Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term power load forecasting based on IWOA-GRU

This paper proposes a short-term load prediction model based on the improved whale optimization algorithm (IW0A) optimized gated recurrent neural network(GRU) to address the issue of strong unpredictability of electric load and low forecast accuracy. First, the whale population is initialized by S chaotic mapping to enhance the population diversity and improve the quality of the initial solution; second, a nonlinear convergence factor is proposed to balance the global and local search ability of the algorithm and improve the convergence speed in order to avoid the defects that the standard whale optimization algorithm is easy to fall into local optimum and slow convergence speed when solving the GRU parameter optimization problem. Finally, WOA is used to automatically determine the best parameters and create the IWOA-GRU load prediction model by optimizing the number of layer neurons, learning rate, and other factors. The results show that when compared to the prediction methods used by LSTM, GRU, PSO-GRU, RSO-GRU, and WOA-GRU, the proposed model may successfully increase convergence speed and prediction accuracy.
Pragmatic Distribution Based Routing Cluster to Improve Energy Efficient Cluster lifetime for Wireless Sensor Networks

— Energy consumed by the sensor nodes are more sporadic in a sensor networks. A skilled way to bring down energy consumption and extend maximum life-time of any sensor present can be of evenly and unevenly distributed random area networks. Cluster heads are more responsible for the links between the source and destination. Energy consumption are much compare to member nodes of the network. Re-clustering will take place if the connectivity in the distributed network failure occurs in between the cluster networks that will affects redundancy in the network efficiency. Hence, we propose pragmatic distribution based routing cluster lifetime using fitness function (PDBRC) prototype is better than the existing protocol using MATLAB 2021a simulation tool.
Hierarchical Bayesian Analysis of Changepoint Problems

SUMMARY A general approach to hierarchical Bayes changepoint models is presented. In particular, desired marginal posterior densities are obtained utilizing the Gibbs sampler, an iterative Monte Carlo method. This approach avoids sophisticated analytic and numerical high dimensional integration procedures. We include an application to changing regressions, changing Poisson processes and changing Markov chains. Within these contexts we handle several previously inaccessible problems.
Design of smart input devices

As the size of the chip is getting smaller and the processing speed is getting faster and faster, various smart products are also constantly being upgraded. Multimedia is widely used in teaching. Traditional laser pointers can no longer meet people's needs. Electronic stylus chips can enhance the interaction between human and computer. The smart electronic stylus uses a gyroscope and accelerometer to collect position and speed information, then transmits the information to the smart display device via Bluetooth.
Rapid relative pressure map computation from velocity-encoded phase-contrast measurements

Introduction Spatially resolved assessment of blood pressure dynamics is desirable for the evaluation of various cardiovascular disorders. For example, the local change of pressure across vascular or valvular stenoses can be used to characterize the severity of the constriction. For the heart chambers, systolic and diastolic efficiency is directly related to the dynamics of the respective filling pressures. Time-resolved velocity data acquired by velocity encoded phase-contrast MRI can be used to derive relative pressure [1,2]. Based on the Navier-Stokes equation relating a 3D velocity vector field to the pressure gradient vector field for an incompressible fluid, taking the pressure divergence and neglecting body forces, a pressure Poisson equation can be formulated [3]. Solving this Poisson equation allows to calculate the relative pressure field, avoiding the noise amplification and path dependence of directly integrating pressure gradients fields. However, the implementation of a solver for the pressure Poisson equation for arbitrarily shaped domains is not straight-forward and boundary conditions have to be chosen carefully. Recently, a multi-grid solver has been proposed for improved and accelerated relative pressure field computation [4]. In this paper, we present an alternate solver for the pressure Poisson equation using a direct generalized minimal residual procedure (GMRES) [5].
Error Estimates on a Stable and Reliable Numerical Scheme for a Fully Discrete Time-Dependent Allen-Cahn Equation

The stability analysis and error estimates are some of the we ll-known techniques carried out on a number of commonly used numerical schemes for Allen-Cahn equation. We exploit these techniques and design a reliable fully-discrete sche me consisting of coupling the Non-standard finite difference with the finit e element method. We show that the solution obtained from thi s scheme is stabled and attains its optimal rate of convergence in bot h theH1 andL2-norms. We further show that this scheme replicates the properties of the exact solution. Some numerical experimen ts are performed to support our theoretical analysis.
Hybrid iterate boosting and space portioning clustering based FCM algorithm

A hybrid Iterate boosting and space portioning clustering based FCM algorithm is proposed.Under the foundation of original portion set,the iterated optimize cluster hypothesis updates with the fraction of distribution weight voting,making use of the weak learning exoteric to remerge with the training set after every iterate process.Compared with the traditional simple repeat training clustering method,a disadvantage has been got over.It confirms that the algorithm has high quality of classification and entirely shape portioning effect in the shape classification experiment and quantity index contrast.
Research on vehicle-borne autonomous velocity measurement method based on sequence image matching

In order to solve the problems of low precision,dependence and low mobility for common vehicle velocity measurement system,a vehicle-borne autonomous velocity measurement model is designed.Therefore,the problem of vehicle velocity measurement is transformed into sequence image matching.Considering the constrained parameters of vehicle movement and the characteristics of sequence images,sequence image matching method is analyzed in four aspects of feature extraction,similarity measurement,searching space and searching strategy.Image matching algorithm is also designed.The simulation result shows that the matching method base on binary images and similarity measurement of mean absolute error function ensure computation complexity greatly reduced with a high computation efficiency.And the proposed matching algorithm exhibits a behavior with a high matching precision and speed.The result of vehicular test proves the effectiveness and reliability of the proposed method.
On a third order iterative method for solving polynomial operator equations

We present a semilocal convergence result for a Newton-type method applied to a polynomial operator equation of degree \(2\). The method consists in fact in evaluating the Jacobian at every two steps, and it has the \(r\)-convergence order at least \(3\). We apply the method in order to approximate the eigenpairs of matrices. We perform some numerical examples on some test matrices and compare the method with the Chebyshev method. The norming function we have proposed in a previous paper shows a better convergence of the iterates than the classical norming function for both the methods.
Investigation on the Mineral Catalytic Graphitization of Anthracite during Series High Temperature Treatment

Graphite can be artificially converted from anthracites under high temperatures; however, the exact mechanism through which inorganic minerals contribute to the graphitization process is still unknown. In light of this, several selected minerals in different amounts were added to demineralized anthracite coal. The anthracite–mineral mixtures were subjected to artificial graphitization experiments under temperatures ranging from 1700 to 2900 °C in the laboratory. The obtained series of coal-based graphites with various levels of graphitization were characterized by X-ray diffraction (XRD), and the derived structural parameters, such as d002 and FWHM (002), La, and Lc were used to compare the carbon structural evolution during the high temperature treatment and mineral catalytic graphitization. Moreover, the amorphous carbon of anthracite is eventually transformed into the highly ordered crystalline carbon of coal-based graphite. The five added minerals show interesting structural variation during the graphitization process, in which pyrite is decomposed into iron (Fe), illite, quartz, and kaolinite, which can react with disordered carbon in organic matter to form moissanite (SiC), while dolomite seems to react with sulfur to form oldhamite (CaS). At temperatures less than 2300 °C, the minerals could significantly enhance the catalytic effect. There is a clear difference in the catalytic effect of different minerals on graphitization. Kaolinite exhibits the strongest catalytic effect. The minerals dolomite, illite, and quartz only show a certain degree of catalysis. Pyrite, however, only has a limited effect on improving the degree of graphitization at a temperature of 1700 °C. However, once the temperature exceeds 2300 °C, the dominant factor controlling the graphitization of anthracite appears to be the temperature. According to the growth pattern at microcrystalline sizes (La and Lc), the minerals’ catalytic effects can be classified into three groups. The first group includes minerals that preferentially promote La growth, such as pyrite, illite, and quartz. The second group includes minerals that preferentially promote Lc growth, such as dolomite. Finally, kaolinite is in a separate group that promotes microcrystal growth in both the lateral and vertical directions simultaneously. The mechanisms of the minerals’ catalytic graphitization are discussed in this paper. The promotion role of minerals in the artificial graphitization process may help to optimize the graphitization process and reduce the process cost in the future.
Insight on Oxygen-Supplying Biomaterials Used to Enhance Cell Survival, Retention, and Engraftment for Tissue Repair

Oxygen is one of the essential requirements for cell survival, retention, and proliferation. The field of regenerative medicine and tissue engineering (TE) has realized considerable achievements for the regeneration of tissues. However, tissue regeneration still lacks the full functionality of solid organ implantations; limited cell survival and retention due to oxidative stress and hypoxia in the deeper parts of tissues remains a perpetual challenge. Especially prior to neovascularization, hypoxia is a major limiting factor, since oxygen delivery becomes crucial for cell survival throughout the tissue-engineered construct. Oxygen diffusion is generally limited in the range 100–200 μm of the thickness of a scaffold, and the cells located beyond this distance face oxygen deprivation, which ultimately leads to hypoxia. Furthermore, before achieving functional anastomosis, implanted tissues will be depleted of oxygen, resulting in hypoxia (<5% dissolved oxygen) followed by anoxic (<0.5% dissolved oxygen) microenvironments. Different types of approaches have been adopted to establish a sustained oxygen supply both in vitro and in vivo. In this review, we have summarized the recent developments in oxygen-generating and/or releasing biomaterials for enhancing cell survival in vitro, as well as for promoting soft and hard tissue repair, including skin, heart, nerve, pancreas, muscle, and bone tissues in vivo. In addition, redox-scavenging biomaterials and oxygenated scaffolds have also been highlighted. The surveyed results have shown significant promise in oxygen-producing biomaterials and oxygen carriers for enhancing cell functionality for regenerative medicine and TE applications. Taken together, this review provides a detailed overview of newer approaches and technologies for oxygen production, as well as their applications for bio-related disciplines.
Relation of Overweight/Obesity to Reward Region Response to Food Reward and the Moderating Effects of Parental History of Eating Pathology in Adolescent Females

Objective: To test whether overweight/obesity is associated with an elevated reward region response to milkshake cues and a low reward region response to milkshake receipt. To test whether the risk for eating pathology moderates the effects of weight status on the neural response to milkshake cues and milkshake receipt. Method: The current study used functional magnetic resonance imaging (fMRI) to examine the neuronal responses of female adolescents (n = 80; M age = 14.6 ± 0.9; M BMI = 21.9 ± 3.6; 41% with a biological parental history of eating pathology) during a food receipt paradigm. Results: Females with overweight/obesity showed a greater ventromedial prefrontal cortex (vmPFC), and ventral anterior cingulate (ACC) response to milkshake cues and a greater ventral striatum, subgenual ACC, and dorsomedial prefrontal cortex response to milkshake receipt than those with a healthy weight. Females with overweight/obesity plus a parental history of eating pathology showed a greater vmPFC/medial orbitofrontal cortex response to milkshake cues than those without a parental history of eating pathology and those with a healthy weight. Females with overweight/obesity and without a parental history of eating pathology showed a greater thalamus and striatum response to milkshake receipt. Conclusions: Overweight/obesity is associated with an elevated reward region response to palatable food cues and food receipt. A risk for eating pathology enhances the reward region response to food cues in those with excess weight.
The Effect of Internal Control on Funds Management by Privately Run Tahfiz Schools in Klang Valley

Mushrooming of privately run Tahfiz schools in Klang Valley in recent years has led to some schools being unable to sustain the school expenditures due to insufficient and inefficient funds management. There are instances whereby the contribution received from the public is being misused for the personal gain of the founder. The aim of this conceptual framework is to determine the relationship between internal control components with Thafiz School’s fund management. This study is to shed light on this aspect as limited prior research has discussed this issue . The results obtained are expected to facilitate the school’s management of fund management. Primary data is going to be collected using questionaries. Surveys are sent to selected privately run Thafiz schools in Klang Valley. Five components of internal control are going to be regressed against fund management using multiple regression analysis. The five components of internal control are control environment, risk assessments, control activities, monitoring, and information and communication. These components of internal control will be hypothesized against the fund management to determine the relationship. A positive relationship between the variables is expected. Future research is hoped to extend the survey to all privately run Thafiz Schools Malaysia-wide.
A Multiprocessor Ocean General Circulation Model Using Message Passing

Abstract Numerical models of the ocean are widely used to study the physics, chemistry, and biology of the ocean and its effect on climate. Unfortunately limits in available computer power often restrict the length of model runs and the amount of detail that can be included in the models. For this reason there is interest in developing a code that can be used either with clusters of workstations or with the new generation of array-processor computers. This paper reports on a new ocean general circulation model code that can be used on either a cluster of workstations or an array-processor computer. The model consists of one master control process and a number of slave oceanic processes, each of the latter being responsible for one subregion of the full ocean. The shapes of the subregions are variable allowing the computation and message passing load to be shared equally among processes. The code has also been designed so that message passing between processes is asynchronous. This allows the message passi.
Shortened first regrowth interval of grass silage as a harvesting strategy to improve nutrient supply for dairy cows: a case study

Dairy cows have a highly valuable ability to convert grass into milk. A modification of the normal three-cut harvesting strategy was evaluated consisting of shortened first grass regrowth period to increase the energy value of the silage crop over the whole growing season under Boreal conditions. Grass was ensiled from timothy-meadow fescue-red clover swards over two years at three consecutive harvests within the growing season. Diets based on the silages (D1, 1st cut; D2, 2nd cut and D3, 3rd cut) were fed to dairy cows in two milk production experiments using change-over designs and an average concentrate proportion of 0.41 on dry matter basis. Consistently high energy value in silages was achieved and despite minor differences in silage D-values, feed intake was highest for D1. The differences in energy-corrected milk yield between treatments were limited to an increase for D2 in Exp 2 so that feed energy conversion into milk was decreased with D1. A shortened first regrowth interval for grass silage harvest was a viable option, but forage area per animal and other farm specific factors should be considered when choosing the silage harvesting strategy.
The sustainable development principles in the Transport company management

Transport is the main source of environmental pollution. The ecology of transport determines the extent of the negative impact on nature and the environment. The authors present a block diagram of the interaction between the interests of public security and the economic entity. The mechanism of interaction between public and commercial interests of a transport enterprise is considered from the standpoint of payment for nature management. The article proposes a scheme for ensuring the quality and efficiency of the railway transport enterprise. The influence of external and internal factors on the quality and efficiency of the transport enterprise is described.
A New Method to Measure the Residual Flux by Magnetic Sensors and a Finite-Element Model

When the power transformer is connected to the power grid, a surge of inrush current may occur as a result of the residual flux. In order to mitigate this effect, the measurement of residual flux is necessary. In this article, a method for measuring the residual flux of ferromagnets was proposed, utilizing magnetic sensors and a finite-element model. Residual flux originated from residual magnetization, which could be obtained from a matrix of spatial magnetic flux density measured by sensors and the conversion matrix calculated from the finite-element model. By combining the residual magnetization with the background magnetic field, the residual flux could be determined. The experimental results confirmed the accuracy of the proposed method, which was particularly suited for measuring nonuniform residual flux, and also enabled the determination of residual flux direction. In comparison to existing methods, this approach offered significant advantages.
Suivi de points robuste aux spécularités et aux changements d'éclairage

Most feature point tracking approaches assume that objects are Lambertian and that no lighting change occurs. To some extent, the tracking method with photometric normalization and the approach proposed by Soatto et al. overcome these issues. From a study on specular reflection models, we explain explicitly the assumptions on which these techniques are implicitly based. Then, we propose an original tracking algorithm based on a more appropriate modeling of the illumination changes. Experimental results demonstrate the robustness of our approach against specular highlights and lighting changes.
The Relationship between Inclusive Education Policy with Teachers’ Attitude to Children with Special Needs at Inclusive School

The aim of this study is to know the relationship between teachers’ understanding to inclusive education policy with teachers’ attitude to children with special needs at inclusive school in Pati. According to the aim, this study used correlational quantitative approach. The subject of this study were 40 teachers of inclusive school in Pati. The sampling technique used was simple random sampling technique. The data collection technique used was likert model scale instrument. The analysis result indicates the hypothesis is accepted, it means that there is a significant relation between the understanding of inclusive education policy with the teachers’ attitude to children with special needs by value of 2,860 > 2,061 ( > ) and significant value of < 0,05 (0,007 < 0,05). Correlation coefficiency value (R) of this study is 0,421. It means that relationship betweem teachers’ understanding to inclusive education policy with teachers’ attitude to children with special needs in Pati has a fairly strong relationship. The precentage of the influence of contribution which has understanding of inclusive education policy to the teachers’ attitude to children with special needs is 17,7% which indicates by determination coefficient () of 0,177, meanwhile the rest of 82,3% is influenced by other not examined factors.
Intelligent Detection Method for Concrete Dam Surface Cracks Based on Two-Stage Transfer Learning

The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model.