Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of Metamorphic and Regenerative Events by Cold Atmospheric Pressure Plasma Exposure in Tadpoles, Xenopus laevis

Atmospheric pressure plasma has found wide clinical applications including wound healing, tissue regeneration, sterilization, and cancer treatment. Here, we have investigated its effect on developmental processes like metamorphosis and tail regeneration in tadpoles. Plasma exposure hastens the process of tail regeneration but delays metamorphic development. The observed differences in these two developmental processes following plasma exposure are indicative of physiological costs associated with developmental plasticity for their survival. Ultrastructural changes in epidermis and mitochondria in response to the stress of tail amputation and plasma exposure show characteristics of cellular hypoxia and oxidative stress. Mitochondria show morphological changes such as swelling with wide and fewer cristae and seem to undergo processes such as fission and fusion. Complex interactions between calcium, peroxisomes, mitochondria and their pore transition pathways are responsible for changes in mitochondrial structure and function, suggesting the subcellular site of action of plasma in this system.
A simulation-based approach to modeling component interactions during design of flapping wing aerial vehicles

A new flapping wing aerial vehicle (FWAV) simulation methodology is presented that combines models of the key subsystems: (1) the actuator, (2) the battery, and (3) the wings. This approach captures component interactions that are inherently coupled in order to realize system-level designs for optimal system performance. The approach demonstrates that coupling between wing sizing, flapping motions, and loading conditions propagate into the motor–battery model to alter system-level performance properties. For the actuator subsystem model, a generalized servo motor using empirically derived coefficients to describe torque and angular velocity bandwidth in terms of voltage and current. This model is coupled with a lithium polymer battery model accounting for the nonlinear voltage drop and capacity derating effects associated with loading conditions. For aerodynamic predictions of the wing subsystem, a blade element model for predicting aerodynamic forces is coupled with an elastic wing deformation model that accounts for bending and twisting of the blade elements. System-level performance is then modeled in a design case study by coupling all of the subsystem models to account for relevant interactions, which generates a design trade space spanning a range of wing sizes, airspeeds, and flapping condition. The results from the simulation offer insight into vehicle configuration settings that provide maximum performance in terms of lift and endurance for the Robo Raven II flapping wing aerial vehicle. Experimental validation of the modeling approach shows good predictive accuracy. In addition, the presented framework offers a generalized approach for coupling interacting subsystems to improve overall predictive accuracy and identify areas where component-level improvements may offer system-level performance gains.
Determining the Responsibility of Three-Phase Unbalanced Sources Based on RICA

Voltage unbalance is one of the main power quality problems, and the source of many negative effects on utilities that are experienced by customers. In this paper, a method based on robust independent component analysis (RICA) for responsibility division of unbalanced sources is proposed for voltage unbalance. According to the weak correlation between negative-sequence voltage and upstream and downstream current fluctuation at the point of common coupling (PCC), the independent component of the negative-sequence voltage and current fluctuation at the point of common coupling is obtained by RICA. The blind source mixing coefficient matrix can be obtained according to the least squares method. The equivalent negative-sequence impedance on both sides of the PCC can be obtained using the linear correlation between the mixing coefficients. Finally, according to the principle of partial pressure, the unbalance contribution of the upstream and downstream at the PCC is calculated. The method is accurate for upstream and downstream impedance estimation compared with the traditional method, and has a strong anti-interference ability. When the background noise or system fluctuation is large, the responsibility division result is still accurate. The correctness and effectiveness of the proposed method are proven by the simulation of the experimental circuit.
Online Strategy Clustering Based on Action Sequences in RoboCupSoccer Small Size League

This paper addresses a strategy learning problem in the RoboCupSoccer Small Size League (SSL). We propose a novel method based on action sequences to cluster an opponent’s strategies online. Our proposed method is composed of the following three steps: (1) extracting typical actions from geometric data to make action sequences, (2) calculating the dissimilarity of the sequences, and (3) clustering the sequences by using the dissimilarity. This method can reduce the amount of data used in the clustering process; handling action sequences instead of geometric data as data-set makes it easier to search actions. As a result, the proposed clustering method is online feasible and also is applicable to countering an opponent’s strategy. The effectiveness of the proposed method was validated by experimental results.
Friction Reduction Effect of Soft Coatings

Finite element analysis is conducted on an elastic-plastic full stick contact between a rigid flat and a deformable coated sphere with a soft coating. A combined normal and tangential load is applied to the rigid flat, and the sliding inception is assumed as a result of the decreasing tangential stiffness of the contact junction. As the coating becomes thicker, the static friction coefficient first decreases reaching a minimum and then increases. This demonstrates a friction reduction effect of soft coatings that is opposite to hard coatings. The static friction coefficient decreases with increasing normal load as the contact junction contains more plasticity and becomes more compliant.
A Deep Learning Approach for Probabilistic Security in Multi-Robot Teams

In this letter, we train a convolutional neural network (CNN) to predict the probability of security of a multi-robot system (MRS) when robot interactions are probabilistic. In the context of MRSs, probabilistic security is defined using the control-theoretic notion of left invertibility, a necessary and sufficient condition to avoid perfect attacks. As the probabilistic security problem is NP-Complete, current solutions fail to generalize as the size of the MRS increases. Fortunately, deep neural networks have shown promising results in the efficient computation of solutions to hard problems, which motivates our CNN-based approach. In this context, formulating a method for data generation is non-trivial due to the large space of available interaction graph topologies and training biases introduced by random sampling. As such, we use a two-step approach for data generation where we first explore the space of available topologies, then populate the sampled topologies with probability distributions, all while preventing any biases from occurring in the data. We then train a CNN with convolution layers specifically tailored for graph adjacency matrices. Finally, the validity of our results is demonstrated through simulations.
Device-Free Tracking via Joint Velocity and AOA Estimation With Commodity WiFi

Device-free localization and tracking (DFT) is a novel technique which can estimate the location of a target without equipping it with any devices. Existing DFT systems mainly rely on machine learning techniques with a labor-intensive training. Recently some training-free DFT systems are designed by deriving the angle-of-arrival (AOA) using the dedicated hardware. However, limited by the hardware imperfection, realizing a training-free DFT system using the commodity Wi-Fi is still a challenging task to solve. To address this issue, we develop a novel DFT system which can track the target motion via both target velocity and AOA estimations simultaneously. First, the motion-induced phase shifts are refined from extremely noisy channel state information (CSI) measurements to detect the Doppler shift based on the signal superposition analysis. Then, according to the phased-array signal processing, we realize joint Doppler velocity and AOA estimation of the target path under the compressive sensing framework. It formulates joint Doppler velocity and AOA estimation as a two-dimensional sparse reconstruction problem, which can achieve a high accuracy to further estimate the target velocity and track the target motion at a decimeter level. We implement the proposed DFT system on commercial Wi-Fi devices and validate its performance with extensive evaluations in three indoor scenarios.
FOSTERING CREATIVITY VIA TECHNOSELF ENHANCED LEARNING WITH EMERGING TECHNOLOGIES

Creative and collaborative learning has profound implications for all parts of the system we have built up in our societies — not only the education systems but also the social, economic and cultural systems. Technology Enhanced Learning (TEL) research has increasingly focused on emerging technologies, Extended Reality (XR) to improve learner’s engagement in enriched multimodal learning environments. This paper recommends technoself pedagogy and investigates XR for creative learning as a frontier in TEL. In partnership with cultural sectors, we introduce the agile working process in the collaboration with the Alexandra Park and Palace Charitable Trust (AP) and report the project development of two pilot apps based on the proposed methodology. As a result, students as co-creators were engaged in pilot collaborative projects to work on the digital solutions that promote unforgettable stories. The prototypes exploited the latest development of Virtual Reality (VR), Augmented Reality (AR), and game and mobile technology. The pilot pedagogical practice focuses on providing a vibrant collaborative learning environment which fosters innovation and creativity, informed by practice, inspired by TEL research across disciplines. The collaborative learning practices also support cultural sectors to inspire their visitors and to help curators think beyond their current boundaries, providing a new, mixed media and technological approach to raise cultural awareness to wider audiences.
Modeling and Analysis of Electric Motors: State-of-the-Art Review

This paper presents a comprehensive state-of-the-art review of the modeling and analysis methods for the multidisciplinary design of electric motors for various applications including vehicular power and propulsion systems and electrified powertrains. It covers the important aspects of different engineering domains, such as dynamic modeling, loss calculations, demagnetization analysis, thermal modeling, acoustic noise and vibration analysis, and mechanical stress modeling. This paper intends to guide the electric motor designers through examples and results on how to apply different analysis techniques on electric motors.
Multilevel Information Fusion for Induction Motor Fault Diagnosis

Condition monitoring and fault diagnosis are of significance to improve the safety and reliability of motors, given their widespread applications in virtually every branch of the industry. Sequential data modeling based on recurrent neural network and its variants have drawn increasing attention because the temporal nature of motor signals can be well leveraged for motor analysis. One common drawback of prior research is that signals measured on motors are typically analyzed with a fixed time window, making it difficult to tradeoff between global state estimation and local feature extraction. This paper presents a deep-learning-based model termed multiresolution & multisensor fusion network for motor fault diagnosis, through multiscale analysis of motor vibration and stator current signals. Specifically, vibration and current signals are first segmented by analysis windows of varying lengths to create a new data stream for the joint representation and temporal encoding of the original sensor signals, based on two network structures: convolutional neural network and long short-term memory. The advantage of the developed method is that it automatically learns the discriminative features through the network training process, without requiring manual feature selection as is typically the case in prior methods. By considering the temporal dependence of the signals being analyzed, the developed multiresolution fusion technique not only improves the effectiveness of feature extraction but is also adaptive to varying motor speed. Two case studies demonstrate the advantages of the developed method.
A Hybrid Integration Strategy for Compact, Broadband, and Highly Efficient Millimeter-Wave On-Chip Antennas

A novel hybrid integration strategy for compact, broadband, and highly efficient millimeter-wave (mmWave) on-chip antennas is demonstrated by realizing a hybrid on-chip antenna, operating in the [27.5–29.5] GHz band. A cavity-backed stacked patch antenna is implemented on a 600 $\mu$m thick silicon substrate by using air-filled substrate-integrated-waveguide technology. A hybrid on-chip approach is adopted in which the antenna feed and an air-filled cavity are integrated on-chip, and the stacked patch configuration is implemented on a high-frequency printed circuit board (PCB) laminate that supports the chip. A prototype of the hybrid on-chip antenna is validated, demonstrating an impedance bandwidth of 3.7 GHz. In free-space conditions, a boresight gain of 7.3 dBi and a front-to-back ratio of 20.3 dB at 28.5 GHz are achieved. Moreover, the antenna is fabricated using standard silicon fabrication techniques and features a total antenna efficiency above 90% in the targeted frequency band of operation. The high performance, in combination with the compact antenna footprint of $\text{0.49}\,\lambda _{\rm min} \times \text{0.49}\,\lambda _{\rm min}$, makes it an ideal building block to construct broadband antenna arrays with a broad steering range.
Tunable Bandpass Filter Using Series Connected Varactor Diodes

The paper presents a novel design of tunable third-order parallel-coupled line filter. The design is based on using short-circuited resonators loaded in series by varactor diodes. Integrating the varactor diodes in the middle of the resonator has made this design unique. It is shown that varying the internal capacitance the varactor diodes has a direct impact on tuning the odd-mode resonance frequency of the resonator. The proposed concept is validated by comparing theoretical equations to circuit simulations. Very good agreement is found. When the concept is applied to the design of a third-order parallel line-coupled filter, the obtained tuning range is ±27.3% (central frequency tuned between 1.74 and 3.05 GHz). In addition, the filter presents good electrical performance and compact size (73% surface miniaturization) with respect to the state of art results.
Analytical Equivalent Circuit Extraction Procedure for Broadband Scalable Modeling of Three-Port Center-Tapped Symmetric On-Chip Inductors

This paper presents a new, almost fully analytical methodology for component value extraction of a double-<inline-formula> <tex-math notation="LaTeX">${\pi }$ </tex-math></inline-formula> equivalent circuit model for three-port on-chip inductors from a given S-parameter dataset. The compact model provides an accurate fit over a wide frequency range from dc to beyond the self-resonance frequency (SRF) to a tabular input <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>-parameter model describing a symmetric center-tapped on-chip inductor. The input dataset may be obtained from a measurement or from an electromagnetic field solver simulation. Using a passive broadband equivalent circuit instead of the original <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>-parameters’ description is advantageous for circuit design, as it facilitates the convergence of transient simulations. The proposed approach carefully considers center-tap parasitics. Hence, the obtained equivalent circuit model fits the input inductor characteristics accurately not only for differential excitation but also in the common-mode and single-ended operation. Due to the fact that the proposed extraction approach is based on physical assumptions and analytical circuit decomposition, the obtained component values are physically meaningful and relate to geometry. Thus, this approach is suitable for the generation of scalable compact models, which can be used to speed-up inductor optimization during the RF circuit design. The proposed methodology has been verified on a three-port inductor realized in a 28-nm CMOS technology and measured up to 60 GHz. The extracted equivalent circuit model exhibits an accurate fit to the measured data over the entire frequency range in all operation modes. Finally, field-solver models are used to verify the scalability.
Design and Analysis of Electromagnetic Tilting Actuators

This paper studies the design and the behavior of bistable electromagnetic tilting actuators (ETA). A serial chain of these actuators can be utilized as a medical or industrial endoscope. With the concept presented here, it is possible to unite flexibility and high stiffness in one shaft. An ETA is able to switch between defined positions through a tilting movement. The mechanical movement is driven by an electromagnetic torque, which is powered by a magneto motive force in the coils of the ETA. The tilting torque is mainly influenced by the design of the magnetic path inside the ETA cores. The ETA chain is operated using a “Follow the Leader” control. Therefore, very fast calculation methods are necessary to evaluate power-on-durations and subsequently influence the tilting process itself. In this paper, the physical effects while operating are analyzed and the necessary calculation methods are elaborated. Since very high currents are applied for tilting, the electric network calculation includes a thermal model to consider the thermally driven asymmetric increase of resistivity as well. The analysis of the operational characteristic of the ETA is the basis of the time-efficient combined calculation method. Using this combined calculation, a complete tilting process of one or more ETA can be calculated in advance considering the chain configuration and external forces. Using this method, optimized power-on-durations can be evaluated, for which the tilting process itself is successful and no unintended impulsive forces are applied to the chain. The validation with measurements has proven the accuracy of the initial model (without parameter fitting) to a deviation of 15% at most with a very small calculation time effort within seconds.
Portable Laboratory Platform With Electrochemical Biosensors for Immunodiagnostic of Hepatitis C Virus

This paper presents a portable laboratory platform (PLP) with electrochemical amperometric biosensors for the detection of hepatitis C virus (HCV). This PLP is composed by a controller unit, a Bluetooth module to provide communication with mobile devices, and a potentiostat circuit to perform cyclic voltammetry. The Bluetooth communication allows the mobile devices to set up the parameters, e.g., a cycling voltage in the range ±1.5 V, a scan rate in the range 5–1000 mV<sup>−1</sup> with a step potential of 2.318 mV, and up to 4000 number of scan cycles. The electrochemical sensors were functionalized with cystamine (C<sub>4</sub>H<sub>12</sub>N<sub>2</sub>S<sub>2</sub>) and glutaraldehyde (C<sub>5</sub>H<sub>8</sub>O<sub>2</sub>) to make the covalent binding with the recombinant core-HCV protein. The experimental results have revealed a positive detection of HCV, using a redox solution of 5 mmol.L<sup>−1</sup> of potassium ferrocyanide (<inline-formula> <tex-math notation="LaTeX">$\text{K}_{{3}}$ </tex-math></inline-formula> Fe(CN)<sub>6</sub>) on 0.1 mol.L<sup>−1</sup> of phosphate-buffered saline (PBS). The sensitivity of the immunosensor was established at the concentration of 1 ng. <inline-formula> <tex-math notation="LaTeX">$\mu \text{L}^{{-1}}$ </tex-math></inline-formula> of the anti-core-HCV IgG class antibody. This PLP has a plug-and-play connection for the biosensors, and at the same time, it offers good ergonomy with easy operation due to its low dimensions (115 mm <inline-formula> <tex-math notation="LaTeX">$\times75$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times30$ </tex-math></inline-formula> mm). This PLP presents a power consumption of 280.5 mW when supplied with a 3.3-V voltage source. This consumption was measured in the worst operation case, e.g., with the Bluetooth working, resulting in an average battery life of 195 min.
Broadband power generation of piezoelectric vibration energy harvester with magnetic coupling

In this work, a magnetic coupling 2-degree-of-freedom bistable piezoelectric energy harvester was designed and developed. The device consisted of a primary beam with a magnet attached to the tip and a parasitic beam. The magnets between the primary beam and the pedestal generated nonlinear repulsive force. By controlling the distance between two magnets, the system could oscillate between two stable equilibrium points which allowed the device to exhibit broadband characteristics. Theoretical and experimental investigations of this energy harvester were presented over a range of excitation frequencies. Lumped-parameter nonlinear equations of the bistable Duffing oscillator with magnetic coupling were introduced to describe the broadband voltage response. Compared with the theoretical results, at an excitation acceleration of 3 m/s2, it is experimentally verified that the proposed device achieved the operation frequency bandwidth adding up to 8 Hz at around the first and second resonance regions which was about 2.7 times higher than conventional 2-degree-of-freedom linear harvester one. In addition, energy harvesting efficiency could be improved by increasing the frequency bandwidth. The results of this work can be applied to optimize the design of piezoelectric energy harvester and harvest low-frequency vibration energy in the environment to supply power for sensor network nodes.
Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death

Imaging polarimetry is a focus of increasing interest in diagnostic medicine because of its non-destructive nature and its potential to distinguish normal from tumor tissue. However, handling and understanding polarimetric images is not an easy task, and different intermediate steps have been proposed in order to introduce helpful physical magnitudes. In this research, we look for a sensitive polarimetric parameter that allows us to detect cell death when cancer cells are treated with chemotherapy drugs. Experiments in two different myelomonocytic leukemia cell lines, U937 and THP1, are performed in triplicate, finding a highly-significant positive correlation between total diattenuation of samples in transmission configuration, D T , and chemotherapy-induced cell death. The location of the diattenuation enhancement gives some insight into the cell death process. The proposed method can be an objective complement to conventional methodologies based on pure observational microscopy and can be easily implemented in regular microscopes.