Miracle-dz/UniFAR-FTUs huggingface dataset:
Attachment to God, Locus of Control, and Outcome in the Brief Alcohol Screening and Intervention for College Students

Research shows that aspects of religion and spirituality can influence drinking behavior among university students. Some believe that an internalized commitment to God, or to another spiritual force, is an important component in explaining relationships between religion, spirituality, and drinking behavior. One way of understanding and measuring this internalized commitment to God is based on attachment theory. Another way of measuring and understanding internalized beliefs about the degree to which God shapes behavior is based on the social learning theory concept called locus of control. Conceptually, low God locus of control indicates that one believes God is passive and not involved in decisions pertaining to drinking. Research shows that college or university students who have anxious God attachments are likely to experience negative consequences as a result of binge drinking. These individuals believe God is inconsistent in their lives. Likewise, low (passive) God locus of control (LOC) seems to increase drinking and negative consequences from drinking. In this study, 19 students (15 males, 4 females) attending Abilene Christian University (ACU) participating in a Brief Alcohol Screening and Intervention for College Students (BASICS) program completed the following four measures: Attachment to God Inventory (AGI), Alcohol-related God Locus of Control Scale for Adolescents (AGLOC-A), General Drinking Questionnaire, and Negative Outcomes of Drinking were assessed to determine if attachment to God and God LOC impacted their drinking. Results indicated that low (passive) God locus of control was associated with increased drinking and that anxious attachment to God was associated with negative consequences of drinking. Implications are discussed. Attachment to God, Locus of Control, and Outcomes in the Brief Alcohol Screening and Intervention for College Students A Thesis Presented To The Faculty of the Graduate School Abilene Christian University In Partial Fulfillment Of the Requirements for the Degree Master of Science
Geometric Allocation Approach in Markov Chain Monte Carlo

The Monte Carlo method was first devised by Ulam et al. [61] in 1947, and it has been enjoyed for a wide variety of applications in mathematics, physics, statistics, etc. According to the literature, the method is defined as a simulation that solves a deterministic problem by means of a stochastic way. Ulam and the coworkers used the Monte Carlo method for neutron diffusion problem in fissile material of atomic bombs and for the eigenvalue problem of the Schrödinger equation at Los Alamos during the World War II. In the both applications, the average of random sampling converges to a target integral. Let s ∈ N be a dimension and D ⊂ Rs be an integral range on the Lebesgue measure (now we assume D is compact as topological space for simplicity), x ∈ D be a continuous state variable, f : Rs → R be an integrand function, and I be a target integral defined as I = ∫
Predicting hydrogen storage in nanoporous materials using meta-learning

Discovering the optimal materials and conditions for hydrogen storage is crucial to the development of fuel cell vehicles. In this work, we present a method towards this goal by combining meta-learning and high-throughput molecular simulations. Our meta-learning model efficiently utilizes the datasets generated by high-throughput simulations and applies the physical domain knowledge of hydrogen storage. Comparing to training many small models each on a material, meta-learning showed more accurate prediction, higher data efficiency and improved generalization capability. Our meta-learning approach not only accelerates the workflow of computational materials discovery, but also introduces a practical application of meta-learning and few-shot learning in the physical sciences.
Monetizing Bowser: A Contingent Valuation of the Statistical Value of Dog Life

Abstract Households in the USA spend about $70 billion annually on pets. Dogs, the most common pet, can be found in nearly half of American households. An important shadow price in the analysis of policies affecting human mortality is the value of statistical life (VSL), which is imputed from how people make decisions involving tradeoffs between small mortality risks and other goods. The value of statistical dog life (VSDL) is also an important, but until now unavailable, shadow price for use in regulation of such goods as pet foods and environmental toxins. Additionally, an estimate of the VSDL would have uses outside the regulatory process in valuing programs involving zooeyia, in setting tort awards for wrongful dog death, and in property divisions in divorce settlements where joint custody of dogs is not feasible. In order to estimate the VSDL, we conducted a contingent valuation of a national sample of dog owners that elicited willingness-to-pay for changes in mortality risk for pet dogs. Specifically, respondents were asked about willingness-to-pay for a vaccine that would reduce the risk of canine influenza. The design included both quantity (different magnitudes of risk reduction from the offered vaccine) and quality (differences in nature of death from the influenza) treatments as scope tests. It also included treatments involving spillover effects to other dogs and a priming question about disposable income. Based on the analysis and consideration of its assumptions, we recommend $10,000 as the VSDL.
Structural elucidation of triclinic and monoclinic SFCA-III – killing two birds with one stone

Synthesis experiments in the system CaO–SiO2–Al2O3–Fe2O3–MgO resulting in the formation of SFCA-III are reported. SFCA-III is a new n = 2 member of a polysomatic series of M 14+6nO20+8n compounds based on pyroxene-spinel modules which are of relevance to iron-ore sintering. Single-crystal diffraction studies using synchrotron radiation revealed that the compound occurs in two polytypes representing maximum degree of order structures which explains the observed allotwinning of the sample.
Classifying documents through existing opinion mining methods

This thesis proposes a solution for document-level opinion mining, a method of finding overall opinion from given sources, for example, product reviews, news articles and blogs. This suggestion was done by using existing methods and an unsupervised self-organizing map for classification. The task is to create a system that can classify documents written in the English language, according to opinion categories, for example, positive, neutral and negative. Also, a design suggestion is made for how the presented solution could be implemented in Cluetail Ltd. systems, using Python programminglanguage. Thesis process started by learning about the underlying techniques (Machine learning, data mining and natural language processing). These techniques create the foundation for learning sentiment analysis. Partially supervised and unsupervised learning methods were chosen for this approach. Lexicon based positive negative term appearance features, and bigram features extracted according to part-of-speech tags and with calculated opinion orientation using an unsupervised statistical method. These features formed a feature vector for each document which describes the found overall opinion. Two review datasets with known opinion categories were used, and the capabilities were tested both with opinion polarities (positive negative) and multiple opinion categories (very positive, positive, neutral, negative, very negative). The findings from these results, issues, improvements and the ways to extend this work in other languages are discussed on the last pages of this thesis. Overall this work is the first step towards functional document-level opinion mining system, and a simple introduction to sentiment analysis meant for anyone who may have interest to learn about it. Subject headings, (keywords) Data mining, Natural language processing, Neural networks, Machine learning, Opinion mining, Python, Sentiment analysis, Self-organizing map
Adult education in Mozambique: between expectations and possibilities

Adult education has been and continues being an important area of education. In developing contexts, it is considered the way for social inclusion and poverty reduction and it is expected to provide knowledge, skills and values to youth and adults who did not get opportunity to education in their early age. Adult education also serves as a way to improve people’s competences throughout their life span. However, the concept of adult education in Mozambique has been used as literacy, referring only to the development of reading, writing and calculation skills. Using content analysis of documents, this research attempts to gain insight of adult education in the context of Mozambique. The findings reveal that adult education in the perspective of change and development as it is in Mozambique is linked to the objectives of poverty reduction and social inclusion. In this sense quality and relevance in adult education, as in other type of education, is needed to be able to develop functional knowledge and skills to youth and adults and allow them continuing learning in their life. Literacy and adult education have been considered an important field for social and economic development in the country, but contextual factors contribute to a narrow perspective of adult education, focusing mainly on literacy and numeracy as its final goal.
Automatic speech based emotion recognition using paralinguistics features

Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A similar effect is seen with male speakers: the first model yields 36%, the second 28% a verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
Impact of Sea Breeze Circulation on the Transport of Ship Emissions in Tangshan Port, China

A sea breeze is a local circulation that occurs in coastal regions from the poles to the equator. The adverse influence of ship emissions on air quality in coastal areas may be aggravated by the onshore flow of sea breeze circulation. However, our knowledge regarding the evolution of ship-emitted pollutants during a specific sea breeze episode is still limited. To address this knowledge gap, this study investigated the evolution of ship emissions during a sea breeze episode that occurred on 29 June, 2014 in Tangshan port in China by employing the WRF/Chem model. NO2, one of the primary pollutants emitted by ships, was selected as the target pollutant for investigation. The results indicate that the ground level NO2 concentration was considerably affected by sea breeze circulation. Although the onset of the sea breeze was delayed until nearly midday due to offshore synoptic winds, ship-emitted NO2 was transported to more than 100 km inland with the penetration of the sea breeze. Further investigation found that the averaged concentration of ship-contributed NO2 during the episode showed an evident downward trend as the distance from the coastline increased. Vertically, the shallow atmospheric boundary layer (ABL) on the sea limited the vertical dispersion of ship emissions, and the pollutant was transported shoreward by the sea breeze within this shallow ABL. The height of the ABLs is lowered in coastal regions due to the cooling effect of sea breezes which brings the cool marine air to the hot land surface. Ship-contributed NO2 was mostly trapped in the shallow ABL; thereby, its concentration increased.
Corporate Governance Structure, Financial Capability, and the R&D Intensity in Chinese Sports Sector: Evidence from Listed Sports Companies

Innovations are the foundation of an enterprise’s sustainable development, which is particularly important for sports firms in an evolving Chinese sport industrial environment. Analyzing publicly-listed sports firms on The New Third Board (NTB) in China, this study examined the influence of corporate financial capability and corporate governance structure on firms’ RD however, other financial capability indicators, such as leverage and cash flow, have negative relationships with R&D intensity. Limited evidence was found to support the notion that corporate governance significantly influences R&D intensity, although sports firms with good governance mechanisms are more likely to increase the positive effects of financial capabilities on R&D intensity while decreasing the negative effects. Discussions were centered on planning and executing R&D activities in sports companies.
REGIONAL CLIMATE SIMULATION OVER ASIA UNDER THE GLOBAL WARMING NESTED IN THE CCSR/NIES AGCM

We have developed a regional climate model, NIES-RAMS, and conducted present and future Asian regional climate simulations which were nested in the results of Atmospheric General Circulation Model (AGCM) experiments. The regional climate model could capture the general simulated features of the AGCM and also some regional phenomena such as orographic precipitation, which were not appeared in the outcome of the AGCM simulation, were successfully produced. The increases of annual mean surface runoff and its large fluctuations are projected in a lot of Asian regions. The seasonal mismatch between water demand and water availability, which should be considered in future water resource assessments, is concerned.
NeuroImage: Clinical Disrupted GABAergic facilitation of working memory performance in people with schizophrenia

Objectives: Gamma-Amiobutyric acid (GABA) is a primary inhibitory neurotransmitter that facilitates neural oscillations that coordinate neural activity between brain networks to facilitate cognition. The present magnetic resonance spectroscopy (MRS) study tests the hypothesis that GABAergic facilitation of working memory is disrupted in people with schizophrenia (PSZ). Methods: 51 healthy participants and 40 PSZ from the UC Davis Early Psychosis Program performed an item and temporal order working memory (WM) task and underwent resting MRS to measure GABA and glutamate concentrations in dorsolateral prefrontal (DLPFC) and anterior cingulate (ACC) regions of interest. MRS was acquired on a 3 Tesla Siemens scanner and GABA and glutamate concentrations were referenced to creatine. Percent correct on the WM task indexed performance and correlation coe ﬃ cients examined GABAergic or Glutamatergic facilitation of WM, with Fisher's Z transformation testing for group di ﬀ erences. Results: There were no group di ﬀ erences in GABA or glutamate concentrations, but WM correlations were re- versed between groups. In patients, higher DLPFC GABA was associated with worse rather than better WM performance. This pattern was not observed for glutamate or in the ACC. Although under-powered, there was no indication of medication e ﬀ ects. Conclusions and Relevance: Results cannot be explained by group di ﬀ erences in DLPFC GABA or glutamate concentrations but, instead, indicate that schizophrenia disrupts the GABAergic facilitation of WM seen in healthy individuals. Results appear to parallel post mortem ﬁ ndings in suggesting that schizophrenia alters the distribution of di ﬀ erent classes of GABAergic interneurons rather than producing a general de ﬁ cit across the total population of neurons.
Interaction networks for the identification of Higgs boson decays to bottom quark-antiquark pairs

We investigate the performance of a jet identification algorithm based on an interaction network to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The algorithm’s inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated to them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, an interaction network is trained to learn a jet representation with which to classify the jet. The algorithm is trained on simulated samples of accurate LHC collisions, released by the CMS collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.
Implementing Open Innovation in Research and Technology Organisations: Approaches and Impact

Research and development organisations (RTOs) are at the heart of innovation systems. They help to connect innovation system actors to foster industrial innovation. Due to this intermediary role, they act as paradigmatic open innovation (OI) actors. In this context, RTOs need to balance their knowledge stocks and flows, while assuring their own innovation capabilities and positively impacting the innovation system they influence. Thus, RTOs need to develop collaboration approaches that support their own performance while increasing their capabilities and not threatening their competitive advantages derived from their knowledge stock. In this study we extend the OI research to research organisations analysing their OI approach and the impact on its own performance, developing a new framework for OI study in RTOs using a partial least squares structural equation modelling (PLS-SEM) approach. The research, based on a sample of Spanish RTOs, arises two substantive conclusions. First, an increased number of collaboration partners (collaboration breadth) and the use of a variety of OI practices have a strong impact on RTOs’ overall performance (scientific, transference, and economic results). Second, RTOs need to foster their aperture and promote an active management to benefit from collaborating partners, whereas managers should pay special attention to questions related to the protection and management of intellectual property when promoting the use of different OI practices. Both results also emphasise the importance of managing firms’ knowledge flows in the implementation of OI in RTOs.
The Impact of Impulse Buying and Network Platforms on Consumer Purchasing Behaviour: A Case Study of a Technical Product

This paper investigates the effects of impulse buying and network platforms on the online purchasing behaviour for experience goods using a 2 (low impulse buying tendency vs. high impulse buying tendency) × 2 (mobile platform vs. computer platform) between-subjects factorial experiment.A technical product is a typical type of experience good. To increase the external validity of the conclusions, this study takes software as a technical product and employs an online scenario-simulated experiment to collect data. The results indicate that low impulse consumers tend to spend less money and conduct more stringent budget control when purchasing a technical product. However, no significant difference existed between high and low impulse consumers regarding the purchasing decision. Furthermore, consumers who shop on a mobile platform tend to spend more money, conduct looser budget control and make more careful purchasing decisions. In addition, a remarkable effect also exists between impulse buying and network platforms regarding the average viewing time of the single page and the commodity details page.
A Novel Lane Line Detection and Tracking System of Car Videos

In recent years, in order to reduce traffic accidents, developing Driver Assistance Systems for safety has attracted much attention. Lane line detection is an essential component of Driver Assistance System. In this thesis, we propose a lane line detection and tracking system utilizing the time slice images. On the other hand, due to the discontinuousness, dashed lane lines are difficult to detect in a single image. We propose the gradient value adjustment method to enhance the recognition of the dashed lane lines. For each frame, we find the candidate lane lines in the image through pre-processing, edge detection, and peak finding and connecting. Then we detect the true lane line positions by the time slice images in lane line verification. Once the lane line positions are located, we predict their new positions by the time slice images and track them in the new frame. Since we only process several rows of an image and we provide the continuous detection of the lane lines by tracking, the processing time of an image is reduced. The experiments use the video sequences of real road scenes containing three
High-Efficiency Skutterudite Modules at a Low Temperature Gradient

Thermoelectric skutterudite materials have been widely investigated for their potential application in mid-temperature waste heat recovery that has not been efficiently utilized A large amount of research has focused on developing materials with a high thermoelectric figure of merit (zT). However, the translation of material properties to device performance has limited success. Here, we demonstrate single-filling n-type Yb0.25Fe0. 25Co3.75Sb12 and multi-filling La0.7Ti0. 1Ga0.1Fe2. 7Co1.3Sb12 skutterudites with a maximum zT of ~1.3 at 740 K and ~0.97 at 760 K. The peak zT of skutterudites usually occurs above 800 K, but, as shown here, the shift in peak zT to lower temperatures is beneficial for enhancing conversion efficiency at a lower hot-side temperature. In this work, we have demonstrated that the Fe-substitution significantly reduces the thermal conductivity of n-type skutterudite, closer to p-type skutterudite thermal conductivity, resulting in a module that is more compatible to operate at elevated temperatures. A uni-couple skutterudite module was fabricated using a molybdenum electrode and Ga–Sn liquid metal as the thermal interface material. A conversion efficiency of 7.27% at a low temperature gradient of 366 K was achieved, which is among the highest efficiencies reported in the literature at this temperature gradient. These results highlight that peak zT shift and optimized module design can improve conversion efficiency of thermoelectric modules at a low temperature gradient.
Embedded Constrained Feature Construction for High-Energy Physics Data Classification

Before any publication, data analysis of high-energy physics experiments must be validated. This validation is granted only if a perfect understanding of the data and the analysis process is demonstrated. Therefore, physicists prefer using transparent machine learning algorithms whose performances highly rely on the suitability of the provided input features. To transform the feature space, feature construction aims at automatically generating new relevant features. Whereas most of previous works in this area perform the feature construction prior to the model training, we propose here a general framework to embed a feature construction technique adapted to the constraints of high-energy physics in the induction of tree-based models. Experiments on two high-energy physics datasets confirm that a significant gain is obtained on the classification scores, while limiting the number of built features. Since the features are built to be interpretable, the whole model is transparent and readable.