Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing gravity at the energy scale of inflation via primordial non-Gaussianity

In this thesis, we review the standard inflationary paradigm and study some inflationary models among the so-called α-attractors. Our main concern is the study of the inflationary dynamics and predictions, such as the primordial non-Gaussianity, of some α-attractor models. In order to study the bispectrum, we make use of the in-in formalism and the δN formalism. First, we focus on single-field models. We study the predictions of the α-attractors and the reasons underneath their universal behaviour. In particular, we analyse the acceptable initial conditions that lead to an inflationary era for the T-models. Secondly, we consider two-field α-attractor models in which one of the fields has a non-canonical kinetic term. We analyse their background dynamics and their predictions, especially for what concern mechanisms that can produce large non-Gaussianities thanks to the parameter α, the potential term and the non-canonical kinetic term. We consider two simple trajectories in field space: a circle and a nearly straight trajectory. In the former case, large non-Gaussianities can be produced thanks to the third derivative of the potential along the isocurvature direction or thanks to the non-canonical kinetic term. In the latter, they can be produced thanks to the α parameter or the non-canonical kinetic term. Finally, we study the background dynamics of two two-field models. In particular, we estimate the nonlinearity parameter fNL for the second one using the δN formalism.
PSNet: Towards Efficient Image Restoration With Self-Attention

Image restoration aims to recover a clean image from various degradations, e.g., haze, snow, and blur, playing an important role in robot vision, autonomous vehicles, and medical imaging. Recently, the use of Transformer has witnessed a significant improvement in multifarious image restoration tasks. However, despite a few remedies to reduce the quadratic complexity of self-attention, these approaches are still impractical for real-world applications, which need high efficiency and speed. To ameliorate this issue, we propose an efficient framework for image restoration based on self-attention. To this end, we combine the strengths of patch-based and strip-based self-attention units to improve efficiency. More specifically, we apply self-attention of different operation scales to features of different resolutions, i.e., we adopt a relatively smaller region for self-attention on high-resolution features while a larger region for low-restoration features. In addition, instead of using global self-attention in each partitioned region, we leverage a strip-based version for low complexity. To further improve efficiency, we insert our design into a U-shaped CNN network to establish our framework, dubbed PSNet. Extensive experiments demonstrate that our network receives state-of-the-art performance on five representative image restoration tasks with low computational complexity and high speed, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, and image denoising.
Reliable IoT Paradigm With Ensemble Machine Learning for Faults Diagnosis of Power Transformers Considering Adversarial Attacks

Power transformer represents an important equipment in electric power systems. Transformers are not only a source of power outages for electric utilities, but they also affect customers because they interrupt power supplies. Transformers require frequent maintenance and service, which can be time-consuming and expensive. Power systems and their maintenance and servicing can be planned in advance when transformer faults are detected early with high accuracy rates. In order to avoid unplanned shutdowns, it is crucial to monitor and diagnose faults of power transformers. Based on how electrical and thermal stresses impact the insulating oil, it is possible to detect or classify transformer faults by checking for dissolved gases. In this article, a new transformer monitoring and fault diagnosis technique are developed by utilizing an industrial Internet of Things (IoT) platform combined with an ensemble machine learning (EML) strategy. In order to prevent overlapping between diverse transformer defects, an innovative feature engineering strategy is developed based on introducing new features for gases’ concentrations and their ratios. Through this, EML efficiency is enhanced due to the separation of overlapping in the datasets between diverse transformer faults. The datasets from Egyptian chemical laboratories and literature are used to train and test the proposed EML. As shown in the experiments, the proposed fault diagnosis model provides the best diagnosis accuracy, which is significantly better than the state-of-the-art machine learning approaches. Finally, the robustness of the developed EML model is further demonstrated by including white Gaussian noise that is based on the fast gradient sign method (FGSM) as well as the zeroth order optimization (Zoo) attack to validate the model against adversarial attack scenarios.
Dynamic target detection based on YOLOv5

In simultaneous localization and map building systems, dynamic targets often have a huge impact on feature point extraction and matching tracking. This paper proposes a dynamic target detection method based on YOLOv5, which effectively improves the accuracy of feature point extraction in dynamic environments and thus avoids front-end mis-matching of dynamic feature points.
Random walks generated by Ewens distribution on the symmetric group

This paper studies Markov chains on the symmetric group $S_n$ where the transition probabilities are given by Ewens distribution with parameter $\theta>0$. The eigenvalues are identified to be content polynomials of partitions divided by n$th$ rising factorial of $\theta. $ The mixing time analysis is carried out for two cases: If $\theta$ is a constant integer, the chain is fast enough that the mixing time does not depend on $n. $ If $\theta=n$, the chain exhibits a total variation cutoff at $\frac{\log n}{\log 2}$ steps.
A Novel Method of Transformer Overloading Detection by Magnetic Flux Leakage Measurements

Power distribution networks in developing economies are often subjected to overloading. Resultantly, typical distribution transformers performing the final high to low voltage (LV) conversion are operated beyond rated capacity during peak demand hours. This overloading exceeds permissible limits prescribed by IEEE C57.91 and results in transformer failure and even catastrophic fire/explosion. Main objective of this work is to develop a contactless overload detection mechanism for distribution transformers. It was anticipated that measurement and quantization of magnetic flux leakage (MFL) outside steel tank can be utilized for overload detection. To validate this, a commercial 200 kVA transformer was modeled and tested by finite element method (FEM) to obtain levels of MFL in different loading states at various sensing points outside steel tank of transformer. Then an experimental test bed was established to validate finite element analysis (FEA) results. Resultantly, a method of transformer overloading is demonstrated and an optimum sensing position was determined. Furthermore, correlation coefficient remained greater than 90% for experiment as well as for FEM indicating high linearity between MFL and loading states of the transformer.
Um Estudo Acerca da Seleção de Features para a Detecção dos Ransomwares WannaCry, Ryuk e CryptoLocker

Ransomware é um malware que sequestra dados e devido a sua detecção frequentemente tardia apresentam uma ameaça significativa para indivíduos e organizações, causando perdas financeiras substanciais e violações de dados. O objetivo deste trabalho consiste em identificar as features relevantes para detecção de três tipos conhecidos de ransomwares: WannaCry, Ryuk e CryptoLocker. Para tanto, é utilizado o ambiente Cuckoo Sandbox no qual a atividade de tais ransomwares são registradas. Com isso, são criados datasets contendo amostras normais e de ataque para cada tipo de ransomware estudado. Por fim, aplicou-se métodos de ranqueamento de features nesses datasets para a identificação das 20 features mais relevantes para a detecção dos ransomwares estudados, fornecendo uma contribuição valiosa para a melhoria dos mecanismos de detecção de ransomware.
Large ferroelectric polarization of TiN/Hf0.5Zr0.5O2/TiN capacitors due to stress-induced crystallization at low thermal budget

We report on atomic layer deposited Hf0.5Zr0. 5O2 (HZO)-based capacitors which exhibit excellent ferroelectric (FE) characteristics featuring a large switching polarization (45 μC/cm2) and a low FE saturation voltage (∼1.5 V) as extracted from pulse write/read measurements. The large FE polarization in HZO is achieved by the formation of a non-centrosymmetric orthorhombic phase, which is enabled by the TiN top electrode (TE) having a thickness of at least 90 nm. The TiN films are deposited at room temperature and annealed at 400 °C in an inert environment for at least 1 min in a rapid thermal annealing system. The room-temperature deposited TiN TE acts as a tensile stressor on the HZO film during the annealing process. The stress-inducing TiN TE is shown to inhibit the formation of the monoclinic phase during HZO crystallization, forming an orthorhombic phase that generates a large FE polarization, even at low process temperatures.
Non-Markovian spontaneous emission dynamics of a quantum emitter near a 
MoS2
 nanodisk

We introduce a photonic nanostructure made of two dimensional materials that can lead to non-Markovian dynamics in the spontaneous emission of a nearby quantum emitter. Specifically, we investigate the spontaneous emission dynamics of a two-level quantum emitter with picosecond free-space decay time, modelling J-aggregates, close to a MoS$_2$ nanodisk. Reversible population dynamics in the quantum emitter is obtained when the emitter's frequency matches the frequency of an exciton-polariton resonance created by the nanodisk. When such isolated resonances exist, decaying Rabi oscillations may occur. The overlapping of exciton-polariton resonances also affects strongly the decay dynamics at close distances to the nanodisk, giving rise to complex decaying population oscillations. At very close distances of the emitter to the nanodisk the ultrastrong coupling regime appears, where after a very fast oscillatory partial decay of the initial population, the emitter rest population remains constant over long times and population trapping occurs. The size and material quality of the nanodisk is shown to be of lesser influence on the above results.
Effects of photobiomodulation therapy (pulsed LASER 904 nm) on muscle oxygenation and performance in exercise-induced skeletal muscle fatigue in young women: a pilot study

Photobiomodulation therapy (PBMt) has been used to increase muscle performance and improve recovery when applied before exercise. We aimed to evaluate the effects of PBMt using LASER on muscle oxygenation and performance. The study was a randomized, participant and assessor-blinded, within-subject crossover trial with placebo control to test the viability of the methods. Five physically active young women were randomly assigned to either placebo, or active PBMt (12 diode cluster probe; 904 nm; 60 mW; 250 Hz; 43.2 J per site, 129.6 J total) in contact over rectus femoris (RF) muscle of the dominant limb immediately before an isokinetic fatigue protocol. A one-week wash-out period preceded cross-over. Electromyography and isokinetic performance measures were evaluated. Absolute concentrations of deoxygenated haemoglobin and myoglobin (deoxy[Hb + Mb]) of the RF, an index of local microvascular fractional O2 extraction, was monitored continuously by near-infrared spectroscopy (NIRS). Total haemoglobin concentration as an indicator of microvascular haematocrit was calculated as the sum of the deoxy[Hb + Mb] and oxy[Hb + Mb] signals. PBMt pre-conditioning reduced time to peak torque when compared to placebo (P<0.05). PBMt resulted in a noticeably reduced trend in deoxy[Hb + Mb] during exercise compared to placebo (P>0.05). PBMt before exercise improves indicators of muscle performance, potentially by increasing local matching of bulk and microvascular O2 delivery relative to skeletal muscle O2 utilisation. Further work is required to understand the effect of PBMt on haemodynamic and metabolic characteristics of muscle.
Classic and Fuzzy Type-2 Control for the Van de Vusse Reactor: A Comparative Study

A Proportional, Integral and Derivative controller with Ziegler-Nichols tuning for the Van de Vusse reactor, whose circuit includes a fuzzy rule-based system of interval type-2, is compared with the classic control. In the nonlinear process with inverse response of the Van de Vusse reactor, it is assumed that it is perfectly agitated and is operating in isothermal and isochoric conditions with constant reaction. The mathematical model that describes the reaction is given by a nonlinear system of ordinary differential equations which, by the Laplace transform method, is associated with a second order transfer function. Simulations built on Simulink R © show the efficacy of the interval type-2 fuzzy control strategy, with better performance in terms of delay and overshoot compared to the classic control.
Analysis of Stair-Like 3-D-Printed Layers for Suppression of Conduction Noise on Microstrip Line

Unwanted high-frequency electromagnetic interference from signal transmission lines or electromagnetic waves adversely affects the performance of electronic systems. Due to electromagnetic wave interference, filters and shields need to be designed to reduce noise and avoid damage to electronic devices. In this letter, 3-D printing is used to create stair-like layers on a microstrip line, which is utilized as a high-frequency power noise attenuator. Polylactic acid with added carbon is used in 3-D printing to prepare the conduction noise suppression layers. The effect of the stair-like 3-D-printed layers on power absorption, which enables high-frequency devices to function correctly, is discussed. The experimental results reveal that the power absorption range of the stair-like 3-D-printed layers is 80%–95% in the frequency range of 1.3–4.5 GHz for 1.21/3.23 mm specimens.
In vitro cytotoxicity of carbon black nanoparticles synthesized from solution plasma on human lung fibroblast cells

Carbon black nanoparticles (CB-NPs) have been synthesized from liquid benzene by a solution plasma method at room temperature and atmospheric pressure. The morphological observation by scanning electron microscopy revealed the agglomeration of aggregated fine particles. The synthesized CB-NPs were predominantly amorphous as confirmed by X-ray diffraction. The in vitro cytotoxicity of CB-NPs on the human lung fibroblast (MRC-5) cell line was assessed by the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and systematically compared with those of two types of commercial carbon blacks (i.e., Vulcan XC-72 and Ketjenblack EC-600JD). Cell viabilities were studied at different concentrations of 32.5, 65, 125, and 250 µg/mL. It was found that the CB-NPs derived from solution plasma exhibited a lower cytotoxicity on the MRC-5 cells than the other two comparative carbon blacks. The viability of MRC-5 cells exposed to CB-NPs remained higher than 90% even at a high concentration of 250 µg/mL. This result preliminarily confirmed the biosafety and potential use of CB-NPs in the field of biological applications.
Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics

Objective: This study investigated to probe ferroptosis-related diagnostic biomarkers and underlying molecular mechanisms in Diabetic nephropathy (DN). Methods: GSE30122 and GSE1009 from GEO database were used as training and verification sets, respectively, to screen differentially expressed ferroptosis-related genes (FRGs). These genes were further analyzed using GO, KEGG, and GSEA methods, and screened with PPI, LASSO, and SVM-RFE to identify ferroptosis-related diagnostic biomarkers for DN. A diagnostic model was established using the Glm function and verified with ROC curve. The relationship between these biomarkers and immune cell was analyzed, and qRT-PCR and Western blot were used to detect the expression of these biomarkers in kidney tissues and identify the effect of TP53 on DN development. Results: Fifty one differentially expressed FRGs were enriched in bioprocesses such as p53 signaling pathway, oxidative stress and chemical stress response, and mTOR signaling pathway. TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were identified as ferroptosis-related diagnostic biomarkers for DN. TP53 showed the most differential expression. ROC analysis showed that AUC values of TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were 0.751, 0.705, 0.725, 0.882, 0.691, and 0.675, respectively. The AUC value of DN diagnosis model was 0.939 in training set and 1.000 in verification set. qRT-PCR results confirmed significant differences in these six biomarkers between DN and normal kidney tissue (p < 0.05), and correlation analysis showed that five biomarkers were significantly correlated with infiltrating immune cells (p < 0.05). Furthermore, western blots showed that TP53 promotes apoptosis through PI3K-AKT signaling in DN. Conclusion: TP53, RB1, NF2, RRM2, PRDX1, and CDC25A have potential as diagnostic biomarkers for DN. The diagnostic model containing the above six biomarkers performs well in the diagnosis of DN. Five of the six biomarkers are strongly associated with several infiltrating immune cells. TP53 may play an essential role in the development of DN.
CD31 Positivity and Prediction of Tumor Invasive Behavior in Colorectal Cancer

Background: Due to the close association of CD31 marker positivity and tumor microvessel density, the relationship between this marker and the pathophysiological behavior of the tumor, such as the ability to invade the surrounding tissues and organ damage, is also quite probable. Objectives: We aimed at evaluating the association of CD31 expression with the likelihood of microvascular invasion and other abnormal histopathological findings in patients with colorectal cancer. Methods: In our cross-sectional study, the pathology numbers of 50 samples that were diagnosed with colorectal cancer in their pathology report during 2021 and 2022 were identified by searching our hospital information system. Immunohistochemical staining (IHC) using CD31 monoclonal antibody was performed for assessing CD31 positivity. Results: CD31 positivity was found in 34.0% of cases, 35.5% in colon masses, and 31.6% in rectal masses. The patients with positive CD31 had significantly higher tumor grades (P < 0.045). The prevalence rate of vascular invasion was significantly higher in patients with positive CD31 compared to those with negative CD31 states (47.1% versus 12.1%, P = 0.006). Also, the rate of lymph node involvement in the groups with positive and negative CD31 was 58.8% and 24.2%, respectively, indicating a significant difference (P = 0.016). In multivariable logistic regression models, CD31 positivity was shown to be associated with the risk for vascular invasion (OR = 6.211, P = 0.020) and lymph node involvement (OR = 6.535, P = 0.011). Conclusions: CD31 positivity in patients with colorectal cancer can effectively predict invasive behavior of the tumor including vascular invasion, lymph node involvement, and high tumor grades.
How to Manage a Budget with ATL+

We study the alternating-time temporal logic ATL+ enriched with one resource (written ATL+(1)) extending ATL+ with the possibility to manage a budget. We propose a game-theoretic semantics via the introduction of two evaluation games so that the compositional semantics is captured by strategies in the games. We show that the model-checking problem for ATL+(1) is in PSpace and we identify several non-trivial fragments that can be solved in PTime. By-products of our investigations include also a simplified Pspace decision procedure for resource-free ATL+, an effective way to synthesize constraints in a version of ATL+(1) with parameters and a PSpace bound to solve an energy game with one counter whose objectives are LTL formulae of temporal depth one.
The Effect of Chayote (Sechium Edule) Extract Cream Can Reduce Matrix Metalloproteinase-2 (MMP-2) Levels and Increase Collagen Density in The Wound Healing Process of Male Wistar Rats (Rattus Novergicus) With Diabetes Mellitus

Introduction: The main complication in Diabetes Mellitus patients is delayed wound healing resulting in chronic wounds. Chayote is known as a vegetable, which has anti-inflammatory properties. The tannin and saponin compounds in chayote produce antibacterial agents. This study aims to prove the role of chayote extract cream on the wound healing process of diabetic rats through MMP-2 markers and collagen density. Methods: This study was an experimental post-test only control group design using male rats. Samples were induced hyperglycaemia with 46 mg of nicotinamide and 10 mg of streptozotocin one week before the experiment. The sample was divided into 2 groups: the placebo group (P0) and the treatment group (P1) with a total of 11 samples per group. The treatment group received cream of chayote extract 60% for 1 week. On the 8th day, both groups were taken by punch biopsy to assess collagen density, then examined for MMP-2 levels in the blood. Results: The average MMP-2 level in the control group was 4.97 ± 0.36 ng/ml, while the average MMP-2 in the treatment group was 4.59 ± 0.42 ng/ml. The average collagen level in the control group was 37.08 ± 12.92 %/ body surface, while the mean collagen in the treatment group was 54.21 ± 15.76 %/ body surface. Conclusion: Administration of ethanol extract of chayote (Sechium edule) significantly reduced matrix metalloproteinase-2 (MMP-2) levels and increased collagen density in the wound healing process of rats.
Dwelling into the Role of Ahamkara in Academic Performance: A Qualitative Inquiry

A student's academic performance is conditioned on factors within the pupil or outside, in the environment. The Western body of knowledge emphasizes the positive role of ‘Self’ and related variables such as self-esteem, self-efficacy, self-confidence, and others on the academic performance of students. An Indian counterpart to the concept of self is ‘Aham’. The present study aimed at qualitatively understanding the role of Ahamkara in the academic performance of school students. For this purpose, a semi-structured interview technique was utilised for data collection. A total of 11 interviews were recorded and transcribed. Thematic Network analysis framework forwarded by Attride-Sterling (2001) was utilized for data analysis. Based on the analysis 4 global themes emerged. They are: Formation and maintenance of a sense of self through Identification; Active engagement of Self in the learning process leads to growth; Ignorance of Self, a path to failure; and Failure an opportunity to maintain the optimal sense of self.