Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

120m高钢筋混凝土烟囱爆破拆除

介绍了用定向控制爆破方法拆除吉化电石厂120m钢筋混凝土烟囱的经验。针对该烟囱下部有1个门和3个窗户的特殊结构,选择倒梯形爆破切口,以增强预留支撑面的强度。为防止烟囱落地产生震动和前冲,在倒塌的前方设置了减震带和阻冲挡土墙。文中同时还阐述了参数的选择与计算,以及爆破效果。文中总结的四点经验值得借鉴。     

Farmers' preferences for adopting on-farm concentration of raw milk: Results from a discrete choice experiment in Germany

Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers' potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Food-grade carrageenans and their implications in health and disease

Food additives, often used to guarantee the texture, shelf-life, taste, and appearance of processed foods, have gained widespread attention due to their increased link to the growing incidence of chronic diseases. As one of the most common additives, carrageenans have been used in human diets for hundreds of years. While classified as generally recognized as safe (GRAS) for human consumption, numerous studies since the 1980s have suggested that carrageenans, particularly those with random coil conformations, may have adverse effects on gastrointestinal health, including aggravating intestinal inflammation. While these studies have provided some evidence of adverse effects, the topic is still controversial. Some have suggested that the negative consequence of the consumption of carrageenans may be structure dependent. Furthermore, pre-existing conditions may predispose individuals to varied outcomes of carrageenan intake. In this review, structure–function relationships of various carrageenans in the context of food safety are discussed. We reviewed the molecular mechanisms by which carrageenans exert their biological effects. We summarized the findings associated with carrageenan intake in animal models and clinical trials. Moreover, we examined the interactions between carrageenans and the gut microbiome in the pathogenesis of gastrointestinal disorders. This review argues for personalized guidance on carrageenan intake based on individuals’ health status. Future research efforts that aim to close the knowledge gap on the effect of low-dose and chronic carrageenan intake as well as interactions among food additives should be conducive to the improved safety profile of carrageenans in processed food products.     

电商平台产品共同购买网络的影响因素研究

共同购买网络的推荐系统应用越加广泛，仅基于网络内生结构变量研究其共同购买链接的经济意义已有局限，故加入网络口碑这一外生变量，进行更全面的分析。采用了社会网络方法中指数随机图模型进行建模，主要围绕产品销售量、产品入度、差评率和评论数4个方面因素，探究其对共同购买网络中共同购买链接形成的影响。结果显示，销售量、产品入度和评论数对共同购买链接形成的影响呈正比关系，而差评率则会显著地降低产品共同购买的几率。该指数随机图构建出的共同购买网络为在线电商平台管理网络口碑和推荐系统优化设计提供有益参考。     

腔磁力系统中的诱导透明和快慢光传播

为了在腔磁力系统中实现可控的磁子诱导透明、磁力诱导透明以及快慢光传播,建立了一个混合腔磁力系统.该系统由一个含有YIG球的微波腔和在z方向对球施加一个均匀的偏置磁场组成,并用强泵浦场驱动磁子和弱探测场驱动微波腔.研究表明,通过调节腔与磁子之间的相互作用强度和微波腔与磁子的耗散比,可以增加磁子诱导透明(MIT)、磁力诱导透明(MMIT)的效果和提高快慢光传播的速度.该研究结果可为磁力诱导放大、量子光学操纵和量子信息存储以及灵敏光开关的研究提供参考.     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

0Cr17Ni4Cu4Nb传感器断裂原因分析

某力学传感器在出厂检验流程中发生断裂失效,为了确定失效原因,防止此类失效事件再次发生,实验对该传感器的力学性能、金相组织和断口形貌进行了分析,确定了断裂原因。结果表明,传感器的失效模式为氢致延迟开裂,材料中氢含量偏高和开裂位置机加工质量较差是导致传感器发生氢致开裂的主要原因。结合失效原因,提出了改进措施。