Miracle-dz/UniFAR-FTUs huggingface dataset:
Children’s Imagination of the Divine: Creativity Across Religions

ABSTRACT Earlier studies have concluded that the religious sphere is less open to creativity than are other areas of human activities. Also, it has been suggested that artistic freedom and creative expression are unwelcome in the domain of religious iconography. In the present study, we address this subject with regard to children’s artistic expression of the divine (n = 1703) in five different cultural and religious environments: Japanese (Buddhism and Shinto), Iranian (Islam), Russian-Buryat (Buddhism, shamanism), Russian Slavic (Christian Orthodoxy) and French-speaking Swiss (Catholic and reformed Christianity). The Consensual Assessment Technique was used to access the creativity. The results globally supported the previous research on the positive role that domain knowledge plays in creativity. It is likely that the exposure to knowledge in the religious domain through education would not negatively impact on children’s creativity in the domain of pictorial representations of the divine, at least in secularized societies. Results also revealed that a child’s creative expression could be a function of his or her age. Obviously, with age and knowledge of the religious domain, older children would go more often beyond the simplistic or conventional representations of the divine. As for gender, no effect was found in four out of five samples.
Spatio-temporal patterns of an invasive species Mimosa bimucronata (DC.) Kuntze under different climate scenarios in China

Invasive alien plants pose a serious threat to native ecosystems and their biodiversity. To control their spread and damage, it is critical to clarify the potential distribution patterns of the invaders at the regional scale and their response to climate change. Mimosa bimucronata (DC.) Kuntze, a shrub extremely tolerant of stressful habitat conditions, is used as firewood and hedgerow in the tropics. Since its introduction to China in the 1950s, it has established a sustainable population and become an aggressive invasive species. Due to serious ecological threats in South China, it has been listed as a Key Management Invasive Alien Species. To understand its invasibility in China, two species distribution models (SDMs), BIOCLIM and DOMAIN, were employed to evaluate the main environmental factors regulating its geographical distribution under the current climate and a future climate change scenario with double CO2 concentration. We found an extensive spread of current suitability habitats in South China, concentrated mainly in the tropical-subtropical provinces of Guangdong, Guangxi, Hainan, and Fujian. The complex interplay of local bioclimatic factors shaped the species distribution. Under the current climate, the predicted range was somewhat larger than the current one. Under the climate change scenario, suitable habitats for Mimosa bimucronata would remain similar to the current one. However, its invasiveness in the current biogeographical range would be more serious. The suitability habitats would expand and shift to the Southeastern coastal and Northern parts of South China under future climate change. We suggest adopting early monitoring and eradication measures in potential invasion areas and practical measures to contain its spread. The results could provide a conceptual basis to formulate prevention and control strategies to improve future containment of its invasion.
Active Clustering Ensemble With Self-Paced Learning.

A clustering ensemble provides an elegant framework to learn a consensus result from multiple prespecified clustering partitions. Though conventional clustering ensemble methods achieve promising performance in various applications, we observe that they may usually be misled by some unreliable instances due to the absence of labels. To tackle this issue, we propose a novel active clustering ensemble method, which selects the uncertain or unreliable data for querying the annotations in the process of the ensemble. To fulfill this idea, we seamlessly integrate the active clustering ensemble method into a self-paced learning framework, leading to a novel self-paced active clustering ensemble (SPACE) method. The proposed SPACE can jointly select unreliable data to label via automatically evaluating their difficulty and applying easy data to ensemble the clusterings. In this way, these two tasks can be boosted by each other, with the aim to achieve better clustering performance. The experimental results on benchmark datasets demonstrate the significant effectiveness of our method. The codes of this article are released in http://Doctor-Nobody.github. io/codes/space.zip.
A randomized controlled trial of varenicline and brief behavioral counseling delivered by lay counselors for adolescent vaping cessation: Study protocol

Background Approximately one-fifth of high-school seniors and college students currently vape nicotine. Adolescents express a desire to quit vaping, and case reports have shown promise for e-cigarette tapering with dual behavioral and pharmacologic therapies. However, there are no published clinical trials to date that test these intervention approaches for adolescent nicotine vaping cessation. In this three-arm randomized, placebo-controlled, parallel-group study, we aim to assess the efficacy of varenicline in combination with brief behavioral counseling and texting support on vaping cessation in adolescents dependent on vaped nicotine. Methods The study will enroll 300 individuals between the ages of 16–25 with daily or near-daily nicotine vaping who reside in the Greater Boston area. Participants will be randomly assigned in a 1:1:1 ratio in blocks of six to one of the three arms: (1) a 12-week course of varenicline titrated to 1 mg bid, brief behavioral counseling delivered by a lay counselor, and an introduction to This is Quitting (TIQ) texting support created by the Truth Initiative; (2) a 12-week course of placebo, brief behavioral counseling, and TIQ; and (3) 12 weeks of enhanced usual care, consisting of advice to quit and an introduction to TIQ. The primary outcome will be biochemically verified continuous vaping abstinence at the end of the treatment (week 12). Secondary outcomes include continuous abstinence at follow-up (week 24), 7-day point prevalence abstinence at weeks 12 and 24, safety and tolerability of varenicline in an adolescent vaping population, as well as change in mood and nicotine withdrawal symptoms across the intervention period. Exploratory outcomes include change in comorbid substance use behaviors and nicotine dependence. Analysis will be intent-to-treat, with multiple imputation sensitivity analyses for participants with missing or incomplete outcome data. Discussion This is the first study to evaluate varenicline in combination with a novel, brief, lay counselor delivered vaping cessation program for adolescents who vape nicotine. Results will inform clinicians on the effectiveness and acceptability of this promising, but not yet tested intervention. Clinical trial registration: ClinicalTrials.gov, identifier NCT05367492.
Effectiveness of Clozapine on Employment Outcomes in Treatment-Resistant Schizophrenia: A Retrospective Bidirectional Mirror-Image Study

Purpose Clozapine is more effective than other antipsychotics and is the only antipsychotic approved for treatment-resistant schizophrenia. The objective of this study is to reveal the effect of clozapine on employment using a bidirectional mirror-image model. Patients and Methods This design was a retrospective observational study that investigated the employment status of patients with treatment-resistant schizophrenia based on medical records. The bidirectional mirror-image model consisted of 1) switching from other antipsychotics to clozapine and 2) switching from clozapine to other antipsychotics. The observation period was 1 year for each pre- and post-clozapine initiation and discontinuation. Results We included 36 patients in the bidirectional mirror-image model. The regular employment plus employment support rate was significantly higher in the clozapine phase than in the other antipsychotic phase in the bidirectional mirror-image model (30.6% vs 11.1%, P = 0.039). The days of regular employment plus employment support were also significantly longer in the clozapine phase (61.3 ± 106.2 vs 24.7 ± 82.7 days, P = 0.032). As per the unidirectional mirror-image model, switching to clozapine resulted in significantly higher regular employment plus employment support rates in the clozapine phase than those in the other antipsychotic phase (33.3% vs 10.0%, P = 0.039). Switching from clozapine to other antipsychotics did not exhibit significant differences in any outcomes. Conclusion The results suggest that clozapine is superior to other antipsychotics with respect to achieving employment in patients with treatment-resistant schizophrenia. However, biases specific to the mirror-image model need to be considered.
COMPARISON OF DIFFERENT SUBGRID TURBULENCE MODELS AND BOUNDARY CONDITIONS FOR LARGE-EDDY-SIMULATIONS OF ROOM AIR FLOWS.

The calculation results applying two equation turbulence models for the time average Navier-Stokes equations suffer sometimes from large difference to measurements of complex room air flows. Using less modelling assumptions the Large-Eddy-Simulation (LES) becomes practical to calculate room air flows because of more powerful CFD codes and computers. This paper focus on different LES-Models and its boundary conditions.
Electromagnetic Source Imaging With a Combination of Sparse Bayesian Learning and Deep Neural Network

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies.
Some Optimal and Sequential Experimental Designs with Potential Applications to Nanostructure Synthesis and Beyond

Design of Experiments (DOE) is an important topic in statistics. Efficient experimentation can help an investigator to extract maximum information from a dataset. In recent times, DOE has found new and challenging applications in science, engineering and technology. In this thesis, two different experimental design problems, motivated by the need for modeling the growth of nanowires are studied. In the first problem, we consider issues of determining an optimal experimental design for estimation of parameters of a complex curve characterizing nanowire growth that is partially exponential and partially linear. A locally D-optimal design for the non-linear change-point growth model is obtained by using a geometric approach. Further, a Bayesian sequential algorithm is proposed for obtaining the D-optimal design. The advantages of the proposed algorithm over traditional approaches adopted in recent nano-experiments are demonstrated using Monte-Carlo simulations. The second problem deals with generating space-filling design in feasible regions of complex response surfaces with unknown constraints. Two different types of sequential design strategies are proposed with the objective of generating a sequence of design points that will quickly carve out the (unknown) infeasible regions and generate more and more points in the (unknown) feasible region. The generated design is
Performance of Counterflow Microchannel Heat Exchangers Subjected to External Heat Transfer

This article analyzes the effect of external heat transfer on the thermal performance of counterflow microchannel heat exchangers. Equations for predicting the axial temperature and the effectiveness of both fluids as well as the heat transferred between the fluids, while operating under external heating or cooling conditions, are provided in this article. External heating may decrease and increase the effectiveness of the hot and cold fluids, respectively. External cooling may improve and degrade the effectiveness of the hot and cold fluids, respectively. For unbalanced flows, the thermal performance of the microchannel heat exchanger subjected to external heat transfer depends on the fluid with the lowest heat capacity. At a particular number of transfer units (NTU), the effectiveness of both the fluids increased with decrease in heat capacity ratio when the hot fluid had the lowest heat capacity. When the cold fluid had the lowest heat capacity, the effectiveness of both fluids increased with decrease in heat capacity ratio at low values of NTU but at high values of NTU the effectiveness increased with increase in heat capacity ratio. A term called the “performance factor” has been introduced in this article to assess the relative change in effectiveness due to external heat transfer.
How Lending Experience and Borrower Credit Influence Rational Herding Behavior in Peer-to-Peer Microloan Platform Markets

ABSTRACT This paper analyzes the herding behavior that characterizes lenders’ lending decisions on a microloan platform and explains how rational herding behavior can resolve the information-asymmetry problem, which is a well-known reason for the failure of online microloan platforms. Using a set of panel data on individual lending decisions acquired from Paipaidai.com (PPDai), an online microloan platform, we examine the influence of the lending decisions of prominent, experienced lenders on novice lenders to identify rational herding behavior. Our empirical analysis demonstrates that rational herding behavior can in fact efficiently reduce lender loss from borrower defaults caused by limited information. Although it is typically assumed that herding behavior is irrational, we find that it can be rational in this context and can thus shed light on why PPDai has succeeded while most other microloan platforms have failed. Accordingly, we make three key contributions: 1) we use heterogeneous herding effects to empirically determine whether lenders’ herding behavior on PPDai is rational based on observational learning; 2) we investigate the moderating effect of borrower credit and novice-lender experience on herding, and we leverage this heterogeneity in lender experience to better explain loan results; and 3) because PPDai publicly provides potential lenders with a transparent credit score—in contrast to platforms like Prosper.com, which leverage hidden proprietary credit information from Experian—we further analyze the credit composition of prominent lenders to better understand the crucial determinants of rational herding. In fact, our follow-up survival simulations indicate that without rational herding, the total number of successful PPDai loans would have decreased by around 46 percent during the study period—a finding that further underlines the crucial influence of rational herding and the unique contextual factors of PPDai that have fostered it.
Towards emotional intelligence and quality of work life: Improving the role of work attitude

Abstract This study aims to examine whether the main job attitudes (job satisfaction, job involvement, employee engagement, organizational commitment, and perceived organizational support) mediate the relationship between emotional intelligence and Quality of Work Life (QWL) or not. This study employed a survey design involving employees from various industries at various levels around Indonesia. We distributed 400 questionnaires to the respondents, and the response rate was 81%. Path analysis using SmartPLS 3.0 was used to check the validity and reliability of the questionnaires. Findings. Findings confirmed that employee satisfaction, employee involvement, and perceived organizational support mediated emotional intelligence and quality of work-life. However, employee engagement and organizational commitment did not mediate the relationship between emotional intelligence and the quality of work-life. Limitations. Our study covered a relatively small scope; thus, it is suggested that further studies involve more respondents and regions. Uniqueness. This research explained the mediation effect of major job attitudes based on the Robbins Model, and this was the first time the model was tested in Indonesia.
Spectrophotometry of the white dwarfs

Spectrophotometrie multicouleur de 319 naines blanches et magnitudes mesurees a six longueurs d'onde. Les resultats permettent d'etudier des particularites de distributions d'energie revelees dans les diagrammes couleur-couleur. Table des resultats, incluant les luminosites
Temporal Changes in SARS-CoV-2 Infection Pattern in Patients Admitted with Hematological Diseases—A Single Center Experience from North India

Previous studies have shown the vulnerability of hematological patients with the Coronavirus disease of 2019 (COVID-19). We aimed to compare the outcomes and risk factors for poor survival in patients with hematological conditions hospitalized with COVID-19 infection. Single centre, retrospective, cohort study included all patients with a hematological condition admitted to Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India between 1 April 2020 and 31 May 2021. Of a total of 154 patients, 81 were in the pre-delta group and 73 were in the delta group out of which 21 (25.97%) in the pre-delta group and 24 (33.88%) patients in the delta group died. Haematological characteristics—age > 60 years, progressive hematological cancer, more than two lines of anti-cancer therapy, and active chemo-immunotherapy or targeted therapy were associated with higher mortality in the delta group. COVID-19 characteristics associated with higher mortality during the delta wave were severity of COVID infection, higher oxygen requirements, and COVID plasma therapy There were no deaths in individuals (n = 15) within the delta group who received COVID-19 vaccination. This study adds to the evidence that patients with hematological diseases are a particularly vulnerable group and the delta variant of the virus is associated with higher mortality. We could identify patient characteristics and features related to COVID-19 infection and underlying hematological conditions that were associated with poor outcomes in the delta sub-group. Vaccination was found to be an effective strategy for overcoming mortality and morbidity in these patients.
Improving market value of textile and garment company through company growth, company financial performance, and macroeconomics

AbstractThere are so many studies on the company's market value. However, most studies do not verify the macroeconomic effect on certain industries. Research on macroeconomic as moderators in increasing the company's market value is still limited also. Therefore, macroeconomics (interest rates) presented in this study as a moderator to examine the effect of company growth (increased sales) and company financial performance (ROA) to the company’s market value (PBV). It expected to provide additional information to individuals, lecturers, and business actors on how to increase company market value and what factors must be considered to increase it. The population is all go-public companies, and the samples are manufacturing companies-textile and garment sub-sectors during 2015-2020. Multiple linear regression and SPSS are used as the data analysis technique. The results imply that the increase in sales has no influence on PBV. Even though interest rates have been presented, it still can’t strengthen the effect of increased sales on PBV. ROA influences PBV. However, after presenting interest rates, it turns out that interest rates weaken the influence of the ROA level on PBV. This result shows that macroeconomics can moderate the connection of company financial performance to the company’s market value.AbstrakTerdapat banyak penelitian terdahulu tentang nilai pasar perusahaan. Namun, sebagian besar penelitian tidak melihat pengaruh variabel makroekonomi pada industri tertentu. Penelitian tentang faktor makroekonomi sebagai moderator dalam meningkatkan nilai pasar perusahaan juga belum banyak. Oleh karena itu, penelitian ini menghadirkan makroekonomi yang diproxy oleh suku bunga sebagai moderator untuk menguji pengaruh pertumbuhan perusahaan yang diproxy oleh peningkatan penjualan dan kinerja keuangan perusahaan yang diproxy oleh ROA terhadap nilai pasar perusahaan yang diproxy oleh PBV. Sehingga diharapkan dapat memberikan tambahan informasi kepada individu, dosen, maupun pelaku usaha tentang bagaimana meningkatkan company market value serta faktor apa saja yang harus diperhatikan untuk meningkatkannya. Populasi penelitian yaitu semua perusahaan yang sudah go public. Sedangkan samplenya adalah perusahan manufaktur, subsektor textile dan garment tahun 2015-2020. Penelitian ini menggunakan analisis regresi linear berganda dengan SPSS sebagai teknik analisis data. Menurut hasil uji, peningkatan penjualan tidak berpengaruh terhadap PBV. Meskipun telah dihadirkan suku bunga, ternyata suku bunga tetap tidak mampu memperkuat pengaruh peningkatan penjualan terhadap PBV. Selain itu, dapat diketahui pula bahwa ROA berpengaruh terhadap PBV. Setelah dihadirkan suku bunga, ternyata suku bunga memperlemah pengaruh tingkat ROA terhadap PBV. Hal ini menunjukkan bahwa makroekonomi mampu menjadi moderator bagi kinerja keuangan perusahaan terhadap nilai pasar perusahaan.