Miracle-dz/UniFAR-FTUs huggingface dataset:
Abrupt permafrost thaw accelerates carbon dioxide and methane release at a tussock tundra site

ABSTRACT Abrupt thaw could cause permafrost ecosystems to release more carbon than is predicted from gradual thaw alone. However, thermokarst feature mapping is limited in scope, and observed responses of carbon fluxes to abrupt thaw are variable. We developed a thermokarst detection algorithm that identifies thermokarst features from a single elevation dataset with 71.5 percent accuracy and applied it in Healy, Alaska. Additionally, we investigated the landscape-level variation in carbon dioxide and methane fluxes by extent of abrupt thaw using eddy covariance. Seven percent of the site was classified as thermokarst. Water tracks were the most extensive form of thermokarst, although small pits were much more numerous. Abrupt thaw was positively correlated with carbon uptake during the growing season, when increases in gross primary productivity outpaced increases in ecosystem respiration in vegetation-dense water tracks. However, this was outweighed by higher carbon release in thermokarst features during the nongrowing season. Additionally, abrupt thaw was positively correlated with methane production nearly year-round. Our findings support the hypothesis that abrupt thaw of permafrost carbon will contribute to the permafrost climate feedback above and beyond that associated with gradual thaw and highlights the need to map thermokarst and incorporate abrupt thaw into Earth System Models.
The Face Deepfake Detection Challenge

Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an “in the wild” scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and “minimum average distance to Manhattan” (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams’ proposed methods with corresponding ranking is presented in this paper.
The search for active black holes in nearby low-mass galaxies using optical and mid-IR data

We investigated AGN activity in low-mass galaxies, an important regime that can shed light onto BH formation and evolution, and their interaction with their host galaxies. We identified 336 AGN candidates from a parent sample of $\sim 48,000$ nearby low-mass galaxies ($M_{\rm \star} \leq 10^{9.5}M_\odot$, $z < 0.1$) in the SDSS. We selected the AGN using the classical BPT diagram, a similar optical emission line diagnostic based on the HeII$\lambda$4686 line, and mid-IR color cuts. Different criteria select host galaxies with different physical properties such as stellar mass and optical color, and only 3 out of 336 sources fulfill all three criteria. This could be in part due to selection biases. The resulting AGN fraction of $\sim 0.7 \%$ is at least one order of magnitude below the one estimated for more massive galaxies. At optical wavelengths, the HeII-based AGN selection appears to be more sensitive to AGN hosted in star-forming galaxies than the classical BPT diagram, at least in the low-mass regime. The archival X-ray and radio data available for some of the optically selected AGN candidates seem to confirm their AGN nature, but follow-up observations are needed to confirm the AGN nature of the rest of the sample, especially in the case of mid-IR selection. Our sample will be important for future follow-up studies aiming to understand the relation between BHs and host galaxies in the low-mass regime.
Design Methodology for Wireless Backhaul/Fronthaul Using Free Space Optics and Fibers

The recent increase in data rates for Free space optics (FSO) transmission technology, means they could be used for designing the backhaul/fronthaul for 5G and beyond cellular networks. The flexibility and cost-effectiveness provided by FSO are the primary reasons for the mobile operators to investigate the potential of the technology as a mobile backhaul/fronthaul. Unfortunately, the reliability of FSO links is weather dependent, especially if the link covers considerable distance. Optical fibers, on the other hand, are expensive but more reliable. Hence, optimally designing a hybrid network consisting of both fiber and FSO connections can bring in the cost-effectiveness of FSO as well as the robustness of fibers. In such a design, the more important links are connected using fibers while the links with higher tolerance towards failure are designed using FSO. Therefore, in this paper, we propose a hybrid FSO/fiber backhaul/fronthauling methodology for connecting wireless base stations (BSs) to the network core. We first formulate a mixed integer non-linear program (MINLP) for determining the number of splitter/FSO distribution points required in the network that optimally provide connectivity to the BSs. The MINLP is designed to identify the locations of the splitters/FSO distribution points as well. Thereafter, we solve the MINLP with the help of particle swarm optimisation (PSO) and mixed integer linear programming (MILP) techniques. We also derive a heuristic for solving the MILP. Finally, we propose another method for determining the number of splitters required in a relatively shorter time: K-means cluster based method. The results verify that the hybrid network is cost-effective while conforming to the data rate and reliability requirements of the links. The proposal allows evaluation of a seamless design solution of the hybrid network with practical time-complexity.
Gestational Diabetes Mellitus—Recent Literature Review

Gestational diabetes mellitus (GDM), which is defined as a state of hyperglycemia that is first recognized during pregnancy, is currently the most common medical complication in pregnancy. GDM affects approximately 15% of pregnancies worldwide, accounting for approximately 18 million births annually. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both the mother and infant. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness. A deeper understanding of the risk factors and pathogenesis becomes a necessity, with particular emphasis on the influence of SARS-CoV-2 and diagnostics, as well as an effective treatment, which may reduce perinatal and metabolic complications. The primary treatments for GDM are diet and increased exercise. Insulin, glibenclamide and metformin can be used to intensify the treatment. This paper provides an overview of the latest reports on the epidemiology, pathogenesis, diagnosis and treatment of GDM based on the literature.
Ultraviolet diversity of Type Ia Supernovae

Ultraviolet (UV) observations of Type Ia supernovae (SNe Ia) probe the outermost layers of the explosion, and UV spectra of SNe Ia are expected to be extremely sensitive to differences in progenitor composition and the details of the explosion. Here we present the first study of a sample of high signal-to-noise ratio SN Ia spectra that extend blueward of 2900 A. We focus on spectra taken within 5 days of maximum brightness. Our sample of ten SNe Ia spans the majority of the parameter space of SN Ia optical diversity. We find that SNe Ia have significantly more diversity in the UV than in the optical, with the spectral variance continuing to increase with decreasing wavelengths until at least 1800 A (the limit of our data). The majority of the UV variance correlates with optical light-curve shape, while there are no obvious and unique correlations between spectral shape and either ejecta velocity or host-galaxy morphology. Using light-curve shape as the primary variable, we create a UV spectral model for SNe Ia at peak brightness. With the model, we can examine how individual SNe vary relative to expectations based on only their light-curve shape. Doing this, we confirm an excess of flux for SN 2011fe at short wavelengths, consistent with its progenitor having a subsolar metallicity. While most other SNe Ia do not show large deviations from the model, ASASSN-14lp has a deficit of flux at short wavelengths, suggesting that its progenitor was relatively metal rich.
Application of Big Data Technology and Visual Neural Network in Emotional Expression Analysis of Oil Painting Theme Creation in Public Environment

With the progress of science and technology and the arrival of the big data era, people increasingly rely on computers to deal with daily life and related affairs. In recent years, machine learning has become more and more popular and has achieved good results in some fields, which also makes machine learning widely used. Among them, visual neural network technology can more intelligently analyze the emotional expression of oil painting, which is one of the current research hotspots, involving machine vision, pattern recognition, image processing, artificial intelligence, and other fields. However, in the art field, oil painting is still very different from other images. At present, there is no deep learning algorithm to identify the application of emotional expression analysis in oil painting theme creation. This paper will start with the neural network algorithm and combine the big data recognition technology to analyze the emotional expression of the oil painting subject in the public environment and establish the emotional expression analysis model of oil painting creation based on big data and neural network. The experiment shows that the graphics synthesized by this model have high resolution and good definition, but the speed is slow in the process of experimental operation. It takes about one hour to complete a round of image optimization.
The Forgotten Quantum Number: A short note on the radial modes of Laguerre-Gauss beams

The orbital angular momentum quantum number of Laguerre-Gauss beams has received an explosively increasing amount of attention over the past twenty years. However, often overlooked is the so-called radial number of these beams. We present a derivation of the differential operator formalism of this "forgotten" quantum number. We then draw some connections between this new formalism and the effect the radial number has on beam stability with possible application to quantum communication. We also briefly outline how the radial number is tied to other physical aspects of the beam (such as the Gouy phase, and radial confinement). These do not necessarily constitute finished results, but are instead meant to stimulate discussion of this interesting and often overlooked physical parameter.
Bulletin of Electrical Engineering and Informatics

Received Aug 29, 2020 Revised Jul 29, 2022 Accepted Sep 8, 2022 Distributed generation (DG) improves the reliability of the system by providing a means of alternate power supply to the load points. DG is integrated to meet the system load along with the utility. In this work roy billinton test system (RBTS) bus 4 is considered for evaluating the effect of DG integration on system reliability. Reliability is evaluated using failure modes and effect analysis (FMEA) technique and the results are validated using Monte Carlo simulation technique. A MATLAB program is developed for both the techniques to evaluate the reliability. The failure rate of DG and the islanding capability of the DG are considered to resemble the practical operating conditions of DG. When DG is operating in islanded mode, if DG fails it affects the outage time of the load points. DG failure rate is also considered as the second order failure event overlapping with the feeder section failures. All the practical operating conditions of DG considered and their effect on the reliability of the system is evaluated.
islatu: A Python package for the reduction of reflectometry data

The interaction between light and matter provides a sensitive probe of the electronic structure of materials, on length scales determined by the difference between the incident and outgoing wavevector of the light. Reflectometry techniques involve scattering off the surface of a material, placing a detector at a point such that any light reaching the detector must scatter through a vector approximately parallel to the material’s surface normal. Typically, for various experiment-specific reasons, the raw data recorded by a detector will not be proportional to the quantity of interest: the modulus squared of the scattering matrix element ⟨? ⃗? ′| ̂ V |? ⃗? ⟩. This is particularly true when the length of the scattering vector |? ⃗? | = |? ⃗? − ? ⃗? ′| is small, as is the case in reflectivity experiments. Then, in addition to corrections that must be applied in any scattering experiment, the finite size of the sample will affect the intensity of the reflected beam, and it is often necessary to also correct for manual changes to the beam’s attenuation. For the above reasons, all reflectivity experiments need at least some form of data reduction, with the exact requirements being experiment specific and often numerous. islatu provides a simple, performant and rigorously tested library and command-line interface for carrying out these correction steps, which aims to substantially simplify the process of converting instrument data to a reflectivity curve. This curve can then be analysed using one of the many widely available reflectivity fitting tools, such as (Björck & Andersson, 2007), (A. R. Nelson & Prescott, 2019) and (A. Nelson, 2006).
A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception

Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor production on an industrial scale also follows the automation trend. However, small and medium producers lag with equipment modernization due to the high costs of industrial equipment. One of the important sensors in automation equipment for distilleries is the alcohol concentration sensor used for fraction separation, process automation, and supervision. This paper proposes a novel low-cost approach to alcohol concentration sensing by employing deep learning on the visual perception of traditional alcoholmeter. For purposes of the training model, dataset acquisition apparatus is developed and a large dataset of labeled images of alcoholmeter readings is acquired. The problem of reading alcohol concentration from an alcoholometer image is treated as a regression and classification problem. Performances of both regression and classification models were investigated with Resnet18 as an architecture of choice. Both models achieved satisfying performance metrics demonstrating the feasibility of the proposed approaches. The proposed system implemented on Raspberry Pi with a camera can be integrated into new distillation equipment. Additionally, it can be used for retrofitting existing equipment due to its non-invasive nature of reading. The scope of use can be further expanded to the reading of other types of analog instruments simply by retraining the model.
Very Low-Volume, High-Intensity Interval Training Mitigates Negative Health Impacts of COVID-19 Pandemic-Induced Physical Inactivity

Initially, we aimed to investigate the impact of a one-year worksite low-volume, high-intensity interval training (LOW-HIIT) on cardiometabolic health in 114 sedentary office workers. Due to the COVID-19 pandemic outbreak, LOW-HIIT was discontinued after 6 months and participants were followed up for 6 months to analyze physical activity/exercise behavior and outcome changes during lockdown. Health examinations, including cardiopulmonary exercise testing and the assessment of cardiometabolic markers were performed baseline (T-1), after 6 months (T-2, termination of worksite LOW-HIIT) and 12 months (T-3, follow-up). Cycle ergometer LOW-HIIT (5 × 1 min at 85–95% HRmax) was performed 2×/week. For follow-up analyses, participants were classified into three groups: HIIT-group (continued home-based LOW-HIIT), EX-group (continued other home-based exercises), and NO-EX-group (discontinued LOW-HIIT/exercise). At T-2, VO2max (+1.5 mL/kg/min, p = 0.002), mean arterial blood pressure (MAB, −4 mmHg, p < 0.001), HbA1c (−0.2%, p = 0.005) and self-reported quality of life (QoL, +5 points, p < 0.001) were improved. At T-3, HIIT-group maintained VO2max and QoL and further improved MAB. EX-group maintained MAB and QoL but experienced a VO2max decrease. In NON-EX, VO2max, MAB and QoL deteriorated. We conclude that LOW-HIIT can be considered a promising option to improve cardiometabolic health in real-life conditions and to mitigate physical inactivity-related negative health impacts during lockdowns.
Pedagogy Leadership during COVID-19 Pandemic: New Kinds of Practice

This research study aims to identify the factors that influence educational leadership pedagogy during the COVID-19 pandemic. The study took place in the girls’ section of an international school in Dammam city, Saudi Arabia. Through the application of a mixed research approach of interviews with four lead teachers and a survey of 72 teachers, a theoretical model that integrates eight factors of educational leadership pedagogy was developed. The model integrates best practices to sustain school effectiveness and students’ performance. The model was developed through thematic analysis and validated by Confirmatory Factor Analysis (CFA). The findings revealed several interventions influencing educational leadership and students’ performance.
Comparing COVID-19 Vaccination Outcomes with Parental Values, Beliefs, Attitudes, and Hesitancy Status, 2021–2022

Despite the availability of safe and effective COVID-19 vaccines, vaccine acceptance has been low, particularly among parents. More information is needed on parental decision-making. We conducted a prospective cohort study from October 2021 to March 2022 among 334 parents in a large urban/suburban pediatric primary care network and linked longitudinal survey responses about attitudes and beliefs on vaccination, social norms, and access to vaccination services for COVID-19 to electronic health-record-derived vaccination outcomes for their eldest age-eligible children in June 2022. The odds of accepting two doses of COVID-19 vaccine for their child was higher in respondents who indicated the COVID-19 vaccine would be very safe (aOR [CI]: 2.69 [1.47–4.99], p = 0.001), as well as those who previously vaccinated their child against influenza (aOR [CI]: 4.07 [2.08–8.12], p < 0.001). The odds of vaccinating their child were lower for respondents who attended suburban vs. urban practices (aOR [CI]: 0.38 [0.21–0.67], p = 0.001). Parents in the cohort were active users of social media; the majority (78%) used their phone to check social media platforms at least once per day. Our findings suggest that healthcare providers and policymakers can focus on improving vaccination coverage among children living in suburban neighborhoods through targeted mobile-based messaging emphasizing safety to their parents.
On the amplification of acoustic phonons in carbon nanotube

We present a theoretical study of acoustic phonons amplification in Carbon Nanotubes (CNT). The phenomenon is via Cerenkov emission (CE) of acoustic phonons using intraband transitions proposed by Mensah et. al.,~\cite{1} in Semiconductor Superlattices (SSL) and confirmed in ~\cite{2}. From this, an asymmetric graph of $\Gamma^{CNT}$ on $\frac{V_d}{V_s}$ and $\Omega\tau$ were obtained where amplification ($\Gamma_{amp}^{CNT}$) $>>$ absorption ($\Gamma_{abs}^{CNT}$). The ratio, $\frac{\vert \Gamma_{amp}^{CNT}\vert}{\vert\Gamma_{abs}^{CNT}\vert}\approx 3.5$, at $V_d = 1. 02V_s$, $\omega_q = 3.0\ \mathrm{THz}$ and $T = 85\ K$ for scattering angle $\theta > 0$ . A threshold field at which $\Gamma_{abs}^{CNT}$ switches over to $\Gamma_{amp}^{CNT}$ was calculated to be $E_{z}^{dc} = 6.2\times 10^3\ \mathrm{V/m}$. This field is far less than that deduced using Bloch-Type Oscillation (BTO)~\cite{3} which is $E_{BTO}^{dc} = 3.0\times 10^5\ \mathrm{V/m}$. The obtained $\Gamma_{amp}^{CNT}$ would enable the use of CNT for the production of SASER.
Results of an advanced nursing triage protocol in emergency departments

OBJECTIVES: The increasing number of patients admitted to emergency departments (EDs) and overcrowding of EDs lead to a global problem. Advanced nursing triage is an important solution in facilitating patient and time management, also increasing the efficiency of the ED. This study was conducted to predict the possible effects of applying advanced nursing triage modeling with predetermined protocols during the current nursing triage in the ED. METHODS: This was a descriptive and cross-sectional study. An advanced “triage assessment protocol,” which was developed previously, was hypothetically applied for 5 days by triage nurses in the adult ED of a university hospital. The hypothetical application was tested by triage nurses in all shifts. The nurses recorded the examination or treatment options which they thought to apply for the patient on the study form. The data recorded on the advanced triage evaluation protocol form by the triage nurses were compared with the patient outcomes and physician examination/treatment requests in the Hospital Information Management System by the researchers. RESULTS: In the study, it was determined that the rate of examination/treatment that could be requested according to the advanced nursing triage protocol was 46%. There were a good level of agreement on X-ray and a moderate level of agreement on urinary test and urinary beta- Human chorionic gonadotropin (hCG) test between physicians and triage nurses regarding examination/treatment requests. In addition, it was found that there was a 61.2% of agreement on decisions made for patients aged between 18 and 35. The rate of agreement between doctors and nurses regarding a gluco-stick request for patients admitted outside the prime time (92.2%) was found to be significantly higher (87.9%) than for patients admitted during prime time (P = 0.046). CONCLUSION: “Advanced triage” practices recommended for busy EDs were tested “hypothetically” at the national level due to the lack of legal regulations and were found to be compatible with the actual results of physicians' practices at an acceptable level, especially for selected medical conditions. The method used in this study can be useful in planning the transition to “advanced triage” practices. These results can show the readiness of nurses for the transition to this practice.