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Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems. Unsorted retired batteries pose recycling challenges due to diverse cathodes. Here, the authors propose a privacy-preserving machine learning system that enables accurate sorting with minimal data, important for a sustainable battery recycling industry.

Background Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management. Methods We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF). Results A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients. Conclusions The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Physiologically Based Pharmacokinetic (PBPK) models can provide forecasts of the drug residues within the organism. Ractopamine (RAC) is a typical β-agonist. In this study, we developed a PBPK model for RAC in goats. The goal was to predict the distribution of the drug after multiple oral administrations. The preliminary PBPK model for RAC in goats performed well in predicting the drug’s distribution in most tissues. In our sensitivity analysis, we found that the parameter of Qclu (Blood Flow Volume through Lungs) had the greatest impact on the RAC concentrations in plasma, liver, and kidney and was the most sensitive parameter. Furthermore, our study aimed to assess the withdrawal time (WT) of RAC in different tissues after RAC long-term exposure in goats. We found that the WT of RAC in the kidney was the longest, lasting for 13  days. Overall, the insights gained from this study have important implications for optimizing drug administration in goats and ensuring appropriate withdrawal times to prevent any potential risks.

[This corrects the article DOI: 10.3389/fvets.2024.1399043.].

The frequent occurrence of extreme cold waves under climate change has attracted widespread attention. Based on the Japanese 55-year Reanalysis daily dataset from 1958 to 2021, we use a newly developed dynamic metric, the local finite-amplitude wave activity (LWA), to explore the precursory signals, outburst conditions, and key dynamic features of extreme cold waves over eastern China from the perspective of synoptic climatology. The statistical results show that approximately 40% of extreme cold waves have the following features. First, the formation of significant positive LWA anomalies over the Balkhash–Baikal region is an evident precursory signal, which is accompanied by significant cold surface air temperature anomalies that accumulate over mid- and high-latitude Eurasia. Second, the appearance of extreme positive LWA anomalies over the region east of Lake Baikal (ELB) is necessary for subsequent outbursts of extreme cold waves. These extreme positive LWA anomalies indicate the meridionally enhanced planetary trough over East Asia and advection of the accumulated cold air masses southeastward to eastern China. Third, the evident positive change in the LWA anomalies over the ELB is mainly attributable to the convergence of the zonal LWA flux due to the zonal wind in the eddy-free state and Stokes drift flux over the eastern area of the ELB and the convergence of the meridional eddy heat flux over the western area. This study demonstrates that the LWA could be used as a simple and feasible metric for monitoring and forecasting extreme cold waves.

Red mud is a highly alkaline industrial solid waste generated by the alumina industry. Its stacking and disposal not only occupy land resources, but also cause environmental pollution. It is of great significance to separate and purify target compounds from complex mixtures of red mud based on their resource utilization properties. Ferrous oxalate and ferric hydroxide were prepared by oriented conversion based on redox reaction and chemical precipitation in this research. The products prepared were characterized by XRD, SEM and IR under different conditions (iron powder, ascorbic acid and photocatalysis). The results show that the two-stage acid leaching process can improve the leaching rate of iron from red mud significantly, and the experimental of oriented conversion of ferrous oxalate and ferric hydroxide from red mud with reproducibility. The sample prepared with ascorbic acid as reducing agent has the best effect. The development of this experiment provides a new idea for the directional classification and purification of ferrous oxalate and iron hydroxide from red mud.

A lithium ion capacitor is a kind of novel energy storage device with the combined merits of a lithium ion battery and a supercapacitor. In order to obtain a design scheme for lithium ion capacitor with as much superior performance as possible, the key research direction is the ratio of battery materials and capacitor materials in lithium ion capacitor composite cathode materials. In this work, an improved electrochemical model of a lithium ion capacitor is proposed, and the simulated results obtained from the model were validated based on experiments, including under the premise of fixed electrode quality, under the current applied to the battery material keeps unchanged, and under the current applied to the capacitor material keeps unchanged. Results show that the improved model can simulate the electrode properties of lithium ion capacitor with high precision, and 0.3~0.4 is recommended as the best volume ratio for improving the specific energy of lithium ion capacitor.

The lithium-ion capacitor (LIC) is a new type of hybrid energy storage device, which combines the advantages of lithium-ion battery and electric double layer capacitor. To achieve efficient and reliable application of LIC in practical scenarios, accurate model and state estimation method are needed. In this work, the electrical behavior of LIC is studied, which is then described by the Thevenin model. A multi-innovation filter- (MIF-) based coestimation method is proposed, in which the multi-innovation linear Kalman filter (MI-LKF) is used for model parameter identification, the multi-innovation cubature Kalman filter (MI-CKF) is used for state of charge estimation, and the multi-innovation extended Kalman filter (MI-EKF) is used for state of health estimation. Compared to traditional methods, this method can significantly improve estimation accuracy by only expanding the innovation used to update the state from a single moment to multiple moments. The experimental results indicate that the estimation errors of SOC and SOH can be constrained within ±0.5%. In addition, the proposed method has good robustness and can achieve high-precision state estimation even in the face of noise interference, uncertain initial values of the algorithm, and uncertain starting operating points.

[Objectives] This study was conducted to analyze the effects of continuous application of biogas slurry for many years on soil ecosystem restoration of rare earth tailings by planting Pennisetum × sinese, in order to provide basis for scientific application of biogas slurry. [Methods] The fields with different years of continuous application of biogas slurry in Dingnan Rare Earth Tailings Ecological Restoration Demonstration Park were selected as the research object, and the differences in soil physical and chemical properties and microbial community structure after application of biogas slurry for different years (0, 3 and 5 years) were studied. [Results] The bulk density of soil with continuous application of biogas slurry showed a downward trend, while the maximum water holding capacity, capillary water holding capacity, porosity, aeration, pH, organic matter, nitrogen, phosphorus and potassium, alkali-hydrolyzable nitrogen and available phosphorus showed an upward trend. Moreover, the effects achieved by application for 5 years were better than those by application for 3 years. Continuous application of biogas slurry could significantly improve the activity of soil urease, acid phosphatase, sucrase and cellulase, and it effects increased with the application year increasing. Continuous application of biogas slurry could significantly improve the abundance of dominant bacteria in soil, and with the increase of application years, the abundances of dominant bacteria also increased. [Conclusions] Continuous application of biogas slurry effectively improved soil physical and chemical properties and soil fertility in rare earth tailings areas where Pennisetum x sinese was planted to restore rare earth tailings. This study provides a theoretical support for establishing key ecological restoration technoiques.

Polyurethane grouting technology is widely employed to maintain critical transportation infrastructure, including pavements, airports, and railways. After injection, foamed polyurethane bonds with surrounding aggregates to form a polyurethane–aggregate composite material (PACM). The gradation of aggregates in PACM, stress levels, and loading frequencies significantly influence fatigue performance under cyclic traffic loading. This study investigates the fatigue behavior of three distinct PACM gradation types through three-point bending fatigue tests under varying stress levels and loading frequencies. Results reveal that the finer gradations of PACM tend to exhibit higher flexural stiffness and longer fatigue life but also greater sensitivity to stress levels. Conversely, coarser gradations show lower stiffness but improved energy dissipation characteristics. Additionally, the flexural stiffness modulus, fatigue life, and cumulative dissipated energy decrease with increasing stress levels, while they grow with higher loading frequencies. In contrast, the dissipated angle follows an opposite trend. Additionally, mathematical models were developed to describe the evolution of dissipated energy, uncovering a three-stage pattern dominated by a prolonged plateau phase accounting for over 80% of the fatigue process. Based on this characteristic plateau, fatigue life prediction models were established for each gradation type, achieving high prediction accuracy with relative errors below 10%. These findings not only highlight the significant impact of aggregate gradation on PACM fatigue performance but also provide practical tools for optimizing material design in pavement maintenance.