Miracle-dz/UniFAR-FTUs huggingface dataset:
AnoDe: A Log-based Self-Supervised Framework to Detect Scrubber Failures in SRAM-FPGA

SRAM-FPGAs used in radiative environment are integrated with a scrubber to protect the configuration memory from radiation effects. Any malfunctions in the scrubber degrades the reliability of the system and can have catastrophic consequences in critical applications. Existing solutions for a reliable scrubber focus on masking or detecting the scrubber errors through redundant modules implemented in the FPGA. While these approaches improve the overall reliability, they are not completely radiation-proof owing to their implementation on FPGA. In order to improve the scrubber reliability, a complementary scheme external to the FPGA is required. Based on this consideration, we propose AnoDe, a failure detection framework running on a supervisory processor external to the FPGA board. AnoDe leverages the logs generated by the scrubber to detect failures in real-time using an Autoencoder network. AnoDe provides a self-supervised solution right from generating the labelled training data to dynamically adapting to the prevailing radiation conditions. We evaluated the effectiveness of our approach on a Xilinx Ultrascale+ MPSoC ZCU104 board using fault injection. The results demonstrated a detection performance comparable to that of a custom approach with an F1 score of 0.85 for single bit upsets and 0.93 for multi bit upsets. Overall, the proposed approach could reduce the scrubber SEU sensitivity from 6 % to 1 %.
Analysis on Forecasting of Loan using Supervised Machine Learning Models

In the current generation as the world is digitalizing so fast that we can’t even imagine, even online banking became very common among lot of people. As we all know, at one point of time lot of people are in need of loan and getting selected for a loan is a huge process and it will take a lot of time. As we are stepping ahead in future this loan has to be done in a faster and a better way that’s why we came up with a solution for that. We have to analyze the details of a person as deep as possible so that we can predict the data more accurately and then the main thing is all the details have to be true and genuine. It seems to be a very big process and it will take lot of time. So, in this paper, we are going to build various supervised machine learning models in order the predict the more accurate value for the prediction of loan using machine in an efficient manner.
Comparison of Initiation and Temporal Evolution of Positive and Negative Ionic Wind Under Needle-to-Plate Electrode

Considerable research efforts have been devoted to the generation, characterization, and optimization of ionic wind produced by corona discharge. The current article tries to explain the mechanism that causes the different behaviors observed in negative and positive dc corona ionic wind. The steady-state and the transient state ionic wind produced by both polarities are studied through particle image velocimetry (PIV) technology. The results show that at transient state, a series of mushroom-like jets are produced in negative corona discharge ionic wind due to the effect of Trichel pulse. These jets merge with each other and combine momentum to form the wide structure at steady state. In comparison, the positive corona discharge current has no pulse, and the ionic wind has only one “I-shape” wave head during the initiation process. The dynamics of the jets is studied in detail. The formation of the jet series in negative corona, or single jet in the case of positive corona, is believed to play a key role in the generation of ionic wind.
Preparation, Characterization, and Biological Properties of Hydroxyapatite from Bigeye Snapper (Priancanthus tayenus) Bone

The optimum condition of acid hydrolysis for hydroxyapatite extraction from bigeye snapper (Priancanthus tayenus) bone and the effects of extraction time (10–60 min) and HCl concentration (2.0–5.0% w/v) on yield and hydroxyapatite properties were determined. The optimum extracted condition was found using 5% HCl for 60 min, which was 13.4% yield; 19.8 g/100 g Ca content; 9.6 g/100 g P content; 2.1 Ca/P ratio; L*, a*, b*; and ΔE as 84.5, 2.8, 16.5, and 15.6, respectively. The using of 5% NaOH solution was optimum for hydroxyapatite precipitation from the extracted solution. The characteristic and biological properties of the obtained hydroxyapatite were studied. Fourier transform infrared spectroscopy and X-ray diffraction results showed a good comparison between the extracted and commercial hydroxyapatite. The microstructure of the extracted hydroxyapatite from a scanning electron microscope showed an irregular and flat-plate shape, large surface area, and roughness. The extracted hydroxyapatite was non- and low-cytotoxicity at a concentration of 50 and 100–400 µg/mL, respectively. Bovine serum albumin (BSA) adsorption and desorption of hydroxyapatite was studied. An increasing BSA concentration, hydroxyapatite amount, and adsorption time significantly increased protein adsorption on hydroxyapatite. Protein desorption from BSA-loaded hydroxyapatite showed an increase of release initially in the first 4 days and became a steady release rate until 14 days.
Electron-phonon interaction and thermal boundary resistance at the crystal-amorphous interface of the phase change compound GeTe

Phonon dispersion relations and electron-phonon coupling of hole-doped trigonal GeTe have been computed by density functional perturbation theory. This compound is a prototypical phase change material of interest for applications in phase change non-volatile memories. The calculations allowed us to estimate the electron-phonon contribution to the thermal boundary resistance at the interface between the crystalline and amorphous phases present in the device. The lattice contribution to the thermal boundary resistance has been computed by non-equilibrium molecular dynamics simulations with an interatomic potential based on a neural network scheme. We find that the electron-phonon term contributes to the thermal boundary resistance to an extent which is strongly dependent on the concentration and mobility of the holes. Further, for measured values of the holes concentration and electrical conductivity, the electron-phonon term is larger than the contribution from the lattice. It is also shown that the presence of Ge vacancies, responsible for the p-type degenerate character of the semiconductor, strongly affects the lattice thermal conductivity of the crystal.
Design of Postbuckled Spinal Structures for Airfoil Camber and Shape Control

In this paper the use of shape definition schemes to design spinal structures for the control of deformable airfoils is examined. The aim is to find strcutures that, when suitably loaded, can be used to alter the aerodynamic properties of a cladding that forms the airfoil, thus obviating the need for flaps or ailerons. Morphing through different cambered airfoils to achieve aerodynamic performances for different maneurvers is then possible by exploiting a range of incremental nonlinear structural solutions. Further, by using structures that are acting in the postbuckling reginme, it is possible to obtain significant changes in shape with only modest changes in applied load. Results are formulated in terms of the aerodynamic properties of the morphed airfoils using a shape optimized beam as the spinal structure with fixed aerodynamic cladding.
Kompatybilność rop badana z zastosowaniem mikroskopu wysokociśnieniowego w warunkach dynamicznych

Ze względu na wysoką cenę lekkich rop większość rafinerii skłania się do zakupu większych ilości tańszych rop „okazyjnych”, które są cięższe i zawierają wyższe stężenia siarki i kwasów naftenowych. Takie podejście doprowadza do większej częstotliwości prac serwisowych związanych z oczyszczaniem instalacji procesowej w rafinerii. Na szczęście zrozumienie przyczyn i metod łagodzenia wytrącania się zanieczyszczeń ropopochodnych uległo ostatnio znacznej poprawie dzięki opracowaniu narzędzi do przewidywania i identyfikacji. Co zaskakujące, 90% zanieczyszczeń ropopochodnych w rafinacji ma tylko kilka wspólnych przyczyn. Analiza osadu zwykle pozwala na zidentyfikowanie pierwotnej przyczyny, którą potwierdza odnalezienie prekursora tego niekorzystnego zjawiska w ropie naftowej przepływającej przez zanieczyszczoną jednostkę instalacji. Odpowiednie rozpoznanie zagadnienia może wprowadzić szereg potencjalnych metod łagodzących, z których dana rafineria może wybrać najlepszą do wdrożenia. Każdy kraj na świecie dąży do osiągnięcia jak największego bezpieczeństwa energetycznego, a tym samym do dywersyfikacji dostaw ropy i innych surowców. W przypadku ropy, gdy pozyskiwana jest ona z różnych źródeł, generuje się dodatkowy problem, a mianowicie kompatybilność. Okazuje się, że po zmieszaniu niektóre ropy wykazują tendencje do wytrącania osadów w zbiornikach magazynowych, a następnie w ciągu logistycznym. W jednej z poprzednich prac opracowano skuteczną metodę filtrowania pozwalającą na określenie kompatybilności rop i ich mieszanin. W obecnej pracy skoncentrowano się na tym, aby nowa metoda pozwoliła na szybszą możliwość wykonania pomiaru kompatybilności przy jednoczesnej możliwości ponownego wykorzystania tej samej próbki. Opracowany nowy sposób badań kompatybilności z wykorzystaniem mikroskopu (ang. high-pressure microscope, HPM) zastosowano do dwóch rop pochodzących z różnych kierunków dostaw. Pomiary wykonano dla rop i ich mieszanin w temperaturze 120°C i pod ciśnieniem 25 bar. Pomiar kompatybilności rop metodą filtrowania wykorzystano do celów porównawczych, gdzie masa odseparowanego osadu na specjalistycznych filtrach stanowiła wyznacznik kompatybilności badanych rop.
Analyzing Multilevel Data in the Presence of Heterogeneous Within-Class Regressions

The concerns of this investigation are multiple sources of complications in the analysis of multilevel educational data. The focus is on problems that arise when within-group regressions of outcome on input are related to teacher/ school characteristics. Single-level and multilevel analytical approaches are applied to hypothetical data for which the relationship between teacher/class quality and the heterogeneity of within-class slopes is varied systematically. It is shown that single-level analyses and the proposed multilevel approaches can all yield mislèading estimates of teacher/class effects on mean class outcomes. However, selected multilevel methods provide some indication of misspecification and can identify the direction of the bias in estimating teacher/class effects on mean class outcomes.
Discovery of 0.5 MK Gas in the Center of the Virgo Cluster

An observation of M87, the central galaxy of the Virgo Cluster, was performed by the deep survey telescope aboard the Extreme-Ultraviolet Explorer, in the 0.065-0.245 keV energy band. A point source and an extended emission halo of radius ~20' are clearly visible in the data, and represent the first detection of cluster gas emission in the EUV. The emission cannot be explained by the well-known cluster gas at X-ray temperatures. Instead, it is necessary to introduce a second gas component, with temperature between 5 × 105 and 106 K. The rapid cooling of plasmas at such temperatures implies a mass accretion rate of more than 300 M☉ yr-1. It is unlikely that the phenomenon is directly related to a cooling flow, which involves a much lower accretion rate of ~10 M☉ yr-1.
PLGA Nanoparticles Loaded with Sorafenib Combined with Thermosensitive Hydrogel System and Microwave Hyperthermia for Multiple Sensitized Radiotherapy

Hypoxia is typically the leading cause of radiotherapy (RT) resistance in solid tumors, and glutathione (GSH) overexpression in tumor cells is a potent antioxidant mechanism that protects tumor cells from radiation damage. Herein, we developed a sorafenib (SFN) loaded-PLGA hydrogel system (SPH) in combination with microwave (MW) hyperthermia for RT sensitization. SPH with stable properties was produced by combining SFN and PLGA in a specific ratio and encapsulating the mixture in agarose hydrogel. Intratumoral injection of SPH to mice combined with MW hyperthermia can not only directly cause thermal damage to tumor cells, but also increase blood oxygen delivery to the tumor site, thus overcoming the problem of intratumoral hypoxia and achieving “first layer” RT sensitization. Moreover, high temperatures can cause the hydrogel to disintegrate and release SFN. Not only can SFN inhibit tumor growth, but it can also achieve the “second layer” of RT sensitization by inhibiting glutathione (GSH) synthesis in cells and increasing reactive oxygen species (ROS) production. Experiments, both in vitro and in vivo, have indicated that SPH and MW hyperthermia can achieve a double RT sensitization effect and a significant tumor inhibition effect. In conclusion, combining our SPH nanosystem and thermoradiotherapy is a promising anti-tumor treatment.
Add-On Effect of Hemagglutinating Virus of Japan Envelope Combined with Chemotherapy or Immune Checkpoint Inhibitor against Malignant Pleural Mesothelioma: An In Vivo Study

Simple Summary In this study, we report that the combination of existing therapeutic agents and hemagglutinating virus of Japan envelope (HVJ-E), a virus-inactivating agent, may be a more effective treatment agent for pleural mesothelioma, a refractory cancer for which no effective treatment has been established. We have already reported that HVJ-E, which activates antitumor immunity and induces apoptosis in cancer, can provide a different mechanism for effective treatment. In this report, the antitumor effect of HVJ-E in combination with chemotherapy or immune checkpoint inhibitors (ICIs) activated antitumor immunity in a mouse model of malignant pleural mesothelioma-bearing tumor and significantly enhanced antitumor efficacy compared to single-agent therapy. We report that the use of HVJ-E in combination with chemotherapy or ICIs, which are already used in clinical practice, may be more effective than single-agent therapy. However, further clinical studies are needed before the combination therapy can be used in clinical practice. Abstract Malignant pleural mesothelioma (MPM) is a refractory tumor because most of the lesions are already disseminated at diagnosis. Previously, the main treatment for MPM was combination chemotherapy. However, recently, immune checkpoint inhibitors (ICIs) are also used. For better efficacy of MPM treatment, we focused on hemagglutinating virus of Japan envelope (HVJ-E), which activates antitumor immunity and induces tumor-specific cell death. In this paper, we aimed to determine whether HVJ-E as a single agent therapy or in combination with chemotherapy or ICIs is effective in MPM bearing mouse. We confirmed its antitumor efficacy in MPM-bearing mouse. HVJ-E significantly prolonged the survival of human MPM-bearing mouse compared to that of control mouse and when combined with CDDP. This efficacy was lost in NOD-SCID mouse, suggesting that activation of innate immunity by HVJ-E was related to the survival rate. HVJ-E also showed antitumor efficacy in murine MPM-bearing mouse. The combination of chemotherapy and HVJ-E caused a significant increase in cytotoxic T cells (CTLs) compared to chemotherapy alone, suggesting that not only innate immunity activated by HVJ-E but also the increase in CTLs contributed to improved survival. The combination of anti-PD-1 antibody and HVJ-E significantly prolonged the survival rate of murine MPM-bearing mouse. Further, HVJ-E might have exhibited antitumor effects by maintaining immunogenicity against tumors. We believe that HVJ-E may be a beneficial therapy to improve MPM treatment in the future.
Numerical Simulations of Vortex Sheet Evolution in Stratified Shear Flow

The nonlinear evolution of an interface subject to a parallel shear flow is studied on the basis of the vortex sheet model. We perform numerical simulations of the vortex sheet in density stratified fluids by using the point vortex method. The numerical algorithm based on two types of regularization method improves the stability of the vortex method and provides solutions with much higher resolutions than previous results. The numerical results show that, for a smaller density ratio, the interface has a larger growth rate and a more singular vortex sheet strength and curvature at late times. It is also found that, for an infinite density ratio, the interface has a solution of a travelling wave with no deformations.
On the aspectuality of the individual-level / stage-level dichotomy

This paper addresses how the contrast known as Individual-­‐Level/Stage-­‐Level (IL/SL) is implemented in the grammar. More specifically, the paper is a critical assessment of the view that the IL/SL distinction is an aspectual distinction. The empirical data I will be using to probe into the IL/SL dichotomy is the contrast between the copular verbs in Spanish ser/estar. I will argue that the Spanish copular contrast reflexes the IL/SL dichotomy and that this dichotomy cannot be reduced to an aspectual difference in the ways it has been proposed in previous literature. Concurring with other authors I will argue that IL/SL-­‐ness ensues from a different syntactic composition, very likely from different heads of prepositional nature, which can be argued to carry aspectual value. Crucially, however, this aspectual heads do not seem to translate into differences at the level of viewpoint or situation aspect in any relevant sense, as has been proposed in the literature.
The intersection number of complete r-partite graphs

Abstract Latin squares C, D of order n are called pseudo-orthogonal if any two rows of the matrices C and D have exactly one common element. We give conditions for existence of families consisting of t pseudo-orthogonal Latin squares of order n. It is proved that the intersection number of a complete r-partite graph equals n 2 if and only if there exists a family consisting of r – 2 pairwise pseudo-orthogonal Latin squares of order n. It is proved that if , where prols(n) is the maximum t such that there exists a set of t pseudo-orthogonal Latin squares of order n, then the intersection number of the graph is equal to n 2. Applications of the obtained results to calculating the intersection number of some graphs are given.
Graph structure inference for high-throughput genomic data

Graph Structured Inference For High-Throughput Genomic Data Hui Zhou Recent advances in high-throughput sequencing technologies enable us to study a large number of biomarkers and use their information collectively. Based on highthroughput experiments, there are many genome-wide networks constructed to characterize the complex physical or functional interactions between the biomarkers. To identify outcome-related biomarkers, it is often advantageous to make use of the known relational structure, because graph structured inference introduces smoothness and reduces complexity in modelling. In this dissertation, we propose models for high-dimensional epigenetic and genomic data that incorporate the network structure and update the network structure based on empirical evidence. In the first part of this dissertation, we propose a penalized conditional logistic regression model for high dimensional DNA methylation data. DNA methylation of CpG sites within genes are often correlated and the number of CpG sites typically far outnumbers the sample size. The new penalty function combines the truncated lasso penalty and a graph fuse-lasso penalty to induce parsimonious and consistent models, and to incorporate the CpG sites network structure without introducing extra bias. An efficient minorization-maximization algorithm that utilizes difference of convex programming and alternating direction method of multipliers is presented. Extensive simulations demonstrated superior performance of the proposed method compared to several existing methods in both model selection consistency and parameter estimation accuracy. We also applied the proposed method to a matched case-control breast invasive carcinoma methylation data from the Cancer Genome Atlas (TCGA), generated from both Illumina Infinium HumanMethylation27 (HM27) and HumanMethylation450 (HM450) Beadchip. The proposed method identified several outcome-related CpG sites that have been missed by the existing methods. In the latter part of this dissertation, we propose a Bayesian hierarchical graphstructured model that integrates a priori network information with empirical evidence. Empirical data may suggest modifications to the given network structure, which could lead to new and interesting biological findings when the prior knowledge on the graphical structure among the variables is limited or partial. We present the full hierarchical model along with the Markov Chain Monte Carlo sampling inference procedure. Using both simulations and brain aging gene pathway data, we showed that the new method can identify discrepancy between data and a prior known graph structure and suggest modifications and updates. Motivated by methylation and gene expression data, the two models we propose in this thesis make use of the available structure in the data and produce better inferential results. The proposed methods can be applied to a wider range of problems.
A spectral re-examination of the Markarian AGN

Abstract The recently published ZORROASTER catalogue is being expanded into what will eventually be a complete list of known ‘nearby’ (z < 0.1) AGN, with optical spectral images, detailed spectral descriptors and waveband-specific flux ratios included where available. The first version of ZORROASTER contained 2843 entries characterised by confirmed or previously suspected broad H-beta emission, and 1000 spectral images. The 2nd version of ZORROASTER was released during the conference, and (in addition to serendipitous updates) includes all z < 0.1 objects from the list of Markarian. Activity classes and spectral descriptions of Markarian objects have been reviewed, and comparisons of flux ratios derived from the 100 ZORROASTER wavelength-specific pass bands are presented for those Markarian AGN for which Sloan Digital Sky Survey spectra exist. The value of the improved parameterizations and classifications of the Markarian objects on AGN studies, and the implications on some past investigations based on the Markarian lists, are briefly discussed.
A State-of-the-Art Survey of Machine Learning in the Agricultural Sector

Food is regarded as a basic human requirement that may be met through agriculture. The globe over, agriculture is seen as a source of employment in addition to providing for humankind’s basic requirements. In emerging nations like India, where agriculture provides a source of employment, it is seen as the foundation of the economy. 15.4% of India’s GDP is derived from agriculture. Pre-harvest, harvest, and post-harvest activities in agriculture are typically divided into these three categories. Gains in agriculture have improved as a result of advancements in the field of machine learning. Machine learning is a cutting-edge technology innovation that assists farmers in lowering their farming losses by providing thorough recommendations and insights into the crops. In order to address problems in the three stages of agriculture namely pre-harvesting, harvesting, and post-harvesting, this study gives an exhaustive assessment of the current machine learning applications in agriculture. Utilizing machine learning techniques in agriculture enables added precise and effective agriculture with high-quality output using less human labor.