Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensor Product-Based Model Transformation Technique Applied to Modeling Magnetic Levitation Systems

The derivation of a Tensor Product (TP)–based model of magnetic levitation systems using the TP–based model transformation method is proposed. The TP model approximates the behavior of the plant, but exhibiting a numerical approximation error. Finally, the behavior of the TP model is compared to the laboratory equipment behavior taking into consideration two testing scenarios. The experimental results obtained for the laboratory equipment show that the mean square error is smaller in the second testing scenario.
Asymmetric SECE Piezoelectric Energy Harvester Under Weak Excitation

Piezoelectric energy harvesters (PEHs) are widely used to convert energy from a piezoelectric transducer into a stable DC form, which enables low-power IoT devices to have an unlimited operating life without using batteries. Under weak excitation conditions, however, the power-extraction efficiency of conventional PEHs is too low to provide power even to low-power IoT devices that requires low operation voltages less than 2 V. This paper proposes an asymmetric synchronous electric charge extraction (ASECE) scheme that improves the extraction efficiency of PEHs at low output voltages under weak excitation. The proposed ASECE is implemented using <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology. The figure-of-merits (FOMs) of the proposed ASECE while operating under 2 V of output voltage are 7.14 and 6.24 at weak and strong excitations, respectively. The maximum FOM for various different excitation levels is observed to be as high as 7.7. The proposed ASECE is superior to prior art with respect to FOM, by at least <inline-formula> <tex-math notation="LaTeX">$1.15\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.63\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$2.21\times $ </tex-math></inline-formula> under 2 V, 1 V, and 0.5 V outputs, respectively, under strong excitation.
Effects of plant‐scale alkali refining and physical refining on the quality of rapeseed oil

The physical refining of soybean oil was introduced as an energy saving and environmentally friendly procedure alternative to the traditional alkali refining, and the process was successfully applied to other vegetable oils. We had compared the two procedures in industrial refining under conditions, which enable a clear comparison. In nine plant-scale experiments, crude rapeseed oil, taken from the same tank of crude oil, was processed on the same day both by alkali refining and by physical refining. Quality changes (free fatty acids, peroxide value, conjugated fatty acids, polar lipids, minor constituents) were determined, and also their stability against oxidation (Rancimat and Schaal Oven Test), and the fatty acid composition. In refined oils, the sensory acceptabilities and the sensory profiles were assayed. Finally deodorized oils, produced by the two methods, did not appreciably differ in their sensory characteristics and chemical composition, excepting slightly higher concentration of isomeric polyunsaturated fatty acids in physically refined oils. During storage for one year in commercial packagings at 15 °C, oxidative and sensory changes were negligible.
Providing Psychological and Emotional Support After Perinatal Loss: Protocol for a Virtual Reality-Based Intervention

The loss of an infant during the perinatal period has been recognized as a complex and potentially traumatic life event and can have a significant impact on women’s mental health. However, often times, psychological aftercare is typically not offered, and manualized interventions are rarely used in clinical care practice and have seldom been evaluated. In recent years, a growing number of studies have demonstrated the efficacy of virtual reality (VR) interventions to facilitate the expression and coping with emotions linked to a traumatic event. The objective of the proposed paper is to present the protocol of a randomized control trial aimed to assess a novel VR-based intervention for mothers who experienced a perinatal loss. We hypothesize that the VR-based intervention group will show significantly reduced symptoms related to grief, postnatal depression and general psychopathology after treatment relative to a treatment-as-usual (TAU) group. Participants would be randomly assigned to the TAU + VR or to the VR + TAU condition. The TAU condition as well as the VR-based intervention will last 3 weeks, after which women will complete a post-assessment. The proposed VR-based intervention will consist in three weekly sessions focused, respectively on: (1) collect information about the loss and psychoeducation about perinatal grief, and introduction to the virtual environment; (2) through the use of the virtual environment, women will be assisted in the elaboration and acceptation of loss; (3) recreate, using the specific features of the virtual environment a positive metaphor representing woman’s future life. VR has proved to be a valid intervention tool in clinical psychology, and in the last years VR technologies have become more affordable to be used in clinical practice. With the present study we propose to answer to the unquestionable need for interventions addressed to ameliorate the emotional effects in women who experienced perinatal loss, by exploiting also the therapeutic opportunities offered by a new technology as VR.
New Stabilization Method for Delayed Discrete-Time Cohen–Grossberg BAM Neural Networks

This paper deals with the state feedback stabilization problem of delayed discrete-time Cohen–Grossberg BAM neural networks. By the mathematical induction method, stabilizable conditions are derived to ensure that the resulting closed-loop system is globally exponentially stable, and thereby, the desired state feedback controller is designed. These stabilizable conditions are very simple, which can easily verified by using the standard toolbox software (for example, MATLAB). The proposed approach is directly based on the definition of global exponential stability, and does not involve the construction of any Lyapunov–Krasovskii functional. For a special case, it is theoretical proven that the proposed method is superior to an existing one. Moreover, several illustrative examples are given to validate the success of the derived theoretical results.
Simulation of Acoustophoretic Separation of Microplastic Particles in mkFluidic Lab-chip

In this research we propose a simulation study of the acoustophoretic motion of microplastic particles suspended in a water-filled microchannel. The mechanism of acoustofluidic microparticles sorting was analyzed in dedicated originally designed Lab-chip device. Under the action of acoustic waves, the microparticles are focused at the points of the acoustic pressure nodes and transported further for detection and analysis.
Extracting Account Attributes for Analyzing Influence on Twitter

The last years has witnessed a surge of auto-generated content on social media. While many uses are legitimate, bots have also been deployed in influence operations to manipulate election results, affect public opinion in a desired direction, or to divert attention from a specific event or phenomenon. Today, many approaches exist to automatically identify bot-like behaviour in order to curb illegitimate influence operations. While progress has been made, existing models are exceedingly complex and nontransparent, rendering validation and model testing difficult. We present a transparent and parsimonious method to study influence operations on Twitter. We define nine different attributes that can be used to describe and reason about different characteristics of a Twitter account. The attributes can be used to group accounts that have similar characteristics and the result can be used to identify accounts that are likely to be used to influence public opinion. The method has been tested on a Twitter data set consisting of 66,000 accounts. Clustering the accounts based on the proposed features show promising results for separating between different groups of reference accounts.
Extraction of Ultra-Low Contact Resistivity by End-Resistance Method

Accuracy of extracting ultra-low contact resistivity by the end-resistance method is evaluated. As the contact length becomes smaller than the transfer length, the end-resistance approaches the contact resistance, and the error decreases with the reduction of contact length and contact resistivity. The contact resistivity lower than $10^{-9}\Omega-\mathrm{c}\mathrm{m}^{2}$ can be extracted with accuracy lower than $3\times 10^{-10}\Omega-\mathrm{c}\mathrm{m}^{2}$. This end-resistance method is verified by self-aligned transmission line model test structure. Statistic analysis of the distribution of contact resistance and the uniformity of the contact interface are also demonstrated.
Considerations about using the Shapley Value for Influence Maximization in the case of the Weighted Cascade Model

This paper explores the influence maximization problem for the weighted cascade model by considering an approach based on Shapley value and Extremal Optimization. The Shapley value is a solution concept in cooperative game theory that, given a total value of the game assigns to each player a value as part of it, computed as its marginal contribution to all possible coalitions of players. In the weighted cascade model we consider adding and updating nodes in the initial set during the extremal optimization search based on their Shapley value in an approach already tested for the independent cascade model. Comparisons with other methods by means of numerical experiments show that results reported by this approach are promising, prompting for further research in this direction.
Adaptive Numerics for Linear ODE Systems with Time-Dependent Data; Application in Photovoltaics

We give a brief account on the numerical approximation of linear evolution systems with time-dependent data. In particular, exponential integrators of Magnus type are considered. As an application we consider a system modeling the effect of photon impact of electrons in photovoltaic cells, in form of a time-dependent system of ordinary differential equations (ODEs) of spatially discrete Schrödinger type. Adaptive tuning of time steps in course of the numerical integration is an essential point; this is based on recently developed computable local error estimators. Numerical results are presented.
Low-Power Weighted Pseudo-Random Test Pattern Generation for Launch-on-Capture Delay Testing*

A new weighted pseudo-random test generator called wPRPG is proposed for low-power launch-on-capture (LOC) transition delay fault testing. The low-power weighted PRPG is implemented by assigning different weights on the test enable signals and applying a gating technique. The new low-power PRPG can achieve much higher transition delay fault coverage in LOC delay testing than the conventional test-per-scan PRPG.
Technical Aspects of in vivo Small Animal CMR Imaging

Cardiovascular magnetic resonance (CMR) imaging has become an accurate and versatile imaging modality to visualize the cardiovascular system in normal or abnormal conditions. In preclinical research, small rodent animal models of human cardiovascular diseases are frequently used to investigate the basic underlying mechanism of normal and abnormal cardiac function and for monitoring the disease progression under therapy. Technical improvements have enabled the transfer of CMR to small animal research, and as such made this non-invasive technique available to provide insights into cardiac morphology, function, perfusion, and pathophysiology in small animal cardiac disease models. This article reviews the basic technical approaches to in vivo small animal magnetic resonance imaging and its variants for the most promising applications.
Retinal and Optic Nerve Integrity Following Monocular Inactivation for the Treatment of Amblyopia

In animal models, monocular deprivation (MD) by lid closure mimics the effects of unilateral amblyopia in humans. Temporary inactivation of one or both eyes with intraocular administration of tetrodotoxin (TTX) has recently been shown to promote recovery from the anatomical effects of MD at post-critical period ages when standard recovery strategies fail. In the current study, the retinae and optic nerves of animals subjected to 10 days of monocular retinal inactivation were assessed for pathological changes as a means of assessing the viability of this potential new amblyopia therapy. Retinal sections from both eyes were subjected to hematoxylin and eosin staining and were then examined for cell density and soma size in the ganglion cell layer (GCL). Sections of the optic nerve from each eye were examined for neurofilament protein, myelin, glial cell density, and glial fibrillary acidic protein (GFAP). Our study revealed no evidence of gross histopathological abnormalities following inactivation for 10 days, nor was there evidence of degeneration of axons or loss of myelin in the optic nerve serving inactivated eyes. On all measurements, the inactivated eye was indistinguishable from the fellow eye, and both were comparable to normal controls. We confirmed that our inactivation protocol obliterated visually-evoked potentials for 10 consecutive days, but visual responses were restored to normal after the effects of inactivation wore off. Notwithstanding the critical need for further assessment of ocular and retinal health following inactivation, these results provide evidence that retinal inactivation as a treatment for amblyopia does not produce significant retinal damage or degeneration.
Surgery Versus ATMPs: An Example From Ophthalmology

Advanced therapy medicinal products (ATMPs) are the new frontier of medicine. Advanced therapy medicinal products are set out to satisfy unmet medical needs and provide new innovative, cutting-edge therapies for serious or life-threatening diseases, thus providing new therapeutic options for people with few or no possibility of treatment. They are divided into four groups including gene therapy medicinal products, cell-based therapy medicinal products, tissue-engineered products, and combined ATMPs, which in Europe refer to products that incorporate one or more medical devices with any of the previously mentioned ATMPs as part of the advanced medicine product (AIFA, 2017; Ten Ham et al., 2018). Advanced therapy medicinal products can potentially have long-term benefits, thus bringing a long-lasting positive impact on patient health. Advanced therapy medicinal product therapies are often administered just once or twice, which gives patients the possibility to heal quickly compared to traditional therapies. They also provide a long-term saving opportunity, both in terms of costs of treatments and procedures that are no longer necessary and in terms of quality of life and productivity. The resolution of the patient’s illness has a monetary impact on the patient, the patient’s caretakers, and especially on the society (Alliance for Regenerative Medicine, 2019). The aim of this paper was to provide an overview on the use of ATMPs approved in Europe, with a focus on blindness and visual impairment and the related economic burden. In this case study, the effective cost of a blind patient in different European countries was compared after treatment with ATMPs or traditional therapies, focusing on visual impairment caused by corneal opacity. Our evaluation includes an overview of the global economic impact of the two types of therapies on the society. We estimated direct healthcare costs, direct non-healthcare costs, and labor productivity losses, to include costs on healthcare, services, patients, their families and for the society in general. We could conclude that the costs of the two therapeutic approaches are comparable.
Rugged - Terrain Traversability Analyzing For Quadruped Robots

This paper presents an algorithm of feasibility analysis of the terrain to improve the adaptability of quadruped bionic robot for arbitrary environments. We use the depth camera to sense the ground environment, and utilize the rasterized Digital Elevation Model (DEM) to process the dense point clouds obtained by the camera to describe the terrain. Through calculating the geometric features in the grid, including slope value, curvature value, roughness and fluctuation, the traversability of terrain could be derived and judged. In this paper, we propose an approach to calculate the roughness characteristics, which increasing the accuracy of calculation and meanwhile adding the calculation of the grid fluctuation. The traversability of the grid is then calculated according to the corresponding formula according to the geometric characteristics of each grid. Finally, a simulation is provided to illustrate the effectiveness of the proposed method.
Increasing Accessibility of Electric Vehicles with Joystick Based Drive-By-Wire Model

The automotive industry is shifting towards a cleaner alternative: electric vehicles. This change can be utilized to bring about an improvement to the usage accessibility of these vehicles. This paper proposes a way to implement a joystick control in an electric vehicle which can be used to drive the vehicle independently for anyone who has the ability to move and have control over their arms and wrists. The joystick, accessible to the driver, can be used for moving the vehicle in the desired direction. The work proposed here makes use of a drive by wire technology to operate the vehicle. The driving motor is connected to the joystick through a microcontroller. Another motor, which is connected to the steering column through a chain and sprocket, is used to steer the vehicle. This joystick controlled driving ensures that multiple amputees or differently abled people can drive it with ease.
Hydropower Generation Forecasting via Deep Neural Network

With the advances of deep learning, its applications in our daily life have attracted considerable attention from both academics and industry. However, most of the existing works focus on computer vision and natural language processing, while few studies in the real industrial manufactures. The main reasons are that the data resources are difficult to obtain and the relationships between industrial data are too complex to be modeled. In this paper, we propose a deep neural network based approach for hydroelectric power generation prediction, which, to our knowledge, is the first attempt modeling power generation data with the combination of residual neural networks and recurrent neural networks. Furthermore, we consider different grains of the hydropower generation by dividing the data into four-levels, i.e. recent, daily, weekly, and time-series sequences, which can greatly improve the prediction performance. To this end, we employ a multi-information fusion method to fuse the four components (i.e. closeness, period, trend, long-period) predicted results, among which different component is learned with different weights to determine their influence on final hydropower prediction. Experiments conducting on the real hydropower generation prove the effectiveness of the proposed model, which significantly outperform the baselines. We hope this research will open a new perspective of improving data usage in the industry, especially in power generation areas.
Fast Fully Adaptive Signalling for Target Matching

In this article, we discuss a novel signal processing technique for adaptive radar that permits joint target-matched illumination and interference avoidance in dynamic spectral environments. This approach allows for spectral coexistence between a radar system and a primary user of the radio frequency space. Spectral coexistence is exploited to allow the use of higher bandwidths than would otherwise be available to conventional radar systems. The technique proposed exploits the relative simplicity of the error reduction algorithm, and also provides a novel use of the masking procedure to allow for target-matched illumination. Practical constraints such as constant modulus are considered in the waveform design procedure, while providing an implied signal-to-interference-plus-noise ratio improvement via the error reduction algorithm. Results for full simulation and hardware-in-the-loop experiments are presented and analyzed. We are able to show a signal-to-interference-plus-noise ratio gain of 40 dB is achieved for the target-matched waveform as compared with a linear frequency modulated waveform. However, the signal-to-interference-plus-noise ratio gain comes at a cost of degraded autocorrelation characteristics of the target-matched illumination waveform, despite only modest levels of primary user spectrum occupancy. Spectral notch depths achieved by the modified error reduction algorithm are approximately 25 dB.
Depth Prediction for Monocular Direct Visual Odometry

Depth prediction from monocular images with deep CNNs is a topic of increasing interest to the community. Advances have lead to models capable of predicting disparity maps with consistent scale, which are an acceptable prior for gradient-based direct methods. With this in consideration, we exploit depth prediction as a candidate prior for the coarse initialization, tracking, and marginalization steps of the direct visual odometry system, enabling the second-order optimizer to converge faster into a precise global minimum. In addition, the given depth prior supports large baseline stereo scenarios, maintaining robust pose estimations against challenging motion states such as in-place rotation. We further refine our pose estimation with semi-online loop closure. The experiments on KITTI demonstrate that our proposed method achieves state- of-the-art performance compared to both traditional direct visual odometry and learning-based counterparts.
Some characteristics of Australian isolates of zucchini yellow mosaic virus.

Isolates of zucchini yellow mosaic virus (ZYMV) were obtained from six cucurbit species from four states of Australia and identified by host range, electron microscopy and serology. A survey of coastal Queensland in 1986-87 showed no ZYMV north of Townsville nor around Rockhampton, but major centres of ZYMV incidence with specific pathotypes, around Ayr, Bowen and in south-east Queensland. Pathotypes were distinguished using Cucumis melo differentials, Trichosanthes anguina and Phaseolus vulgaris. Three ZYMV isolates were compared with watermelon mosaic virus 2 and papaya ringspot virus, cucurbit strain, on 20 hosts from 5 families. The effect of ZYMV on 19 cvv. of pumpkin, squash, zucchini, cucumber, rockmelon and watermelon was examined in the field where marketable fruit production was most affected in Cucurbita maxima and C. pepo, moderately in C. melo and Citrullus lanatus and least in C. moschata and C. sativus. One instance of ZYMV seed transmission in C. pepo and transmission by Aphisgossypii was demonstrated. Resistance breeding progress with C. maxima using a C. ecuadorensis source and in C. lanatus using Citrullus sp. cv. Egusi is reported. Virus purification was achieved using carbon tetrachloride and Triton X-100 clarification with sucrose pad and gradient centrifugations. An antiserum was produced and reacted well in agarose gel-diffusion tests without prior virus particle disruption.