators are developed with new materials and advanced methods in microfabrication/encapsulation to avoid the toxicity of conventional lead‐based piezoelectric materials. Intriguingly, some piezoelectric materials are biodegradable in nature, which eliminates the need for invasive implant extraction. Together, these advancements in the field of piezoelectric materials and microsystems can spark a new age in the field of medicine.