Miracle-dz/UniFAR-FTUs huggingface dataset:
Error Analysis of Instantaneous Frequency Estimation of Second-Order SST and Its Application on Analyzing Gear Meshing Stiffness From Motor Current

Motor gearbox systems are widely used in industrial production, and the variations of gear meshing stiffness generate transient frequency modulation (FM) effects on motor current. The 2nd-order synchrosqueezing transform (SST) is effective for revealing the time-varying frequency characteristics of motor current, thus enabling the monitoring of the condition of the electromechanical system; however, the selection of the window parameter in 2nd-order SST analysis is subjective and lacks specific theoretical guidance. To quantify the analytical limits, this study derives the instantaneous frequency (IF) estimation error function of the 2nd-order SST. The function demonstrates that the factors affecting the IF estimation are the window parameter and the 2nd-order and 3rd-order derivatives of the signal phase. To validate the practical application of the IF estimation error function, a rotating vector (RV) gearbox test bench is constructed. First, the motor current model of the test bench that contains the meshing stiffness is established, which allows the definition of the phase and FM amplitude of the signal. In the analysis of the model simulation, the optimal window parameters for identifying the FM characteristics are determined by comparing the FM amplitudes with the calculated IF estimation errors. With the window parameter determined by model analysis as previous knowledge, the healthy and faulty meshing stiffness characteristics are successfully extracted from the experimental motor currents. As a result, local gear faults in the RV gearbox can be diagnosed intuitively and accurately.
GTEA: Guided Taylor Expansion Approximation Network for Optical Flow Estimation

Deep neural network (DNN) has proven its powerful ability in feature representation and has been widely applied in computer vision tasks, especially optical flow estimation (OFE). However, these DNN-based OFE approaches have a black-box nature, resulting in lacking a clear interpretation for their success. More unfortunately, they usually suffer from the loss of objects’ shape, which is not beneficial for performance improvement. To address these issues, we develop a novel guided Taylor expansion approximation (GTEA) network, including the following key procedures: First, we present the detail analysis of the relationship between Taylor expansion and optical flow. Then, a convolutional guided filter (ConvGF) network is developed to refine the shape of objects. Finally, the final output is obtained by employing basic network, ConvGF, and detail network to approximate the Taylor expansion. Quantitatively and qualitatively experimental results on the Sintel and KITTI datasets demonstrate that the proposed GTEA is effective and even performs better than the state-of-the-art OFE methods on performance improvement and shape preservation.
Recognizing and Responding to Human Emotions: A Survey of Artificial Emotional Intelligence for Cooperative Social Human-Machine Interactions

This paper represents a survey of Artificial Emotional Intelligence (AEI) for cooperative social human-machine interactions. Additionally, it explores the potential benefits and challenges of integrating AEI into various applications, including virtual assistants, customer service, and healthcare. To imbue machines with the ability to understand human emotions, interest in AEI has grown to develop machines capable of natural human-machine interactions. The rapid advancement of Artificial Intelligence (AI) has enabled machines to perform tasks that were once exclusive to humans. However, current AI systems cannot understand emotions and social cues, which limits their ability to interact with humans. Social cues refer to the various nonverbal and verbal signals that people use to convey their emotions, intentions, and thoughts during social interactions. These cues include facial expressions, tone of voice, gestures, body language, and other subtle indicators that humans use to communicate with each other. There are several challenges associated with developing AEI systems that can navigate the complexity of human social interactions and emotions. One of the biggest challenges is developing machine learning algorithms that can accurately recognize and interpret human emotions. Emotions are complex, often subtle, and heavily context-dependent. Machines must recognize and respond to them in a way similar to how humans do through contextual, nonverbal, and verbal cues. Another challenge is developing natural language processing algorithms that accurately recognize the sentiment behind spoken or written communication. Sentiment analysis is a challenging problem because language is inherent ambiguity, and the meaning of a word or phrase can depend on situational, historical, and cultural context. Sentiment can depend on subtle factors such as tone of voice, facial expressions, and body language. Despite these challenges, there are also significant opportunities for improving the quality of human-machine interactions. By developing machines that recognize and respond to human emotions, we can achieve the goal of creating more natural and satisfying interactions between humans and machines. This work-in-progress paper is a preliminary analysis of the technical challenges and opportunities of developing AEI and assesses potential implications for human-machine interaction (e.g., human trust in technology). The paper presents future research directions for AEI, highlighting the need for interdisciplinary collaborations to develop a more human-centered and responsible AI system, while surveying the technology and the potential impacts of human trust in technology. In this literature review, the guiding question is: Can a method for AI in combination with interdisciplinary collaborations be developed to take a logical leap to make a determination of a human's emotional state for a more human-centered and responsible AI system.
Convolutional Autoencoder for Real-Time PPG Based Blood Pressure Monitoring Using TinyML

In this paper, we propose an efficient and robust convolutional autoencoder (CAE) model for continuous realtime blood pressure (BP) monitoring. The proposed model was implemented on a resource-constrained edge device. The model was built to capture the hidden patterns among successive segments and alleviate the effects of momentary glitches and outliers. The model was deployed and assessed on the Arduino Nano 33 BLE Sense in a real-time environment by means of Tiny Machine Learning (TinyML). Extensive results revealed that the proposed model improved BP prediction accuracy on both offline and real-time experiments. With 4 features, the model achieved a mean absolute error±standard deviation (MAE±SD) of 2.81±2.84 and 1.51±1.85 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively, on a dataset of 40 subjects. Whereas microcontroller unit (MCU) based real-time continuous predictions attained 2.25±2.82 for SBP and 5.01±2.10 mmHg for DBP, on 8 volunteers. Compared to the state-of-the-art models implemented on tiny devices, our model showed superior robustness and accuracy. Overall, the study offered some important insights into the significance of compact and impactful feature set and the effectiveness of the proposed model in a real-time setting.
Learning Locomotion for Quadruped Robots via Distributional Ensemble Actor-Critic

Domain randomization introduces perturbations in the simulation to make controllers less susceptible to the reality gap, which enables remarkable sim-to-real transfer on real quadruped robots. However, aleatoric uncertainty originating from perturbations could often lead to suboptimal controllers. In this work, we present a novel algorithm called Distributional Ensemble Actor-Critic (DEAC) that blends three ideas: distributional representation of a critic, lower bounds of the value distribution, and ensembling of multiple critics and actors. Distributional representation and ensembling provide reasonable uncertainty estimates, while lower bounds of the value distribution offer finer-grained error control. The simulation results show that the controller trained by DEAC outperforms the other baselines in the domain randomization setting. The trained controller is deployed on an A1-like robot, demonstrating high-speed running and the ability to traverse diverse terrains such as slippery plates, grassland, and wet dirt.
The Role of Teacher Initiative on the Quality of Early Childhood Education through Service Quality and Teacher Knowledge

Inovasi inisiatif guru merupakan kemampuan kepala sekolah membangkitkan inisiatif guru untuk terus berkontribusi dalam pendidikan anak usia dini. Tujuan penelitian ini adalah untuk mengoptimalkan peran mediasi (kualitas pelayanan dan pengetahuan guru di lingkungan sekolah PAUD, dan pengaruh inisiatif guru terhadap mutu pendidikan PAUD. Jenis penelitian adalah kuantitatif berupa data responden yang dikumpulkan melalui sebaran kuesioner pada guru PAUD di Kota Cilegon, dan dianalisis dengan alat statistic structural Equation Modelling (SEM). Hasil penelitian antara lain: 1) ada pengaruh inisiatif guru terhadap kualitas pelayanan. 2) ada pengaruh inisiatif guru terhadap pengetahuan guru. 3) ada pengaruh inisiatif guru terhadap kualitas mutu pendidikan PAUD. 4) tidak ada pengaruh kualitas pelayanan terhadap kualitas mutu pendidikan PAUD. 5) ada pengaruh Pengetahuan guru terhadap kualitas mutu pendidikan PAUD. 6) ada pengaruh mediasi antar inisiatif guru terhadap kualitas mutu pendidikan PAUD. Simpulan penelitian ini yaitu inisiatif guru berdampak pada berbagai aspek seperti kualitas pelayanan, pengetahuan guru, dan kualitas mutu pendidikan PAUD.
A micro-opto-mechanical glass interferometer for megahertz modulation of optical signals

Waveguide-based interferometric circuits are widely employed in optical communications, sensing and computing applications. In particular, glass-based devices are appealing due to the transparency and bio-compatibility of this substrate, or where low-loss interfacing with fiber networks is required. However, fast electro-optic phase modulation is hard to achieve in glass materials. Here, we demonstrate an optical phase and intensity modulator in glass, working in the megahertz range. This modulator exploits the elasto-optic effect inside a mechanical microstructure, brought to oscillation at resonance, and is entirely realized by femtosecond laser micromachining. In detail, we demonstrate 23-dB optical intensity modulation at 1.17 MHz, with an internal optical loss of the phase-modulator component as low as 0.04 dB.
A Compact Cuk-Based Differential Power Processing IC With Integrated Magnetics and Soft-Switching Controller for Maximized Cell-Level Power Extraction

In this article, a cell-level differential power processing integrated circuit (IC) is presented to perform maximum power point tracking on photovoltaic cells under mismatch conditions, such as partial shading. To reduce the form-factor of the design, a bidirectional Cuk-based converter is implemented by integrating magnetics on the packaging bond wires. Using variable frequency TCM, a novel closed-loop soft switching controller is proposed to guarantee zero voltage switching (ZVS) for a wide operating range, regardless of process and component variations. To evaluate the stability and performance of the proposed nonlinear soft switching controller, a detailed analysis is provided using the phase plane portrait. In addition, a modified low-drop bootstrap circuit is presented to provide the floating voltage supply for the high-side switches in low-voltage applications. The proposed differential power processing IC is implemented in a 130 nm complementary metal-oxide semiconductor (CMOS) with an area of 3 by 3 $\text{mm}^{2}$. Simulation and experimental results are provided to validate the performance of the circuit, and a system efficiency above 93% is achieved for mismatch currents up to 3 A.
Analysis of Polycyclic Aromatic Hydrocarbons via GC-MS/MS and Heterocyclic Amines via UPLC-MS/MS in Crispy Pork Spareribs for Studying Their Formation during Frying

This study aims to explore the effects of frying conditions on the formation of HAs and PAHs in crispy pork spareribs, a popular meat commodity sold on Taiwan’s market. Raw pork spareribs were marinated, coated with sweet potato powder, and fried in soybean oil and palm oil at 190 °C/6 min or 150 °C/12 min, followed by an analysis of HAs and PAHs via QuEChERS coupled with UPLC-MS/MS and GC-MS/MS, respectively. Both HAs and PAHs in pork spareribs during frying followed a temperature- and time-dependent rise. A total of 7 HAs (20.34–25.97 μg/kg) and 12 PAHs (67.69–85.10 μg/kg) were detected in pork spareribs fried in soybean oil and palm oil at 150 °C/12 min or 190 °C/6 min, with palm oil producing a higher level of total HAs and a lower level of total PAHs than soybean oil. The content changes of amino acid, reducing sugar, and creatinine played a vital role in affecting HA formation, while the degree of oil unsaturation and the contents of precursors including benzaldehyde, 2-cyclohexene-1-one, and trans,trans-2,4-decadienal showed a crucial role in affecting PAH formation. The principal component analysis revealed that HAs and PAHs were formed by different mechanisms, with the latter being more liable to formation in pork spareribs during frying, while the two-factorial analysis indicated that the interaction between oil type and frying condition was insignificant for HAs and PAHs generated in crispy pork spareribs. Both CcdP (22.67–32.78 μg/kg) and Pyr (16.70–22.36 μg/kg) dominated in PAH formation, while Harman (14.46–17.91 μg/kg) and Norharman (3.41–4.55 μg/kg) dominated in HA formation in crispy pork spareribs during frying. The outcome of this study forms a basis for learning both the variety and content of HAs and PAHs generated during the frying of pork spareribs and the optimum frying condition to minimize their formation.
Life Skills Oriented Natural Schools

A key characteristic of positive youth development (PYD) programs is the opportunity to develop life skills, such as social, behavioral, and moral competencies, which can be generalized to domains beyond hands-on activities. The purpose of our study was to examine whether life skills-oriented nature schools have been integrated into their students. Population and sample of 29 students of Qurani Al-Hikmah Nature School. Method: quantitative descriptive with survey approach. The life skills instrument used the Life Skills Transfer Survey (LSTS), a measure of perceived life skills transfer. The results revealed that Qurani Al-Hikmah natural school oriented life skills have been integrated into the school curriculum and given daily to students bringing changes in attitudes, thoughts and behaviors by providing a pleasant school environment .
Comparison of Student Learning Outcomes Among SQL Problem-Solving Patterns

Structured Query Language (SQL) plays a pivotal role in the effective management of relational databases and is a key skill across domains that engage with database systems, including research, development, and business management. However, mastering SQL can be challenging. To comprehend the approaches employed by students when solving SQL problems and address the challenges they faced during the learning process, our study analyzes submissions from the Database Systems course at the University of Illinois Urbana-Champaign during the Fall 2022 semester. We extend prior research involving line chart visualizations that facilitate instructors in identifying struggling students and understanding their submission behaviors. Yet, we acknowledge the limitations of this approach in providing timely feedback and actionable insights due to the sheer volume of visualizations. To address this, we developed an innovative technique using global sequence alignment scores and regular expression algorithms to compress student submission sequences. Our approach reveals submission patterns and pattern elements, leading to recommendations for instructors to enhance database education. By integrating student performance data, such as the number of submission attempts on a particular SQL problem and whether the student arrived at a correct final solution query, we aim to empirically support these recommendations, thereby enabling instructors to more accurately differentiate between struggling and excelling students.
Walking in nature may improve affect but not cognition

Beneficial effects of natural environments on affect have been consistently reported, but effects on cognition have been less consistent. We examined affect and cognitive performance in the domains of attention, working memory, executive function, and recall and recognition memory in a sample of 188 undergraduate participants who completed a walk in one of three environments: an outdoor nature environment, an outdoor urban environment, or an indoor (treadmill) environment. Supporting the hypotheses, the outdoor nature environment resulted in the greatest increase in positive affect and decrease in negative affect from pre-to post-walk. However, there were no effects of location on any cognitive measure. These results suggest that cognitive effects do not always occur in tandem with affective benefits. Possible explanations, including prior frequent exposure to nature in our participants and extremity of the natural environment, are discussed.
RELATIONSHIP BETWEEN HABITAT SUITABILITY INDEX VALUES AND GROUND COUNTS OF BEAVER COLONIES IN KANSAS

Beavers (Castor canadensis) occur near a wide variety of aquatic habitats throughout North America (Novak 1987) and have received a great deal of attention because of their value as a fur resource and the damage they can cause by their feeding and dam-building activities. Their population levels generally reflect habitat quality and the level of exploitation by humans. Early assessments of beaver habitat quality stressed food availability and water suitability (Atwater 1940, Packard 1947). Retzer et al. (1956) included physical features of the watershed in their attempt to evaluate beaver habitat in mountainous regions. More recently, Slough and Sadlier (1977), Willis (1978), Allen (1983), and Howard and Larson (1985) developed models to evaluate and classify beaver habitat. The most widely used of these models is Allen's (1983) Habitat Suitability Index (HSI) model. This model includes what are thought to be key habitat variables that affect beaver populations and evaluates the suitability of habitat for beaver, but does not estimate beaver populations. However, one would expect high HSI values to reflect high numbers of beaver if the HSI model is indeed composed of key habitat variables. Van Horne (1983) correctly cautioned against assuming that habitat quality and population numbers are positively correlated. Beavers are colonial, so colony counts do not pose the problems raised by Van Horne (1983), such as social interactions and seasonal shifts. Therefore, high-quality beaver habitat should have more beaver colonies than low-quality habitat, and one would expect values produced by HSI models to be positively correlated with beaver colony counts. As an outgrowth of comparing beaver survey techniques in Kansas, we initiated this study to determine if HSI values (Allen 1983) were correlated with beaver colony counts obtained during ground surveys of typical Kansas riverine habitat that is common in the Great Plains. Additionally, we attempted to modify the model based on the ground survey data to increase the model's ability to predict beaver colony abundance in Kansas riverine habitat.
A dual mortar contact method for porous media and its application to clay‐core rockfill dams

The clay‐core rockfill dam is a multibody contact system in which the hydromechanical response of the clay core plays a crucial role. This complex problem is highly challenging to model numerically. We present a numerical approach that considers the multibody contact, consolidation, and strong geometric and material nonlinearities for the modeling of clay‐core rockfill dams. Within the framework of the dual mortar finite element method, the presented approach considers the contact bodies as independent porous media continuums. The nonlinear contact conditions are derived based on the effective contact traction on contact interfaces and pore pressure continuity. The weak forms are obtained by introducing Lagrange multipliers as additional unknowns, which are then condensed through an extended general transformation. The presented method is first validated with a patch test considering the contact between two porous media. Then, a three‐dimensional analysis of the Rumei clay‐core rockfill dam is performed. The main numerical analysis concerns are the two observation galleries planned for construction inside the clay core. The galleries consist of dozens of tunnel‐like concrete blocks, giving rise to complex concrete‐concrete and concrete‐clay contacts. The discontinuous separation and sliding between concrete blocks are investigated. For the concrete‐concrete contact, both hard and soft joint approaches are evaluated and compared. The pore pressure results of the concrete structures are also analyzed.
Multi-Disease Detection of Diabetes and Breast Cancer Using The Doctor Framework

Machine Learning (ML) models using physiological signals and historical medical data to identify ailments are slowly gaining popularity. However, data about diseases becomes available incrementally over time and the behavior of diseases themselves could evolve over time. These factors require the ML models to be re-trained with the new data. The common approach of fine-tuning with new data can lead to catastrophic forgetting, where the model forgets what it learnt in prior iterations. Additionally, most disease detection models are trained for the detection of a single disease only. This increases the computational costs associated with ML model training and deployments, which would be critical when considering usage in cost-sensitive locales such as Africa. In this study, we tried to address these challenges by applying the DOCTOR framework, based on a Multi-head Deep Neural Network (DNN) and a Continual Learning, for the detection of diabetes and breast cancer. In a single-disease incremental scenario, the DOCTOR framework beat the baseline performance of the ideal joint scenario with average accuracy and f1 scores of 0.970 and 0.788 for diabetes, 0.875 and 0.805 for breast cancer. In the multi-disease setting, the DOCTOR framework showed the best performance for diabetes, even though it didn't better the ideal joint training scenario on breast cancer. Also, the results on the diabetes were even better than the single-disease scenario, which shows that such a multi-task learning scenario can help improve model generalization.