Miracle-dz/UniFAR-FTUs huggingface dataset:
Input Current Ripple Reduction in a Step-Up DC–DC Switched-Capacitor Switched-Inductor Converter

This paper presents research results related to the concept of a high-voltage-gain DC-DC converter with a low input current ripple. In the proposed topology, a low-volume DC-DC switch-mode boost converter operates in parallel with a switched-capacitor voltage multiplier (SCVM). The overall converter achieves a four-fold voltage gain, but the voltage stress of the transistor and the diode of the boost converter is only half of the output voltage. This is achieved by applying the specific topology of the proposed converter. Furthermore, the boost part uses a low-volume choke as it operates in the discontinuous conduction mode (DCM). The parallel operation of the boost converter and the SCVM decreases the current stress in some components of the multiplier. This paper presents a concept of the hybrid converter, an analytical model for the selection of components and switching parameters, an efficiency model, and the verification of the converter operation through simulation tests and experiments.
Noise in Datasets: What Are the Impacts on Classification Performance?

Classification is one of the fundamental tasks in machine learning. The quality of data is important in constructing any machine learning model with good prediction performance. Real-world data often suffer from noise which is usually referred to as errors, irregularities, and corruptions in a dataset. However, we have no control over the quality of data used in classification tasks. The presence of noise in a dataset poses three major negative consequences, viz. (i) a decrease in the classification accuracy (ii) an increase in the complexity of the induced classifier (iii) an increase in the training time. Therefore, it is important to systematically explore the effects of noise in classification performance. Even though there have been published studies on the effect of noise either for some particular learner or for some particular noise type, there is a lack of study where the impact of different noise on different learners has been investigated. In this work, we focus on both scenarios: various learners and various noise types and provide a detailed analysis of their effects on the prediction performance. We use five different classifiers (J48, Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Random Forest) and 10 benchmark datasets from the UCI machine learning repository and three publicly available image datasets. Our results can be used to guide the development of noise handling mechanisms.
A Polynomial Equation Model for Fatigue Crack Propagation in an Aeronautical Steel Material

The model widely used in the fatigue crack growth study in materials science and fracture mechanics is the Paris-Erdogan, which relates the stress intensity factor with the subcritical crack growth, under a fatigue regime. In this work, a seven degree polynomial is used as an alternative to model the crack propagation behavior and to be able to analyze the three regions charts of the cycles by load, in contrast to the common model that it performs at a slight approximation of the propagation phase of the trinca (region II). Finally, a comparison has been made between the proposed model and the secant method by the ASTM 647,2000 standard that adjusts the points obtained experimentally. This proposed equation is new as another alternative analytical model for which the adjustment parameter “R” are compared in relation to the classical approach in a aeronautical ABNT 4130 steel.
Observers predict actions from facial emotional expressions during real-time social interactions

In face-to-face social interactions, emotional expressions provide insights into the mental state of an interactive partner. This information can be crucial to infer action intentions and react adaptively towards another person’s actions. Here we investigate how facial emotional expressions impact subjective experience and physiological and behavioral responses to social actions during real-time interactions. Thirty-two participants interacted with virtual agents while fully immersed in Virtual Reality. Agents displayed an angry or happy facial expression before they directed an appetitive (fist bump) or aversive (punch) social action towards the participant. Participants responded to these actions, either by reciprocating the fist bump or by defending the punch. For all interactions, subjective experience was measured using ratings. In addition, physiological responses (electrodermal activity, electrocardiogram) and participants’ response times were recorded. Aversive actions were judged to be more arousing and less pleasant relative to appetitive actions. In addition, angry expressions increased heart rate relative to happy expressions. Crucially, interaction effects between facial emotion and action were observed. Angry expressions reduced pleasantness stronger for appetitive compared to aversive actions. Furthermore, skin conductance responses to aversive actions were increased for happy compared to angry expressions and reaction times were faster to aversive compared to appetitive actions when agents showed an angry expression. These data indicate that observers used facial emotional expression to generate expectations for particular actions. Consequently, the present study demonstrates that observers integrate information from facial emotional expressions with actions during social interactions.
Improvement of Indophenol Blue Colorimetric Method on Activity of Urease in Soil

Among all the measurement methods on activity of urease,indophenol blue colorimetric method,higher accuracy of the measurement results and better reproducibility,is the most widely used measurement method on activity of urease.However,this kind of method also owns some drawbacks,for examples,filtered fluid of cultivated soil is muddy and concentration of substrate has a great influence on the activity of urease.Based on the former measurement methods,four crucial parameters was taken to,such as method of filtration,time of cultivation,concentration of substrate and choice of buffer solution,the comparative tests in order to improve the accuracy of the measurement results of indophenol blue colorimetric method.The result of improved method is closer to the real activity of urease in soil with the conditions that choosing 5% concentration of substrate and pH10.0 borate buffer solution during the cultivation,extracting and filtrating with potassium chloride solution after 24-hours cultivation.The result of improved indophenol blue colorimetric method is 2.46 times better than the traditional method.
Critical Success Factors of Blockchain adoption in Green Supply Chain Management: Contribution through an Interpretive Structural Model

ABSTRACT The main purpose of this paper is to study the critical success factors (CSFs) of the blockchain technology (BCT) adoption in green supply chain management (GSCM) which might be literally afirst attempt and also propose aconceptual framework of aGSCM model adopting the BCT, which will be promoting the combination of these two areas in the future. Acritical literature review of the BCT, GSCM, and BCT-based GSCM was conducted to identify the most relevant factors of BCT adoption, followed by the model formulation with the help of interpretive structural modelling (ISM) consisting of CSFs and relationships between those based on experts’ views. The overall results emphasized that ‘recording and trading’-related factors may contribute to the BCT adoption, while others like smart contract must be enhanced. This study supports previous conceptual work on BCT and GSCM and could serve as astarting point to assist in decision-making.
Zebrafish Model-Based Assessment of Indoxyl Sulfate-Induced Oxidative Stress and Its Impact on Renal and Cardiac Development

Kidney disease patients may have concurrent chronic kidney disease-associated mineral bone disorder and hypertension. Cardiovascular disease (CVD) and neuropathy occur due to kidney failure-induced accumulation of uremic toxins in the body. Indoxyl sulfate (IS), a product of indole metabolism in the liver, is produced from tryptophan by the intestinal flora and is ultimately excreted through the kidneys. Hemodialysis helps renal failure patients eliminate many nephrotoxins, except for IS, which leads to a poor prognosis. Although the impacts of IS on cardiac and renal development have been well documented using mouse and rat models, other model organisms, such as zebrafish, have rarely been studied. The zebrafish genome shares at least 70% similarity with the human genome; therefore, zebrafish are ideal model organisms for studying vertebrate development, including renal development. In this study, we aimed to investigate the impact of IS on the development of zebrafish embryos, especially cardiac and renal development. At 24 h postfertilization (hpf), zebrafish were exposed to IS at concentrations ranging from 2.5 to 10 mM. IS reduced survival and the hatching rate, caused cardiac edema, increased mortality, and shortened the body length of zebrafish embryos. In addition, IS decreased heart rates and renal function. IS affected zebrafish development via the ROS and MAPK pathways, which subsequently led to inflammation in the embryos. The results suggest that IS interferes with cardiac and renal development in zebrafish embryos, providing new evidence about the toxicity of IS to aquatic organisms and new insights for the assessment of human health risks. Accordingly, we suggest that zebrafish studies can ideally complement mouse model studies to allow the simultaneous and comprehensive investigation of the physiological impacts of uremic endotheliotoxins, such as IS, on cardiac and renal development.
Management Accounting Practices in the Hospitality Industry: A Systematic Review and Critical Approach

The hospitality industry has been making a remarkable contribution to the growth of several countries’ economies. From a business perspective, the best management accounting (MA) techniques and practices are fundamental to the success of companies. However, studies in this specific area in the hotel industry are scattered in the scientific literature in different types of documents and different languages, and with an irregular distribution throughout the years (2000–2020). To fill this gap, a thorough analysis of the global performance of management accounting practices in hospitality is crucial. This study accessed the Web of Science database in three different languages and systematized the articles to be included in this research through the PRISMA guidelines, which allowed an empirical basis for the critical approach to this topic. The greatest relevance of the study is the fact that it presents a systematic review of the literature on hotel management accounting practices, for which these results were enriched with a critical approach. The innovative character of the study focuses on evidence of the increasing implementation of some hotel management accounting practices over the years, such as some operating ratios and the USALI. In practical terms, the results of this study explain the overall performance of management accounting practices in the lodging industry and which ones are most widely used. The importance of the practices to support the decision-making of hoteliers and the challenges that they need to face in their implementation are also shown.
Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

The casting mono-like silicon (Si) grown by directional solidification (DS) is promising for high-efficiency solar cells. However, high dislocation clusters around the top region are still the practical drawbacks, which limit its competitiveness to the monocrystalline Si. To optimize the DS-Si process, we applied the framework, which integrates the growing experiments, transient global simulations, artificial neuron network (ANN) training, and genetic algorithms (GAs). First, we grew the Si ingot by the original recipe and reproduced it with transient global modeling. Second, predictions of the Si ingot domain from different recipes were used to train the ANN, which acts as the instant predictor of ingot properties from specific recipes. Finally, the GA equipped with the predictor searched for the optimal recipe according to multi-objective combination, such as the lowest residual stress and dislocation density. We also implemented the optimal recipe in our mono-like DS-Si process for verification and comparison. According to the optimal recipe, we could reduce the dislocation density and smooth the growth rate during the Si ingot growing process. Comparisons of the growth interface and grain boundary evolutions showed the decrease of the interface concavity and the multi-crystallization in the top part of the ingot. The well-trained ANN combined with the GA could derive the optimal growth parameter combinations instantly and quantitatively for the multi-objective processes.
Diversification and phylogenetic correlation of functional traits for co‐occurring understory species in the Chinese boreal forest

Functional traits impact species interactions, community composition, and ecosystem functioning. However, few studies have focused on the diversification and phylogenetic correlation of multiple functional traits over geological time. We conducted phylogenetic comparative analysis for boreal forest understory species in northeast China to examine the diversification and phylogenetic correlation in several functional traits: leaf area (LA), leaf carbon content (LCC), leaf dry matter content (LDMC), leaf nitrogen content (LNC), plant height (PH), and specific leaf area (SLA). Phylogenetic signals showed that there were very low levels of phylogenetic niche conservatism (PNC) in understory leaf‐related traits and plant height, suggesting divergence of functional traits for the co‐occurring understory species. The disparity through time analyses (DTT) indicated that trait disparities mainly originated during recent divergence events and there were no differences in the observed trait disparities compared with that expected under Brownian motion. Furthermore, we found both positive and negative phylogenetic correlations among the measured functional traits. The very low levels of PNC suggest that these functional traits diverged among co‐occurring understory species, and that those species are distantly phylogenetically related. The phylogenetic correlations among traits may be caused by both positively and negatively correlated adaptions that correspond to resource acquisition strategies. This study provides evidence that divergence in functional traits may reflect understory adaptions to boreal conditions.
Optimization schedule for preventing slippage on tandem cold rolling mills based on IAGA

To tackle the problem of slippage during the cold rolling process of a tandem mill,a schedule optimization model for preventing slippage was established based on slip factor. Aiming to the shortages of the standard genetic algorithm,such as premature convergence,oscillation and over-randomization,an improved adaptive genetic algorithm was applied to optimize the system. In the algorithm a population-sorted multi-roulette-wheel selection operator was put forward for reducing the selected error generated by roulette-wheel randomization and promoting the probability of selecting better individuals. Simultaneously the algorithm decides the crossover rate and mutation rate of chromosome based on the individual adaptive value in the calculation process,makes the start phase variable obviously and the later phase stable slowly,and ensures population development,seeking balance and entire convergence. Experimental results and actual application show that the schedule optimization model can reduce the slip probability prominently,improve the product quality,and achieve a better economic benefit.
The 6 A’s model of social worker associations and COVID-19: A preliminary insight

This paper offers the 6 A’s model of social worker associations and COVID-19, which includes (i) ‘Apprehend’, (ii) ‘act’, (iii) ‘advocate’, (iv) ‘alliance’, (v) ‘an emphasis on solidarity and resilience’ and (vi) ‘a future prospect’. The model is based on the findings of qualitative analysis of social worker associations’ reports on COVID-19. It also offers insights that can be utilised in similar crises in the future.
Substance use and participation in Temporary Assistance for Needy Families: the role of unobserved heterogeneity

ABSTRACT Background The 1996 Personal Responsibility and Work Opportunity Reconciliation Act created Temporary Assistance for Needy Families (TANF) and granted states permission to implement drug testing as an eligibility requirement. As more states pass legislation requiring suspected drug users submit to drug tests, it is necessary to understand how substance use impacts participation. While several studies have estimated the association between substance use and participation, the literature has generally ignored the potential endogeneity of substance use. Method Using data from the 1997 National Longitudinal Survey of Youth, we estimate the effect of substance use on mothers’ TANF participation using a system of equations, addressing the potential endogeneity of substance use via unobserved heterogeneity that affects both substance use and TANF participation decisions. Results Our estimates suggest past-30-day substance use decreases the probability of current participation. Unobserved characteristics that increase the probability of participation also increase the probability of substance use. Our results are consistent across a range of sensitivity analyses. Conclusions Endogeneity plays an important role in substance use and TANF participation decisions. At face value, our results suggest legislative efforts to identify and exclude substance users from TANF may be misplaced. More research is needed to understand the causal mechanisms at work before making definitive policy recommendations.
Feelings of Knowing - Fundamental Interoceptive Patterns (FoK-FIP): a magnetic monopole-like “pure mental” process fundamental to subjective feelings and self-awareness

ABSTRACT The Feelings of Knowing - Fundamental Interoceptive Patterns (FoK-FIP) is a transdisciplinary theory developed to explain elusive phenomena suspected to exist that do not easily lend themselves to empirical measurement. The FoK-FIP theory posits that specialized self-generated biomagnetism and “pure mental” process share similarities with the hypothetical elementary particle described in particle physics, magnetic monopoles with a magnetic charge. Feelings of Knowing (FoK) are “awareness charge” that are self-generated events. Fundamental Interoceptive Patterns (FIP) are restricted oscillatory magnetic fields that are FoK caused phenomena. Further, FoK produces “cognitive force,” an observing ego representing specialized interoceptive awareness. Through embodied states, FoK-FIP acts as a “biological node,” an informational processing unit in which physiological signals and an observing ego’s sensations or feelings are centered. An observing ego cognitively broadcasts using specialized small magnetic signals and four phases of a narrowed range of interoceptive signals. By defining interoceptive signals (i.e., signals of the body’s internal state) using FoK-FIP through cognitive broadcasting, an observing ego creates a world it projects around itself. This process is understood through the components map with interoceptive markers (IMs), a novel algorithm based on biological evolution. FoK-FIP-related predictions are described as are empirical studies to test aspects of the theory. The FoK-FIP theory details a path to wellbeing based on a sense of control and capacity for self-care. Mental stability is thought to change as a function of an observing ego’s volitional reactions.
Assessing the Role of Digital Finance on Shadow Economy and Financial Instability: An Empirical Analysis of Selected South Asian Countries

: The advancement in ﬁntech technological development in emerging countries has accelerated the role of digital ﬁnance in economic development. Digital ﬁnance assists in ﬁnancial inclusion; however, it may also increase the chances of ﬁnancial instability due to systematic risks. Emerging countries are also in the clutches of shadow economic growth, which reduces taxable income revenue and creates pressure on ﬁnancial inclusion prospects. The current study attempts to measure the impact of digital ﬁnance on the shadow economic growth and ﬁnancial stability among the selected South Asian emerging countries. We have used the CUP-FM and CUP-BC estimation methods to measure the above relationship on two model frameworks from 2004 to 2018, with the former measuring the inﬂuence of digital ﬁnance on the shadow economy and the latter examining the relationship between digital ﬁnance and ﬁnancial stability. In addition, the second-generation unit root test, and the Westerlund cointegration analysis are also employed to conﬁrm the stationarity and cointegration among the variables. The result of the Westerlund’s cointegration conﬁrms a long cointegration between the explanatory and outcome variables. Furthermore, the long-run estimation results conclude that an increase in digital ﬁnance helps in reducing the growth of the shadow economy among the selected sample countries. However, it also increases the likelihood of systematic risks and increases ﬁnancial instability. The study also reveals that the control variables like unemployment and industrial productivity also have a signiﬁcant inﬂuence on ﬁnancial stability and the shadow economy. The ﬁndings will assist readers in comprehending how digital ﬁnance inﬂuences the shadow economy and promotes ﬁnancial inclusion and stability in emerging nations.
Effects of Nitrogen Fertilizer on Quality Characteristics of Wheat with the Absence of Different Individual High-Molecular-Weight Glutenin Subunits (HMW-GSs)

High-molecular-weight glutenin subunits (HMW-GSs) are important components of gluten, which determine the grain quality of wheat. In this study, we investigated the effects of nitrogen (N) fertilizer application on the synthesis and accumulation of grain protein and gluten quality in wheat lines with different HMW-GSs absent. The results showed that the absence of the HMW-GS in the wheat variety Ningmai 9 significantly decreased the contents of gluten, glutenin macropolymer (GMP), protein compositions, HMW-GS and HMW-GS/LMW-GS. The reduction in glutenins was compensated to some extent by an increase of gliadins. The absence of x-type HMW-GSs (1, 7 and 2 subunits) had a greater effect on gluten and GMP properties than y-type HMW-GSs (8 and 12 subunits). The content of protein compositions, gluten and GMP increased with an increase of N level; however, the increment in wheat lines with the absence of HMW-GS, especially in Ax1a, Bx7a and Dx2a, was lower than that in the wild type under various N levels. The expression level of genes encoding HMW-GSs, and activities of nitrate reductase (NR) and glutamine synthetase (GS), differed significantly among the investigated wheat lines. The reduction in gene expression and activities in Ax1a and Dx2a may account for the reductions in gluten, GMP, protein compositions, HMW-GS and HMW-GS/LMW-GS.