Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Speckle Noise Filtering Technique for Medical Images

1Research Scholar, Department of Mathematics, Erode Arts and Science College, Erode, Tamilnadu, India 2Associate Professor (Rtd), Department of Mathematics, Erode Arts and Science College, Erode, Tamilnadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Speckle noise filtering has become a challenging role in medical ultrasound image processing. Based on the concepts of fuzzy logic and fractional integral operation, an adaptive speckle noise filtering technique for medical images is presented in this paper. This filtering technique has two steps. In the first step, using the coefficient of variation concept and Gaussian membership function, the image area is classified into three different regions such as homogeneous, edge and detail region. In second step, a simple average filter and fractional integral operation are employed for filtering the classified noisy pixels. The proposed filtering technique increases image quality by eliminating maximum unwanted noise whereas protecting the important image particulars.
Effect of cation ordering on oxygen vacancy diffusion pathways in double perovskites

Perovskite structured oxides (ABO3) are attractive for a number of technological applications, including as superionics because of the high oxygen conductivities they exhibit. Double perovskites (AA′BB′O6) provide even more flexibility for tailoring properties. Using accelerated molecular dynamics, we examine the role of cation ordering on oxygen vacancy mobility in one model double perovskite SrLaTiAlO6. We find that the mobility of the vacancy is very sensitive to the cation ordering, with a migration energy that varies from 0.6 to 2.7 eV. In the extreme cases, the mobility is both higher and lower than either of the two end member single perovskites. Further, the nature of oxygen vacancy diffusion, whether one-dimensional, two-dimensional, or three-dimensional, also varies with cation ordering. We correlate the dependence of oxygen mobility on cation structure to the distribution of Ti4+ cations, which provide unfavorable environments for the positively charged oxygen vacancy. Our results demonstrate t.
Wassonite: A new titanium monosulfide mineral in the Yamato 691 enstatite chondrite

Abstract Wassonite, ideally stoichiometric TiS, is a titanium monosulfide not previously observed in nature, that was discovered within the Yamato 691 EH3 enstatite chondrite. Twelve Ti-S phase grains were identified in a rare barred olivine (BO) chondrule; three of the grains were extracted by the focused ion beam technique. Because of the submicrometer size of the wassonite grains, it was not possible to determine conventional macroscopic properties. However, the chemical composition and crystal structure were well constrained by extensive quantitative energy-dispersive X-ray analysis and electron diffraction using transmission electron microscopy (TEM). The crystal system for wassonite is rhombohedral (a = 3.42 ± 0.07, c = 26.50 ± 0.53 Å) with space group: R3̅m, cell volume: 268.4 ± 0.53 Å3, Z = 9, density (calculated): 4.452 g/cm3, empirical formula: (Ti0.93,Fe0.06,Cr0.01)S. The wassonite grains crystallized from the chondrule melt that was itself formed in the solar nebula, not on the parent asteroid. The other crystalline phases in the BO chondrule include forsterite, enstatite, troilite, metallic Fe-Ni, and osbornite (as well as the new Ti-S-bearing minerals and schollhornite) are highly reduced and indicate formation at low-oxygen fugacities.
Transactions of the Korean Nuclear Society Virtual Autumn Meeting December 17-18 Re-exploration of pretrained artificial intelligence model for a nuclear power plant autonomous operation

An autonomous system is a technology that is in the spotlight in industrial fields because of an advantage of excluding human error. Nuclear industry has also implemented automated systems that support nuclear power plants (NPPs) operation in full power mode. However, tasks of adjusting key parameters to suit the situation as the plant temperature and pressure gradually decrease or increase are performed by human operators. Thus, it is difficult to stabilize an NPP only with automatic systems during startup or shutdown operation. In the previous work, the framework to develop autonomous system for startup and shutdown operation was suggested [1]. As an extension of the previous study, actor-critic algorithm was implemented to stabilize a pressurizer (PZR) pressure, one of tasks during startup operation. Training process consists of two stages: first, training is performed from the beginning, then the model is saved, and second, the saved model is started again as a starting point. Through this process, a PZR pressure control model was successfully obtained for a system operating block.
The Factors determining the Degree of Severity of the Penalties imposed on Independent Auditors in Brazil

There has been a rise in the importance of auditing in capital markets through trust and credibility, as a result of business expansion. In light of this, the purpose of this research study is to discover the factors that determine the kind of penalties imposed on independent auditors, on the basis of the Punitive Administrative Proceedings filed by the Securities and Exchange Commission (SEC) in the period 2000-2017. This study makes a contribution to this scientific area by being the first to seek variables that can explain these penalties in quantitative terms. The sample of 76 proceedings was taken from the CVM and B3 sites. The regression models show that the penalties imposed on auditors are: (i) positive and related to a significant degree of breaches that occurred in the customer acceptance, execution of work and reports, repeated offenders and business clients that are listed in the Stock Exchange; (ii) negative and related in a significant way to the occurrence of breaches in audit planning, the existence of corporate governance in the audited clients, the big four firms and procedural timelines.
An Approach to Detect Fake Reviews based on Logistic Regression using Review-Centric Features

- The impact of reviews on any e-commerce site is of great importance, as it can be the base for a buyer's decision to buy any product. Buyer tries to evaluate the authenticity and quality of the product using the feedback given by other previous buyers in the form of review. But, sellers are taking advantage of the reviews by posting reviews in an attempt to promote or defame a product. Such reviews which are not a genuine opinion of an individual are termed as fake reviews. The existence of such fake reviews makes the buyer unable to make the right judgments of sellers, which can also cause the credibility of the platform to be downgraded. Thus, it is very important to identify the fake reviews on the platform. In this paper, we propose a method to detect such fake reviews using a logistic regression model by considering review centric features achieving an overall accuracy of 82%. Also, our study illustrates the impact of the “verified purchase” feature in fake review classification.
Deep Learning over Conventional Image Processing for Contrast Enhancement and Auto-Segmentation of Super-Resolved Neuronal Brain Images: A Comparative Study

- Segmentation and analysis of neuronal and fibril structures obtained by electron microscopy to develop 3D maps of connections in the brain is an involved research domain in computer vision and neuroscience. Interactive understanding of neuronal and fibril connections in the brain can enable neuroscientists to draw significant conclusions for applications in artificial intelligence, better understand the disease and possibly arrive at a cure. Conventional methods used for biomedical image enhancement and segmentation often fail to generalize and identify non-convex morphologies and deep connections in the brain. In this paper, deep learning is compared with traditional mathematical image processing to understand its applicability as a promising solution for contrast enhancement and auto-segmentation of super-resolved neuronal brain images. The paper also throws light on existing non deep learning techniques for image enhancement and segmentation to develop a comparative opinion. In addition to this, the open research challenges for ensuring robustness in the field of deep learning-based bio-medical imaging have been discussed.
Characteristic-Based Expected Returns and Corporate Events

We propose that expected returns estimated for the broad market based on observable firm characteristics provide a simple and useful benchmark for assessing whether returns to a given set of stocks are abnormal. To illustrate, we document that the apparently abnormal long-run returns after six important corporate events, including initial and seasoned public equity offerings, mergers and acquisitions, dividend initiations, share repurchases and stock splits, are substantially reduced or eliminated when actual event stock returns are compared to characteristic-based expected returns. A simple five-characteristic specification relying only on firm size, book-to-market ratio, profitability, asset growth, and return momentum performs as well as more complex specifications. This analysis supports the conclusion that returns after corporate events are largely explained by the return-relevant characteristics of the firms engaging in the events.
Private Emotion Recognition with Secure Multiparty Computation

We present the ﬁrst privacy-preserving solution for deep learning-based audio classiﬁcation using Secure Multiparty Computation (MPC). Our approach allows to classify a speech signal of one party (Alice) with a deep neural network of another party (Bob) without Bob having access to Alice’s speech signal in an unencrypted manner. As threat models, we consider both passive security and active security. We evaluate the efﬁciency-security-accuracy trade-off of the proposed solution in a use case for privacy-preserving emotion detection from speech with a convolutional neural network, answering a question that has remained open in the literature thus far, namely to what extent MPC-based protocols can enable provably secure and highly accurate real-time speech classiﬁcation.
Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling

Light Detection And Ranging (LiDAR) is an active remote sensing technology for acquiring high resolution 3D terrain and vegetation structure parameters over multiple tempo-spatial scales, which can help to accurately monitor and model forest ecosystem dynamics. Consequently it plays an important role in understanding the impact of such dynamics on the carbon cycle and global climate change, and promoting biodiversity conservation. In this paper, we review the concepts and recent developments of LiDAR and explore the application of LiDAR in generating terrain models (e.g. digital elevation and digital surface models) and retrieving forest biophysical parameters (e.g. individual tree locations, leaf area index, volumes, biomass and carbon stocks). Finally, we present challenges for LiDAR in forestry applications, and suggest three major research issues for future study. We believe that there is an inevitable trend to constructing a digital ecosystem research network via combining spaceborne, airborne, and ground-based sensor measurements in an integrated platform, where LiDAR can provide an accurate and efficient solution for capturing 3D environmental variables. Thus, LiDAR will contribute to improving the relationship between man and nature in decision-making processes and eventually realizing a harmonious coexistence.
Remimazolam Protects Against LPS-Induced Endotoxicity Improving Survival of Endotoxemia Mice

Remimazolam is a new benzodiazepine of sedative drugs with an ultra-short-acting anesthetic effect, commonly used for critically ill patients (especially septic patients) in intensive care units (ICUs). Although some anesthetics have been reported to show certain anti-inflammatory effects, the role of remimazolam in inflammation is still remained unknown. Here, we studied the effects of remimazolam on macrophage in response to LPS both in vivo and in vitro. Interestingly, compared with LPS treatment group, remimazolam remarkably improved survival rate of endotoxemia mice and decreased the release of LPS-induced inflammatory mediators (such as TNF-α, IL-6, and IL-1β). We further found that remimazolam not only inhibited the activation of MAPK signal pathway at 15 min after LPS treatment but also disturbed Rab5a related TLR4 expression at cell surface in response to LPS at a later time. Such evidence suggests that remimazolam might be beneficial to septic patients who are suffering from uncontrolled inflammatory responses.
Does Compliance Strategy Increase Compliance? Evidence from Malaysia

Due to the significance of tax revenue as the primary source of a country’s income, any leakage due to non-compliance behaviour may impact the country, which will eventually affect citizens. One way to reduce tax non-compliance is through compliance strategies which can be categorised into two; deterrence and accommodative approach. It is advisable that tax authority should not only focus on the deterrent approach (tax audit and penalty), mainly on tax offenders. Instead, tax authority also needs to find ways to preserve good taxpayers’ continuous fulfilment of their tax responsibilities, encourage future taxpayers to commit to tax laws, and transform tax offenders to become good taxpayers using the accommodative approach. This study uses the survey method to determine individual taxpayers’ perception of tax authority’s compliance strategies (threat of punishment, tax education programme, treatment provided by tax authority, special voluntary disclosure programme (SVDP)). This study then investigates the influence of those compliance strategies towards tax compliance behaviour. Our findings indicate that individual taxpayers have a positive perception towards compliance strategies provided by the tax authority in Malaysia (refer to Inland Revenue Board of Malaysia (IRBM)). In addition, the threat of punishment, treatment provided by IRBM and SVDP are significantly proven to have a positive relationship with tax compliance. However, it is suggested that IRBM should improve their tax education programmes as it is found to be insignificant. This study contributes to current tax compliance literature in a relatively important field of compliance strategies by providing comprehensive insights into the influence of both deterrence and accommodative apporaches in influencing tax compliance behaviour, as well as focusing more on an accommodative approach that is receiving attention lately. With both approaches in one study, the results of this study provide a clear, complete and comprehensive understanding since most of previous studies conducted separately. Finally, the findings of this study also provide valuable and significant input to the tax authorities and policymakers, especially IRBM in assessing and making a necessary improvement on their compliance strategies with the mission to enhance tax compliance behaviour, especially among individual taxpayers.
FERRAMENTA MULTIESCALA PARA IDENTIFICAÇÃO DE TEMPESTADES GEOMAGNÉTICAS: UM ESTUDO PRELIMINAR

Among the major challenges of the Space Weather community is the comprehensive knowledge of the electrodynamical processes occurring due to the Solar-wind and the magnetosphere coupling, in which substantial transfers of energy, moment and particles occur. As direct consequence of the enhancement of this kind of coupling, magnetic disturbances at the Earth surface related to a magnetosphere-ionosphere current system can be produced and they are possible to be recorded by magnetometers. This work aims to implement a tool to identify the behavior of the energy, i.e. impulsive variations, during the main phase of geomagnetic storms which are related to this complex current system. Instead of the use of a pos-processed index available the most of the time with a delay of months, for instance, equatorial geomagnetic index Dst, a real-time diagnosis has been proposed in order to identify geomagnetic storms. This diagnosis is based on a wavelet analysis technique to deal directly with raw data from a chosen magnetometer station.
STABILITY OF THE PERIODIC TODA LATTICE UNDER SHORT RANGE PERTURBATIONS

We consider the stability of the periodic Toda lattice (and slightly more generally of the algebro-geometric finitegap lattice) under a short range perturbation. We prove that the perturbed lattice asymptotically approaches a modulated lattice. More precisely, let g be the genus of the hyperelliptic curve associated with the unperturbed solution. We show that, apart from the phenomenon of the solitons travelling on the quasi-periodic background, the n/t-pane contains g+2 areas where the perturbed solution is close to a finite-gap solution in the same isospectral torus. In between there are g + 1 regions where the perturbed solution is asymptotically close to a modulated lattice which undergoes a continuous phase transition (in the Jacobian variety) and which interpolates between these isospectral solutions. In the special case of the free lattice (g = 0) the isospectral torus consists of just one point and we recover the known result. Both the solutions in the isospectral torus and the phase transition are explicitly characterized in terms of Abelian integrals on the underlying hyperelliptic curve. Our method relies on the equivalence of the inverse spectral problem to a matrix Riemann–Hilbert problem defined on the hyperelliptic curve and generalizes the so-called nonlinear stationary phase/steepest descent method for Riemann–Hilbert problem deformations to Riemann surfaces.
Isolation and structure elucidation of anti-malarial principles from Terminalia mantaly H. Perrier stem bark

Emergence of malaria parasite resistance to drugs has raised global public health concerns for a compelling need to develop improved malaria therapy. This study is a bio-guided isolation of triterpenoid antimalarial compounds from Terminalia mantaly. Methanol extract of the plant was subjected to column chromatography, and eluted with a ternary solvent system gradient-wise. Two compounds, 1 and 2, were isolated and characterised by spectroscopic data (IR, 1H and 13C NMR, COSY, HMQC, HMBC) and by comparison with literature. Isolated compounds were investigated for antimalarial property by spectrophotometric determination of inhibition of β-Hematin formation, absorbance taken at 405 nm. Results were analysed using Graghpad Prism® (6.0) and presented as mean IC50±SEM. Statistical significance, determined using Student’s t-test and one-way ANOVA, set at p-value of 0.05. Quantitative β-Hematin formation inhibitory activities gave IC50±SEM values of (compound 1; 4.434±0.47), (compound 2; 5.140±4.2) with (chloroquine; 0.335±0.1 mg/ml). Compound 1 was identified as 2,3,19,23-tetrahydroxyolean-12-en-28-oic acid glucopyranoside (arjunglucoside I), and compound2 as its aglycone, 2,3,19,23-tetrahydroxyolean-12-en-28-oic acid (arjungenin). This study provided credence for folkloric use of Terminalia mantaly to treat malaria, and this observed activity was probably due to these isolated triterpenoids.Keywords: β-Hematin, triterpenoids, nuclear magnetic resonance spectroscopy
Surface roughness of thermally treated wood cut with different parameters in CNC router machine

The effects of different machining parameters on surface roughness values of thermally treated pine, beech, and linden woods cut in a computer numerical control (CNC) router machine were examined. Wood specimens were thermally treated at 170, 190, and 210 °C for 2 h. Then, specimens were cut in the radial and tangential directions with three different spindle speeds (12000, 15000, and 18000 rpm) and three different feed rates (3000, 4000, and 6000 mm/min) using two different end mill tools (spiral and straight) on the CNC machine. The end mill type significantly affected the roughness values of the untreated and thermally treated specimens in both directions. Lower roughness values were found in the specimens (especially pine) machined with the straight end mill compared to those machined with the spiral end mill. Roughness generally decreased in the thermally treated specimens. However, thermal treatment temperature did not have a notable effect on roughness. As the spindle speed increased, the roughness values of all specimens decreased. In contrast, as the feed rate increased, the roughness values increased. Therefore, the end mill type, feed rate, and spindle speed were the most influential parameters on the roughness.
A NOVEL IMPLEMENTATION OF ROBUST CT SCAN BASED BRAIN DIAGNOSIS PROCESS USING CNN-GB TECHNIQUE

Background: Brain diagnosis process is an important medical activity; it can be diagnosis through many techniques. The numerous diagnosis processes are available for Brain scanning, such as CT, MRI, X-ray and CTA. If Medical applications are fast and real, then diagnosis process is easy; these can help the patient’s life. Objective: In this work CT scan-based brain diagnosis system is proposed through CNN-GB technique. A real and accurate brain diagnosis process gives the better treatment with efficient success rate. Method: In this work the brain abnormalities have been identified through machine and deep learning mechanisms. This work requires dataset (Kaggle dataset) and real time CT scan images. CNN and GBML techniques are applied on CT brain images for identify the disorders. Results: This proposed method achieves 0.992 accuracy and 0.993 Tp these are good results compared to earlier method. Conclusion: In this investigation a CT scan-based brain diagnosis application is designed. This architecture is a combination of adaptive median filter, CNN deep learning model and gradient boosting machine learning. Due to combination of three algorithms getting the accurate results compared to earlier models. This application is achieving PSNR = 56.20, SSIM is 0.99, accuracy is 0.992, error rate is 0.05 average time 0.07 have achieved. Moreover, this method is verified on various high density of noise, at all type of densities implemented application attains the more improvement.
Debugging a Policy: A Framework for Automatic Action Policy Testing

Neural network (NN) action policies are an attractive option for real-time action decisions in dynamic environments. Yet this requires a high degree of trust in the NN. How to gain such trust? Systematic testing certainly is one possible answer, in analogy to program testing. The input to the program becomes the start state for the policy; and erroneous program behaviors – “bugs” – become bad policy behavior, e.g. not reaching the goal from a solvable state. We introduce a framework spelling out this concept. The framework is generic and in principle applicable to arbitrary planning models. We discuss how this form of testing can be operationalized, i.e., how to confirm a bug has been found, and how potential bugs might be identified in the first place. This essentially involves seeing standard planning concepts through the new lense of policy testing. The implementation and practical exploration of this framework remains open for future work. We believe that action policy testing is an important topic for ICAPS, and we hope that our framework will serve to start its discussion.