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Human swimming posture recognition is a key technology to improve training effect and reduce sports injury by analyzing and recognizing swimmer's movement posture. However, the existing technical means cannot accomplish the accurate recognition of human swimming posture in underwater environment with high standard. For this reason, the study takes the 3D convolutional neural network as the model basis, and introduces the global average pooling and batch normalization to optimize its network structure and data processing, respectively. Meanwhile, full pre-activation residual network and three-branch structure convolutional attention mechanism are added to improve the feature extraction and recognition. Finally, a novel human swimming posture recognition model is proposed. The outcomes revealed that this model had the highest recognition accuracy of 95%, the highest recall of 93.26% and the highest F1 value of 92.87%. The lowest pose recognition errors were up to 4.7%, 4.9%, 2.1% and 6.6% for freestyle, breaststroke, butterfly and backstroke, respectively. The shortest recognition time was 6.78 s for the freestyle item, which minimized the recognition time and reduced the recognition error compared with the same type of recognition model. The new model proposed by the research shows significant advantages in recognition accuracy and computational efficiency. It can provide more effective support for recognizing athletes' swimming posture for future swimming endeavors.

Polyethylene oxide (PEO) based electrolytes critically govern the security and energy density of solid-state batteries, but typically suffer from poor oxidation resistance at high voltages, which limits the energy density of batteries. Here, we report a Lewis-acid coordinated strategy to significantly improve the cyclic stability of 4.8 V-class PEO-based battery. The introduced Mg 2+ and Al 3+ with strong electron-withdrawing capability weaken the electron density of ether oxygen (EO) chains via chelation in the coordination structure, resulting in a locally limited interaction between the EO chains and the surface of cathodes at high state of charge. The batteries using Lewis-acid coordinated electrolytes and Ni-rich cathodes achieve high voltage stability of 4.8 V over 300 cycles. Further, the realization of industrial-scale electrolyte membranes, and Ah-level pouch cells over 586 Wh kg ‒1 with good cyclic stability, suggests the potential of our strategy in practical applications of all-solid-state batteries. This work reports a Lewis-acid coordinated strategy to improve stability of a 4.8 V-class PEO-based battery. The batteries using Lewis-acid coordinated electrolytes and Ni-rich cathodes achieve high voltage stability of 4.8 V over 300 cycles.

As a compulsory course, College English should not only help students master professional knowledge, but also promote the development of their comprehensive qualities, cultivating high-quality and innovative talents who can meet the needs of times. 21st century is an era of digitization and globalization, which places high demands not only on students’ English language skills but also their non-cognitive skills of communication, collaboration, critical thinking, and creativity as well as their digital literacy. Based on knowledge and aiming at improving students thinking mode and abilities step by step, high-order teaching emphasizes the facilitation of students’ critical thinking and creativity through a combination of teachers’ higher-order teaching and the students’ deep learning through various teaching methods. This article aims to investigate whether technology-supported high-order College English teaching can foster students’ 21st century skills gradually in one class. A teaching practice was conducted for freshmen major in journalism and the results confirm the positive role of technology-supported high-order college teaching in fostering students’ 21st skills.

Background Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies. Results Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation. Conclusion All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.

Blockchain technology (BT) allows market participants to keep track of digital transactions without central recordkeeping. The features of blockchain, including decentralization, persistency, and attack resistance, allow data security and privacy. Machine learning (ML) involves the analytical platform on a massive amount of data to provide precise decisions. Since data reliability, integration, and data security are crucial in machine learning, the emergence of blockchain technology and machine learning has become a unique, most disruptive, and trending research in the last few years, achieving comparable and precise performance. The combination of blockchain and machine learning (BT–ML) has been applied across different applications to assist decision-makers in retrieving valuable data insights while preserving privacy and integration. This paper summarizes the state-of-the-art research in combing BT and ML in e-commerce and other various applications, including healthcare, smart transportation, and the Internet of Things (IoT). The challenges and benefits of integrating machine learning and blockchain technologies are outlined in the paper. We also discuss the advantages and limitations of current algorithms in the BT–ML integration. This paper provides a roadmap for researchers to pave the way for current and future research directions in combing the BT and ML research areas.

Band degeneracy is effective in optimizing the power factors of thermoelectric (TE) materials by enhancing the Seebeck coefficients. In this study, we demonstrate this effect in model systems of layered oxyselenide family by the density functional theory (DFT) combined with semi-classical Boltzmann transport theory. TE transport performance of layered LaCuOSe and BiCuOSe are fully compared. The results show that due to the larger electrical conductivities caused by longer electron relaxation times, the n-type systems show better TE performance than p-type systems for both LaCuOSe and BiCuOSe. Besides, the conduction band degeneracy of LaCuOSe leads to a larger Seebeck coefficient and a higher optimal carrier concentration than n-type BiCuOSe, and thus a higher power factor. The optimal figure of merit (ZT) value of 1.46 for n-type LaCuOSe is 22% larger than that of 1.2 for n-type BiCuOSe. This study highlights the potential of wide band gap material LaCuOSe for highly efficient TE applications, and demonstrates that inducing band degeneracy by cations substitution is an effective way to enhance the TE performance of layered oxyselenides.

This paper selects the panel data of 13 cities in Beijing Tianjin Hebei region from 2008 to 2016, and uses the fixed effect model to study the relationship between environmental regulation, industrial structure upgrading and economic growth in Beijing Tianjin Hebei region. The results show that: strengthening environmental regulation can promote the upgrading of industrial structure in Beijing Tianjin Hebei region by reducing the emission of pollutants; the upgrading of industrial structure is conducive to promoting the economic development of Beijing Tianjin Hebei region.

A facile and economic colorimetric strategy was designed for ATP detection by rationally using urease, a pH-responsive molecule, and a metal-mediated switchable DNA probe. By utilizing metal ions as a modulator of urease activity, the concentration of ATP is translated into pH change, which can be readily visualized by naked eye. An unmodified single-stranded DNA probe was designed, which consists of a target binding sequence and two flanked cytosine (C)-rich sequences. This C-rich single-stranded DNA can form a hairpin structure triggered by Ag+ ions via C-Ag+-C base mismatch. Upon introduction of ATP, Ag+-coordinated hairpin DNA structure will be broken and release the included Ag+, thus inhibiting the activity of urease. Conversely, urease can hydrolyze urea and raise pH value of the solution, resulting in the color change of the sensing solution. The proposed assay allows determination of ATP as low as 1.6 nM and shows a satisfactory result in human serum. Because of simple operation and low cost of this method, we believe it has a potential in point-of-care (POC) testing in resource-limited areas.

An increasing number of studies have shown that the consumption of soybeans and soybeans products is beneficial to human health, and the biological activity of soy products may be attributed to the presence of Soy Isoflavones (SI) in soybeans. In the intestinal tracts of humans and animals, certain specific bacteria can metabolize soy isoflavones into equol. Equol has a similar chemical structure to endogenous estradiol in the human body, which can bind with estrogen receptors and exert weak estrogen effects. Therefore, equol plays an important role in the occurrence and development of a variety of hormone-dependent malignancies such as breast cancer and prostate cancer. Despite the numerous health benefits of equol for humans, only 30-50% of the population can metabolize soy isoflavones into equol, with individual variation in gut microbiota being the main reason. This article provides an overview of the relevant gut microbiota involved in the synthesis of equol and its anti-tumor effects in various types of cancer. It also summarizes the molecular mechanisms underlying its anti-tumor properties, aiming to provide a more reliable theoretical basis for the rational utilization of equol in the field of cancer treatment.

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.