Sustainable ammonia production through process synthesis and global optimization

Current ammonia production technologies have a significant carbon footprint. In this study, we present a process synthesis and global optimization framework to discover the efficient utilization of renewable resources in ammonia production. Competing technologies are incorporated in a process superstructure where biomass, wind, and solar routes are compared with the natural gas-based reference case. A deterministic global optimization-based branch-and-bound algorithm is used to solve the resulting large-scale nonconvex mixed-integer nonlinear programming problem (MINLP). Case studies for Texas, California, and Iowa are conducted to examine the effects of different feedstock prices and availabilities. Results indicate that profitability of ammonia production is highly sensitive to feedstock and electricity prices, as well as greenhouse gas (GHG) restrictions. Under strict 75% GHG reductions, biomass to ammonia route is found to be competitive with natural gas route, whereas wind and solar to ammonia routes require further improvement to compete with those two routes. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16498 2019     

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.     

Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

On the performance of improved quadrature spatial modulation

Quadrature spatial modulation (QSM) utilizes the in‐phase and quadrature spatial dimensions to transmit the real and imaginary parts of a single signal symbol, respectively. The improved QSM (IQSM) transmits two signal symbols per channel use through a combination of two antennas for each of the real and imaginary parts. The main contributions of this study can be summarized as follows. First, we derive an upper bound for the error performance of the IQSM. We then design constellation sets that minimize the error performance of the IQSM for several system configurations. Second, we propose a double QSM (DQSM) that transmits the real and imaginary parts of two signal symbols through any available transmit antennas. Finally, we propose a parallel IQSM (PIQSM) that splits the antenna set into equal subsets and performs IQSM within each subset using the same two signal symbols. Simulation results demonstrate that the proposed constellations significantly outperform conventional constellations. Additionally, DQSM and PIQSM provide a performance similar to that of IQSM while requiring a smaller number of transmit antennas and outperform IQSM with the same number of transmit antennas.     

柠檬酸铋合成新工艺研究

针对现有柠檬酸铋制备工艺合成时间长、能耗高、产品纯度不高等不足,介绍了一种常温下制备柠檬酸铋的新工艺。新工艺制备时间短,制备的柠檬酸铋产品质量好,合成过程产生的废水可循环利用,废气经碱液循环吸收可生产硝酸钠副产品。     

胡萝卜丁中维生素C在热风辅助射频干燥过程中降解的动力学模型

目的研究热风辅助射频(hot air-assisted radio frequency,HA-RF)干燥过程中维生素C随温度变化的降解动力学模型。方法在6.5 cm极板间距和60℃热风系统条件下进行HA-RF干燥,重点研究胡萝卜丁中维生素C在干燥过程中的降解动力学,包括脱氢抗坏血酸(dehydroascorbic acid,DHAA)和抗坏血酸(ascorbic acid,AA)。总维生素C含量采用2,4-硝基苯肼分光光度法测定,AA采用2,6-二氯吲哚酚滴定法测定。结果HA-RF干燥过程中维生素C的降解规律符合一级可逆模型,其降解活化能为40.54 kJ/mol。DHAA活化能为35.83 kJ/mol,表明DHAA的稳定性低于AA。结论本研究使用的干燥温度为62.5~77.5℃,较高的干燥温度下维生素C降解速率较高,较低处理温度下的降解过程具有更好的模型适应度(R2>0.98)。     

How Cells Communicate with Each Other in the Tumor Microenvironment: Suggestions to Design Novel Therapeutic Strategies in Cancer Disease

Connexin- and pannexin (Panx)-formed hemichannels (HCs) and gap junctions (GJs) operate an interaction with the extracellular matrix and GJ intercellular communication (GJIC), and on account of this they are involved in cancer onset and progression towards invasiveness and metastatization. When we deal with cancer, it is not correct to omit the immune system, as well as neglecting its role in resisting or succumbing to formation and progression of incipient neoplasia until the formation of micrometastasis, nevertheless what really occurs in the tumor microenvironment (TME), which are the main players and which are the tumor or body allies, is still unclear. The goal of this article is to discuss how the pivotal players act, which can enhance or contrast cancer progression during two important process: “Activating Invasion and Metastasis” and the “Avoiding Immune Destruction”, with a particular emphasis on the interplay among GJIC, Panx-HCs, and the purinergic system in the TME without disregarding the inflammasome and cytokines thereof derived. In particular, the complex and contrasting roles of Panx1/P2X7R signalosome in tumor facilitation and/or inhibition is discussed in regard to the early/late phases of the carcinogenesis. Finally, considering this complex interplay in the TME between cancer cells, stromal cells, immune cells, and focusing on their means of communication, we should be capable of revealing harmful messages that help the cancer growth and transform them in body allies, thus designing novel therapeutic strategies to fight cancer in a personalized manner.     

High-resolution FIB-TEM-STEM structural characterization of grain boundaries in the high dielectric constant perovskite CaCu3Ti4O12

In this work, the grain boundaries composition of the polycrystalline CaCu₃Ti₄O₁₂ (CCTO) was investigated. A Focused Ion Beam (FIB)/lift-out technique was used to prepare site-specific thin samples of the grain boundaries interface of CCTO ceramics. Scanning transmission electron microscopy (STEM) coupled with energy dispersive X-ray spectrometry (EDXS) and Electron Energy Loss Spectroscopy (EELS) systems were used to characterize the composition and nanostructure of the grain and grain boundaries region. It is known that during conventional sintering, discontinuous grain growth occurs and a Cu-rich phase appears at grain boundaries. This Cu-rich phase may affect the final dielectric properties of CCTO but its structure and chemical composition remained unknown. For the first time, this high-resolution FIB-TEM-STEM study of CCTO interfacial region highlights the composition of the phases segregated at grain boundaries namely CuO, Cu₂O and the metastable phase Cu₃TiO_4.