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Resource flow supports the delivery of products and services and plays a vital role in the low-carbon urban distribution grid. Therefore, reasonable forecasting of the resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and oneclick forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact the forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines-longand short term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations. [ABSTRACT FROM AUTHOR]

Resource flow supports the delivery of products and services and plays a vital role in the low-carbon urban distribution grid. Therefore, reasonable forecasting of the resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and one-click forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact the forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines–long- and short term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations.

The rapid development of internet technology has brought convenience to people’s lives, increasingly making their work and personal activities inseparable from the internet. However, the various types of information data in the Internet are very large, and accurately obtaining useful information from them has become particularly important. Therefore, clustering analysis technology is widely applied in various fields. The elastic network algorithm is vulnerable to noise and other factors in clustering analysis, resulting in low accuracy of clustering results. To solve this problem, this study proposes an elastic network algorithm based on weighted properties for clustering analysis. After comparative experiments, the results showed that the accuracy of the proposed algorithm was 98%. The area under the precision recall curve was 0.86. The maximum F-value was 0.93. The area under the working characteristic curve of the subject was 0.94. The precision ultimately stabilized at 97%. All values were higher than the comparison algorithm. In the experiment, the running time of the algorithm was 11 seconds and the average error was 7%, both of which were lower than the comparison algorithm. When the algorithm was affected by Statistical dispersion and threshold parameters, the accuracy of the proposed algorithm was 71%, 69% and 67% respectively, which were better than the comparison algorithm. In conclusion, the proposed elastic network algorithm based on the weighted characteristics has high accuracy, strong stability and advantages in clustering.

Tactical communication networks has high dynamic, weak connection, low bandwidth, multi-link backup, SDN (software defined network) technology decouples from hardware and software through traditional network control devices, software the core control functions, get a global view through centralized control policies, so as to realize the flexible scheduling of resources and the efficient utilization of channel resources, promote the development of tactical communication networks more intelligent and flexible adaptive.The development status and advantages of software defined network were firstly introduced, the problems of traditional architecture of tactical communication networks were analyzed, including the application of SDN in the field of foreign military tactical communication.Then, the feasible idea of the application of SDN in the field of military communication was put forward, as well as the application scenarios were conceived.Finally, the feasibility of the application of SDN in military communication was summarized.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model. [ABSTRACT FROM AUTHOR]

The creep-recovery test was used to analyze the rheological properties of black bean-wheat flour blends in linear and nonlinear viscoelastic regions. After adding black bean powder to wheat flour, the water absorption rate and development time of the dough were both increased, and the stable time of the dough showed a trend of increasing first and then decreasing. Creep and recovery data fitted well with the 4-element Burgers model (R2>0.94). During the creep time of 3、5、10 and 15 min, the λc、μc、Jcm、and Jmax of the dough gradually increased, the Jro and the recovery rate gradually decreased. Jco、Jro、Jcm generally showed a decreasing trend. After the deformation, the recovery rate of the dough in the recovery time of 3、5、10 and 15 min was 63.51%～73.6%. Under the shear stress of 150、250、500 and 1 000 Pa, the Jco、Jcm and Jmax of the dough gradually increased, μc、Jro、λr and recovery rate dropped from 89.99% to 32.36%. When the amount of black bean flour added in wheat flour was 10%, the weakening of the dough was the smallest and the stable time was relatively longer. The Linear creep-response analysis results showed that the dough had reached steady shear during the creep time of 3 min, prolonging of the creep time affected the elastic recovery and recovery speed of the dough, and the elastic network of the dough was broken partially; the recovery rate of the dough was 73.6% at the recovery time of 10 min. The nonlinear creep-analysis results showed that the dough was sensitive to shear stress outside the linear viscoelastic region, but it still had typical viscoelastic properties,improved the shear stress, reduced the steady shear viscosity of the dough, and accelerated the delayed elastic recovery.

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.

Electric fuses are protection devices with a long history. They are widely used in both high voltage and low voltage power systems. The pre-arc I-t (current versus time) characteristic is an important parameter of electric fuse. The traditional method of obtaining this parameter is designing the electrical fuse by experience, then conducting experiments to obtain the I-t characteristic. However, customers request specific of I-t characters first, and then the engineers have to design electric fuses based on the requirements, this is an inverse design problem of electric fuses. There is no research in this area yet. The objective of this paper is to propose a new method to solve the inverse design problem of electrical fuse with machine learning. The method contains two steps. First, finite element analysis is used to obtain training samples for machine learning. A total of 252 samples (pre-arc current-time result) are obtained by simulations. Second, an elastic network is used to solve the probabilistic inference model by utilizing 217 samples from the total of 252. The other 35 samples are used to evaluate inverse design problem solution results. The results show that this method can accurately predict the design parameters of the restricted zone of fuse element, including the thickness (T1) and radius (R1) of restricted zone. The average relative errors value of T1 and R1 are 15.7% and 3.4%, respectively. Increasing the penalty factor and elastic error decreases the relative error. By changing the penalty error to 0.8, the average relative errors value of T1 and R1 are reduced by 14.1% and 1.7%, respectively. By changing the elastic error to 0.8, the average relative errors value of T1 and R1 are reduced by 13.1% and 2.1%, respectively. The research results provide a new solution for the electrical fuse design problem.

In this paper, we conduct quantitative research on the atmospheric environmental corrosion of aluminum alloy based on the atmospheric environmental factors. we apply the elastic network regression method to construct a regression model for corrosion rates based on the identified damage factors, which allows us to calculate the dependent variable as long as we know the main environmental factors in a specific year. Finally, we introduce a measure of atmospheric environment corrosion based on the established regression which can characterize the severity of corrosion with different transformations.

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops. [ABSTRACT FROM AUTHOR]

Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model.

Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis.Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we developed an iterative method based on disease similarity (IBDS). We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method.Results: We selected 39 diseases which are highly related to AD. They correspond 1,481 kinds of proteins. One hundred and eighty-four proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods.Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. Seven proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD.Availability and Implementation:https://github.com/zty2009/Identifying-Protein-Biomarkers-in-Blood-for-Alzheimer-s-Disease

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD. Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity = 100%, Specificity = 82.86%) in a three-year ahead prediction of MCI to AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion. [ABSTRACT FROM AUTHOR]

To ease the traffic pressure on the plain urban road net work and reduce t he traffic accident risk an annular radial pattern was applied, with elastic topology network and the topology theory as the reference, in determining the road network path at the same time, the aggregation coefficient of the main road was calculated to complete the study of urban road network on the basic form. On this basis, the actual planning capacity of urban road was calculated according to the construction demand of step-by-step network, and then the planning layout model of urban road network was designed by balancing supply and demand of layout. It was found that after the application of the layout model, the TPI value matching with the trunk road decreased to 0.15, and the traffic flow at the fixed junction per unit time also increased effectively. The results show that compared with the original road network layout model, this model alleviates the traffic congestion pressure of urban road network effectively. [ABSTRACT FROM AUTHOR]

Forage intake is one of the most important indicators of health and productivity of ruminant livestock, such as sheep. Forage intake estimation can also make an important contribution to the design and implementation of rotational grazing systems. In this paper, sheep forage intake estimation models were developed based on acoustic analysis from data gathered by an audio recorder mounted on a sheep. A data set with 114 pieces of audio was constructed by collecting ingestion sound of eight female sheep (2 years old with 50 ± 3 kg body mass). A Gaussian kernel-based support vector machine (SVM) classifier was trained to identify chewing sound segments from sheep ingestion audio. Seven explanatory variables were extracted from each chewing sound segment. These variables were used to establish single variable and multiple variable-based forage estimation models. Least squares regression and elastic network approach were employed to determine the coefficients of the single variable and multiple variable-based forage intake estimation models, respectively. Validation results showed that the best single variable and multiple variable-based model could explain 71.02% and 80.94% of forage intake changes, respectively. The average accuracy of the two best models was 86.13% and 89.32%, respectively. The results suggested that an automatic system to estimate the forage intake of sheep based on a wearable audio-recorder could be developed in the future. This could contribute to sheep health disorder identification, decisions on the number of sheep for a given grassland unit, and so on. The latter is the foundation of sheep rotational grazing. • Two types of models were established to estimate sheep forage intake. • Trained SVM classifier to identify chewing sound segment with an accuracy of 95.34%. • Model coefficients of correlated variables were determined by using elastic network. • The best model can explain 80.94% of the forage intake changes. [ABSTRACT FROM AUTHOR]

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

Ubiquitin, discovered less than 50 years ago, tags thousands of diseased proteins for destruction. It is small (only 76 amino acids), and is found unchanged in mammals, birds, fish and even worms. Key features of its functionality are identified here using critical point thermodynamic scaling theory. These include Fano interference between first- and second-order elements of correlated long-range globular surface shape transitions. Comparison with its closest relative, 76 amino acid Nedd8, shows that the latter lacks these features. A cracked elastic network model is proposed for the common target shared by many diseased proteins. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a xylem-limited bacterium that infects a wide variety of plants. Stationary phase survival protein E is classified as a nucleotidase, which is expressed when bacterial cells are in the stationary growth phase and subjected to environmental stresses. Here, we report four refined X-ray structures of this protein from X. fastidiosa in four different crystal forms in the presence and/or absence of the substrate 3′-AMP. In all chains, the conserved loop verified in family members assumes a closed conformation in either condition. Therefore, the enzymatic mechanism for the target protein might be different of its homologs. Two crystal forms exhibit two monomers whereas the other two show four monomers in the asymmetric unit. While the biological unit has been characterized as a tetramer, differences of their sizes and symmetry are remarkable. Four conformers identified by Small-Angle X-ray Scattering (SAXS) in a ligand-free solution are related to the low frequency normal modes of the crystallographic structures associated with rigid body-like protomer arrangements responsible for the longitudinal and symmetric adjustments between tetramers. When the substrate is present in solution, only two conformers are selected. The most prominent conformer for each case is associated to a normal mode able to elongate the protein by moving apart two dimers. To our knowledge, this work was the first investigation based on the normal modes that analyzed the quaternary structure variability for an enzyme of the SurE family followed by crystallography and SAXS validation. The combined results raise new directions to study allosteric features of XfSurE protein. [ABSTRACT FROM AUTHOR]

The present study compares fibroblasts extracted from intact and ruptured human anterior cruciate ligaments (ACL) for creation of a tissue engineered ACL-construct, made of porcine small intestinal submucosal extracellular matrix (SIS-ECM) seeded with these ACL cells. The comparison is based on histological, immunohistochemical and RT-PCR analyses. Differences were observed between cells in a ruptured ACL (rACL) and cells in an intact ACL (iACL), particularly with regard to the expression of integrin subunits and smooth muscle actin (SMA). Despite these differences in the cell source, both cell populations behaved similarly when seeded on an SIS-ECM scaffold, with similar cell morphology, connective tissue organization and composition, SMA and integrin expression. This study shows the usefulness of naturally occurring scaffolds such as SIS-ECM for the study of cell behaviour in vitro, and illustrates the possibility to use autologous cells extracted from ruptured ACL biopsies as a source for tissue engineered ACL constructs.

We present results on spatially localized oscillations in some inhomogeneous nonlinear lattices of Fermi–Pasta–Ulam (FPU) type derived from phenomenological nonlinear elastic network models proposed to study localized protein vibrations. The main feature of the FPU lattices we consider is that the number of interacting neighbors varies from site to site, and we see numerically that this spatial inhomogeneity leads to spatially localized normal modes in the linearized problem. This property is seen in 1-D models, and in a 3-D model with a geometry obtained from protein data. The spectral analysis of these examples suggests some non-resonance assumptions that we use to show the existence of invariant subspaces of spatially localized solutions in quartic Birkhoff normal forms of the FPU systems. The invariant subspaces have an additional symmetry and this fact allows us to compute periodic orbits of the quartic normal form in a relatively simple way. [ABSTRACT FROM AUTHOR]

Diffusion in colloidal suspensions can be very slow due to the cage effect, which confines each particle within a short radius on one hand, and involves large-scale cooperative motions on the other. In search of insight into this cooperativity, here the authors develop a formalism to calculate the displacement correlation in colloidal systems, mainly in the two-dimensional (2D) case. To clarify the idea for it, studies are reviewed on cooperativity among the particles in the one-dimensional (1D) case, i.e. the single-file diffusion (SFD). As an improvement over the celebrated formula by Alexander and Pincus on the mean-square displacement (MSD) in SFD, it is shown that the displacement correlation in SFD can be calculated from Lagrangian correlation of the particle interval in the one-dimensional case, and also that the formula can be extended to higher dimensions. The improved formula becomes exact for large systems. By combining the formula with a nonlinear theory for correlation, a correction to the asymptotic law for the MSD in SFD is obtained. In the 2D case, the linear theory gives description of vortical cooperative motion. [ABSTRACT FROM AUTHOR]