Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposed ontology for cognitive radar systems

Cognitive radar is a rapidly developing area of research with many opportunities for innovation. A significant obstacle to development in this discipline is the absence of a common understanding of what constitutes a cognitive radar. The proposition in this study is that radar systems should not be classed as cognitive, or not cognitive, but should be graded by the degree of cognition exhibited. The authors introduce a new taxonomy framework for cognitive radar against which research, experimental and production systems can be benchmarked, enabling clear communication regarding the level of cognition being discussed.
Pre-clinical toxicity assessment of Artemisia absinthium extract-loaded polymeric nanoparticles associated with their oral administration

Background: This study was designed to quantify the composition of the ethanolic extract of Artemisia absinthium through gas chromatography–mass spectrometry analysis and ensure in vivo safety of A. absinthium extract-loaded polymeric nanoparticles (ANPs) before considering their application as a drug carrier via the oral route. Methods: We synthesized N-isopropylacrylamide, N-vinyl pyrrolidone, and acrylic acid crosslinked polymeric NPs by free-radical polymerization reaction and characterized them by Fourier-transform infrared spectroscopy, transmission electron microscopy, and dynamic light scattering spectroscopy. Different concentrations of extract (50 mg/kg, 300 mg/kg, and 2,000 mg/kg body weight) were encapsulated into the hydrophobic core of polymeric micelles for the assessment of acute oral toxicity and their LD50 cut-off value as per the test procedure of OECD guideline 423. Orally administered female Wistar rats were observed for general appearance, behavioral changes, and mortality for the first 30 min, 4 h, 24 h, and then, daily once for 14 days. Result: ANPs at the dose of 300 mg/kg body weight were used as an initial dose, and rats showed few short-lived signs of toxicity, with few histological alterations in the kidney and intestine. Based on these observations, the next set of rats were treated at a lower dose of 50 mg/kg and a higher dose of 2,000 mg/kg ANPs. Rats administered with 50 mg/kg ANPs remained normal throughout the study with insignificant histological disintegration; however, rats treated at 2,000 mg/kg ANPs showed some signs of toxicity followed by mortality among all three rats within 24–36 h, affecting the intestine, liver, and kidney. There were no significant differences in hematological and biochemical parameters among rats treated at 50 mg/kg and 300 mg/kg ANPs. Conclusion: We conclude that the LD50 cut-off value of these ANPs will be 500 mg/kg extract loaded in polymeric NPs.
Improved depth map upsampling using iterative joint trilateral filter

Typical depth sensor cameras have a lower resolution compared to RGB cameras. In this work, inspired by the Joint Trilateral Filter (JTF), an iterative JTF (JTFite) approach is presented to improve the visual quality of the depth discontinuity regions. Our framework takes into consideration the utilization of the difference of refined pixels’ values and the canny edge of the JTF, to obtain sharper edges. The recent work on JTF is revisited by setting varying parameters which employs local gradients information to avoid texture artifacts in a structure-aware onion filtering. We extend the JTF by using traits of its local filtering technique to improve further the edges of the objects in the depth map by post-filtering the updated region. The proposed method enhances details of the textures in the depth map by emphasizing on gradients of the drastic changes of the pixels values. The primary goal of this work is to increase the depth resolution by a high-resolution RGB image to preserve sharp edges and details of the textures. Visual and quantitative experimental results show that the proposed method outperforms the JTF for depth map upsampling. We evaluate the upsampled and filtered low-resolution (LR) depth maps with their associated ground truth (GT) images.
View-Semantic Transformer With Enhancing Diversity for Sparse-View SAR Target Recognition

With the rapid development of supervised learning-based synthetic aperture radar (SAR) target recognition technology, it is easy to find that the recognition performance is proportional to the number of training samples. However, the biased data distribution and under-representation of the model caused by incomplete data within categories exacerbate the challenge of SAR interpretation. In this article, we propose a new view-semantic transformer network (VSTNet) that generates synthesized samples to complete the statistical distribution of training data and improve the discriminative representation of the model. First, SAR images from different views are encoded into a disentangled latent space, which allows us to synthesize data with more diverse views by manipulating view-semantic features. Second, the synthesized data as a complement effectively expands the training set and alleviates the overfitting problem of limited data in sparse views. Third, the proposed method unifies SAR image synthesis and SAR target recognition into an end-to-end framework to boost their performance against each other. Experiments conducted on moving and stationary target acquisition and recognition (MSTAR) data demonstrate the robustness and effectiveness of the proposed method.
Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

The electrical capacitance tomography (ECT) technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area, in which the performances of reconstruction algorithms play a crucial role in the reliability of measurement results. According to the Tikhonov regularization technique, a new cost function with the total least squares technique and the ℓ1-norm based regularizer is presented, in which measurement noises, model deviations and the influence of the outliers in the measurement data are simultaneously considered. The split Bregman technique and the fast-iterative shrinkage-thresholding method are combined into a new iterative scheme to solve the proposed cost function efficiently. Numerical experiment results show that the proposed algorithm achieves the boost in the precision of reconstruction, and under the noise-free condition the image errors for the imaging targets simulated in this paper, that is, 8.4%, 12.4%, 13.5% and 6.4%, are smaller than the linear backprojection (LBP) algorithm, the Tikhonov regularization (TR) algorithm, the truncated singular value decomposition (TSVD) algorithm, the Landweber algorithm and the algebraic reconstruction technique (ART).
Applying RIS-Based Communication for Collaborative Computing in a Swarm of Drones

A swarm of autonomous and heterogeneous drones has many benefits in various scenarios e.g. search and rescue, disaster management, agriculture, delivery and logistics, mapping and surveying, environmental monitoring, etc. However, the presence of obstacles in the environment poses challenges to communication between drones, including network coverage, received signal power, latency, and power consumption. To improve the drones’ communication in real-time scenarios, reconfigurable intelligent surfaces (RIS) can be used. RIS is a promising technology for empowering millimeter-waves and sub-millimeter waves communication. It also can provide improved communication links with significantly higher received signal strength in non-line-of-sight situations, which should be taken into account by drones to decide when and with which other drone(s) to perform computation offloading. To this end, we provide two federated learning-based computation offloading strategies through direct and indirect communications. These approaches are based on an advanced rating technique including some key computation and communication parameters. The core of the algorithm also involves two separate deep learning models that are helpful to efficiently transfer and update the decreased model weights, drones’ properties, angle of arrival, and angle of departure. Simulation results show that the efficiency of the proposed approaches are superior to a reference strategy in terms of energy consumption by −32%, latency −18%, throughput +50%, and cost of communication and computation by −35%.
Network Intrusion Detection System using Reinforcement learning

Our research on the efficacy of deep reinforcement learning helps us comprehend the challenges encountered by NIDS (DRL). To find network anomalies, we suggest integrating Adversarial/Multi Agent Reinforcement Learning with Deep QLearning (AE-DQN). We compare our suggestions on the NSL-KDD dataset with the KDDTest+ dataset. In this article, we take a look at the difficulty of reducing an infinite number of possible categories down to only five. Our strategy yielded an overall F1 score of 79% and an accuracy of 80% across the board. Furthermore, our proposed method outperforms the Recurrent Neural Network (RNN) IDS (2) and the Adversarial Reinforcement Learning with SMOTE (AESMOTE) IDS in terms of the variety of assaults it can identify, as shown by its performance on the NSL-KDD dataset (3). Our major aim going forward is to enhance detection efficiency against different kinds of threats.
Theoretical Study of Density Distributions and Size Radii of 8B and 17Ne

The proton, neutron and matter density distributions, the corresponding size radii and elastic electron scattering form factors of one-proton 8 B and two-proton 17 Ne halo nuclei are calculated. The theoretical technique used to fulfill calculations is by assuming that both nuclei under study are composed of two main parts; the first is the compact core and the second is the unstable halo part. The single-particle radial wavefunctions of harmonic-oscillator (HO) and Woods-Saxon (WS) potentials are used to study core and halo parts, respectively. And other approach is studied by using HO potential for both core and halo parts, but using two HO size parameters for both supposed parts. The long tail behavior which is the main characteristic of halo nuclei are well produced for both 8 B and 17 Ne. The calculated size radii are in general in good agreement with the available experimental data. The electron scattering form factors of the C0+C2 and C0 components are also calculated for 8 B and 17 Ne, respectively and compared with corresponding stable 10 B and 20 Ne nuclei. For 8 B calculations, the core-polarization (CP) effects are taken into account by using Tassie and Bohr-Mottelson models. The contribution from model-space (MS) part C2 component is taken through pwt interaction. The results of the calculated charge form factors are left for the planned electron-radioactive ion beam colliders where the study of skin or halo on the charge form factors are going to be studied.
Swinging mass for energy-efficient quadrupedal locomotion

Quadrupedal animal, such as dogs, typically have soft but heavy abdomens, whose swinging motion significantly contribute to the energy efficiency of their entire locomotion. This study examines the effect of a swinging mass on quadrupedal locomotion by simulating the soft abdomen of its biological counterparts. A quadruped robot is designed with a swinging mass beneath its trunk. Its pitching motion was evaluated by a mechanical model. The experiment results demonstrate that the swinging mass effectively reduces the pitching motion, thereby improving the energy efficiency of the robot. Although the swinging mass does not always guarantee the energy efficiency of locomotion, the spring stiffness that supports the mass can be obtained for energy-efficient locomotion. GRAPHICAL ABSTRACT
Gain enhancement of triple-band patch antenna by using triple-band artificial magnetic conductor

A microstrip line fed rectangular patch antenna with slots in the patch radiator is designed to realise triple resonant frequencies with satisfactory impedance matching (| S 11 |≤-10 dB) at 3.36, 5.96, and 9.09 GHz. Current distribution analysis is presented to provide an insight to the mechanism of triple resonance generation. A triple-band artificial magnetic conductor (AMC) is designed and analysed based on resonance phenomenon of stepped impedance resonators. The resonant frequencies of the AMC coincide with the antenna. A rectangular split-ring slot is embedded in the metallic ground of the antenna. The designed AMC is placed beneath the antenna maintaining an air gap between the structures. The slot in the ground plane enables the surface waves to interact with the AMC. The proposed arrangement enhances the gain from 1.38 to 3.77 dBi at 3.36 GHz, 3.22 to 6.29 dBi at 5.96 GHz, and 7.58 to 11.16 dBi at 9.09 GHz. The proposed structure exhibits good radiation patterns with low cross-polarisation and high front to back ratio at all the three resonant frequencies. A prototype of the proposed structure is fabricated and the measured results are in good agreement with the simulated results.
ISSR Analysis to Detect Genetic Variation Among Some Lentil Genotypes in Syria

Lentil (Lens culinaris Medik) plays a significant role in human and animal nutrition. It is important to generate baseline information on genetic diversity of local and introduced lentil in Syria. Therefore, this study aimed to assess diversity and relationship among two local varieties and 35 introduced lentil lines from ICARDA using 20 Inter Simple Sequence Repeats (ISSR) primers.18 primers were able to amplify DNA bands with a total of 184 bands. Primers ISSR5, ISSR11, ISSR13, ISSR15 and ISSR16 presented 100% polymorphism percentage. The mean value of polymorphism percentage was 87.31%. ISSR primers could identify 19 unique bands in all genotypes, 12 of them were positive bands (present) and 7 were negative bands (absent). Primer ISSR11 was able to detect 3 positive unique bands in genotype Ln20. The UPGMA (unweighted pair group method with arithmetical averages) clustering based on ISSR data grouped 37 lentil genotypes into 6 clusters. The lowest genetic distance (0.20) was observed between (Ln15 and Ln16, Ln21 and Ln22, Ln27 and Ln28) which proves that they are closely related. While Ln24 presented the highest genetic distance (0.49) with both Ln18 and Idleb3 which refers to their genetic divergence.
Identifying structural damage with data driven impedance response calibration

The impedance/admittance measurements of a piezoelectric transducer circuit bonded to or embedded in a host structure can be used as damage indicator, since damage will introduce notable impedance shifts. When a credible model of the healthy structure, such as the finite element model, is available, using the impedance/admittance change information as input, it is possible to identify both the location and severity of damage. In this research we cast the damage identification problem into a many-objective optimization framework through impedance response calibration using Gaussian Process. With damage location and severity as unknown variables, the objective functions are response surfaces calibrated using emulated damaged scenarios assisted by Gaussian Process. Subsequently, a ε - dominance enabled many-objective algorithm based on multi-objective Simulated Annealing is devised to facilitate the many-objective optimization. The proposed approach yields high-quality results that can be further investigated for model updating.
Topics: structure and function

Following a feature-based approach to information structure and a new approach to the definition of topics, a structure for different kinds of topics is proposed, showing that the Finnish and Hungarian left peripheries are more similar than previously thought, in line with cartographic models. The most popular definition of topics in terms of aboutness is reanalysed as a consequence of the relationship between the comment of the utterance and support entities: processing effort minimisers, thanks to the application of a principle inspired by Relevance Theory, where comments are expected to be relevant contributions to the discourse. The contrastive feature is proposed to project a head which encodes a true mathematical function, which applies to non-topics, as well. Furthermore, an interface mechanism is proposed for nanosyntax, the feature-based approach being employed.
Exploring the Therapeutic Potential of Apigenin in Obesity-Associated Fibrinolytic Dysfunction: Insights From an Animal Study

Introduction: Obesity (Obe) is a chronic metabolic disorder usually complicated by impaired fibrinolytic activity. Apigenin (Api) is one of the flavonoids that have anti-adiposity effects. This study aimed to explore the therapeutic potential of Api in high-fat diet (HFD)-induced obese rats. Methods: Twenty-four Wistar adult male rats were randomly allocated into control group, supplemented with a normal pellet diet (NPD); Api group, supplemented with Api (10 mg/kg) for eight weeks; Obe group, obesity was induced by feeding HFD for eight weeks; and Obe/Api group, obese rats supplemented with Api for eight weeks. Body mass index (BMI), homeostatic model assessment of insulin resistance (HOMA-IR), tumor necrosis factor-α (TNF-α), malondialdehyde (MDA), total superoxide dismutase (t-SOD) activity, and plasminogen activator inhibitor-1 (PAI-1) were measured. Results: Compared to the control group, Obe group exhibited a significant increase in BMI, HOMA-IR, TNF-α, MDA, and PAI-1. These results were also associated with a significant decrease in serum t-SOD activity. Supplementation of Api alleviated the measured deteriorated parameters and ameliorated visceral adiposity in obese rats. Conclusion: This study provides compelling evidence regarding a promising role for Api in ameliorating the impairment of fibrinolytic activity in an Obe animal model. The observed effects are likely mediated through Api's anti-obesity properties, as well as its indirect modulation of PAI-1, oxidative stress, and inflammation. Future clinical studies are recommended that may make benefit of the preclinical therapeutic use of apigenin in obesity-associated fibrinolytic dysfunctions.
Impact ionization in β-Ga2O3

A theoretical investigation of extremely high field transport in an emerging wide-bandgap material β-Ga2O3 is reported from first principles. The signature high-field effect explored here is impact ionization. The interaction between a valence-band electron and an excited electron is computed from the matrix elements of a screened Coulomb operator. Maximally localized Wannier functions are utilized in computing the impact ionization rates. A full-band Monte Carlo simulation is carried out incorporating the impact ionization rates and electron-phonon scattering rates. This work brings out valuable insights into the impact ionization coefficient (IIC) of electrons in β-Ga2O3. The isolation of the Γ point conduction band minimum by a significantly high energy from other satellite band pockets plays a vital role in determining ionization co-efficients. IICs are calculated for electric fields ranging up to 8 MV/cm for different crystal directions. A Chynoweth fitting of the computed IICs is done to calibrate ionization models in device simulators.A theoretical investigation of extremely high field transport in an emerging wide-bandgap material β-Ga2O3 is reported from first principles. The signature high-field effect explored here is impact ionization. The interaction between a valence-band electron and an excited electron is computed from the matrix elements of a screened Coulomb operator. Maximally localized Wannier functions are utilized in computing the impact ionization rates. A full-band Monte Carlo simulation is carried out incorporating the impact ionization rates and electron-phonon scattering rates. This work brings out valuable insights into the impact ionization coefficient (IIC) of electrons in β-Ga2O3. The isolation of the Γ point conduction band minimum by a significantly high energy from other satellite band pockets plays a vital role in determining ionization co-efficients. IICs are calculated for electric fields ranging up to 8 MV/cm for different crystal directions. A Chynoweth fitting of the computed IICs is done to calibrate i.
Sous-algèbres des algèbres de Lie semi-simples

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Control Schemes for Autonomous Car‐following Systems with two Classical Compensators

In this paper, we improve the dynamics of an autonomous car‐following system with two classical compensation methods: proportional‐differential (PD) and velocity feedback (VF) control methods. Based on an optimal velocity model, the system transfer function was derived and control block diagram was given with an open‐loop transfer function. Three theorems were proposed and proven with the use of small gain theorem. The analytical results show that two compensation factors play important roles in stabilizing the car‐following system. In the simulation, three dynamical models were derived in reverse from the closed‐loop transfer function and the numerical results show that, with increasing factors, the stability of car‐following was enhanced and traffic jams were eliminated. The analytical results are supported by the numerical results.
Bax mRNA Expression as A Potential Biomarker of Placental Apoptosis in Early-onset Preeclampsia

BACKGROUND: Early-onset preeclampsia is characterized by higher oxidative stress and apoptosis level than late-onset one. Studies comparing the expression of the Bcl-2 family protein in early and late-onset preeclampsia are still lacking and show inconclusive evidence. This study aimed to compare the expression of Bax and Bcl-2 messenger RNA (mRNA) as a biomarker of placental apoptosis between early-onset and late-onset preeclampsia.METHODS: A cross-sectional study was conducted using formalin-fixed, paraffin-embedded preeclamptic placental samples and dividing them into early-onset and late-onset preeclampsia groups. Bax and Bcl-2 mRNA expressions were assessed using the quantitative real-time polymerase chain reaction method. Apoptosis was assessed through DNA fragmentation examination by the ligation-mediated real-time polymerase chain reaction method.RESULTS: Thirty early-onset and 30 late-onset preeclamptic placental samples were included. The mean fold change Bax mRNA in early-onset was higher than in late-onset preeclampsia (6.02±3.59 vs. 2.82±1.97; p=0.00). The mean fold change Bcl-2 mRNA early-onset was not different from late-onset preeclampsia (31.20±17.94 vs. 31.01±27.60; p=0.98). The mean DNA fragmentation cycle threshold in early-onset preeclampsia was lower than in late-onset preeclampsia (28.07±0.64 vs. 30.63±0.96; p=0.00). A weak negative correlation exists between fold change Bax mRNA and DNA fragmentation cycle threshold (r=-0.30; p=0.02). CONCLUSION: Bax mRNA showed significant correlation in DNA fragmentation compared to Bcl-2 mRNA; hence, might show more role in apoptotic pathway. Early-onset preeclampsia has higher Bax mRNA relative expression and apoptosis than late-onset preeclampsia. Therefore, Bax mRNA can be potential biomarker in early-onset preeclampsia.KEYWORDS: mRNA, Bax, Bcl-2, apoptosis, DNA fragmentation, early-onset, preeclampsia