Research on Population Development Trend in Huizhou of China Forecast Based on Optimal Weighted Combination Method and Fractional Grey Model

In this paper, the optimal weighted combination model and fractional grey model are constructed. The coefficients of the optimal weighted combination model are determined by minimizing the sum of squares of resists of each model. On the other hand, the optimal conformable fractional order and dynamic background value coefficient are determined by the quantum inspired evolutionary algorithm (QIEA). Taking the resident population from 2008 to 2018 as the research object, the optimal weighted combination model and fractional grey model were used to study the estimated and predicted values. The results are compared and analyzed. The results show that the fractional grey model is better than the optimal weighted combination model in the estimation of the values. The optimal weighted combination model is better than the fractional grey model in predicting. Meanwhile, the fractional grey model is found to be very suitable for the data values that are large, and the changes between the data are relatively small. The research results expand the application of the fractional grey model and have important implications for the policy implementation activities of Huizhou government according to the population growth trend in Huizhou.

Noise prediction of chemical industry park based on multi-station Prophet and multivariate LSTM fitting model

With the gradual transformation of chemical industry park to digital and intelligent, various types of environmental data in the park are extremely rich. It has high application value to provide safe production environment by deeply mining environmental data law and providing data support for industrial safety and workers’ health in the park through prediction means. This paper takes the noise data of the chemical industry park as the main research object, and innovatively applies the 3σ principle to the zero-value processing of the noise data, and builds an LSTM model that integrates multivariate information based on the characteristics of the wind direction classification noise data combined with the wind speed and vehicle flow information. The Prophet model integrating multi-site noise information was adopted, and the Multi-PL model was constructed by fitting the above two models to predict the noise. This paper designs and implements a comparative experiment with Kalman filter, BP neural network, Prophet, LSTM, Prophet + LSTM weighted combination prediction model. R2 was used to evaluate the fitting effect of single model in Multi-PL, RMSE and MAE that were used to evaluate the prediction effect of Multi-PL on noise time series. The experimental results show that the RMSE and MAE of the data processed by the 3σ principle are reduced by 32.2% and 23.3% in the multi-station ordered Prophet method, respectively. Compared with the above comparison models, the Multi-PL model prediction method is more stable and accurate. Therefore, the Multi-PL method proposed in this paper can provide a new idea for noise prediction in digital chemical parks.

A Riemannian approach to predicting brain function from the structural connectome

Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

Targeting current species ranges and carbon stocks fails to conserve biodiversity in a changing climate: opportunities to support climate adaptation under 30x30

Protecting areas for climate adaptation will be essential to ensuring greater opportunity for species conservation well into the future. However, many proposals for protected areas expansion focus on our understanding of current spatial patterns, which may be ineffective surrogates for future needs. A science-driven call to address the biodiversity and climate crises by conserving at least 30% of lands and waters by 2030, 30x30, presents new opportunities to inform the siting of new protections globally and in the U.S. Here we identify climate refugia and corridors based on a weighted combination of currently available models; compare them to current biodiversity hotspots and carbon-rich areas to understand how 30x30 protections siting may be biased by data omission; and compare identified refugia and corridors to the Protected Areas Database to assess current levels of protection. Available data indicate that 20.5% and 27.5% of identified climate adaptation areas (refugia and/or corridor) coincides with current imperiled species hotspots and carbon-rich areas, respectively. With only 12.5% of climate refugia and corridors protected, a continued focus on current spatial patterns in species and carbon richness will not inherently conserve places critical for climate adaptation. However, there is ample opportunity for establishing future-minded protections: 52% of the contiguous U.S. falls into the top quartile of values for at least one class of climate refugia. Nearly 27% is already part of the protected areas network, but managed for multiple uses that may limit their ability to contribute to the goals of 30x30. Additionally, nearly two-thirds of nationally identified refugia coincide with ecoregion-specific refugia suggesting representation of nearly all ecoregions in national efforts focused on conserving climate refugia. Based on these results, we recommend that land planners and managers make more explicit policy priorities and strategic decisions for future-minded protections and climate adaptation.

Combining outlier analysis algorithms to identify new physics at the LHC

The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a β-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using super- symmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.

Spatial and Temporal Consistency of Brain Networks for different Multi-Echo fMRI Combination Methods

The application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to its superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine the fMRI time-series derived at different echo times to improve the data quality. Here we evaluated three multi-echo combination schemes, i.e. ‘optimal combination’ (T2*-weighted), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio (tCNR) weighted combination. For the first time, the effect of these multi-echo combinations on functional resting-state networks was assessed in the temporal and spatial domain, and compared to networks derived from the second echo (35 ms) functional images. Sixteen healthy volunteers were scanned during a 5 minutes resting-state fMRI session. After obtaining the networks, several temporal and spatial metrics were calculated for their time-series and spatial maps. Our results showed that, compared to the second echo network time-series, the Pearson correlation and root mean square error were the most consistent for the optimal combination time-series and the least with those derived from tSNR-weighted combination. The frequency analysis further suggested that the time-series from the tSNR-weighted combination method reduced hardware- and physiological-related artifacts as reflected by the reduced power for the associated frequencies in almost all networks. Moreover, the spatial stability and extent of the networks significantly increased after multi-echo combination, primarily for the optimal combination, followed by the tSNR-weighted combination. The performance of the tCNR-weighted combination lacked robustness and instead varied remarkedly between resting-state networks in both the temporal and spatial domain. The results highlight the benefits of multi-echo sequences on resting-state networks as well as the importance of adjusting the choice of multi-echo combination method to the research question and domain of interest.

A Hybrid Recommendation Approach for Viral Food Based on Online Reviews

Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user’s specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based recommendation approach in a weighted combination. Compared with the traditional recommendation approaches, it is found that the hybrid recommendation approach performs more accurately in identifying the sentiments of user evaluations, and takes into account the similarities between users and foods. We can conclude that the proposed hybrid recommendation approach combined with the sentimental value of food reviews provides novel insights into improving the existing recommendation system used by e-commerce platforms.