Direct ink writing of vancomycin-loaded polycaprolactone/ polyethylene oxide/ hydroxyapatite 3D scaffolds

Novel inks were formulated by dissolving polycaprolactone (PCL), a hydrophobic polymer, in organic solvent systems; polyethylene oxide (PEO) was incorporated to extend the range of hydrophilicity of the system. Hydroxyapatite (HAp) with a weight ratio of 55–85% was added to the polymer-based solution to mimic the material composition of natural bone tissue. The direct ink writing (DIW) technique was applied to extrude the formulated inks to fabricate the predesigned tissue scaffold structures; the influence of HAp concentration was investigated. The results indicate that in comparison to other inks containing HAp (55%, 75%, and 85%w/w), the ink containing 65% w/w HAp had faster ink recovery behavior; the fabricated scaffold had a rougher surface as well as better mechanical properties and wettability. It is noted that the 65% w/w HAp concentration is similar to the inorganic composition of natural bone tissue. The elastic modulus values of PCL/PEO/HAp scaffolds were in the range of 4–12 MPa; the values were dependent on the HAp concentration. Furthermore, vancomycin as a model drug was successfully encapsulated in the PCL/PEO/HAp composite scaffold for drug release applications. This paper presents novel drug-loaded PCL/PEO/HAp inks for 3D scaffold fabrication using the DIW printing technique for potential bone scaffold applications.     

“数字信号处理”课程的几何视角

“数字信号处理”的经典课程体系以“系统”为中心，以离散时间傅立叶变换、z变换和离散傅立叶变换为分析工具，以滤波器设计为目的，注重定理和公式推导及其性质分析，很难让学生建立一个形象的、完整统一的知识体系框架。本文从几何视角将 “系统”化和“代数”化的经典课程体系重构为面向“信号”的“几何”知识体系，从而提升学生对本质问题的“洞察力”，帮助学生建立系统的知识体系。     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

Tribological and mechanical characteristics of AA5083 alloy reinforced by hybridising heavy ceramic particles Ta2C & VC with light GNP and Al2O3 nanoparticles

In this study, AA5083 sheets were reinforced with four different hybrid nanoparticles by friction stir processing (FSP) for the development of surface nanocomposites used in advanced engineering applications. The present research focused on improving the properties and tribological behaviour of AA5083 alloy surfaces, including novel hybrid nanoparticles and the intermetallic phase formed during FSP. A tribometer tester with a constant normal load was used to examine the tribological performance of the hybrid composites. After the wear test, a surface profiler inspector was used to analyse the morphology and surface roughness of the examined materials. The Vickers micro-hardness of the base metal and the manufactured composites were measured. During FSP, a new intermetallic phase of AlV₃ was successfully formed at 300–400 °C in the hybrid nanocomposites containing VC particles. The reinforcements resulted in additional grain refining than FSP. The AA5083/Ta2C–Al₂O₃ exhibited the greatest grain refinement, a sixty-fold reduction in grain size compared to that of the base alloy. The results revealed that the hybrid nanocomposites containing VC particles demonstrated the most significant microhardness values inside the stirred zone as a result of the presence of the AlV₃ phase, which was increased by 25–30%. Moreover, the mechanical properties were significantly improved for all manufactured nanocomposites. The tensile strength was increased by 28% through the hybridisation of AA5083 using a hybrid of VC-GNPs. The dispersion of Ta₂C-GNPs and VC-GNPs in the matrix led to excellent interfacial adhesion, resulting in an enhancement in the mechanical properties. The AA5083/VC-GNPs surface composite outperformed other manufactured composites regarding wear resistance. In addition, due to GNPs soft nature, it reduced the coefficient of friction (COF) of the manufactured composites by 20–25% compared to other reinforcements.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Iodine from brown algae in human nutrition,with an emphasis on bioaccessibility,bioavailability, chemistry,and effects of processing: A systematic review

Brown algae are becoming increasingly popular as a food source and dietary supplement in Europe and other Western countries. As they are highly rich in iodine, they represent a potential new dietary iodine source. Iodine deficiency has been re-emerging in Europe, and it is important to ensure adequate intake through one's diet. However, macroalgae, and especially brown algae, may contain very high amounts of iodine, and both iodine deficiency and excessive iodine may increase the risk of negative health effects. The iodine content of algae or foods containing algae is currently not regulated in the European Union. The aim of this paper is to review the literature to determine the chemical species of iodine in brown algae, the loss of iodine during processing, and the bioavailability and bioaccessibility of iodine. A systematic search of the literature was performed in April 2021, via the databases Web of Science and PubMed. The review includes studies of iodine in brown macroalgae in relation to bioavailability, bioaccessibility, processing and speciation. A meta-analysis was conducted in relation to the following topics: (i) the correlation between total iodine and iodide (I^–) content in brown algae; (ii) the correlation between the loss of iodine during processing and the I^– content; and (iii) the correlation between bioavailability and the I^– content. The bioavailability of iodine from brown algae was generally high, with in vivo bioavailability ranging from 31% to 90%. The in vitro bioavailability of iodine (2%–28%) was systematically lower than in vivo bioavailability (31%–90%), indicating an inadequate in vitro methodology. Processing may reduce the iodine content of brown algae, and a higher I^– content was positively correlated with increased iodine loss during processing. Although processing strategies may reduce the iodine content of brown algae significantly, the iodine content may still be high after processing. These findings may be used in food safety evaluations of brown algae as well as in the development of macroalgae-containing foods with iodine contents suitable for human consumption. Further research on processing techniques to reduce the iodine content in brown macroalgae are warranted.     

Synthesis of thermoresponsive copolymer/silica-coated magnetite nanoparticle composite and its application for heavy metal ion recovery

To enhance chemical stability and suppress of aggregation of magnetite nanoparticles (MNPs), which are used as a support for thermoresponsive copolymer immobilization, silica coating of the MNPs is applied via the electrooxidation method. Although the resulting silica coated-MNPs also formed aggregates, the size distribution of the aggregate shifted to smaller size range. Because of that, the surface area available for copolymer immobilization increased approximately 6.7 times at maximum as compared with that of the uncoated MNPs. It contributed to the increase of the amount of the immobilized copolymer on the silica-coated MNPs, which is approximately four times larger than that on the uncoated MNPs. Fe₃O₄ dissolution test confirmed enhancement of chemical stability of MNPs. The thermoresponsive copolymer immobilized on the silica-coated MNPs shows the ability to recycle Cu(II) ion from Cu(II) containing solution by changing temperature with significantly shorter time than those in other thermoresponsive adsorbents in gel form.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

Mathematical Modeling of Drying Processes of Selected Fruits and Vegetables

This study investigates the behavior of fruit and vegetable samples during drying. The experimental data are fitted to several different thin-layer drying models. Regression analysis is used to determine model parameters, while statistical indicators serve to evaluate the goodness of fit. The power function model gives the best fit for all examined samples. Based on this model, different drying and heat storage technologies can be combined to ensure that the required residual moisture content of an agricultural product is reached. It is demonstrated on the case of a specific Togolese processing plant that under favorable conditions, fossil fuel consumption can be decreased by 33 %.     

Main trends on the simulation of the drying of refractory castables - Review

Refractory castables develop microstructures after curing that behave as partially saturated porous media. Upon heating (during its drying stage), the steam generated by the physical and chemically bond water can result in pore pressurization and explosive spalling. Numerical modeling can provide guidelines for designing safer heat-up profiles and also a better understanding of the mechanisms that lead to catastrophic damage. This work aims to review the fundamentals and models available, providing insightful thoughts on the current trends of the drying phenomena of ceramic compositions. The review also highlights that there are models better oriented to result in reasonable predictions of pore pressure values and others focused on a more accurate representation of the main physical phenomena that take place during heating. According to the findings, there are still various challenges to attain accurate models with high applicability capable of yielding safer and more efficient drying of refractory castables.