Time-consuming Problem Research Articles

Experimental screening and molecular dynamics simulation study on wetting agent in dry suspension

Suspension is the common preparation whose absorption rate is second only to the solution. However, problems like the difficulty in screening excipients and the complicated screening process limit its application to a certain extent. In this study, trimethoprim (TMP) was used as the research object. Compound sulfachlorpyridazine sodium Powder(CSSP) containing TMP was used as the research vector to explore the effects of wetting agent(WA), mixing processes between excipients and drug on the preparation stability from the perspectives of the change of the surface free energy between the drug powder and solvent, the reduction of the particle size of the drug, and the interaction between the drug and the excipients, serving the contact angle, sedimentation volume ratio, re-dispersibility, particle size, and appearance as indicators. In addition to that, taking the number density distribution, mean square displacement (MSD), and van der Waals force (VDW) as indicators, the molecular dynamics (MD) simulation technique was employed to explore the differences in the wetting ability of wetting agents (WAs) and the wetting mechanism of WAs on drug molecules. Suitable indicators were screened to provide new ideas for the initial screening of wetting agents from the simulation level and other solutions in tedious and time-consuming problems of experimental excipients screening.

Two-Phase Adaptive Kriging Model Based Importance Sampling Method for Estimating Time-Dependent Failure Probability

To improve the computational efficiency of the time-dependent failure probability (TDFP) to system with dynamic loads, materials degeneration and other time-varying factors, a two-phase adaptive Kriging model-based importance sampling (IS) method is proposed. The main contribution of this article is that a single Kriging model is established and adaptively refined to gradually approach the optimal IS probability density function (PDF) and estimate TDFP in two sequential phases. In the first phase, a generally approximate IS PDF is constructed by Kriging model based on its prediction characteristic and subsequently updated until convergence. Then, a simple rejection sampling technique is used to robustly generate IS samples for the estimation of TDFP. In the second phase, current Kriging model is subsequently refined for accurately identifying the classification of the IS samples generated in the last phase. Finally, the TDFP can be estimated using the products obtained in the above two phases. As the IS PDF established by Kriging model is used for directly approximating the optimal one, the proposed method has the capacity to deal with the complicated problem containing multiple failure regions. Additionally, since IS procedure can dramatically improve the sampling efficiency, the proposed method is an effective technique to the time-consuming TDFP analysis problems.

Automatic identification of transformer excitation inrush based on second-order Taylor coefficient

In order to solve the time-consuming problem of the existing automatic identification methods of transformer inrush current, an automatic identification method of transformer inrush current based on second-order Taylor coefficient was designed. Ignore the leakage resistance and winding resistance of transformer winding, get the second harmonic characteristic quantity, assume the transformer variable ratio value is fixed, identify the transformer winding parameters, introduce the decay coefficient to describe the characteristic of excitation inrush current gradually decaying with time, construct the excitation inrush current Taylor expansion phase model, and set the automatic identification mode based on the second order Taylor coefficient. Experimental results: The designed transformer excitation inrush current automatic identification method and the other two transformer excitation inrush current automatic identification method of the elapsed time were: 55.32ms, 71.52ms, 71.04ms, indicating that after the integration of the second-order Taylor coefficient, the designed transformer excitation inrush current automatic identification method better performance.

Small extracellular vesicles purification and scale-up.

Exosomes are small extracellular vesicles (sEVs) secreted by cells. With advances in the study of sEVs, they have shown great potential in the diagnosis and treatment of disease. However, sEV therapy usually requires a certain dose and purity of sEVs to achieve the therapeutic effect, but the existing sEV purification technology exists in the form of low yield, low purity, time-consuming, complex operation and many other problems, which greatly limits the application of sEVs. Therefore, how to obtain high-purity and high-quality sEVs quickly and efficiently, and make them realize large-scale production is a major problem in current sEV research. This paper discusses how to improve the purity and yield of sEVs from the whole production process of sEVs, including the upstream cell line selection and cell culture process, to the downstream isolation and purification, quality testing and the final storage technology.

Fatigue Reliability Assessment for Orthotropic Steel Decks: Considering Multicrack Coupling Effects

Multiple fatigue cracks are generally present in practical engineering due to the existence of welding; the size and number of cracks of orthotropic steel bridge decks are greatly uncertain. The component failure conditions caused by these cracks may have correlations. Currently, it is still a challenging issue to develop a physical model of multiple fatigue crack propagation in bridge decks and perform a fatigue reliability assessment, which is also the motivation that drives the innovation of this study. A fatigue reliability evaluation method is presented for orthotropic steel bridge decks, considering the coupling effect of multiple cracks and the randomness of vehicle loading. A numerical simulation method for multifatigue crack growth is developed by combining the ABAQUS and FRANC3D programs. The equivalent crack depth under different spacing and depths of collinear cracks is calculated by using numerical simulation and the multicrack equivalent characterization method. The critical damage accumulation function of multiple fatigue cracks is established using linear elastic fracture mechanics. Subsequently, the critical damage accumulation function of multiple fatigue cracks is established based on linear elastic fracture mechanics. In order to solve the time-consuming problem of traditional Monte Carlo method, the iHL-RF method and AK-MCS method are developed for fatigue reliability analysis. The results show that compared with the single-crack model, the fatigue reliability of orthotropic steel deck will be crucially reduced considering the coupling effect of double cracks. The MCS, iHL-RF and AK-MCS methods can effectively solve the fatigue reliability analysis problem. Compared with the MCS method, the reliability calculation time based on AK-MCS method is significantly reduced. The AK-MCS method-based method reduces the time for calculating the reliability of orthotropic steel decks by 50% compared with the iHL-RF method. The reliability analysis of orthotropic steel deck bridge based on AK-MCS method is proved to be efficient and accurate.

Recognition of mulberry leaf diseases based on multi-scale residual network fusion SENet.

Silkworms are insects with important economic value, and mulberry leaves are the food of silkworms. The quality and quantity of mulberry leaves have a direct impact on cocooning. Mulberry leaves are often infected with various diseases during the growth process. Because of the subjectivity and time-consuming problems in artificial identification of mulberry leaf diseases. In this work, a multi-scale residual network fusion Squeeze-and-Excitation Networks (SENet) is proposed for mulberry leaf disease recognition. The mulberry leaf disease dataset was expanded by performing operations such as brightness enhancement, contrast enhancement, level flipping and adding Gaussian noise. Multi-scale convolution was used instead of the traditional single-scale convolution, allowing the network to be widened to obtain more feature information and avoiding the overfitting phenomenon caused by the network piling up too deep. SENet was introduced into the residual network to enhance the extraction of key feature information of the model, thus improving the recognition accuracy of the model. The experimental results showed that the method proposed in this paper can effectively improve the recognition performance of the model. The recognition accuracy reached 98.72%. The recall and F1 score were 98.73% and 98.72% respectively. Compared with some other models, this model has better recognition effect and can provide technical reference for intelligent mulberry leaf disease detection.

Modern Trends in Multi-Agent Systems

The term “multi-agent system” is generally understood as an interconnected set of independent entities that can effectively solve complex and time-consuming problems exceeding the individual abilities of common problem solvers [...]

MMPL: Multi-Objective Multi-Party Learning via Diverse Steps

Multi-party learning is a specific framework of distributed learning which is widely exploited in the medical system and mobile data analysis. By installing a central server, individual devices update the model parameters instead of sharing sensitive data. This method can protect data privacy with the highest measures. But during the communication round, it is difficult to balance the model performance and the computational costs. In this article, we propose a novel framework for multiparty learning, named Multi-objective Multi-Party Learning via Diverse Steps (MMPL). We regard multi-party learning as a multi-objective problem and employ evolutionary optimization for analysis. Within the design of our framework, we try to use the neural network as a bridge to connect evolutionary optimization with multi-party learning. During the study, we propose a novel space-searching strategy for complex encoding problems. An individual wised value is installed on each private device for a differentiated treatment. Experimental results between our method and the comparative algorithms show that the proposed algorithm can achieve better performance while partly ameliorating the time-consuming problem.

Multiobjective Optimization of Composite Material Seat Plate for Mortar Based on the Hybrid Surrogate Model

As an important force transmission component of mortars, the seat plate affects some core indicators of mortars such as range, shooting accuracy, and maneuverability. In order to withstand huge impact loads, the seat plate was previously made of metal, which accounts for approximately 30%–45% of the total weight of the gun. The drawbacks of the heavy weight of the seat plate, which are not conducive to transportation and transfer, run counter to the current direction of the mortar’s lightweight development. The application of composite materials can greatly reduce the weight of the seat plate, but it exacerbates the contradiction between the mobility and combat effectiveness of mortars. In order to achieve the best match between mortar stability and maneuverability, a multiobjective optimization of composite material layers for seat plates is proposed, utilizing the designability of composite material layers. First, a fiber continuity model based on dropout sequence is adopted to solve the problems existing in the design of inherent continuity classes for composite layered fibers. Second, a hybrid surrogate model that considers the composite material seat plate quality, structural strength, shooting stability, shooting accuracy, and various working conditions is considered. Then, in order to improve the optimization efficiency and robustness of the algorithm, a multiobjective optimization algorithm based on the Chebyshev combination pattern is used to solve the mixed surrogate model. Finally, the optimization results are comprehensively evaluated against the optimization objectives. Research has shown that the method proposed in this article can effectively solve the time-consuming problem of multiobjective optimization, improve the accuracy of hybrid surrogate models, and meet the expected requirements of multiobjective optimization of composite material seat plates. While ensuring shooting stability, the weight of the seat plate is reduced by 18.43% compared to the metal seat plate, which has important application value for lightweight design of mortars.

Selective ensemble method for anomaly detection based on parallel learning

Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction.

Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) often takes prolonged saturation duration (Ts) and relaxation delay (Td) to reach the steady state, and yet the insufficiently long Ts and Td in actual experiments may underestimate the CEST measurement. In this study, we aimed to develop a deep learning-based model for quasi-steady-state (QUASS) prediction from non-steady-state CEST acquired in experiments, therefore overcoming the limitation of the CEST effect which needs prolonged saturation time to reach a steady state. To support network training, a multi-pool Bloch-McConnell equation was designed to derive wide-ranging simulated Z-spectra, so as to solve the problem of time and labor consumption in manual annotation work. Following this, we formulated a hybrid architecture of long short-term memory (LSTM)-Attention to improve the predictive ability. The multilayer perceptron, recurrent neural network, LSTM, gated recurrent unit, BiLSTM, and LSTM-Attention were included in comparative experiments of QUASS CEST prediction, and the best performance was obtained by the proposed LSTM-Attention model. In terms of the linear regression analysis, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE), the results of LSTM-Attention demonstrate that the coefficient of determination in the linear regression analysis was at least R2 = 0.9748 for six different representative frequency offsets, the mean values of prediction accuracies in terms of SSIM, PSNR and MSE were 0.9991, 49.6714, and 1.68 × 10-4 for all frequency offsets. It was concluded that the LSTM-Attention model enabled high-quality QUASS CEST prediction.

Positioning by floors based on WiFi fingerprint

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms.

Home Care Nursing: Factors Related to Burden of Patient Encounters Perceived by Home Care Nurses

Due to demographic changes and structural developments in the healthcare system, an increasing number of patients receive home care nursing in Denmark. Little is known about the complexity of home care. The aim of this paper was to investigate the challenges that home care nurses experience in their clinical practice by studying factors related to home care nurses’ experiences of encounters with patients as burdensome. A population-based survey was undertaken involving all nurses in homecare services in one municipality in Denmark. The participating nurses registered all home care contacts by the patients during the daytime for 1 week. A registration form was used including a series of items designed to identify factors related to the perceived burden of each visit. Statistical frequency analyses were used to examine factors related to the home care nurses’ experiences of encounters with patients as burdensome, and to investigate the challenges that home care nurses meet in their clinical practice. Home care nurses estimated 7.6% of 941 representative homecare visits as burdensome. Assessment of burden was associated with patients being below 60 years of age, time consumption and unexpected problems during the visit, especially of a psychosocial nature. Even though <10% of the encounters were considered burdensome, there is reason to believe that an increase can be expected due to ongoing emphasis on achieving greater efficiency in the healthcare system.

Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales.

Modeling of Proton Exchange Membrane Fuel Cell Stack and Start Strategy Optimization with a Multi-Objective Genetic Algorithm

This paper proposes a non-dominated ranking genetic algorithm (NSGA-II) method for fast optimizing the loading strategy at the start of a proton exchange membrane fuel cell (PEMFC) stack to optimize the dynamic response capability, power density and system efficiency. First, a Simulink model of the PEMFC stack including anode module, cathode module, water transfer module and output voltage module is established as the base model for optimization, and the accuracy of the stack model is verified through experiments. The power consumption of the air compressor is the main parasitic power considered, and it is modelled using the air compressor map by taking a table look-up method directly. The three performance indicators of dynamic response capability, power density and system efficiency are then optimized simultaneously based on NSGA-II. The results show that the optimized start-up loading strategy results in a PEMFC stack that outperforms the base model in all three indicators, demonstrating the success of the method in solving time-consuming multiple optimization problems. This study presents an effective and rapid approach for the multi-objective optimization of PEMFC stack, which is of guidance for engineering practice.

Analysis of the effect of music therapy on psychological anxiety relief based on artificial intelligence recognition

Abstract In order to improve the accuracy and reliability of EEG emotion recognition and avoid the problems of poor decomposition effect and long time consumption caused by manual parameter selection, this paper constructs an EEG emotion recognition model based on optimized variational modal decomposition. Aiming at the modal aliasing problem existing in traditional decomposition methods, the KH algorithm is used to search for the optimal penalty factor and the number of decomposition layers of the VMD, and KH-VMD decomposition is performed on the EEG signals in the DEAP dataset. The time-domain, frequency-domain, and nonlinear features of IMFs under different time windows are extracted, respectively, and the Catboost classifier completes the construction of the EEG emotion recognition model and emotion classification. Considering the two conditions of the complexity of the network structure of the KH-VMD model and the average classification accuracy of different brain regions in different music environments, the WEE features of the target EEG can constitute the optimal classification network by taking the WEE features of the target EEG as the input of the KH-VMD classification model. At this time, the average classification accuracy that can be obtained with differentiated brain regions and differentiated music environments is 0.8314 and 0.8204. After 8 weeks of music therapy, the experimental group’s low anxiety scores of pleasure and arousal on the Negative Picture SAM scale were 3.11 and 3.2, which were significantly lower than those of the control group’s low-anxiety subjects. The experimental group with high anxiety had anxiety scores and sleep quality scores that were 5.23 and 3.01 points lower than before the intervention. Therefore, music therapy can effectively alleviate psychological anxiety and enhance sleep quality.

An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images

With the increasing global focus on renewable energy, distributed rooftop photovoltaics (PVs) are gradually becoming an important form of energy generation. Effective monitoring of rooftop PV information can obtain their spatial distribution and installed capacity, which is the basis used by management departments to formulate regulatory policies. Due to the time-consuming and labor-intensive problems involved in manual monitoring, remote-sensing-based monitoring methods are getting more attention. Currently, remote-sensing-based distributed rooftop PV monitoring methods are mainly used as household rooftop PVs, and most of them use aerial or satellite images with a resolution higher than 0.3 m; there is no research on industrial and commercial rooftop PVs. This study focuses on the distributed industrial and commercial rooftop PV information extraction method based on the Gaofen-7 satellite with a resolution of 0.65 m. First, the distributed industrial and commercial rooftop PV dataset based on Gaofen-7 satellite and the optimized public PV datasets were constructed. Second, an advanced MANet model was proposed. Compared to MANet, the proposed model removed the downsample operation in the first stage of the encoder and added an auxiliary branch containing the Atrous Spatial Pyramid Pooling (ASPP) module in the decoder. Comparative experiments were conducted between the advanced MANet and state-of-the-art semantic segmentation models. In the Gaofen-7 satellite PV dataset, the Intersection over Union (IoU) of the advanced MANet in the test set was improved by 13.5%, 8.96%, 2.67%, 0.63%, and 0.75% over Deeplabv3+, U2net-lite, U2net-full, Unet, and MANet. In order to further verify the performance of the proposed model, experiments were conducted on optimized public PV datasets. The IoU was improved by 3.18%, 3.78%, 3.29%, 4.98%, and 0.42%, demonstrating that it outperformed the other models.

A Least Squares Fitting Method for Uncertain Parameter Estimation in Solidification Model

This study proposes an automated method for estimating the uncertain parameters of the solidification model in response to the inefficient and time-consuming problem of manually estimating multiple uncertain parameters of the solidification model. The method establishes an uncertain parameter estimation model based on the relationship between the simulated images equiaxed crystal ratio and the uncertain parameters of the solidification model, fits the parameters of the model by the least squares method, and finally estimates the uncertain parameters in the solidification model using the parameters of the fitted model. In comparison with the traditional method of calculating uncertain parameters manually through empirical formulas, this method reduces the difficulty of tuning parameters and solves the problem of tuning multiple parameters simultaneously in the nonlinear solidification model. The experimental results show that the proposed method can accurately estimate the uncertain parameters of the solidification model, improve the efficiency and accuracy of the solidification model estimation parameters, and play a guiding role in simulating the solidification process of continuously casting billet to control the solidification structure.

Temperature mapping model of cables considering the coupling of electromagnetic and thermal field

Background During cable operation, its internal temperature reflects the actual working condition of the cable. Once overload occurs, its conductor temperature will rise rapidly. Under high temperature conditions, the insulation material is very prone to breakdown accidents, which seriously threatens the safety of the power system. The traditional method of calculating the internal temperature of cables suffers from low accuracy and time-consuming problems. Methods To quickly and accurately calculate the cable’s internal temperature, a cable temperature mapping model is proposed with the coupling of electromagnetic and thermal field taken into consideration. Firstly, a finite element model is formulated based on the cable structure and material parameters. Secondly, the coupling between electromagnetic and thermal field is analyzed, and multiple coupling calculations are performed iteratively according to the operating conditions. Finally, the mapping between temperature and current is established using the exponential function. The cable surface temperatures under five operating conditions are measured online and compared with the calculated results of the temperature mapping model. Results Under five operating conditions, the average absolute error of the proposed model is 0.628 °C, and the average calculation time is 0.005 s. The average absolute error of the traditional model is 0.635 °C, and the average calculation time is 201.86 s. Conclusions The temperature mapping model developed in this paper can quickly and accurately calculate the cable’s internal temperature and forms an important part of the digital twin model of the cable.

Methods, algorithms and programs of computer algebra in problems of registration and analysis of random point structures

An original approach to solving difficult time-consuming problems of registration and analysis of random point images is described. The approach is based on the development and application of high-performance specialized computer algebra systems. Three software packages have been created specifically for carrying out equivalent analytical transformations on a computer. The first software system is designed to calculate formulas describing the volumes of convex polyhedra with parametrically specified boundaries in n-dimensional space. The second system is based on the calculation of multidimensional integral expressions by the method of cyclic differentiation of the integral with respect to the parameter. The third system is based on the accelerated implementation of complex combinatorial-recursive transformations on a computer. Another distinctive feature of the work is the extension of the classical Catalan numbers to the multidimensional case (they were required to solve a number of intermediate probabilistic-combinatorial problems). The implementation of the above computer algebra software systems on a multi-core cluster of Novosibirsk State University, together with the direct use of the explicit form of generalized Catalan numbers, allowed the authors to obtain several new previously unknown probabilistic formulas and dependencies required for solving problems in the field of analysis of random point images.