Isogeometric Analysis of Hyperelastic Material Characteristics for Calcified Aortic Valve

This study explores the implementation of computed tomography(CT)reconstruction and simulation techniques for patient-specific valves,aiming to dissect the mechanical attributes of calcified valves within transcatheter heart valve replacement(TAVR)procedures.In order to facilitate this exploration,it derives pertinent formulas for 3D multi-material isogeometric hyperelastic analysis based on Hounsfield unit(HU)values,thereby unlocking foundational capabilities for isogeometric analysis in calcified aortic valves.A series of uniaxial and biaxial tensile tests is executed to obtain an accurate constitutive model for calcified active valves.To mitigate discretization errors,methodologies for reconstructing volumetric parametric models,integrating both geometric and material attributes,are introduced.Applying these analytical formulas,constitutive models,and precise analytical models to isogeometric analyses of calcified valves,the research ascertains their close alignment with experimental results through the close fit in displacement-stress curves,compellingly validating the accuracy and reliability of the method.This study presents a step-by-step approach to analyzing themechanical characteristics of patient-specific valves obtained fromCT images,holding significant clinical implications and assisting in the selection of treatment strategies and surgical intervention approaches in TAVR procedures.

A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

具有电子转移路径的金属化氮化碳选择性再生NADH和光酶偶联CO_(2)还原

将CO_(2)转化为燃料和化学品被认为是缓解能源危机的一种有效策略.受自然光合作用的启发,光酶偶联结合了光催化和酶催化的优点,在绿色生物制造中具有较好的应用前景.铑(Rh)络合物是选择性再生还原型烟酰胺腺嘌呤二核苷酸(M-NADH)的关键介体,其固定化可以提高体系的可持续性,并有效缩短电子传递路径,因而受到广泛关注.本文制备了联吡啶功能化的金属化氮化碳(PCNRhbpy_(4)),用于光酶偶联催化CO_(2)还原.首先以双氰胺为前驱体,通过两步退火法制备氮化碳(PCN),再与5,5’-二氨基-2,2’-联吡啶(DABP)进一步热缩合,然后锚定[Cp*RhCl2]2获得PCNRhbpy4,并通过透射电镜、扫描电镜、粉末X射线衍射、紫外可见光吸收光谱、瞬态表面光电压和荧光发射光谱等进行表征.结果表明,合成的PCNRhbpy4材料具有掺杂石墨烯结构,且通过残余的末端联吡啶结构均匀地固定了Rh络合物.以PCNRhbpy4作为光催化剂实现了1,4-NADH的100%选择性再生,并在2.0 min内实现了80%的NADH再生.进一步耦合固定在疏水膜上的甲酸脱氢酶,可以有效地将CO_(2)还原为甲酸盐,48 h内甲酸盐浓度达到7 mmol L^(-1).此外,光催化剂的循环实验结果表明,PCNRhbpy4具有较高的稳定性.反应机理研究结果表明,光生电子经N-掺杂石墨烯传递到Rh活性位点,并结合溶液中的质子,随后氢负离子通过环滑移机制传递到NAD+,高选择性再生1,4-NADH.综上,本文为Rh的固定化开辟了一条新路径,实现了光酶催化CO_(2)还原,为光酶催化在绿色生物制造的实际应用提供参考.

Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Preparation of activated carbon with high surface area for high-capacity methane storage

Activated carbon （AC） was fabricated from corncob, which is cheap and abundant. Experimental parameters such as particle size of corncob, KOHlchar weight ratio, and activation temperature and time were optimized to generate AC, which shows high methane sorption capacity. AC has high specific surface area （3227 m^2/g）, with pore volume and pore size distribution equal to 1.829 cm^3/g and ca. 1.7-2.2 nm, respectively. Under the condition of 2℃ and less than 7.8 MPa, methane sorption in the presence of water （Rw = 1.4） was as high as 43.7 wt% methane per unit mass of dry AC. The result is significantly higher than those of coconut-derived AC （32 wt%） and ordered mesoporous carbon （41.2 wt%, Rw = 4.07） under the same condition. The physical properties and amorphous chaotic structure of AC were characterized by N2 adsorption isotherms, XRD, SEM and HRTEM. Hence, the corncob-derived AC can be considered as a competitive methane-storage material for vehicles, which are run by natural gas. Key words

Grading of products utilizing bamboo as a substitute for plastic based on environmental effects

To achieve sustainable development goals,mitigate plastic pollution,and promote eco-friendly products,it is crucial to identify key products in the bamboo as a substitute for plastic(BSP)industry and assess their environmental effects.This study proposed a novel evaluation method for the environmental effect of bamboo as a substitute for plastic(EBSP).It focused on the contributions of BSP products in reducing plastic pollution and greenhouse gas emissions.We established a set of EBSP evaluation indicators and developed a grading model,evaluating 30 typical BSP products across six categories.The results showed that the EBSP evaluation model,based on the emission reduction rate of substitution(ERRS),substitution rate of material(SRM),and product renewal ratio(PRR),can accurately quantify the environmental benefits of BSP products.This model has successfully facilitated precise quantification of the EBSP and established a rational and effective grading system for BSP products.The results also demonstrated that the average EBSP ranking across the six categories of BSP products,in descending order,is:disposable bamboo products,bamboo household goods,bamboo packaging products,bamboo engineering materials,bamboo furniture products,and bamboo craft products.Specifically,disposable bamboo products scored an EBSP 1.96 times the overall average,indicating significant environmental benefits.The PRR emerged as a critical factor influencing EBSP.Among BSP products with the same lifespan,those with higher substitution emission reduction efficiency offered more pronounced environmental benefits.Ultimately,the BSP industry should strategically prioritize disposable bamboo products,such as bamboo toothbrushes,cutlery,and lunch boxes.These products should be the primary focus of policy support and central to efforts in product development,design innovation,and market promotion.

聚合物-微生物杂合体的构建及催化应用

设计材料-微生物杂合体系,利用太阳能等可再生能源进行绿色生物合成,有望为人类社会提供解决全球能源短缺和环境危机的可行方案。将人工材料优异的物理化学特性与微生物的生物合成功能进行耦合,构建杂合体系的研究在近年来受到了广泛关注。聚合物材料因其功能多样、可设计性强、生物兼容性好等特点被广泛应用于构建材料-微生物杂合体,在生物能源领域展现出广阔的应用前景。本文从聚合物材料的功能特点出发,系统总结了不同类型的聚合物-微生物杂合体,并论述了聚合物材料通过增强光能利用、加速电子传递、稳定生物活性等机制对杂合体催化性能的强化。最后,对聚合物-微生物杂合体系面临的挑战和未来发展前景进行展望。

Heart failure with preserved ejection fraction (HFpEF) in type 2 diabetes melitus: from pathophysiology to therapeutics

Type 2 diabetes mellitus(T2DM or T2D)is a devastating metabolic abnormality featured by insulin resistance,hyperglycemia,and hyperlipidemia.T2D provokes unique metabolic changes and compromises cardiovascular geometry and function.Meanwhile,T2D increases the overall risk for heart failure(HF)and acts independent of classical risk factors including coronary artery disease,hypertension,and valvular heart diseases.The incidence of HF is extremely high in patients with T2D and is manifested as HF with preserved,reduced,and midrange ejection fraction(HFpEF,HFrEF,and HFmrEF,respectively),all of which significantly worsen the prognosis for T2D.HFpEFis seen in approximately half of the HF cases and is defined as a heterogenous syndrome with discrete phenotypes,particularly in close association with metabolic syndrome.Nonetheless,management of HFpEF in T2D remains unclear,largely due to the poorly defined pathophysiology behind HFpEF.Here,in this review,we will summarize findings from multiple preclinical and clinical studies as well as recent clinical trials,mainly focusing on the pathophysiology,potential mechanisms,and therapies of HFpEF in T2D.

Effects of feature selection on lane-change maneuver recognition:an analysis of naturalistic driving data

Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor performance.Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition.This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach–In total,1,375 LC cases are analyzed.To comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid.Then the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each scenario.Findings–It has been found that the common lateral features,e.g.yaw rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical knowledge.Finally,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic.Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data.The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

Computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these problems.This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft Kinect.It then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole detection.The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation.We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.