Beihua University (simplified Chinese: 北华大学; traditional Chinese: 北華大學; pinyin: Běihuá Dàxué) is a state-owned public university in Jilin City, Jilin, China.Beihua University (BEIHUA) is a provincial comprehensive university with the most extensive scope in Jilin Province. Developing from 1906 and through the merger of three colleges in 1999, now BEIHUA has become a university with three campuses, which together occupy an area of 1,263,700 square meters with a floor space up to 830,300 square meters..
Background Two-dimensional porous carbon nanosheets are promising electrode materials for zinc-ion hybrid capacitors (ZIHC) due to large surface area, rapid ion transport kinetics and abundant exposed electroactive sites. Nevertheless, fabricating porous carbon nanosheets through a facile synthetic route remains a challenge. Method This study develops a template-free, one-step pyrolysis strategy utilizing a self-activation route with potassium L-aspartate as the precursor to synthesize porous carbon nanosheets. Findings The high-temperature pyrolysis process not only enables structural transformation from hollow polyhedra to porous nanosheets, but also realizes heteroatom self-doping and in-situ self-activation to generate porous frameworks. The optimized PCS-700 samples display a nanosheet-like architecture with a large specific surface area of 698.7 m2 g-1, abundant oxygen-containing functional groups, and trace nitrogen functional groups. The electrode delivers a specific capacitance of 339.4 F g-1 at 0.5 A g-1 and superior electrochemical stability. More importantly, the assembled PCS-700//ZnSO4(aq)//Zn ZIHC achieves a specific capacity of 151.3 mAh g-1 at 0.1 A g-1, a high energy density of 121 Wh kg-1 at 80 W kg-1, and 97.9% capacitance retention after 10,000 cycles. This method avoids multi-step operations, toxic reagents and complicated template removal procedures, thus offering a facile and scalable route to fabricate porous carbon nanosheets for ZIHCs.
Preserving lignin reactivity during fractionation remains a central challenge in lignocellulosic biorefining, because conventional acidic extraction systems often promote ether bond cleavage and undesired condensation, thereby diminishing the value of lignin for downstream catalytic upgrading. Herein, an alkali-assisted neutral deep eutectic solvent (DES) strategy was developed for the selective isolation of reactive lignin from pine. Neutral DESs composed of choline chloride with ethylene glycol or glycerol were combined with NaOH to extract lignin under comparatively mild conditions, and the resulting lignin fractions were subsequently subjected to oxidative depolymerization using H3PMo12O40 in a methanol/water medium. Increasing pretreatment severity enhanced lignin extraction but reduced the beta-O-4 content, whereas the glycerol-based DES afforded a higher lignin yield at the expense of structural preservation. Among the lignin samples obtained, T100t5EGL exhibited the highest beta-O-4 content (84.7%) and the highest molecular weight, and consequently showed the best performance in oxidative depolymerization. Under the optimized catalytic conditions of 0.1 g H3PMo12O40 per 0.1 g lignin, 160 degrees C, 3 h, and 1.0 MPa O2, the total aromatic monomer yield reached 11.74 wt%, with vanillin and methyl vanillate as the major products. The results suggest that alkali-assisted neutral DES fractionation better preserves native lignin ether linkages than more acidic fractionation environments, likely because lignin release proceeds with less extensive beta-O-4 scission and reduced condensation. This work provides a feasible strategy for coupling structure-preserving lignin extraction with downstream aromatic monomer production.
The stock market is highly volatile, making accurate price prediction a challenging task in financial time-series analysis. This study proposes a hybrid deep learning framework based on Wavelet Packet Transform (WPT) and a Dual-Stage Attention-based Recurrent Event Network (DSA-RE-NET) for stock price forecasting. Historical financial data are pre-processed using Hot Deck Imputation for missing values and Elliptic Envelope for outlier detection. Exploratory data analysis is performed to capture key statistical properties, including price variation, volume distribution, and volatility patterns. Multi-scale features are extracted using WPT to represent short-term and long-term price dynamics. The DSA-RE-NET model utilizes these features to learn temporal dependencies and event-driven patterns for prediction. The model predicts min–max normalized stock prices in the range [0,1], and all reported error metrics are computed on this normalized scale; selected metrics are additionally converted to approximate USD magnitudes for interpretability. Furthermore, the high R2 value of 0.997 is partly attributable to the strong autocorrelation inherent in stock price time series, where consecutive prices are naturally highly correlated, rather than solely reflecting the model’s predictive superiority. The model achieves relative MAE reductions of 33.6% and 25.5% over naïve and random-walk baselines respectively, with a directional accuracy of 73.5% ± 0.8%.
Background Schizophrenia (SCZ) is a highly heritability psychological disorder, however the exact etiology remains unclear, and lack of the reliable and effective biomarkers for diagnosis and treatments in the management of SCZ, thus exploring the novel biomarkers in SCZ may enhance the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Methods Based on differentially expressed genes (DEGs) and weighted gene co-expression network (WGCNA) analyses from five brain datasets, we screened SCZ-key genes and then developed a novel machine learning (ML) framework that incorporated 12 MLs and their 84 combinations to construct a consensus diagnostic signature. Meanwhile, we constructed the nomogram with aforementioned signatures to provide a quantitative clinical practice tool for predicting SCZ. Subsequently, we performed the consensus clustering and nonnegative matrix factorization (NMF) algorithms for clustering analysis in SCZ patients. On this basis, the regulation factors of diagnostic signature, enrichment patterns and immune infiltration analysis in SCZ, and protein level among SCZ subtypes were evaluated. Results We identified 53 SCZ-key genes by intersecting DEGs and module genes of WGCNA, then developed a consensus diagnostic signature using a 84-combination ML framework, and established a nomogram diagnosis model with aforementioned signature for clinical practice, demonstrating promising discriminative performance and potential clinical utility benefits in predicting SCZ. Moreover, consensus clustering analysis could divide SCZ patients into two distinct clusters, and two subgroups were distinguished using NMF algorithm with DEGs of two clusters. Furthermore, we observed distinct biological functions, immune cells and protein functions between subtypes. Finally, hub genes of subgroups, which were closely associated with SCZ. Conclusion Our study constructed a novel diagnostic signature and a nomogram, which all achieved higher accuracy and maybe as the potential diagnostic tools for SCZ. Meanwhile, SCZ subtypes showed distinct inflammation, immune and metabolic patterns, incorporating the subtypes into the 3PM framework will provide a unique opportunity for clinical intelligence and new management approaches.
The systemic inflammation response index (SIRI) is an emerging inflammation-immune indicator calculated from counts of neutrophils, monocytes, and lymphocytes. The potential prognostic value of SIRI in various tumors has been reported in several studies. However, updated and comprehensive evidence regarding its prognostic value in lung cancer (LC) remains insufficient. This study, through a systematic review and meta-analysis, intends to comprehensively analyze the relationship of SIRI with overall survival (OS) and progression-free survival (PFS) among individuals experiencing LC. Cochrane Library, Web of Science, Embase, and PubMed were systematically searched from the commencement of the databases to October 2025. Cohort studies reporting the relation of SIRI with OS or PFS were included. Hazard ratios (HRs) alongside their 95
