Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Effect of Physical Injection Parameters on Biological Cell Deformation

Intracytoplasmic Sperm Injection (ICSI) is an assisted reproductive technology in which the zona pellucida and the cell membrane (oolemma) are pierced by a micropipette and a sperm is injected into the cytoplasm for fertilisation. However, cell damage is often spotted during the penetration process mainly due to oocyte deformation, which may lead to oocyte degeneration. Cell injection parameters are the main contributing factors to the cell damage, which may cause cell degeneration. In this study, the effects of injection speed and pipette diameter as two key physical injection parameters were investigated using a motorised micro injection system and zebrafish embryo as the biological model. It was demonstrated that the deformation increased from low to medium injection speeds, however, it subsequently decreases from medium to high speeds. It was successfully shown that tip diameter of the injection pipette is a contributing factor in cell deformation as the deformation ratio increased when a larger tip diameter pipette was used. The deformation is a potential damage to biological cells which may contribute to cell degeneration post injection.
An Improved Asymmetric Hurricane Parametric Model Based on Cross-Polarization SAR Observations

Synthetic aperture radar (SAR) has been proven to be a useful tool in monitoring hurricane structure and intensity. By far, SAR is the most promising spaceborne sensor to obtain high-resolution hurricane wind field on the ocean surface. In this article, an improved asymmetric hurricane parametric (IMAHP) model has been proposed to reconstruct the asymmetric wind speed, where the high-resolution cross-polarization SAR imagery is used to determine the value of model parameters. Compared with other models, the new model can better reconstruct hurricane wind speed with a more concise model function. For verification, taking SAR-retrieved wind speed as a reference, the root-mean-square error and bias of the wind speed estimated by the IMAHP model are 1.86 m/s, 1.89 m/s for Hurricane Arthur (2014), 2.01 m/s, 1.77 m/s for Iselle (2014), and 1.99 m/s, 1.74 m/s for Norbert (2014), respectively. Finally, comparisons with airborne stepped-frequency microwave radiometer and dropwindsondes measurements show that the wind speed simulated by the IMAHP model is close to these measurements.
On the Sum Rate of MCM-Based NOMA and MCM-Based OMA Systems

In this paper, we report on the performance of different Multi-Carrier Modulation (MCM) techniques combined with both Non-Orthogonal Multiple Access (NOMA) and the conventional Orthogonal Multiple Access (OMA) systems. MCM-NOMA and MCM-OMA systems are analyzed in terms of efficient sum rate in mobility channel. In order to perform a fair comparison, we take into consideration all the different parameters among the MCM techniques, in addition to the distinct resource allocation in both NOMA and OMA systems. In addition, the performance of both MCM-NOMA and MCM-OMA systems is analyzed at different terminals speeds. The comparisons illustrate that all MCM techniques performs better when combined with NOMA systems comparing to OMA based systems. Furthermore, numerical simulations prove that Filter Bank Multi-Carrier (FBMC)-NOMA combination provides the best performance when compared to other MCM techniques in both NOMA and OMA systems.
Broadband Compact Circularly Polarized Spiral Antenna Array Fed by Printed Gap Waveguide for Millimeter-Wave Applications

This paper presents a new design for a wideband circularly polarized (CP) spiral antenna array fed by a differential aperture coupling. The differential excitation is realized by two vias located at the lateral sides of a transverse slot etched on the lower substrate. The proposed two-arm spiral antenna has a smaller size compared to conventional spiral antennas with several turns. The presented single radiating element provides the impedance bandwidth of (<inline-formula> <tex-math notation="LaTeX">$|\text{S}11| < -10$ </tex-math></inline-formula> dB) 40.4% and 3-dB axial ratio (AR) of 23.2% with a peak RHCP gain of 7.2 dBic over the operating frequency band. To further enhance the AR bandwidth and the maximum gain, the presented CP spiral element is employed in a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> sequential feeding arrangement. The sequential feeding network is implemented using the low-loss printed gap waveguide (PGW) line, which is compatible with low-cost printed circuit board (PCB) technology. A prototype of the proposed <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> antenna array is fabricated and measured experimentally. Measured results reveal that the designed antenna array has a wide impedance bandwidth of over 48.5%, the 3-dB AR bandwidth of 34.2%, and the 3-dB gain bandwidth of 32.4% with a maximum gain of 11.74 dBic and excellent radiation efficiency of 85% over the entire operating frequency band.
High Performance Flexible Structure Three-Level DC-DC Converter: A Candidate DC Interface for Microgrids With Distributed Energy Resources

High-performance DC interfaces play a pivotal role in microgrids with distributed energy resources (DERs), which would significantly improve the utilization of DERs and the flexibility of the prosumers. For a candidate DC interface converter, how to maintain the performance over a wide operation range becomes the biggest challenge. This paper proposes a flexible structure soft-switching three-level (TL) dc-dc converter, which adds four high-speed MOSFETs and one low-speed electronic relay to achieve overall optimum performance over a wide range. Under high output or low input voltage conditions, the relay and the extra MOSFETs on the primary side are ON permanently. The proposed converter is a zero-voltage switching (ZVS) converter, which guarantees ZVS for all primary switches even under 0 load currents. Under low output or high input voltage conditions, the relay and the extra MOSFETs in the secondary side are OFF permanently. The proposed converter is a zero-voltage and zero-current switching (ZVZCS) converter, and two added MOSFETs provide ZVZCS operation not only for the main primary switches but also themselves over a wide range. Other advantages of the presented converter include reduced filter size, no primary side circulating currents, and reduced current stress of the clamping capacitor. Besides, the switching loss caused by the added power devices is low because of the full range of soft-switching operation and low on-state resistance of these devices. This paper discusses the circuit configuration, operation principle, soft switching characteristics, technical comparison, and experimental results from a 1-kW prototype prove the rightness of the presented converter.
User association and power allocation for UAV-assisted networks: A distributed reinforcement learning approach

Unmanned aerial vehicles (UAVs) can be employed as aerial base stations (BSs) due to their high mobility and flexible deployment. This paper focuses on a UAV-assisted wireless network, where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission. In contrast to state-of-the-art designs focusing on the instantaneous cost of the network, this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot. Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process (MDP). Unfortunately, solving such an MDP problem with the conventional relative value iteration (RVI) can suffer from the curses of dimensionality, in the presence of a large number of users. As a countermeasure, we propose a distributed RVI algorithm to reduce the dimension of the MDP problem, such that the original problem can be decoupled into multiple solvable small-scale MDP problems. Simulation results reveal that the proposed algorithm can yield lower long-term average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.
The Effect of Code Smells on the Relationship Between Design Patterns and Defects

The relationship between design patterns and defects was investigated in the literature, but with mixed results. While the majority of studies found the presence of patterns to be positively correlated with defects, other works reported the opposite conclusions. This may suggest that contextual factors affect this relationship. In this study we analyze the role of code smells as a confounding variable in the relationship between design patterns and defects in Java classes. To investigate this, we applied statistical tests to capture the difference in the impact on defects between pattern classes with/without code smells in 10 Java systems from the PROMISE dataset, with respect to 13 design patterns and 10 code smells. The presence of code smells in patterns appears to be a valid factor affecting defect-proneness. Pattern classes with smells attract more defects than non-smelly pattern classes, and in most systems smelly design patterns are positively correlated with the presence of defects, while non-smelly patterns tend to have no impact, or a slightly negative impact on the presence of defects. As a result, the presence of code smells in design pattern classes appears to be a contextual factor affecting the defect-proneness of the subject code.
Patterns of the Formation of Transition Metal Diboride Superhard Coatings

An analysis of the effect of magnetron sputtering parameters on the formation of structure and properties of transition metals diboride films was carried out. It was shown that main contribution in the energetic of deposition process is made by substrate temperature, substrate bias and ion current density on substrate. All these parameters are significantly depended on constructional features of a magnetron sputtering system. Depending on these parameters different structural states of transition metals diboride films from amorphouslike to nanocrystallite with growth texture normal to (00.1) plane are formed. Strongly-textured diboride coatings are superhard and overstoichiometric relative to boron.
Critical properties study of exactly solved spin models for ferromagnets and ferroelectics

Development of the hydrokinetic models of superdense matter and systems with phase transition points of a critical type is of great fundamental interest. The exact analytical expressions for the critical exponents of the model with the surface tension are known for the case of critical and tricritical points, that enables to fix their parameters directly, depending on the universality class, as well as demonstrate the possibility of non-Fisher’s universality classes where the conventional scaling relations are violated. The thermodynamic method for the investigation of critical states allows determining the type of critical behavior and the behavior of all thermodynamic quantities characterizing the system under consideration, depending on the values of a critical slope of the phase equilibrium curve and adiabatic coefficients of stability even when information of the system is limited. In this work, the critical properties of certain spin models were examined. The properties of the two-dimensional exactly solvable Lieb and Baxter models in the critical region are considered. From the point of view of the thermodynamic stability, the behavior of adiabatic and isodynamic parameters for these models is analyzed, and the types of their critical behavior are determined. The reasons for the violation of the scaling law hypothesis and the universality hypothesis for the models are clarified. It is shown that the type of phase transition depends on the value of the interaction parameter.
Deep Learning for Real-time ECG R-peak Prediction

In this work, we propose a novel algorithm to achieve real-time R-peak prediction during ECG signal recording. More specifically, from the current frame of ECG signal, we aim to predict how far into the future the next R-peak will occur, taking into account information about the variability in the intervals between beats seen in previous frames of the ECG signal. Currently there has been little work in this area. However, the real-time prediction of the next beat has important research significance, including timing of artificial heart pumps and integration into the cardiopulmonary support heart (CPS-heart), as well as narrowing the search range of R-peaks to assist R-peak detection. This paper proposes use of an integrated network using one-dimensional convolution network (1D CNN) with long short-term memory (LSTM) network. The deep learning model we have proposed is shown to effectively predict R-peaks with results of preliminary studies achieving a prediction accuracy of 90.61%.
Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. Glottal flow waveform directly reflects the state of glottal excitation. Extracting acoustic features from glottal source signals may contribute to the detection of pathological voice. To improve the performance of voice pathology detection, this article investigates the contribution of the glottal flow waveform for pathological voice detection by evaluating the classification result using features extracted from raw speech utterances and corresponding glottal flow waveforms. The individual feature sets used are extracted from raw or glottal voice utterances with identical parameter settings, which are openSMILE acoustic features, audio features computed by Moving Picture Experts Group-7 standard and classical glottal source features. In addition, a feature selection method in terms of the wrapper approach is used to combine the single features ranked by using the Fisher discrimination ratio. Voice pathology detection experiments were carried out using Random Forest. The best accuracies of 88.52% for the Saarbrücken Voice database and 100.00% for the Massachusetts Eye and Ear Infirmary database are achieved using the combined feature set extracted from the glottal source signal, with improvement of 0.44-3.13% in the accuracies obtained by using raw speech utterances. Compared to state-of-the-art methods, the proposed method achieves the highest accuracy for the Massachusetts Eye and Ear Infirmary database and an increase of 2.75-17.16% in detection accuracy compared to other conventional pipeline systems for the Saarbrücken Voice databse. The experimental results demonstrate that using glottal flow waveform as source signal can improve the performance of pathological voice detection.
Properties of epitaxially grown In2S3:V thin films for intermediate band solar cell application

Indium sulfide hyperdoped with vanadium has been predicted to be a promising material to realize an intermediate band (IB) solar cell. Using co-evaporation of the elements we grew In2S3:V on (111)-oriented $p$-Si substrates. As transparent top electrode n-ZnO:Al is used to realize heterostructure $pin$-solar cells. Investigations of structural properties reveal epitaxial growth independent of the vanadium doping content. We further characterize pin-heterostructure solar cells by current-voltage characteristics in the dark and under illumination. We do not observe an increase of short circuit current density for V-doped samples, which indicates that the IB is fully occupied.
Financial time-series analysis of Brazilian stock market using machine learning

The recent profound changes in technological development have allowed the application of complex computational techniques for modeling and predicting price movements in the Stock Market. In this context, this paper compares the performance of different Machine Learning classifiers in predicting the trend of future financial asset price movements, in addition to performing the stock market trading simulation to assess financial gains provided by the trading strategy that considers the predictions as buying and selling signals. The paper considers five single classifiers, three ensemble classifiers that use Decision Tree as weak classifiers and four ensemble classifiers that combine the eight other classifiers, in addition to two benchmark classifiers. The simulation uses the best classifier and compares its efficiency with the buy and hold strategy. Results show that the precision of the Convolutional Neural Network surpasses that of the other classifiers and the simulation indicates that the use of classification as a trading strategy can reduce the potential for greater gains, but also avoids large losses, reducing the risk of investment.
Field Comparison Study of Fixed-Tilted and Single-Axis Tracking PV Structures in the Desert Environment of Dubai, UAE

This paper evaluates a fixed-tilted and a single-axis sun tracking photovoltaic (PV) test setups located within a harsh desert environment. We compare multiple parameters, including energy yields, temperature losses, and soiling losses. Four monocrystalline (sc-Si) PV modules and one thin-film (TF) module are considered in this study during the period January-December 2019. The improvement in the energy yield from the east-west sun tracking system compared to the fixed-tilted system at 25° was found to be 16% and 24% on average for the sc-Si and TF PV technologies, respectively. The single-axis tracking modules showed higher month-to-month variability in energy yields, where the highest yields occurred in summer months. The differences in temperature losses between the two test setups were found to be negligible; however, the losses due to soiling were significantly lower for the single-axis tracker as characterized by an average daily soiling rate of −0.156%/day compared to −0.366%/day for the fixed-tilted modules.
Improvement of Mixture-of-Experts-Type Model to Construct Dynamic Saliency Maps for Predicting Drivers’ Attention

Recently, there have been many studies on driving assistance systems to reduce traffic accidents. In such a situation, we proposed a method to construct dynamic saliency maps for predicting driver attention while driving in our previous work. In this construction method, multiple feature maps are first created from the video data of a car-mounted camera. Subsequently, this method computes dynamic saliency maps as combinations of the feature maps that perform center-biasing and normalization. Additionally, this method has been successfully applied to a real-world open dataset, including car-mounted camera videos. However, there is room for further improvement in the model used in this method, since the model cannot capture drivers’ characteristics, so that the degree of center bias changes depending on the driving situation. In this paper, we propose an improved model that incorporates multiple center biases into the center-biasing process. It is expected that, by using this improved model, one can appropriately consider changes in the center bias depending on driving situations, and the improved model outperforms the previous model. In addition, the performance of the improved model is compared with that of the conventional model for a real-world open dataset.
Size control of Si nanocrystal with visible Photoluminescence in a-Si:H/nc-Si multilayer structures

We report strong visible photoluminescence at room temperature in a-Si:H/nc-Si multilayer structures. The peak emission energy was controlled by the thickness of the nc-Si layer. The peak energy decreases as the nc-Si thickness was increased from 5 nm to 30 nm, indicating quantum size effects. The structures show photoluminescence at visible to near-infrared, from radiative recombination at the interface between the nc-Si and a-Si:H layers. Thicker layer structures exhibit persistent photoconductivity from carriers trapped at interface states. These nanocrystalline-amorphous Si superlattices offer a pathway for synthesizing embedded Si nanocrystals with controlled sizes and can be integrated with electronic and photonic devices.
Improved Null-Space Expansion Scheme under Presence of Channel Estimation Error for Multiuser Massive MIMO

This paper improves the previously proposed null-space expansion scheme for multiuser massive MIMO down-link in time varying channels. By exploiting past estimated channel state information (CSI), it expands the null-space even against future interference channel. Its fundamental feasibility was proven, however, it assumed perfect knowledge of CSI at estimation instant. CSI imperfection due to the additive noise effect limits its performance. We proposes a subspace dimension reduction of interference channel to avoid excessive null-steering. Its effectiveness is validated through computer simulation.
Structure and Surface Texture Characterisation of Fibres and Nanoparticles in Silver(I):6-Guanosine Hydrogel

In this work, silver nanoparticles (AgNPs) have been synthesised via friendly method without adding any reducing agents or exposure to artificial light such as a UV lamp. The method is based on preparing highly stable silver(I):guanosine hydrogel with stoichiometry 1:1. None of monovalent cations were used in this method to induce the formation of the hydrogel. Transmission electron microscopy (TEM) and atomic force microscopy (AFM) techniques were used to investigate the morphology of the formed gel. X-ray diffraction analysis was carried out using the Rietveld method. In addition, surface texture analysis of the self-assembled polymer of silver(I) hydrogel and the silver nanoparticles, that in situ formed in the hydrogel, was carried out.
Analysis of Short-Circuit Behavior for SiC MOSFETs with Various Circuit Characteristics

In this paper, 1200 V-class planar SiC MOSFETs are investigated by experiment and finite-element simulation under single-pulse short circuit stress. Experimental results show that the failure mode of the device is gate damage. The simulations obtain the temperature, current and voltage changes inside the devices under short circuit conditions. The effect of various circuit characteristics for short-circuit behavior is presented. The simulation results reveal the relationship between total loss, switching speed and short-circuit capability of device.
ON DETERMINING VOLUMETRIC SOIL MOISTURE VARIATIONS IN PEAT BY THE GRAVIMETRIC METHOD

We collected samples from peat layers in the Beverly Swamp, over three summer periods, to document variations in the water content of the soil. The normally adopted practice of multiplying in situ bulk density by the sample weight ratio of water to solid produced unreasonable results, however. Realistic results were obtained from an alternative method that determines the ratio of water in the sample to total sample volume. The success of the latter method suggests that it is much less sensitive to disturbance caused by the sampling procedure.
Interactive Effect of Soil Salinity and Copper Application on Growth and Chemical Composition of Pistachio Seedlings (cv. Badami)

The effects of five salinity levels and four Cu levels on growth and chemical composition of Badami pistachio seedlings were studied under greenhouse conditions in a completely randomized design with three replications. Growth parameters were determined on the 24th week after planting. Total elemental uptake amounts in shoot and root of plant were measured. Results showed that salinity decreased leaf area, stem height, and shoot and root dry weights. Application of 2.5 and 5 mg copper (Cu) kg−1 soil significantly increased root dry weight, whereas it had no significant effects on shoot dry weight and leaf area. Application of 7.5 mg Cu kg−1 soil had a negative effect on stem height. Salinity declined shoot and root total Cu and phosphorus (P) uptake amounts but increased shoot and root total sodium (Na) and chlorine (Cl) uptake amounts. Copper increased shoot and root total Cu uptake amounts, root total P uptake, and shoot total Na uptake but decreased shoot total Cl uptake.