Miracle-dz/UniFAR-FTUs huggingface dataset:
MyoSpring: 3D Printing Mechanomyographic Sensors for Subtle Finger Gesture Recognition

We introduce MyoSpring, a novel technique to fabricate customised mechanomyographic sensors with 3D printed springs. MyoSpring averts from a traditional ‘one-size-fits-all’ approach and facilitates the development of functionally customisable wrist-worn sensors, which can be used to recognise subtle finger gestures with high accuracy. We automate the design process of MyoSpring sensors with an intuitive Graphical User Interface enabling novices to quickly design and fabricate customised sensors. We conducted a user study with 15 participants and 12 gestures, including partial and full flexion finger gestures, to verify the functionality of MyoSpring. Through the evaluation, we show that 1) MyoSpring can achieve high average accuracy of finger gesture sensing of 94.11% (SD = 9.73%), 2) mechanical customisation allows MyoSpring to significantly improve the average accuracy of overall gesture sensing and 3) MyoSpring better supports partial finger flexion gesture sensing, achieving an accuracy of 91.44% (SD = 11.76%) for partial gestures.
From Motivation Components to Academic Achievement Prediction

This research aims to determine the relationship among the mental and motivation components include self-efficacy (SE), the self-regulation learning (SRL), the intrinsic motivation and the use of cognitive strategies, and test anxiety, as well as their effects on academic performance in the blended-learning environments during the COVID-19 pandemic. By studying the academic achievement and motivation components of 392 Accounting major Sophomores and Juniors, reducing test anxiety and improving self-efficacy were revealed to be the important methods toward success. Also, the use of self-regulation and cognitive strategies as the intermediary variables could have impact on the academic performance. Through the empirical study and a review of academic motivation and academic performance literature before and after COVID-19, this research proposed 4E (Effort, Enthusiasm, Expectation, and Easiness) as the predictive framework and key motivation factors for potential intelligent tutoring systems and adaptive learning systems on the early academic warning and intervention of students’ academic progress.
Predictive Model of Student Academic Performance from LMS data based on Learning Analytics

The present research aims to implement a predictive model in the KNIME platform to analyze and compare the prediction of academic performance using data from a Learning Management System (LMS), identifying students at academic risk in order to generate timely and timely interventions. The CRISP-DM methodology was used, structured in six phases: Problem analysis, data analysis, data understanding, data preparation, modeling, evaluation and implementation. Based on the analysis of online learning behavior through 22 behavioral indicators observed in the LMS of the Faculty of Educational Sciences of the National University of San Agustin. These indicators are distributed in five dimensions: Academic Performance, Access, Homework, Social Aspects and Quizzes. The model has been implemented in the KNIME platform using the Simple Regression Tree Learner training algorithm. The total population consists of 30,000 student records from which a sample of 1,000 records has been taken by simple random sampling. The accuracy of the model for early prediction of students' academic performance is evaluated, the 22 observed behavioral indicators are compared with the means of academic performance in three courses. The prediction results of the implemented model are satisfactory where the mean absolute error compared to the mean of the first course was 3. 813 and with an accuracy of 89.7%, the mean absolute error compared to the mean of the second course was 2.809 with an accuracy of 94.2% and the mean absolute error compared to the mean of the third course was 2.779 with an accuracy of 93.8%. These results demonstrate that the proposed model can be used to predict students' future academic performance from an LMS data set.
Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

We propose PASS, a O(n) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line chart. Visualization of large trend line data is a challenge and current sampling approaches do produce reduction but result in loss of semantics and anomalous behavior. We have evaluated PASS using seven large and well-vetted datasets (Taxi, Temperature, DEBS challenge 2012-2014 dataset, New York Stock Exchange data, and Integrated Surface Data) and found that it has several benefits when compared to existing state-of-the-art time series data reduction techniques. First, it can preserve the semantics of the trend. Second, the visualization quality using the reduced data from PASS is very close to the original visualization. Third, the anomalous behavior is preserved and can be well observed from the visualizations created using the reduced data. We have conducted two user surveys collecting 3,000+ users’ responses for visual preference as well as perceptual effectiveness and found that the users prefer PASS over other techniques for different datasets. We also compare PASS using visualization metrics where it outperforms other techniques in five out of the seven datasets.
Mixed Dimensions: Exploring Novel Material and Extrusion Tool for 2.5D Shape Painting

This paper explores a novel tool and materials for brush-like expression in 3D forms. We envision a 2.5D painting device that aims at closing the gap between painterly expression, digital fabrication, and the incorporation of expressive elements present in traditional painting with structural capability. The paper describes the development of the new pigment material, a custom handheld extrusion device, and discusses a user study investigating emerging relationships with the developed medium. We find the reactivity and variability of the medium enabling strong expressive agency and new interaction opportunities. With these, we discuss what are aspects in making that could be embraced to enhance users’ creative agency and ownership in making, postulating a space between artistic expression and utilitarian making–e.g. digital fabrication.
Incorporating Shape-Changing Food Materials Into Everyday Culinary Practices: Guidelines Informed by Participatory Sessions with Chefs Involving Edible pH-responsive Origami Structures

In recent studies researchers have developed compelling and exciting shape-changing food (SCF) materials. Unfortunately, the adoption of SCFs in culinary practice is still complex. We present a research-through-design exploration of a novel SCF material that can be developed in restaurant kitchens using everyday kitchen appliances with the only addition of a commercial desktop 3D food printer. Our lightweight process involves the preparation of edible paper, which can be folded into origami shapes and is actuated through the addition of an acidic liquid. We facilitated participatory sessions with five chefs in their restaurants and evaluated our material. Our qualitative study contributes design guidelines on how to incorporate SCFs into chefs’ everyday culinary practices. Building on the chefs’ perspectives of our material and its culinary potential we conclude with a speculation of how SCF materials could enable novel multi-sensory and interactive food experiences.
Glaze Epochs: Understanding Lifelong Material Relationships within Ceramics Studios

The “material turn” in HCI has placed a renewed focus on informing design from the relationships found in material-based interactions. While several ethnographic works provide insight into how practitioners converse with materials, it is less understood how these conversations transform into a skilled practitioner’s relationship with a material. We examine the material practice of glazing that gives ceramics its decorative and functional characteristics and involves fusing mixtures of silica, alumina, and flux onto a clay body through kiln firing. This practice evolves over decades developing from multiple trajectories including theoretical foundations, systematic experimentation, and happy accidents. This work describes virtual site visits with six expert ceramicists and documents how material knowledge is externalized in practice, teaching, and the studio environment. We synthesize our findings into a framework to inform the design of material interactions that moves beyond discrete and momentary material encounters towards lifelong material epochs.
EMStriker: Potentials of Enhancing the Training Process of Racket-based Sports via Electrical Muscle Stimulation

Racket sports offer an enjoyable form of physical activity and are fertile ground for interactive technologies supporting new players. Yet, current research has neglected its potential to support not only active players but also coaches in their training methods. To investigate how interactive technologies can support skill acquisition in training, we designed an Electrical Muscle Stimulation (EMS) system that helps maintain the ready position in crossminton. We compared the system with a vibrotactile solution in a user study, interviewing novice players and experienced coaches about their perception of the system. The system allowed coaches to effectively and immediately guide players to the ready position. An EMS-based feedback system for coaches can potentially reduce delay (physical and cognitive) for trainees, as stated by coaches. Our work contributes insights into designing systems that facilitate learning sports techniques using interactive feedback.
Automatic Classroom Question Classification Based on Bloom's Taxonomy

Asking questions is usually used by teachers to guide students to think and to interact with students. Bloom's Taxonomy has been used widely in the educational field to assess students’ intellectual abilities and skills. However, most questions lack a scientific basis and design; too many low-level questions constrain students’ thought. Some works classify these questions manually, which is inefficient. To improve the efficiency and provide implications for teachers to design curriculums, this study utilized machine learning to automatically classify teachers’ questions by building up keywords and extracting TF-IDF (Term frequency inverse document frequency) features. The result showed that keywords are significant in classifying questions, and we obtained an accuracy of 86.0%.
A procedure for the extraction of a nonlinear microwave GaN FET model

In this paper, we describe an extraction procedure of nonlinear models for microwave field‐effect transistor (FET). We use a nonlinear model available in commercial CAD tools, and we extract the parameters by combining direct extraction and numerical optimization. We determine a first estimate of the model parameters by few DC and S‐parameter measurements. Next, we refine the parameters by optimization against low‐frequency and high‐frequency vector‐calibrated large‐signal measurements gathered with a Large‐Signal‐Network Analyzer (LSNA). As case study we consider a 0.25 × 200 µm2 GaN FET on SiC for power amplifier applications. Ultimately, we want to show that a good accuracy level can be achieved while minimizing the extraction effort and that an accurate model can be built and suitably tailored depending on the final application. Copyright © 2016 John Wiley & Sons, Ltd.
A study on the link between design brief structure and stimulus fidelity to optimize novelty and usefulness

Abstract This academic-based investigation is focused on identifying elements that contribute toward the generation of efficient design briefs and their correlation with design outcomes of a sketching exercise. Four conditions are compared: a baseline group, an abstract group, a contextual information group, and a group that was given various example solutions. Via more in-depth surveys, we sought to elicit correlations between the students’ design creativity and stimuli permutations of the different design conditions. Results show that the contextual information groups, which were presented with higher levels of stimulus fidelity, had higher novelty scores, while abstract groups performed well in usefulness. These findings contribute to the formulation of design briefs where the goal is to stimulate the creativity of design outcomes and examine their relationships with student's perceptions of design exercises.
RaPID-Query for fast identity by descent search and genealogical analysis

The size of genetic databases has grown large enough such that, genetic genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. However, a fast and accurate method is needed to search an out-of-database individual against millions of individuals in such databases. Most existing approaches only offer all-vs-all within panel match. Some prototype algorithms offer 1-vs-all query from out-of-panel individual, but they do not tolerate errors. A new method, random projection-based identical-by-descent (IBD) detection (RaPID) query, referred as RaPID-Query, is introduced to make fast genealogical search possible. RaPID-Query method identifies IBD segments between a query haplotype and a panel of haplotypes. By integrating matches over multiple PBWT indexes, RaPID-Query method is able to locate IBD segments quickly with a given cutoff length while allowing mismatched sites in IBD segments. A single query against all UK biobank autosomal chromosomes can be completed within 2.76 seconds CPU time on average, with the minimum 7 cM IBD segment length and minimum 700 markers. Using the same criteria, RaPID-Query can achieve 0.099 false negative rate and 0.017 false positive rate at the same time on a chromosome 20 sequencing panel having 92,296 sites, which is comparable to the state-of-the-art IBD detection method Hap-IBD. For the relatedness degree separation experiments, RaPID-Query is able to distinguish up to fourth degree of the familial relatedness for a given individual pair, and the area under the receiver operating characteristic curve values are at least 97.28%. It is anticipated that RaPID-Query will make genealogical search convenient and effective, potentially with the integration of complex inference models.
Equilibrium dynamics of the pre- and post-cleavage regions of SP1 are separately shifted by the HIV-1 maturation inhibitor Bevirimat

Human immunodeficiency virus type 1 (HIV-1) assembly and maturation proceeds in two distinct steps. During assembly, viral Gag oligomerizes into a hexagonal polyprotein lattice incorporating the capsid protein (CA) and spacer peptide 1 (SP1) domains, that constitute the immature Gag lattice. During maturation, CTD-SP1 hexamers formed in the previous step are cleaved by HIV-1 protease, causing a dramatic rearrangement of the immature virion to its mature, infectious form. The first-generation maturation inhibitor (MI) bevirimat (BVM) is reported to block the final cleavage between CA and SP1, thus blocking HIV maturation. In contrast, the host factor inositol hexakisphosphate (IP6) is a co-factor of Gag assembly and facilitates the formation of a quaternary arrangement of SP1 known as the six helix bundle (6HB). Here, starting from a MAS NMR structure and using atomistic free energy calculations, we establish that binding of BVM and IP6 to the immature lattice lacks any cooperativity or avidity. Furthermore, we rationalize the molecular origin of HIV resistance to BVM by determining the role of BVM on the stability of the 6HB and by revealing that SP1 shows independent dynamics for its pre- and post-cleavage regions. Finally, results from our simulations permit us to propose a novel chemical scaffold for the design of maturation inhibitors based on BVM and IP6.
Dopamine transporter and synaptic vesicle sorting defects initiate auxilin-linked Parkinson’s disease

Auxilin participates in the uncoating of clathrin-coated vesicles (CCVs), thereby facilitating synaptic vesicle (SV) regeneration at presynaptic sites. Auxilin (DNAJC6/PARK19) loss-of- function mutations cause early-onset Parkinson’s disease (PD). Here, we utilized auxilin-knockout (KO) mice to elucidate the mechanisms through which auxilin deficiency and clathrin-uncoating deficits lead to PD. We demonstrate that auxilin KO mice display the cardinal features of PD, including progressive motor deficits, α-synuclein pathology, nigral dopaminergic loss, and neuroinflammation. Through unbiased proteomic and neurochemical analyses, we demonstrate that dopamine homeostasis is disrupted in auxilin KO brains, including via slower dopamine reuptake kinetics in vivo, an effect associated with dopamine transporter misrouting into axonal membrane deformities in the dorsal striatum. We also show that elevated macroautophagy and defective SV protein sorting contribute to ineffective dopamine sequestration and homeostasis, ultimately leading to neurodegeneration. This study advances our knowledge of how presynaptic endocytosis deficits lead to dopaminergic vulnerability and pathogenesis of PD.
A Novel Design Of An NTC Thermistor Linearization Circuit

A novel design of a circuit used for NTC thermistor linearization is proposed. The novelty of the proposed design consists in a specific combination of two linearization circuits, a serial-parallel resistive voltage divider and a twostage piecewise linear analog-to-digital converter. At the output of the first linearization circuit the quasi-linear voltage is obtained. To remove the residual voltage nonlinearity, the second linearization circuit, i.e., a two-stage piecewise linear analog-to-digital converter is employed. This circuit is composed of two flash analog-to-digital converters. The first analog-to-digital converter is piecewise linear and it is actually performing the linearization, while the second analog-to-digital converter is linear and it is performing the reduction of the quantization error introduced by the first converter. After the linearization is performed, the maximal absolute value of a difference between the measured and real temperatures is 0.014oC for the temperature range between −25 and 75oC, and 0.001oC for the temperature range between 10 and 40oC.
Surface Elevation Changes Estimation Underneath Mangrove Canopy Using SNERL Filtering Algorithm and DoD Technique on UAV-Derived DSM Data

Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs of unmanned aerial vehicle (UAV) data carried out during 2016 and 2017. A novel filtering algorithm named Surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) is used to estimate the elevation height underneath the mangrove canopy. Consequently, DoD technique is used to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation. The significant findings showed that region of interest (ROI) 5 experienced the highest volumetric accretion (surface raising) at 0.566 cm3. The most increased erosion (surface lowering) was identified at ROI 8 at −2.469 cm3. In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m. In comparison, the most increased vertical erosion (surface lowering) was spotted at ROI 3 at −0.568 m. The change detection map and the rates of surface elevation changes at Kilim River enabled authorities to understand the situation thoroughly and indicate the future situation, including its interaction with sea-level rise impacts.
Scabbard: Single-Node Fault-Tolerant Stream Processing

Single-node multi-core stream processing engines (SPEs) can process hundreds of millions of tuples per second. Yet making them fault-tolerant with exactly-once semantics while retaining this performance is an open challenge: due to the limited I/O bandwidth of a single-node, it becomes infeasible to persist all stream data and operator state during execution. Instead, single-node SPEs rely on upstream distributed systems, such as Apache Kafka, to recover stream data after failure, necessitating complex cluster-based deployments. This lack of built-in fault-tolerance features has hindered the adoption of single-node SPEs. We describe Scabbard, the first single-node SPE that supports exactly-once fault-tolerance semantics despite limited local I/O bandwidth. Scabbard achieves this by integrating persistence operations with the query workload. Within the operator graph, Scabbard determines when to persist streams based on the selectivity of operators: by persisting streams after operators that discard data, it can substantially reduce the required I/O bandwidth. As part of the operator graph, Scabbard supports parallel persistence operations and uses markers to decide when to discard persisted data. The persisted data volume is further reduced using workload-specific compression: Scabbard monitors stream statistics and dynamically generates computationally efficient compression operators. Our experiments show that Scabbard can execute stream queries that process over 200 million tuples per second while recovering from failures with sub-second latencies.
The Social Bifurcation of Reality: Symmetrical Construction of Knowledge in Science-Trusting and Science-Distrusting Discourses

This article proposes a conceptual framework to study the social bifurcation of reality in polarized science-trusting and science-distrusting lay worldviews, by analyzing and integrating five concepts: science work, number work, emotion work, time work, and boundary work. Despite the epistemological asymmetry between accounts relying on mainstream science and science-distrusting or denialist ones, there are symmetrical social processes contributing to the construction of lay discourses. Through conceptual analysis, we synthesize an alternative to the deficit model of contrarian discourses, replacing the model of social actors as “defective scientists” with a focus on their culturally competent agency. The proposed framework is useful for observing the parallel construction of polarized realities in interaction and their ongoing articulation through hinge objects, such as vaccines, seatbelts, guns, or sanitary masks in the Covid-19 context. We illustrate the framework through a comparative approach, presenting arguments and memes from contemporary online media in two controversies: namely, vaccine-trusting versus vaccine-distrusting views and Covid-convinced versus Covid-suspicious discourses.
Growth Differentiation Factor-15 in Immunity and Aging

Aging increases susceptibility to and severity of a variety of chronic and infectious diseases. Underlying this is dysfunction of the immune system, including chronic increases in low-grade inflammation (inflammaging) and age-related immunosuppression (immunosenescence). Growth differentiation factor-15 (GDF-15) is a stress-, infection-, and inflammation-induced cytokine which is increased in aging and suppresses immune responses. This mini review briefly covers existing knowledge on the immunoregulatory and anti-inflammatory roles of GDF-15, as well as its potential importance in aging and immune function.