Miracle-dz/UniFAR-FTUs huggingface dataset:
Annealing Effect on Some Optical Properties of Cr2O3 Thin Films Prepared by Spray Pyrolysis Technique

Cr2O3 thin films have been prepared by spray pyrolysis on a glass substrate. Absorbance and transmittance spectra were recorded in the wavelength range (300900) nm before and after annealing. The effects of annealing temperature on absorption coefficient, refractive index, extinction coefficient, real and imaginary parts of dielectric constant and optical conductivity were expected. It was found that all these parameters increase as the annealing temperature increased to 550°C.
INVESTIGATION OF NEW NSAIDS FOR THE TREATMENT OF INFLAMMATORY DISEASES OF KIDNEY AND URINARY TRACT

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used in the world, primarily due to their high efficiency for the treatment of inflammatory induced pain syndrome. The main feature of NSAIDs is a combination of anti-inflammatory, analgesic, antipyretic, and anticoagulant properties. However, their long-term use is associated with side effects in the gastrointestinal tract including peptic ulcers and other. We developed and synthesized molecule of methyl (4-О-β-glucopyranosyloxy)-benzoic acid. The anti-inflammatory effect of methyl (4-О-β-glucopyranosyloxy)-benzoic acid evaluated using the carrageenan-induced hindpaw edema model. The study shows that the intragastrically administration of test substance to animals reduces inflammatory process.
RDF Literal Data Types in Practice

One of the more mysterious aspects of RDF (Resource Description Framework) is typed literals. For instance, confusion over the difference between a plain character string (“foo”) and a string that is explicitly typed (“foo”ˆˆxsd:string) is common. Also, questions often arise about comparisons between literals of the various numeric types (e.g., long, integer, decimal, and float). This paper explores how several popular triple stores handle literals via direct testing, and also compares their behavior to the relevant standards. Along the way, we highlight a number of implementation inconsistencies and some surprising aspects of the standards themselves.
Review of Literature on The Impact of Foreign Workers on Wages Keywords:Foreign Workers; Wages

This paper reviews the current theoretical and foreign workers research with a particular emphasis on the impact of foreign workers on wages level. In general, the model that is used to measure the impact of foreign workers on wages level is Constant Elasticity of Substitutes model (CES), assimilation economic model, cohort effect model, spatial correlation model, Total Factors Productivity (TFT), Skill-Biased Productivity (SBP), standard cross-section and other estimation models. The research results found are different, thus emerging the positive and negative effect. The positive impact is the optimism of the impact of foreign workers’ presence on local workers with high skills and experience. The group of high skill and experience shows that the relations between local and foreign workers are complementary and causes the transfer of technology and knowledge level, as well as the wages of local workers, tend to increase. Meanwhile, the negative impact is the pessimism of the impact of foreign workers’ presence on local workers with low skills. The group of low skill and experience are substitutionary and causes increased unemployment, as well as the wages of local workers, tend decline.
SOLID STATE SELF-HEALING SYSTEM: EFFECTS OF USING IMMISCIBLE HEALING AGENTS

The solid state self-healing system was obtained by employs a thermosetting epoxy resin, into which a thermoplastic is dissolved. The aim of this study is to identify the effect of using immiscible healing agents, which are polyvinyl chloride and polyvinyl alcohol, on solid state self-healing system. Healing was achieved by heating the fractured resins to a specific temperature; above their glass transition temperature (Tg) which obtained from dynamic mechanical analysis (DMA) in order for thermal expansion to occur. The thermal properties and bonding formed in the epoxy resins were characterized by means of Fourier Transform Infrared Spectroscopy (FTIR). Izod impact test was performed in preliminary work. Further work then has been done using compact tension test to demonstrate details self-healing capability of the different specimens. Under compact tension test, it was found that healable resin with PVC has highest healing efficiency followed PVA with 7.4% and 3% of average percentage healing efficiencies respectively. These results are due to the different solubility parameters of the thermoset/network and thermoplastic polymer which led to the phase separation. Morphological studies using microscope optic prove the fracture-healing process and morphological properties of the resins.
Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection

Physical activity (PA) is critical for reducing the risk of obesity, a prevalent health concern that burdens low-socioeconomic status (SES) households. While self-tracking apps can increase PA, encouraging app engagement remains a challenge, thus limiting the app's efficacy. To understand how to better support caregiver's motivation to use family health apps, we designed and evaluated Storywell? a mobile app for promoting family PA. Guided by Self-Determination Theory, Storywell provides social rewards (e.g., storybooks with interactive reflective questions) aimed at supporting relatedness and motivation. Our 3-month qualitative study with 18 families revealed satisfying moments that can affect caregiver's motivation. We contribute new knowledge on designing satisfying moments that heighten the motivation to use health apps, especially for low-SES families who face many barriers to using such systems.
Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.
Towards Production-Grade, Platform-Independent Distributed ML

Most existing frameworks for distributed machine learning are either tied to a specific data platform, or focus on novel computational and communication abstractions. The latter often neglect the constraints of shared-use clusters, such as fault tolerance, fair resource (network, CPU) usage, and isolation. This paper proposes a new distributed ML framework, SALMON, that abstracts the key components (control flow, partitioned data store, group communication) and relies only on above-resource-manager platform dependencies (via Apache REEF). The resulting framework is both expressive for common ML algorithm patterns (e.g., iterative MapReduce and parameter server), and flexible to operate on a variety of conventional, shared-use platforms (e.g., Apache Hadoop and HPC). Early experiments demonstrate the promise of this approach via comparisons with Apache Spark on a large-scale production dataset.
How do user-centered design studies contribute to cartography?

I ask in this essay: How do user-centered design studies contribute to cartography? Scholars in related fields increasingly recognize the intellectual value of employing user-centered processes to improve a single product and identify new design considerations for future products. To this end, I propose an analytical framework for organizing the contributions of user-centered design studies that includes eight opportunities for advancing cartography: (1) domain gap analyses, (2) adapted or novel user-centered methods, (3) streamlined user-centered design processes, (4) transferable design insights, (5) comprehensive user-centered design case studies, (6) novel or unique maps and visualizations, (7) summative controlled experiments, and (8) new insights into pressing geographic problems. I apply this framework against my own collaborative work in a retrospective analysis of three UCD case studies: the GeoVISTA CrimeViz visual analytics tool, the NOAA Lake Level Viewer climate change visualization, and the UW Cart Lab Global Madison mobile map.
Emote-Controlled

In recent years, streaming platforms for video games have seen increasingly large interest, as so-called esports have developed into a lucrative branch of business. Like for other sports, watching esports has become a new kind of entertainment medium, which is possible due to platforms that allow gamers to live stream their gameplay, the most popular platform being Twitch.tv. On these platforms, users can comment on streams in real time and thereby express their opinion about the events in the stream. Due to the popularity of Twitch.tv, this can be a valuable source of feedback for streamers aiming to improve their reception in a gaming-oriented audience. In this work, we explore the possibility of deriving feedback for video streams on Twitch.tv by analyzing the sentiment of live text comments made by stream viewers in highly active channels. Automatic sentiment analysis on these comments is a challenging task, as one can compare the language used in Twitch.tv with that used by an audience in a stadium, shouting as loud as possible in sometimes nonorganized ways. This language is very different from common English, mixing Internet slang and gaming-related language with abbreviations, intentional and unintentional grammatical and orthographic mistakes, and emoji-like images called emotes. Classic lexicon-based sentiment analysis techniques therefore fail when applied to Twitch comments. To overcome the challenge posed by the nonstandard language, we propose two unsupervised lexicon-based approaches that make heavy use of the information encoded in emotes, as well as a weakly supervised neural network–based classifier trained on the lexicon-based outputs, which is supposed to help generalization to unknown words by use of domain-specific word embeddings. To enable better understanding of Twitch.tv comments, we analyze a large dataset of comments, uncovering specific properties of their language, and provide a smaller set of comments labeled with sentiment information by crowdsourcing. We present two case studies showing the effectiveness of our methods in generating sentiment trajectories for events live streamed on Twitch.tv that correlate well with specific topics in the given stream. This allows for a new kind of implicit real-time feedback gathering for Twitch streamers and companies producing games or streaming content on Twitch. We make our datasets and code publicly available for further research.1
Fleck Retina Syndrome in Thailand

Purpose: To study presenting symptoms, signs, and electroretinographic findings of patients diagnosed with fleck retina syndrome in Siriraj Hospital, Bangkok, Thailand. Design: Retrospective chart review. Methods: 67 charts of patients diagnosed with fleck retina syndrome during 2003 2009 were reviewed. Demographic data including gender, age, visual acuity, underlying diseases, presenting symptoms, signs and electroretinographic (ERG) patterns were recorded. Nineteen charts were excluded due to incomplete data. In total 48 cases were analyzed. Results: Forty-eight patients (19 male and 29 female), mean age 35+19.15 years (range from 11-79 years), were included. The mean visual acuity at presentation using logMAR was 0.81+0.66 and 0.82+0.69 for right eye and left eye, respectively. There were 12 patients (25%) presenting with fleck retina and nyctalopia, 11 patients (22.9%) were diagnosed with retinitis punctata albescens with nonrecordable ERG and 1 patient (2.1%) was diagnosed with fundus albipunctatus with prolonged dark adaptation of rod ERG. Thirty-six patients (75%) of fleck retina presented without nyctalopia, of which 25 patients (52%) were diagnosed with Stargardtûs disease with decreased flicker and cone ERG. The other 5 patients (10.4%) were diagnosed with fundus flavimaculatus and 6 patients (12.5%) were diagnosed with familial drusen with minimal change in ERG. Conclusion: Fleck retina syndrome can present with nyctalopia, blurred vision or incidental findings during routine ophthalmological examination. Electroretinography is the specific test for diagnosis of the subgroup of fleck retina syndrome. Specification of diagnosis can predict the clinical course and disease progression for each subgroup of disease. Thai J Ophthalmol 2017; July-December 31(2): 55-65.
Improving Text-Independent Speaker Identification Performance Using Gaussian Mixture Speaker Models

Systems that automatically recognize a speaker are increasingly important in humancomputer interaction because speech communication has always been and will continue to be the dominant mode of human social bonding and information exchange. This paper investigates the use of Gaussian mixture models (GMMs) for robust text-independent speaker identification. The experiments performed in this research examine several aspects and parameters of GMM usage: algorithmic issues, amount of training data, modeling different languages, and small and large population performance. We found that increasing the amount of training data and decreasing the number of speakers improved the accuracy of text-independent speaker identification using statistical models based on Gaussian mixture models. There also appears to be a maximum number of Gaussian mixture components needed to adequately model speakers and achieve good identification performance for different amounts of training data.
Urban Climate News Quarterly Newsletter of the IAUC Inside the Summer issue . . .

Gerald Mills gerald.mills@ucd.ie Many of these themes were explored at the AAG conference held in Washington DC in April and reported upon by Chandana Mitra. As the World Cup approaches its climax, Winston Chow provides some insights into the microclimates associated with stadiums like the match venues, which are 16 in all and 10 of which are over 1200m above sea-level. Finally, I would like to thank the editor David Pearlmutter for the quality of this production. Urban Climate News is the best advertisement for the IAUC and its vigour can be judged by the involvement of its members. I would encourage readers to consider sending in reports on projects, on urban climate Colleagues, welcome to the 36th edition of Urban Climate News.
Asymmetric information in secondary insurance markets: Evidence from the life settlements market

We use data from a large US life expectancy provider to test for asymmetric information in the secondary life insurance—or life settlements —market. We compare realized lifetimes for a subsample of settled policies relative to all (settled and nonsettled) policies, and find a positive settlement‐survival correlation indicating the existence of informational asymmetry between policyholders and investors. Estimates of the “excess hazard” associated with settling show the effect is temporary and wears off over approximately 8 years. This indicates individuals in our sample possess private information with regards to their near‐term survival prospects and make use of it, which has economic consequences for this market and beyond. Asymmetric information life settlements life expectancy secondary insurance market D12 G22 J10
Systemic Importance and Optimal Capital Regulation

This paper tests the effectiveness of a newly proposed systemic risk tax to be levied on systematically important banks and have identified that such tax could force the banks to build up capital holdings in recessions, while it seems opposite in booms. The systemic tax might also affect the social welfare through the equilibrium loan rate, significantly influencing the non-systemically bank which is assumed to be the loan rate taker. As for the optimal capital requirements, we have discovered the existence of a countercyclical capital requirement (at around 2.3%) that requires a higher capital in booms, which is in line with Basel III. Moreover, we have also suggested that contagion effect, bank capital supply, deposit insurance might also influence the optimal capital requirements profoundly.
FindStat – a database and search engine for combinatorial statistics and maps

FindStat is a collaborative online database of combinatorial statistics on combinatorial collections and of maps between such collections. A search engine allows users to identify statistics and maps given a few known values, using for example the web interface. As of April 2019, the database contains 1387 statistics and 146 maps on 18 collections by 70 contributors.
Using a coach to improve team performance when the team uses a Kanban process methodology

Teams are increasing their use of the Kanban process methodology across a range of information system projects, including software development and data science projects. While the use of Kanban is growing, little has been done to explore how to improve team performance for teams that use Kanban. One possibility is to introduce a Kanban Coach (KC). This work reports on exploring the use of a Kanban Coach, with respect to both how the coach could interact with the team as well as how the use of a coach impacts team results. Specifically, this paper reports on an experiment where teams either had, or did not have, a Kanban Coach. A quantitative and qualitative analysis of the data collected during the experiment found that introducing KC led to significant improvement of team performance. Coordination Theory and Shared Mental Models were then employed to provide an explanation as to why a KC leads to better project results. While this experiment was done within a data science project context, the results are likely applicable across a range of information system projects.
Running head: NEURAL VALUATION, HEALTH CAMPAIGNS, AND RISKY PEER INFLUENCE 1 Neural Valuation of Anti-Drinking Campaigns and Risky Peer Influence in Daily Life

Objective: Health behavior is affected by competing sources of influence like media messages and peers. In the context of alcohol consumption, college students are targeted by anti-drinking media messages, but tend to have pro-alcohol conversations with peers. How do humans integrate competing sources of influence on daily behavior? We observed individuals under exposure to anti-alcohol media messages and pro-alcohol conversations and tested a “common neural value” account of how contradictory influences are integrated to affect behavior. Methods: Participants were instructed to cognitively regulate responses to anti-drinking media messages while undergoing fMRI at baseline. Individual differences in success in messageconsistent or -derogating regulation were indexed by changes in activity within the neural valuation system (ventral striatum/VS, ventromedial prefrontal cortex/VMPFC), providing a proxy for success in finding value in message-consistent/-derogating engagement. To measure peer influence, we tracked daily drinking-related conversations and drinking behavior for 30 days using mobile electronic diaries. Results: Peer conversations, on average, were positive towards drinking. More positive conversations led to more future drinking, particularly for participants who showed greater neural value signals when derogating anti-drinking media. Susceptibility to risky peer influence decreased with increasing success in up-regulating message-consistent neural valuation responses to anti-drinking media. Neural effects were driven by VS-activity. Conclusions: Results are consistent with a dynamic value integration process where contradictory influences inform a common neural value signal. Reductions in the value of a behavior (through anti-drinking campaigns) may buffer against future value increases after exposure to competing influences (pro-alcohol peers) with important real-world consequences.
FINAL REPORT Committee for Aid and Education in Neurochemistry ( CAEN ) CATEGORY 1 B : Research supplies for use in the applicant ’ s home laboratory December 2012 round

In this report we show the results obtained from the original proposal. This study was sent to the Journal of Neurochemistry (Changes in trafficking of NMDARs to lipid raft and synaptic membrane in nucleus accumbens after acute administration of cocaine and methylphenidate). However the rotor was used for the development of three additional studies, one article was accepted and two more articles were submitted.
QuickDedup: Efficient VMdeduplication in cloud computing environments

Deduplication is one of the major storage optimisation techniques for Virtual Machines (VMs) in cloud environment. Usually, hashing of blocks helps in identifying duplicate data blocks. This paper proposes a novel deduplication approach, QuickDedup that reduces the overall deduplication time, metadata overhead and the number of hash computations, and subsequent comparisons for the VM disk images. In addition to minimising the deduplication related metadata, which is a necessary by-product useful in checking deduplication, QuickDedup, follows novel byte comparison scheme to prepare various block classes. This way, QuickDedup eliminates or minimises the need for hash calculation and subsequent comparisons. QuickDedup performs the calculation and comparisons of hashes within the respective categories only. QuickDedup saves the space required for hash storage during deduplication and makes deduplication of VM disk images much faster. We conducted a detailed evaluation of QuickDedup on various metrics with different kinds and sizes of VM images taken from publicly available datasets. The evaluation results show a substantial improvement of up to 96% in the overall deduplication time required to deduplicate VM images apart from significant savings in metadata and storage overhead. © 2020 Elsevier Inc. All rights reserved.