Miracle-dz/UniFAR-FTUs huggingface dataset:
Has accounting gone bad ?

We investigate two aspects of earnings smoothing via accruals that are consistent with accounting rule changes causing a decline in accounting quality. First, since 2000 accruals cause earnings to be far more volatile than cash flow from operations (CFO). We find this surprising result is unrepresentative, driven by the disproportionate influence of firms with negative CFO and low total assets (the deflator). We consider three other deflators—sales, market capitalization, and number of shares—as well as different diagnostics to determine distortion caused by deflation. We find number of shares performs best, and volatility of earnings is only about half that of CFO. Second, “smoothing coefficients”, slopes from annual cross-sectional regressions of accruals on CFO, have become less negative over time. This trend too is likely due to changes in operating characteristics, not changes in accounting rules. It is related mainly to a decline in the level of working capital, especially inventories, and is unrelated to factors such as increases in negative special items and the fraction of firms reporting losses.
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.
Exploration of alcohol use disorder-associated brain miRNA-mRNA regulatory networks

Alcohol use disorder (AUD) is due to gene expression changes in specific brain regions, but the underlying mechanism is not fully understood. We investigated AUD-associated miRNA-mRNA regulatory networks in multiple brain regions by analyzing transcriptomic changes in two sets of postmortem brain tissue samples and ethanol-exposed human embryonic stem cell (hESC)-derived cortical interneurons. miRNA and mRNA transcriptomes were profiled in 192 postmortem tissue samples (Set 1) from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control Caucasians. Nineteen differentially expressed miRNAs (fold-change>2.0 & P<0.05) and 97 differentially expressed mRNAs (fold-change>2.0 & P<0.001) were identified in one or multiple brain regions of AUD subjects. AUD-associated miRNA-mRNA regulatory networks in each brain region were constructed using differentially expressed and negatively correlated miRNA-mRNA pairs. AUD-relevant pathways (including CREB Signaling, IL-8 Signaling, and Axonal Guidance Signaling) were potentially regulated by AUD-associated brain miRNA-mRNA pairs. Moreover, miRNA and mRNA transcriptomes were mapped in additional 96 postmortem tissue samples (Set 2) from six of the above eight brain regions of eight AUD and eight control Caucasians, and some of the AUD-associated miRNA-mRNA regulatory networks were confirmed. Additionally, miRNA and mRNA transcriptomes were analyzed in hESC-derived cortical interneurons with and without ethanol exposure, and ethanol-influenced miRNA-mRNA regulatory networks were constructed. This study provided evidence that alcohol could induce concerted miRNA and mRNA expression changes in reward-related or alcohol-responsive brain regions. We concluded that altered brain miRNA-mRNA regulatory networks might contribute to AUD development.
Communicating about COVID-19 vaccine development and safety

Purpose. Beliefs that the risks from the vaccine outweigh the risks from getting COVID-19 and concerns that the vaccine development process was rushed and lacking rigor have been identified as important drivers of hesitancy and refusal to get a COVID-19 vaccine. We tested whether messages designed to address these beliefs and concerns might promote intentions to get a COVID-19 vaccine. Method. An online survey fielded between March 8-March 23, 2021 with US Veteran (n=688) and non-Veteran (n=387) respondents. In a between-subjects experiment, respondents were randomly assigned to a control group (with no message) or to read one of two intervention messages: 1. a fact-box styled message comparing the risks of getting COVID-19 compared to the vaccine, and 2. a timeline styled message describing the development process of the COVID-19 mRNA vaccines. Results. Most respondents (60%) wanted a COVID-19 vaccine. However, 17% expressed hesitancy and 23% did not want to get a COVID-19 vaccine. The fact-box styled message and the timeline message did not significantly improve vaccination intentions, F(2,359)=0.91, p=. 402, partial eta squared=. 005, or reduce the time respondents wanted to wait before getting vaccinated, F(2,307)=0.76, p=. 468, partial eta squared=. 005, compared to no messages. Discussion. We did not find an impact on vaccine intention based on providing information about vaccine risks and development. Further research is needed to identify how to effectively address concerns about the risks associated with COVID-19 vaccines and the development process and to understand additional factors that influence vaccine intentions.
Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors

Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-andplay framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-andplay algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://github.com/ucker/SCI-TVFFDNet.
The Effect of Partial Defoliation on Development and Distribution of Roots ofVitis viniferaL. cv. Cabernet Sauvignon Grafted onto Rootstock 99 Richter

The effect of partial defoliation (33%) over the whole canopy from different developmental stages of the vine on development and distribution of roots of Vitis vinifera L. cv. Cabernet Sauvignon was investigated under field conditions. Partial defoliation significantly stimulated root density, commencing from berry set. Root density apparently increased the later during the growth season defoliation commenced. Yield and cane mass of partially defoliated vines tended to coincide with root densities. This relationship did not hold for non-defoliated vines. Root development in fine to medium diameter classes was increased by partial defoliation. However, the development of thick roots apparently decreased. Partial defoliation from pea size seemed to create the most effective root system. Early defoliation (from bud break and berry set) reduced root development compared to later defoliations (from pea size and veraison). Partially defoliated vines had higher total root numbers in all soil layers, although differences were not significant. Regardless of treatments, roots were mainly located in 0 to 800 mm soil layers, with deeper layers containing much less roots. Higher root densities and generally larger numbers of particularly fine roots in all soil layers for partially defoliated vines suggested a more efficient nutrient absorption capacity and utilization of soil for these vines. Partially defoliated vines responded to the loss in leaf area by forming new roots, which may improve vine performance.
Fundamental limits of concentration in luminescent solar concentrators revised: the effect of reabsorption and nonunity quantum yield

Luminescent solar concentrators (LSCs) are devices theoretically able to condense both direct and diffuse solar radiation into thin dielectric layers with extremely high efficiencies. A theory based on thermodynamic principles was developed in the past to estimate the concentration limits that can be achieved with an LSC and facilitate researchers’ efforts to predict the potential of their designs to convert optical to electrical power. However, while concentration efficiencies of thousands or even tens of thousands of suns are supported by this model, values of only a fraction of those have ever been recorded experimentally. This is because in the calculation of the thermodynamic limits the quantum yield of the luminophores is assumed to be equal to unity and any processes that quench the intensity of the trapped field are completely ignored. In an attempt to better match theory with reality and provide more accurate performance estimates, we have revised the limits of concentration based on a statistical optics framework. The new model gives insight into the main mechanisms inhibiting the concentration of LSCs and can be used to extract design rules for efficient LSCs. Comparisons between the method presented in this paper and results obtained with Monte Carlo ray-tracing simulations demonstrate excellent agreement between the two. Finally, we discuss the conditions for validity of the thermodynamic limits, and we show that in some circumstances these can actually be surpassed.
Greywater Environmental Management: A Review

The growth in population leads to significantly increasing the water demand. Thus, the disparity between demand and supply for water has become an important problem in human life. One of the most effective solutions to overcome this disparity is to manage urban water. Greywater, a significant part of domestic wastewater or sewage, can play an essential role in the urban environment. To address this role, the current paper aims to provide families with some efficient, affordable and sustainable systems to treat greywater. For non-drinking services, treated greywater may be used in various applications such as irrigation, toilet-flushing, car-washing, etc. Around the world, there are several types of greywater treatment systems. A study of these processes has been undertaken to assess the most appropriate activities at the family and community level. The best techniques for treating greywater in this study, constructions wetland (C.W.) instead of other techniques, as this is a sustainable, low-cost and easy-to-apply technology. The use of grey water is important because it decreases the demand for fresh water and decreases treatment stress.
Shrub growth in the Alps diverges from air temperature since the 1990s

In the European Alps, air temperature has increased almost twice as much as the global average over the last century and, as a corollary, snow cover duration has decreased substantially. In the Arctic, dendroecological studies have evidenced that shrub growth is highly sensitive to temperature—this phenomenon has often been linked to shrub expansion and ecosystem greening. Yet, the impacts of climate change on mountain shrub radial growth have not been studied with a comparable level of detail so far. Moreover, dendroecological studies performed in mountain environments did not account for the potential modulation and/or buffering of global warming impacts by topography, despite its possible crucial role in complex alpine environments. To fill this gap, we analyzed a network of eight sites dominated by the dwarf shrub Rhododendron ferrugineum. The sites selected for analysis represent the diversity of continentality, elevation and slope aspect that can be found across the French Alps. We quantified annual radial increment growth for 119 individuals, assembled meteorological reanalyzes specifically accounting for topographic effects (elevation, slope and aspect) and assessed climate-growth relations using a mixed modeling approach. In agreement with a vast majority of dendroecological work conducted in alpine and arctic environments, we find that the number of growing degree days during the snow-free period snow-free growing degree days (SFGDDs) is a strong and consistent driver of R. ferrugineum growth across all sites since 1960 until the late 1980s. We also document a marked loss of sensitivity of radial growth to increasing SFGDD since the 1990s, with this decoupling being more pronounced at the driest sites. Our observations of the spatial and temporal variability of shrub sensitivity to limiting factors can be compared to the ‘divergence’ problem observed in tree-ring series from circumpolar and alpine regions and, accordingly, sheds light on possible future trajectories of alpine shrub growth in response to ongoing climate change.
Review of decomposition characteristics of eco-friendly gas insulating medium for high-voltage gas-insulated equipment

Sulfur hexafluoride (SF6), one of the most potent greenhouse gases with a global warming potential of 23 500 and an atmospheric lifetime of 3200 years, has been widely used as an insulating gas. The search for eco-friendly gas insulating medium to replace SF6 has been a hot topic in the power industry over the past 5 years. The performance evaluation of eco-friendly gas insulating medium concentrates on several dimensions including stability and decomposition characteristics. This review focuses on recent advances in knowledge about the decomposition characteristics of eco-friendly gas insulating medium. The basic theoretical and experimental methods, discharge decomposition, thermal stability and gas–solid interface interaction properties of several potential eco-friendly gas insulating medium are summarized. The existing problems and future research directions are also discussed.
Effect of anisotropic scattering for rotational collisions on electron transport parameters in CO

The role of anisotropic scattering in rotational collisions of electrons with CO molecules is investigated numerically with Monte Carlo (MC) simulations and with calculations using the Lisbon KInetics two-term Boltzmann solver (LoKI-B). The study adopts integral cross sections taken from the IST-Lisbon database of LXCat or extracted from Biagi’s code Magboltz v11.10. Different angular scattering models for rotational collisions are implemented and compared in MC simulations, and a novel anisotropic scattering model is derived from the dipole-Born differential cross sections, to describe the strongly forward-peaked nature of rotational collisions. This model is also implemented in LoKI-B, to describe the anisotropic inelastic/superelastic scattering in dipole rotational collisions, using coherent expressions for the corresponding integral and momentum transfer cross sections. The comparison between MC and LoKI-B results shows that the calculation of swarm parameters is more influenced by the choice of the angular scattering model than the adoption of the two-term approximation, yielding deviations up to 50% in the reduced mobility for different angular distributions. The consequences in the swarm derivation of cross sections are also discussed. Finally, it is shown that inclusion of electric-quadrupole interactions, usually neglected in electron swarm studies, can improve the agreement between numerical results and measurements.
Polyol Synthesis of Size-Controlled Rh Nanoparticles and Their Application to Photocatalytic Overall Water Splitting under Visible Light

A gradual size control of poly(N-vinyl-2-pyrrolidone)-protected Rh nanoparticles (PVP-Rh NPs) was successfully achieved in the range of 1.7–7.7 nm by tuning the pH value and the reaction temperature of the ethylene glycol (EG) solution; smaller NPs were formed at higher pH and at higher temperature. This trend can be interpreted by the change in the nucleation rate caused by tuning the pH value and/or the temperature of the solution. When the size tuned PVP-Rh NPs were applied for use as cocatalysts in a photocatalyst (solid solution of GaN and ZnO (Ga1–xZnx)(N1–xOx)) for overall water splitting under visible light, it was demonstrated that smaller Rh cores gave higher activity than the larger ones.
Probing the nickel corrosion phenomena in alkaline electrolyte using tender x-ray ambient pressure x-ray photoelectron spectroscopy

Corrosion poses a significant challenge to many materials, from industrial structures to electrochemical catalysts. Nickel (Ni) is considered one of the most active metal catalysts for electrochemical water oxidation reaction in the alkaline environment. To gain more insight into corroded nickel electrodes in the water oxidation reaction, we employed operando ambient pressure x-ray photoelectron spectroscopy to detect the nickel/electrolyte interface under various potentials. To probe the influence of various halide anions on cycled Ni electrode/electrolyte interfaces undergoing the oxygen evolution reaction, we examined the surface changes in nickel electrodes after repeated electrochemical cycles in the presence of KOH mixed with KBr and KI under various potentials. As the applied potential became more anodic, we detected the formation of surface hydroxide and bromide when Br− was present in the electrolyte, whereas in I− -containing electrolytes, we found only hydroxides. The formation of oxidized Ni species was highly dependent on the type and concentration of halide ions. Br− facilitated the formation of more oxidized Ni species than I− did under the same concentration. In the presence of higher concentrations of potassium halide (100 mM) in the bulk electrolyte, metallic nickel could be completely oxidized at the most anodic potentials with surface hydroxide formation. This work reports the observation of cationic metal ions in the bulk electrolyte and describes in situ observation of degradation at the metal/electrolyte interface.
Computer Vision for Elderly Care Based on Deep Learning CNN and SVM

Computer vision has wide application in medical sciences such as health care and home automation. This study on computer vision for elderly care is based on a Microsoft Kinect sensor considers an inexpensive, three dimensional, non-contact technique, that is comfortable for patients while being highly reliable and suitable for long term monitoring. This paper proposes a hand gesture system for elderly health care based on deep learning convolutional neural network (CNN) that is used to extract features and to classify five gestures according to five categories using a support vector machine (SVM). The proposed system is beneficial for elderly patients who are voiceless or deaf-mute and unable to communicate with others. Each gesture indicates a specific request such as “Water”, “Meal”, “Toilet”, “Help” and “Medicine” and translates as a command sending to a Microcontroller circuit that sends the request to the caregiver’s mobile phone via the global system for mobile communication (GSM). The system was tested in an indoor environment and provides reliable outcomes and a useful interface for older people with disabilities in their limbs to communicate with their families and caregivers.
Design and Implementation of Intelligent Controller Based on hybrid renewable energy source

This paper presents the design and implementation of Artificial Neural Networks (ANN) that control a hybrid wind-solar system based on Field Programmable Gate Arrays. the controller designed to direct the output from the clean energy systems where the output might be from the wind, solar, the sum of the two systems, or from the battery charging system. The ANN designed and trained using MATLAB2019a, two training algorithms were explored Levenberg-Marquardt and Practical Swarm Optimization and the one with best training results have been chosen to be Co-Simulated on Xilinx Spartan 6 FPGA XC6SLX9FTG256 using Xilinx System Generator (XSG) with MATLAB R2012b Simulink then the FPGA card Hardware Implementation using ISE Design Suite 14.7 program.
Automatic Structure Elucidation through Data Base Search and 2D NMR Spectral Analysis

This work shows how two expert systems, LSD and SISTEMAT, can be used together to solve structure elucidation problems that were selected from recent literature articles. The LSD system is a structure generator that mainly relies on homo- and heteronuclear 2D NMR data. It lacks the knowledge of chemical shift values and of natural product chemistry. Conversely, the SISTEMAT data base contains about 20000 natural compounds and refers to both their 13C NMR chemical shifts and their botanical origin. When exploited by dedicated computer programs it yields structural constraints such as skeletal types and ring systems. The botanical and spectroscopic data in SISTEMAT proved to be very complementary in the constraints extraction process. Several application examples of the proposed methodology are described in detail.
Simulink Implementation of Voltage Stability Improvements Using STATCOM based 5-level Diode Clamped Converter

In this paper, a STATCOM based on Five-level Diode Clamped Multilevel Converter is used to maintain the bus voltage at specific bus bar in a suggested grid under various abnormal conditions like 2-phase to ground fault, load disturbance and source disturbance. The suggested grid consists of a single machine, transmission line and loads. The compensation procedure is carried out and compared between the two suggested control methods, the first method was the traditional control method using Phase Shifted-PWM controller which is used to improve the voltage profile. The Second one was the Finite Set-Model Predictive Controller for improving the voltage profile. The simulation results show that the two methods provided significant improvements of the bus voltage profile. Moreover, the improvement of voltage profile using FS-MPC method is less over shoot and attains a steady state better than the conventional PS-PWM when the system is subjected under many disturbances. The results of the harmonic effect show low values of total harmonic distortion (THD) of supply voltage and current.
Faults Diagnosis of BLDC Motors Using Artificial Neural Networks

Recently, brushless DC motor (BLDC) has been implemented in many applications, especially critical applications. Due to many reasons, BLDC motor subjects to many types of faults including electrical and mechanical faults, therefore detect and diagnosis faults is very important in order to keep safety to the motor also reduce cost and maintenance. In this paper, an approach has been presented to diagnose the stator winding faults, control circuit switches fault and bearing faults. Artificial Neural Network (ANN) has been applied to diagnose faults at different operation conditions. The simulation result shows the ability of the proposed technique to diagnose faults with high accuracy.