Miracle-dz/UniFAR-FTUs huggingface dataset:
ICE: a scalable, low-cost FPGA-based telescope signal processing and networking system

We present an overview of the 'ICE' hardware and software framework that implements large arrays of interconnected FPGA-based data acquisition, signal processing and networking nodes economically. The system was conceived for application to radio, millimeter and sub-millimeter telescope readout systems that have requirements beyond typical off-the-shelf processing systems, such as careful control of interference signals produced by the digital electronics, and clocking of all elements in the system from a single precise observatory-derived oscillator. A new generation of telescopes operating at these frequency bands and designed with a vastly increased emphasis on digital signal processing to support their detector multiplexing technology or high-bandwidth correlators---data rates exceeding a terabyte per second---are becoming common. The ICE system is built around a custom FPGA motherboard that makes use of an Xilinx Kintex-7 FPGA and ARM-based co-processor. The system is specialized for specific applications through software, firmware, and custom mezzanine daughter boards that interface to the FPGA through the industry-standard FMC specifications. For high density applications, the motherboards are packaged in 16-slot crates with ICE backplanes that implement a low-cost passive full-mesh network between the motherboards in a crate, allow high bandwidth interconnection between crates, and enable data offload to a computer cluster. A Python-based control software library automatically detects and operates the hardware in the array. Examples of specific telescope applications of the ICE framework are presented, namely the frequency-multiplexed bolometer readout systems used for the SPT and Simons Array and the digitizer, F-engine, and networking engine for the CHIME and HIRAX radio interferometers.
Geometrical scaling of inclusive charged hadron $p_{\rm T}$ spectra in Pb-Pb collsions at 2.76 TeV

In Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV, we report on a geometrical scaling in the transverse momentum ($p_{\rm T}$) spectra of inclusive charged hadrons at 0-5$\%$, 5-10$\%$, 10-20$\%$, 20-30$\%$, 30-40$\%$, 40-50$\%$, 50-60$\%$, 60-70$\%$ and 70-80$\%$ centralities. This geometrical scaling is exhibited when these spectra are expressed in terms of a new scaling variable, $p_{\rm T}^{\prime}=p_{\rm T}(191.5/N_{A})^{1/(3\lambda+6)}$, where $N_{A}$ is the number of participating nucleon pairs, $\lambda$ is the scaling parameter. With the method of ratios, this scaling parameter is determined to be 1.68. We then use a single Tsallis distribution to parameterize the spectra at different centralities, and find that the Tsallis temperature $T$ is proportional to $N_{A}^{1/(3\lambda+6)}$, which is exactly the prediction of the geometrical scaling. The geometrical scaling also predicts that the charged-particle density per participating nucleon pair ($(dN_{\rm ch}/d\eta)/N_{A}$) grows as a power of $N_{A}$, $N_{A}^{2/(3\lambda+6)}$, which is confirmed by the data collected by the ALICE collaboration.
Doped Ferrite Nanoparticles Exhibiting Self-Regulating Temperature as Magnetic Fluid Hyperthermia Antitumoral Agents, with Diagnostic Capability in Magnetic Resonance Imaging and Magnetic Particle Imaging

Simple Summary Hyperthermia, a limited increase in tumor tissue temperature up to a maximum of 45 °C, has long been used in cancer treatment, either as a stand-alone therapy or in conjunction with chemo-/radiotherapy. Among the different physical procedures used, a particularly promising technique is magnetic fluid hyperthermia (MFH): this consists of the local administration of magnetic nanoparticles, followed by the application of alternating magnetic fields. One concern with this technique is the possibility of damage to healthy peritumoral tissue. The present study investigates innovative nanoparticles with self-regulating temperature, which should reduce this risk and thus mark a significant step forward in MFH. In an experimental model of aggressive breast cancer, we demonstrated a substantial reduction of tumor growth rate by using an experimental MFH protocol, transferable to clinical practice. These innovative nanomaterials present the added advantage of allowing non-invasive monitoring of temperature, by magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). Abstract This paper reports a comprehensive investigation of a magnetic nanoparticle (MNP), named M55, which belongs to a class of innovative doped ferrite nanomaterials, characterized by a self-limiting temperature. M55 is obtained from M48, an MNP previously described by our group, by implementing an additional purification step in the synthesis. M55, after citrate and glucose coating, is named G-M55. The present study aimed to demonstrate the properties of G-M55 as a diagnostic contrast agent for MRI and magnetic particle imaging (MPI), and as an antitumoral agent in magnetic fluid hyperthermia (MFH). Similar specific absorption rate values were obtained by standard MFH and by an MPI apparatus. This result is of interest in relation to the application of localized MFH by MPI apparatus. We demonstrated the biocompatibility of G-M55 in a triple-negative human breast cancer line (MDA-MB-231), and its efficacy as an MFH agent in the same cell line. We also demonstrated the efficacy of MFH treatment with G-M55 in an experimental model of breast cancer. Overall, our results pave the way for the clinical application of G-M55 as an MFH agent in breast cancer therapy, allowing not only efficient treatment by both standard MFH apparatus and MPI but also temperature monitoring.
EXCITATION OF TRAPPED WAVES IN SIMULATIONS OF TILTED BLACK HOLE ACCRETION DISKS WITH MAGNETOROTATIONAL TURBULENCE

We analyze the time dependence of fluid variables in general relativistic, magnetohydrodynamic simulations of accretion flows onto a black hole with dimensionless spin parameter a/M = 0.9. We consider both the cases where the angular momentum of the accretion material is aligned with the black hole spin axis (an untilted flow) and where it is misaligned by 15° (a tilted flow). In comparison to the untilted simulation, the tilted simulation exhibits a clear excess of inertial variability, that is, variability at frequencies below the local radial epicyclic frequency. We further study the radial structure of this inertial-like power by focusing on a radially extended band at 118(M/10 M☉)−1 Hz found in each of the three analyzed fluid variables. The three-dimensional density structure at this frequency suggests that the power is a composite oscillation whose dominant components are an over dense clump corotating with the background flow, a low-order inertial wave, and a low-order inertial-acoustic wave. Our results provide preliminary confirmation of earlier suggestions that disk tilt can be an important excitation mechanism for inertial waves.
FIRST DETECTION OF GALACTIC LATITUDE DEPENDENCE OF NEAR-INFRARED DIFFUSE GALACTIC LIGHT FROM DIRBE RENALYSIS

Observational study on near-infrared (IR) scattering properties of interstellar dust grains has been limited due to its faintness. Using all-sky maps obtained from the Diffuse Infrared Background Experiment, we investigate the scattering property from diffuse Galactic light (DGL) measurements at 1.25, 2.2, and 3.5 μ m , in addition to our recent analyses of diffuse near-IR emission. As a result, we first find that the intensity ratios of near-IR DGL to 100 μ m emission increase toward low Galactic latitudes at 1.25 and 2.2 μ m . The derived latitude dependence can be reproduced by a scattered light model of interstellar dust with a large scattering asymmetry factor g ≡ 〈 cos θ 〉 of 0.8 − 0.3 + 0.2 at 1.25 and 2.2 μ m , assuming an infinite Galaxy disk as an illuminating source. The derived asymmetry factor is comparable to the values obtained in the optical, but several times larger than that expected from a recent dust model. Since a possible latitude dependence of ultraviolet-excited dust emission at 1.25 and 2.2 μ m would reduce the large asymmetry factor to the reasonable value, our result may indicate the first detection of such an additional emission component in the diffuse interstellar medium.
Dynamics of biodiversity loss and financial system stability nexus in developing countries

Global warming has been escalating along with its major damaging effects. One such effect is its negative impact on economic growth. This paper is premised on the fact that, in addition to its threats to economic growth, climate change can affect financial institutions unfavorably and become a significant cause of financial risk. Therefore, this study aims to investigate the nexus between the dynamics of biodiversity loss and financial system stability in sub-Saharan Africa (SSA). Using annualized data from World Bank indicators, the study adopted panel techniques. The panel data results indicate that carbon emission has no positive impact on bank non-performing loans in the short run. The findings strongly support that carbon emission in this region does not contribute significantly to financial instability in the short run. This shows that Africa may well be able to assist the world to counteract climate change by providing an essential carbon sink while resisting deforestation and effectively managing the continent’s marine resources. Based on the research findings, it is recommended that policymakers in SSA should promote economic activities that reduce climate fragility while ensuring sustainable economic development.
Detecting Reconnaissance and Discovery Tactics from the MITRE ATT&CK Framework in Zeek Conn Logs Using Spark’s Machine Learning in the Big Data Framework

While computer networks and the massive amount of communication taking place on these networks grow, the amount of damage that can be done by network intrusions grows in tandem. The need is for an effective and scalable intrusion detection system (IDS) to address these potential damages that come with the growth of these networks. A great deal of contemporary research on near real-time IDS focuses on applying machine learning classifiers to labeled network intrusion datasets, but these datasets need be relevant pertaining to the currency of the network intrusions. This paper focuses on a newly created dataset, UWF-ZeekData22, that analyzes data from Zeek’s Connection Logs collected using Security Onion 2 network security monitor and labelled using the MITRE ATT&CK framework TTPs. Due to the volume of data, Spark, in the big data framework, was used to run many of the well-known classifiers (naïve Bayes, random forest, decision tree, support vector classifier, gradient boosted trees, and logistic regression) to classify the reconnaissance and discovery tactics from this dataset. In addition to looking at the performance of these classifiers using Spark, scalability and response time were also analyzed.
The non-minimal ekpyrotic trispectrum

Employing the covariant formalism, we derive the evolution equations for two scalar fields with non-canonical field space metric up to third order in perturbation theory. These equations can be used to derive predictions for local bi- and trispectra of multi-field cosmological models. Our main application is to ekpyrotic models in which the primordial curvature perturbations are generated via the non-minimal entropic mechanism. In these models, nearly scale-invariant entropy perturbations are generated first due to a non-minimal kinetic coupling between two scalar fields, and subsequently these perturbations are converted into curvature perturbations. Remarkably, the entropy perturbations have vanishing bi- and trispectra during the ekpyrotic phase. However, as we show, the conversion process to curvature perturbations induces local non-Gaussianity parameters fNL and gNL at levels that should be detectable by near-future observations. In fact, in order to obtain a large enough amplitude and small enough bispectrum of the curvature perturbations, as seen in current measurements, the conversion process must be very efficient. Interestingly, for such efficient conversions the trispectrum parameter gNL remains negative and typically of a magnitude 𝒪(102)–𝒪(103), resulting in a distinguishing feature of non-minimally coupled ekpyrotic models.
Exotic Hadrons

In this talk, we review the most recent progress in the searching for the exotic hadrons, including hybrids, multi-quark states, molecules and so on. We only focus on the studies with a charmonium and one or more light mesons in the final states. This covers the X(3872), the $XYZ$ states at around 3.940 GeV, the Y(4140) and X(4350) in two-photon collisions, the $Y$ states from $ISR$ processes, and the charged $Z$ states.
Brazilian discussion about COVID-19 lockdown policies on Twitter

The COVID-19 pandemic affected all countries worldwide, causing big changes in people's routines due to public policies for disease spreading control. Among the most impacting measures were social distancing policies and lockdown, leading to an intense discussion by the population. To describe this discussion in Brazil, this research applied data science and natural language methods to analyze posts on Twitter. It processed more than 12.9 million tweets between 2020 and 2021, and the results highlighted the main topics discussed by Brazilian Twitter users, such as the ideological-political component. The approach employed in this research proved to help extract valuable information in massive data mass.
Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.
Integrability and the AdS/CFT correspondence

In this paper, we review the recently discovered asymptotic integrability in the planar SYM theory and discuss its breakdown beyond the asymptotic region due to the wrapping interactions. We also discuss novel dynamical tests of the AdS/CFT correspondence one can perform in the special cases when the wrapping interactions may be neglected.
HERSCHEL FAR-INFRARED PHOTOMETRY OF THE SWIFT BURST ALERT TELESCOPE ACTIVE GALACTIC NUCLEI SAMPLE OF THE LOCAL UNIVERSE. I. PACS OBSERVATIONS

Far-Infrared (FIR) photometry from the Photodetector Array Camera and Spectrometer on the Herschel Space Observatory is presented for 313 nearby, hard X-ray selected galaxies from the 58 month Swift Burst Alert Telescope (BAT) Active Galactic Nuclei catalog. The present data do not distinguish between the FIR luminosity distributions at 70 and 160 μm for Seyfert 1 and Seyfert 2 galaxies. This result suggests that if the FIR emission is from the nuclear obscuring material surrounding the accretion disk, then it emits isotropically, independent of orientation. Alternatively, a significant fraction of the 70 and 160 μm luminosity could be from star formation, independent of active galactic nucleus (AGN) type. Using a non-parametric test for partial correlation with censored data, we find a statistically significant correlation between the AGN intrinsic power (in the 14–195 keV band) and the FIR emission at 70 and 160 μm for Seyfert 1 galaxies. We find no correlation between the 14–195 keV and FIR luminosities in Seyfert 2 galaxies. The observed correlations suggest two possible scenarios: (1) if we assume that the FIR luminosity is a good tracer of star formation, then there is a connection between star formation and the AGN at sub-kiloparsec scales, or (2) dust heated by the AGN has a statistically significant contribution to the FIR emission. Using a Spearman rank-order analysis, the 14–195 keV luminosities for the Seyfert 1 and 2 galaxies are weakly statistically correlated with the F70/F160 ratios.
Spin hopping in a discontinuous La0.7Ca0.3MnO3 film

Magnetization and magnetotransport properties of a discontinuous La0.7Ca0. 3MnO3 film on LaAlO3 are presented. The sample shows transport characteristics of a granular ferromagnet with an extraordinarily large magnetoresistance. The data are successfully interpreted within a model of spin-dependent tunneling between grains taking into account magnetic intergrain correlation effects.
Bearing fault diagnosis method based on Gramian angular field and ensemble deep learning

Inspired by the successful experience of convolutional neural networks (CNN) in image classification, encoding vibration signals to images and then using deep learning for image analysis to obtain better performance in bearing fault diagnosis has become a highly promising approach. Based on this, we propose a novel approach to identify bearing faults in this study, which includes image-interpreted signals and integrating machine learning. In our method, each vibration signal is first encoded into two Gramian angular fields (GAF) matrices. Next, the encoded results are used to train a CNN to obtain the initial decision results. Finally, we introduce the random forest regression method to learn the distribution of the initial decision results to make the final decisions for bearing faults. To verify the effectiveness of the proposed method, we designed two case analyses using Case Western Reserve University (CWRU) bearing data. One is to verify the effectiveness of mapping the vibration signal to the GAFs, and the other is to demonstrate that integrated deep learning can improve the performance of bearing fault detection. The experimental results show that our method can effectively identify different faults and significantly outperform the comparative approach.
Prueba de impacto: ensayo Charpy

En este trabajo se presentan algunos resultados interesantes de pruebas de impacto Charpy. A trav´es del comportamiento a la fractura de diversos metales y pol´imeros, han sido analizados, de una forma did´actica, diversos factores, tales como: el incremento de la velocidad de impacto, la presencia de entallas y la transici´on d´uctil-fr´agil con el decrecimiento de la temperatura
The role of pericytes on the efficacy of bevacizumab in colorectal cancer

Background/aim Pericytes are mesenchymal cells surrounding capillary vessels and are known to play an essential role in tumor angiogenesis. Chondroitin sulfate proteoglycan 4 (CSPG4) is a cell surface proteoglycan and its release from pericytes and vascular smooth muscle cells is very important in tumor angiogenesis. Bevacizumab, which is a monoclonal antibody frequently used in the treatment of metastatic colorectal cancer, binds to the ligand of vascular endothelial growth factor A (VEGFA) and inhibits tumor angiogenesis. However, no reliable biomarker for predicting patients that will show a good response to this therapy has been established yet. In this study, we aimed to identify the significance of the presence of pericyte and VEGFA and CSPG4 expressions on the efficacy of Bevacizumab. Materials and methods Fifty patients with metastatic or recurrent colorectal cancer who had been treated with Bevacizumab combined chemotherapy treatment were included in the study. The expressions of VEGFA and CSPG4 genes and also human β-actin as the reference gene were examined using the quantitative real-time polymerase chain reaction method in the formalin-fixed paraffin-embedded tumor tissues. For determining vascular and pericyte density in tumor tissue, immunohistochemical analysis was performed with CD31, alpha-smooth muscle actin, and CD34 antibodies. Results CSPG4 positive group had better objective response rate, as well as longer progression-free and overall survival than CSPG4 negative ones. Progression-free survival was significantly longer in VEGFA low group and CD31 low group. No significant correlation was found between CD34 positivity, SMA positivity, and progression-free and overall survival. Conclusion Our results suggested that bevacizumab may be more effective in patients having less vascular density in the tumor tissue. But further studies are needed to support this finding.
Crafting and Assessing Design Science Research for Entrepreneurship

Recognizing the importance of various types of artifacts for entrepreneurship, design science (DS) has been proposed as an inclusive approach that combines relevance and rigor. By enabling researchers to go beyond their traditional roles as observers and analysts of established artifacts to help design new artifacts, DS can improve the relevance of entrepreneurship research. However, there is a paucity of knowledge on how this type of research can be published in leading entrepreneurship journals. In this editorial, we seek to provide practical guidance on how to craft and assess DS studies that target Entrepreneurship Theory & Practice (ETP) and other top-tier journals.
Machine Assessment of Student Discussion-Board Formal-Style Debates

This paper explores machine identification of argumentative moves in asynchronous online student debates. Student debate is a technique for engaging with topics which have no clear answer. Online debates differ from less-structured discussion-board class discussions in that students are restricted to formal debating moves such as advancing a new argument, providing evidence, or rebutting an argument. They are also assigned to debate a particular side. This research explores whether the different argumentative skills in student debate can be distinguished from each other by reading the texts of the students’ discussion. The data for this research are the postings from 20 week-long student debates, an assignment in an educational technology class. Utilizing exclusively the text the students wrote, machine classifiers were trained to recognize the argumentative role of a message. If machines can detect an average difference between these debate moves from the text of the debate, and if they can detect the side of the debate, then the students are likely exercising different argumentative skills. The experiment shows it is possible to identify the skills which make debating different than free-form discussion with accuracy significantly better than chance.