Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression of anthocyanin biosynthetic genes in Malus sylvestris L. ‘Mutsu’ non-red apples

Summary The expression of five anthocyanin biosynthetic genes of chalcone synthase (MdCHS). flavanane 3-hydroxylase (MdF3H). dihydroflavonol 4-reducase (pDFR), anthocyanidin synthase (MdANS) and UDP glucose-flavonoid 3-O-glucosyltransferase (pUFGluT) was investigated during fruit development in the skin of non-red ‘Mutsu’ apples (Malus sylvestris (L) Mill. van domestica (Borkh) Mansf. ). MdCHS, MdF3H and pDFR appeared in the skin of non-bagged fruit from 81 d after full bloom (DAFB) to 167 DAFB, but MdANS and pUFGluT did not appear. When the fruit was bagged during fruit development and unbagged before the maturation, anthocyanin was produced in the exposed portion but not in the shaded portion. All five genes including MdANS and pUFGluT were detected in the exposed portion at 165 DAFB. In the shaded portion, MdANS was detected, but pUFGluT was not. The skin colour of the shaded portion after unbagging in the bagged fruit was more yellow than that in non-bagged fruit. This result suggests that the yellowing of the skin may relate to the expression of MdANS. All five anthocyanin biosynthetic genes were detected at 20 DAFB despite the absence of anthocyanin. The roles of genes in the anthocyanin biosynthetic pathway may differ from one development stage to the next.
Hybrid SPAD/PD Receiver for Reliable Free-Space Optical Communication

Free-space optical (FSO) communication provides wireless optical connectivity with high data rates and low-cost implementation; however, its performance is strongly influenced by the power attenuation due to the infrequent adverse weather conditions. This article proposes a novel highly sensitive dual-mode receiver comprising an array of single-photon avalanche diodes (SPADs) and a PIN photodiode (PD) to enhance the availability of FSO links. In adverse weather conditions, the receiver operates in the SPAD-mode; whereas, in the clear weather conditions, the receiver works in the PD-mode. A hybrid receiver controller is employed in the proposed receiver to adaptively control the switching process based on the received light levels. The adaptive controller also adjusts the incident photon rate of the SPAD array using a variable optical attenuator (VOA) to optimize the performance of the SPAD unit. Our extensive performance analysis illustrates the superior achievable data rates of the proposed receiver under various weather conditions compared to the traditional FSO receivers with either PD or SPAD array.
The Security Risk Analysis Methodology for Smart Network Environments

Adequate and accurate security risk assessment and analysis of any smart network environment is an extremely important task, the results of which allow us to build an adequate protection and evaluate its effectiveness. Nowadays, traditional information security risk management strategy cannot be adequately applied to compose and assure the protection systems for smart network environments, because new infrastructures (e.g. smart homes, smart buildings, smart cities, smart plants, smart hospitals, smart farms, smart transportation) are heterogenous, movable, flexible, highly scalable and reconfigurable with a huge number of peer-to-peer inter-device connections. The paper discusses our approach and experience for security risk analysis at the smart network environment. The character features of the typical smart network environment are revised, the existing methods for risks analysis are reviewed. New requirements for the risk analysis method adequate for the new features of a smart digital landscape are offered. Also, the quantitative risk assessment for the smart network environment is proposed. The proposed method has been experimentally verified on our test bench of a ‘smart crossroad’, the cyberphysical component of the intelligent transportation system (ITS) of a smart city.
A Fast Time-Step Selection Method for Explicit Solver-Based Simulation of High Frequency Low Loss Circuit and Its Application on EMI Filter

Electromagnetic interference (EMI) modeling and prediction are essential for the design of most power electronics apparatuses. This article aims at finding a fast method to select time step for explicit solver-based simulation of high frequency low loss (HFLL) circuits like EMI filter. The state-space model of HFLL circuit is constructed and its eigenvalues are proved to be very close to the imaginary axis. Both the nondegenerate and degenerate circuit cases are discussed. During the analysis, a circuit lemma is summarized on how to transform degenerate circuit into nondegenerate circuit and the corresponding inversion of its coefficient matrix is derived based on Sherman–Morrison's formula. Then the Laguerre–Samuelson's inequality is employed to find the upper bound of HFLL circuit's eigenvalues. This process only requires two matrix multiplications and traces of the matrix operation results, thus keeping the computational complexity retaining in $O(N^2)$. A typical EMI filter is constructed and its equivalent circuit including the parasitic effects is extracted from ANSYS. This filter is simulated in application between a dc/ac converter and the grid using the fourth-order Runge–Kutta (RK4) solver with a time step selected by the proposed method. Numerical test shows that the spectrum results are very close to those obtained by experiment while being much more efficient than traditional methods, which demonstrates that this time-step selection method could benefit the analysis and time-domain simulation of HFLL circuits.
Integrated Framework of Autonomous Vehicle with Traffic Sign Recognition in Simulation Environment

This study proposes an integrated framework for autonomous vehicle research in a simulated environment. There has been much research on the simulations of vehicles and environments, the recognition of traffic signs, and the lateral/longitudinal controls of a vehicle. Yet, not many systems are available for autonomous vehicle researchers to test and improve their algorithms in a realistic simulated environment with sensor suites in their own car model. We aim to provide an integrated framework for a programmable autonomous vehicle in a simulated environment. The simulated vehicle is capable of autonomous driving with traffic sign recognition using deep learning-based object detection capability as well as lateral and longitudinal controllers. To show the feasibility of the proposed system, we built a simulated robotic vehicle with an environment where traffic signs are placed alongside a road. We also integrated a module for object detection and recognition to determine the longitudinal behavior of the vehicle. In addition, the current study implemented a lateral controller based on a convolutional neural network for the vehicle to make it drive by itself. We believe that the proposed integrated framework can be utilized by researchers and educators and lower entry barriers in the prosperous autonomous vehicle research.
Simulation of Polymer Multimode Interference Thermo-Optic Switch

Thermo-optic switch is one of the most matures optical switching technologies that play a vital role in developing an optical network. This paper demonstrates the simulation of Multimode Interference (MMI) Thermo-Optic (TO) Switch based on polymer material using Beam Propagation Method (BPM). By using BeamProp software from RSoft®, the simulation result for crosstalk shows small percentage of deviation of 1% compared to previous research work.
Third Quadrant Conduction Loss of 1.2–10 kV SiC MOSFETs: Impact of Gate Bias Control

The third quadrant (3rd-quad) conduction of power <sc>MOSFET</sc>s involves competing current sharing between the metal-oxide-semiconductor (MOS) channel and the body diode controlled by the gate bias (<italic>V</italic><sub>G</sub>). For 1.2 kV SiC planar <sc>MOSFET</sc>s, it is well known that a positive <italic>V</italic><sub>G</sub> higher than the threshold voltage enables parallel conduction through both channels, which reduces the 3rd-quad voltage drop and conduction loss. This work, for the first time, unveils that this fact does not hold for higher voltage (e.g., 3.3 kV and 10 kV) SiC planar <sc>MOSFET</sc>s. By combining the static characterization, simulation, and modeling, it is revealed that, once the MOS channel turns <sc>on</sc>, the body diode in high-voltage <sc>MOSFET</sc>s turns <sc>on</sc> at a source-to-drain voltage (<italic>V</italic><sub>SD</sub>) much higher than the built-in potential of the PN junction. In 10 kV SiC <sc>MOSFET</sc>s, the body diode does not turn <sc>on</sc> over the entire practical <italic>V</italic><sub>SD</sub> range if the MOS channel is on. As a result, the positive V<sub>G</sub> leads to completely unipolar conduction, which could induce a higher voltage drop than the bipolar body diode at high temperatures. A buck converter based on a 10 kV SiC <sc>MOSFET</sc> half-bridge module was built and tested, which validated that a negative <italic>V</italic><sub>G</sub> control provides the smallest 3rd-quad voltage drop and conduction loss at high temperatures. Finally, based on the revealed physics for planar <sc>MOSFET</sc>s, the optimal <italic>V</italic><sub>G</sub> control for the 3rd-quad conduction in trench <sc>MOSFET</sc>s is discussed. These results provide critical device understandings of 1.2–10 kV SiC <sc>MOSFET</sc>s and important application guidelines for 10 kV SiC <sc>MOSFET</sc>s.
Nonparametric Bayesian Analysis of Hazard Rate Functions using the Gamma Process Prior

When failure time data are modelled using an inhomogeneous Poisson process, it is necessary to model the underlying hazard rate function λ(t). The most common approaches to the problem either select some parametric form for λ(t), or alternatively – conditional on some collected data set – approximate it using the non-parametric Kaplan-Meier estimator. In this paper we present simulation and inference for a non-parametric hazard rate function drawn from a Gamma Process Prior. We use a gamma-scaled Dirichlet Process prior to implement the Gamma Process prior, and construct a Markov Chain Monte Carlo sampler to carry out inference. We demon-strate the methodology with the simulation of a process with an increasing failure rate.
Improved Loss Minimization Control for IPMSM Using Equivalent Conversion Method

This article proposes an equivalent conversion-based improved loss minimization control method for interior permanent magnet synchronous motor (IPMSM). In the proposed control method, both the copper loss and iron loss are studied and the efficiency optimization problem due to the unequal inductance parameters of IPMSM is considered and solved, thus achieving the optimal efficiency. First, according to the mathematical model of the IPMSM considering iron loss resistance, the equivalent conversion method is presented to derive the relationship between electromagnetic torque, d-axis and q-axis currents. Then, by the polynomial fitting method, the d-axis and q-axis currents which minimize the electrical loss (copper loss and iron loss) are calculated. Additionally, to further show the performance of the proposed control method, the maximum torque per ampere control method and the approximate processing control method are studied for the comparative analysis. Both the simulation and experimental results show that the proposed control method can comprehensively reduce the electrical loss of the IPMSM, showing the effectiveness of the proposed control method.
Characterization of NMC Lithium-ion Battery Degradation for Improved Online State Estimation

This paper presents a characterization of Nickel Manganese Cobalt (NMC) Lithium-Ion (Li-ion) batteries based on empirical tests to improve online state estimation. It investigates how battery degradation affects the battery management systems (BMS) ability to estimate the state of charge (SOC) and state of health (SOH). The testing is conducted on 8 NMC 18650 Li-ion battery cells split up in two groups of four new and four degraded cells with an SOH of 90%. As both the SOC and the SOH estimations are dependent on the relation between the SOC and the open circuit voltage (OCV), the necessary relaxation time to achieve a stable OCV as well as the SOC-OCV relations dependency on temperature and ageing are derived. The relaxation time needed to achieve a stabilised terminal voltage is, in the worst case, found to be equal to 20 minutes for both the new and the degraded cells. The SOC-OCV curve is not changing significantly when the operation temperature is increased from 23°C to 45°C, which is also the case for both the new and the degraded cells.The internal battery resistance and capacitance are however found to depend significantly on both the SOC and operation temperature. A first order RC representation is derived for different SOC and temperatures levels.
Effects of Fire and Disturbance on Plants and Birds in a Florida Oak/ Palmetto Scrub Community

-The vegetation and avian communities of a site consisting of oak/palmetto scrub and disturbed oak scrub were characterized. Nearly half of the oak/palmetto scrub was intentionally burned midway through the study. The prescribed fire altered avian community composition for at least 6 months in areas where nearly all the vegetation burned. Twenty years after clearing of an area of oak/palmetto scrub, the regenerated oak-scrub habitat differed from adjacent oak/palmetto scrub in having more bare ground (15.5 vs. 0.8%), a taller shrub layer (272 vs. 161 cm) composed primarily of oak species without saw palmetto and more herbaceous species (11 vs. 3). Compared with oak/palmetto scrub, disturbed oak scrub had more breeding bird species that were abundant enough to allow density estimation (8 vs. 4) and supported a higher density of the Florida scrub jay (Aphelocoma coerulescens coerulescens), a threatened subspecies. These data suggest that disturbed areas can represent important wildlife habitat in scrub-dominated landscapes, but because effects of mechanical disturbance last for very long periods, the use of mechanical disturbance as a habitat management tool should be applied cautiously.
Population dynamics of the burrowing ghost shrimp Callianassa filholi on an intertidal sandflat in New Zealand (Decapoda: Thalassinidea)

Abstract Population samples of the burrowing ghost shrimp Callianassa filholi Milne-Edwards, 1878 were taken on an intertidal sandflat in Otago Harbour, south-eastern New Zealand, monthly over a period of 16 months. The spatial population distribution appeared to be contagious in autumn and early winter, and random from mid-winter to summer. The population density did not show any significant difference over the sampling period, with a mean density of 16 individuals m-2. Overall the sex ratio was significantly biased towards females (mean male:female ratio = 1:1.5). Visual inspection of size frequencies indicated the presence of a bimodal population. Large animals (7–14 mm carapace length [CL]) were present throughout the year and no significant difference in CL was observed between sexes. Post-larval shrimp (CL ≤ 3 mm) were present in spring and summer, which together with developing juveniles constituted a second group of smaller animals (< 7 mm CL). Regression analysis of allometric growth showed the s.
3D Mapping Methods and Consistency Checks to Exclude GNSS Multipath/NLOS Effects

In urban canyons, the positioning accuracy obtainable from global navigation satellite systems (GNSS) is mainly impaired by signal interference due to multipath and non-lineof-sight (NLOS) effects. GNSS is one of the sensors used in connected autonomous vehicles (CAVs) for positioning, navigation and timing (PNT). Hence, it is essential that GNSS receivers in CAVs are robust and resilient. In this paper, a method consisting of two layers of GNSS observation checks is suggested to exclude these effects in order to improve the positioning accuracy. The first layer excludes all non-consistent measurements identified by a chi-square test threshold. The second layer uses a decision tree for the exclusion of any remaining multipath/NLOS affected measurements, based on a data set obtained from a ray tracer for a 3D mapped model environment. The simulation results show an enhancement in positioning accuracy greater than 95%.
A Quantum Entanglement-Based Approach for Computing Sentence Similarity

It is important to learn directly from original texts in natural language processing (NLP). Many deep learning (DP) models needing a large number of manually annotated data are not effective in deriving much information from corpora with few annotated labels. Existing methods using unlabeled language information to provide valuable messages consume considerable time and cost. Our provided sentence representation based on quantum computation (called Model I) needs no prior knowledge except word2vec. To reduce some semantic noise caused by the tensor product on the entangled words vector, two improved models (called Model II and Model III) are proposed to reduce the dimensions of the sentence embedding stimulated by Model I. The provided models are evaluated in the STS tasks of 2012, 2014, 2015 and 2016, for a total of 21 corpora. Experimental results show that using quantum entanglement and dimensionality reduction in sentence embedding yields state-of-the-art performances on semantic relations and syntactic structures. Compared to the Pearson correlation coefficient (Pcc) and mean squared error (MSE), the results of 16 out of 16 corpora are better than the results of the comparative methods.
Methodological Improvement for Reconstructing the Paleo-topography of Lombok Island before the Samalas AD 1257 Eruption

Paleo-topographic reconstruction due to volcanic eruptions is a fascinating study, especially if the erupting volcano has a large magnitude of the eruption. Recently, at least three previous studies present well-organized methods and computational analysis for paleo-topography reconstruction. By reviewing those research and did a simple test, we found that there are small gaps that need to be improved. This paper is proposed an improvement method for paleo-topography reconstruction. It is important to notice that this improved methodology is not technically based, but only focused on a conceptual framework. The improvement review involves five variables, i.e., 1) the present-day topography, 2) stratigraphic composite, 3) data correlation, 4) paleo-channel validation, and 5) final paleo-topography validation. This improvement will be applied in future research in a complete reconstruction of Lombok island paleo-topography before the Samalas AD 1257 eruption.
Frame-Angle Controlled Wavelet Modulated Inverter and Self-Recurrent Wavelet Neural Network-Based Maximum Power Point Tracking for Wind Energy Conversion System

In this work, a new control methodology is proposed for Type -IV wind energy conversion system (WECS) using a self-recurrent wavelet neural network (SRWNN) control with a Vienna rectifier as the machine side converter (MSC). A SRWNN combines excellent dynamic properties of recurrent neural networks and the fast convergence speed of wavelet neural network. Hidden neurons of SRWNN contains local self-feedback loops, which provide the memory feature and the necessary information of past values of the signals, allowing it to track maximum power from WECS under varying wind speeds. The Vienna rectifier allows unity power factor operation to increase electrical efficiency. Frame angle-controlled wavelet modulation is proposed for the grid side converter (GSC). Wavelet modulated inverter produces output voltage fundamental components with higher magnitudes than those obtained from the pulse width modulated inverters. The non-linear load compensation and power quality enhancement are achieved by executing frame angle control for WM inverter. The overall system is modeled, and performance is verified in MATLAB Simulink. The hardware prototype is developed, and the switching pulses for the rectifier and inverter are generated using dSPACE1104 controller. The results prove that the system provides low harmonic content and high magnitude of the fundamental current component at the machine and grid sides and ensures maximum power operation at various wind speeds.
Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

Fatigue driving has become one of the most common causes for traffic accidents. In this article, we proposed an effective fatigue detection method based on eye status with pupil and iris segmentation. The segmented feature map can guide the detection to focus on pupil and iris. A streamlined network, consisting of a segmentation network and a decision network, is designed, which greatly improves the accuracy and generalization of eye openness estimation. Specifically, the segmentation network that uses light U-Net structure performs a pixel-level classification on the eye images, which can accurately extract pupil and iris features from the video’s images. Then, the extracted feature map is used to guide the decision network to estimate eye openness. Finally, the detection method is test by the National Tsing Hua University Drowsy Driver Detection (NTHU-DDD) Video Dataset and the precision of fatigue detection achieves 96.72%. Experimental results demonstrate that the proposed method can accurately detect the driver fatigue in-time and possesses superior accuracy over the state-of-the-art techniques.
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics

Nowadays, humans and robots are working more closely together. This increases business productivity and product quality, leading to efficiency and growth. However, human and robot collaboration is rather static; robots move to a specific position then humans perform their tasks while being assisted by the robots. In order to get a dynamic collaboration, robots need to understand the human’s intention and learn to recognize the performed actions complementing therefore his capabilities and relieving him of arduous tasks. Consequently, there is a need for a human action recognition dataset for Machine Learning algorithms. Currently available depth-based and RGB+D+S based human action recognition datasets have a number of limitations, counting the lack of training samples along with distinct class labels, camera views, diversity of subjects and more importantly the absence of actual industrial human actions in an industrial environment. Actual action recognition datasets include simple daily, mutual, or healthrelated actions. Therefore, in this paper we introduce an RGB+ S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.
Gender Effects in Mobile Application Development

Growing interest in the impact of gender on technology includes concerns with regard to gender bias and its implication in the user experience. Past explorations of gender and design revealed significant differences between female and male users with regard to the perception and the acceptance of dating apps. A novel study was designed to investigate whether gender differences in the user experience could be caused by gender bias in mobile app development. Significant differences in the definition of design priorities emerged with regard to the participants’ gender and the persona’s perceived gender.