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.     

Novel TiC-based composites with enhanced mechanical properties

Novel TiC-based composites were synthesized by reactive hot-pressing at 1800 °C for 1 h with ZrB₂ addition as a sintering aid for the first time. The effects of ZrB₂ contents on the phase composition, microstructure evolution, and mechanical properties were reported. Based on the reaction and solid solution coupling effects between ZrB₂ and TiC, the product ZrC may be partially or completely dissolved into the TiC matrix, and then phase separation within the miscibility gap is observed to form lamellar nanostructured ZrC-rich (Zr, Ti)C. The TiC-10 mol.% ZrB₂ (starting batch composition) exhibits good comprehensive mechanical properties of hardness 27.7 ± 1.3 GPa, flexural strength 659 ± 48 MPa, and fracture toughness of 6.5 ± 0.6 MPa m^1/2, respectively, which reach or exceed most TiC-based composites using ceramics as sintering aids in the previous reports.     

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.     

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.     

Rectangular Section Control Technology for Silicon Steel Rolling

Rectangular section control technology(RSCT)was introduced to achieve high-precision profile control during silicon steel rolling.The RSCT principle and method were designed,and the whole RSCT control strategy was developed.Specifically,RSCT included roll contour design,rolling technology optimization,and control strategy development,aiming at both hot strip mills(HSMs)and cold strip mills(CSMs).Firstly,through the high-performance variable crown(HVC)work roll optimization design in the upper-stream stands and the limited shifting technology for schedule-free rolling in the downstream stands of HSMs,a hot strip with a stable crown and limited wedge,local spot,and single wave was obtained,which was suitable for cold rolling.Secondly,an approximately rectangular section was obtained by edge varying contact(EVC)work roll contour design,edge-drop setting control,and closed loop control in the upper-stream stands of CSMs.Moreover,complex-mode flatness control was realized by coordinating multiple shape-control methods in the downstream stands of CSMs.In addition,the RSCT approach was applied in several silicon-steel production plants,where an outstanding performance and remarkable economic benefits were observed.