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To date, the knowledge of benthic macroinvertebrates in the upper reaches of the Jinsha River remains quite limited compared to other rivers in China due to the difficulty of sampling and special geographical environment. In this study, benthic macroinvertebrates in the upper reaches of the Jinsha River were investigated to address the knowledge gap. The results showed that dipterans dominated the communities, followed by amphipods and EPT taxa. The density, biomass and biodiversity were at a low level. As a high sediment-loaded river, unlike the Yellow River and the Wei River, oligochaetes are not commonly found in the upper reaches of the Jinsha River, and no mollusks were collected. The results of db-RDA showed that some natural factors influenced the spatial distribution of benthic macroinvertebrates in both dry and wet seasons, but during the flood season (summer and autumn), nitrogen and phosphorus became important factors due to severe soil erosion and high sediment loads in the water. The sections between above Batang and below Batang have obviously different substrates, resulting in the significantly different species composition and functional feeding groups. Moreover, higher habitat diversity can lead to higher benthic macroinvertebrate diversity after comparing the substrate types of the two sections. We consider that the unstable habitat conditions in the upper reaches of the Jinsha River are not conducive to completing the entire life cycle of benthic macroinvertebrates within a localized small habitat, and the communities are likely influenced more by seasonal contributions from populations in tributaries. Considering the impact of the existing cascade hydropower stations in the middle and lower reaches, and the realistic and potential impacts expected from the planned cascade hydropower stations in the upper reaches, it is imperative to prioritize the conservation of the tributaries along both banks of the upper Jinsha River. This enhanced conservation effort aims to preserve the diversity and integrity of benthic macroinvertebrates, which is of great significance in providing reliable food sources for the rare cold-water fish species and ensuring the safety of the aquatic ecosystem in the region. [ABSTRACT FROM AUTHOR]

Reservoirs have experienced varied degrees of eutrophication in recent years as the effects of climate change and human activities on reservoir ecosystems have intensified. This study looked at the patterns of integrated nutrient status and total phosphorus (TP) concentration in 30 reservoirs in major reservoirs across Fujian Province. Significant connections were identified between TP concentration, trophic status, and total phytoplankton density. 88.9% of the reservoirs were at the middle trophic level by the nutritional status (EI). The average EI of the 25 reservoirs with water supply functions is 39.2, eight reservoirs in coastal areas having EI values more than 45.0. The EI of the Baisha Reservoir in Jiulong River Basin reached 50.3, indicating modest eutrophication. TP concentration levels in the 30 reservoirs of Fujian Province range from 0.01 to 0.08 mg·L−1, representing different functional categories. Algal bloom is more likely to occur in a reservoir with a high nutrient status value. The N/P mass ratios of the 30 reservoirs were all significantly greater than 7.0, with the minimum is 11.0 and the maximum is 136.0, which indicating that these reservoirs were typically phosphorus‐limited. [ABSTRACT FROM AUTHOR]

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method. [ABSTRACT FROM AUTHOR]