Impacts of CaO Solid Particles in Carbon Dioxide Absorption Process from Ship Emission with NaOH Solution

CO₂ emitted from ship exhaust is one of the major sources of atmospheric pollution. In order to reduce ship CO₂ emissions, this paper comes up with the idea of recovering CO₂ from ship exhaust by NaOH solution and improves the absorption rate by adding CaO solid particles. The effect mechanism of CaO solid particles on CO₂ absorption efficiency is analyzed in detail, and the mathematical model is deduced and the CaO enhancement factor is calculated through experiments. Experiment result demonstrates that the effect of CaO solid particles on the absorption of CO₂ in alkali solution is significant. The absorption rate of pure CO₂ gas, the simulated ship exhaust gas and 6135AZG marine diesel engine emission can be increased by 10%, 15.85% and 10.30%, respectively. So it can be seen that CaO solid particles play an important role in improving the absorption efficiency of ship CO₂ emission.     

E-Shop Transshipment Selection Evaluation Based on Cloud Model

In e-commerce, a critical problem is how to transship the products between different e-shops to lessen the risk of out of stock, especially in network shopping mall of China. Most of importance is how to select the optimal one in the waiting list of e-shops to make the transshipment economical. However, many factors that influence the effect of transshipment are uncertain and fuzzy, such as customer demand, product price and inventory level. Based on the cloud model theory, a novel method for evaluation of e-shop transshipment selection in the same network shopping mall is proposed to solve the problem. The factors that influence the transshipment effect are discussed in detail. An example of selecting the optimal e-shop to transship products to the out of stock e-shop is given on the basis of the steps of our evaluation calculation. Finally, the result shows that it can effectively optimize transshipment in online shopping economically.     

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.     

Prototype Loading Tests on Full-Ring Segmental Lining of Rectangular Shield Tunnel

A series of full-scale loading tests are performed for a prospective subway tunnel with a rectangular shape including two reliability tests: one stagger-jointed three-ring reliability test, and one ultimate failure test on a single ring. Comprehensive measuring programs are designed to record the deformation of both lining structure and joints and the stresses of concrete, bolts and reinforcements. Experimental results show that in both the single-ring and three-ring loading cases, the long sides of tunnel cross section bend inwards while the short sides of tunnel cross section bend outwards. The inner part of joints opens while the outer part of joints closes at places experiencing positive moment and vice versa. Joint’s rotational stiffness varies at different locations. Concrete cracking and crushing are the chief damage modes, and they are closely related to the distribution of bending moment. Stagger-jointed fabrication significantly increases the overall rigidity of lining system, which thereby greatly reduces the deformation of both concrete lining and joints in comparison with the single-ring case. It is shown that the routinely-used uniform rigidity model is conservative and the preliminary design can be optimized by applying an effective rigidity ratio (ERR) of 0.5.     

Error Analysis of Liquid Holdup Measurement in Gas-Liquid Annular Flow Through Circular Pipes Using High-Speed Camera Method

Accurate measurement of gas-liquid phase fraction is essential for the proper modelling of the pressure drop, heat transfer coefficient, mass transfer rate and interfacial area in two-phase flows. In this paper, taking the issue of optical distortion into account, an analytical model was proposed to estimate and correct the liquid holdup in gas-liquid annular flow through a circular pipe using high-speed camera method. The error in the liquid holdup measurement generated from different refractive indices among transparent circular pipe, liquid film and air core was firstly theoretically analyzed based on the geometric optics. Experimental tests were then carried out to identify the difference as well as to validate the proposed model. Results indicated that the prediction of the liquid holdup has a good performance with the experimental data (i.e., mean relative error is 4.1%) and the measured liquid holdup is larger than the real one. It was found that the measured liquid holdup is larger than the real one. Generally, when the real liquid holdup gets smaller, the discrepancy between the measured liquid holdup by image and the real liquid holdup becomes more significant. Thus, after measuring the liquid holdup from the images, the value of the measured liquid holdup must be corrected by the present model in order to obtain the real liquid holdup.     

Theoretical,Simulation and Experimental Analysis of Microfluidic Droplet Generation and Recovering Process with Applications in Frying Oil Assessments

The research on microfluidic droplet size prediction has been extensive and fruitful, while the droplet deforming process has been seldom studied. In this paper, a frying-oil-assessing microfluidic device was designed to study the droplet deforming and recovering processes, which were dominated by channel geometry, flow rates, sheath flow viscosity and interfacial tension of the two phases. Theoretical expressions of the deforming process and its extreme value were obtained for the first time, supported by simulation and experiments. Theoretical, simulation and experimental results indicated that the steady-state droplet length could be a useful parameter for frying oil assessment.     

Simplified Markov Model for Reliability Analysis of Phased-Mission System Using States Merging Method

This paper presents a simplified Markov model to evaluate the reliability of phased-mission system (PMS). The time cost and storage requirement are very huge for traditional Markov model to analyze the PMS reliability as the number of components increases to a large scale. The states merging method proposed in this paper can account for the PMS with subsystems consisting of identical components, and similar PMSs are common in real-world systems. The simplified Markov model by states merging has smaller number of system states, compared with the traditional one. Furthermore, for the above subsystems, the size of our model increases only linearly as the number of components increases, while the size of the traditional model exponentially increases. Finally, the effectiveness and correctness of our approach are analyzed by comparing with the traditional Markov method.     

Modeling Nd<Superscript>3+</Superscript>-Yb<Superscript>3+</Superscript>-Tm<Superscript>3+</Superscript>-Er<Superscript>3+</Superscript> Codoped Telluride Glass Fiber for 0.4 to 2.0 μm Emission Spectra

The modeling of rare-earth-doped fiber amplifier is accomplished by utilizing the rate and propagation equations of distinct levels for a laser medium. A complex theoretical model for neodymium (Nd³⁺), erbium (Er³⁺), thulium (Tm³⁺) and ytterbium (Yb³⁺) codoped telluride glass fiber covering 0.4—2.0 μm emission spectra is presented. The emission spectra of Nd³⁺-Er³⁺-Tm³⁺-Yb³⁺ codoped telluride fiber are realized with the excitation of both 808 and 980 nm lasers pumped at 500mW. Numerical methods are used to calculate the emission spectra covering 0.4—2.0 μm. With the Nd³⁺, Tm³⁺ and Yb³⁺ ion concentrations fixed at 2 × 10²⁰ ion/m³, the Er³⁺ ion concentration optimized to 8 × 1020 ion/m3 and the fiber length spanning from 0.5 to 2 m, a peak amplified spontaneous emission (ASE) power of 19.8mW is attainable, and a minimum ASE power of 7.96mW can also be achieved. The analytical techniques and results indicate that when a telluride codoped fiber with suitable ion concentrations of Nd³⁺, Er³⁺, Tm³⁺ and Yb³⁺ is excited by both 980 and 808 nm pump lasers, 0.4—2.0 μm emission spectra are attainable for vast optical applications.     

Research of Clinical Named Entity Recognition Based on Bi-LSTM-CRF

Electronic Medical Records (EMR) with unstructured sentences and various conceptual expressions provide rich information for medical information extraction. However, common Named Entity Recognition (NER) in Natural Language Processing (NLP) are not well suitable for clinical NER in EMR. This study aims at applying neural networks to clinical concept extractions. We integrate Bidirectional Long Short-Term Memory Networks (Bi-LSTM) with a Conditional Random Fields (CRF) layer to detect three types of clinical named entities. Word representations fed into the neural networks are concatenated by character-based word embeddings and Continuous Bag of Words (CBOW) embeddings trained both on domain and non-domain corpus. We test our NER system on i2b2/VA open datasets and compare the performance with six related works, achieving the best result of NER with F1 value 0.853 7. We also point out a few specific problems in clinical concept extractions which will give some hints to deeper studies.     

Multilingual financial news retrieval and smart recommendation based on big data

In view of the study of finance and economics information, we research on the real-time financial news posted on the authority sites in the world’s major advanced economies. Analyzing the massive financial news of different information sources and language origins, we come up with a basic theory model and its algorithm on financial news, which is capable of intelligent collection, quick access, deduplication, correction and integration with financial news’ backgrounds. Furthermore, we can find out connections between financial news and readers’ interest. So we can achieve a real-time and on-demand financial news feed, as well as provide a theoretical basis and verification of the scientific problems on real-time processing of massive information. Finally, the simulation experiment shows that the multilingual financial news matching technology can give more help to distinguish the similar financial news in different languages than the traditional method.