Vermiform animals from the Lower Cambrian Chengjiang Lagerstätte

The exceptional preservation of the Lower Cambrian Chengjiang Lagerstätte provides a unique insight into the early evolution of vermiform animals. This study presents new vermiform taxa, describes their morphological features, hypothesizes possible modes of life and discusses phylogenetic relationships among early metazoan phyla. Morphological features are re-assessed in the Cambrian lobopodian Luolishania longicruris, and Miraluolishania haikouensis is considered to be its junior synonym. Evidence indicates that L. longicruris may have had a filter feeding lifestyle. Cladistic analysis suggests that Cambrian lobopodians are paraphyletic or even polyphyletic, and that L. longicruris with well developed sensory structures (‘antennae’, eyes and setae) and tagmosis (a distinct head and two trunk sections) may be an important representative of the stem lineage leading to arthropods. A new fossil priapulid Eximipriapulus globocaudatus is reported and described on the basis of specimens that reveal exquisite morphological details. Possible internal fertilization is suggested and a putative juvenile is described. Evidence indicates that the animal was an active burrower using a double-anchor strategy. Cladistic analysis resolves E. globocaudatus as one of the most derived Cambrian stem priapulids. The eyes of Hallucigenia fortis and Cardiodictyon catenulum are reported, along with a re-description of eyes from L. longicruris. Three visual units are found within the eyes of H. fortis and L. longicruris, suggesting that they resemble arthropod lateral visual organs and appear to represent the primitive visual systems of arthropods. Three new vermiform taxa, Acanthipos torquatus, Hamuscolex bosolveri, and Palaeomyzon discus are described. Comparison with extant taxa suggests that they may be stem group representatives of three separate phyla of extant parasitic worms. The oral disc of Palaeomyzon discus indicates a parasitic lifestyle. This study extends both the biodiversity and ecological diversity of known Early Cambrian ecosystems

Structural model of the back-propagation neural network [30].

Structural model of the back-propagation neural network [30].</p

Data used in the back-propagation long short-term memory model.

Data used in the back-propagation long short-term memory model.</p

BP-LSTM model training process.

Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.</div

Comparison of parameters of different model prediction levels.

Comparison of parameters of different model prediction levels.</p

Structure of the combined back-propagation long short-term memory model.

Structure of the combined back-propagation long short-term memory model.</p

S1 Data -

Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.</div

BP-LSTM model test set prediction results.

Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.</div

Comparative test on partitions B₁₀ and B₁₁.

<p>(A) Original state, (B) displacement result using proximity graphs with local grouping information, and (C) displacement result using proximity graphs without local grouping information.</p

Testing data sets and partitions.

<p>(A) Partitions of data set A, and (B) partitions of data set B.</p