Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of The Problem Based Learning (PBL) Learning Model To Improve Skills Creative Thinking of Students on The Material Coligative Properties of Solutions

One of the chemicals that is considered difficult is the colligative properties of solutions. Difficulty understanding material colligative properties of solutions can hinder students' understanding of other concepts. The purpose of this research was to find out the increase in creative thinking skills and mastery of students' material after participating in the Problem Based Learning (PBL) model of learning. The research method used was quasi-experimental research, with the research design used being one group pretest-posttest design. In this study, the experimental class was treated using the Problem Based Learning learning model on colligative properties of solutions. The population in this study were undergraduate students at HKBP Nommensen Pematangsiantar University. The sample in this study were Bachelor of Chemistry Education students at HKBP Nommensen Pematangsiantar University. The results of research on student learning outcomes in the material Colligative Properties of Solutions using the PBL learning model increased from 42.6% students' pretest scores and 84.16% students' posttests, the increase obtained was 41.56%. The conclusion of the research is that the Problem Based Learning (PBL) learning model has a significant effect on students' creative thinking skills. This can be proven from the average value of students' creative thinking skills in the experimental class at each meeting has increased
On the Topology of Simple Fold Maps

The purpose of this note is to study the topology of simple fold maps of a closed n-manifold into a p-manifold $(n>p)$ and special generic maps of a closed orientable 4-manifold into an orientable 3-manifold. We call a smooth map $f$ with only fold singularities a fold map and it is said to be simple if the connected component of$f^{-1}(f(x))$ containing $x$ intersects $S(f)$ only at $x$ for every $x\in S(f)$ . Special generic maps are smooth maps which have only definite fold sigularities. Precise definitions of fold maps, simple fold maps and special generic maps are given in \S 2. The results here extend and depend upon some related results in the author’s previous paper [13], where special generic maps of simply connected $2n$-manifolds into
The Relationship and Influencing Factors between Endangered Plant Tetraena mongolica and Soil Microorganisms in West Ordos Desert Ecosystem, Northern China

Soil microorganisms play crucial roles in improving nutrient cycling, maintaining soil fertility in desert ecosystems such as the West Ordos desert ecosystem in Northern China, which is home to a variety of endangered plants. However, the relationship between the plants–microorganisms–soil in the West Ordos desert ecosystem is still unclear. Tetraena mongolica, an endangered and dominant plant species in West Ordos, was selected as the research object in the present study. Results showed that (1) there were ten plant species in the Tetraena mongolica community, belonging to seven families and nine genera, respectively. The soil was strongly alkaline (pH = 9.22 ± 0.12) and the soil nutrients were relatively poor; (2) fungal diversity was more closely related to shrub diversity than bacterial and archaeal diversity; (3) among the fungal functional groups, endomycorrhizal led to a significant negative correlation between shrub diversity and fungal diversity, because endomycorrhizal had a significant positive effect on the dominance of T. mongolica, but had no significant effect on other shrubs; (4) plant diversity had a significant positive correlation with the soil inorganic carbon (SIC), total carbon (TC), available phosphorus (AVP) and available potassium (AVK). This study revealed the effects of soil properties and soil microorganisms on the community structure and the growth of T. mongolica and provided a theoretical basis for the conservation of T. mongolica and the maintenance of biodiversity in desert ecosystems.
Non-Intrusive Load Monitoring Method for Multi-Energy Coupling Appliances Considering Spatio-Temporal Coupling

The multi-energy coupling (MEC) appliances couple electricity, gas, heat, and cold, of which the gas, heat, and cold have long transient processes. However, these transient features have never been considered in past non-intrusive load monitoring (NILM) methods. This paper proposes a novel NILM method for MEC appliances considering spatio-temporal coupling, which combines semi-supervised learning and weakly supervised learning. Firstly, the unlabeled historical events are labeled by graph-based semi-supervised learning (GBSSL) and distinguished into three categories, i.e., reliable, uncertain, and unreliable samples. Then, to fully use of spatio-temporal coupling information, the unreliable samples are discarded and the feature vectors of other samples are expanded into spatio-temporal coupling feature vectors. To improve the accuracy of load identification, this paper proposes a two-stage deep learning framework “Teaching and Mutual Learning” (TML). In the teaching stage, two convolutional neural networks (CNN) are pre-trained with reliable samples. In the mutual learning stage, the noise samples are filtered by using the different learning abilities of the two networks. The effectiveness and superiority of the proposed method are verified on five types of MEC appliances in a park IES.
Geodetic residual time series: A combined series by minimization of their internal noise level

This study aims to assess the hydrological effects of polar motion calculated from different combinations of geophysical excitations at decadal, seasonal, and non-seasonal periods. The geodetic residuals GAO, being a difference between observed geodetic excitation function of polar motion Geodetic Angular Momentum (GAM) and atmospheric (Atmospheric Angular Momentum—AAM) plus oceanic excitation functions (Oceanic Angular Momentum—OAM), are compared. Estimating hydrological effects on Earth’s rotation differs significantly, especially when using various oceanic models. Up to now, studies of geophysical excitations of polar motion containing AAM, OAM, and hydrological angular momentum (HAM) have not achieved entire agreement between geophysical (sum of AAM, OAM, and HAM obtained from the models) and geodetic excitation. Many geophysical models of the atmosphere, oceans, and land hydrology can be used to compute polar motion excitation. However, these models are very complex and still have uncertainties in the process descriptions, parametrization, and forcing. This work aims to show differences between various GAO solutions calculated from different mass and motion terms of various AAM and OAM models. Justifying to use one combination of GAO to estimate geodetic residuals is comparing those time series to hydrological excitations computed from Gravity Recovery and Climate Experiment (GRACE) data and the Land Surface Discharge Model (LSDM) model. Especially the quality of each geodetic residual time series is determined by estimating their noise level using a generalized formulation of the “three-cornered hat method” (3CH). This study presents a combined series of geodetic residuals GAO in polar motion (PM), wherein the internal noise level is shortened to a minimum by using the 3CH method. The combined GAO time series are compared with results obtained from the GRACE/GRACE Follow-On (GRACE-FO) solution provided by International Combination Service for Time variable Gravity Fields (COST-G) and the single solution elaborated by the Center for Space Research (CSR) and from the HAM LSDM hydrological model. The results show that higher consistency between GAO and HAM excitations can be achieved by minimizing the internal noise level in the GAO combined excitation series using the 3CH method, especially for the overall broadband and seasonal oscillations. For seasonal spectral bands, an agreement between combined GAO and the best-correlated series of GRACE CSR achieve correlations as high as 0.97 and 0.83 for the χ 1 and χ 2 equatorial components of PM excitation, respectively. This study’s combined geodetic residual time series slightly improved consistency between observed geodetic polar motion excitations and geophysical ones.
Highly Ordered Anodic Porous Alumina by Imprinting Using Ni Molds Prepared from Ordered Array of Polystyrene Particles

Anodic porous alumina with a highly ordered pore arrangement was prepared by imprinting using a metal (Ni) mold prepared from a two-dimensional ordered array of polystyrene particles as a template. The ordered array of shallow concaves of Al prepared by imprinting could initiate pore development and could guide pore growth during anodization. This process enables the high-throughput preparation of highly ordered anodic porous alumina because the metal mold can be used repeatedly.
Controller Design for Electrohydraulic Actuator of Heavy-Duty Automatic Transmission Using Model Predictive Control Algorithm

High-quality shifting is an inelastic demand for the automotive industry, and it is worth exploring the design of controllers that can shift precisely according to the planned shifting strategy. This article proposes a dynamic model of the automatic transmission (AT) up/downshift process, and the transfer function based on pressure is first given. The model was verified by comparing it with the experimentally obtained fluid pressure. Subsequently, a pressure control strategy suitable for heavy-duty AT shifting was explored by establishing a model predictive controller with pressure feedback control. Meanwhile, a Kalman filter-based observer was designed to solve the inconvenient installation of internal sensors. Finally, the proposed control model was optimized using a multiobjective genetic algorithm for the controller. The simulation results indicate that the proposed control strategy can be applied to heavy-duty vehicles for improving the shift quality.
Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System

The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw precipitation forecasts because of their significant bias when they feed hydrological models. In this research, ensemble precipitation forecasts (EPFs) of three NWP models (National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) (Exeter, UK), and Korea Meteorological Administration (KMA) (SEOUL, REPUBLIC OF KOREA)) were investigated for six historical storms leading to heavy floods in the Dez basin, Iran. To post-process EPFs, the raw output of every single NWP model was corrected using regression models. Then, two proposed models, the Group Method of Data Handling (GMDH) deep learning model and the Weighted Average–Weighted Least Square Regression (WA-WLSR) model, were employed to construct a multi-model ensemble (MME) system. The ensemble reservoir inflow was simulated using the HBV hydrological model under the two modeling approaches involving deterministic forecasts (simulation using observed precipitation data as input) and ensemble forecasts (simulation using post-processed EPFs as input). The results demonstrated that both GMDH and WA-WLSR models had a positive impact on improving the forecast skill of the NWP models, but more accurate results were obtained by the WA-WLSR model. Ensemble forecasts outperformed coupled atmospheric–hydrological modeling in comparison with deterministic forecasts to simulate inflow hydrographs. Our proposed approach lends itself to quantifying uncertainty of ensemble forecasts in hydrometeorological the models, making it possible to have more reliable strategies for extreme-weather event management.
Extraction of Gold Based on Ionic Liquid Immobilized in UiO-66: An Efficient and Reusable Way to Avoid IL Loss Caused by Ion Exchange in Solvent Extraction

Ionic liquids (ILs) have received considerable attention as a promising green solvent for extracting metal ions from aqueous solutions. However, the recycling of ILs remains difficult and challenging because of the leaching of ILs, which is caused by the ion exchange extraction mechanism and hydrolysis of ILs in acidic aqueous conditions. In this study, a series of imidazolium-based ILs were confined in a metal–organic framework (MOF) material (UiO-66) to overcome the limitations when used in solvent extraction. The effect of the various anions and cations of the ILs on the adsorption ability of AuCl4− was studied, and 1-hexyl-3-methylimidazole tetrafluoroborate ([HMIm]+[BF4]−@UiO-66) was used for the construction of a stable composite. The adsorption properties and mechanism of [HMIm]+[BF4]−@UiO-66 for Au(III) adsorption were also studied. The concentrations of tetrafluoroborate ([BF4]−) in the aqueous phase after Au(III) adsorption by [HMIm]+[BF4]−@UiO-66 and liquid–liquid extraction by [HMIm]+[BF4]− IL were 0.122 mg/L and 18040 mg/L, respectively. The results reveal that Au(III) coordinated with the N-containing functional groups, while [BF4]− was effectively confined in UiO-66, instead of undergoing anion exchange in liquid–liquid extraction. Electrostatic interactions and the reduction of Au(III) to Au(0) were also important factors determining the adsorption ability of Au(III). [HMIm]+[BF4]−@UiO-66 could be easily regenerated and reused for three cycles without any significant drop in the adsorption capacity.
Implementation of Hyper Parameter Optimization in Liver Disease Prediction

Liver disease can be genetic. The liver can be damaged by alcohol use, obesity and hepatitis viruses. Chronic liver disease has many causes like Liver cancer, Cirrhosis and life threatening conditions. So prevention treatment can help to heal the damage and prevent liver failure. Hence, the time required to introduce the machine learning techniques which minimize the error and give more accuracy. Aim of this research paper is categories into two phases. In the first phase six classification algorithms (LR, KNN, SVM, NB, DT, and RF) are compared with ML Classification evaluation metrics. As a consequence, Logistic Regression obtained a higher accuracy of 71.6% in liver disease prediction than the other classification algorithms. In Phase 2, we selected a sample from a classification method with lower accuracy, such as Random Forest, Decision Tree algorithms and we adjusted the hyper parameters. RF accuracy is improved after Hyper Parameter Optimization by +4%, whereas Decision Tree algorithm accuracy is improved by +12%.
POINTWISE BOUNDS AND SPATIAL DECAY ESTIMATES IN HEAT CONDUCTION PROBLEMS

In this paper we derive a new maximum principle for the absolute value of the gradient of a solution to the heat equation. We then apply this principle to obtain explicit bounds in the associated Dirichlet problem. Finally we derive explicit pointwise St-Venant type spatial decay estimates for solutions of certain initial-boundary value problems and their gradients in the case of unbounded domains.
A non-intrusive load decomposition method of resident by multi-scale attention mechanism

Non-intrusive load monitoring (NILM) is one of the important technologies in home energy management and power demand response scenario. However, the presence of multi-mode appliances and appliances with close power values have affected in diminishing the accuracy of identification based NILM algorithms. To tackle these challenges, the work proposes a resident load decomposition method combining multi-scale attention mechanism and convolutional neural network. At the first stage, the attention scores of the normal load data at the previous few moments of the attention model are smoothed dynamically against the abnormal scores at the current moment. The load identification attention model is optimized by constraint factors. Then, on this basis, convolution filters of different sizes are used to model the mixed load data of different electrical equipment, to mine more abundant characteristic information. Finally, to illustrate the proposed processes and validate its effectiveness, taking the PLAID data set as an example, the method proposed in the article is compared with respect to the existing NILM techniques. The experimental results show that the method based on the multi-scale attention mechanism in this paper can greatly improve the effect of load decomposition. Moreover, it reduces the confusion problem of electrical appliance identification with similar load characteristics.
The Anthropometric Measurement of Nasal Landmark Locations by Digital 2D Photogrammetry Using the Convolutional Neural Network

Measuring and labeling human face landmarks are time-consuming jobs that are conducted by experts. Currently, the applications of the Convolutional Neural Network (CNN) for image segmentation and classification have made great progress. The nose is arguably one of the most attractive parts of the human face. Rhinoplasty surgery is increasingly performed in females and also in males since surgery can help to enhance patient satisfaction with the resulting perceived beautiful ratio following the neoclassical proportions. In this study, the CNN model is introduced to extract facial landmarks based on medical theories: it learns the landmarks and recognizes them based on feature extraction during training. The comparison between experiments has proved that the CNN model can detect landmarks depending on desired requirements. Anthropometric measurements are carried out by automatic measurement divided into three images with frontal, lateral, and mental views. Measurements are performed including 12 linear distances and 10 angles. The results of the study were evaluated as satisfactory with a normalized mean error (NME) of 1.05, an average error for linear measurements of 0.508 mm, and 0.498° for angle measurements. Through its results, this study proposed a low-cost automatic anthropometric measurement system with high accuracy and stability.
Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica

Abstract. Landfast ice (LFI) plays a crucial role for both the climate and the ecosystem of the Antarctic coastal regions. We investigate the snow and LFI mass balance in Prydz Bay using observations from 11 sea ice mass balance buoys (IMBs). The buoys were deployed offshore from the Chinese Zhongshan Station (ZS) and Australian Davis Station (DS), with the measurements covering the ice seasons of 2009–2010, 2013–2016, and 2018. The observed annual maximum ice thickness and snow depth were 1.59 ± 0.17 and 0.11–0.76 m off ZS and 1.64 ± 0.08 and 0.11–0.38 m off DS, respectively. Early in the ice growth season (May–September), the LFI basal growth rate near DS (0.6 ± 0.2 cm d−1) was larger than that around ZS (0.5 ± 0.2 cm d−1). This is attributed to cooler air temperature (AT) and lower oceanic heat flux at that time in the DS region. Air temperature anomalies were more important in regulating the LFI growth rate at that time because of thinner sea ice having a weaker thermal inertia relative to thick ice in later seasons. Interannual and local spatial variabilities for the seasonality of LFI mass balance identified at ZS are larger than at DS due to local differences in topography and katabatic wind regime. Snow ice contributed up to 27 % of the LFI total ice thickness at the offshore site close to ground icebergs off ZS because of the substantial snow accumulation. Offshore from ZS, the supercooled water was observed at the sites close to the Dålk Glacier from July to October, which reduced the oceanic heat flux and promoted the LFI growth. During late austral spring and summer, the increased oceanic heat flux led to a reduction of LFI growth at all investigated sites. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades. Based on the results derived from this study, we argue that an increased understanding of snow (on LFI) processes, local atmospheric and oceanic conditions, as well as coastal morphology and bathymetry, are required to improve the Antarctic LFI modeling.
On the two‐way interaction between homogeneous turbulence and dispersed solid particles. II. Particle dispersion

Part I of this paper [Elghobashi and Truesdell, Phys. Fluids A 5, 1790 (1993)] examined the modulation of turbulence by the particles. Here the effects of the two‐way interaction on particle dispersion are discussed. In zero gravity, the two‐way coupling enhances the alignment of the surrounding fluid velocity vector with the direction of the solid particle trajectory. This alignment reduces the mean‐square relative velocity and increases the Lagrangian velocity autocorrelation coefficient of the solid particle, the fluid point and the surrounding fluid, and the mean‐square displacement of the solid particles. However, the fluid point mean‐square displacement decreases because the larger inertia of the solid particles increases the decay rate of turbulence energy. In gravity environment, the particles augment the component of turbulence energy in the gravity direction, and thus increase the mean‐square displacement of the solid particles and fluid points in that direction. However, their dispersion in the.
FAST DUAL-BASED LINEARIZED BREGMAN ALGORITHM FOR COMPRESSIVE SENSING OF DIGITAL IMAGES

A central problem in compressive sensing is the recovery of a sparse signal using a relatively small number of linear measurements. The basis pursuit (BP) has been a successful formulation for this signal reconstruction problem. Among other things, linearized Bregman (LB) methods proposedrecentlyare foundeffectiveto solve BP. In this paper, we present a fast linearized Bregmanalgorithm applied to a dual formulationthat accelerates the conventionalLB iterations considerably. Performance of the proposed algorithm is evaluated and compared with the conventional LB algorithm in compressive sampling of 1-D sparse signals and digital images.
Social Media Addiction and Aggressive Behaviors in Early Adolescents: The Mediating Role of Nighttime Social Media Use and Sleep Quality

Social media addiction (SMA) is considered a risk factor for aggressive behaviors in adolescents, while its underlying mechanisms remains unclear. In this study, we investigated the possible mediating roles of nighttime social media use and sleep quality in the relationship between SMA and aggressive behaviors. A sample of 773 Italian secondary school students (49.9% female, age range = 11–15 years) completed a self-report questionnaire that included information on SMA, nighttime social media use, sleep quality, and aggressive behaviors. Results indicated that SMA was positively associated with aggressive behaviors. In addition, SMA was positively related to nighttime social media use and negatively related to sleep quality. Nighttime social media use was associated with a poorer sleep quality, which in turn associated with more aggressive behaviors. Analyses indicated that both nighttime social media use and sleep quality were mediators in the relationship between SMA and aggressive behaviors in adolescents.