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The rapidly growing volume of spent lithium-ion batteries (LIBs) poses an urgent recycling challenge, as conventional pyro-/hydro-metallurgical methods remain inefficient and require harsh operating conditions. Herein, we develop an efficient, pilot-scale radiation-driven leaching strategy for processing kilogram quantities of spent ternary cathode material dispersed in aqueous citric acid. In minutes, ambient and controllable electron beam irradiation achieves near-complete (>99%) leaching of valuable transition metals and lithium. Mechanistic studies reveal that the hydrated electrons and citric acid-derived radicals generated jointly during water radiolysis drive rapid cathode amorphization by reducing high-valence metal centers and cleaving metal-oxygen bonds. Concurrently, direct irradiation induces lattice defects and oxygen vacancies, synergistically enhancing amorphization and strengthening radical-metal site coupling, thereby accelerating dissolution. Furthermore, the leachate is repurposed into effective cathode materials and oxygen evolution reaction catalysts, enabling an economically viable closed-loop recycling process. Energy efficiency and techno-economic analyses indicate that industrial-scale radiation technology offers a practical solution for scalable spent LIB recycling. The study presents a radiation-based method that rapidly recovers valuable metals from spent lithium-ion batteries under mild conditions and converts them into new battery materials and catalysts, supporting scalable closed-loop recycling.

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches.

Dual-use biotechnology faces the risks of availability, novel biological agents, knowledge, normative, and other dual-use risks. If left unchecked, these may destroy human living conditions and social order. Despite the benefits of dual-use technology, good governance is needed to mitigate its risks. The predicaments facing all governments in managing the dual-use risks of biotechnology deserve special attention. On the one hand, the information asymmetry risk of dual-use biotechnology prevents the traditional self-governance model in the field of biotechnology from playing its role. On the other hand, top-down public regulation often lags behind technological iteration due to the difficulty of predicting the human-made risks of dual-use biotechnology. Therefore, we argue that governance of the dual-use risks of biotechnology should avoid the traditional bottom-up or top-down modes. We suggest the governance for dual-use biotechnology could be improved if the four-stage experimentalist governance model is followed. The first stage is to achieve consensus on a broad governance framework with open-ended principles. The second stage is for countries to take action based on local conditions and the open-ended framework. The third stage is to establish a dynamic consultation mechanism for transnational information sharing and action review. The fourth and final stage is to evaluate and revise the global governance framework.

Acute myeloid leukemia (AML) is a blood cancer with high heterogeneity and stratified as M0–M7 subtypes in the French-American-British (FAB) diagnosis system. Improved diagnosis with leverage of key molecular inputs will assist precisive medicine. Through deep-analyzing the transcriptomic data and mutations of AML, we report that a modern clustering algorithm, t -distributed Stochastic Neighbor Embedding ( t -SNE), successfully demarcates M2, M3 and M5 territories while M4 bias to M5 and M0 & M1 bias to M2, consistent with the traditional FAB classification. Combining with mutation profiles, the results show that top recurrent AML mutations were unbiasedly allocated into M2 and M5 territories, indicating the t -SNE instructed transcriptomic stratification profoundly outperforms mutation profiling in the FAB system. Further functional data mining prioritizes several myeloid-specific genes as potential regulators of AML progression and treatment by Venetoclax, a BCL2 inhibitor. Among them two encode membrane proteins, LILRB4 and LRRC25 , which could be utilized as cell surface biomarkers for monocytic AML or for innovative immuno-therapy candidates in future. In summary, our deep functional data-mining analysis warrants several unappreciated immune signaling-encoding genes as novel diagnostic biomarkers and potential therapeutic targets. Key points t -SNE instructed transcriptomic stratification discriminates FAB subgroups of AML. Potential association of the prioritized genes with Venetoclax resistance at single-cell resolution.

COVID-19 vaccines are in short supply worldwide. China was among the first countries to pledge supplies of the COVID-19 vaccine as a global public product, and to date, the country has provided more than 600 million vaccines to more than 200 countries and regions with low COVID-19 vaccination rates. Understanding the public's attitude in China toward the global distribution of COVID-19 vaccines could inform global and national decisions, policies, and debates. The aim of this study was to determine the attitudes of adults living in China regarding the global allocation of COVID-19 vaccines developed in China and how these attitudes vary across provinces and by sociodemographic characteristics. We conducted a cross-sectional online survey among adults registered with the survey company KuRunData. The survey asked participants 31 questions about their attitudes regarding the global allocation of COVID-19 vaccines developed in China. We disaggregated responses by province and sociodemographic characteristics. All analyses used survey sampling weights. A total of 10,000 participants completed the questionnaire. Participants generally favored providing COVID-19 vaccines to foreign countries before fulfilling domestic needs (75.6%, 95% CI 74.6%-76.5%). Women (3778/4921, 76.8%; odds ratio 1.18, 95% CI 1.07-1.32; P=.002) and those living in rural areas (3123/4065, 76.8%; odds ratio 1.13, 95% CI 1.01-1.27; P=.03) were especially likely to hold this opinion. Most respondents preferred providing financial support through international platforms rather than directly offering support to individual countries (72.1%, 95% CI 71%-73.1%), while for vaccine products they preferred direct provision to relevant countries instead of via a delivery platform such as COVAX (77.3%, 95% CI 76.3%-78.2%). Among our survey sample, we found that adults are generally supportive of the international distribution of COVID-19 vaccines, which may encourage policy makers to support and implement the distribution of COVID-19 vaccines developed in China worldwide. Conducting similar surveys in other countries could help align policy makers' actions on COVID-19 vaccine distribution with the preferences of their constituencies.

Regime complex has been widely recognized in many governance issues, but the evolutionary dynamics in regime complex are largely overlooked. This article explores the evolutionary dynamics by conducting a case study on the regime complex for human genetic data, which has evolved as an alternation between stable and unstable periods, known as punctuated equilibrium. Given that existing theories fail to explain the evolutionary pattern, a multiactor analysis framework is set up with the core argument that the evolution of regime complex is shaped by interactions between governance issues outside the regime complex, and a combination of actor power and institutional logics within them. Besides its specific contribution to the literature on regime complex, this article has general implications for research in global governance.

Abstract Regime complex has been widely recognized in many governance issues, but the evolutionary dynamics in regime complex are largely overlooked. This article explores the evolutionary dynamics by conducting a case study on the regime complex for human genetic data, which has evolved as an alternation between stable and unstable periods, known as punctuated equilibrium. Given that existing theories fail to explain the evolutionary pattern, a multiactor analysis framework is set up with the core argument that the evolution of regime complex is shaped by interactions between governance issues outside the regime complex, and a combination of actor power and institutional logics within them. Besides its specific contribution to the literature on regime complex, this article has general implications for research in global governance.

The Paris Agreement design follows the Global Experimental Governance mode, which once achieved success in ozone protection. However, the implementation of the Paris Agreement encountered difficulties, as it inherited the traditional dichotomy country classification established at the 1992 Rio Summit. Still, over time, the capability and motivation in Annex I and non-Annex I countries developed so differently that incentive and constraint policies do not encourage more ambitious mitigation commitments using the previous classification. For this reason, according to a country’s capability and motivation, this research divided these countries into four categories: Leader, Reserve Force, Waverer, and Obscurity, and proposed a potential climate action roadmap for different types of countries to mobilize their internal forces by dynamically classifying a country’s character and to improve overall global climate governance.