Spatial variation in the community structure and response of benthic macroinvertebrates to multiple environmental factors in mountain rivers.

Exploring the response between benthic community changes and environmental variables has significance for restoring the health of river ecosystems. However, little is known of the impact on communities of interactions between multiple environmental factors, and frequent changes in the flow of mountain rivers are different from those in the flow of plain river networks, which also impact differently the benthic community. Thus, there is a need for research on the response of benthic communities to environmental changes in mountain rivers under flow regulation. In this study, we collected samples from the Jiangshan River in the dry season (November 2021) and the wet season (July 2022) to investigate the aquatic ecology and benthic macroinvertebrate communities in the watershed. Multi-dimension analyses were used to analyze the spatial variation in the community structure and response of benthic macroinvertebrates to multiple environmental factors. In addition, the explanatory power of the interaction between multiple factors on the spatial variation of communities, and the distribution characteristics of benthic community and their causes were investigated. The results showed that herbivores are the most abundant forms in the benthic community of mountain rivers. The structure of benthic community in Jiangshan River was significantly affected by water quality and substrate, whereas the overall community structure was affected by river flow conditions. Furthermore, nitrite nitrogen and ammonium nitrogen were the key environmental factors impacting the spatial heterogeneity of communities during the dry and wet season, respectively. Meanwhile, the interaction between these environmental factors showed a synergistic effect, enhancing the influence of these environmental factors on community structure. Thus, controlling urban and agricultural pollution and releasing ecological flow would be effective strategies to improve benthic biodiversity. Our study showed that using the interaction of environmental factors was a suitable way to evaluate the association between environmental variables and variation in benthic macroinvertebrate community structure in river ecosystems.

A multi-attribute decision-making-based site selection assessment algorithm for garbage disposal plant using interval q-rung orthopair fuzzy power Muirhead mean operator.

With the increase of the global population and the improvement of people's living standards, the output of garbage generated by human activities is also increasing day by day. Choosing an appropriate garbage disposal site is one of the key links for the harmless disposal of garbage. However, due to the uncertainty and complexity of socio-economic development and the limited cognitive ability of decision-makers, how to rationally select the garbage disposal site has become a challenging task. This study drew a new multi-attribute decision-making method based on interval q-rung orthopair fuzzy weighted power Muirhead mean (Iq-ROFPWMM) operator to evaluate site selection scheme of garbage disposal plant, and support for garbage disposal site selection. In this method, firstly, power average and Muirhead mean operators are integrated and introduced into the interval q-rung orthopair fuzzy environment to construct an Iq-ROFPWMM operator. Meanwhile, some properties of idempotence, boundedness and monotonicity for the Iq-ROFPWMM operator are analyzed. Then, a multi-attribute decision-making method using Iq-ROFPWMM operator is established. After that, a practical case on the evaluation of garbage disposal site selection scheme is given to verify the effectiveness of the proposed method. Further, parameter analysis and comparative analysis are applied to demonstrate the superiority of our method. The results show that this method boasts wider space for evaluation information representation, more flexible adaptation to the environment evaluation, and stronger robustness of the evaluation results. Finally, some conclusions of this study are drawn and the development direction is revealed.

The ensemble deep learning model for novel COVID-19 on CT images.

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

Under the different sectors: the relationship between low-carbon economic development, health and GDP.

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China's carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

A decision-making algorithm combining the aspect-based sentiment analysis and intuitionistic fuzzy-VIKOR for online hotel reservation.

In the process of hotel reservation on online traveling platforms, online reviews, as a fundamental source where the actual information of a product can be had access to, have been attached with high importance by customers when they have difficulty making a decision on which hotel to pick. However, with enormous amount of online reviews distributed in diverse online traveling platforms, customers tend to have few patience or time to manually read all these reviews and get the exact information they want. Inspired by the widespread application of aspect-based sentiment analysis in the field of data mining, a bidirectional long short-term memory (Bi-LSTM) and attention mechanism based model to predict multiple attributes of a product from online review texts is proposed. Experimental result shows that such Bi-LSTM with attention mechanism model apparently improves the accuracy of the prediction, compared with single LSTM model. Meanwhile, based on the output of the prediction, we analyze and transfer it into a statistical matrix. With an intuitionistic fuzzy compromise decision-making method VIKOR applied, an overall ranking, according to multiple product attributes can be made, in which way to help customers make decisions. To prove the rationality of the algorithm, online hotel reviews from three stream online travelling platforms are crawled as a case.

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment.

With the rapid outbreak of COVID-19, most people are facing antivirus mask shortages. Therefore, it is necessary to reasonably select antivirus masks and optimize the use of them for everyone. However, the uncertainty of the effects of COVID-19 and limits of human cognition add to the difficulty for decision makers to perfectly realize the purpose. To maximize the utility of the antivirus mask, we proposed a decision support algorithm based on the novel concept of the spherical normal fuzzy (SpNoF) set. In it, firstly, we analyzed the new score and accuracy function, improved operational rules, and their properties. Then, in line with these operations, we developed the SpNoF Bonferroni mean operator and the weighted Bonferroni mean operator, some properties of which are also examined. Furthermore, we established a multi-criteria decision-making method, based on the proposed operators, with SpNoF information. Finally, a numerical example on antivirus mask selection over the COVID-19 pandemic was given to verify the practicability of the proposed method, which the sensitive and comparative analysis was based on and was conducted to demonstrate the availability and superiority of our method.