Miracle-dz/UniFAR-FTUs huggingface dataset:
A Continuum Robot Surface of Woven, McKibben Muscles Embedded in and Giving Shape to Rooms

Robots are typically designed as occupants of rooms, adapting to, and navigating within them. “Robot surfaces,” an emerging robot typology, are not occupants of but integral with rooms, physically shaping rooms to support human activity. We report on an advancement of robot surfaces formed by weaving McKibben Pneumatic Air Muscles that, when actuated, morph a 2D planar surface to generate 3D geometries including a “spherical cap. ” Following our foundational study at different scales with different materials, we developed a full-scale prototype that offers an intimate and private space for people meeting in open plan environments. We report on our research, focusing on a design case, and validate the full-scale prototype as compared to our Non-Uniform Rational B-Splines (NURBS) model for three useful configurations. Our quantitative and qualitative results suggest that our robot surface can support human activity as envisioned. This research contributes foundational understanding of an emerging category of robotics from which our team and peers can build.
Detection of Thermal Covert Channel Attacks Based on Classification of Components of the Thermal Signal Features

In response to growing security challenges facing many-core systems imposed by thermal covert channel (TCC) attacks, a number of threshold-based detection methods have been proposed. In this paper, we show that these threshold-based detection methods are inadequate to detect TCCs that harness advanced signaling and specific modulation techniques. Since the frequency representation of a TCC signal is found to have multiple side lobes, this important feature shall be explored to enhance the TCC detection capability. To this end, we present a pattern-classification-based TCC detection method using an artificial neural network that is trained with a large volume of spectrum traces of TCC signals. After proper training, this classifier is applied at runtime to infer TCCs, should they exist. The proposed detection method is able to achieve a detection accuracy of 99%, even in the presence of the stealthiest TCCs ever discovered. Because of its low runtime overhead (<inline-formula><tex-math notation="LaTeX">$< 0.187\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>187</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3189578.gif"/></alternatives></inline-formula>) and low energy overhead (<inline-formula><tex-math notation="LaTeX">$< 0.072\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>072</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq2-3189578.gif"/></alternatives></inline-formula>), this proposed detection method can be indispensable in fighting against TCC attacks in many-core systems. With such a high accuracy in detecting TCCs, powerful countermeasures, like the ones based on dynamic voltage and frequency scaling (DVFS), can be rightfully applied to neutralize any malicious core participating in a TCC attack.
A Linearization of Centroidal Dynamics for the Model-Predictive Control of Quadruped Robots

Centroidal dynamics, which describes the overall linear and angular motion of a robot, is often used in locomotion generation and control of legged robots. However, the equation of centroidal dynamics contains nonlinear terms mainly caused by the robot's angular motion and needs to be linearized for deriving a linear model-predictive motion controller. This paper proposes a new linearization of the robot's centroidal dynamics. By expressing the angular motion with exponential coordinates, more linear terms are identified and retained than in the existing methods to reduce the loss from the model linearization. As a consequence, a model-predictive control (MPC) algorithm is derived and shows a good performance in tracking angular motions on a quadruped robot.
Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units

ABSTRACT Solar energy is a feasible alternative to traditional sources of energy. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the reliable and cost-effective functioning of active distribution systems. Photovoltaic power forecasting is a critical instrument in solar photovoltaic power plants for improving energy delivery quality to the grid and lowering weather-related ancillary expenses. This paper proposes a new hybrid deep learning model called convolutional neural network (CNN) and gated recurrent unit (GRU). Convolutional layers have the potential to learn complicated characteristics from raw data automatically. GRU layers, on the other hand, can learn numerous parallel sequences of input data immediately. The generated prediction model was compared to newly established deep-learning-based algorithms of CNN, GRU, recurrent neural network (RNN), and long short-term memory (LSTM) to test CNN-GRU performance. The photovoltaic power data collected in Alice Springs, Australia, is utilized. In terms of accuracy and efficacy, the values of mean absolute error (MAE), root mean squared error (RMSE), mean bias error (MBE), mean squared error (MSE) and the coefficient of determination (R2) were 0.068 kW, 0.103 kW, 0.032 kW, 0.010 kW, and 99.91%, respectively. The experimental findings show that the proposed model outperforms alternative baseline models.
A Passively Adaptable Toroidal Continuously Variable Transmission Combined with Twisted String Actuator

Robots performing close physical interaction with humans would require a continuously variable transmission to operate in the region around the peak efficiency or peak power of the driving system. Conventional continuously variable transmission (CVT) has shown advantages in energy-efficient driving systems. However, these CVT designs are heavy and large for robotic applications. This paper presents a passively adaptable toroidal-CVT (pat-CVT) that is coupled with a twisted string actuator (TSA). The proposed combination of pat-CVT with TSA expands the operation range of TSA and mimics the torque-speed characteristics of artificial muscles. The contributions of the proposed system are as follows: 1) It has a high transmission ratio (4.6:1) compared to the level of existing CVT, and compact design; 2) We propose a structure that increases the power transmission efficiency by reducing slip rate during rotation through the application of soft material on the input/output disk and roller surfaces of the CVT; 3) Relations between the external load and the transmission ratio can be determined by selecting the spring in the system to optimize the motor operating conditions over the entire range of loads. We show theoretical modeling of pat-CVT with TSA and characterization of the transmission ratio, energy efficiency, and force-velocity curve.
Religious/spiritual beliefs and behaviours and study participation in a prospective cohort study (ALSPAC) in Southwest England

Background Longitudinal studies are key to understanding risk factors for health, well-being, and disease, yet associations may be biased if study invitation and participation are non-random. Religious/spiritual beliefs and behaviours (RSBB) are increasingly recognised as having potentially important relationships with health. However, it is unclear whether RSBB is associated with study participation. We examine whether RSBB is associated with participation in the longitudinal birth cohort ALSPAC (Avon Longitudinal Study of Parents and Children). Methods Three RSBB factors were used: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other), and religious attendance (frequency of attendance at a place of worship). Participation was measured in three ways: i) total number of questionnaires/clinics completed; ii) completion of the most recent questionnaire (in 2020); and iii) length of participation. Analyses were repeated for the ALSPAC mothers, their partners, and the study children, and were adjusted for relevant socio-demographic confounders. Results Religious attendance was positively associated with participation in all adjusted models in all three cohorts. For example, study mothers who attended a place of worship at least once a month on average completed two more questionnaires (out of a possible 50), had 50% greater odds of having completed the most recent questionnaire, and had 25% reduced risk of drop-out, relative to those who did not attend a place of worship. In the adjusted analyses, religious belief and attendance were not associated with participation. However, the majority of unadjusted models showed associations between RSBB and participation. Conclusion After adjusting for confounders, religious attendance – not religious belief or affiliation – was associated with participation in ALSPAC. These results indicate that use of RSBB variables (and religious attendance in particular) may result in selection bias and spurious associations; these potential biases should be explored and discussed in future studies using these data.
L2 self of Omani college and university students: An exploration on L2 Learning Experience

Most studies on L2 Motivational System focus on the ideal and ought-to self-guides leaving the L2 Learning Experience (L2LE) behind in that insufficient empirical information about it exists. The present study is a follow up study of Cruz and Shabibi (2019) to report on the L2 Motivational Self System of Omani college students; thus, a classification of the L2LE with respect to the learning context of Omani college students was developed. It specifically seeks to know which aspects of the students‟ L2LE make an impact on their L2 motivation. Through the data obtained from 294 Omani college and university students, it was found that the Omani college and university students make use of their ideal self and L2LE more than the ought-to self. Statistical data found positive correlations between the L2 self-guides and L2LE components. It was also found that the formal and informal education are tantamount to being sources of L2 motivation for Omani students. Theoretical and pedagogical implications on the L2LE are also offered in the study.
Centralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids

Grid-forming inverters can establish frequency and voltage regulation in autonomous microgrids by themselves without relying on the external grid and as such are significant enablers for the integration of inverter-interfaced renewable energy sources. The most prominent grid-forming strategies, the droop-based techniques, cannot provide an error-free frequency regulation after a step-wise disturbance and thus are completed by the secondary integral action. In this paper, we propose a model predictive control for fast frequency response in an AC, purely inverter-based microgrid operating in islanded mode. The controller acts as a secondary control unit and adjusts the control actions of the underlying droop controllers leading to an error-free frequency transient control. The controller has been designed leveraging the concept of frequency divider - a simple analytical formulation that estimates the frequency at every system bus. The proposed control is successful in providing a fast frequency response, i.e., it assures a system-wide error-free frequency regulation under different operating circumstances including load changes and varying X/R-ratios. Numerical simulations are carried out in the WSCC 9-Bus and IEEE 39-Bus systems to verify the performance of the proposed method during transients and a comparison with respect to the more common secondary frequency control is given.
Low-Complexity Techniques for Ultra-Wideband Communication Systems

Ultra-wideband communication systems use radio signals whose bandwidth is in the range of some hundred MHz to several GHz. The first application of these signals was military radar. The reasons for using signals with such an extremely large bandwidth are manifold. The resulting high temoral signal-resolution is a prerequisit for precise radar systems. Radio channels with dense multipath propagation achieve high multipath diversity, which can be used to improve the robustness and capacity of the communication channel. Furthermore, the large bandwidth allows to transmit signals with a small power spectral density such that, the interference to other radio signals will be negligible, even if they lie within the same frequency band. With the advances in integrated circuit technology made in the past years, the opportunity has come to use these signals also for civil communication, position location, and radar applications. However, the large signal bandwidth is still an obstacle when it comes to implementing low power devices that are simple and at the same time take full advantage of the capabilities of ultra-wideband radio. Therefore, practical systems cannot exploit all the benefits offered by UWB signals. In this work the focus is on low-complexity receiver architectures for communication systems. These architectures are found by identifying the receiver tasks whose implementation is complex or which require a high signal-processing speed. Accordingly, a simple receiver should not perform channel estimation or equalization. As a consequence, the receiver must be noncoherent. This conclusion is confirmed by deriving the optimum (maximum-likelihood) receiver, under the assumption that the receiver has no information about the propagation channel. Many important properties of the noncohrerent receiver are discussed, such as sensitivity, vulnerability to narrowband interference, and robustness to synchronization inaccuracies. Furthermore, an approach for the robust design of a key system parameter, the integration duration, is derived for some simplifying assumptions on the propagation channel. A meaningful extension of this simple noncoherent receiver is obtained by assuming that the receiver knows the power delay profile of the propagation channel. The resulting maximumlikelihood receiver is derived and its performance is compared with that of the simpler receiver which has no channel information. Another extension is to use more than one receiver antenna, each, leading to a separate noncoherent receiver. The reasonable distance between the receiver antennas is discussed, i.e., whether micro diversity or macro diversity can be exploited. The optimum scheme to combine the individual received signals is derived and the performance of this optimum scheme is compared with the suboptimum selection-combining scheme. A totally different approach to simplify receivers consists in designing the transmit signal such that the signal acquisition gets simple. A novel multiple access scheme for uncoordinated users, called rate division multiple access, follows this idea.
A Python Code for Detecting True Repeating Earthquakes from Self-Similar Waveforms (FINDRES)

Seismic data are generally scrutinized for repeating earthquakes (REs) to evaluate slip rates, changes in the mechanical properties of a fault zone, and accelerating nucleation processes in foreshock and aftershock sequences. They are also used to study velocity changes in the medium, earthquake physics and prediction, and for constraining creep rate models at depth. For a robust detection of repeaters, multiple constraints and different parameter configurations related to waveform similarity have been proposed to measure cross-correlation values at a local seismic network and evaluate the location of overlapping sources. In this work, we developed a Python code to identify REs (FINDRES), inspired by previous literature, which combines both seismic waveform similarity and differential S-P travel time measured at each seismic station. A cross-spectral method is applied to evaluate precise differential arrival travel times between earthquake pairs, allowing a subsample precision and increasing the capacity to resolve an overlapping common source radius. FINDRES is versatile and works with and without P- and S-wave phase pickings, and has been validated using synthetic and real data, and provides reliable results. It would contribute to the implementation of open-source Python packages in seismology, supporting the activities of researchers and the reproducibility of scientific results.
Motion planning in complex urban environments: An industrial application on autonomous last‐mile delivery vehicles

In this article, a motion planning framework for autonomous driving is explored to achieve unmanned last‐mile delivery vehicle application in complicated urban scenario. This approach can dramatically improve the intelligence, driving safety, driving robustness, and scalability of autonomous vehicles. In this framework, a specific High‐Definition (HD) map representation was proposed for last‐mile delivery applications, and a Route Planning layer generates a kinematically feasible reference line with smoothness condition based on Routing and HD Map components. Then a Scenario Planning layer considers routing results and both static and dynamic obstacles into account to select a corresponding scenario to execute, such as cruise on‐road scenario, cross intersection scenario, parking scenario, and so forth. Finally, a Trajectory Planning layer which is classified with trajectory generation and optimization modules is described. In the trajectory generation part, a rough path–speed profile and corresponding decisions, such as nudge, stop, yield, and so forth, are generated. Then, the rough path–speed profile is postprocessed by optimization algorithms in the trajectory optimization part. On the basis of the real road test results from thousands of times for JD.com's real autonomous delivery vehicle operations and approximate 115,475 km of fully autonomous driving mode in an urban scenario, the proposed motion planning framework demonstrates the efficiency of autonomous driving, improves the driving quality and reduces the manual intervention.
Adaptive-Discretization Based Dynamic Optimal Energy Flow for the Heat-Electricity Integrated Energy Systems With Hybrid AC/DC Power Sources

With renewable energy becoming more and more important, the heat and electricity integrated energy system (HE-IES) has been widely used because of its potential benefit in accommodating renewable energy. This work for the first time formulated a dynamic optimal energy flow (OEF) problem for the HE-IES with hybrid AC/DC sources. Firstly, to effectively and accurately assess the operation state of district heating network (DHN), the temperature dynamics are focused and solved with a set of difference equations where an adaptive discretization method is proposed. The method based on the delay characteristics of pipelines is proposed to select temporal and spatial steps for reasonable approximation of DHN, and to achieve satisfactory accuracy results with a lower computational burden. Secondly, a two-stage OEF model for HE-IES with new linear approximations is developed. In this model, a detailed converter station model coupled AC grid and DC grid is investigated, and a linearization constraint method is proposed to handle the highly nonlinear of converter station by adding extended branches to the AC grid. Finally, simulations demonstrate that the effectiveness of the proposed model, which can provide more intact information of state variables for optimal planning. Note to Practitioners—This work is motivated by the demand for optimal energy management in heat and electricity integrated energy system (HE-IES). On the one hand, the slow dynamic characteristic of the district heating network (DHN) is crucial for calculating the optimal results. On the other hand, the improvement of electronic technology has diversified energy supplies and enriched the way of using electricity, such as converter-based generators for electric power grid (EPG) and converter-based boilers for DHN. Therefore, the model of HE-IES exhibits highly nonlinearity, which brings huge challenges to optimal energy flow analysis, especially for the real-time OEF analysis with state information updated every 15–30 min. Existing studies focus on computing optimal energy flow without considering the slow dynamic characteristic of DHN or the hybrid AC/DC scenarios brought by the converter-based sources. This paper presents an optimal energy flow problem that integrates the slow dynamic characteristic and the converter-based sources to retain the complete state information of the system as much as possible in the optimization process. The work is used on an integrated IEEE-33 bus and Denmark-61 node system to show the effectiveness.
JmjC Family of Histone Demethylases Form Nuclear Condensates

The Jumonji-C (JmjC) family of lysine demethylases (KDMs) (JMJC-KDMs) plays an essential role in controlling gene expression and chromatin structure. In most cases, their function has been attributed to the demethylase activity. However, accumulating evidence demonstrates that these proteins play roles distinct from histone demethylation. This raises the possibility that they might share domains that contribute to their functional outcome. Here, we show that the JMJC-KDMs contain low-complexity domains and intrinsically disordered regions (IDR), which in some cases reached 70% of the protein. Our data revealed that plant homeodomain finger protein (PHF2), KDM2A, and KDM4B cluster by phase separation. Moreover, our molecular analysis implies that PHF2 IDR contributes to transcription regulation. These data suggest that clustering via phase separation is a common feature that JMJC-KDMs utilize to facilitate their functional responses. Our study uncovers a novel potential function for the JMJC-KDM family that sheds light on the mechanisms to achieve the competent concentration of molecules in time and space within the cell nucleus.