Production of Benzoic Acid through Catalytic Transformation of Renewable Lignocellulosic Biomass

In the present work, we reported a novel route for the conversion of lignocellulosic biomass (sawdust) to a high-value chemical of benzoic acid under atmospheric pressure. The trans- formation involved the catalytic pyrolysis of sawdust into aromatics, the decomposition of heavier alkylaromatics to toluene, and the liquid-phase oxidation of toluene-rich aromatics to benzoic acid. The production of the desired benzoic acid from the sawdust-derived aro- matics, with the benzoic acid selectivity of 85.1 C-mol% and nearly complete conversion of toluene, was achieved using the MnO₂/NHPI catalyst at 100 ℃ for 5 h. The in uence of adding methanol on the catalytic conversion of sawdust to the core intermediate of toluene was also investigated in detail.     

The Cauchy problem for a Bardina–Oldroyd model to the incompressible viscoelastic flow

In this paper, we study the Cauchy problem for a regularized viscoelastic fluid model in space dimension two, the Bardina–Oldroyd model, which is inspired by the simplified Bardina model for the turbulent flows of fluids, introduced by Cao et al. (2006). In particular, we obtain the local existence of smooth solutions to this model via the contraction mapping principle. Furthermore, we prove the global existence of smooth solutions to this system.     

Gas-Mediated Tumor Energy Remodeling for Sensitizing Mild Photothermal Therapy

The metabolic reprogramming of tumors requires high levels of adenosine triphosphate (ATP) to maintain therapeutic resistance, posing a major challenge for photothermal therapy (PTT). Although raising the temperature helps in tumor ablation, it frequently leads to severe side effects. Therefore, improving the therapeutic response and promoting healing are critical considerations in the development of PTT. Here, we proposed a gas-mediated energy remodeling strategy to improve mild PTT efficacy while minimizing side effects. In the proof-of-concept study, a Food and Drug Administration (FDA)-approved drug-based hydrogen sulfide (H₂S) donor was developed to provide a sustained supply of H₂S to tumor sites, serving as an adjuvant to PTT. This approach proved to be highly effective in disrupting the mitochondrial respiratory chain, inhibiting ATP generation, and reducing the overexpression of heat shock protein 90 (HSP90), which ultimately amplified the therapeutic outcome. With the ability to reverse tumor thermotolerance, this strategy delivered a greatly potent antitumor response, achieving complete tumor ablation in a single treatment while minimizing harm to healthy tissues. Thus, it holds great promise to be a universal solution for overcoming the limitations of PTT and may serve as a valuable paradigm for the future clinical translation of photothermal nanoagents.     

Sharp bounds of the zeroth-order general Randi? index of bicyclic graphs with given pendent vertices

Let G be a simple connected graph and α be a given real number. The zeroth-order general Randi? index of ⁰R_α(G) is defined as ∑_v∈V(G)[d_G(v)]^α, where d_G(v) denotes the degree of the vertex v of G. In this paper, for any α(≠0,1), we give sharp bounds of the zeroth-order general Randi? index ⁰R_α of all bicyclic graphs with n vertices and k pendent vertices.     

高温红外光谱电化学薄层池的制作与表征

为了拓宽光谱电化学研究范围,开展高温下电化学反应过程,本文研制了一种高温红外光谱电化学薄层池(HTC)。 研制的HTC清洗方便,操作简单,适用于水体系和有机体系。 该HTC可在室温至373 K(根据溶剂沸点,控温精度为±0.5 K)温度范围内使用,具有良好的电化学性能,红外光谱采集谱图清晰信噪比好。 利用铁氰化钾水溶液和对苯醌离子液体溶液的红外光谱电化学行为对HTC进行了表征,得到较满意的结果。     

基于增益媒质的亚波长纳米阵列超传输特性研究

增益媒质因其优良的放大特性和广阔的应用前景吸引了国内外学者的广泛关注,然而,激发增益媒质补偿欧姆损耗需要较强的外部能量,极大地限制了增益媒质的发展前景。本文使用辅助位微分方程的时域有限差分方法研究了麦克斯韦方程与半经典的电子速率方程相耦合的自洽仿真过程,并基于四能级原子系统描述的增益媒质和异常光传输现象间的耦合机制,提出了一种新颖的含亚波长周期裂缝的增益/金属/增益纳米阵列结构。研究结果表明,本文提出的纳米结构可以使用较低外部能量实现完全补偿欧姆损耗的目的。该结果对深入了解纳米结构和增益媒质之间的相互作用有着重要的意义。     

木质纤维素生物质催化转化制备苯甲酸

In the present work, we reported a novel route for the conversion of lignocellulosic biomass (sawdust) to a high-value chemical of benzoic acid under atmospheric pressure. The trans- formation involved the catalytic pyrolysis of sawdust into aromatics, the decomposition of heavier alkylaromatics to toluene, and the liquid-phase oxidation of toluene-rich aromatics to benzoic acid. The production of the desired benzoic acid from the sawdust-derived aro- matics, with the benzoic acid selectivity of 85.1 C-mol% and nearly complete conversion of toluene, was achieved using the MnO₂/NHPI catalyst at 100 ℃ for 5 h. The in uence of adding methanol on the catalytic conversion of sawdust to the core intermediate of toluene was also investigated in detail.     

基于阿基米德范数的广义直觉模糊Bonferroni平均及其多属性决策方法

主要提出了一种基于阿基米德T-范数和S-范数的广义直觉模糊Bonferroni平均算子.首先利用广义直觉模糊运算法则和阿基米德T-范数和S-范数,构建了一种新的广义直觉模糊Bonferroni平均算子,并详细研究了该算子的一些优良性质,包括幂等性、单调性、有界性以及置换不变性等;然后探究了广义直觉模糊Bonferroni平均算子的几类特殊形式;最后在直觉模糊环境下,基于提出的算子建立了一种新的多属性决策方法,并以图书馆空调系统的选择为例,分析说明了提出的决策方法的可行性和有效性.     

基于DEA模型的我国城市水务企业经营绩效评价研究

我国城镇化进程的日益深化以及环境污染的日益严峻,使得科学评价并提升城市水务企业经营绩效成为当务之急。基于企业类型的维度,从城市水务企业区域分布、数量分布及综合实力等方面选取了12家代表性企业,运用DEA模型对其2012-2014年度的经营绩效进行了评价。结果表明:2012~2014年样本水务企业的整体绩效水平不高,其中有6家经营绩效达到最优水平,有5家纯技术效率与规模效率无效同时存在,其余1家纯技术效率有效而规模效率无效。另外,不同类型的水务企业经营绩效存在着差距,民营企业优于国有企业;基于评价结果提出了“国有企业应加强技术研发与提高管理效率、民营企业应积极开拓市场以及建立两类水务企业的利益共同体”等政策建议,以期提升水务企业经营绩效。     

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.