Miracle-dz/UniFAR-FTUs huggingface dataset:
Ready, Set, Slow: How Aspiration-Relative Product Quality Impacts the Rate of New Product Introduction

Performance feedback research addresses how firms respond to performance that diverges from their aspirations. Whereas the majority of research in this vein involves financial performance, we apply this framework to product quality performance, arguing that when performance diverges either below or above aspirations, firms will pursue a slower subsequent product introduction rate, either to identify the cause of the underperformance or to incorporate the successful product characteristics in the case of overperformance. We also investigate whether our predictions hold when two boundary conditions are applied. Since product quality aspirations are derived from the “reputations for quality” of the firm and its peers, we argue that the stability of these reputations will amplify the delaying effects of below- and above-aspiration performance. Consistent with research on firm responses to financial performance, we also predict that greater sales revenues relative to sales aspirations will attenuate the delaying effects of aspiration-relative performance divergence. Our analysis of 1,332 video games released by 48 publishers from 2006 to 2009 is largely consistent with these predictions.
The effect on judgment of attributing religious affiliation to a terrorist suspect

The purpose of this study was to examine the effects and correlations of the perceived religious affiliation on a fictitious terrorism case. Participants were 402 French adults who completed a questionnaire after reading a scenario involving the arrest of a person (French vs. North African; man vs. woman) wearing an explosive belt. They indicated the level of the perpetrator’s religious affiliation and judged her/him and the act. The participants’ level of social dominance orientation (SDO) was measured and studied along in its two dimensions. The results showed an effect of ethnicity on perceived religious affiliation, which was correlated with judgment and mediated the effect of social dominance on judgment. The implications of this study are discussed in terms of intergroup interactions and religious prejudice.
The Effect of Corporate Governance, Integrated Quality Management and Social Responsibility on Competitiveness and Operational Performance

The purpose of this study is to analyze the effect of corporate governance, integrated quality management, and social responsibility on the competitiveness of manufacturing companies, analyze. The study was conducted on manufacturing companies with a population of 43 companies and determined a sample of 129 employees as respondents. The data from the questionnaire were analyzed using the SEM-AMOS. The study's results found social responsibility had insignificant effect on the competitiveness. This is because responsibility tends to be a moral spirit of the company which is not too related to the competitiveness that the company wants to achieve, so social responsibility in the form of environmental, social, economic, stakeholder, and voluntary dimensions has an insignificant impact on the company's competitiveness. Social responsibility has a positive and negative effect on the competitiveness. Besides that, social responsibility is related to the worker's social spirit and the social environment. Social responsibility has insignificant effect on the competitiveness. This is because the actualization of social responsibility is not directly related to competitiveness and improving corporate performance. Responsibility tends to be oriented towards aspects of worker psychology and working environment conditions, while competitiveness and operational performance are oriented towards achieving the results of the company's work.
C-Reactive Protein and Serum Albumin Ratio: A Feasible Prognostic Marker in Hospitalized Patients with COVID-19

(1) Background: C-reactive protein (CRP) and albumin are inflammatory markers. We analyzed the prognostic capacity of serum albumin (SA) and CRP for an outcome comprising mortality, length of stay, ICU admission, and non-invasive mechanical ventilation in hospitalized COVID-19 patients. (2) Methods: We conducted a retrospective cohort study based on the Spanish national SEMI-COVID-19 Registry. Two multivariate logistic models were adjusted for SA, CRP, and their combination. Training and testing samples were used to validate the models. (3) Results: The outcome was present in 41.1% of the 3471 participants, who had lower SA (mean [SD], 3.5 [0.6] g/dL vs. 3.8 [0.5] g/dL; p < 0.001) and higher CRP (108.9 [96.5] mg/L vs. 70.6 [70.3] mg/L; p < 0.001). In the adjusted multivariate model, both were associated with poorer evolution: SA, OR 0.674 (95% CI, 0.551–0.826; p < 0.001); CRP, OR 1.002 (95% CI, 1.001–1.004; p = 0.003). The CRP/SA model had a similar predictive capacity (honest AUC, 0.8135 [0.7865–0.8405]), with a continuously increasing risk and cutoff value of 25 showing the highest predictive capacity (OR, 1.470; 95% CI, 1.188–1.819; p < 0.001). (4) Conclusions: SA and CRP are good independent predictors of patients hospitalized with COVID-19. For the CRP/SA ratio value, 25 is the cutoff for poor clinical course.
Calcifediol for Use in Treatment of Respiratory Disease

Calcifediol is the prohormone of the vitamin D endocrine system (VDES). It requires hydroxylation to move to 1,25(OH)2D3 or calcitriol, the active form that exerts its functions by activating the vitamin D receptor (VDR) that is expressed in many organs, including the lungs. Due to its rapid oral absorption and because it does not require first hepatic hydroxylation, it is a good option to replace the prevalent deficiency of vitamin D (25 hydroxyvitamin D; 25OHD), to which patients with respiratory pathologies are no strangers. Correcting 25OHD deficiency can decrease the risk of upper respiratory infections and thus improve asthma and COPD control. The same happens with other respiratory pathologies and, in particular, COVID-19. Calcifediol may be a good option for raising 25OHD serum levels quickly because the profile of inflammatory cytokines exhibited by patients with inflammatory respiratory diseases, such as asthma, COPD or COVID-19, can increase the degradation of the active metabolites of the VDES. The aim of this narrative revision is to report the current evidence on the role of calcifediol in main respiratory diseases. In conclusion, good 25OHD status may have beneficial effects on the clinical course of respiratory diseases, including COVID-19. This hypothesis should be confirmed in large, randomized trials. Otherwise, a rapid correction of 25(OH)D deficiency can be useful for patients with respiratory disease.
Customized Appliance Device for Force Detection in Bruxism Individuals: An Observational Study

Objective This study aims to test a customised device for detecting contact-related sleep bruxism in adult patients and to show the efficacy of an established biofeedback method incorporated within the device. Methods Four volunteers, three of whom suffered from bruxism and one did not, underwent four tests to assess bruxism-related force detection during sleep with concurrent electromyographic recording and to compare SB activity with and without biofeedback stimuli. Results The device detected sleep bruxism in bruxer individuals, whilst no activity emerged in the control individual. A correlation between EMG and device signals for bruxism-related events emerged. Moreover, bruxism activity showed a significant decrease on the nights when the biofeedback treatment was applied. Conclusion The force-based device can detect appliance-contacting SB events as reliably as EMG recording. Finally, biofeedback stimuli allowed achieving a reduction in the severity and frequency of SB events.
The Role of Methyl-(Z)-11-tetradecenoate Acid from the Bacterial Membrane Lipid Composition in Escherichia coli Antibiotic Resistance

Background The bacterial membrane plays a critical role in the survival of bacteria and the effectiveness of antimicrobial peptides in protecting the host. The lipid constituents of the bacterial membrane are not evenly distributed, and they could be affected by clustering anionic lipids with cationic peptides with multiple positive charges. That could be harmful to bacteria because it prevents lipids from interacting with other molecular components of the cell membrane, disrupts existing natural domains, or creates phase boundary defects between the clustered lipids and the bulk of the membrane. This preliminary quantitative study is aimed at assembling a correlation between antibiotic resistance and bacterial lipid composition in E. coli, based on the function and arrangement of the bilipid coating of the bacterial cell, intimately associated with the path of antimicrobials through membranes. Methods Fifteen multiresistant E. coli samples are collected from swine with enterocolitis tested for resistance levels using the disc diffusimetric method (Kirby-Bauer disc diffusion). Pathogen identification completed using the API 20E multitest system revealed the E. coli presence in 11 samples. In these samples, bacterial membrane detection of fatty acid methyl esters (FAME) operating a 240 MS Ion Trap (Varian) GC/MS (Agilent Technologies, Santa Clara, CA, USA) was performed, using the MIDI Sherlock recognition software model. Results Interpreting the descriptive statistical method, the correlation matrix, and regression curves and after ANOVA analysis, we ascertained that the studied E. coli population statistically confirmed different degrees of resistance in most of the samples analyzed in this test. Conclusions In one case, the methyl-(Z)-11-tetradecenoate acid was observed to have a relationship with the susceptibility evaluation by using the disc diffusimetric method, which has revealed the lowest rate of antimicrobial resistance, so it has importance in further resistance evaluation studies.
Prior to Implementation of Digital Pathology—Assessment of Expectations among Staff by Means of Normalization Process Theory

The Region of Southern Denmark is the first in Denmark to implement digital pathology (DIPA), starting at the end of 2020. The DIPA process involves changes in workflow, and the pathologist will have to diagnose based on digital whole slide imaging instead of through the traditional use of the conventional light microscope and glass slides. In addition, in the laboratory, the employees will have to implement one more step to their workflow—scanning of tissue. The aim of our study was to assess the expectations and readiness among employees and management towards the implementation of DIPA, including their thoughts and motivations for starting to use DIPA. We used a mixed-method approach. Based on the findings derived from 18 semi-structured interviews with employees from the region’s departments of pathology, we designed a questionnaire, including questions from the normalization measure development tool. The questionnaires were e-mailed to 181 employees. Of these employees, 131 responded to the survey. Overall, they reported feeling sufficiently tech-savvy to be able to use DIPA, and they had high expectations as well as motivation and readiness for the upcoming changes. However, the employees were skeptical regarding the allocation of resources, and few were aware of reports about the effects of DIPA. Based on the findings, it seems to be important to provide not only a thorough introduction to the new intervention and the changes it will entail, but also to continue to ensure that the staff know how it works and why it is necessary to implement.
Impact of intellectual capital on profitability: Evidence from software development companies in the Slovak Republic

Intellectual capital is the total value of all entity’s intangible resources (organizational, human, and customer). Effective management of intellectual capital in high-tech industries needs determination of its role in ensuring profitability and clarifying the direction of managerial and investment policy in intangible resources. The aim of this study is to investigate the impact of intellectual capital on the profitability of Slovak software development companies. Panel data regression analysis was used as the main research method to analyze the data of 16 Slovak software development companies for 2015–2019. The study designed and analyzed four panel data regression models with different dependent variables (Return on Assets, Net Profit Margin, Gross Profit Margin, Earnings Before Interest and Taxes Margin) and similar independent variables (Capitalized Development Costs, Software, Acquired Intangible Fixed Assets, Personnel Costs, Social Security Costs, Social Costs, and Total Costs of Economic Activity). The analysis of these models was carried out based on the fixed effects method. It was found that intellectual capital reflected in the financial statements of software development companies does not meet the information needs of stakeholders and does not have a significant direct impact on profitability. Only Acquired Intangible Fixed Assets had a direct positive impact on the profitability of software development companies in all four analyzed models, and some independent variables had a negative impact. It is proposed to expand the structure of financial reporting items that characterize the intellectual capital and improve the method of recognizing costs of various types as intangibles.
Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature

Downscaling and simulating various meteorological variables at different time scales are fundamental topics for making climate change studies in a geographic region. Here, a new approach for downscaling the mean daily temperature was implemented using a vine copula‐based approach and considering the best CanESM2 predictors. The accuracy of the copula‐based approach was compared with genetic programming (GP), optimized support vector regression (OSVR), support vector machine (SVM), adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) models at Birjand synoptic station in Iran. In the proposed approach, after examining the different vine copulas, the D‐vine copula was selected as the best copula according to the evaluation statistics and tree sequences. According to the root‐mean‐square error (RMSE) and Nash–Sutcliff efficiency (NSE), the accuracy of the ANN model in downscaling the mean daily temperature data was not acceptable and the other considered models were slightly overestimated. The results indicated that the copula‐based approach outperformed the other models in downscaling the mean daily temperature with NSE = 0.61. However, given the 99% confidence interval of the simulations, a slightly overestimation at temperatures above 20°C was observed for the copula‐based approach, which has better performance than the other considered models. The copula‐based approach was able to reduce RMSE by about 82, 20, 24, 47 and 34% compared to ANN, OSVR, GP, SVM and ANFIS models, respectively. The results also showed that the performance of the support vector regression model optimized by the ant colony algorithm is also acceptable and is in the second rank after the copula‐based approach. The accuracy of the copula‐based approach was also confirmed according to Taylor diagram and violin plot. The proposed approach has a higher accuracy than data‐driven models due to use of the conditional density of vine copulas, and the joint distribution of the mean daily temperature and selected predictors.
Investigating the role of enterprise social networks in facilitating organisational change in GCC countries

The importance of enhancing internal communication and its content during organisational change appears to be neglected by many organisations. Although change management literature agrees generally about the role that communication plays in facilitating change, there is still a dearth of empirical studies that focus on improving internal communication during organisational change phases. The evolution of communication technology has provided some new tools that can enhance internal communication within an organisation. This study explores the role of using of one of these new communication technologies in communicating organisational change through the development of a novel conceptual model. The developed model covers the communication needs in each phase of a planned change, and combines the benefits of communicating organisational change with the benefits of using Enterprise Social Networks (ESN), as found in the literature. The aim is to investigate empirically how ESN as a new internal communication technology can be employed to communicate organisational change effectively in order to facilitate that change. To do so, the researcher in this study has applied a qualitative approach through a case study strategy in order to validate the conceptual model being proposed. The researcher conducted 32 interviews and analysed all of them qualitatively using Nvivo software. The findings of the conducted study revealed that using ESN had many positive impacts on employees, such as increasing their awareness, engagement and participation, which helped to facilitate the overall change projects. Moreover, the study proved the suitability of the validated novel model to contribute in facilitating organisational change through ESN, which can guide leaders, managers, change agents and academics on how ESN can be used to communicate planned change effectively in order to facilitate it.
The detection of distributional discrepancy for language GANs

ABSTRACT A pre-trained neural language model (LM) is usually used to generate texts. Due to exposure bias, the generated text is not as good as real text. Many researchers claimed they employed the Generative Adversarial Nets (GAN) to alleviate this issue by feeding reward signals from a discriminator to update the LM (generator). However, some researchers argued that GAN did not work by evaluating the generated texts with a quality-diversity metric such as Bleu versus self-Bleu, and language model score versus reverse language model score. Unfortunately, these two-dimension metrics are not reliable. Furthermore, the existing methods only assessed the final generated texts, thus neglecting the dynamic evaluating the adversarial learning process. Different from the above-mentioned methods, we adopted the most recent metric functions, which measure the distributional discrepancy between real and generated text. Besides that, we design a comprehensive experiment to investigate the performance during the learning process. First, we evaluate a language model with two functions and identify a large discrepancy. Then, several methods with the detected discrepancy signal to improve the generator were tried. Experimenting with two language GANs on two benchmark datasets, we found that the distributional discrepancy increases with more adversarial learning rounds. Our research provides convicted evidence that the language GANs fail.
EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation

Imbalanced data cause deep neural networks to output biased results, and it becomes more serious when facing extremely imbalanced data regarding the outliers with tiny size (the ratio of the outlier size to the image size is around 0.05%). Many data argumentation models are proposed to supplement imbalanced data to alleviate biased results. However, the existing augmentation models cannot synthesize tiny outliers, which make the generated data unavailable. In this article, we propose a new augmentation model named extremely imbalanced data augmentation generative adversarial nets (EID-GANs) to address the extremely imbalanced data augmentation problem. First, we design a new penalty function by subtracting the outliers from the cropped region of generated instance to guide the generator to learn the features of outliers. After this, we combine the output value of the penalty function with the generator loss to jointly update the generator’s parameters with backpropagation. Second, we propose a new evaluation approach that adopts two outlier detectors with k-fold cross-validation to assess the availability of generated instances. We conduct extensive experiments to demonstrate the significant performance improvement of EID-GAN on two extremely imbalanced datasets, which are the industrial Piston and the Fabric datasets, and one general imbalanced dataset, i.e., the public DAGM dataset. The experimental results show that our EID-GAN outperforms the state-of-the-art (SOTA) augmentation models on different imbalanced datasets.
Rotational Core Losses in Hydro Generators

Accurate estimation of core losses in hydro generators is invaluable as it has implications of monetary value to both machine designers and utilities. For machine designers to meet the maximum acceptable total loss design targets, accurate loss estimation techniques are important to avoid the penalty of the extra losses generated. On the other hand, increase in the demand for electricity requires utilities to increase the output of their hydro generators as an alternative to building new stations, which is a more cost efficient option. Therefore, accurate prediction of core losses and their distribution, as part of the uprate studies, is key for any proposed uprate to determine the machine’s hot spots and core capability. This will permit the increase in machine rating without compromising its life time. This study is towards an accurate quantification of core losses and their distribution in hydro generators stators for uprate studies. The emphasis is on understanding the distribution of rotational flux in the generator stator core, rotational core loss measurements in lamination steel, and core loss estimation including both the rotational and non-sinusoidal flux density components. This entails 2D electromagnetic finite element modelling, validation and simulation of hydro generators, which are subsequently used for the analysis. The distribution of rotational flux was found to be dependent on the stator dimensions and material BH curve operating point. Moreover, the associated rotational core losses resulted in a non-uniform distribution of losses in the stator, with higher localized losses at the back of the tooth. This new distribution of core losses is important in the thermal modelling to determine the distribution of hotspots in the machine. In this work, the numerical analysis provides an understanding of what is happening in the machine beyond the current measurement capability in the real machine. The experimental measurements performed also support the numerical results in the loss estimation.