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According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been applied widely in mineralized-anomaly identification. However, they easily ignore spatial coupling information of multi-source ore-indicating factors have lower generalization abilities caused by excessive feature redundancies, and rely on known non-mineralization samples when they are utilized in mineralized-anomaly identification. This paper utilizes a convolutional sparse autoencoder network for realizing mineralized-anomaly identification. The proposed method retains the extraction of spatial coupling correlations of geological and geochemical variables through the addition of convolutional operations, learning the relationship between mineralized-features and the locations of mineralization, and being conducive to analyzing results with geological structures. The establishment of sparse terms of using ReLU activation functions and adding sparsity constraints into the objective loss function improves the generalization ability of the whole network through suppression of several feature units to reduce redundant features. Moreover, the isolated forest is employed as an autonomous extractor of background multichannel image samples, overcoming the limitation of traditional autoencoder networks that rely on labeled non-mineralization samples. The integration of convolutional sparse autoencoder network and isolated forest can accurately predict more known mineral deposits (68.96%) in smaller prospective areas (16.77%) in the Fengxian District, Shaanxi Province, China. The obtained mineralized-anomaly map reveals that most of the known mineralization is distributed in the delineated areas of larger reconstruction errors, demonstrating that this approach can effectively identify mineralized-anomalies without relying on prior knowledge. [ABSTRACT FROM AUTHOR]

This paper examines the impact of home country institutions on corporate social responsibility (CSR) of multinational enterprises from emerging markets (EM-MNEs). Taking the Belt and Road Initiative (BRI) as an example and using a sample with 2052 firm-year observations from China over the period 2008–2018, we find that the BRI exerts a positive and significant effect on overall CSR of Chinese MNEs involved in the BRI and the positive effect is stronger for Chinese state-owned MNEs. In addition, only two dimensions of CSR (Employee Relations and Products) are improved significantly under the pressure of BRI. Finally, we examine the interactive effect of home- and host- country institutions on Chinese MNEs' CSR and find the positive impact of the BRI on MNEs' CSR performance is stronger in host countries with a higher level of CSR related institutional pressure. These results provide practical suggestions for the Chinese government and MNEs to further improve CSR under the BRI, and enrich our understanding of the interactive effect between home- and host-country institutions on enhancing Chinese MNEs' reputation and promoting regional cooperation with countries along the BRI. [ABSTRACT FROM AUTHOR]

The convolutional neural networks used widely in mineral prospectivity prediction usually perform mixed feature extraction for multichannel inputs. This results in redundant features and impacts further improvement of predictive performance. To solve this limitation, this paper utilized convolutional autoencoder networks to mine latent high-level features for each predictor variable in parallel to prevent the mixing of features and to enhance the feature mapping outcomes of each channel. Convolutional autoencoder networks, as unsupervised learning methods, can handle input images with high-dimensional features. They can train samples without differences such that the reconstructed outputs can restore inputs as accurately as possible to reduce the extraction of irrelevant feature information. Moreover, convolutional autoencoder networks focus on finding the fewest features for representing all inputs, and the extracted features express internal spatial relations at a high level. It is helpful to improve the performance of metallogenic prediction. Hence, this paper arranged these obtained features into one-dimensional vectors to establish the inputs of classifiers. Through modeling with four classifiers (logistic regression, support vector machine, artificial neural network, and random forest), we achieved different models for mineral prospectivity prediction. According to the comprehensive evaluations, the random forest model outperformed the other models. Taking the prediction of gold deposits in the Fengxian region of Southern Qinling in China as an example, the predictive capability of the proposed integrated method was shown to be effective and reliable. The predicted high-potential areas can provide significant guidance for gold deposit exploration in the study area. [ABSTRACT FROM AUTHOR]

Bark is an essential component of tree branches, yet its role in controlling branch-leached dissolved organic matter (DOM) characteristics remains unknown in forests. Here, we collected branches (about 1.5 cm in diameter) of two evergreen coniferous trees, two deciduous broadleaf trees, and three evergreen broadleaf trees from a subtropical forest in southern China, and subsequently used a bark removal experiment to determine the effects of bark on branch-derived DOM quantity and bioavailability. Regardless of tree type, the presence of bark reduced tree branch-leached dissolved organic carbon (DOC), dissolved total nitrogen (DTN), and dissolved total phosphorus (DTP) productions. Moreover, DOC, DTN, and DTP productions leached from the branches containing bark were always much lower than the expected values summed from barks and the branches without bark. The presence of bark increased DOM aromaticity in the broadleaf tree branch leachates but reduced DOM aromaticity in the coniferous tree branch leachates. During 42 days of incubation, the presence of bark decreased broadleaf tree branch-leached DOM bioavailability and the relative increments of aromaticity, whereas the opposite trends were observed for the coniferous tree branch-leached DOM. Tree branch-derived DOM bioavailability correlated negatively with SUVA254 values, but exhibited no relationship with either DOC:DTN ratio or DOC:DTP ratio. These observations highlight that tree bark can prevent DOM leaching from branches and regulate branch-leached DOM bioavailability via its effect on DOM aromaticity in subtropical forests. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural networks applied in mineral prospectivity mapping usually extract features from only one scale at each iteration, resulting in plain features. To combat this limitation, this study utilized a convolutional neural network based on GoogLeNet to predict prospectivity for gold deposits in the Fengxian study area, China. The GoogLeNet adopted four groups of convolution kernels to extract and integrate features from multiple scales, obtaining abundant and comprehensive features related to mineralization. According to a multi-source geoinformation analysis, we selected 11 exploration criteria, including three geological factors (NW-trending brittle-ductile faults, NE-trending brittle faults, and anticline axes) and eight geochemical exploration data layers (Au, Ag, As, Hg, Pb, Zn, Cu, and Sb). Then, we created predictor samples to train the models to mine evidential features. Following to a comprehensive analysis, we formed a fusion model of GoogLeNet for mineral prospectivity modeling. The results demonstrated that the fusion model achieved an optimized predictive accuracy of 93.1% and an area under curve of 0.968. This fusion model outperformed the other models with superior success rate and prediction area rate performances, capturing 72% of the known gold deposits in just 27.3% of the research area. The results indicate the effectiveness of GoogLeNet in mineral prospectivity mapping. Finally, we classified the Fengxian district into three areas according to their different mineral prospectivity. The high-prospectivity areas provide significant implications for further exploration of gold deposits in the study area. [ABSTRACT FROM AUTHOR]

China's Mars exploration mission has stimulated tremendous interest in planetary science exploration recently. To propose potential scientific research projects, this study presents a concept simulation for the measurement of Martian atmospheric winds using the Doppler Michelson interferometry technique. The simulation is based on the satellite instrument initially designed for the Dynamic Atmosphere Mars Observer (DYNAMO) project to measure vertical profiles of winds from the 1.27 μm airglow observations in the Martian atmosphere. A comprehensive DYNAMO measurement simulation forward model based on an orbit submodel, an atmospheric background field submodel, and an instrument submodel is developed using the Michelson equation. The simulated interferogram signal over the field of view (FOV) calculated by the forward model is associated with the filter transmittance function, column emission rate of airglow, wind velocity, temperature, and the Michelson phase. The agreement between the derived atmospheric signals from the simulated interferogram without altitude inversion and the input parameters used to initiate the forward model confirms the validity of the forward model. [ABSTRACT FROM AUTHOR]

More than ten species of the genus Sinocyclocheilus (Cyprinidae: Barbinae) have been found to exhibit striking variation related to cave-dwelling traits. Until now, the molecular phylogeny and evolutionary history of this genus have remained unclear. To understand the molecular phylogeny and biogeographic evolution of Sinocyclocheilus, we analyzed the complete mitogenomes of 12 Sinocyclocheilus species based on Bayesian Inference (BI) and Maximum Likelihood (ML) analyses. The results of phylogenetic analyses strongly suggest that the genus Sinocyclocheilus is a monophyletic group, and S. jii is the most basal species. Molecular clock estimates showed that the diversification of this genus occurred in the late Miocene (about 6.49 Ma), which was associated with the intensive uplift of the Tibetan Plateau. Selective pressure analyses showed the dN/dS ratios of all Sinocyclocheilus species were < 1, indicating that this genus suffered strong purifying selection on its mitochondrial function, and then adapted to the contrasting environments. [ABSTRACT FROM AUTHOR]

Many studies have indicated that structural strain will be significantly influenced by temperature variations, and a good understanding of the effect of temperature on structural strain is essential. A structural health monitoring system has been installed in a typical Tibetan timber building to measure the structural strains and ambient temperature since 2012. This paper presents the correlation between temperature and strain data from the monitored structure. A method combining singular spectrum analysis and polynomial regression is proposed for modeling the temperature induced strains in the structure. Singular spectrum analysis is applied to smooth the temperature data, and the correlation between the resulting temperature time series and the measured strains is obtained by polynomial regression. Parameters of the singular spectrum analysis and the regression model are selected to have the least regression error. Results show that the proposed method has both good reproduction and prediction capabilities for temperature induced strains, and that the method is accurate and effective for eliminating the effect of temperature from the measured strain. A standardized Novelty Index based on the residual strain is also used for the condition assessment of the structure. [ABSTRACT FROM AUTHOR]