Miracle-dz/UniFAR-FTUs huggingface dataset:
The Enhancing Effect of Stable Oxygen Functional Groups on Porous-Carbon-Supported Pt Catalysts for Alkaline Hydrogen Evolution

Highlights What are the main findings? The HCl treatment of porous carbon can generate abundant hydroxyl and carboxyl groups, while the further heat treatment can transform into thermally stable carbonyl and ether groups. Carbonyl and ether groups within porous carbon supports is beneficial to the improvement of HER performance of catalysts. What is the implication of the main finding? The surface properties of porous carbon supports can be well tuned via a HCl treatment followed by an appropriate heat treatment. The feasible improvement of HER performance by regulating surface oxygen functional groups of porous carbon supports. Abstract The oxygen functionalization of carbon materials has widely been employed to improve the catalytic performance of carbon-supported Pt (Pt/C) catalysts. Hydrochloric acid (HCl) has often been employed to clean carbons during the preparation of carbon materials. However, the effect of oxygen functionalization through a HCl treatment of porous carbon (PC) supports on the performance of the alkaline hydrogen evolution reaction (HER) has rarely been investigated. Herein, the impact of HCl combined with the heat treatment of PC supports on the HER performance of Pt/C catalysts has been comprehensively investigated. The structural characterizations revealed similar structures of pristine and modified PC. Nevertheless, the HCl treatment resulted in abundant hydroxyl and carboxyl groups and the further heat treatment formed thermally stable carbonyl and ether groups. Among the catalysts, Pt loading on the HCl-treated PC followed by a heat treatment at 700 °C (Pt/PC-H-700) exhibited elevated HER activity with a lower overpotential of 50 mV at 10 mA cm−2 when compared to the unmodified Pt/PC (89 mV). Pt/PC-H-700 also exhibited better durability than the Pt/PC. Overall, novel insights into the impact of the surface chemistry properties of porous carbon supports on the HER performance of Pt/C catalysts were provided, which were useful for highlighting the feasible improvement of HER performances by regulating the surface oxygen species of porous carbon supports.
Monitoring of indicators of chromatin status in quails ovarian follicles granulosa cells of different directions of productivity

Relevance. Identification and analysis of the reasons for reducing egg productivity is an urgent problem in quail breeding. The aim of this study is to assess the destructive processes in the somatic cells in ovarian follicles (the level of apoptosis and pycnosis) of mature quail layers of different productivity in connection with egg production and quality indicators of eggs.Methods. In experiments, the level of apoptosis and pyknosis in granulosa cells of ovarian follicles of 3–8 mm females of Estonian, Manchurian, English white, tuxedo, English black and Pharaoh breeds was evaluated. The quails were kept in individual cages in monopreed pairs with a muted 14-hour light day. The age of the females was 160–180 days. The level of pycnotic cells was assessed by the Tarkowski cytological method, apoptotic processes were analyzed by staining with fluorochromes, propidium iodide and acridine orange (10 mcg/mlPI/AO). Results. chromatin. The results of the study of chromatin destruction using PI/AO showed a high average percentage of apoptotic cells in almost all breeds except thep < 0.05). The maximum proportion of dead granulosa cells was tested in Tuxedo and English Black layers (52% and 43% respectively). At the same time, the English white breed of quails at the age of 183 days has one of the best indicators of egg production and the proportion of yolk in relation to egg weight, which indicates the possibility of prolonging the period of their use as laying hens.
Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model

Abstract. Tropical cyclones (TCs) are one of the most severe meteorological disasters, making rapid and accurate track forecasts crucial for disaster prevention and mitigation. Because TC tracks are affected by various factors (the steering flow, the thermal structure of the underlying surface, and the atmospheric circulation), their trajectories present highly complex nonlinear behavior. Deep learning has many advantages in simulating nonlinear systems. In this paper, based on deep-learning technology, we explore the movement of TCs in the northwestern Pacific from 1979 to 2021, divided into training (1979–2014), validation (2015–2018), and test sets (2019–2021), and we create 6–72 h TC track forecasts. Only historical trajectory data are used as input for evaluating the forecasts of the following three recurrent neural networks utilized: recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The GRU approach performed best; to further improve forecast accuracy, a model combining GRU and a convolutional neural network (CNN) called GRU_CNN is proposed to capture the characteristics that vary with time. By adding reanalysis data of the steering flow, sea surface temperatures, and geopotential height around the cyclone, we can extract sufficient information on the historical trajectory features and three-dimensional spatial features. The results show that GRU_CNN outperforms other deep-learning models without CNN layers. Furthermore, by analyzing three additional environmental factors through control experiments, it can be concluded that the historical steering flow of TCs plays a key role, especially for short-term predictions within 24 h, while sea surface temperatures and geopotential height can gradually improve the 24–72 h forecast. The average distance errors at 6 and 12 h are 17.22 and 43.90 km, respectively. Compared with the 6 and 12 h forecast results (27.57 and 59.09 km) of the Central Meteorological Observatory, the model proposed herein is suitable for short-term forecasting of TC tracks.
Differential gene expression in the Longissimus dorsi of Nguni and Bonsmara bulls finished on low and high energy diets

Objectives of this research were to examine differential gene expression profiles of Nguni and Bonsmara cattle fed diets differing in their energy density. The ultimate goal was to improve understanding of the mechanisms that underlie differences between these breeds and the potential interactions of the differences between breeds with the nutritive environment. The experiment was designed as a 2 × 2 factorial arrangement of breed and diet (12.5 MJ/kg DM vs. 10.9 MJ/kg DM). The initial feeding trial had 10 bull calves per treatment. However, financial constraints limited RNA sequencing to six animals per treatment and the RNA generated from one animal was of insufficient quality to be useful. Transcripts with false discovery rate P-values <0.05 and fold-changes >2.0 were considered significant. Bonsmara had a faster growth rate, heavier live and carcass weights, and better feed conversion compared to Nguni. However, lower levels of fat were observed in Nguni. Twenty different genes were differentially expressed, with three exhibiting interaction effects and all 20 having differences in transcript abundance between the breeds. A dietary effect was only observed for the one gene and that gene was also subject to an interaction effect with breed. Observed differences in gene expression between Bonsmara and Nguni by several genes affecting the structure or function of the mitochondria imply differences in energy metabolism between the breeds. Interaction effects on the abundance of some gene transcripts indicate the need to consider the diet when evaluating breed differences and conversely, consider breed when evaluating diets.
The Impact of Experiential Learning on Professional Identity: Comparing Community Service-Learning to Traditional Practica Pedagogy

Community service-learning (CSL) has been referred to as a “pedagogy for citizenship,” as it enhances ethical behavior and social responsibility among student participants. It represents a pedagogical and philosophical approach that promotes experiential learning by incorporating intentional course-based lessons with service in the community. Despite the numerous studies outlining the benefits of CSL initiatives, there is a dearth of research on how CSL courses can impact students already in the “helping professions. ” More specifically, there is very little research on the benefits of CSL in social work field education courses. For this study, the researchers found that developing a CSL practicum led to a substantive shift in professional understanding for the students who participated in a CSL learning opportunity. Although the traditional and CSL groups began their practicum experiences believing the primary role of a social worker was to build and maintain healthy relationships with service users, the CSL group saw their primary role switch from a “micro practice” to a “macro practice” worker. CSL offers social work education additional unique opportunities to support the development of student's social work core values, knowledge, and skills.
A localized ensemble of approximate Gaussian processes for fast sequential emulation

More attention has been given to the computational cost associated with the fitting of an emulator. Substantially less attention is given to the computational cost of using that emulator for prediction. This is primarily because the cost of fitting an emulator is usually far greater than that of obtaining a single prediction, and predictions can often be obtained in parallel. In many settings, especially those requiring Markov Chain Monte Carlo, predictions may arrive sequentially and parallelization is not possible. In this case, using an emulator procedure which can produce accurate predictions efficiently can lead to substantial time savings in practice. In this paper, we propose a global model approximate Gaussian process framework via extension of a popular local approximate Gaussian process (laGP) framework. Our proposed emulator can be viewed as a treed Gaussian process where the leaf nodes are laGP models, and the tree structure is learned greedily as a function of the prediction stream. The suggested method (called leapGP) has interpretable tuning parameters which control the time‐memory trade‐off. One reasonable choice of settings leads to an emulator with a O(N2) training cost and makes predictions rapidly with an asymptotic amortized cost of O(N) .
Antibacterial and Antioxidant Activity of Endophytic Fungi Extract Isolated from Leaves of Sungkai (Peronema canescens)

Sungkai is a plant that is widely found in Indonesia. This plant is often used in traditional medicine so the biotechnology of endophytic fungi is highly needed due to natural resources from plants that have been reduced. This study reported on endophytic fungi found in sungkai leaves and the compound produced. Morphological and molecular identification through phylogenetic tree analysis was carried out to determine the endophytic fungal species found. Potato Dextrose Broth media was used for the cultivation process. Ethyl acetate was used as a solvent for the extraction and the evaporation process used a rotary evaporator. Antioxidant and antibacterial tests were carried out using the DPPH method and paper disc diffusion. Chromatographic techniques were used to isolate the compound and spectroscopic analysis was performed to identify its chemical structure. The results of the morphological and molecular analysis showed Trichoderma asperellum as an endophytic fungus identified. The pure compound obtained from this endophytic fungus was 4-hydroxybenzoic acid. The antioxidant and antibacterial activity showed a strong category (IC50 = 43.88 ? g/mL; MIC 64 ? g/mL). This compound was very likely to be a raw material for new antibiotics and antioxidants through further research with various modifications.
CS-IntroVAE: Cauchy-Schwarz Divergence-Based Introspective Variational Autoencoder

Although generative models are still being developed, image reconstruction and generation tasks have evolved dramatically. Since the most popular generative models still have some limitations, it is still challenging. For example, while generative adversarial network (GAN) produces clear images, it is hard to train. The hybrid VAE-GAN incorporates the benefits of both, although it is computationally intensive and prone to drawbacks such as overfitting and gradient disappearance. A novel generative model called the Cauchy-Schwarz Divergence-based Introspective Variational Autoencoder (CS-IntroVAE) is based for this challenge. Extensive experiments show that our model has good performance on both tasks by employing mixed Gaussian distributions as prior distributions and Cauchy-Schwarz divergence as a measure of the distance between prior and posterior distributions.
Propulsion of a Plunging Flexible Airfoil using a Torsion Spring Model

The aim of the present study is to understand the mechanism of propulsion in biological flyers, initially starting from rest, using a computational analysis of the plunging motion of a flexible wing. Simulations are performed on a self-propelled flexible wing unconstrained to move in direction perpendicular to the heaving motion. A two-dimensional lumped torsional flexibility model has been developed for a multi-component system which mimics a real insect wing. Using the lattice-Boltzmann method, this model is used to investigate the role of chordwise flexibility on fluid mechanics associated with forward propulsion. The aerodynamics and vortical wake patterns at different frequencies are investigated for a range of Reynolds number and bending stiffnesses. In addition, the behavior of input power and efficiency with stiffness has been examined. This work is anticipated to pave the way towards an improved understanding of propulsion in biological flyers employing flexible wings and thereby contribute to development of micro-air vehicles employing the same concept.
Weighted support vector machines for regression and its application

In the standard support vector machines for regression, the required error of regression estimation and the penalty for violation of the required error are equally considered for every training sample, which is unsuitable in case there exists significant difference among the training samples. In the proposed weighted support vector machines, each training sample had different approximation error requirement and different penalty. The dual quadratic optimization of weighted support vector machines for regression and its sequential minimal optimization (SMO) algorithms were given in detail. The experiments on the measurement of gasoline octane numbers by near-infrared spectroscopy, where the importance of each training sample was characterized by the geometrical distance from the test sample, show that the measurement accuracy is improved with the proposed weighted support vector machines.
Development and Evaluation of a Lumbar Assisted Exoskeleton With Mixed Lifting Tasks by Various Postures

A large number of the WRLSs (wearable robots lumbar support) research have been presented for working efficient increase and injure risk reduction in recent years. However, the previous research can only complete the sagittal-plane lifting task, which can not adapt to the mixed lifting tasks in the actual work scene. Therefore, we presented a novel lumbar assisted exoskeleton with mixed lifting tasks by various postures based on position control, which can not only carry out the lifting tasks of sagittal-plane, but also complete the lifting tasks of sides. First, we proposed a new generation method of raising reference curves that can generate assistance curve for each user with each task, which is very convenient in mixed lifting tasks. Then, an adaptive predictive controller was designed to track the reference curves of different users under different loads, the maximum tracking errors of the angles are <inline-formula> <tex-math notation="LaTeX">${2}. {2}^{\circ }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${3}. {3}^{\circ }$ </tex-math></inline-formula> respectively at 5kg and 15kg, and all the errors are within 3%. Compared to the condition of no exoskeleton, the average RMS (root mean square) of EMG (electromyography) for six muscles are reduced by <inline-formula> <tex-math notation="LaTeX">$10.33\pm 1.44\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$9.62\pm 0.69\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$10.97\pm 0.81\%$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$14.48\pm 2.11\%$ </tex-math></inline-formula> by lifting loads with stoop, squat, left-asymmetric and right-asymmetric respectively. The results demonstrate that our lumbar assisted exoskeleton presents outperformance in mixed lifting tasks by various postures.
Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.
Modeling Classical Dynamics and Quantum Effects in Superconducting Circuit Systems

In recent years, superconducting circuits have come to the forefront of certain areas of physics. They have shown to be particularly useful in research related to quantum computing and information, as well as fundamental physics. This is largely because they provide a very flexible way to implement complicated quantum systems that can be relatively easily manipulated and measured. In this thesis we look at three different applications where superconducting circuits play a central role, and explore their classical and quantum dynamics and behavior. The first part consists of studying the Casimir [20] and Casimir–Polder like [19] effects. These effects have been discovered in 1948 and show that under certain conditions, vacuum field fluctuations can mediate forces between neutral objects. In our work, we analyze analogous behavior in a superconducting system which consists of a stripline cavity with a DC–SQUID on one of its boundaries, as well as, in a Casimir–Polder case, a charge qubit coupled to the field of the cavity. Instead of a force, in the system considered here, we show that the Casimir and Casimir– Polder like effects are mediated through a circulating current around the loop of the boundary DC–SQUID. Using detailed analysis, we examine how the values of these currents change as we vary different physical circuit parameters. We show that for the set of physical parameters that can be easily obtained experimentally, the Casimir and Casimir–Polder currents can be of the order of 10−8 A and 10−13 A respectively. In the second part, we theoretically model an experiment which was performed by Britton Plourde’s group at Syracuse University, and which studied the transient dynamics of a nonlinear superconducting oscillator, based on a capacitively shunted DC–SQUID. Such DC–SQUID oscillators are used in many areas of physics and engineering, for example, as building blocks of amplifiers or qubits, qubit couplers, or as sensitive magnetic field detectors. In many of these situations, their steady state behavior is often considered, while in the experiment performed at Syracuse, of specific interest, was the response of a DC–SQUID oscillator to a short radiation that only briefly excited the system. In this thesis, we simulate this response at the experimental temperature, by numerically solving a set of classical stochastic differential equations that mimic the behavior of the circuit. This is done for different settings of the flux that is threaded through the DC–SQUID as well as different input pulse amplitudes. Furthermore, we briefly outline just how these kinds of brief excitations could be useful when applied in flux measurement protocols. We find that our simulations show good agreement with the experimentally obtained data. The final part considered in this thesis, looks at the dynamics of a qubit coupled to a measuring probe, which is modeled as a harmonic oscillator. An example superconducting circuit, that could be used to implement such a setup, consists of a flux qubit inductively coupled to a DC–SQUID. This measurement scenario has already