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Abstract Finding a real thermoelectric (TE) material that excels in various aspects of TE performance, mechanical properties, TE power generation, and cooling is challenging for its commercialization. Herein, we report a novel multifunctional Ge0.78Cd0.06Pb0.1Sb0.06Te material with excellent TE performance and mechanical strength, which is utilized to construct candidate TE power generation and cooling devices near room temperature. Specifically, the effectiveness of band convergence, combined with optimized carrier concentration and electronic quality factor, distinctly boosts the Seebeck coefficient, thus greatly improving the power factor. Advanced electron microscopy observation indicates that complex multi‐scale hierarchical structures and strain field distributions lead to ultra‐low lattice thermal conductivity, and also effectively enhance mechanical properties. High ZT ~ 0.6 at 303 K, average ZTave ~ 1.18 from 303 to 553 K, and Vickers hardness of ~200 Hv in Ge0.78Cd0.06Pb0.1Sb0.06Te are obtained synchronously. Particularly, a 7‐pair TE cooling device with a maximum ΔT of ~45.9 K at Th = 328 K, and a conversion efficiency of ~5.2% at Th = 553 K is achieved in a single‐leg device. The present findings demonstrate a unique approach to developing superior multifunctional GeTe‐based alloys, opening up a promising avenue for commercial applications.

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

To explore the influence of raw materials from different cultivars on the quality of Sichuan dark tea, the raw materials of 4 tea cultivars were used in this study. The main chemicals and aroma components were analyzed by sensory evaluation, chemical analysis and gas chromato graphy-mass spectrometry (GC-MS) . The main components of Sichuan dark teas made from different tea cultivars were significantly different. The contents of water extract, tea polyphenols, catechins and caffeine of Sichuan dark teas from 'Zijuan' were the highest, which were 49.45%, 18.54%, 4.77%, and 4.21%, respectively, followed by Sichuan dark tea processed with 'Huangjinya' cultivar, all of them were significantly (P

“Golden-flower” Tibetan tea (GTT) is an innovative dark tea fermented via fungus Eurotium cristatum. To study GTT effects on alleviating the symptoms of type 1 diabetes mellitus (T1DM), GTT's extract (GTTE) was prepared. GTTE chemical compositions were analyzed via HPLC, pyrolysis-gas chromatography-mass (Py-GC-MS) spectrometry analysis, and chemistry analyses. GTTE effects on T1DM were explored on T1DM mice model induced by streptozotocin (STZ). GTTE was composed mainly of tea pigment theabrownin (TB) (49.18%), with high percentages of polysaccharide (16.93%), protein (10.15%), polyphenols (13.90%), amino acids (5.89%), caffeine (1.83%), and flavonoids (0.67%). Py-GC-MS results exhibited that GTTE constituted of phenols, lipids, sugars, and proteins. GTTE attenuated T1DM conditions of mice, relieved their liver and pancreatic injury, restored damaged islet cells, decreased oxidative stress by increasing superoxide dismutase (SOD) and catalase (CAT) levels, modulated cytokine expression leading to the decreasing pro-inflammatory cytokines TNF-α and IL-6, increased anti-inflammatory cytokines IL-4 to improve inflammatory responses, and optimized gut microbiota composition and structure based on high-throughput 16S rDNA sequencing, suggesting multi-channel anti-diabetes mechanisms.