Miracle-dz/UniFAR-FTUs huggingface dataset:
Photodegradation of Natural Wood Veneer and Studies on Its Color Stabilization for Automotive Interior Materials

The aim of this study was to investigate the color and chemical changes of natural wood veneers after ultraviolet (UV) exposure for automotive interior materials. To control the photodegradation, chemical treatments with hydrogen peroxide (H2O2) and sodium hypochlorite (NaClO) solution were applied to the veneer under different concentration conditions. The veneers treated with H2O2 showed strong color changes on the surface after the UV test. On the other hand, the veneers treated with NaClO solution (2–3% concentration) showed excellent color stability compared to the untreated veneer because NaClO partially removed lignin compounds on the veneer surface. NaClO can improve the color stability by destroying the lignin structures. However, 3% NaClO condition was seen to cause surface damage although this treatment condition had the best effect on photoprotection. Therefore, treatment with 2% NaClO was the best condition in this study for against color change and photodegradation caused by UV light.
Determining Factors and Nature of Innovation in the Process of a Paradigmatic Mexican Innovative Social Entrepreneurship

The process of a social entrepreneurship based on innovation, from identifying a social need to implementing a novel solution, is an unattended subject by the literature on social-purpose entrepreneurship and innovation. This paper analyzes the factors that brought about the evolution of this process and discusses the nature of its innovations. Based on a detailed case study of Isla Urbana AC —one of the most successful and renowned Mexican social entrepreneurship cases— this paper deduces that these factors are divided into two levels, which can not be analyzed in isolation: internal (associated to characteristics of the entrepreneur as an individual and strategy of the organization) and external (conditions imposed by the context). It is shown that these set of factors bring about certain types and distinctive attributes of innovation, which is an essential tool for the creation of social value and sustainability of the entrepreneurship. It points out that innovations, radical and/or incremental, are not only technological and tangible (of products, services, etc.), but has a strong non-technological and intangible component (eg. business model and social inclusion strategy).
Another New Wittgenstein: The Scientific and Engineering Background of the <italic>Tractatus</italic>

Introduction In recent years an entirely “New Wittgenstein”1 has grown up around the idea that the Tractatus should be read as a critical engagement with Frege’s notion of ‘elucidation’ and thus with a particular conception of philosophy. This is supposed to solve the puzzle of how Wittgenstein’s sentences can really be nonsensical while there is yet a way to understand their author and learn to see the world right (TLP 6.54). 2 Less conspicuously than this “American” school of interpretation,3 there has also grown up in recent years another New Wittgenstein. This one brings together rather heterogeneous strands of investigation. They help solve a puzzle that has been declared to be unsolvable by Brian McGuinness: How did Wittgenstein become a philosopher rather than an engineer? McGuinness argues that looking for a cause here, e.g., for the question or intellectual problem that prompted the transition, is a misguided attempt to construct a kind of teleology:
Oil Discoveries and Protectionism

Can oil discovery shocks affect the demand for protectionism? A two-period model of Dutch disease indicates that if the tradable sector is politically dominant then an oil discovery induces protectionism. If the economy is also credit constrained, this effect is intensified upon discovery, but partially reversed when oil revenues start to flow. We test these predictions using detailed bilateral tariff data that cover 96 products in 155 countries over the period 1988-2012, and worldwide discoveries of giant oil and gas fields. Our identification strategy rests on the exogeneity of the timing of discoveries. We find that an oil discovery increases tariffs during pre-production years and decreases tariffs in the years to follow yet to a lesser extent, most notably in capital scarce economies with a relatively dominant tradable sector. Our baseline estimates indicate that a giant oil field discovery induces a rise of approximately 15% in the average tariff over the course of 10 years; this increase is about 1.8 times larger during the preproduction period when the oil discovery represents a pure news shock.
sklvq: Scikit Learning Vector Quantization

The sklvq package is an open-source Python implementation of a set of learning vector quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ, GMLVQ, and LGMLVQ algorithms, sklvq is distinctive by putting emphasis on its modular and customizable design. Not only resulting in a feature-rich implementation for users but enabling easy extensions of the algorithms for researchers. The theory behind this design is described in this paper. To facilitate adoptions and inspire future contributions, sklvq is publicly available on Github (under the BSD license) and can be installed through the Python package index (PyPI). Next to being well-covered by automated testing to ensure code quality, it is accompanied by detailed online documentation. The documentation covers usage examples and provides an in-depth API including theory and scientiﬁc references.
Applying a Kernel PCA Method to Reveal Coexisting Attractors within a Generalized Lorenz Model

Natural ecosystems are complex network of biotic and abiotic interactions of species and their biological, physical and chemical constituents. Over the century, mathematical models have played primary roles in understanding the mystery behind the ecosystems processes and interesting dynamics of natural ecosystems. The ditrophic predator-prey interactions are the basic building blocks for complex food web models (multiple trophic interactions). The pioneering work of Lotka-Volterra (1926), explaining the abrupt deviations in species abundance and existence of oscillations in a simple predator-prey interaction. Studies in previous decades, it has been assumed that the functional responses are the main cause for chaotic and non-chaotic behavior. In this paper, we investigate how functional responses affect the system dynamics by using its different combinations in a simple two prey-one predator population model. Based on our present investigation, we concluded that stabilizing properties of functional responses dominate oscillatory behavior. Keyboard: Controlling Limit cycles, Two parameter bifurcation comparison, Leslie Gower model, Holling type Functional Responses, stabilizing property, dominating oscillatory property. Chaos and Anderson Localisation in Disordered Classical Chains: Hertzian vs FPUT models A. Ngapasare, G. Theocharis, O. Richoux, Ch. Skokos, V. Achilleos 1 Laboratoire d’Acoustique de l’Universit ́e du Maine, UMR CNRS 6613 Av. O. Messiaen, F-72085 LE MANS Cedex 9, France. 2 Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa. We numerically investigate the dynamics of a one-dimensional disordered lattice using the Hertzian model, describing a granular chain, and the α + β Fermi-Pasta-Ulam-Tsingou model (FPUT). The most profound difference between the two systems is the discontinuous nonlinearity of the granular chain appearing whenever neighboring particles are detached. We therefore sought to unravel the role of these discontinuities in the destruction of Anderson localization and their CMSIM Virtual Conference, 22-24 October 2020 31 influence on the system’s chaotic dynamics. Our results show that both models exhibit an energy range where localization coexists with chaos. However, the discontinuous nonlinearity is found to be capable of triggering energy spreading of initially localized modes, at lower energies than the FPUT model. A transition from Anderson localization to chaotic dynamics and energy equipartition is found for the granular chain and is associated with the “propagation” of the discontinuous nonlinearity in the chain. On the contrary, the FPUT chain exhibits an alternate behavior between localized and delocalized chaotic behavior which is strongly dependent on the initial energy of excitation.
Generative Adversarial Networks used for Latent Space Optimization: A Comparative Study for Partial Discharge Analysis

Hydrogenerators are complex equipment with many components where more than 100 failure mechanisms can be active. During normal operation, the high voltage stator, one of the main components of hydrogenerators, is always subjected to Partial Discharge (PD) activity. Multiple sources of PD activity can be active simultaneously. Global PD signals which include all active PD sources are obtained from periodic measurements made on hydrogenerators while they are in operation. PD measurements are an effective diagnostic tool for evaluating the integrity of the stator winding, similar to signals coming from an electrocardiogram for the health status of a human. Quantifying PD activity is still a challenge in the industry since the recognition of the type of PD is not trivial and still requires expert judgement. Since the degradation rate of all active PD sources is different, automatic classification of PD source is thus essential to monitor their evolution. With that goal in mind, an extensive effort was initiated in 2019 to automatically recognize individual PD sources from 2D Partial Discharge Analyzer (PDA) files using Deep Learning (DL) techniques. In this context, this paper presents the use of a Generative Adversarial Network (GAN) in combination with A Variational Autoencoder (VAE) for increasing the representativeness of each PD sources in the VAE latent space.
TWITTER SENTIMENT ANALYSIS DURING COVID-19 IN FLORIDA

by JINGYI LI (Under the Direction of Tianming Liu) ABSTRACT Sentiment analysis is a popular approach to figure out people’s thoughts by digging into human-generated text content from online users. This study focuses on tweets from the Twitter platform to investigate people’s sentiments and emotions related to COVID-19 pandemic. Tweets from Florida users have been collected and preprocessed on three periods of time, April 22-28, July 15-21, and October 14-20, to study the changes in sentiments, subjectivities, and emotions. Two sentiment analysis approaches, TextBlob and NRCLex, have been used and compared in this study: TextBlob has strength in sentiment classifications and defining subjectivities, while the NRC Lexicon method provides more detailed emotional states analysis. The results from both methods show that neutral sentiments are the majority and positive sentiments outweigh negative sentiments. However, TextBlob indicates a more significant difference between positive and negative than NRC. We also observe that the positive attitudes decrease during the week in July and increase back in October.
Deep Sleep Stage Detection

Sleep quality is very important to human health. To detect sleep disorders, sleep scoring is performed by sleep experts on the polysomnograms that record the activities of different parts of the human body, like electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG). Current automatic sleep scoring approaches are mostly based on single-channel EEGs and the few multi-channel models that exist do not obtain a satisfying performance. In this master assignment, we firstly perform a module evaluation to test the performance of useful deep learning modules developed for optimizing single-channel models in multi-channel sleep scoring. Based on the results, we build a well-performing multi-channel automatic sleep scoring model, where temporal learning is applied to extract temporal features from sleep epochs, spatial learning is designed to capture correlation information among the channels of a modality, sequential learning is performed to extract transition rules from sleep sequences and the residual connection is used to consider temporal and sequential information together for sleep stage classification. We evaluate our model on two public datasets — the SleepEDF-13 and SHHS-1 datasets. Our model obtains an accuracy of 84.6%, macro F1 score of 78.3% and Cohen’s kappa of 0.79 for the SleepEDF-13 dataset and an accuracy of 86.4%, macro F1 score of 77.7% and Cohen’s kappa of 0.81 for the SHHS-1 dataset. Additionally, we employ two methods — the layer-wise relevance propagation (LRP) and an embedded channel attention network (Embedded CAN) to investigate the channel and feature importance in automatic sleep scoring. Results show that our multi-channel sleep scoring model performs well on different datasets compared to the state-ofthe-art, and channel and feature importance obtained comply with the AASM rules and can be a guidance for further optimizing automatic sleep scoring models.
Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective

This study focuses on the machine learning bias when predicting teacher grades. The experimental phase consists of predicting the student grades of 11th and 12thgrade Portuguese high school grades and computing the bias and variance decomposition. In the base implementation, only the academic achievement critical factors are considered. In the second implementation, the preceding year’s grade is appended as an input variable. The machine learning algorithms in use are random forest, support vector machine, and extreme boosting machine. The reasons behind the poor performance of the machine learning algorithms are either the input space poor preciseness or the lack of a sound record of student performance. We introduce the new concept of knowledge bias and a new predictive model classification. Precision education would reduce bias by providing low-bias intensive-knowledge models. To avoid bias, it is not necessary to add knowledge to the input space. Low-bias extensive-knowledge models are achievable simply by appending the student’s earlier performance record to the model. The low-bias intensive-knowledge learning models promoted by precision education are suited to designing new policies and actions toward academic attainments. If the aim is solely prediction, deciding for a low bias knowledge-extensive model can be appropriate and correct. Doi: 10.28991/esj-2021-01298 Full Text: PDF
Deaning from the Middle: Academic Deans’ Emotional Intelligence and Leadership Effectiveness

As higher education changes, academic deans’ roles also adapt to meet the demands of increased enrollment and serving diverse student populations. Academic deans lead from the middle of their institutions; they must report to university administration, while serving the faculty members within their respective colleges or schools (Bright & Richards, 2001; Buller, 2007; Butin, 2016; Dill, 1980; Gallos, 2002; Morris, 1981; Perlmutter, 2017). To meet these demands, academic deans must develop emotional intelligence to lead effectively. Emotional intelligence serves as a skillset for academic deans to use in navigating their administrative duties and serving as leaders for their faculty and staff. This study focused on determining whether academic deans’ emotional intelligence is related to their leadership effectiveness. The researcher hypothesized that emotional intelligence and leadership effectiveness were related. A survey consisting of demographic questions, the Genos Emotional Intelligence Inventory, and the Leadership Practices Inventory were used to measure whether emotional intelligence served as a predictor for leadership effectiveness. Results indicated that emotional intelligence indeed served as an indicator of leadership effectiveness for academic deans.
Lecture 20: Multi-Agent Deep RL & Emergent Communication

The field of Deep RL ultimately advances as researchers try to tackle increasingly complicated and more realistic problems. It is well understood that most real-life tasks involve multiple actors, especially when cooperation is involved. Hence, Multi-Agent Deep RL (MADRL) is the extension of single-agent Deep RL to multi-agent problem settings. The following notes will describe Multi-Agent Systems, and the MADRL problem setup. We will then cover two key challenge with MADRL, namely non-stationarity and partial observability. Next, we step back into the single-agent setting and examine how DQN’s were extended to handle partial observability via RNN’s, hence the term Deep Recurrent Q Network. We will also see how DQN’s were extended to multi-agent settings through Independent DQN’s. Lastly, we will see how researchers combined elements of DRQN’s and Independent DQN, and with some slight modifications, to successfully tackle multi-agent problems via an architecure called Distributed Deep Recurrent Q Networks.
Responsibility system nursing intervention for cerebral infarction complicated with hypertension can effectively control blood pressure and improve neurological function

Objective: This study aimed to explore the application value of responsibility system nursing intervention in patients with cerebral infarction and hypertension. Methods: Cerebral infarction patients complicated with hypertension who were admitted to our hospital were divided into groups according to different nursing methods. Altogether 131 patients who underwent responsibility system nursing intervention were included as the research group, and 112 who underwent conventional nursing intervention were regarded as the control group. Blood pressure and nerve function were observed. Results: We found that the blood pressure levels of both groups showed a fine range, and the diastolic and systolic blood pressure levels in the research group were better than those in the control group (P<0.05). Self-management ability of patients before discharge from the hospital was evaluated via a self-made questionnaire, and the results showed that patients in the research group were better than those in the control group in medication compliance, emotional management and disease knowledge (P<0.05). After nursing intervention, the NIHSS scores of both groups were improved compared with those before intervention (P<0.05), while the scores of the observation group were dramatically lower than those of the control group (P<0.05). The negative emotions of both groups were improved, while the negative emotion scores of the research group were lower than those of the control group. After different nursing intervention, the quality of life score of the observation group was higher than that of the control group (P<0.05). By investigating the satisfaction of discharged patients with nursing in our hospital, we discovered that the satisfaction in the research group was higher than that of the control group (P<0.05). Conclusion: Responsibility system nursing intervention for cerebral infarction complicated with hypertension can effectively control blood pressure and improve neurological function.
World Saving; by Francesco Grigoli, Alexander Herman, and Klaus Schmidt-Hebbel; IMF Working Paper No. 14/204; November 1, 2014

This paper presents new evidence on the behavior of saving in the world, by extending previous empirical research in five dimensions. First, it is based on a very large and recent database, covering 165 countries from 1981 to 2012. Second, it conducts a robustness analysis across different estimation techniques. Third, the empirical search is expanded by including potential saving determinants identified by theory but not previously considered in the empirical literature. Fourth, the paper explores differences in saving behavior nesting the 2008-10 crisis period and four different country groups. Finally, it also searches for commonalities and differences in behavior across national, private, household, and corporate saving rates. The results confirm in part existing research, shed light on some ambiguous or contradictory findings, and highlight the role of neglected determinants. Compared to the literature, we find a larger number of significant determinants of saving rates, using different estimators, for different periods and country groups, and for different saving aggregates. JEL Classification Numbers: C23, E21, H30
Likelihood of pregnancy after the transfer of embryos derived from follicle aspiration and embryo production sessions with in vitro di erent relative e ciencies ff ffi

The aim of the present study was to evaluate the likelihood of pregnancy of produced in vitro(IVP) embryos from batches with distinct relative e ciencies. Data were retrospectively analyzed ffi from 605 transvaginal ultrasonic-guided follicle aspiration sessions (OPU) followed by in vitro embryo production (IVEP) and 2456 fresh embryo transfers (ET), performed between 2008 and 2012 in individuals of the Gir (dairy ) breed. The OPU and IVEP were performed using Bos indicus standard procedures by a single group of technicians at the same laboratory facility. Records were strati ed into quartiles (I to IV) according to the total of cumulus-oocytes complexes (COC) fi produced per donor, or in percentile ranges (0% 25%, 26% 50%, 51% 75%, and 76% 100%) – – – – for endpoints related to COC quality or e ciency of embryo production. Pregnancy per embryo ffi transfer (P/ET) was compared among quartiles or ranges using the chi-squared test. Donors producing a greater number of total COC (quartile I) also had more viable and grade I COC, and a greater number of embryos than donors ranked in quartiles II, III or IV, respectively ( < 0.0001). Nevertheless, P/ET did not di er ( > 0.05) among embryos produced by donors P ff P ranked in Quartiles I to IV. Similarly, there was no di erence ( > 0.05) in P/ET for embryos ff P derived from OPU sessions with a relatively greater or lesser percentage of viable or Grade I COC. Cleavage and blastocyst rates within each IVEP batch had no e ect ( > 0.05) on P/ET. In ff P conclusion, data suggest that there is no relationship among oocyte yield after OPU, or e ciency ffi of IVEP, and the likelihood of pregnancy after ET of fresh IVP embryos.
Exploring Citizens’ Perception of Abu Dhabi Policing e-Service Quality

The purpose of the study is to explore citizens’ perception on the quality of service of the newly transformed e-policing system in Abu Dhabi. The Abu Dhabi policing system has undergone a major change by utilizing information and communication technology in all aspects of service operations. The changes require an evaluation from the citizens as an assessment of the success. Hence, it is timely to assess the citizens’ perceptions on the policing system’s integrity, transparency, responsivity, interactivity and serviceability and to compare for gender differences in the perception. The study participants were citizens of Abu Dhabi. The sample was selected based on the convenience sampling technique. The data was collected from 260 respondents via an online survey. The results of the descriptive analysis show an acceptable level of e-service quality perception on integrity, transparency, responsivity, interactivity and serviceability. In addition, the results also indicate there is no significant difference in the perception of Abu Dhabi policing e-service between male and female respondents. At present, studies on Abu Dhabi policing are rather limited. Therefore, the findings demonstrate how smart policing could drive a greater acceptance of the e-service quality especially for the United Arab Emirates (UAE). In addition, the high perception of the policing integrity, transparency, responsivity, interactivity and serviceability will provide greater opportunities for future research to embark on how society-authority could work together for sustainable peace and harmony.