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.     

Complex study of protective Cr3C2–NiAl coatings deposited by vacuum electro-spark alloying,pulsed cathodic arc evaporation,magnetron sputtering,and hybrid technology

Coatings were obtained by vacuum electro-spark alloying (VESA), pulsed cathodic arc evaporation (PCAE), magnetron sputtering (MS) techniques and VESA-PCAE-MS hybrid technology using Cr₃C₂–NiAl electrodes. The structure of the coatings was analyzed using scanning and transmission electron microscopy, X-ray diffraction and energy-dispersive spectroscopy. Mechanical properties were determined by nanoindentation, while tribological properties were assessed using pin-on-disk tribometer. Corrosion resistance was estimated by voltammetry in 1 N H₂SO₄ and 3.5%NaCl solutions. Oxidation resistance tests were performed at 800°С in air. The VESA coating had the highest thickness, low friction coefficient and high wear resistance. PCAE coating demonstrated the highest hardness (24 GPa) and elastic recovery (59%), oxidation resistance and superior corrosion resistance both in 1 N H₂SO₄ (i_corr = 70 μА/cm²) and 3.5%NaCl (i_corr = 0.74 μА/cm²) solutions. The MS coating had average mechanical properties and low corrosion current density (71 μА/cm²) in 1 N H₂SO₄. Deposition of coatings using VESA-PCAE-MS hybrid technology led to an increase in corrosion and oxidation resistance at least by 1.5 times in comparison with the VESA coating.     

Investigation the correlation between chemical structure and swelling,thermal and flocculation properties of carboxymethylcellulose hydrogels

Hydrophilic polymer networks (hydrogels) based on sodium carboxymethylcellulose (NaCMC) and polycarboxylic acids (oxalic, succinic, citric and adipic) as cross-linking agents are synthesized by esterification reaction; one series of NaCMC hydrogels cross-linked with citric acid is prepared with acrylamide and acrylic acid (Aam/Aac) copolymers using the design of semi-interpenetrating polymer networks (semi-IPN), in order to increase their potential application for flocculation purposes. The Infrared spectroscopy (FTIR) of hydrogels confirms the esterification reaction between NaCMC and cross-linking agents. Results of swelling measurements show that citric acid in the amount of 15 wt% gives the hydrogels with the best absorption capacity. The results of Differential scanning calorimetry (DSC) and Thermal gravimetric analysis (TGA) show no significant difference in thermal properties of neat and semi-interpenetrating NaCMC hydrogels. The amorphous nature of hydrogels is confirmed by X-ray diffraction analysis (XRD). The results of flocculation study show that combination of NaCMC network and Aam/Aac copolymer with initial mass ratio of 10/90 creates a theoretical platform for the production of flocculant which could show high efficacy in purifying of water dominated by positively charged particles.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

基于物联网的智慧林业可持续发展策略研究

随着我国科技的不断进步,在“互联网+”大数据的背景下,林业的智慧化发展是必经之路。虽然我国智慧林业的发展已经取得了一定的突破,但是仍然存在着许多问题。基于此,文章在物联网技术的基础上,对智慧林业的可持续发展策略进行探讨。     

干扰条件下的“北斗”信号解扩重构DOA估计

在干扰条件下，卫星导航抗干扰波束形成算法往往需要卫星信号波达方向（Direction-of-Arrival，DOA）的先验信息。但当存在低信噪比信号或主动干扰源时，常规的DOA估计算法性能急剧下降甚至失效。针对此问题，提出了一种被干扰信号压制的低信噪比“北斗”信号的DOA估计算法。该算法首先通过对接收信号进行子空间投影抑制干扰信号，然后对抑制干扰后的信号进行解扩重构处理，最后通过多重信号分类算法完成对“北斗”信号的DOA估计。仿真结果表明，在干扰信号干信比80 dB条件下，“北斗”信号DOA估计误差在5°以内，为下一步进行波束形成计算提供了高精度的入射角信息。     

Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

Microwave assisted-in situ synthesis of porous titanium/calcium phosphate composites and their in vitro apatite-forming capability

Microwave irradiation has been proven to be an effective heating source in synthetic chemistry, and can accelerate the reaction rate, provide more uniform heating and help in developing better synthetic routes for the fabrication of bone-grafting implant materials. In this study, a new technique, which comprises microwave heating and powder metallurgy for in situ synthesis of Ti/CaP composites by using Ti powders, calcium carbonate (CaCO₃) powders and dicalcium phosphate dihydrate (CaHPO₄·2H₂O) powders, has been developed. Three different compositions of Ti:CaCO₃:CaHPO₄·2H₂O powdered mixture were employed to investigate the effect of the starting atomic ratio of the CaCO₃ to CaHPO₄·2H₂O on the phase, microstructural formation and compressive properties of the microwave synthesized composites. When the starting atomic ratio reaches 1.67, composites containing mainly alpha-titanium (α-Ti), hydroxyapatite (HA), beta-tricalcium phosphate (β-TCP) and calcium titanate (CaTiO₃) with porosity of 26%, pore size up to 152 μm, compressive strength of 212 MPa and compressive modulus of 12 GPa were formed. The in vitro apatite-forming capability of the composite was evaluated by immersing the composite into a simulated body fluid (SBF) for up to 14 days. The results showed that biodissolution occurred, followed by apatite precipitation after immersion in the SBF, suggesting that the composites are suitable for bone implant applications as apatite is an essential intermediate layer for bone cells attachment. The quantity and size of the apatite globules increased over the immersion time. After 14 days of immersion, the composite surface was fully covered by an apatite layer with a Ca/P atomic ratio approximately of 1.68, which is similar to the bone-like apatite appearing in human hard tissue. The results suggested that the microwave assisted-in situ synthesis technique can be used as an alternative to traditional powder metallurgy for the fabrication of Ti/CaP biocomposites.     

A new numerical algorithm to check the observability and bad-data processing ability of the metering scheme

This paper provides a unified approach for the optimization of measurements placements employed for power system online monitoring through state estimation. The proposed methodology, which can be suitable for the mixed measure system, preserves state estimation observability and bad-data processing capability by employing numerical algorithms for observability checking, critical measurements and critical couple identification. First, node injection radix measurements and measurement categories are defined. According to the above definitions, the coefficient matrix can be solved. The analysis on the column vectors of the coefficient matrix can determine each measurement classification. Furthermore, the numbers of each measurement class contains can determine bad-data processing capability. The observability can be checked by the type number of measurements. The proposed method is illustrated with the IEEE39-bus system and the IEEE118-bus system. Results from the case studies are presented to demonstrate that the approach adequately fulfills the desired properties related to observability, bad-data processing, cost, and robustness.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.