Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Standardized Patients in the Training and Evaluation of Physician Assistant Students

Purpose: Standardized patients (SPs) are used extensively in physician and nursing education, where they have proven to be valuable educational tools. Practice with SPs may provide durable benefits regarding communication skills. However, the extent to which SPs are used in physician assistant (PA) training is unknown. The primary aim of this study was to determine the extent to which programs use SPs and whether SP utilization varied by the program's affiliation with an academic center or medical school. Methods: Self‐administered surveys were sent to one academic coordinator at each of the then 134 accredited PA programs. Results: Ninety‐six programs returned surveys. At programs represented by survey participants, SPs play various roles, including providing medical histories and participating in complaint‐specific and organ‐specific “sensitive” examinations (rectal, pelvic, etc). Programs housed in medical schools were more likely to use professional patients. Respondents from programs not using SPs provided reasons why they were not used. Most common among these were issues related to cost, access, training, and recruitment. To a lesser extent, liability issues were also cited, as was a perceived lack of evidence that SPs add value to PA education. Details about SP compensation were provided by only 48 (50%) respondents, but reflected a broad range of remuneration. Conclusions: The majority of accredited PA training programs use SPs. Although they play a range of educational roles, they are most commonly used for the most sensitive components of physical examination. Use of SP patients was particularly common in programs affiliated with medical schools and community colleges.
Area- and Power-Efficient Staircase Encoder Implementation for High-Throughput Fiber-Optical Communications

This brief presents a VLSI architecture of a high-throughput, low-latency, and power staircase forward error correction (FEC) encoder. The designed encoder achieves low latency and memory overhead by splitting the parity generation matrix and precomputing partial parity bits for the next staircase block while generating the current staircase block. The proposed encoder is designed with a multistage pipelined architecture that enables high efficiency in terms of throughput and area. Using 65-nm CMOS technology, the synthesized encoder achieves 432 Gb/s when operating at 909 MHz, with the power consumption of 323 mW.
Towards Actionable Forms of Communicating and Sharing Design Knowledge

Design research aims to construct knowledge that is useful for designers and non-designers in the processes of designing for various types of challenges: from making products to solving complex social problems. Designers and non-designers seek information and inspiration for their work both in a non-design world, e.g., in films, illustrated magazines, and in various sources and forms of design research. Conference papers are one of many sources of design research insights. Unfortunately, the textual format of conference papers does not allow to convey the richness of design research insights and express them in forms that are actionable and available to others, e.g., non-designers. As a result, members of design teams might feel disempowered or do not trust and accept provided design research outputs. Therefore, they do not act upon the provided design research insights and find it challenging to apply them in collaboration. In this paper, we present the actionable palette that consists of nine qualities that act as building blocks of actionable forms of sharing and communicating design knowledge. Using the actionable palette to review design research outputs from 51 pictorials, we identified six forms of capturing design research insights. We characterize these six forms and analyze them in terms of actionability to inspire designers and non-designers to experiment with forms of sharing design research insights and first design ideas based on design research insights. Finally, we provide a set of guidelines to inspire and inform the process of reaching the particular qualities of actionability for design research outputs
Finite volume computation and verification of fluid flow and het transfer phenomena in domains with moving boundaries and complex geometries

Problems where complex geometries or where moving boundaries exist arises in numerous scientific and engineering applications. Illustrative examples can be found in the thermal machinery field, such in reciprocating compressors, in internal combustion engines, or in air handling units. In the design and optimisation studies of theses systems, the detailed analysis of the heat and mass transfer phenomena becomes an essential issue. From the Computational Fluid Dynamics (CFD) point of view, a common characteristic to all these problems is the need to develop specific treatments from the standard CFD methodologies to deal with the geometry of the domain under study. The works conducted in this thesis tackle the modelization and the numerical methodology required for the fluid flow and heat transfer computation within this range of applications. The flow computation in domains with moving boundaries is approached from the moving grid method. In this method, the governing equations are discretized over control volumes which change their geometry at every time-step. This non-Eulerian, or arbitrary Lagrangian-Eulerian (ALE), formulation introduces the necessity to satisfy the Space Conservation Law (SCL) together with the rest of governing equations. Failing in preserving this principle might lead to additional sources of error in the solutions, and, as it is demonstrated in this work, to introduce oscillatory behaviour in the convergence of the error. The studies conducted in this thesis are focused on the implementation of the moving grid method on Finite Volume (FV) computations, and on the analysis of the effects that parameters such as the mesh velocity, or the time and the SCL discretization, have on the accuracy and on the asymptotic or oscillatory convergence of both the space and time discretization errors. Test cases analysed include manufactured solutions, and the incompressible and compressible flow on different piston-cylinder configurations.
An Unobtrusive System for Heart Rate Monitoring Based on Ballistocardiogram Using Hilbert Transform and Viterbi Decoding

This paper presents an unobtrusive system for monitoring heart rate (HR) based on ballistocardiogram (BCG). The system contains a piezoelectric sensor, which can be embedded into a chair or bed, to convert small body vibrations caused by heart beating into BCG signal and a novel algorithm to estimate HR from the BCG. The algorithm employs the Hilbert Transform to reveal the frequency content of J-peak in the BCG signal for HR estimation. The Viterbi Decoding is applied to improve the HR accuracy for noisy BCG signal by finding the most likely path through time-frequency state-space plane. The performance of the proposed algorithm is evaluated by BCG recordings from 32 subjects. Mean absolute error (MAE) of 2.17 beats per minute (BPM) and standard deviation of absolute error (SDAE) of 2.34 BPM are achieved. Pearson correlation coefficient of 0.956 between estimated HR and true HR is obtained.
Unsupervised word discovery for computational language documentation

Language diversity is under considerable pressure: half of the world’s languages could disappear by the end of this century. This realization has sparked many initiatives in documentary linguistics in the past two decades, and 2019 has been proclaimed the International Year of Indigenous Languages by the United Nations, to raise public awareness of the issue and foster initiatives for language documentation and preservation. Yet documentation and preservation are time-consuming processes, and the supply of field linguists is limited. Consequently, the emerging field of computational language documentation (CLD) seeks to assist linguists in providing them with automatic processing tools. The Breaking the Unwritten Language Barrier (BULB) project, for instance, constitutes one of the efforts defining this new field, bringing together linguists and computer scientists. This thesis examines the particular problem of discovering words in an unsegmented stream of characters, or phonemes, transcribed from speech in a very-low-resource setting. This primarily involves a segmentation procedure, which can also be paired with an alignment procedure when a translation is available. Using two realistic Bantu corpora for language documentation, one in Mboshi (Republic of the Congo) and the other in Myene (Gabon), we benchmark various monolingual and bilingual unsupervised word discovery methods. We then show that using expert knowledge in the Adaptor Grammar framework can vastly improve segmentation results, and we indicate ways to use this framework as a decision tool for the linguist. We also propose a tonal variant for a strong nonparametric Bayesian segmentation algorithm, making use of a modified backoff scheme designed to capture tonal structure. To leverage the weak supervision given by a translation, we finally propose and extend an attention-based neural segmentation method, improving significantly the segmentation performance of an existing bilingual method.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level

Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.
A benchmark suite for control algorithms of retractable wind-energy harvesters

Wind power is well known for being variable. Our main insight is that one can take advantage of variability by appropriately building wind-energy harvesters that may be stowed/retracted when winds are calm. We refer to harvesters that can be deployed and retracted on command as retractable wind-energy harvesters (RWEHs). Among other advantages, stowed harvesters do not block views, do not constrain avian life, and do not make noise, and thus can increase the neighborliness of harvesting wind near or within a residential community.RWEH control algorithms help owners to achieve the neighborliness that might be required by an RWEH-hosting community while helping RWEHs’ efficiency. The stowing requirements, or operation limitation agreements (OLAs) specify conditions when the retractable harvesters should be stowed (e.g., when it is not windy). In this work, we contribute a suite of benchmarks to compare RWEH control algorithms. The benchmark suite provides workloads formed from the following workload components: 1. specifications of a harvester to be controlled, 2. a set of historical windspeeds from 30 weather stations, and 3. a variety of stowing requirements.We derived OLAs from a survey of 304 respondents in which survey-takers were asked whether they would support RWEHs viewable from where they live and when the RWEHs should be hidden or stowed.
Auto Correction of Setswana Real-word Errors

Spell checkers are used to detect and where possible correct spelling errors. Errors are classified as nonword errors and real-word errors. Real-word errors require the consideration of the context of the sentence to detect and correct. Setswana language has several commonly used words which are often misspelled by either separating or merging them. The misspelling results in real-word errors. In this paper we propose contextual rules that look at neighbor words to determine whether the correct word is written as two separate words or merged as one word. For some words the rules require that the parts of speech category of neighbor words be determined whereas some depend on specific neighbor words or position in a sentence. Implemented rules show that the rules are very consistent with a 88% success rate. Our tool only looks at neighbor words and therefore does not look at the context of the whole sentence. Hence, for words that require context of the whole sentence to disambiguate correctly our rules fail. This module can be incorporated into a spell checker to detect and correct real world errors for some words. That is, help users to determine the correct orthography of certain words.
A Recent Survey on Authentication Schemes with Privacy Preservation in VANETs

Vehicular Ad-Hoc Networks (VANETs) are one of the quintessential elements in imminent transportation systems. Vehicles in the VANET are interconnected with them and the infrastructures through a Dedicated Short Range Communication channel (DSRC) and utilize the IEEE 802.11p legacy as its communication protocol. Wireless communications are susceptible to a variety of attacks. Some of the assaults are false identification claim, monitoring victims and others. Hence, the communication among the automobiles and the infrastructures want to be blanketed towards attacks. VANETs furnish security services like authentication, anonymity, privacy protection, message integrity and others to mitigate the assaults on automobiles and the infrastructures in the network. Many research options which tackle these protection problems in the past. In this paper, some of the posted works of literature are reviewed which presents options to safety troubles like authentication and secluded safety and the solutions had been in contrast to get better understanding.
Local Fractions – a method for the calculation of local source
contributions to air pollution, illustrated by examples using the
EMEP MSC-W model (rv4_33)

Abstract. We present a computationally inexpensive method for individually quantifying the contributions from different sources to local air pollution. It can explicitly distinguish between regional/background and local/urban air pollution, allowing fully consistent downscaling schemes. The method can be implemented in existing Eulerian chemical transport models and can be used to distinguish a large number of emission sources to air pollution in every receptor grid cell within one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence it can be used for time-critical operational services providing scientific information as input to local policy decisions on air pollution abatement. The main limitation in its current version is that only primary pollutants can be addressed. In this paper we provide a technical description of the method and discuss various applications for scientific and policy purposes.
Applying logarithmic density function to develop a segmentation model for intensity and noisy inhomogeneity images: towards informed decisions in computational and applied mathematics

This study examined the how a logarithmic intensity function could be used to develop a segmentation model for intensity and noisy homogeneity images. Whereas inhomogeneous images demand the use of local image data, it remains notable that it remains defective for noisy images. The eventuality is that active contour motions tend to be misguided by the local information. In the proposed model, the logarithmic function was able to capture minute details contained in selected images and also counter or ignore the perceived noise. As such, the model proved effective and robust, worth applying to such images. To verify the effectiveness of the model, its results were compared to the experimental outcomes previously reported after employing local Chan-Vese Model. Indeed, the proposed model exhibited superior performance in relation to the treatment of intensity and noisy inhomogeneity images.
Succinct Palette and Color Model Generation and Manipulation Using Hierarchical Representation

We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom‐up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.
Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models

Members of communities often share topics of interest. However, usually not all members are interested in all topics, and participation in topics changes over time. Prediction models based on temporal hypergraphs that—in contrast to state-of-the-art models—exploit group structures in the communication network can be used to an- ticipate changes of interests. In practice, there is a need to assess these models in detail. While loss functions used in the training process can provide initial cues on the model’s global quality, local quality can be investigated with visual analytics. In this paper, we present a visual analytics framework for the assessment of temporal hypergraph prediction models. We introduce its core components: a sliding window approach to prediction and an interactive visualization for partially fuzzy temporal hypergraphs.
Childhood Tuberculosis: Update of Guidelines

Tuberculosis (TB) contributes significantly to the global burden of pediatric morbidity and mortality, particularly in the developing countries. However, management and control of childhood TB has not received as much attention as its adult counterpart. Earlier, most TB treatment guidelines included the childhood TB only in the passing or as a small component. Only recently efforts are being made to provide focussed guidance for the management of childhood TB, which is a positive step as there are important issues specific largely to TB in children. Universal acceptability of a single set of guidelines may not be feasible, given the variable disease prevalence and differing infrastructure capacity in various parts of the world. The clinical and laboratory diagnosis and management of childhood TB is fast evolving and the need to constantly update the guidelines according to the needs of the target population cannot be overemphasized. This review provides an insight into the World Health Organization guidelines for childhood TB, particularly with respect to their significance in the current scenario and its comparison with various nation-specific
BILATERAL BIFID MANDIBULAR CONDYLE WITH TEMPORO-MANDIBULAR JOINT ANKYLOSIS

We report a case of 10years old female child who presented with presenting complaints of progressive difficulty in jaw opening. The parents gave history of facial trauma suffered five years back. A radiograph obtained at that time was unremarkable. This time she was advised computed tomography (CT) examination of temporo-mandibular joint (TMJ) to evaluate the severity and extent of ankylosis. Examination was done on a 64 slice CT scanner (GE) with isotropic images and additional volume rendered and multiplanar acquisitions. Findings were suggestive of bilateral mediolateral bifid mandibular condyles with fibrous, partial bony ankylosis. Both mandibular condylar head were enlarged with abnormal contour. Condyles were bifid separated by a distinct
Managing the Implementation and Evaluation of the School Reading Program

Aims: Non-reading phenomenon among students is a worldwide problem. This problem continues to balloon despite the various governments’ program. This study was conducted to determine the positive and negative experiences met by the school managers during the implementation and evaluation of the reading program in their respective area. Study Design: Qualitative- phenomenology. Place and Duration of Study: Digos City, Davao del Sur, Philippines, School Year 2018-2019. Methodology: Purposive; Focus-Group Discussions; Validated Researcher-made questionnaire. Results: Nine themes surfaced from the data analysis; implementation of the program; appointment of reading coordinator; amelioration of reading teachers; initiative of school administration; availability of reading materials; involvement in SIP/AIP creation; variations on identified challenges; heartening situations; satisfaction with co-workers and DepEd personnel; and recommendations for improvement. Conclusion: Contextualization of the reading program allows learners to connect and interact with the texts. School heads need to be consistent with their policies. They need to involve the teachers concern not only those chosen few because it creates partition and conflict. Teachers’ dedication and initiative to the program is essential; however, school managers need to provide relevant materials for its smooth implementation. Further, success of any program depends on the cooperation among the implementers. Unity and understanding among workers are essential in order to achieve the purpose of any endeavor.
Machine Learning based Signal Generation Strategies for High-Speed Optical Transmitters

Optical communication is the only viable solution to respond to the demand for a high bit rate and long transmission distance. Directly modulated lasers (DMLs) are a cheap solution for modulating the light in optical fibre. Moreover, their hardware is simpler than externally modulated lasers. However, DML is inherently chirped and the transmission length with high bit rate is limited. This work explores and implements neural networks based signal predistortion schemes to create transmitters.
Predicting student understanding by modeling interactive exploration of evidence during an online science investigation

ABSTRACT This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students’ interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the interactive map to make scientific observations for an evidence-based hypothesis. The examination of classification error using an artificial neural network, compared against the majority class for prediction, suggests that student performance on several metrics of critical thinking can be classified based on different patterns in interactions with visual representations. Two alternative methods are compared in this study for training and evaluating data-mined models of student performance. In accordance with the general consensus in the literature, the error estimates for models’ predictions were less variable using a student-level cross-validation. Implications of these findings for open-ended inquiry-based learning environments are discussed.
Towards a new SDN NFV approach for the management of MPLS infrastructures

Network infrastructure virtualization has become an active research area. Indeed, network feature virtualization (NFV) brings unparalleled flexibility to next-generation networks and goes far beyond cost reduction. This technology reduces vendor dependency and allows new features to be deployed faster than ever before. In this article we propose a new solution for the virtualization of network services, particularly in relation to concepts related to MPLS technology. Our solution ensures fast access to the access network while guaranteeing quality of service.