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The conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival.

Background: Psoriasis and dementia are both inflammatory diseases. The association between psoriasis and dementia has rarely been investigated, and the existing results are conflicting. Thus, we conducted this study to evaluate whether an association exists between psoriasis and dementia.Methods: We searched for studies from six databases from inception to July 30, 2020, using subject and free words. RevMan 5.4 was used to calculate the risk ratio (RR) of dementia in the subjects with psoriasis. When heterogeneity was present, a random-effects model was used. Subgroup, sensitivity, and meta-regression analyses were performed using Stata 15.1.Results: Nine studies were identified and included in the study, of which seven that involved a total of 3,638,487 participants were included in the meta-analysis. We found that among the patients with psoriasis (RR: 1.14, 95% confidence interval [CI]: 1.06–1.24, p = 0.0009) and psoriatic arthritis (RR: 2.20, 95% CI: 1.29–3.78, p = 0.004), the proportions of those with non-vascular dementia (RR: 1.13, 95% CI: 1.11–1.15, p < 0.00001) and vascular dementia (RR: 1.41, 95% CI: 1.09–1.82, p = 0.009) were higher than that among the patients without psoriasis. Those with dementia were also more likely to develop psoriasis, and those with severe psoriasis were less likely to die from dementia (RR: 1.88, 95% CI: 0.72–4.90, p = 0.020). The meta-regression analysis did not show any significant sources of heterogeneity.Conclusions: The patients with psoriasis and psoriatic arthritis show high prevalence of different types of dementia. Based on the findings of this study, dementia may not be considered a high-risk factor of death from severe psoriasis. However, identification of this potential risk allows for early intervention, thereby reducing comorbidities and deaths.

The bolted rotor is a typical rotor structure with light weight, high strength, good rigidity, flexible selection of all kinds of wheel materials, etc., which is formed by pressing and combining several sections of rotors or discs and is widely used in aero-engines and gas turbines. Aiming at blind assembly, trail assembly and inconsistent assembly tightness of bolted rotor, and the degradation and loosening of bolt tightness in service, a quantitative detection method of bolt assembly quality based on GHM multiwavelet was proposed. Using the GHM multiwavelet with excellent characteristics such as symmetry, compactness, orthogonality, vanishing moment and high order vanishing moment, feature extraction of the bolted rotor assembly tightness states was performed. The quantitative detection index of assembly quality was defined by the relative energy band distribution based on multiwavelet. The experimental verification shows that the proposed method can accurately identify six kinds of bolt states from tightness to looseness.

With the development of the country and the improvement of the level of science and technology, the use of water resources in China is on the rise. Among them, the domestic water consumption is increasing rapidly, and it is predicted that the domestic water consumption in China will reach two to three times that in the early 21st century in the middle of the 21st century. Based on the data of domestic water consumption in ten typical countries in the world, such as the United States, Germany and Japan, this paper studies the variation of per capita domestic water consumption in these countries with per capita GDP, urbanization rate and other factors. Taking these data as samples, the domestic water consumption in China is predicted. The prediction results can further help relevant departments to formulate reasonable policies in water resources planning.

Impulsive noise, generated by inverters and other equipment used in the field, tends to easily enter test channels in scenarios where continuous frequency conversion signals are employed to test the frequency response of electrohydraulic proportional valves. Interference caused by such noise reduces the signal-to-response ratio of response signals, thereby influencing the accuracy of the frequency response of proportional valves. To address this concern, in this article, an integrated filtering method that combines ensemble empirical mode decomposition with median filtering is proposed. The proposed method first preprocesses the response signals of the systems and subsequently obtains frequency-response diagrams using fast Fourier transforms. Simulation results demonstrate that the proposed method reduces the root mean square error associated with the amplitude–frequency characteristic curves of the proportional directional valve considered from 2.1 to 0.3, whereas that associated with phase–frequency characteristic curves is reduced from 78.0 to 1.4 with signal-to-noise ratios in the high-frequency band being of the order of –20 dB. Experimental results also reveal that the proposed method reduces the root mean square error of the amplitude–frequency characteristic curves of the proportional directional valve by 52.5% and that of the phase–frequency characteristic curves by 71.2%.

Objective: Metabolic syndrome (MS) is a combination of medical disorders that increase the risk for cardiovascular disease and diabetes mellitus. It suggests an association between an elevated serum aminotransferase level and MS. Little data show the relationship between the levels of serum aminotransferase and the incidence of MS in Ningxia, China. Methods: A total of 5415 subjects who received medical health checkups from 2007 to 2009 were enrolled in the study. The participants were interviewed by trained health workers under a structured questionnaire. MS was defined according to the modified ATPIII criteria for Asian Americans by the American Heart Association (AHA-ATP III). Results: The prevalence of elevated aspartate aminotransferase (AST) and ALT (>40 U/L) were 7.1% and 22.2% in males, and 2.1% and 4.8% in females respectively. The prevalence of MS was 32.1% in males and 15.4% in females. The components of MS were significantly more in the group with elevated aminotransferase levels than in the group with normal aminotransferase levels. The odds ratios (95% CI) for elevated AST were 1.90 (1.49, 2.42), 2.59 (2.01, 3.39), 1.68 (1.32, 2.15), and 1.81 (1.36, 2.42) in the adults with abdominal obesity, high serum triglycerides levels, high blood pressure, and high plasma glucose levels respectively. After adjustment for age, the odds ratios (95% CI) for elevated ALT were 3.08 (2.63, 3.61), 4.30 (3.64, 5.08), 1.26 (1.08, 1.48), 2.16 (1.93, 2.65) and 2.38 (1.96, 2.87) in adults with abdominal obesity, high serum triglycerides levels, low serum high-density lipoproteincholesterol (HDL-C), high blood pressure, and high plasma glucose levels respectively. The odds ratios (95% CI) for elevated AST were 1.67 (1.06, 2.63), 2.28 (1.46, 3.63), 2.59 (1.59, 4.21) and for elevated ALT 2.02 (1.50, 2.73), 2.68 (1.96, 3.65), 3.94 (2.86, 5.43) for the subjects with 1, 2, and â¥3 risk factors after adjustment for age, gender, and BMI. Conclusion: The serum aminotransferase levels were higher in males compared to females, and serum ALT level was more closely associated with MS than the AST level in adults in Ningxia, China. With an increasing the number of components of MS, the aminotransferase levels and the risks for elevated aminotransferase increase, whereas the AST/ALT ratios decrease. Keywords: Metabolic syndrome, Serum aminotransferase, Odds ratios

The microgrid trading market can effectively solve the problem of in-situ consumption of distributed energy and reduce the impact of distributed generation (DG) on the grid. However, the traditional microgrid trading model has some shortcomings, such as high operation cost and poor security. Therefore, in this paper, a microgrid market trading model was developed using consortium blockchain technology and Nash game equilibrium theory. Firstly, blockchain container is used to authenticate the users who want to participate in the transaction. Then, the pricing system collects and integrates transaction requests submitted by users, then formulates transaction pricing strategy of microgrid using Nash equilibrium theory. Finally, the price, trading volume, and user information are submitted to the blockchain container for transaction matching to achieve the transaction. After the transaction is completed, its related information is recorded in the hyperledger and the dispatch system is called. The scene simulation was implemented on Fabric 1.1 platform and the results analyzed. Results show that the trading model proposed in this paper greatly reduces the cost of electricity purchase and improves the benefits of electricity sales. Besides, the model is far more capable of handling transactions than the models based on Bitcoin and Ethereum.

Effective cancer treatment requires a clear subtype. Due to the small sample size, high dimensionality, and class imbalances of cancer gene data, classifying cancer subtypes by traditional machine learning methods remains challenging. The gcForest algorithm is a combination of machine learning methods and a deep neural network and has been indicated to achieve better classification of small samples of data. However, the gcForest algorithm still faces many challenges when this method is applied to the classification of cancer subtypes. In this paper, we propose an improved gcForest algorithm (MLW-gcForest) to study the applicability of this method to the small sample sizes, high dimensionality, and class imbalances of genetic data. The main contributions of this algorithm are as follows: (1) Different weights are assigned to different random forests according to the classification ability of the forests. (2) We propose a sorting optimization algorithm that assigns different weights to the feature vectors generated under different sliding windows. The MLW-gcForest model is trained on the methylation data of five data sets from the cancer genome atlas (TCGA). The experimental results show that the MLW-gcForest algorithm achieves high accuracy and area under curve (AUC) values for the classification of cancer subtypes compared with those of traditional machine learning methods and state of the art methods. The results also show that methylation data can be effectively used to diagnose cancer.

With the rapid development of the energy internet, the transaction of distributed renewable energy (DRE) is playing an increasingly important role in the energy market. However, in the transaction model of distributed renewable energy combined with public blockchain technology, nodes in the trading network can join or leave the network at any time without any permission, which hinders the regulation of electricity institutions. Corresponding to the transaction principle, a distributed renewable energy transaction authentication mechanism based on consortium blockchain is proposed in this paper. First, certificate authority nodes were set in the transaction network to provide nodes with access authority by controlling the public keys and private keys of trading participants so that they can complete their identity authentication. Next, essential chaincodes in the transaction authentication were designed and deployed on a Hyperledger Fabric blockchain site, and a simulation experiment of a simple DRE transaction was used to elaborate the details on the transaction process. Finally, the proposed model was evaluated according to its performance and proved to be practical and effective.

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences.

Through the investigation and analysis of the students’ sports life in the Chinese Culture University of Taipei, it can reflect that different regional cultures have different cultural characteristics. The investigation and analysis of the students’ participation in sports activities, the degree of awareness, the selection of sports items, the motivation of participating in sports activities and the related sports consumption, is based on the Chinese Culture University of Taipei. This paper finds out the problems existing in the students’ sports life of Chinese Culture University and puts forward some reasonable suggestions.

Wrestling has a long history, and wrestling activities are deeply loved and respected by the people of Shilin which is known as the "Hometown of Wrestling." With the continuous development and progress of society, Yi Nationality’s wrestling is facing new opportunities as well as new challenges. Through the methods of literature, data statistics, questionnaire, expert interview and logical analysis, this paper studies 50 long-term wrestlers in Shilin County, analyzes the present situation and finds out the existing problems, thus providing the necessary reference for the development of the Yi wrestling sport.

Heat transfer performances and flow structures of laminar impinging slot jets with power-law non-Newtonian fluids and corresponding typical industrial fluids (Carboxyl Methyl Cellulose (CMC) solutions and Xanthangum (XG) solutions) have been studied in this work. Investigations are performed for Reynolds number Re less than 200, power-law index n ranging from 0.5 to 1.5 and consistency index K varying from 0.001 to 0.5 to explore heat transfer and flow structure of shear-thinning fluid and shear-thickening fluid. Results indicate that with the increase of n, K for a given Re, wall Nusselt number increases mainly attributing to the increase of inlet velocity U. For a given inlet velocity, wall Nusselt number decreases with the increase of n and K, which mainly attributes to the increase of apparent viscosity and the reduction of momentum diffusion. For the same Re, U and Pr, wall Nusselt number decreases with the increase of n. Among the study of industrial power-law shear-thinning fluid, CMC solution with 100 ppm shows the best heat transfer performance at a given velocity. Moreover, new correlation of Nusselt number about industrial fluid is proposed. In general, for the heat transfer of laminar confined impinging jet, it is best to use the working fluid with low viscosity.

Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently.

Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules.Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences.Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences.

Since wireless sensor networks (WSNs) consist of nodes with limited battery power, collaborative data processing and balanced energy consumption should be considered as the key issue. This paper proposes an efficient cluster head selection approach for collaborative data processing in WSNs. The proposed algorithm designs an effective energy-efficient model to select the optimal cluster heads among all nodes fairly, which helps to reduce the impact of the monitoring scheme on the lifetime of network. Experimental results show that the proposed protocol is able to reduce energy consumption and obtain higher efficiency as well as effectively prolonging the lifetime of network more than a few existing cluster-based routing protocols.

BackgroundIntegrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.MethodOur proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.ResultsOur proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).