Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Ukuran Perusahaan, Utang, Profitabilitas Terhadap Praktik Income Smoothing Sebelum dan Saat Pandemi Covid-19

This study aims to determine whether there are differences in the income smoothing practice before and during the Covid-19 and to examine whether there is an effect of firm size, debt, and profitability on income smoothing practice before the Covid-19 (2017-2019), during the Covid-19 (2020-2021), and before and during the Covid-19 (2017-2021). The population of the study is all sub-industrial business entities providing health and pharmaceutical services on the IDX (Indonesian Stock Exchange). The sampling technique used was non-probability purposive sampling. The number of samples is 11 business entities. To test whether there are differences in income smoothing practice before and during the Covid-19, the Wilcoxon Signed Rank Test was used. Meanwhile, to examine the effect of firm size, debt, and profitability on income smoothing practice, binary logistic regression was used. The results of descriptive statistical analysis show that there is an increase in income smoothing practice during the Covid-19 compared to before the Covid-19. However, the results of the Wilcoxon Signed Rank Test show that there is no significant difference between the income smoothing practice before and during the Covid-19. The results of binary logistic regression show that firm size, debt, and profitability have no effect on income smoothing practice during the Covid-19 (2020-2021). While the period before the Covid-19 (2017-2019) and the period before and during the Covid-19 (2017-2021) firm size and profitability did not have a significant effect on income smoothing practice, but the debt had a significant effect on income smoothing practice.
Mycobacterium bovis Transmission between Cattle and a Farmer in Central Poland

Introduction: Zoonoses have recently become an increasing public health problem. Zoonoses are estimated to account for 60% of all emerging infectious diseases. One particularly important zoonosis is human tuberculosis, especially tuberculosis due to Mycobacterium bovis (M. bovis), which is naturally resistant to pyrazinamide (PZA). Material and Methods: The patient had a pulmonary form of tuberculosis accompanied by a cough and fever. At the same time, the disease was also confirmed in 20 out of 25 cattle on the farm. The clinical specimen (sputum) was examined in accordance with the European Union (EU) laboratories’ methodology. Tissue materials from cattle were verified in the National Veterinary Research Institute (NVRI), in the Bovine tuberculosis (BTB) Reference Laboratory, Pulawy, Poland and tested in accordance with the guidelines for the laboratory diagnosis of BTB. Results: All M. bovis isolates represented one spoligotype, SB0120. The results of mycobacterial interspersed repetitive unit variable number tandem repeat (MIRU-VNTR) evaluation showed the same genetic pattern. Conclusions: Findings from this study suggest the first confirmed interspecific transmission of Mycobacterium bovis, between a farmer and his cattle, in Poland. Present findings support the increasing concern regarding zoonotic TB that has been highlighted elsewhere.
Effect of Fabric Layering Sequence and Architecture on Mechanical Properties of Clay, Jute, and Glass Fibers Reinforced Polyester Laminated Hybrid Composites

ABSTRACT Hybrid laminated composites are attracting a lot of attention these days. Indeed, mixing natural and synthetic fabrics in the same matrix completely exploits the components’ greatest properties. However, it has been proven that the use of particles such as clays increases the material performance. Hence, the aim of this study is to produce a new hybrid laminated composite system using the hand lay-up method and compression molding techniques. For this, a layering sequence of plain-woven jute fabrics and different architecture of E-Glass fibers (matt and plain-woven roving fabrics with different grammage) were selected to strengthen a polyester resin modified with a tiny amount of clays. The effects of hybridization, layering sequence, and weaving architecture were evaluated by means of mechanical tests. The dispersion and distribution of the charges in the polymeric matrix were also investigated by studying the material rheological properties. We conclude that the proposed hybridization may result in an optimal, superior, and cost-effective laminated composite that fits all the requirements of a given structure.
Efficacy of Neuro-Feedback Training for PTSD Symptoms: A Systematic Review and Meta-Analysis

If the negative emotions experienced in life become trauma, they affect daily life. Neuro-feedback technology has recently been introduced as a treatment, but many different neuro-feedback protocols and methods exits. This study conducted a meta-analysis of neuro-feedback training for post-traumatic stress disorder (PTSD) symptoms to evaluate the effects of functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG)-based neuro-feedback training. A search of PubMed, the Cochrane Library, Web of Science, Science Direct, and ClinicalTrials.gov was conducted from January 2011 to December 2021. The studies’ quality was assessed using the Cochrane risk of bias tool and publication bias was assessed by Egger’s regression test. Seven studies that met the inclusion criteria were used for the systematic review and meta-analysis. EEG was more effective than fMRI for PTSD symptoms, and the effect on PTSD symptoms was higher than on anxiety and depression. There was no difference in the effectiveness of the training sessions. Our findings showed that EEG-based neuro-feedback training was more helpful for training PTSD symptoms. Additionally, the methods were also shown to be valid for evaluating clinical PTSD diagnoses. Further research is needed to establish a gold standard protocol for the EEG-based neuro-feedback training (EEG-NFT) method for PTSD symptoms.
Formation of large-scale magnetic structures associated with the Fermi bubbles

The Fermi bubbles are part of a complex region of the Milky Way. This region presents broadband extended non-thermal radiation, apparently coming from a physical structure rooted in the Galactic Centre and with a partly-ordered magnetic field threading it. We explore the possibility of an explosive origin for the Fermi bubble region to explain its morphology, in particular that of the large-scale magnetic fields, and provide context for the broadband non-thermal radiation. We perform 3D magnetohydrodynamical simulations of an explosion from a few million years ago that pushed and sheared a surrounding magnetic loop, anchored in the molecular torus around the Galactic Centre. Our results can explain the formation of the large-scale magnetic structure in the Fermi bubble region. Consecutive explosive events may match better the morphology of the region. Faster velocities at the top of the shocks than at their sides may explain the hardening with distance from the Galactic Plane found in the GeV emission. In the framework of our scenario, we estimate the lifetime of the Fermi bubbles as $2\times10^6$ yr, with a total energy injected in the explosion(s) $> 10^{55}$ ergs. The broadband non-thermal radiation from the region may be explained by leptonic emission, more extended in radio and X-rays, and confined to the Fermi bubbles in gamma rays.
Dietary Hempseed Decreases Femur Maximum Load in a Young Female C57BL/6 Mouse Model but Does Not Influence Bone Mineral Density or Micro-Architecture

Numerous seed and seed extract diets have been investigated as a means of combating age-related bone loss, with many findings suggesting that the seeds/extracts confer positive effects on bone. Recently, there has been rising interest in the use of dietary hempseed in human and animal diets due to a perceived health benefit from the seed. Despite this, there has been a lack of research investigating the physiologic effects of dietary hempseed on bone. Previous studies have suggested that hempseed may enhance bone strength. However, a complete understanding of the effects of hempseed on bone mineralization, bone micro-architecture, and bone biomechanical properties is lacking. Using a young and developing female C57BL/6 mouse model, we aimed to fill these gaps in knowledge. From five to twenty-nine weeks of age, the mice were raised on either a control (0%), 50 g/kg (5%), or 150 g/kg (15%) hempseed diet (n = 8 per group). It was found that the diet did not influence the bone mineral density or micro-architecture of either the right femur or L5 vertebrae. Furthermore, it did not influence the stiffness, yield load, post-yield displacement, or work-to-fracture of the right femur. Interestingly, it reduced the maximum load of the right femur in the 15% hempseed group compared to the control group. This finding suggests that a hempseed-enriched diet provides no benefit to bone in young, developing C57BL/6 mice and may even reduce bone strength.
Fast In-Loop Single-Tube Open-Circuit Fault Diagnosis for Voltage Source Inverter Based on Duality of Current-Time Pairs

In this article, the method extracting fault characteristics by utilizing the duality of current-time pairs is proposed to diagnose the single switch tube open-circuit (OC) fault for the inverter in the traction motor drive system. This method develops a neural-network-liked structure for OC fault diagnosis. The input layer is a fixed-period integral mean (FPIM) filter within one carrier cycle to smooth sampled discrete current-time pairs for effectively counteracting current PWM-notches and noise interference. The filtered current-time pairs are fed forward to the middle layer including functional activation functions. One of them is to normalize the current such that the diagnosis algorithm can be self-adaptive to different loads. Another one is to eliminate the singular problem near the current peak. Finally, the diagnostic functions are constructed by the dual-difference of discrete current-time pairs to quickly diagnose and locate the single-tube OC fault. It does not require additional sensors and other auxiliary detection circuits, and can accurately locate faults with few current signal nodes. It greatly reduces fault detection time and avoids triggering a vicious chain reaction due to detection delays. In addition, it also has excellent robustness under varying frequency conditions, since it is only concerned with the local property. This algorithm can be embedded in the closed-loop system due to its simple structure and low computation and storage burden. The effectiveness of the proposed method is verified through experiments.
Information content in the angular power spectrum of weak lensing: wavelet method

We quantify the performance of a non-linear Wiener filter, constructed in wavelet space, at recovering some of the Fisher information that was lost in the weak lensing convergence field. The proposed method consists of a separation of the original field into the sum of a Gaussian and a non-Gaussian contribution. After filtering an ensemble of such fields, which are obtained from N-body simulations, we find that we can recapture about four times more Fisher information, an effect that can potentially improve by a significant amount the constraining power of weak lensing surveys on cosmological parameters, including the dark energy equation of state ω. We compare this performance with that of a logarithmic mapping and find that the wavelet method can recover up to three times more information.
Detecting roads from high-resolution aerial images: a position iteration algorithm for linear target detection

Detecting roads from high-resolution photographs can serve forestry, agriculture, traffic and even military areas, and produce significant social and economic value. In this paper, we present a novel method that utilizes the flatness and the connectivity to detect the road in high-resolution aerial images. The method iterates the probable locations of the roads by using the flatness and connects the roads by using the connectivity. Firstly, we introduce a concept of ‘footprint’, which reveals the probable location and extension direction of a road. Given an initial footprint, we assess the flatness between locations to search the resulting footprint. By iterating and connecting the footprints, our approach produces a set of connected line segments that reflect the road to be detected. In addition, a footprints initialization algorithm is introduced to make our method totally automatic, and a road network pruning algorithm is designed to make the result clearer and more accurate. Tested under three high-resolution aerial photographs, our method achieved an accuracy of more than 80%. The algorithm is adapted for road detection and still linear target detection in high-resolution aerial photographs. Since the algorithm does not require artificial features or training data, it can be quickly deployed in application.
Behavior Change Training for Health Professionals: Evaluation of a 2-Hour Workshop

Background Rates of noncommunicable diseases continue to rise worldwide. Many of these diseases are a result of engaging in risk behaviors. Without lifestyle and behavioral intervention, noncommunicable diseases can worsen and develop into more debilitating diseases. Behavioral interventions are an effective strategy to reduce the burden of disease. Behavior change techniques can be described as the “active ingredients” in behavior change and address the components that need to be altered in order for the target behavior to change. Health professionals, such as pharmacists and nurses, can engage in opportunistic behavior change with their patients, to encourage positive health behaviors. Objective We aimed to develop, implement, and evaluate a behavior change workshop targeted at health professionals in Australia, with the goal of increasing knowledge of behavior change techniques and psychological variables. Methods A prospective study design was used to develop and evaluate a 2-hour behavior change workshop targeted at health professionals. The workshop was developed based on the Capability, Opportunity, Motivation, and Behavior Model and had five core objectives: (1) to detail the role of health professionals in delivering optimal care, (2) to demonstrate opportunities to change behavior, (3) to describe principles of behavior change, (4) to explain behavior change techniques, and (5) to determine the most appropriate behavior change techniques to use and when to use them. A total of 10 workshops were conducted. To evaluate the workshops and identify any potential long-term changes in behavior, we collected pre- and postworkshop data on knowledge and psychological constructs from the attendees. Results A final sample of 41 health professionals comprising general practitioners, nurses, and pharmacists completed the pre- and postworkshop surveys. Following the workshops, there were significant improvements in knowledge of behavior change techniques (t40=–5.27, P<.001), subjective norms (t40=–3.49, P=.001), descriptive norms (t40=–3.65, P<.001), perceived behavioral control (t40=–3.30, P=.002), and intention (t36=–3.32, P=.002); each had a large effect size. There was no significant difference in postworkshop attitude (t40=0.78, P=.44). The participants also found the workshops to be highly acceptable. Conclusions A 2-hour, theoretically informed workshop designed to facilitate the use of behavior change techniques by health professionals was shown to be largely effective. The workshops resulted in increases in knowledge, descriptive and subjective norms, perceived behavioral control, and intention, but not in attitude. The intervention was also shown to be highly acceptable, with the large majority of participants deeming the intervention to be needed, useful, appropriate, and applicable, as well as interesting and worth their time. Future research should examine the lasting impacts of the workshop on health professionals’ practices.
The effect of intermediate-mass close binaries on the chemical evolution of globular clusters

Context. The chemical processes during the asymptotic giant branch (AGB) evolution of intermediate-mass single stars predict most of the observations of the different populations in globular clusters although some important questions still need to be further clarified. In particular, to reproduce the observed anticorrelations of Na-O and Al-Mg, chemically enriched gas lost during the AGB phase of intermediate-mass single stars must be mixed with matter with a pristine chemical composition. The source of this matter is still debated. Furthermore, observations reveal that a significant fraction of the intermediate-mass and massive stars are born as components of close binaries. Aims. We investigate the effects of binaries on the chemical evolution of globular clusters and on the origin of matter with a pristine chemical composition that is needed for the single-star AGB scenario to work. Methods. We used a population synthesis code that accounts for binary physics to estimate the amount and the composition of the matter returned to the interstellar medium of a population of binaries. Results. We demonstrate that the mass lost by a significant population of intermediate-mass close binaries in combination with the single-star AGB pollution scenario may help to explain the chemical properties of the different populations of stars in globular clusters.
Stochastic Differential Equations and Applications

This advanced undergraduate and graduate text has now been revised and updated to cover the basic principles and applications of various types of stochastic systems, with much on theory and applications not previously available in book form. The text is also useful as a reference source for pure and applied mathematicians, statisticians and probabilists, engineers in control and communications, and information scientists, physicists and economists.
ON THE DYNAMICS OF ULTRA COMPACT X-RAY BINARIES: 4U 1850-087, 4U 0513-40, AND M15 X-2

In this work, we extend our dynamical study of ultra compact X-ray binaries (UCXB) 4U 1820-30 from Prodan & Murray to three more UCXBs in globular clusters: 4U 1850-087, 4U 0513-40, and M15 X-2. These three UCXBs have orbital periods ≲ 20 minutes. Two of them, 4U 1850-087 and 4U 0513-40, have suspected luminosity variations of the order of ∼1 yr. There is insufficient observational data to make any statements regarding the long periodicity in the light curve of M15 X-2 at this point. The properties of these three systems are quite similar to 4U 1820-30, which prompt us to model their dynamics in the same manner. As in the case of 4U 1820-30, we interpret the suspected long periods as the period of small oscillations around a stable fixed point in the Kozai resonance. We provide a lower limit on the tidal dissipation factor Q, which is in agreement with results obtained for the case of 4U 1820-30.
Emerging technology can guide ecosystem restoration for future water security

Water is the primary medium through which society will experience the effects of climate change. Altered precipitation patterns, enhanced evapotranspiration rates, loss of snow and ice, declines in groundwater storage, and increased risk of flooding and droughts will all have important implications for our water resources. In fact, we are already experiencing climate-related perturbations to the water cycle, as manifested through increases in temperatures and frequency of weather extremes across the globe (IPCC, 2021). However, a critical but often neglected aspect of predicting future hydrological change is recognizing the implications of historical land-use decisions that can act to either enhance or reduce the resilience of both the quality and quantity of water. Such land use decisions may create ‘locked-in’ effects reflecting the policies and path dependencies that underpin management inertia (Unruh & Carrillo-Hermosilla, 2006), as well as physical disturbances that caused hydrological changes that now are difficult to reverse (Lindenmayer et al., 2011). While water challenges are global, the water cycle in boreal regions is perhaps uniquely vulnerable due to cumulative effects of land use and climate warming, with the latter proceeding at faster rates at high latitudes (Buntgen et al., 2021). Climate change in the north is predicted to give rise to increasingly snow-free winters and warmer summers in boreal landscapes, which collectively will reduce water storage. Superimposed on these changes are barriers or inertia due to past land-use decisions, that may exacerbate pressures on groundand surface waters. The most widespread management activity in many boreal countries is the historical ditching of wet soils. These ditches are constructed waterways that were dug, often by hand, to remove excess water affecting several million hectares of land, especially in Finland and Sweden, but also in many other northern countries (Strack, 2008; Figure 1). This resulted in improved forest growth and transformed wet soils and peatlands to productive forests, but also came with unwanted environmental consequences. Drainage ditches are sources of enhanced methane emissions (Peacock et al., 2021) and elevated dissolved organic carbon concentrations (Nieminen et al., 2021). Low water tables also lead to enhanced soil carbon mineralization and increased terrestrial greenhouse gas (GHG) emissions (Evans et al., 2021). The biogeochemical properties of these extensions to the drainage network dramatically alter our understanding of running waters as sources of GHGs to the atmosphere and the overall provisions of ecosystem services. Most importantly, in an era increasingly affected by climate change, we now face conditions that create the opposite obstacle that the ditches aimed to solve—insufficient amounts of water—due to the expected increase in frequency, duration and severity of drought (Spinoni Received: 13 September 2022 Revised: 29 September 2022 Accepted: 30 September 2022
: A Learning-Based Framework to Augment Permanence

For over three decades, the Gabor-based IrisCode approach has been acknowledged as the gold standard for iris recognition, mainly due to the high entropy and binary nature of its signatures. This method is highly effective in large scale environments (e.g., national ID applications), where millions of comparisons per second are required. However, it is known that non-linear deformations in the iris texture, with fibers vanishing/appearing in response to pupil dilation/contraction, often flip the signature coefficients, being the main cause for the increase of false rejections. This paper addresses this problem, describing a customised Deep Learning (DL) framework that: 1) virtually emulates the IrisCode feature encoding phase; while also 2) detects the deformations in the iris texture that may lead to bit flipping, and autonomously adapts the filter configurations for such cases. The proposed DL architecture seamlessly integrates the Gabor kernels that extract the IrisCode and a multi-scale texture analyzer, from where the biometric signatures yield. In this sense, it can be seen as an adaptive encoder that is fully compatible to the IrisCode approach, while increasing the permanence of the signatures. The experiments were conducted in two well known datasets (CASIA-Iris-Lamp and CASIA-Iris-Thousand) and showed a notorious decrease of the mean/standard deviation values of the genuines distribution, at expenses of only a marginal deterioration in the impostors scores. The resulting decision environments consistently reduce the levels of false rejections with respect to the baseline for most operating levels (e.g., over 50% at $1e^{-3}$ FAR values).