Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Purpose With the availability and utilization of Inter-Country Input-Output (ICIO) tables, it is possible to construct quantitative indices to assess its impact on the Global Value Chain (GVC). For the sake of visualization, ICIO networks with tremendous low- weight edges are too dense to show the substantial structure. These redundant edges, inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis (SNA). In this case, we need a method to filter such edges and obtain a sparser network with only the meaningful connections. Design/methodology/approach In this paper, we propose two parameterless pruning algorithms from the global and local perspectives respectively, then the performance of them is examined using the ICIO table from different databases. Findings The Searching Paths (SP) method extracts the strongest association paths from the global perspective, while Filtering Edges (FE) method captures the key links according to the local weight ratio. The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks. Research limitations There are still two limitations in this research. One is that the computational complexity may increase rapidly while processing the large-scale networks, so the proposed method should be further improved. The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology. Practical implications The network pruning methods we proposed will promote the analysis of the ICIO network, in terms of community detection, link prediction, and spatial econometrics, etc. Also, they can be applied to many other complex networks with similar characteristics. Originality/value This paper improves the existing research from two aspects, namely, considering the heterogeneity of weights and avoiding the interference of parameters. Therefore, it provides a new idea for the research of network backbone extraction.

Application of Bayesian network and regression method in treatment cost prediction

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Slugging Fatigue Assessment for Steel Lazy Wave Risers

The objective of this paper is to introduce a new analysis methodology for assessment of riser fatigue due to slugging. Under certain flow regimes, a multiphase (oil-gas-water) flow can result in slug flow, in which a sequence of relatively high density slugs and relatively low density bubbles propagate along the flowline and the riser. The variation of slug and bubble density at a location with time is random, and slug characteristics can also change significantly along the riser length. Due to local and global weight variations, the riser undergoes cycles of bending which cause fatigue. By explicitly modeling full spatial and temporal variability and randomness of slugs, the new analysis method is significantly more accurate than other methods and it captures physics of riser's slugging response. The slugging fatigue of a steel lazy wave riser was analyzed in Orcaflex software by modeling a repeating pair of slug and bubble with constant slug and bubble densities and associated lengths over the 3-hour simulation time. A separate slug train was propagated in five sub-segments of the riser. To model a more accurate and realistic representation of slugging behavior, the time series of density was extracted at each node from the multiphase flow simulator Olga. Statistical and spectral analysis of the Olga output showed that assumptions of constant slug-bubble density, and of slug behavior being uniform over long segments of riser are too simplistic. Therefore, full time series of density at each node was input into the riser analysis using the existing capabilities of Orcaflex software. As the Orcaflex slug form approach was computationally expensive, we also developed an extrenal slug loader, which provides same level of accuracy while being computationally fast and full automated. The new method shows that the cyclic riser response at the touchdown point (TDP) is composed of two parts. One is the relatively short period (~20-60 seconds) fluctuations that occur because of local weight variations as a slug-bubble passes a riser node. The other is the relatively long period (~10-30 minutes) fluctuations that occur due to global weight variations, which are due to spatial integration of density time series over the lower catenary. These long period fluctuations drive the TDP fatigue. Preliminary field measurements with an ROV, while inducing temporary slugging in the riser, confirmed analytical predictions of long period and high amplitude motions at hog bend. This paper presents a new and significantly more accurate method for analyzing riser fatigue due to slugging. Previously unknown behavior of very long period and high amplitude riser motions is identified and explained. SLWR response to slugging can be an important contributor to the overall fatigue design budget especially at the TDP. This work reflects ExxonMobil's on-going efforts to ensure that we maintain safe designs as we adopt systems new to us in new and challenging environments.

An Improved Term Weighting Method Based on Relevance Frequency for Text Classification

As a vital step of text classification (TC) task, the assignment of term weight has a great influence on the performance of TC. Currently, masses of term weighting schemes can be utilized, such as term frequency-inverse documents frequency (TF-IDF) and term frequency-relevance frequency (TF-RF), and they are all consisted of local part (TF) and global part (e.g., IDF, RF). However, most of these schemes adopt the logarithmic processing on their respective global parts, and it is natural to consider whether the logarithmic processing apply to all these schemes or not. Actually, for a specific term weighting scheme, due to its different ratio of local weight and global weight resulting from logarithmic processing, it usually shows diverse text clasification results on different text sets, which presents poor robustness. To explore the influence of logarithmic processing imposed on the global weight on the classification result of term weighting schemes, TF-RF is selected as the representative because it can achieve a better performance among these schemes adopting logarithmic processing. Then, two propositions along with corresponding methods about the relation between TF part and RF part are proposed based on TF-RF. In addition, two groups of experiments are conducted on the two methods. The first group of experiments proves that one method (denoted as TF-ERF) is more helpful to the improvement than the other one (denoted as ETF-RF). The second group of experiments shows that TF-ERF not only ourperforms TF-RF but also obtains better performance than other existing term weighting schemes.

Application of a New Improved Weighting Method, ESO Method Combined with Fuzzy Synthetic Method, in Water Quality Evaluation of Chagan Lake

The existing weighting methods mainly comprise subjective and objective weighting and have a certain degree of subjectivity, with certain requirements for the professional ability of the users and unstable results. Therefore, an improved weighting method based on the entropy weight, over-standard multiple, and single-factor evaluation methods, referred to as the ESO method, is proposed. The advantages and advancements of the ESO method are demonstrated in this study by combining it with the fuzzy synthetic evaluation method to evaluate the water quality of Chagan Lake wetland from 2007 to 2016. The main conclusions of this study are as follows: 1. The ESO method has more comprehensive consideration factors, lower requirements for the professional ability of users, and more stable weighting results than the traditional weighting method. Therefore, it is highly suitable for beginners and frontline staff who are not professionally qualified and cannot accurately conduct subjective weighting. Meanwhile, owing to the amendment rule and emphasis on the local weight of the sample in the ESO method, it is applicable to time-series samples. 2. The ESO method better allocates the amendment weights to indicators with a higher degree of pollution; thus, the final comprehensive evaluation results are relatively conservative. However, in contrast to the single-factor evaluation, the conservatism of ESO method is the result of the comprehensive effect of all samples; thus, the conservative result of the ESO method is more reasonable. 3. The water quality of Chagan Lake in 2009 and 2015 was class IV, which did not meet the standard, while that in remaining the eight years was class III, which met the requirements of the national 13th Five-Year Plan. The results of this study can provide a new approach to weighting calculation methods and a basis for the protection and treatment of the ecological environment of the Chagan Lake wetland.

The weight of nonstrongly complete profinite groups

We compute the local weight of the completion of a nonstrongly complete profinite group and conclude that, if a profinite group is abstractly isomorphic to its own profinite completion, then they are equal. The local weights of all the groups in the tower of completions are computed as well.

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.