Miracle-dz/UniFAR-FTUs huggingface dataset:
Perinatal Oral Health: A Novel Collaborative Initiative to Improve Access, Attitudes, Comfort Level, and Knowledge of Pregnant Women and Dental Providers

Abstract Objectives The objectives of this program were to increase access to dental care among pregnant women and to improve dental students' exposure, comfort level, and knowledge of the potential impact of poor oral health on pregnancy outcomes. Study Design Through collaborative efforts of a School of Dentistry and a School of Medicine, the Oral Health Pregnancy Day Initiative (OHPDI) was developed. Dental students were educated on the impact poor oral health may have on pregnancy outcomes and the importance of access to care. Pregnant women received perinatal oral health education and needed dental care. Results Thirty-four pregnant women presented for the OHPDI. Thirty-nine dental students participated. Eighty-five percent of students reported they learned how poor oral health may have a negative impact on pregnancy and birth outcomes; 79% agreed as a result of the event they were more likely to treat pregnant women. Ninety-four percent of pregnant women reported not having a dentist and 100% received perinatal oral health education and needed dental care. Eighty-eight pregnant women were seen subsequent to the OHPDI. Conclusion This initiative resulted in increased students' knowledge, exposure, and comfort level to treating pregnant women and pregnant women received needed oral health care education and dental treatment.
SmartCity - planning a sensor network based on LoRaWAN technology

New cientiﬁc developments made possible to monitorize and control all kinds of objects and services, by connecting them all in between themselves, giving birth to a new paradigm: the Internet of Things. This way has become possible to improve cities eﬀciency and accuracy on dealing with all kinds of problems. This thesis is based on the projection of a wireless telecommunications system based on LoRaWAN technology, capable of dealing with the sensor network of the project ”SmartCity Abrantes”. These sensors may or not have a ﬁxed location. Firstly, a study of capacity and coverage has been developed considering only the sensor that don’t change their position and the initial system conﬁguration. For this analysis the real scenario was theoretical simulated. It was noticed that some of these sensors were not covered, which motivated the study of the inﬂuence of the introduction of a new gateway on the system. Finnally, it was made a study on network coverage for the mobile sensores, considering all the area of Abrantes region, so that it would be possible to understand were these sensors would be able to communicate. It was found that half of this territory was covered.
Visualizing Differences to Improve End-User Understanding of Trigger-Action Programs

Trigger-action programming lets end-users automate and connect IoT devices and online services through if-this-then-that rules. Early research demonstrated this paradigm's usability, but more recent work has highlighted complexities that arise in realistic scenarios. As users manually modify or debug their programs, or as they use recently proposed automated tools to the same end, they may struggle to understand how modifying a trigger-action program changes its ultimate behavior. To aid in this understanding, we prototype user interfaces that visualize differences between trigger-action programs in syntax, behavior, and properties.
Improvement in gas sensitivity of ZnO thin film by doping with Ti

In this work, we explore immaculate and doped ZnO films with Ti for CO gas detecting. Immaculate and doped ZnO layers have been manufactured by puls Laser deposition procedure (PLD) with different doping proportion. Structure and morphological properties have been studied by XRD and AFM to concentrate on the parameters that enhance the gas detecting. Pure ZnO films and Low doping with Ti at room temperature indicating low affectability. Expanding the working temperature and dopant proportion prompts expanding the responsively and lessening stall the reaction time and recuperation time.
An Approach for Reducing the Search Space for Simheuristics Applications in Logistics Networks in Trading

: For the purpose of improving the performance of logistics networks, so-called logistics assistance systems (LASs) are used. A LAS supports decision makers in taking actions, such as increasing the stock level of a stock keeping unit. A LAS can be combined with simulation to evaluate possible actions. With the size of the logistics network increasing, the number of actions to be evaluated raises and, therefore, the response time of the LAS. In this paper, the authors present two novel approaches for increasing efficiency and effectiveness of a given LAS by defining redundant and interchangeable actions. The results show that the number of simulation runs has been reduced by utilising the presented approaches; thus, the LAS’s performance could be improved.
Detecting Popularity and Innovation on Twitter to Find the Best stocks in SP500

In the past years, social media has become one of the main sources related to the up and down movement of the financial market. The interest of investors in minimizing the risks when wagering in the financial market has increased with the possibility of predicting its up’s and down’s based on the sentiment classification of Twitter’s user’s opinions. In this paper, it is demonstrated how Twitter can be used to detect innovative and popular companies in the S&P500 index market. The collection of data is done through a robot that collects tweets from Twitter search API based on words, previously selected and related to those two subjects of interest. In order to analyse the text content of tweets and to classify a company’s innovation/popularity opinions either as a positive, negative or neutral opinions, it is implemented a Naïve Bayes classifier, with an accuracy of 70,6%. To infer which of the selected words are more influent in the detection of innovative and popular companies, a Genetic Algorithm is implemented. A forecasting of real-time outcomes of the S&P500 stock market values is calculated based on results obtained from the Genetic Algorithm, to detect which are the most innovative and popular companies for investors to wager. The best results were interesting, obtaining a profit of 11,8% for the Innovation case and 5,5% for the Popular case. KeywordsTwitter, Stock Market, Genetic Algorithms, Naïve Bayes, Popularity/Innovation detection;
Investigation of Herbal Tea Production from Purslane (Portulaca oleracea)

Purslane (Portulaca oleracea L.) deserves special attention from agriculturalists and nutritionists. It is a common weed in turfgrass areas as well as in field crops. The mixture of phytochemicals present in many of these plants contributes to their protective and health effects. The stems and leaves of the plant are succulent and edible with a slightly acidic and salty taste similar to spinach. Objective of the present study was focused on the effect of blanching, drying and storage of herbal tea produced from Portulaca oleracea. Optimal results found that herbal tea would have stable antioxidant capacity while blanching at 95C in 4 seconds with the presence of 3% CaCl2 submersed in blanching solution; drying at 40C with 1.5 cm in dimenstion of Portulaca oleracea; storage under vaccum in PET/AL/PE bag within 6 months without any deterioration. Purslane (Portulaca oleracea) could be used as a good source of minerals, antioxidants and omega-3 especially for functional food in form of herbal tea.
Pessimism About Unknown Unknowns Inspires Conservatism

If we could define the set of all bad outcomes, we could hard-code an agent which avoids them; however, in sufficiently complex environments, this is infeasible. We do not know of any general-purpose approaches in the literature to avoiding novel failure modes. Motivated by this, we define an idealized Bayesian reinforcement learner which follows a policy that maximizes the worst-case expected reward over a set of world-models. We call this agent pessimistic, since it optimizes assuming the worst case. A scalar parameter tunes the agent's pessimism by changing the size of the set of world-models taken into account. Our first main contribution is: given an assumption about the agent's model class, a sufficiently pessimistic agent does not cause "unprecedented events" with probability $1-\delta$, whether or not designers know how to precisely specify those precedents they are concerned with. Since pessimism discourages exploration, at each timestep, the agent may defer to a mentor, who may be a human or some known-safe policy we would like to improve. Our other main contribution is that the agent's policy's value approaches at least that of the mentor, while the probability of deferring to the mentor goes to 0. In high-stakes environments, we might like advanced artificial agents to pursue goals cautiously, which is a non-trivial problem even if the agent were allowed arbitrary computing power; we present a formal solution.
ESTIMATION OF GENETIC VARIABILITY PARAMETERS IN INDIAN HF CATTLE USING MICROSATELLITE MARKERS

The study was undertaken with an objective to estimate polymorphism of 12 microsatellite markers in Indian HF cattle. Allele frequencies, polymorphism information content, heterozygosity and exclusion probability were calculated. A panel of 12 microsatellite markers (BM1824, BM2113, INRA023, SPS115, TGLA122, TGLA126, TGLA227, ETH10, ETH225, BM1818, ETH3, TGLA53) was amplified in a single multiplex reaction and analyzed by capillary electrophoresis on an automated DNA sequencer. The expected heterozygosity ranged from 0.599 to 0.892 (mean 0.7734). The total exclusion probability using 12 microsatellite loci with 1 known parent was 0.9988. Nine out of 12 microsatellite loci revealed relatively high polymorphic information content (>0.7). The results of this study showed a relatively low pedigree error rate of 4.34%.
Geographic Tracking of Portuguese Army Vehicles in Operations with Friendly Forces

In this document, it will be approached the difficulties about the communications established in joint operations within the Portuguese Army and other external forces. As a civil protection agent, the Portuguese Army must be integrated into operations related to it. So, it's analyzed the main forms of communications established nowadays between this two parties. As there isn't a proper solution for an efficient tracking and communication system for the operations between the Portuguese Army and external forces, this master theses aims to present an engineering solution, based on an independent, efficient and lightweight applications to solve that matters. All of this will be achieved in accordance with the existing interoperability standards defined by NATO. It was developed a vehicle tracking system which provides commanders with a tool for controlling forces in civil protection operations, solving interoperability problems, between exchanging vehicle location information between entities cooperating together. This system was presented to the entities in charge of missions of this scope, inside and outside the Army, having been approved as a pertinent project with required applicability.
Task Control for Robots with Learning from Demonstration

When humans teach how to perform a task to others, they commonly use a mixture of labels for what do to, indications for sequence of action order, and feedback for perceived performance evaluation. For a natural and efficient teaching experience it should be expected for a collaborative robot to learn a task using a method that accepts aforementioned inputs. Moreover, when a demonstrator is not available, it would be desirable for a robot to autonomously improve its performance on a task. Even though previous work shows that the ability to adapt is crucial for robots to succeed in real world tasks [1], [2], existing Learning from Demonstration algorithms are unable to combine all of the above input modalities [3]. With this thesis we plan to combine ideas from two different approaches, supervised learning and reinforcement learning. We aim to contribute a novel learning from demonstration technique for a robot to learn a task. More specifically we seek to give a robot the ability to learn from initial demonstrations, and task execution feedback, while enabling the robot to: i) improve its own performance in the absence of a human demonstrator, and ii) transfer an acquired skill to a new task to jump start the learning process. In previous work we designed a user study for evaluating the differences between three fundamentally distinct Learning from Demonstration algorithms (Behavior Networks, Interactive Reinforcement Learning, and Confidence Based Autonomy) [4]. This comparison provided us insights on the relative strengths of these approaches; naive users achieved better performance in teaching the task with supervised learning (CBA), whereas the Interactive Reinforcement Learning algorithm modeled user behavior most accurately. In another work we investigated the use of learning from demonstration in transfer learning, and introduced HumanAgent Transfer (HAT): an algorithm that combines transfer learning, learning from demonstration, and reinforcement learning to achieve rapid learning and high performance in complex domains [5]. In this work we have shown that human demonstrations transferred into a rule-based baseline policy for an agent and rened using reinforcement learning signicantly improve both learning time and policy performance. Our algorithm leads to statistically signicant improvement in performance over learning without demonstration. The comparative user study and HAT algorithm motivated us to think about how we can take advantage of the strength of these approaches combined together. A third important
Video Game Addiction in Workplaces

The purpose of this study is to investigate the impact of organizational politics on employees’ video game addiction and how it influences their job satisfaction and organizational citizenship behaviour. And we explore if leader-member exchange can moderate the relationship between organizational politics and video game addiction. For this, we collected data from 305 employees in Korean companies through a survey method. In the results, first, organizational politics increases conflict and loss of control among the sub-factors of video game addiction. Second, each phenomena of video game addiction such as conflict, loss of control and withdrawal decrease each relevant factors of job satisfaction and organizational citizenship behaviour. However, on the contrary to our expectation, some of subfactors of video game addiction increase pay or promotion satisfaction. Finally, leader member exchange quality decrease the effect of each characteristics of organizational politics on conflict and loss of control of video game addiction. Keywords— video game addiction, organizational politics, job satisfaction, organizational citizenship behaviour, leader member exchange quality
IJSRD-International Journal for Scientific Research & Development| Vol. 3, Issue 01, 2015 | ISSN (online): 2321-0613

With the advent of Internet & E-Commerce applications people are exposed to mines of information and variety of products to choose from. In such situation it is quite obvious to get lost in, exploring abundance of products and related information while zeroing down the product to buy from the available many. This causes the waste of large amount of precious time and may at the same time, create confusion in the minds of prospective on-line shoppers regarding purchasing decision to be made, leading to in-vein search and unfruitful buying attempt. As a consequence, the role of user modeling and personalized information access is becoming crucial, users need a personalized support in sifting through large amounts of available information, according to their interests and tastes. Recommender systems play an important role in such scenario by presenting the buyer with the ranked list of products preferred by existing buyers for the same/similar products. The number of features of the product used to determine the order of the product in the ranked list will define the approach used by Recommender system.
Instruments to Measure Preoperative Acute Situational Anxiety, AANA Journal, February 2017

AANA Journal  February 2017  Vol. 85, No. 1 31 Acute situational anxiety is a subjective fearful feeling of emotion that is influenced by an immediate situation. It can vary in intensity and duration among patients in the preoperative period of hospitalization. In adults, the incidence of preoperative acute situational anxiety ranges from 11% to 80%. Untreated anxiety in the perioperative period can lead to multiple deleterious effects for patients. Previous reviews on instruments to measure anxiety have not focused on the preoperative period of hospitalization for surgical patients. The objective of this integrative review was to synthesize and describe the instruments used over the last decade to measure preoperative anxiety in the surgical setting. A systematic search strategy of the PubMed, Cumulative Index to Nursing and Allied Health Literature, and PsycINFO databases was used to review the literature. A total of 370 articles were identified, but only 5 met the inclusion criteria for this review. In the 5 articles, varying levels of reliability, validity, and feasibility of the instruments were reported as well as context considerations. Reliability and validity are not consistently reported among instruments that measure preoperational anxiety, making it difficult for providers to measure preoperational anxiety and provide treatment based on the instrument results.
Computational Analysis of Tumour Heterogeneity

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of nonresponding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity.
The Copper for CVD Graphene: its cleaning and oxidation in relation to graphene quality

Abstract The efforts toward mass production of large area graphene have stressed even more the need to control and improve the quality and reproducibility of the CVD growth and transfer of graphene [1]. Copper foil is the most common substrate and a variety of work has already allowed to progress in increasing grain size and controlling the nucleation site. Nevertheless, there are still unsolved issues and challenges that make CVD graphene growth and quality irreproducible dependent on copper foil supplier and batch. Furthermore, recently some contradictory results are out about the graphene as a barrier to oxidation of metals underneath. This latter aspect is important in relation also to the adhesion of graphene on copper and consequently to the quality of its transferring. It is clear that a correlation exists between the copper substrate, the growth, the graphene quality and goodness of transferring [2]. This contribution aims at elucidating and controlling this correlation. We show significant improvement in reproducibility and control of CVD graphene growth and quality by a H2 plasma processing of copper. Implementation of commercial CVD reactors with a simple H2 remote plasma configuration will be shown, which is also applicable to other 2D materials. Various copper foils and wafers from different suppliers and with different morphology are tested and discussed. The significant advantage over the common H2-Ar annealing will be presented, which leads to consistent growth conditions and high-quality continuous graphene. This step will also be related to properties of graphene as oxidation resistance layer [3], discussing the role of the copper oxides on the adhesion of graphene to the Cu foil and consequences for the quality of the transferring. Therefore, we also correlate results on copper/graphene oxidation to the copper processing and graphene quality.
Hankel-type model reduction based on frequency response matching

In this paper, a stability preserving model reduction algorithm for single-input single-output linear time invariant systems is presented. It performs a data fitting in the frequency domain using semidefinite programming methods. Computing the frequency response of a model can be done efficiently even for large scale models making this approach applicable to those. The relaxation used to obtain a semidefinite program is similar to one used in Hankel model reduction. Therefore accuracy of approximation is also similar to Hankel model reduction one. The approach can be easily extended to frequency-weighted and parameter dependent model reduction problems.
Antioxidant and Antidiabetic Effects of Garcinia schomburgkiana Extracts and Fermented Juices

This study investigated total phenolic contents, antioxidant activities, and antidiabetic activities in flesh, seed and leaf extracts, and the fermented juices of Garcinia schomburgkiana Pierre (Madan). Seed extract showed the highest total phenolic content, radical scavenging activity and amylase inhibitory activity, whereas the leaf extract had the strongest ferrous ion chelating activity and inhibitory activities on glucosidase and lipase. The very strong positive correlation between total phenolic content and radical scavenging activity, and between ferrous ion chelating and glucosidase inhibitory activities were significantly found (r = 1.000 and 0.998 at P<0.05, respectively). Moreover, the Madan juice revealed the highest total phenolic content at 72 hour fermentation. However, production of radical scavenging and ferrous ion chelating activities of fermented juices increased at the start of fermentation. Additionally, amylase inhibitory activity increased during 24 hour fermentation, whereas glucosidase and lipase inhibitory activity reached maximal levels during 17 day and 48 hour fermentation, respectively. The total phenolic contents of the fermented juices were very strongly negatively correlated with radical scavenging activity, and ferrous ion chelating activity, significantly (r = -0.869 and -0.937 at P<0.05, respectively). In addition, the very strong positive correlation between radical scavenging activity and ferrous ion chelating activity was significantly found (r = 0.804, P<0.05). Our results indicated that G. schomburgkiana Pierre is a potential nutraceutical source, and its fermented juice can be further improved as a healthy fruit drink.