Miracle-dz/UniFAR-FTUs huggingface dataset:
ROLLER ELECTROSPINNING IN VARIOUS AMBIENT PARAMETERS

Ambient parameters include humidity and temperature of surrounding environment of spinning area. Many studies have been conducted to examine effects of ambient parameters. Their results show that the average fibers diameter decreases with increasing temperature in the needle electrospinning. With increasing humidity some small circular pores may appear on the surface of the fibers. In the needleless electrospinning, the effect of relative humidity and temperature are different. In this work, poly (vinyl alcohol), molecular weight of 80,000 (g/mol), degree of saponification of 88 per cent, as 12 per cent solution in water was electrospun on a roller electrospinner. Two sets of humidity and temperature values were established to verify the effects of ambient parameters on roller electrospinning. The first set keeps the temperature at constant value of 25 degrees Celsius and change humidity in a following series: 20%; 25%; 30%; 35%; 40%; and 45%. The second set keeps the humidity at constant value of 27.5% and change temperature follow series: 20 0 C; 23 0 C; 25 0 C; 27 0 C; and 30 0 C. Some process and product properties such as spinning performance, average fibers diameter, fibers diameter distribution, content of non-fibrous particles were determined and discussed in relation to ambient parameters.
Knowledge of the role of vaccinations, physical activity and other cancer prevention methods among Polish outpatients

Background. Mortality rates connected with cancer are increasing nowadays due to demographic changes in the worldwide population and the increasing number of health risks leading to cancer. Primary cancer prevention is based on avoiding exposure to identified cancer risk factors. therefore, awareness of basic health hazards is necessary. Objectives. the purpose of this study was to assess the knowledge of the role of vaccinations, physical activity, second-hand smoke exposure and other factors in cancer prevention in Polish outpatients. Material and methods. this was a survey involving Polish primary care patients. the study was based on an original questionnaire designed for this purpose and was distributed among primary care patients in either hardcopy (among primary care patients in the urban settings of the city of lodz) or over the internet. Results. 612 respondents took part in the study. among the respondents, 28.4% claimed that vaccinations do not play any role in cancer prevention. Physical activity may prevent cancer according to 58.6% of the surveyed population. tobacco smoking is a well-known cancer risk factor, which was admitted to by 97.2% of the respondents. second-hand smoke exposure in the workplace was reported by 18.3% of the respondents, more often by those with secondary education than with a university degree. second-hand smoke exposure was strongly connected with the age of the respondents. Conclusions. there is little knowledge about vaccines and physical activity as cancer prevention methods. tobacco smoking is a well-known cancer risk factor. second-hand smoke exposure remains a serious problem in Poland despite a total ban on smoking in the workplace and public places.
First record of Pistia stratiotes (water lettuce) from Gilan province (North of Iran)

In order to study the conservation status of the plant species and ecosystems of Gilan province (North of Iran), an alien species was observed in some ponds in the east of Rasht, which covered the surface of ponds. The plant samples were collectedand photographed; also the relevant environmental data were recorded in situ. The collected specimens were studied with various scientific resources and led to the identification of the species as Pistia stratiotes which is commonly known as “water lettuce”. This species is recorded for the first time from Iran. The plantis pan-tropical and is considered to be one of the most widely distributed aquatic invasive plants in the world. The paper presents data on taxonomic remarks of this plant along with some notes on its geographical distribution and habitat. This report also highlights the need for implementing early control measures with special emphasis on all freshwater habitats in the northern parts of the country.
Interventional strategies to combat muscle disuse atrophy

Short periods of inactivity, for instance induced by bed rest or leg immobilisation, are often necessary for recovery from injury or disease. This thesis describes the effects of short periods of inactivity on loss of muscle mass and strength, and examines the use of a high-protein diet and exercise, which may reduce these negative effects. As it is often difficult for patients to contract a muscle during a period of inactivity, the studies in this thesis focus on the use of neuromuscular electrical stimulation (NMES). The thesis concludes that the combination of daily electrical stimulation and a high-protein diet is the most promising strategy to combat muscle disuse atrophy.
Brazil: the institutional changes in the recent period and the new government

This text discusses institutional changes implemented in the recent period in Brazil. If they are not reversed in a future government, they will have, on the one hand, redefined the place of the state in the economy and society, resulting, among other consequences, in the shrinking of public policies in general, and especially of social policies; on the other hand, they will have substantially altered the country’s labor market, expanding the presence of the already important and structural segment of informal workers. They consist of the Constitutional Amendment 95 (EC 95) and the set of articles and provisions of the Consolidation of Labor Laws (CLT) that were amended by the labor reform. In the first section, aspects concerning the conditions inherited by the new government are analyzed; in the second one, we discuss the Constitutional Amendment 95; in the following section, we go over the labor reform and the labor market, both inherited from Michel Temer’s government. In section four, some of the ongoing measures of the new government are presented into detail.
SINGLE STIMULI, MULTIPLE RESPONSES: PERFORMANCE FEEDBACK AND FIRMS' R&D CHANGES*

The behavioral theory of the firm assumes that firms react on performance feedback by increasing their search for alternative courses of action. However, the empirical literature is full of contradictory findings. This paper puts forward the idea that at least part of these contradictions can be explained if we can identify groups of firms behaving differently from firms in other groups and theoretical propositions. The paper uses exploratory analysis of US and German industrial firms and changes in R&D expense as their response to financial performance feedback. The cluster analysis of behavioural patterns of these firms results in identifying several behaviourally distinctive groups. The findings support the idea that contradictions in previous studies may partially stem from having a different mix of heterogeneously behaving firms. Also, they point to the proposition that for further understanding of responses to performance feedback, these groups of firms should be analysed separately.
An Analysis of the Implementation of Kafka in High-Frequency Electronic Trading Environments

—Electronic trading amounts for the vast majority of all financial transactions with bonds and equities in the world. This type of trading is based largely on the brokering of messages from the buy side, brokers or financial institutions, to a sell side (usually an exchange). Since not all trading is done at the same time these messaging systems need to account for server down times, sequencing, high throughput and performance requirements. Therefore, most fintech companies employ queuing mechanism to manage the message flow. This paper analyzes the way in which Kafka, one of the premiere messaging systems currently in use, can simplify various systems in use such as message brokering, persistence or fault tolerance. The authors of this paper hope to demonstrate the use of Kafka as a messaging system, backup solution and alert broker for software operators and developers with low fault-tolerance and rigid up-time requirements.
ON THE COMPOSITION OF THE PERRON-VANNIER REPRESENTATION AND THE NATURAL MAP P n 7 → P 2 n

We study the composition of F.R. Cohen’s map Pn 7→ P2n with the Perron and Vannier representation, where Pn is the pure braid group on n strings. We prove that the obtained representation of Pn has one of its composition factors the inverse of the Gassner representation of the pure braid group. AMS Subject Classification: 20F36
GIFTED OR AUTISTIC ? THE ‘ GREY ZONE

In 2004, the first draft-design of the nowadays called ‘Dimensional Discrepancy model’ (DD-model) was introduced. During the last two years, this model, as well as its conceptual premises, has had a first round of validation, by means of a theoretical exploratory (literature) study. This was recently published in the Dutch scientific journal of special education (Burger-Veltmeijer, 2006a, 2006b). In this article, the running thread out of these two publications is summarized in a compacted way. The leading question is: How can we improve correct dual diagnoses and assessment of gifted children with (symptoms of) a disorder in the autism spectrum? In the first paragraph, the causes of incorrect dual diagnoses of the twiceexceptionality ‘giftedness and autism spectrum disorder’ (GFT+ASD) are summed up. These mutually related causes are synthesized in the so called ‘grey zone’. This core part of the DD-model is the borderline area between giftedness with and without ASD (paragraph two). A solution to the diagnostic issue is found in assessment of children in this grey zone, in a needs-based way. For that purpose, this borderline area is made operational in the draft-design of the so called ‘DD-checklist’, shown in paragraph three. In the near future, this assessment-tool will be validated by methodology of ‘design research’ (paragraph four). Meantime, creative professionals might already start using the basic ideas.
High Dimensional Structured Inference Using Deep Markov Models for Short Term Load Forecasting.

GAIWAK, PARITOSH. High-dimensional Structured Inference Using Deep Markov Models for Short Term Load Forecasting. (Under the direction of Dr William Davis). Short term load forecasting (STLF) is a challenging research problem, especially when conducted for individual houses. Numerical and simple machine learning approaches are not useful for STLF, and as the number of houses for which the forecasting needs to be done increases, the problem becomes intractable. Various machine learning approaches have been used to solve this problem but none of them has achieved any significant and impactful results. This project models STLF as a time series problem and explores applicability of deep Markov models (DMM) for STLF. It compares the results obtained by using DMM to the result obtained by long short-term memory networks (LSTM) and presents the findings.
LEVERAGING STREAM PROCESSING ENGINES IN SUPPORT OF PHYSIOLOGICAL DATA PROCESSING Submitted

LEVERAGING STREAM PROCESSING ENGINES IN SUPPORT OF PHYSIOLOGICAL DATA PROCESSING Over the last decade there has been an exponential growth in unbounded streaming data generated by sensing devices in different settings including the Internet-of-Things. Several frameworks have been developed to facilitate effective monitoring, processing, and analysis of the continuous flow of streams generated in such settings. Real time data collected from patient monitoring systems, wearable devices etc. can take advantage of stream processing engines in distributed computing environments to provide better care and services to both individuals and medical practitioners. This thesis proposes a methodology for monitoring multiple users using stream data processing pipelines. We have designed data processing pipelines using the two dominant stream processing frameworks – Storm and Spark. We used the University of Queensland’s Vital Sign Dataset in our assessments. Our assessments contrast these systems based on processing latencies, throughput, and also the number of concurrent users that can be supported in a given pipeline.
Time-Series E � cient Factors December 2019

Factors in prominent asset pricing models are positively serially correlated. Momentum strategies profit by timing these factors; they pick up factor “ine ciencies. ” We show that, rather than augmenting a model with the momentum factor, each factor can instead be made time-series e cient. Time-series e cient factors earn significantly higher Sharpe ratios than the original factors; they typically contain all the information found in the original factors; and an asset pricing model with time-series e cient factors, such as an e cient Fama-French five-factor model, prices momentum. Time-series e cient factors also explain more of the covariance structure of returns; they therefore appear to align more closely with the true SDF. JEL classification: G11, G12, G40
Vibrational spectra and assignment of fundamental vibrational modes of O-Bromobenzoyl chloride based on DFT calulations

Introduction Quantum chemical computational methods have proved to be an essential tool for interpreting and predicting the vibrational spectra. A significant advancement in this area was made by combining semi empirical quantum mechanical method; ab initio quantum mechanical method and density functional theory (DFT), each method having its own advantage. In scaled quantum mechanical (SQM) approach (Pulay et al., 1983; Rauhut et al., 1995 and Baker et al., 1998), the systematic errors of the computed harmonic force field are corrected by a few scale factors which are found to be well transferable between chemically related molecules and were recommended for general use. It requires transformation of the QM force field to internal co-ordinates and applies a limited number of independent scaling that is common within groups of similar internal co-ordinates. Ideally, the number of scale factors applied is kept to a possible minimum to safeguard against arbitrariness in the effective force field produced. Uniform scaling (or frequency scaling) has been criticized by Pulay et al., (1990) pointing out that no scaling and uniform scaling of the force field may often lead to misassignments in case of large molecules, since proper correction for systematic errors of theory is impossible by a single factor. To enhance the utility of the SQM force field method, a set of 11 transferable scaling factors have been proposed for use with the B3LYP/6-311+G **
Revision Notes for : CENG-D-07-00160 “ Non-Linear Transfer Function Modelling of Beach Morphology at Duck , North Carolina ” submitted by

This paper presents a simple nonlinear data-based modelling approach for predicting the beach profile volume at Duck, North Carolina, USA. The state-dependent parameter form of the general transfer function (SDP TF) model is used to describe nonlinearity influencing these morphological data in two case examples. Case 1 investigates the nonlinearity associated with the dependency of wave forcing on the preceding beach volume. Case 2 investigates the ability to model the variables within the well known diffusion equation for beach volume using this data-based approach. The results of this study show that the SDP TF approach can be used successfully to develop statistically robust models for describing nonlinearity in beach morphological systems. Furthermore, these models are shown to predict the beach volumes over both short (1 month ahead) and long (2 years ahead) time periods, and thus show great potential for practical applications in coastal zone management and engineering.
Examination of the phenomenological characteristics, reconsolidation update, and perceived temporal distance of intrusive memories in university students with high and low depressive symptoms (eng)

Objective: It is known that experiential and coginitive features of intrusive memories differ in accordance with the type and severity of psychological disorders. This investigation sought to gather all the experiential and cognitive dispositions of intrusive memories that take place in literature and to make comparisons in terms of the mentioned dispositions between participants with high and low depressive symptoms. It is also aimed to reveal perceived temporal distance and reconsolidation processes of intrusive memories. Method: 134 participants are asked to describe narratives of their intrusive memories experienced in the preceding week. Beck Depression Inventory was applied in order to determine depression severity while the Post Traumatic Stress Symptom Inventory-Intrusion was applied in order to determine intrusions of memory. Results: It has been found that when the time of the memories intrude, the group with high depressive symptoms experience more helpless and sad and make more negative attributions to themselves and future compared to the group with low depressive symptoms. Finally, intrusion serve as mediator variable in the relation between depressive symptoms and reconsolidation processes while reconsolidation processes serve as mediator variable in the relation between intrusions percieved temporal distance. Discussion: Phenomenological differences in accordance with depression severity have been discussed. Additionally, the mediator role of intrusion in the relation between depressive symptoms and reconsolidation process and the mediator role of reconsolidation process in the relation between intrusions and percieved temporal distance have been discussed. It is thought that more studies in the area of intrusive memories are needed.
A bibliometric analysis of research based on the Roy Adaptation Model: a contribution to Nursing

Objective. To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s. Method. A descriptive and longitudinal study. We used information from the two dominant scientific databases, Web Of Science and SCOPUS. We obtained 137 publications from the Core Collection of WoS, and 338 publications from SCOPUS. We conducted our analysis using the software Bibliometrix, an R-package specialized in creating bibliometric analyses from a perspective of descriptive statistics and network analysis, including co-citation, co-keyword occurrence and collaboration networks. Results. Our quantitative results show the main actors around the research based on the model and the founding literature or references on which this research was based. We analyze the main keywords and how they are linked. Furthermore, we present the most prolific authors both in number of publications and in centrality in the network of coauthors. We present the most central institutions in the global network of collaboration. Conclusions. We highlight the relevance of this theoretical model in nursing and detail its evolution. The United States is the dominant country in production of documents on the topic, and the University of Massachusetts Boston and Boston College are the most influential institutions. The network of collaboration also describes clusters in Mexico, Turkey and Spain. Our findings are useful to acquire a general vision of the field.
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems

Image prior modeling is the key issue in image recovery, computational imaging, compresses sensing, and other inverse problems. Recent algorithms combining multiple effective priors such as the sparse or low-rank models, have demonstrated superior performance in various applications. However, the relationships among the popular image models are unclear, and no theory in general is available to demonstrate their connections. In this paper, we present a theoretical analysis on the image models, to bridge the gap between applications and image prior understanding, including sparsity, group-wise sparsity, joint sparsity, and low-rankness, etc. We systematically study how effective each image model is for image restoration. Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships. Extensive experiments are conducted to compare the denoising results which are consistent with our analysis. On top of the model-based methods, we quantitatively demonstrate the image properties that are inexplicitly exploited by deep learning method, of which can further boost the denoising performance by combining with its complementary image models.
Statistical Quantile Learning for Large, Nonlinear, and Additive Latent Variable Models

The studies of large-scale, high-dimensional data in fields such as genomics and neuroscience have injected new insights into science. Yet, despite advances, they are confronting several challenges, often simultaneously: lack of interpretability, nonlinearity, slow computation, inconsistency and uncertain convergence, and small sample sizes compared to high feature dimensions. Here, we propose a relatively simple, scalable, and consistent nonlinear dimension reduction method that can potentially address these issues in unsupervised settings. We call this method Statistical Quantile Learning (SQL) because, methodologically, it leverages on a quantile approximation of the latent variables together with standard nonparametric techniques (sieve or penalyzed methods). We show that estimating the model simplifies into a convex assignment matching problem; we derive its asymptotic properties; we show that the model is identifiable under few conditions. Compared to its linear competitors, SQL explains more variance, yields better separation and explanation, and delivers more accurate outcome prediction. Compared to its nonlinear competitors, SQL shows considerable advantage in interpretability, ease of use and computations in large-dimensional settings. Finally, we apply SQL to high-dimensional gene expression data (consisting of 20,263 genes from 801 subjects), where the proposed method identified latent factors predictive of five cancer types. The SQL package is available at https://github.com/jbodelet/SQL.
The one-way communication complexity of submodular maximization with applications to streaming and robustness

We consider the classical problem of maximizing a monotone submodular function subject to a cardinality constraint, which, due to its numerous applications, has recently been studied in various computational models. We consider a clean multi-player model that lies between the offline and streaming model, and study it under the aspect of one-way communication complexity. Our model captures the streaming setting (by considering a large number of players), and, in addition, two player approximation results for it translate into the robust setting. We present tight one-way communication complexity results for our model, which, due to the above-mentioned connections, have multiple implications in the data stream and robust setting. Even for just two players, a prior information-theoretic hardness result implies that no approximation factor above 1/2 can be achieved in our model, if only queries to feasible sets, i.e., sets respecting the cardinality constraint, are allowed. We show that the possibility of querying infeasible sets can actually be exploited to beat this bound, by presenting a tight 2/3-approximation taking exponential time, and an efficient 0.514-approximation. To the best of our knowledge, this is the first example where querying a submodular function on infeasible sets leads to provably better results. Through the above-mentioned link to the robust setting, both of these algorithms improve on the current state-of-the-art for robust submodular maximization, showing that approximation factors beyond 1/2 are possible. Moreover, exploiting the link of our model to streaming, we settle the approximability for streaming algorithms by presenting a tight 1/2+ε hardness result, based on the construction of a new family of coverage functions. This improves on a prior 1−1/e+ε hardness and matches, up to an arbitrarily small margin, the best known approximation algorithm.