Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Progression of Surface Partial Discharge in Power Cables

Surface discharge or surface tracking is one of the destructive types of discharge that travels along the insulation surface. In cables, surface partial discharge (SPD) can be caused due to contamination and moisture. Poor installation and maintenance are also major causes of SPD. Partial discharge (PD) measurement is an efficient technique, used for the assessment of insulation conditions in medium and high voltage assets. PD pulses contain information that describes the nature of PD and the severity of a defect in the insulation. Several characteristics such as accumulated charge, average discharging current, and discharge power are used that indirectly describe behaviour of a PD activity within the insulation. This study investigates the behaviour of quantities related to partial discharge pulse such as the number of pulses (observed during full AC cycle of the applied voltage) and pulse amplitude (peak and average) at progressive electrical stress. An algorithm has been developed that efficiently identifies the first occurring PD pulse of individual PD event, its polarity, and peak amplitude. The probabilistic analysis is also performed to investigate the behavior of pulse characteristics at progressive degradation.
Selecting Test Cases based on Similarity of Runtime Information: A Case Study of an Industrial Simulator

Regression testing is required to check the changes in behavior whenever developers make any changes to a software system. The cost of regression testing is a major problem because developers have to frequently update dependent components to minimize security risks and potential bugs. In this paper, we report a current practice in a company that maintains an industrial simulator as a critical component of their business. The simulator automatically records all the users’ requests and the simulation results in storage. The feature provides a huge number of test cases for regression testing to developers; however, their time budget for testing is limited (i.e., at most one night). Hence, the developers need to select a small number of test cases to confirm both the simulation result and execution performance are unaffected by an update of a dependent component. In other words, the test cases should achieve high coverage while keeping diversity of execution time. To solve the problem, we have developed a clustering-based method to select test cases, using the similarity of execution traces produced by them. The developers have used the method for a half year; they recognize that the method is better than the previous rule-based method used in the company.
The serum activities of alkaline phosphatase isoenzymes measured using two approved methods in zoo-managed Asian elephants (Elephas maximus)

The approved Japanese measurement method of circulating alkaline phosphatase (ALP) has changed from that of the Japan Society of Clinical Chemistry (JSCC) to that of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). We measured the serum levels of total ALP (t-ALP) and those of the isoenzymes ALP2 and ALP3 in 50 Asian elephant (Elephas maximus) specimens using both methods. The activities determined by the IFCC method were roughly one-third lower than those determined by the JSCC method. We present conversion formulae. Our results enable comparisons of historical and current data on serum ALP activities in endangered, zoo-managed Asian elephants.
Self-Filtered Learning for Semantic Segmentation of Buildings in Remote Sensing Imagery With Noisy Labels

Not all building labels for training improve the performance of the deep learning model. Some labels can be falsely labeled or too ambiguous to represent their ground truths, resulting in poor performance of the model. For example, building labels in OpenStreetMap (OSM) and Microsoft Building Footprints (MBF) are publicly available training sources that have great potential to train deep models, but directly using those labels for training can limit the model's performance as their labels are incomplete and inaccurate, called noisy labels. This article presents self-filtered learning (SFL) that helps a deep model learn well with noisy labels for building extraction in remote sensing images. SFL iteratively filters out noisy labels during the training process based on loss of samples. Through a multiround manner, SFL makes a deep model learn progressively more on refined samples from which the noisy labels have been removed. Extensive experiments with the simulated noisy map as well as real-world noisy maps, OSM and MBF, showed that SFL can improve the deep model's performance in diverse error types and different noise levels.
Noise Enhancement of Neural Information Processing

Cortical neurons in vivo function in highly fluctuating and seemingly noisy conditions, and the understanding of how information is processed in such complex states is still incomplete. In this perspective article, we first overview that an intense “synaptic noise” was measured first in single neurons, and computational models were built based on such measurements. Recent progress in recording techniques has enabled the measurement of highly complex activity in large numbers of neurons in animals and human subjects, and models were also built to account for these complex dynamics. Here, we attempt to link these two cellular and population aspects, where the complexity of network dynamics in awake cortex seems to link to the synaptic noise seen in single cells. We show that noise in single cells, in networks, or structural noise, all participate to enhance responsiveness and boost the propagation of information. We propose that such noisy states are fundamental to providing favorable conditions for information processing at large-scale levels in the brain, and may be involved in sensory perception.
Enzymatic Characterization of Unused Biomass Degradation Using the Clostridium cellulovorans Cellulosome

The cellulolytic system of Clostridium cellulovorans mainly consisting of a cellulosome that synergistically collaborates with non-complexed enzymes was investigated using cellulosic biomass. The cellulosomes were isolated from the culture supernatants with shredded paper, rice straw and sugarcane bagasse using crystalline cellulose. Enzyme solutions, including the cellulosome fractions, were analyzed by SDS-PAGE and Western blot using an anti-CbpA antibody. As a result, C. cellulovorans was able to completely degrade shredded paper for 9 days and to be continuously cultivated by the addition of new culture medium containing shredded paper, indicating, through TLC analysis, that its degradative products were glucose and cellobiose. Regarding the rice straw and sugarcane bagasse, while the degradative activity of rice straw was most active using the cellulosome in the culture supernatant of rice straw medium, that of sugarcane bagasse was most active using the cellulosome from the supernatant of cellobiose medium. Based on these results, no alcohols were found when C. acetobutylicum was cultivated in the absence of C. cellulovorans as it cannot degrade the cellulose. While 1.5 mM of ethanol was produced with C. cellulovorans cultivation, both n-butanol (1.67 mM) and ethanol (1.89 mM) were detected with the cocultivation of C. cellulovorans and C. acetobutylicum. Regarding the enzymatic activity evaluation against rice straw and sugarcane bagasse, the rice straw cellulosome fraction was the most active when compared against rice straw. Furthermore, since we attempted to choose reaction conditions more efficiently for the degradation of sugarcane bagasse, a wet jet milling device together with L-cysteine as a reducing agent was used. As a result, we found that the degradation activity was almost twice as high with 10 mM L-cysteine compared with without it. These results will provide new insights for biomass utilization.
The Impact of Internet Use on Rural Women’s Off-Farm Work Participation: Empirical Evidence from China

Promoting rural women’s participation in off-farm work is an important way to increase their income and alleviate a shortage of off-farm labor supply. The widespread use of the Internet provides new opportunities to promote their participation in off-farm work. This paper draws on data from the 2020 China Family Panel Studies (CFPS) to analyze the impact of Internet use on rural women’s off-farm work participation by using a logit model and propensity score matching method. The results show that, relative to groups who do not use the Internet, rural women who use the Internet demonstrate an eight-percentage point increase in their participation in off-farm work and a greater enhancement effect on the employed model than the self-employed model. Further analysis reveals that Internet use primarily affects rural women’s participation in off-farm work in four aspects: increasing the efficiency of access to information; increasing human capital; increasing the accumulation of social capital; and changing the perception of gender roles. The heterogeneity analysis finds that when rural women are younger and more educated, Internet use will have a more pronounced effect on their off-farm work participation. As the number of minors in the household increases, Internet use has a more positive effect on rural women’s off-farm work participation. Additionally, rural women in China’s eastern and central regions are more likely to be positively impacted by Internet use than counterparts from the west. The findings provide new empirical evidence that contributes to the government’s use of the Internet to promote rural female off-farm work participation.
Characteristics of Epitaxial Graphene on SiC/Si Substrates in the Radio Frequency Spectrum

Graphene is expected to bring substantial benefits for high-frequency applications, however, most of the studies in this area are based on theory. Here, the properties of epitaxial graphene grown on intrinsic silicon carbide on silicon substrates are investigated for potential radio frequency (RF) applications. Metal coplanar waveguides (CPWs) are fabricated that employ graphene as a shunt between the signal and ground planes. The CPWs are used for characterizing the frequency-dependent behavior of the sheet resistance of the graphene shunt from 10 MHz to 10 GHz. The process involves evaluating the CPW’s RLCG transmission line parameters and comparing them to a reference un-shunted CPW to extract the sheet resistance. We find that the quality of the metal contact with graphene is one key parameter to observe adequate current injection in the 2D material in the RF spectrum. A mild argon plasma treatment was applied to reach an adequate contact quality. Furthermore, we observe a monotonic decrease of the sheet resistance of the epitaxial graphene for frequencies roughly above 100 MHz. We attribute this behavior to the progressively smaller influence of small-scale discontinuities, such as grain sizes, at those higher frequencies.
Quasi-linear viscoelastic properties of PC-12 neuron-like cells measured using atomic force microscopy

Atomic force microscopy has been widely used to measure the mechanical properties of living cells. Models based on Hertz theory are often utilized to estimate the Young's modulus of cells. In practice, the nonlinear viscoelastic behavior of cells can often be observed in indentation experiments. In this article, the quasi-linear viscoelasticity (QLV) theory, which has been successfully applied to many biological tissues, was employed to construct the constitutive equation of neuron-like PC-12 cells. The values of Young's modulus obtained via our proposed method were one or two orders of magnitude, depending on the indenting rate, less than those obtained using methods based on the modified Hertzian model. The Kelvin model was also introduced to describe the solid and liquid behavior of cells. The prediction error of the Kelvin model was larger than that of the proposed QLV model, especially at the peak force and the toe portion of the force response. The results revealed that the Young's modulus estimated by the QLV model was less affected by indenting rates than that estimated by the modified Hertzian model or the Kelvin model. The proposed QLV model accounting for the indenting conditions was capable of describing both the nonlinear elastic response and relaxation behavior and, therefore, was more appropriate for modeling the biomechanical behaviors of PC-12 cells than the nonlinear elastic model or the linear viscoelastic model.
On the universality of global modes in low-density axisymmetric jets

Experiments conducted in axisymmetric low-density jets reveal that the transition to global instability and the frequency selection of the global mode depend on the operating parameters of density ratio, momentum thickness, and Reynolds number. The onset of the global mode was mapped in the Reynolds number–momentum thickness operating domain for density ratios $S \,{=}\, \rho_{j}/\rho_{\infty}$ ranging from 0.14 to 0.5. The results provide convincing evidence of the universality of global oscillations in low-density jets and indicate that conditions in the jet exit plane are responsible for driving the global instability.
Misfit functions for full waveform inversion based on instantaneous phase and envelope measurements

Resolution in seismic tomography intimately depends on data coverage, with different parts of seismograms sensitive to different parts of Earth's structure. In classical seismic tomography, the usable amount of data is often restricted because of approximations to the wave equation. 3-D numerical simulations of wave propagation provide new opportunities for increasing the amount of usable data in seismograms by choosing appropriate misfit functions which have direct control on Frechet derivatives. We propose new misfit functions for full waveform tomography based on instantaneous phase differences and envelope ratios between observed and synthetic seismograms. The aim is to extract as much information as possible from a single seismogram. Using the properties of the Hilbert transform, we separate phase and amplitude information in the time domain. To gain insight in the advantages and disadvantages of chosen misfit functions, we make qualitative comparisons of the corresponding finite-frequency adjoint sensitivity kernels with those from commonly used misfit functions based on cross-correlation traveltime, amplitude and waveform differences. The major advantages of our misfit functions are: (1) working in the Hilbert domain reduces non-linear behaviour of waveforms due to interaction of phase and amplitude information, and (2) we show with noise-free synthetic seismograms that it is possible to use a complete seismogram without losing information from low-amplitude phases. Complementary to instantaneous phase measurements, envelope measurements provide a way of using amplitude information of waveforms, which may also easily be extended to constrain anelastic properties. The properties of the kernels allow us to simplify the tomography problem by separating elastic and anelastic inversions. First indications are that the kernels remain well behaved in the presence of noise.
Antidiabetic Potential of Novel 1,3,5-Trisubstituted-2-Thioxoimidazloidin-4-One Analogues: Insights into α-Glucosidase, α-Amylase, and Antioxidant Activities

As the ninth leading cause of death globally, diabetes mellitus (DM) is considered to be the worst chronic metabolic disease requiring an enormous need for healthcare with over 578 million expected cases by 2023. Several recent findings have demonstrated that mediating the activity of carbohydrate-hydrolyzing enzymes, including α-amylase and α-glucosidase, could be a potential strategy for managing the development of DM. In the presented study, a novel set of 1,3,5-trisubstituted-2-thioxoimidazolidin-4-ones was designed, synthesized, and characterized. The antidiabetic activity of the synthesized compounds was explored by assessing their inhibitory activity toward α-amylase and α-glucosidase enzymes. The results demonstrated that this class of compounds exhibits considerable inhibitory activity toward both α-amylase and α-glucosidase enzymes. Among the synthesized compounds, compound 5a demonstrated the most inhibitory activity with IC50 of 5.08 and µg/mL and 0.21 µg/mL toward α-glucosidase and α-amylase activities, respectively, as compared to the drug Acarbose (IC50 = 5.76 µg/mL and 0.39 µg/mL, respectively). To gain insights into the antidiabetic potential of compound 5a, we assessed the cytotoxic and antioxidant activities. Our findings indicated that compound 5a displays considerable cytotoxicity toward WI-38 cells with an IC50 of 88.54 µg/mL, as compared to the drug Celecoxib (IC50 = 93.05 µg/mL). Further, compound 5a exhibited a high scavenging activity toward 2,2-Diphenyl1-picrylhydrazyl (DPPH) free radicals (IC50 = 51.75 µg/mL) and showed a low potential to produce ROS as indicated by the monitoring of the generated H2O2 (132.4 pg/mL), as compared to Trolox (IC50 = 58.09 µg/mL) and Celecoxib (171.6 pg/mL). Finally, we performed extensive molecular modeling studies to affirm the binding affinity of this class of compounds to the binding pocket of α-amylase and α-glucosidase enzymes. Collectively, our findings indicate that this class of compounds, particularly compound 5a, could be utilized as a lead structure for the development of novel compounds with potential antidiabetic and antioxidant activities.
Sine Averaging and Duty Ratio Approach PWM Techniques at Low Switching Frequencies

The concept of a Sine PWM inverter is based on the assumption that a sine wave can be assumed to be constant for one switching cycle. This assumption holds true for low voltage high switching frequency needs, but as power or voltage requirements increase, having a high switching frequency can result in high switching losses, necessitating the use of a PWM inverting technique that can efficiently work with low switching frequencies. This paper examines several possible versions of sine PWM algorithms for low-frequency applications, focusing on their fundamental frequency content and total harmonic distortion. A comparison is made for inverters (utilising Pure sine wave as a reference and stepped sine wave as a reference) with existing and proposed method taking the cases of scaled and non-scaled. With the thorough analysis of all the cases, A stepped sine methodology Sine Averaging and Duty Ratio Approach that offers comparatively better results than existing techniques has been proposed.
Preliminary Analysis of Voluntary Information on Organic Milk Labels in Four European Union Countries

The concern for the environment among European consumers is growing and in the future the need for sustainable shopping is expected to increase. Through transparent on-packaging communication with consumers, organic producers have the opportunity to show attributes of organic production system and build a strong market position. The aim of the study was to analyse voluntary packaging information on organic milk from four European markets in the context of organic food quality, i.e., Germany, the Netherlands, Italy and Poland. More specifically, the textual content of 106 organic milk packages was analysed and voluntary information on each package was categorized according to process- and product-related organic milk attributes. The assortment and content of voluntary packaging information varied across the four countries. The largest number of products was found on the German market (37) and the smallest on the Polish market (14). Dutch milk had the greatest amount of voluntary information on animal welfare, product locality, environmental protection, quality confirmation, naturalness and nutritional value. German milk had the most information on enjoyment and conditions of processing, while the Italian milk on the social perspective. The products available on the Polish market had the least voluntary information. Pasteurized organic milk had noticeably more information about organic quality attributes than micro filtrated and UHT milk.
A Framework to Assess Melodic Effectiveness in Training Computational Thinking to Visually Impaired People

Computational Thinking education and training are considered crucial activities to achieve success in later challenges like computer programming. To train the brain to think in logical terms it is necessary to use adequate resources. In this paper we address the difficult problem that is assessing efficacy of a given resource to the Computation Thinking development. To discuss that topic we will use the Melodic System which is a physical learning resource capable of constructing melodic sequences in a logical manner to be used by visually impaired people. Melodic was created aiming at training Computational Thinking through music and sounds and its impact in the learning process must be evaluated. This paper presents the tests and results applied to this intent, while also providing a general and summarized presentation of the Melodic System itself.
A novel hybrid deep learning model for aspect based sentiment analysis

The usage of social media, forums, and e‐commerce websites have been widely increased. Feedback from customers has a big impact on the final product. A service provider, merchant, or manufacturer need all the information, even if it is just a comment or a review about a service or a product. So, it is vital to look at input from users, and therefore sentiment analysis has received a lot of interest. Sentiment analysis is a method for identifying and analyzing text in order to determine the features, qualities, and viewpoints of particular user. Extracting user aspects is the main part of this process, and it is used to group the user aspects. In recent years, convolutional neural network (CNN) models have gained popularity in natural language processing. Thus, this research proposes a novel hybrid CNN model by concatenating the bidirectional long short‐term memory and CNN models to process the data sequentially by learning their high‐level features. The concatenated method minimizes the loss of critical information. Benchmark product reviews and hotel review datasets are employed in the experiments, and accuracies of 93.6% for the product review dataset and 92.7% for the hotel review dataset are achieved by the proposed hybrid model when compared to state‐of‐the‐art techniques.
Offshore Ground Motion Models for Arias Intensity and Cumulative Absolute Velocity in the Japan Trench Area

ABSTRACT This study proposes offshore ground motion models (GMMs) for Arias intensity and cumulative absolute velocity by using the seafloor ground motion database in the Japan Trench area. Whether the station is buried or not will have a great influence on the Arias intensity and cumulative absolute velocity. Compared with existing GMMs, as the magnitude increases, the energy accumulation effect of offshore GMMs is greater than that of onshore GMMs with similar magnitude and distance, and the attenuation rate is slower than other models. The proposed GMMs can be used for seismic hazard and risk analysis in the Japan Trench area.