Miracle-dz/UniFAR-FTUs huggingface dataset:
A soft sensor of stator winding temperature prediction for PMSMs based on extreme learning machine

Permanent magnet synchronous motor (PMSM) plays an effective role in electric vehicle applications. Monitoring PMSM's temperature in real time is critical to its safety and reliability. The traditional method of PMSM temperature monitoring is to install temperature sensors into the motor, and it is a very expensive method. Currently, the lumped-parameter thermal networks (LPTNs) are the appropriate alternative for determining PMSM components' temperatures. However, they lack physical interpretability once the degrees of freedom are reduced in order to meet real-time requirements. The approach based on soft sensors is an efficient and economical way to solve such problems. In this paper, a soft sensor is developed to predict the stator winding temperature using an extreme learning machine (ELM). Furthermore, the Principal Component Analysis (PCA) technique is used to select effective and relevant variables. The performance of the model is evaluated based on five statistical indicators: the correlation coefficient (R2), the root relative squared error (RRSE), the mean square error (MSE), the mean absolute error (MAE), and the root-mean-squared error (RMSE). The results showed that the PCA-ELM model has a high efficiency to predict the stator winding temperature with RMSE = 0.0622, MAE = 0.0480, MSE = 0.0039, RRSE = 6.73% and R2 = 0.9955. Moreover, it has low computational complexity. Due to its low computational complexity and high performance, this application could have a direct influence and economic savings on the development and design of PMSMs temperature monitoring systems.
Self-Regulated Learning Instructions in Engineering Education: A Systematic Scoping Review

While self-regulated learning has been studied in various academic domains in educational psychology research, the topic of self-regulated learning in engineering education has not been examined systematically and comprehensively. Therefore, we conducted a systematic scoping review to examine the existing literature on the intersection of self-regulated learning and engineering education. Specifically, through reviewing the literature, we examined how the theoretical perspectives of self-regulated learning have been adopted and adapted into engineering classrooms in post-secondary education. We also examined whether self-regulated learning has been contextualized into engineering entrepreneurship education, specifically. Reports of the current literature and implications for future research in engineering education are discussed.
Considering Aspirations and Impact: Using Storytelling to Encourage Engineering Students to Reframe their Experiences

This research full paper describes an eight-week, extra-curricular, multi-institutional program centered around sharing personal narratives. Over the course of the experience, we provided (n = 24) engineering students with scaffolding materials to develop their storytelling skills and opportunities for them to craft and present stories around specific prompts. The program culminated in a Story Slam where students orally delivered four-minute stories that communicated "who you are, what has shaped you, and the story you want to tell with your life’s work and why. " The Kern Entrepreneurial Engineering Network (KEEN) framework of the 3Cs (Curiosity, Connections, and Creating value) for the development of an entrepreneurial mindset guided the programmatic development and shaped our inquiry to explore: 1) how storytelling prompts can encourage students’ consideration of their goals and potential impact on society; and 2) beneficial approaches to story-driven learning. To better understand students’ motivations and thoughts about the program, we qualitatively examined their open-ended responses. We also applied a rubric to quantify aspects of KEEN’s 3Cs observed in videos of students’ stories shared during the final Story Slam. The results suggest that storytelling can promote communication, allow students to reframe their experiences, and help them to think about their professional aspirations. Students’ curiosity not only shaped their desire to participate in the program, but for many students, was also a major contributor in their pathways into engineering. The content and materials offered in the program provided foundations that students connected with, as evidenced by the information and mentions they brought into their own stories. Furthermore, the stories presented illustrate that students want to create value both personally and professionally. Students preferred to share their stories in smaller groups and also appreciated positive feedback and comments from their peers and the facilitators. Additionally, they described how listening to other students’ stories helped them to grow and raised their awareness of others’ lived experiences. These insights serve to inform educators about the value of storytelling and to identify opportunities to support students when employing story-driven learning.
Acetic Acid in the Hot Cores of Sagitarrius B2(N) and W51

We have detected interstellar acetic acid (CH3COOH) toward the hot core source W51e2. This is the first new source of interstellar CH3COOH since its discovery by Mehringer et al. toward the hot core source Sgr B2(N-LMH). In this paper, we report CH3COOH observations at two new frequencies toward Sgr B2(N-LMH) with the OVRO array and at 10 frequencies toward W51e2 with the Berkeley-Illinois-Maryland Association array. Toward Sgr B2(N-LMH) the agreement in positions, intensities, and velocities between the two lines from the previous study and the two new lines strongly indicates that all four CH3COOH lines are coming from a common source. Using all four detected transitions, we find an average column density of 6.1(6) × 1015 cm-2, a fractional abundance of (0.8-6) × 10-10 relative to H2 and (3-6) × 10-2 relative to its isomer methyl formate (HCOOCH3). Toward W51e2, we find a CH3COOH column density of 1.7(5) × 1016 cm-2 with a fractional abundance of 1.7 × 10-9 relative to H2 and (1-6) × 10-2 relative to HCOOCH3. Furthermore, we find the distribution of CH3COOH toward W51e2 is coincident with HCOOCH3, thus suggesting a similar formation mechanism.
A Polarity-Driven Radiation-Hardened Latch design for Single Event Upset Tolerance

The ability of tolerating a radiation-induced single event upset (SEU) is required for nanoscale latches in most dependable applications. This is becoming a strict requirement, because an SEU in a latch node may corrupt its outcome and then, possibly cause a system failure. Moreover, the impact of an SEU further deteriorates for latch designs at reduced CMOS nano-scaled technology because it can result in double node upset (DNU) in addition to single node upset (SNU). Existing approaches of designing a radiation-hardened latch do not achieve complete SNU/DNU tolerance at low hardware overhead. The goal of this paper is to propose a high-performance latch design for SEU tolerance. By exploiting the polarity of the upset in different types of transistors, the proposed design has a small number of sensitive nodes, so incurring in a low protection overhead. Moreover, due to its configuration, the proposed design achieves SEU tolerance at circuit-level without requiring additional layout protection. These advantages make the proposed design superior to all existing hardened latches found in the technical literature; simulation results using 65 nm CMOS technology show that the proposed design achieves a reduction in the range of 14.53% to 98.76% in hardware overhead while providing a complete SNU/DNU recovery.
Psychology-Inspired Interaction Process Analysis based on Time Series

Interaction portrays the process of exchanging emotions and states between individuals in a specific temporal and spatial context. Interactions are layered on a temporal scale, including social signals at an instant, intrinsic structures of a process, and enduring relationships between the participants. Existing studies of affective computing focus on the identification of social signals at specific moments, which are precise but limited to the microscopic level; while psychological studies focus on the process and relationship analysis, which are semantic but qualitative. In this paper, we focused on interaction process analysis. By combining psychological knowledge and time series analysis techniques, we proposed quantitative methods for features of personal status, dyad correlation, synchrony coefficients, and ‘lead-follow’ structures. We conducted experiments on the mother-infant interaction dataset. We first compared the differences in interaction patterns between social signals. We found that gaze direction in the free-play phase, emotion synchrony in the recov-ery phase, and engagement in both phases, play an important role in differentiating attachment styles. We then explored the differences in interaction patterns. We found the ‘infant dropping while mother rising’ asynchrony coefficient and the ‘infant leads, mother follows’ structure related to secure attachment styles. Finally, we successfully predicted infant attachment formed one year later based on machine learning methods, confirming the effectiveness of our feature extraction method.
Learning Discriminative Features with Region Attention and Refinement Network for Facial Expression Recognition in the Wild

Facial Expression Recognition (FER) has achieved significant performance in recent years. However, learning discriminative expression features is still a pending issue, due to disturbances in the wild and high similarities across different expressions. To remedy this issue, we present an innovative region attention and refinement network (RARN) capable of learning discriminative and robust features for accurate FER in the wild. Specifically, RARN mainly learn discriminative features from two different aspects: Multi-head Local Attention Network (MLAN) and a Latent Feature Mining Network (LFMN). MLAN can accurately locate the regions of interest and extract robust features in the wild by employing two modules, in which the region generation module divides the midlevel feature maps into several subregions and local attention module highlights the local discriminative features of regions of interest through attention block. LFMN combines a series of latent expression features associated with their corresponding importance weights to further obtain the final discriminative expression features. Experimental results on three widely-used in-the-wild FER datasets demonstrate proposed algorithm achieves favorable performance against state-of-the-art methods. The source code will be found at https://github.com/lizoe/RARN.
Oscillatory two- and three-dimensional thermocapillary convection

The character and stability of two- and three-dimensional thermocapillary driven convection are investigated by numerical simulations. In two dimensions, Hopf bifurcation neutral curves are delineated for fluids with Prandtl numbers (Pr) 10.0, 6.78, 4.4 and 1.0 in the Reynolds number (Re)–cavity aspect ratio (Ax) plane corresponding to Re[les ]1.3×104 and Ax[les ]7.0. It is found that time-dependent motion is only possible if Ax exceeds a critical value, Axcr, which increases with decreasing Pr. There are two coexisting neutral curves for Pr[ges ]4.4. Streamline and isotherm patterns are presented at different Re and Ax corresponding to stationary and oscillatory states. Energy analyses of oscillatory flows are performed in the neighbourhood of critical points to determine the mechanisms leading to instability. Results are provided for flows near both critical points of the first unstable region with Ax=3.0 and Pr=10. In three dimensions, attention is focused on the influence of sidewalls, located at y=0 and y=Ay, and spanwise motion on the transition. In general, sidewalls have a damping effect on oscillations and hence increase the magnitude of the first critical Re. However, the existence of spanwise waves can reduce this critical Re. At large aspect ratios Ax=Ay=15, our results with Pr=13.9 at the lower critical Reynolds number of the first unstable region are in good agreement with those from infinite layer linear stability analysis.
3D point clouds simplification based on geometric primitives and graph-structured optimization

We present a new method for the simplification of 3D point clouds for digital twin city models. Such data usually contains a large amount of redundant information, noise and outliers. This implies that most of subsequent processing tasks are costly both in terms of processing times and hardware infrastructure. The core idea of this paper is that most of the objects present in such scenes can be approximated as a combination of simple geometric primitives. This approximation can, in turn, be simplified by keeping only points and edges that effectively describe the shape of the input scene. Our main contribution is a formulation of the approximation of 3D point clouds with simple geometric primitives as a global optimization problem. We then introduce a new algorithm to efficiently solve this problem with a graph-cut-based approach. We measure the performances of our approach against state-of-the-art methods by comparing the geometric quality of the approximations with the amount of information needed to represent the simplified models. The evaluation of our approach is done on Mobile Laser Scanning (MLS) acquisitions in urban areas. Test areas include single objects and street portions to show the adaptability of our method to various geometries. We show improvements both in terms of geometrical error and final model size.
Design and Analysis of SAW Delay Lines for Sensors Applications

The purpose of this study is to model the surface acoustic wave (SAW) delay line for enhancing the device performance of sensors applications. We use, the acousto-mechanical properties, obtained based on the density functional theory (DFT) approach, to calculate the insertion loss of two port for the interdigital transducer (IDT)/AlScN/Sapphire SAW delay line. We investigate the parameters, which are obtained by using different types of SAW delay line modeling. We show that the P-matrix model combined with the coupling-of-modes (COM) approach provides an accurate result in comparison to the experimental results. In addition, we exhibit a decrease in the insertion losses value, an increase in the Sc concentration, and a left shift in the resonance frequency. This obtained result is very promising for SAW sensors design. This promising result will contribute significantly to the SAW sensors design.
Joint Intensity Classification and Specimen Segmentation on HEp-2 Images: a Deep Learning Approach

Antinuclear antibody (ANA) testing is performed to help diagnose patients with possible autoimmune diseases. The indirect immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference method for ANA screening. It allows for the detection of antibody binding to specific intracellular targets, resulting in distinct staining patterns whose recognition may be helpful for diagnosis purposes. In recent years, there has been an increasing interest in devising deep learning methods for automatic segmentation and classification of staining patterns, as well as for other tasks related to this diagnostic technique. However, little attention has been paid to architectures aimed at managing more functions related to ANA testing by simultaneously training tasks that use a shared representation. In this paper, we propose a deep neural network model based on U-Net that exploits an end-to-end approach for joint intensity classification and specimen segmentation on HEp-2 cell images. To the best of our knowledge, this is the first work proposing the adoption of a single framework tailored to address these two mandatory tasks in the HEp-2 clinical workflow. The experiments were conducted on I3A, the largest publicly available dataset of HEp-2 images. The results showed that the proposed approach outperformed the competing state-of-the-art methods for both the considered tasks, achieving (in 5-fold cross validation) 93.83% and 90.21% for intensity classification accuracy and segmentation accuracy, respectively.
Effects of COVID-19 on Stress and Mental Health of Community College Pre-Engineering Students

This research full paper presents screening rates for mental health issues and life-stress events in engineering-focused community college students during the initial phases of the COVID-19 pandemic in the US. Specifically, it attempts to answer the following research questions: 1) What is the overall rate of various mental health conditions among engineering-focused community college students, 2) What effects has the pandemic had on baseline stress levels engineering-focused community college, and 3) What effects has the pandemic had on quality of life, such as sleep habits and financial security of engineering-focused community college students? Data for this paper was collected via survey from May–July 2020 and includes responses from 84 students at 24 community colleges. The survey itself was a compilation of several widely-used instruments for measuring overall mental health and stress levels in a population. These instruments include the Kessler-6 for psychological distress, the PHQ for anxiety, depression, and eating disorders, the PC-PTSD for PTSD-like symptoms, and the SRRS for inventorying stressful life events.Among the major findings, 32% of respondents reported a major change in financial situation, 27% reported loss of employment, and 13% reported ceasing formal schooling because of the COVID-19 pandemic. Additionally, 32% of respondents reported that the COVID-19 pandemic worsened their housing security situation, 38% reported that COVID-19 has worsened their food security situation, and 36% report that COVID-19 has decreased their ability to access instruction, course materials, or course supplies. Finally, of respondents who completed at least one mental health screening instrument, 70% screened positive for at least one potentially diagnosable condition, while only 9% reported ever receiving a mental health diagnosis.
Big Data Analytics Quality in Enhancing Healthcare Organizational Performance: A Conceptual Model Development

—The advancement of Big Data Analytics (BDA) has aided numerous organizations in effectively and efficiently adopting BDA as a holistic solution. However, BDA quality assessment has not yet been fully addressed, therefore it is necessary to identify essential BDA quality factors to assure the enhancement of organizational performance, particularly in the healthcare sector. Hence, the goals of this study are to recognize and analyse the determining factors of BDA quality as well as to suggest a conceptual model for enhancing the performance of healthcare organizations via BDA quality assessment. The proposed conceptual model is based on a related theoretical model and previous research on BDA quality. The essential BDA quality factors being selected as determinants consist of reliability, completeness, accuracy, timeliness, format, accessibility, usability, maintainability, and portability. The findings of this ongoing study are used to develop a conceptual model that is proposed in line with the ten-research hypothesis and may offer a better assessment quality model to improve the performance of healthcare organizations.
Roles of Ghrelin and Leptin in Body Mass Regulation under Food Restriction Based on the AMPK Pathway in the Red-Backed Vole, Eothenomys miletus, from Kunming and Dali Regions

Simple Summary Animals can adapt to food shortages through phenotypic changes. In this study, food restriction reduced the body mass, RMR, serum leptin level, and POMC and CART gene expression in the red-backed vole, Eothenomys miletus; increased AMPK activity; and increased the concentration of ghrelin in the serum and stomach. The ghrelin-and-leptin-combined AMPK signaling pathway in the hypothalamus could play a role in the body mass regulation of E. miletus under food restriction. Moreover, regional differences in physiological indicators under food restriction may be related to the different temperatures or food resources in different regions. Abstract The phenotype plasticity of animals’ physiological characteristics is an important survival strategy to cope with environmental changes, especially the change in climate factors. Small mammals that inhabit seasonally changing environments often face the stress of food shortage in winter. This study measured and compared the thermogenic characteristics and related physiological indicators in the adenosine-5′-monophosphate-activated protein kinase (AMPK) pathway in Eothenomys miletus between Kunming (KM, n = 18) and Dali (DL, n = 18) under food restriction and refeeding. The results showed that food restriction and the region have significant effects on body mass, the resting metabolic rate (RMR), hypothalamic neuropeptide gene expression, ghrelin levels in the stomach and serum, serum leptin level and the activity of AMPK, and malonyl CoA and carnitine palmitoyltransferase 1 (CPT-1) activity. Food restriction reduced the body mass, the gene expression of neuropeptide proopiomelanocortin (POMC), cocaine- and amphetamine-regulated transcription peptide (CART), and leptin level. However, the ghrelin concentration and AMPK activity increased. After refeeding, there was no difference in these physiological indexes between the food restriction and control groups. Moreover, the physiological indicators also showed regional differences, such as the body mass, POMC and CART gene expression, ghrelin concentration in the stomach and serum, and AMPK activity in DL changed more significantly. All these results showed that food restriction reduces energy metabolism in E. miletus. After refeeding, most of the relevant physiological indicators can return to the control level, indicating that E. miletus has strong phenotypic plasticity. Ghrelin, leptin, and the AMPK pathway play an important role in the energy metabolism of E. miletus under food restriction. Moreover, regional differences in physiological indicators under food restriction may be related to the different temperatures or food resources in different regions.
Disjointed Equivalence of Gravitational and Inertial Mass

Problem- Contemporary physics offers no underlying reason for the equivalence of inertial and gravitational mass. Approach- The equivalence is examined from the new physics provided by the cordus theory, being a non-local hidden-variable (NLHV) theory. Mathematical formalisms are derived for masses and observers in different fabric densities. Findings- A disjointed equivalence is predicted, whereby inertial and gravitational masses are equivalent in any one situation, but a different equivalence holds when the fabric densities change. Consequently this theory predicts that the gravitational constant G varies with fabric density, and hence would be different across the universe and across time. Not only is the gravitational constant non-constant, but the formulation of gravitation changes with fabric density. Specifically, the theory predicts gravity is stronger at genesis (and the end of the universe) such that orbit velocity 𝑣 𝐵 ∝ √ 𝑟 𝐵 (where 𝑟 𝐵 is orbit radius), compared to weaker gravitation at middle life epochs with 𝑣 𝐵 ∝ 1 √𝑟 𝐵 . The current Earth location and epoch correspond to the latter case, i.e. Newtonian gravitation is recovered. The findings disfavour the existence of both dark energy and dark matter, and instead attribute these effects to differences in the fabric density. Originality – The work makes the contribution of deriving a mass equivalence relationship that includes fabric density, identifying a disjointed mass equivalence, and showing that the gravitation formulation itself changes with relative fabric densities.