Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditors' deviant decision-making model based on conflict of interest

When auditors intentionally or unintentionally approve financial statements in line with the views of their employers, the public interest and the auditing profession are at serious risk. The purpose of this study was to examine the process by which auditors' interests will influence decision making. To achieve this goal, first in the qualitative part of the research, by studying the background of accounting research and interviewing experts through the fuzzy Delphi method, the effective components on conflict of interest were identified. In the second part of the study, using social-cognitive meta-theory and operational decision-making power in the stimulus-organism-response (S-O-R) paradigm, data obtained from 362 certified public accountants. Smart-PLS software and Structural equations have been implemented for data analysis and hypotheses testing. Cognitive processes, including expected positive outcomes, understanding the problems of consistent decision-making, and ethical judgment, play a mediating role between the conflict of interest and deviant decision-making, and through these intervening variables, conflict of interest can be monitored and reduced. The results show that professional ethics with the confirmation of all hypotheses are considered as the most effective component. It also showed that reducing the expected positive results, increasing the understanding of problems, and reducing the auditors' ethical judgment in the assigned tasks will lead to deviant decisions. By implementing the proposed algorithm, cognition processes can be directed towards consistent decision making.
Triple-Mesoscopic Carbon Perovskite Solar Cells: Materials, Processing and Applications

Perovskite solar cells (PSCs) have already achieved comparable performance to industrially established silicon technologies. However, high performance and stability must be also be achieved at large area and low cost to be truly commercially viable. The fully printable triple-mesoscopic carbon perovskite solar cell (mCPSC) has demonstrated unprecedented stability and can be produced at low capital cost with inexpensive materials. These devices are inherently scalable, and large-area modules have already been fabricated using low-cost screen printing. As a uniquely stable, scalable and low-cost architecture, mCPSC research has advanced significantly in recent years. This review provides a detailed overview of advancements in the materials and processing of each individual stack layer as well as in-depth coverage of work on perovskite formulations, with the view of highlighting potential areas for future research. Long term stability studies will also be discussed, to emphasise the impressive achievements of mCPSCs for both indoor and outdoor applications.
HSI Denoising: Global Non-Location Low-Rank Factorization and Sparse Factorization Based on Iteratively Boost Method

This study proposes global and non-location low-rank factorization and sparse factorization based on iteratively boost (GNS-ITboost) scheme from the perspective of the iteration method to improve Hyperspectral image (HSI) denoising results. First, this study establishes a novel iterative boost method by jointing global low-rank factorization, non-location low-rank factorization and sparse factorization to HSI denoising. Second, we propose the non-convex method, weight schatten p-norm method to more effectively approximate the rank function to obtain better non-location low-rank factorization. Third, this study provides a new noise estimation method by prior knowledge to accelerate obtaining a better denoising effect. Finally, proximal alternating minimization and weight sparse ideas are used to obtain the solution of denoising optimization problems. Furmore, evaluation results indicate that the proposed method can outperform the state-of-the-art methods in terms of FastHyDe, GLF, LRMR, SNLRSF, SSTV.
Possibilities of Decreasing Hygroscopicity of Resonance Wood Used in Piano Soundboards Using Thermal Treatment

This article presents the possibilities of decreasing moisture sorption properties via thermal modification of Norway spruce wood in musical instruments. The 202 resonance wood specimens that were used to produce piano soundboards have been conditioned and divided into three density groups. The first specimen group had natural untreated properties, the second was thermally treated at 180 °C, and the third group was treated at 200 °C. All specimens were isothermally conditioned at 20 °C with relative humidity values of 40, 60, and 80%. The equilibrium moisture content (EMC), swelling, and acoustical properties, such as the longitudinal dynamic modulus (E’L), bending dynamic modulus (Eb), damping coefficient (tan δ), acoustic conversion efficiency (ACEL), and relative acoustic conversion efficiency (RACEL) were evaluated on every moisture content level. Treatment at 180 °C caused the EMC to decrease by 36% and the volume swelling to decrease by 9.9%. Treatment at 200 °C decreased the EMC by 42% and the swelling by 39.6%. The 180 °C treatment decreased the value of the longitudinal sound velocity by 1.6%, whereas the treatment at 200 °C increased the velocity by 2.1%. The acoustical properties EL′, Eb, ACEL, and RACEL were lower due to the higher moisture content of the samples, and only the tanδ increased. Although both treatments significantly affected the swelling and EMC, the treatment at 180 °C did not significantly affect the acoustical properties.
Contactless Fall Detection Using Time-Frequency Analysis and Convolutional Neural Networks

Automatic detection of a falling person based on noncontact sensing is a challenging problem with applications in smart homes for elderly care. In this article, we propose a radar-based fall detection technique based on time-frequency analysis and convolutional neural networks. The time-frequency analysis is performed by applying the short-time Fourier transform to each radar return signal. The resulting spectrograms are converted into binary images, which are fed into the convolutional neural network. The network is trained using labeled examples of fall and nonfall activities. Our method employs high-level feature learning, which distinguishes it from previously studied methods that use heuristic feature extraction. The performance of the proposed method is evaluated by conducting several experiments on a set of radar return signals. We show that our method distinguishes falls from nonfalls with 98.37% precision and 97.82% specificity, while maintaining a low false-alarm rate, which is superior to existing methods. We also show that our proposed method is robust in that it successfully distinguishes falls from nonfalls when trained on subjects in one room, but tested on different subjects in a different room. In the proposed convolutional neural network, the hierarchical features extracted from the radar return signals are the key to understand the fundamental composition of human activities and determine whether or not a fall has occurred during human daily activities. Our method may be extended to other radar-based applications such as apnea detection and gesture detection.
Endemic Fluorosis and Cognitive Dysfunction in School Going Children: Any Link?

One female case with a heterozygous variant reported by Arjona et al.[6] had a mild intellectual disability, apart from myoclonus, muscle spasm, and dysesthesia from 16 years of age, without any definite clinical seizures in early childhood. A similar mild clinical presentation has also been reported by Khalaff‐Nazall et al.,[8] in multiple family members spanning over three generations in a family harboring heterozygous CNNM2 mutation with autosomal dominant inheritance. Apart from mild impairment in fine motor skills like dyslalia and dyslexia, they had no other neurological abnormalities. However, all of them had hypomagnesemia and hypermagnesuria, refractory to oral magnesium supplementation. The same authors described another family cohort with heterozygous CNNM2 variants presenting with a magnesium independent neurodevelopmental phenotype with intellectual disability, autism spectrum disorder, and cone‐rod dystrophy causing visual impairment, but no clinical seizures. [8] Thus, clinicians need to remember although CNNM2 mutations associated disorder has been initially described as an autosomal recessive disorder, it can present with neurological symptoms, even with epileptic spasms and low serum magnesium levels, in the heterozygous state and accordingly should provide appropriate genetic counseling to parents.
Between Scylla and Charybdis: The Bermudan Swaptions Pricing Odyssey

Bermudan swaptions are options on interest rate swaps which can be exercised on one or more dates before the final maturity of the swap. Because the exercise boundary between the continuation area and stopping area is inherently complex and multi-dimensional for interest rate products, there is an inherent “tug of war” between the pursuit of calibration and pricing precision, tractability, and implementation efficiency. After reviewing the main ideas and implementation techniques underlying both single- and multi-factor models, we offer our own approach based on dimension reduction via Markovian projection. Specifically, on the theoretical side, we provide a reinterpretation and extension of the classic result due to Gyöngy which covers non-probabilistic, discounted, distributions relevant in option pricing. Thus, we show that for purposes of swaption pricing, a potentially complex and multidimensional process for the underlying swap rate can be collapsed to a one-dimensional one. The empirical contribution of the paper consists in demonstrating that even though we only match the marginal distributions of the two processes, Bermudan swaptions prices calculated using such an approach appear well-behaved and closely aligned to counterparts from more sophisticated models.
Effects of Two Sources of Tannins on Performance, Nitrogen Utilization and Efficiency of Microbial Nitrogen Synthesis in Dairy Goats

The aim of this study was to evaluate the effects of tannic acid (TA) and pistachio by-product tannin extract (PBE) on N utilization and efficiency of microbial nitrogen synthesis (EMNS) in Saanen dairy goats. The experimental design was a change over design with three treatments and three periods using six mid lactation multiparous dairy goats. Tannic acid solution and PBE were added to fresh alfalfa at ensiling to get the final concentration of about 1% tannin as dry matter (DM) basis. Dietary treatments were as follows: 1) untreated alfalfa silage (AS), 2) AS treated with TA (AS+TA) and 3) AS treated with PBE (AS+PBE). Intake of DM, organic matter (OM) and N and also milk yield and composition (as percent or as g/d) were not affected by tannin additions. A considerable shift occurred in the pattern of N excretion in goats fed PBE diet compared to control silage diet. Urinary N losses had a tendency (P<0.10) to lower (17.4 vs. 19.2 g/d) and fecal N losses was higher for goats fed AS + PBE compared with those fed the AS silage diet (14.6 vs. 12.6 g/d). As a proportion of N intake or as g N loss per d, retained N was higher (P<0.05) in goats fed AS + PBE compared to those fed the control diet. Microbial nitrogen synthesis (MN) was not affected by tannin additions. However, a trend (P<0.10) for less efficiency of microbial nitrogen synthesis (EMN) was observed in tannin fed goats compared to those fed the control diet. Although, tannins added to AS may exert some beneficial effects on N utilization and also environmental N outputs, they may interfere with rumen fermentation lead to decrease in EMN in goats especially those fed with PBE.
Ionization Mechanism in a Thermal Spark Discharge

The formation of thermal sparks generated by nanosecond pulses is studied experimentally and numerically. The increase of the electron number density, up to 10 19 cm-3 , is measured with sub-nanosecond resolution using Stark broadening of N + , Stark broadening of Hα, continuum emission of the electron, and the intensity of N + lines. Our experimental results are then compared to 0-D kinetic simulations including the electron-impact ionization of excited electronic states of N and O. The electron temperature, in equilibrium with the gas temperature, reaches 42,000K, which agrees with our experimental findings and the literature on the thermal spark.
“A Study on The Levels Of Stress Among Post Graduate Students Of Dibrugarh University’’

Stress is normally an unavoidable part of everyone’s life living in this world. It portrays a negative notion that can have an impact on one’s mental and physical well-being. Stress is always regarded as a psychological process that involves an individual’s personal interpretation and response to any threatening event. Stress is an unavoidable part of life everybody meet with stress in his/ her life; it can take a toll on students‟ physical health, mental health, and academic success and even on every part of life unless they discover to cope with it appropriately. Stress has a negative impact on one’s mental and physical wellbeing. Throughout a lifetime, one may experience thousands of different episodes of stress. It means that stress may be a normal, adaptive reaction to threat. Its role is to signal and prepare individuals to take defensive action. Take for instance, fear of things that present realistic threats motivates individuals to deal with them or avoid them. College and Post Graduation students experience stress connected to changes in lifestyle, increased workload, new tasks, interpersonal relationships and a lot of other things. Tremendous levels of stress can hinder work efficacy and lead to meagre academic performance and at the same time it affects one’s overall happiness.
Governance Effectiveness: The Interaction of Ethno-Racial Diversity and Social Capital

This research examines the relationship among Board Diversity, Social Capital, and Governance Effectiveness by asking, “does board ethno-racial diversity moderate the relationship between Social Capital and Governance Effectiveness, and if so, how? ” Exploring the direct and interacting effects of demographic diversity and Social Capital, and their relation to governing-group effectiveness using a two-sample field survey design, we illustrate whether heterogeneous or homogeneous group compositions amplify or attenuate Governance Effectiveness, and to what degree. Primary analyses find no support for Board Diversity moderating the Social Capital-Governance Effectiveness relationship, with secondary analysis revealing a more complex interaction for Governance Effectiveness, albeit inconsistently, across samples. Our investigation points to the value of social resources in understanding governance as an inherently socially complex activity or capability, predicated on truce or mutual agreement and shaped by the composition and connections of boards.
Determination of Ranolazine in Tablet Formulations by High-Performance Thin-Layer Chromatography—Mass Spectrometry Using Reflectance Scanning Densitometry

A new quantitative densitometric high-performance thin-layer chromatographic (HPTLC) method was developed and validated for the analysis of ranolazine (RZ) both in bulk and formulations. RZ was separated and identified on silica gel 60 F254 HPTLC plates with butanol—acetic acid—water (6:2:2 v/v) as the mobile phase. Densitometric quantification was performed at λ = 270 nm by reflectance scanning which facilitated well-resolved band for the main drug (RF 0.56 ± 0.02). Response to RZ was a linear function of concentration in the range of 100–400 ng, with a correlation coefficient, slope, and intercept of 0.99912 ± 0.00017, 8.684 ± 0.582, and 492.147 ± 2.67, respectively. The minimum amount of RZ that could be authentically detected and quantified was 14.90 and 49.67 ng band−1, respectively. Additionally, the peak identities as well as the purities were confirmed by mass spectrometry. The electrospray ionization (ESI+) mass spectra showed the [M + H]+ ion for RZ detected at m/z 428.3 being acquired directly from the sample bands by an elution-based interface. The proposed method was validated with respect to linearity, precision, accuracy, and specificity.
Mind the gap: CEO–employee pay ratios and shareholder say‐on‐pay votes

We examine whether the ratio of CEO to employee pay (the pay ratio) informs shareholders' say on pay (SOP) votes for a broad panel of U.S. commercial banks. Voting dissent on SOP proposals is increasing in the level of the pay ratio, particularly for banks in the highest pay ratio decile. Results are robust to a number of sensitivity tests, including controlling for proxy advisor recommendations and information about executive compensation already disclosed in proxy statements. In falsification tests, we show that pay ratios do not inform shareholders' votes on the ratification of the firm's auditor or the election of directors.
On the theory of Brownian aggregation in colloidal dispersions

Kelkar, Aniruddha V. Ph.D., Purdue University, December 2014. On the Theory of Brownian Aggregation in Colloidal Dispersions. Major Professors: Elias I. Franses and David S. Corti. Brownian aggregation kinetics is a topic of interest for colloidal dispersions, macromolecular fluids, nanoparticle self-assembly, and biochemical reactions. The manipulation of colloidal dispersion stability by containing or promoting aggregation rates is manifest in several natural phenomena and engineering applications. The destabilization of silt dispersions as river water mixes with the high-ionic-strength seawater near river system deltas leads to the deposition of fertile soil on the river banks. Pigments used in the stone age paintings found in the Lascaux caves of France and in the written records of Egyptian pharaohs are early examples of stabilized colloidal dispersions. Present day applications include the manufacturing of paints and coatings, inkjet printing, formulation of food and pharmaceutical dispersions, waste-water treatment, and in the transport of solids during oil production. Aspects of this problem are also important in biological systems and affect cellular organization, enzyme catalysis, protein aggregation, and macromolecular binding. The primary focus of the thesis is on the fundamental study of this problem. Brownian aggregation in concentrated dispersions of hard spheres, interacting spheres — van der Waals or DLVO potential, and binary mixtures of hard spheres is examined. Much of the current understanding of Brownian aggregation kinetics stems from the models of Smoluchowski and Fuchs. These classical approaches are evaluated using a more rigorous coarse-grained technique — Brownian dynamics simulation. The predictions of these two models agree with simulation results only for very dilute dispersions, φ < 0.005, with errors of up to three orders of magnitude in
A principled approach to designing computational thinking concepts and practices assessments for upper elementary grades

ABSTRACT Background and Context: In today’s increasingly digital world, it is critical that all students learn to think computationally from an early age. Assessments of Computational Thinking (CT) are essential for capturing information about student learning and challenges. When programming is used as a vehicle to foster CT skills, assessment of CT skills needs to measure understanding of programming concepts alongside general problem-solving practices applicable in contexts beyond programming. Objective: In this paper, we discuss the development of and results from separate validated assessments of programming concepts and generic CT practices for 4th-6th grade students. Method: An Evidence-Centered Design approach was used for the development and validation of the assessments, that were used with 15,000 upper elementary students in Hong Kong. Findings: Results from assessment implementation indicate that the assessments worked as designed and reveal student challenges with CT concepts and practices. Implications: These results point to the utility of our assessment as a curricular tool and the need for emphasizing certain CT concepts and practices in future curricular initiatives and teacher professional development.
Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements

Abstract. In this study, we present the assimilation of data from the Orbiting Carbon Observatory-2 (OCO-2) to estimate the Australian CO2 surface fluxes for the year 2015. We used a regional-scale atmospheric transport-dispersion model and a four-dimensional variational assimilation scheme. Our results suggest that Australia was a carbon sink of −0.3 ± 0.09 PgC y−1 compared to the prior estimate 0.09 ± 0.17 PgC y−1 (excluding fossil fuel emissions). Most of the uptake occurred over northern savannas, the Mediterranean ecotype in southern Australia and the sparsely vegetated ecotype in central Australia. Our results suggest that the majority of the carbon uptake over Mediterranean was associated with positive EVI anomalies (relative to 2000–2014). However, the stronger posterior carbon uptake estimated over savanna and sparsely vegetated ecosystem was due primarily to underestimation of the gross primary productivity by the land surface model (CABLE-BIOS3 model). To evaluate the accuracy of our posterior flux estimates, we compare our posterior CO2 concentration simulations against the column- averaged carbon retrievals from the Total Carbon Column Observing Network (TCCON) and ground-based in-situ monitoring sites located around our Australia domain. In general, the performance of our posterior concentration compared well with TCCON observations, except when TCCON concentrations were dominated by ocean fluxes which were tightly constrained to their prior values. Comparisons with in-situ measurements also show encouraging results though with similar difficulties for coastal stations. For stations located far from the coast, the comparison with in situ data was more variable, suggesting difficulties to match the column-integrated and surface data by the inversion, most likely linked to model vertical transport.
Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM

Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.