Based on the in-depth study and research of construction project cost estimation methods and economic benefit analysis methods at home and abroad, this paper proposes an investment forecasting model based on the combination of fuzzy mathematics and improved neural network. First, the classical fuzzy mathematics method is used to classify and screen historical data samples to improve the quality of the samples and improve the accuracy and practicability of the investment prediction model. The composition and other important contents of the project are extracted, and their characteristics are extracted and connected with the investment estimation and benefit analysis of the project, and a hydropower project investment estimation model and an investment benefit analysis model based on an improved neural network are constructed, and the prediction accuracy is as high as 97.2%

Pseudorabies virus (PRV) is a pathogen that causes an acute infectious disease in pigs, which could lead to huge losses to the farming industry. The Bartha-K61 strain of PRV, commonly used as a gE-deleted vaccine, does not always protect against the wild-type virus infection effectively. Therefore, the prompt detection of viral infection in gE-deleted vaccine vaccinated pigs is crucial for in-time measures to prevent the spread of diseases. In this study, we developed a Surface-enhanced Raman spectroscopy(SERS) based lateral flow assay based on antigen-antibody reaction to meet the demand. Our method was rapid (15 min), sensitive (LOD: 5 ng mL(-1)), selective for wild-type PRV detection, and quantitatively or semi-quantitatively (DLR: 41-650 ng mL(-1)) compatible. The detection results from this method were consistent with results from the gE-specific PCR, indicating that this SERS-based lateral flow assay could be used as a new tool to differentially diagnose wild-type PRV and gE-deleted vaccine.

Figure S7. Optimization of the amount of tween-20 addition. Each point was photographed with two copies. (DOC 609 kb)

The development of biosensors that are portable, low-cost, and quantitative has long been sought for rapid, on-site, and timely detection of avian influenza virus (AIV). In this study, an antibody-based Raman lateral flow immunoassay strip was developed to detect AIV H7N9. This LFIA strip used a novel core-shell structure material, AuAg4-ATP@AgNPs, as a Raman probe. An antibody specific for AIV and goat anti-mouse IgG antibody were immobilized on a nitrocellulose membrane as the test and control lines, respectively. Accumulation of antibody-virus-antibody-Raman probe complex at the test line could be visualized by the naked eye, and the Raman signal could be quantified using a portable Raman instrument. The testing process for the SERS-based LFIA strips could be completed in 20 min, which avoided the time-cost of current methods for AIV analysis. In our SERS-based biosensor, we estimated the limit of detection (LOD) for H7N9 to be 0.0018 HAU. This value is approximately three orders of magnitude more sensitive than the corresponding HA assays. When testing real sample, the results of the strip test were in accordance with those from real-time PCR testing. In conclusion, the SERS-based LFIA strip proposed in this study shows tremendous potential to detect targets quickly and sensitively using an elegantly simple method. (C) 2018 Elsevier B.V. All rights reserved.

Experimental methods play a crucial role in identifying the subcellular localization of proteins and building high-quality databases. However, more efficient, automated computational methods are required to predict the subcellular localization of proteins on a large scale. Various efficient feature extraction methods have been proposed to predict subcellular localization, but challenges remain. In this paper, three novel feature extraction methods are established to improve multi-site prediction. The first novel feature extraction method utilizes repetitive information via moving windows based on a dipeptide pseudo amino acid composition method (R-Dipeptide). The second novel feature extraction method utilizes the impact of each amino acid residue on its following residues based on pseudo amino acids (I-PseAAC). The third novel feature extraction method provides local information about protein sequences that reflects the strength of the physicochemical properties of residues (PseAAC2). The multi-label k-nearest neighbor algorithm (MLKNN) is used to predict the subcellular localization of multi-site virus proteins. The best overall accuracy values of R-Dipeptide, I-PseAAC, and PseAAC2 when applied to dataset S from Virus-mPloc are 59.92%, 59.13%, and 57.94% respectively.

Prediction of subcellular localization is critical for the analysis of mechanism and functions of proteins and biological research. A series of efficient methods have been proposed to identify subcellular localization, but challenges still exist. In this paper, a novel feature extraction method, denoted as F-Dipe, is proposed to identify subcellular localization. F-Dipe, which is based on dipeptide pseudo amino acid composition method, improves the performance of multi-site prediction by increasing the focus information of proteins. Besides, convolution neural networks, denoted as CNN, is utilized to predict the subcellular localization of multi-site virus proteins. The multi-label k-nearest neighbor algorithm, denoted as MLKNN, is a base classifier to verify the performance of F-Dipe and CNN. The best overall accuracy of F-Dipe on dataset S from the predictor of MLKNN is 59.92%, higher than the accuracy of pseudo amino acid based features method, denoted as PseAAC, 57.14% and the best overall accuracy of F-Dipe on database S from the predictor of CNN is 62.3%, better than from the predictor of MLKNN 59.92%.

Prediction of subcellular localization of Gram-negative bacterial proteins plays a vital role in the development of antibacterial drugs. Computational approaches have made remarkable progress in bacterial protein subcellular localization, but disadvantages still exist. Recently, deep learning has received significant attention in bioinformatics and one of the key steps in prediction of subcellular localization is developing a powerful predictor. Therefore, improved convolutional neural networks (ICNN) is used to improve the performance of multi-site prediction. First of all, Amphiphilic pseudo amino acid based features (Ampseaac) is used to extract features. Then, compared to the multi-label k-nearest neighbor algorithm (MLKNN), ICNN is developed to identify the subcellular localization of Gram-negative bacterial proteins. The best overall accuracy of Ampseaac from ICNN predictor is 65.25%, better than MLKNN predictor 58.58%.

For long-term monitoring of the midspan deflection of Songjiazhuang cloverleaf junction on 309 national roads in Zibo city, this paper proposes Zhang’s calibration-based DIC deflection monitoring method. CCD cameras are used to track the change of targets’ position, Zhang’s calibration algorithm is introduced to acquire the intrinsic and extrinsic parameters of CCD cameras, and the DIC method is combined with Zhang’s calibration algorithm to measure bridge deflection. The comparative test between Zhang’s calibration and scale calibration is conducted in lab, and experimental results indicate that the proposed method has higher precision. According to the deflection monitoring scheme, the deflection monitoring software for Songjiazhuang cloverleaf junction is developed by MATLAB, and a 4-channel CCD deflection monitoring system for Songjiazhuang cloverleaf junction is integrated in this paper. This deflection monitoring system includes functions such as image preview, simultaneous collection, camera calibration, deflection display, and data storage. In situ deflection curves show a consistent trend; this suggests that the proposed method is reliable and is suitable for the long-term monitoring of bridge deflection.

Credit management is a key factor in reducing credit risk of companies. The performance of credit departments in good standing guarantees stability and profitability of small and medium-sized loan companies. However, loan companies encounter credit risk more and more seriously. Credit managers cannot do very well on supporting the credit decision because credit risk factors are complicated and diversified. Besides, there are different credit risk factors in different cities in terms of their economic development, consumption levels, and competition in the market etc. Loan companies in Ji'nan City, Shandong Province are regarded as critical and competitive financial organizations that make contribution to the economic development of Ji'nan City and control loan risk. This paper analyses the original customer data from a company of Ji'nan City and then predicts overdue status of customers with improved Back Propagation algorithm (improved BP algorithm) by adding momentum coefficient and learning rate, which makes contribution to reducing credit risk. The overall accuracy rate of training has reached 90% and its testing accuracy rate has reached 80%, which contributes to any credit decision objectively instead of the subjective shortage of credit managers in analysis, judgment, and not accuracy.