Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Antecedents of Organizational Citizenship Behavior and Its Consequence on Employee Performance

: The purposes of this study are to measure and analyze the direct influences of commitment, competency, and work culture on organizational citizenship behaviour (OCB) and employee performance. It also attempts to measure and analyze the indirect effects of commitment, competency, and work culture on employee performance through OCB. This study was conducted at the Financial Management Agency, Aceh Province, Indonesia. Of 231 employees at the agency, 192 of them were selected as the sample of the study using a purposive sampling technique. Based on the Structural Equation Modeling (SEM) approach, the study documented that organizational commitment, employee competence, and work culture have a positive and significant effect on employees’ OCB. Organizational commitment, competence, and work culture are also found to have a positive and significant effect on employee performance. Finally, the study found an indirect influence of organizational commitment, competence, and work culture on the performance of the employees through OCB. These findings suggested that to promote the employees’ performance the focus should be given on improving OCB based on improving employees’ commitment, competency, and the organizational working culture.
Lagrangian Modeling of Electrical Properties of the Growing Convective Boundary Layer

Using a Lagrangian stochastic approach to the growing convective boundary layer (CBL), we have calculated the turbulent transport and drift in atmospheric electric field of such natural electrically charged substances as air ions and aerosols. The proposed numerical model is based on balance equations for the light air ions, single-charged and neutral aerosol particles which are size distributed. We have obtained the time evolution of high-altitude profiles of electric space charge, electric conductivity, vertical electric field intensity, and total vertical electric current density. We have ascertained that the spectral distribution of calculated variances of space charge density fluctuations in frequency ranges f = 0.001- 0.5 Hz is satisfied to a power dependence S ~ f -α where 0.7 < α < 2.
Bulletin of Electrical Engineering and Informatics

Received Nov 30, 2022 Revised Dec 22, 2022 Accepted May 2, 2023 Speech quality significantly affects the performance of speech dependent systems. Noise in the background lowers the clarity and intelligibility of speech. The augmentation of speech can increase its quality. We propose a single-channel speech improvement framework that combines particle swarm optimization (PSO), gravitational search algorithm (GSA), and harmonic regeneration noise reduction (HRNR) to minimize speech signal noise and increase speech intelligibility. The proposed hybrid algorithm optimizes the amount of overlap between the noisy speech frames. This helps in reducing the overlapped noise. Then HRNR algorithm is applied to retain the speech harmonics. The algorithm gives improvement in the speech intelligibility for babble, car and exhibition noise. The segmental signal to noise ratio (SNR) is also improved for these noise types. There is improvement in speech intelligibility with minimal speech distortion.
Dollar Asset Holding

We analyze a large number of industryand company-level filings of global institutional investors to provide the first comprehensive estimates of foreign investors’ U.S. dollar (USD) security holdings and currency hedging practices. We find that foreign investors increased their holdings of USD securities by six fold over the past two decade, and showed preference for debt over equities. After the financial crisis of 2007-09, foreign investors maintained persistent and high hedge ratios for their USD holdings despite significant and fluctuating deviations from covered-interest rate parity. We estimate that USD hedging volume by insurance, pensions and mutual funds amounts to $2 trillion per annum. We benchmark foreign investors’ USD holdings and hedging patterns using a mean-variance framework, and discuss important empirical drivers of USD allocation and hedging, including the role of the yield curve slope, spot exchange rate, and CIP deviations. JEL Classifications : F21, F31, G11, G15, G22, G23
Gender quotas and strategic voters: Experimental evidence from Chile’s constitutional convention∗

Citizens’ perceptions of certain groups influence voting behavior and policy choices worldwide, frequently reducing the representation of historically disadvantaged people. Can laws limiting the group identity of elected officials undo these effects? And if so, how does this impact legislator competence and how legislatures represent policy preferences of different groups? I examine these issues in the context of Chile’s constitutional conventional election, which was the first election to mandate gender parity in both candidate lists and elected officials without limiting voter choice. I induce experimental variation in voter awareness of these mandates by randomizing an electoral booth-level voter information campaign. In treated booths, voters were informed that gender parity for elected officials would be enforced. I have three experimental results. First, treatment increased women’s average vote share by 1%. Second, voters in treated booths voted for the gender they believed would be electorally favored by the mandate. This effect varied by electoral coalitions and was concentrated among front-runners. I show that the effects are large enough to influence outcomes in close elections. Third, treatment reduced votes for less competent men as measured by test scores. Finally, data on individual bills indicate that elected female legislators voted more liberally on social issues such as abortion and domestic violence. In contrast, there are no gender-based voting differences for economic and administrative bills. Overall, these findings support the use of electoral mandates as a coordinating device that, when well-designed, can increase the average legislator’s competence and the extent to which policy-making processes reflect voter preferences. ∗Email: antonia.paredeshaz@yale.edu. I am extremely grateful to my advisors Rohini Pande, Gerard Padró i Miquel, and Ebonya Washington for their guidance and encouragement. Christopher Neilson and Nicholas Ryan provided valuable feedback that improved many parts of the paper. I also thank Josefa Aguirre, Felipe Ajenjo, Ceren Baysan, Lauren Falcao Bergquist, Lucas Finamor, Alvaro Garćıa, Winston Hovekamp, Dominic Leggett, Kaivan Munshi, Yusuf Neggers, Ricardo Paredes, Natalia Rigol, Cony Stipicic, as well as many seminar participants at Pontificia Universidad Católica de Chile and Yale. Finally, I acknowledge generous financial support from the Yale Economic Growth Center and the Cowles Foundation for Research in Economics that made this project possible. This project is registered in AEA RCT Registry at AEARCTR-0007657 and was approved by IRBs at Yale University and Pontificia Universidad Católica de Chile.
Cu-doped zeolitic imidazolate framework catalysed highly selective conversion of alkynes to β -keto and vinyl sulfones using sodium sulﬁnates

Cu 2 + -doped zeolitic imidazolate framework-8 (ZIF-8)-catalyzed one-pot procedure to synthesize β -keto and vinyl sulfones by the direct oxysulfonylation and hydrosulfonylation of alkynes via radical reaction under mild conditions has been described. The advantages of this protocol included broad substrate scope and excellent β -keto and E-stereoselectivity. The Cu/ZIF-8 catalyst not only exhibited excellent performance but also had a great stability in the reaction, successfully allowing its reuse up to ﬁve cycles. This efﬁcient Cu/ZIF-8 heterogeneous catalyst is explored for the ﬁrst time to generate β -keto and vinyl sulfones.
PRESENCE OF WOMEN IN LOCAL GOVERNMENT IN SPAIN. HISTORICAL AND TERRITORIAL PERSPECTIVES (1979- 2019)

The aim of this study is, firstly, to assess women’s pres-ence in local government in Spain from 1979 to 2019, a period which coincides with forty years of democracy and has witnessed more than ten elections, and second-ly, to determine whether the gender gap has narrowed. In particular, we wanted to identify any possible changes produced as a result of the entry into force of Organ-ic Law 3/2007 on the Effective Equality of Women and Men. Our results show that women’s presence in town councils has increased, but that there are marked dif-ferences in internal trends. Besides the historical view-point, this research also includes a territorial perspec-tive. Thus, in order to explain the territorial distribution of the results, Moran’s Index of Spatial Autocorrelation was used, analysing data on the presence of female mayors and councillors in more than 8,100 town councils. The variables considered were: territorial proximity, measured using the univariate Moran’s I; degree of rurality, defined according to municipality size; and the presence of women for the first time. For the latter two variables, we conducted a bivariate analysis (bivariate Moran’s I), correlating them with the presence of female mayors and the percentage of female councillors as dependent variables. We found few territorial patterns; instead, our results indicate a high degree of randomness, which could be attributed to the diverse nature of municipali-ties in the country. KeywoRds Elections; Female leadership; Gender differences; Municipal politics; Parity; Spain; Territorial differences. RIS [online] 2023, 81 (2), e228. REVISTA INTERNACIONAL DE SOCIOLOGÍA. ISSN-L: 0034-9712 https://doi.org/10.3989/ris.2023. 81.2. 21-01320 2 . CÁNDIDA GAGO-GARCÍA Y JUANA Ma RODRÍGUEZ MOYA
Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course

Prediction of student performance in Introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Ex-plainable Machine Learning models can effectively help students and instructors gain insights into students’ different programming behaviors and problem-solving strategies that can lead to good or poor performance. This study develops an explainable model that predicts students’ performance based on programming assignment submission information. We extract different data-driven features from students’ programming submissions and employ a stacked ensemble model to predict students’ final exam grades. We use SHAP, a game-theory-based framework, to explain the model’s predictions to help the stakeholders understand the impact of different programming behaviors on students’ success. Moreover, we analyze the impact of important features and utilize a combination of descriptive statistics and mixture models to identify different profiles of students based on their problem-solving patterns to bolster explainability. The experimental results suggest that our model significantly outperforms other Machine Learning models, including KNN, SVM, XGBoost, Bagging, Boosting, and Linear regression. Our explainable and transparent model can help explain students’ common problem-solving patterns in relationship with their level of expertise resulting in effective intervention and adaptive support to students.
The Impact of a High-Fidelity Wave-to-Wire Model in Control Parameter Optimisation and Power Production Assessment

Accurate control parameter optimisation and power production assessment is essential to evaluate the performance of a wave energy converter (WEC). However, commonly used numerical models excessively simplify the power take-off (PTO) system of the WEC, which may strongly affect power production predictions. Therefore, the present paper compares a commonly used WEC model that includes nonlinear viscous losses and an ideal PTO system model, referred to as NLideal, with a high-fidelity wave-to-wire model (HFW2W) model. Results show the incapacity of the commonly used NLideal model to accurately optimise control parameters, particularly using reactive control. Likewise, the annual mean power production (AMPP) predicted using the NLideal model is significantly overestimated, with differences of up to 160% with respect to the more realistic HFW2W model. More dramatically, the use of control parameters optimised with the NLideal model in the HFW2W model results in negative AMPP, meaning that the WEC consumes more energy than it produces.
ENHANCING PROVIDER COMPETENCIES THROUGH THE LOW-DOSE, HIGH-FREQUENCY TRAINING APPROACH

A well-trained and skilled health workforce is key to enhancing provider readiness to deliver quality health care, especially in lowand middle-income countries (LMICs) where health outcomes are not progressing fast enough to meet optimal health targets like the Sustainable Development Goals (Lassi et al. 2016). LMICs recognize the need for capacity development of health workers and often implement refresher trainings aimed at boosting their skills, knowledge, and competencies (Murphy et al. 2014). Such trainings are in most instances carried out off-site, often requiring staff to be away from their duty stations for prolonged durations, depriving their already human resource constrained health facilities of essential care providers (BankeThomas et al. 2017). Training content is often delivered in a didactic manner focusing more on lectures and take-home reading material, with few practical real-life simulation sessions or work with clients, minimizing knowledge retention (Ugwa et al. 2018). A promising and increasingly used alternative approach to health workforce training is low-dose, high-frequency (LDHF), developed by Jhpiego. LDHF has shown much better longterm competency and skill retention compared to classical training approaches (Evans et al. 2018). The rationale for the LDHF approach is based on an extensive review of health worker trainings conducted in LMICs that revealed the majority of the trainings did not improve the quality of health care delivery among the trained health providers (Jhpiego 2013). LDHF has had promising results in different settings, improving provider performance in countries such as Ghana, Uganda, Tanzania, and Ethiopia (Gomez et al. 2018; Willcox et al. 2019; Krishnan et al. 2021; Tadesse et al. 2021).
FTIR study of silicon carbide amorphization by heavy ion irradiations

We have measured at room temperature (RT) the Fourier-transform infra-red (FTIR) absorption spectra of ion-irradiated thin epitaxial films of cubic silicon carbide (3C–SiC) with 1.1 µm thickness on a 500 µm thick (1 0 0) silicon wafer substrate. Irradiations were carried out at RT with 2.3 MeV 28Si+ ions and 3.0 MeV 84Kr+ ions for various fluences in order to induce amorphization of the SiC film. Ion projected ranges were adjusted to be slightly larger than the film thickness so that the whole SiC layers were homogeneously damaged. FTIR spectra of virgin and irradiated samples were recorded for various incidence angles from normal incidence to Brewster’s angle. We show that the amorphization process in ion-irradiated 3C–SiC films can be monitored non-destructively by FTIR absorption spectroscopy without any major interference of the substrate. The compared evolutions of TO and LO peaks upon ion irradiation yield valuable information on the damage process. Complementary test experiments were also performed on virgin silicon nitride (Si3N4) self-standing films for similar conditions. Asymmetrical shapes were found for TO peaks of SiC, whereas Gaussian profiles are found for LO peaks. Skewed Gaussian profiles, with a standard deviation depending on wave number, were used to fit asymmetrical peaks for both materials. A new methodology for following the amorphization process is proposed on the basis of the evolution of fitted IR absorption peak parameters with ion fluence. Results are discussed with respect to Rutherford backscattering spectrometry channeling and Raman spectroscopy analysis.
AdaSearch: Many-to-One Unified Neural Architecture Search via A Smooth Curriculum

The cost of Neural Architecture Search (NAS) has been largely reduced, thanks to one-shot SuperNet methods that exploit the weight-sharing strategy as the proxy. However, challenges remain especially when we target for simultaneously deriving many high-performance, yet diverse models that can meet various resource constraints, by training just one SuperNet. If we treat SuperNet as a large ensemble of all those candidate neural networks with highly varied complexity levels , a fundamental question then arises: how might we train those neural networks all (close) to their optimal performance, with their weights entangled due to sharing, under one unified optimization procedure? To tackle this question, we propose AdaSearch , whose idea is inspired from curriculum learning . Towards our end goal of training all neural networks well, we start by focusing our SuperNet training on a “simple” subset of them, i.e., training a shallow SuperNet that consists of only low-complexity neural networks. We then iteratively grow and train the SuperNet, so that higher-complexity neural networks are gradually included and taken care of. For smoothly transiting the SuperNet curriculum, we also develop a key enabling technique called SuperNet2SuperNet , that is, using distillation to initialize the deeper SuperNet by inheriting knowledge from the shallower one each time. AdaSearch demonstrates state-of-the-art accuracy-efficiency trade-offs on ImageNet, while significantly trimming down search GPU hours and CO 2 emission by reducing N search times to 1 procedure. All our codes and pre-trained models will be publicly released upon paper acceptance.
Ovarian cancer accounts for the seventh-leading cause of female cancer mortality and the most lethal gynecological

Background: Whether or not patients with stage I epithelial ovarian cancer (EOC) benefit from postoperative chemotherapy in the Asian population remains unclear. This retrospective cohort study was aimed at investigating the use of adjuvant chemotherapy in clinical practice to treat patients with early-stage EOC considering clinical factors. Materials and Methods: A total of 414 patients with the International Federation of Gynecology and Obstetrics (FIGO) stage IA–IC and grade 1 EOC were enrolled from the Taiwan Cancer Registry. We used multivariable Cox proportional-hazards models to control for clinical factors. The overall survival (OS) and disease-free survival (DFS) were estimated with the Kaplan–Meier method. Results: DFS did not improve significantly for patients with FIGO stage IA/IB EOC receiving adjuvant chemotherapy, with a 10-year DFS rate of 98% and 88% for those with and without adjuvant chemotherapy, respectively (hazard ratio [HR] = 0.41, 95% confidence interval [CI]: 0.05–3.36). OS did not improve significantly for patients with FIGO stage IA/IB EOC with adjuvant chemotherapy (HR = 0.86, 95% CI: 0.18–4.22) or stage IC (HR = 0.50, 95% CI: 0.10–2.45). OS did not differ significantly for patients with optimal (10-year OS: 92% with chemotherapy and 86% without chemotherapy in the log-rank test, P = 0.629) or nonoptimal staging (10-year OS: 73% with chemotherapy and 90% without chemotherapy in the log-rank test, P = 0.959). Conclusion: Adjuvant chemotherapy did not improve the prognosis of patients with low to intermediate-risk EOC and optimal/nonoptimal surgery. This result should be considered in clinical practice.
Implementing BIM on conservation heritage projects: Lessons from renovation case studies

Purpose: Heritage or Historic BIM, often referred to as HBIM, is becoming an established feature in both research and practice. The advancement of data capture technologies such as laser scanning and improved photogrammetry, along with the continued power of BIM authoring tools has provided the ability to generate more accurate digital representations of heritage buildings which can then be used during renovation and refurbishment projects. Very often these representations of HBIM are developed to support the design process. What appears to be often overlooked is the issue of conservation and how this can be linked to the BIM process to support the conservation management plan for the building once it is given a new lease of life following the refurbishment process. Approach: The paper presents a review of the context of conservation and HBIM then subsequently presents two case studies of how HBIM was applied to high profile renovation and conservation projects in the UK. In presenting the case studies, a range of issues is identified which support findings from the literature noting that HBIM is predominantly a tool for the geometric modelling of historic fabric with less regard for the actual process of renovation and conservation in historic buildings. Findings: Lessons learnt from the
Some implicatures are conversational, but others are conventional: a developmental implicature study

15 INTRODUCTION 16 EXPERIMENT 1 18 METHOD 19 RESULTS 21 DISCUSSION 22 EXPERIMENT 2 23 METHOD 23 RESULTS 24 DISCUSSION 26 EXPERIMENT 3 27 METHOD 27 RESULTS 28 GENERAL DISCUSSION 29 REFERENCES 33 CHAPTER 3 ‘BUT’ HOW DO WE REASON WITH IT: AN EXPERIMENTAL INVESTIGATION OF THE IMPLICATURE STEMMING FROM ‘BUT’ 35 ABSTRACT 35 INTRODUCTION 36 EXPERIMENT 1 44 METHOD 4435 INTRODUCTION 36 EXPERIMENT 1 44 METHOD 44 RESULTS 46 DISCUSSION 47 EXPERIMENT 2 49 METHOD 49 RESULTS 50 GENERAL DISCUSSION 52 REFERENCES 55 CHAPTER 4 BUT: DO AGE AND WORKING MEMORY INFLUENCE CONVENTIONAL IMPLICATURE PROCESSING? 69 ABSTRACT 69 INTRODUCTION 70 METHOD 75 PARTICIPANTS 75 WORKING MEMORY TASK 75 IMPLICATURE TASK 76 PROCEDURE 78 RESULTS 78 GENERAL DISCUSSION 81 REFERENCES 8569 INTRODUCTION 70 METHOD 75 PARTICIPANTS 75 WORKING MEMORY TASK 75 IMPLICATURE TASK 76 PROCEDURE 78 RESULTS 78 GENERAL DISCUSSION 81 REFERENCES 85 CHAPTER 5 ‘BUT’ HOW DO CHILDREN JUDGE IT ON A SCALE? 87 ABSTRACT 87 INTRODUCTION 88 EXPERIMENT 1 93 METHOD 93 RESULTS 95 DISCUSSION 96 EXPERIMENT 2 97 METHOD 97 RESULTS 97 GENERAL DISCUSSION 98 REFERENCES 10187 INTRODUCTION 88 EXPERIMENT 1 93 METHOD 93 RESULTS 95 DISCUSSION 96 EXPERIMENT 2 97 METHOD 97 RESULTS 97 GENERAL DISCUSSION 98 REFERENCES 101 CHAPTER 6 ‘BUT’ LOAD DOESN’T MATTER: THE AUTOMATICITY OF PROCESSING CONVENTIONAL IMPLICATURES 103 ABSTRACT 103 INTRODUCTION 104 EXPERIMENT 109 METHOD 109 RESULTS 111103 INTRODUCTION 104 EXPERIMENT 109 METHOD 109 RESULTS 111 GENERAL DISCUSSION 116 REFERENCES 120 CHAPTER 7 IT’S MAYBE SOMEWHAT DIFFICULT BUT I UNDERSTAND IT! 123 ABSTRACT 123 INTRODUCTION 124 EXPERIMENT 130 METHOD 130 RESULTS AND DISCUSSION 131 GENERAL DISCUSSION 132 REFERENCES 134123 INTRODUCTION 124 EXPERIMENT 130 METHOD 130 RESULTS AND DISCUSSION 131 GENERAL DISCUSSION 132 REFERENCES 134 CHAPTER 8 SUMMARY AND FINAL DISCUSSION 137 SUMMARY OF THE EXPERIMENTAL FINDINGS 137 CHAPTER 2 137 CHAPTER 3 139 CHAPTER 4 139 CHAPTER 5 140 CHAPTER 6 140 CHAPTER 7 141 FINAL DISCUSSION 141 TASK CHARACTERISTICS 141 DEVELOPMENT 144 WORKING MEMORY 149 REFERENCES 157
Model construction and elaboration in spatial linear syllogisms

When faced with linear syllogisms, reasoners usually construct an integrated spatial representation of the premise content. As a consequence multi-model problems are mon difficult than one-model problems, because the representation is more complex in multi-model problems. In the present chapter, the question is raised whether three-model problems are indeed more difficult than two-model problems. Data show that there is a reliable difference between two-model and three-model problems. However, the difference is so small that it cannot be explained by assuming that the reasoner builds an explicit representation of all three models. Overall, the last premise, the one that discloses the multi-model nature of the problem, requires more reading and processing time than other premises, and this difference is larger in multi-model than in one-model problems. The data suggest that the representation constructed leaves the additional models implicit by entering a marker that can be easily fleshed out when needed.