Beijing Founder Electronics (China)

This study investigated the aroma characteristics of three grades of raw tea leaves and their corresponding jasmine tea products from Guangxi, China. Aromatic profiles of jasmine tea varieties were analysed using two-dimensional gas chromatography-olfactory-mass spectrometry (GC×GC-O-MS), stir bar sorptive extraction (SBSE), and descriptive sensory evaluation. Chemometric methods were applied to compare sensory scores with instrumental data. Volatile compound concentrations and relative odour activity values (r-OAVs) were calculated. The results indicated significant differences in base tea leaf quality: high-grade tea leaf G1 exhibited pure, sweet characteristics, serving as an excellent aroma-absorbing carrier. The scenting process significantly imparted jasmine fragrance to the finished product, although its efficacy was constrained by tea leaf grade. GH1 finished tea exhibited a fresh, vibrant, and rich aroma with a sweet, mellow fragrance and high floral integration. In contrast, GH3, due to its inferior base material quality, yielded a weak aroma after scenting with limited quality improvement. The initial quality of the tea base is the fundamental determinant of the upper limit of the finished jasmine tea’s sensory quality, while the scenting process is the core means of shaping its signature floral aroma. The combination of high-quality tea leaves and precise scenting techniques is essential for developing the fresh, vibrant, and rich flavour profile of premium jasmine tea. This study reveals that the flavour formation of jasmine tea originates from the foundational quality of the tea leaves, providing a theoretical basis for monitoring the aroma quality of jasmine tea produced from different grades of tea leaves.

Umami taste in lager beer not only determined body fullness and the backbone of aftertaste, but also affected the controllability and interpretability of flavor expression across the entire brewing process. Based on stage-wise sampling, peptidomic profiles were established on wort fermentation day 0, day 1, day 3, and day 9. A total of 25,592 peptides were identified by reversed-phase liquid chromatography-quadrupole time-of-flight mass spectrometry (RPLC-QTOF-MS). Molecular informatics screening was performed using UMPred-FRL (a feature representation learning-based meta-predictor for umami peptides) and TastePeptides-Meta (a one-stop platform for taste peptides and prediction models), yielding 7255 potential umami peptides. From these, 145 peptides were further selected for molecular docking. In addition, 6 representative umami peptides were selected for receptor-level validation and structural analysis. Mechanistically, the umami receptor taste receptor type 1 member 1/taste receptor type 1 member 3 (T1R1/T1R3) belonged to class C G protein-coupled receptor (GPCR) and relied on the extracellular Venus flytrap (VFT) domain for ligand capture. Ligand-induced VFT conformational convergence transmitted changes to the transmembrane region and triggered signal transduction. Docking and energy decomposition indicated that the ionic group primarily contributed to orientation and anchoring. Salt-bridge or hydrogen-bond networks were formed around Lys228, Arg240, Glu206, Asp210, Asn141, and Gln138, thereby reducing conformational freedom. Meanwhile, hydrophobic side chains obtained major binding gains within a hydrophobic microenvironment formed by Val135, Ile137, Leu165, Tyr166, Trp78, and His79. These results reflected a synergistic mode in which charge pairing enabled positioning and hydro-phobic complementarity promoted VFT closure. To experimentally confirm sensory relevance, 6 representative peptides were individually spiked into 4 brewing-stage beer samples, which produced a clear stratification pattern across stages. Notably, peptides with favorable docking-derived binding propensity did not necessarily enhance umami perception, and several longer peptides showed persistent negative sensory shifts, supporting that binding affinity alone could not be treated as a proxy for perceived umami in the beer matrix. At the node level, the cumulative abundance of umami peptides showed a significant positive correlation with umami scores, with a Pearson correlation coefficient of r = 0.963 and p = 0.037. This result indicated good linear consistency between umami peptide content and the upward shift in umami taste in lager beer. Umami peptide clusters were further proposed as a more appropriate functional unit, and an umami peptide cluster database spanning the full process was constructed. This database provided a reusable resource for process control and flavor prediction.

ABSTRACT In the node classification task on graphs, mainstream graph neural networks often adopt the same neighborhood aggregation strategy for all nodes. This approach results in biased learning toward features from the majority nodes while devaluing those from the minority nodes under class imbalance. In particular, for marginal nodes, limited neighboring information can severely impact classification performance. To address this challenge, this paper proposes a feature fusion‐based hybrid quantum‐classical graph residual neural network (QGRNN). Leveraging the nonlinear expressive capacity of qubits in modeling complex feature interactions, the model innovatively integrates a structure‐driven node selection mechanism with a quantum feature enhancement module, while also dynamically fusing classical features and Hamiltonian expectation values through a gated residual fusion mechanism to compensate for representational deficiencies of marginal nodes overlooked by classical methods. Experimental results show that QGRNN consistently outperforms baselines across a range of node classification tasks. In binary and ternary classification settings, it exhibits strong discriminative capability and robustness, especially maintaining high accuracy under severe class imbalance. In addition, QGRNN also demonstrates strong generality in other tasks, in recommendation scenarios, achieving an average improvement of 36.3% on NDCG@5, 44.3% on Recall@5, and 21.9% on Recall@10 compared to the baseline.

Selective 137Cs+ sequestration is challenging due to its high solubility, environmental mobility, and the influence of excessive competitive ions. Herein, we demonstrate an effective strategy for Cs+ separation by constructing zeolite-type metal sulfide as "cesium ion sieve" (CIS), obtaining GaGeS-1 with "ion-sieving effect". Its structure features a zeolite-type metal sulfide framework with sodalite (SOD) topology based on supertetrahedral T2 clusters. GaGeS-1 with radiation resistance possesses a maximum Cs+ adsorption capacity of 332.52mg/g and ultrafast adsorption kinetics with removal rate (RCs) of 97.06% within 1.5 min. It achieves highly selective Cs+ capture under excessive competing ions, even for actual industrial 137Cs⁺-liquid-waste (RCs > 90%). Single crystal structure analysis and density functional theory calculation reveal that "ion-sieving" originates from strong Cs+···S2- interaction, flexible-robust framework, and optimal Cs+ coordination in SOD cage. Furthermore, GaGeS-1 is integrated with gelatin to fabricate GaGeS-1/Gel nanofibrous electrospun membranes for a multi-stage filtration system, enabling efficient, continuous, and recyclable treatment of mixed Na/Cs wastewater (3.58 L/m2, RCs = 99.39%) and Cs+-contaminated river water (2.63 L/m2, RCs = 99.23%). This study pioneers an efficient "CIS" by developing zeolite-type metal sulfide, and provides a universal assembly method toward practical application, opening new insight into radiocesium remediation and advanced nuclear waste management.

The intergranular corrosion (IGC) and pitting corrosion behavior of selective laser melted (SLMed) 316L stainless steel (316L SS) after sensitization were systematically investigated and compared with its wrought counterpart. The results reveal that SLMed 316L SS exhibits inferior IGC resistance compared with the wrought material due to its lower proportion of Σ3 coincident-site lattice (CSL) boundaries. However, it shows significantly higher pitting corrosion resistance, which is attributed to the refinement of non-metallic inclusions. Following sensitization treatment, both materials experienced a significant reduction in pitting resistance alongside an increase in metastable pitting events. The underlying degradation mechanisms are elucidated through the analysis of preferential initiation sites and passive film composition evolution.

This paper presents an operational amplifier(opamp) with an improved recursive folding cascode structure featuring a regulated resistor (RIRFC) at the input stage, a floating current source controlled Class AB structure at the output stage, and Miller capacitors and zeroing resistors bridging the input and output stages. Additionally, chopping units are embedded in both the input and output stages. The cooperative operation of these modules enables the circuit to achieve high gain, wide bandwidth, high stability, and low noise performance. Simulation results based on a $0.18 \mu \mathrm{~m}$ CMOS process show that the operational amplifier has a gain of up to 128.4 dB at low frequencies, a gain-bandwidth product of $\mathbf{4 0. 2 5 ~ M H z}$, a slew rate of $11.62 \mathrm{MV} / \mathrm{s}$, an equivalent input noise of $11.94 \mathrm{nV} / \sqrt{\mathrm{Hz}}$ at 100 Hz, as well as a high power supply rejection ratio and good common-mode rejection ratio.