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The effects of co-fermentation with Torulaspora delbrueckii on the composition of volatile compounds and sensory characteristics of strawberry fruit wines were investigated by fermenting strawberry pulp with T. delbrueckii in pure fermentation, as well as in simultaneous and sequential inoculations with Saccharomyces cerevisiae. E-nose analysis results showed that these strawberry fruit wines with different fermentation patterns exhibited distinct aromatic characteristics. The sensory attributes intensities of 'fruit', 'strawberry', 'sweet' and 'floral' were significantly enhanced in sequential fermentation compared to pure S. cerevisiae fermentation. Altogether, 87 volatile compounds were detected with HS-SPME-GC/MS in strawberry products, with 32 of them identified as key contributors to the overall aroma due to their high odor activity values. The sequential fermentation increased the concentration of desirable aroma-active compounds, including isoamyl alcohol, phenylethanol, isoamyl acetate, and some ethyl esters in strawberry fruit wines. The partial least squares regression analysis revealed that the fruity, floral and sweet characteristics were primarily attributed to specific aroma-active compounds, including esters such as ethyl caproate, ethyl caprylate, ethyl cinnamate, isoamyl acetate, ethyl cinnamate, ethyl 3-phenylpropionate and ethyl butyrate, as well as terpenes such as limonene, α-terpineol and nerolidol. [ABSTRACT FROM AUTHOR]

Background and purpose : Health-related quality of life (HRQoL) is a key aspect of care for cancer survivors that can be improved by physical activity. Our aim was to explore the relationship between physical activity and time to deterioration (TTD) of the HRQoL in patients with lung adenocarcinoma (LUAD). Methods: We conducted a hospital-based prospective study. The International Physical Activity Questionnaire long-form (IPAQ-L) was used to investigate the pre-treatment physical activity levels, and the EORTC Quality of Life Questionnaire version 3.0 (EORTC QLQ-C30) and EORTC Quality of Life Questionnaire-Lung Cancer (EORTC QLQ-LC13) were used to assess HRQoL at baseline and during follow-up. The QoLR package was used to calculate the HRQoL scores and determine TTD events (minimal clinically important difference=5 points). The effect of physical activity on the HRQoL was assessed using Cox regression analysis. Results: For EORTC QLQ-C30, TTD events of physical functioning (PF) and dyspnea (DY) in functional scales and symptom scales were the most common during follow-up. Pre-treatment physical activity was found to significantly delay TTD of insomnia (HR=0.635, 95%CI: 0.437–0.922, P=0.017) and diarrhea (HR=0.475, 95%CI: 0.291–0.774, P=0.003). For EORTC QLQ-LC13 scales, deterioration of dyspnea (LC-DY) was the most common event. Physical activity was found to delay the TTD of dyspnea (HR=0.654, 95%CI: 0.474–0.903, P=0.010), sore mouth (HR=0.457, 95%CI: 0.244–0.856, P=0.015), and dysphagia (HR=0.315, 95%CI: 0.172–0.580, P<0.001). Conclusions: Pre-treatment physical activity of LUAD patients may delay the TTD of multiple HRQoL indicators in EORTC QLQ-C30 and EORTC QLQ-LC13. Implication for Cancer Survivors: Health-related quality of life (HRQoL) is a key aspect of care for cancer survivors (someone who is living with or beyond cancer), that can be improved by physical activity. Our aim was to explore the relationship between physical activity and time to deterioration (TTD) of the HRQoL in patients with lung adenocarcinoma (LUAD). [ABSTRACT FROM AUTHOR]

In 2022, COVID-19 solutions in China have entered a normal stage, and the solutions imported from ports have been transformed from emergency prevention and control measures to investigative long-term prevention and control measures. Therefore, it is necessary to study solutions for COVID-19 at border ports. In this study, 170 research papers related to the prevention and control measures of COVID-19 at ports from 2020 to September 2022 were retrieved from Wanfang database, HowNet database, Wip database, and WoS core collection. Citespace 6.1.R2 software was used to research institutions visualize and analyze researchers and keywords to explore their research hotspots and trends. After analysis, the overall volume of documents issued in the past 3 years was stable. The major contributors are scientific research teams such as the Chinese Academy of Inspection and Quarantine Sciences (Han Hui et al.) and Beijing Customs (Sun Xiaodong et al.), with less cross-agency cooperation. The top five high-frequency keywords with cumulative frequency are as follows: COVID-19 (29 times), epidemic prevention and control (29 times), ports (28 times), health quarantine (16 times), and risk assessment (16 times). The research hotspots in the field of prevention and control measures for COVID-19 at ports are constantly changing with the progress of epidemic prevention and control. Cooperation between research institutions needs to be strengthened urgently. The research hotspots are the imported epidemic prevention and control, risk assessment, port health quarantine, and the normalized epidemic prevention and control mechanism, which is the trend of research and needs further exploration in the future. [ABSTRACT FROM AUTHOR]

In this paper, we study an initial value problem of the Navier-Stokes-Poisson equations for compressible, viscous, heat-conducting flows on the whole space R 3 . The global well-posedness of strong solutions subject to large and non-flat doping profile is established. The initial data is of small energy but possible large oscillations, and the initial density is allowed to contain vacuum states. [ABSTRACT FROM AUTHOR]

Cognitive computing involves discovering hidden rules and patterns in massive volumes of data. Density peaks clustering (DPC) is a powerful data mining tool that can identify density peaks in decision graphs and assign labels to them without requiring iterations. It can efficiently and simply detect clusters of arbitrary shapes. However, on the one hand, density measurement using the ϵ neighbor or Gaussian kernel only reflects the global structure of the data, so that correct density peaks cannot be found, and performance on manifold datasets is weakened. On the other hand, the one-step allocation strategy results in chain reaction. Once a point with high density is misallocated, a series of points will be incorrectly assigned. To solve this problem, this paper proposes the Jaccard coefficient to measure the similarity between points. The proposed density measurement based on Jaccard coefficient is only related to the k points that share the max similarity with the given point, which can reflect the local structure of manifold datasets, and the density peaks can be identified accurately. Aiming at the chain reaction caused by the assignment strategy of DPC, we develop a two-step allocation strategy based on label propagation and the proposed measurement of similarity. The first step is to assign labels to points close to the clustering centers, where these are equal to labeled points in the label propagation algorithm. The second step is to complete the assignment of labels to the remaining points according to labeled data which is the nearest to each unassigned sample. We compared the proposed algorithm with four algorithms on synthetic datasets and real-world datasets. The three metrics among these algorithms show that the proposed algorithm outperforms other algorithms. The results of clustering on synthetic datasets verified the effectiveness of the proposed method for manifold datasets, and three metrics on the UCI datasets and the Olivetti Faces dataset show that it can reveal the patterns and associations of real-world datasets. [ABSTRACT FROM AUTHOR]

In this study, we consider the multiple traveling salesman problem (MTSP) with multiple depots, closed paths, and a constraint on the number of cities visited by each traveling salesman. In our previous study, we proposed a state transition simulated annealing algorithm (STASA) that employs the Metropolitan criterion for simulated annealing to solve the traveling salesman problem (TSP). The results obtained were significantly improved according to our previous experiments. In the present study, we propose a new intelligent operator to solve the MTSP called an elementary breakpoint operator, which can generate the solution space for MTSP through elementary matrix operations. A neighborhood search structure called 2opt, which can improve the quality of the solution by connecting two shorter non-adjacent arcs, is then introduced into STASA to enhance the ability to capture the global optimal solution. Finally, comparative experiments were conducted based on a wide range of TSPLIB benchmark instances using our proposed algorithm and other algorithms, and the results demonstrated the superior performance of our proposed approach compared with all other state-of-the-art approaches for MTSP. [ABSTRACT FROM AUTHOR]

In this article, a novel multi-objective optimization algorithm based on a state-transition simulated annealing algorithm (MOSTASA) is proposed, in which four state-transition operators for generating candidate solutions and the Pareto optimal solution is obtained by combining it with the concept of Pareto dominance and then storing it in a Pareto archive. To ensure the uniform distribution of the Pareto optimal solution, we define a crowded comparison operator to update the Pareto archive. Simulation experiments were conducted on several standard constrained and unconstrained multi-objective problems, in which convergence and spacing metrics were used to assess the performance of the MOSTASA. The test results manifest that the MOSTASA can converge to the true Pareto-optimal front, and the solution distribution is uniform. Compared to the performance of other multi-objective optimization algorithms, the proposed algorithm is more efficient and reliable. [ABSTRACT FROM AUTHOR]

PM2.5 (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM2.5 are non-linear and non-stationary, it is difficult to predict future PM2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM2.5 prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability: in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM2.5 data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques. [ABSTRACT FROM AUTHOR]

The moth search algorithm (MS) is a novel intelligent optimization algorithm based on moth population behavior, which can solve many problems in different fields. However, the algorithm is easy to fall into local optimization when solving complex optimization problems. This study develops a new hybrid moth search-fireworks algorithm (MSFWA) to solve numerical and constrained engineering optimization problems. The explosion and mutation operators from the fireworks algorithm are introduced into the MS, which not only preserves the advantages of fast convergence and strong exploitation capability of the algorithm, but also significantly enhances the exploration capability. The performance of the MSFWA is tested using 23 benchmark functions. The hybrid algorithm is superior to other highly advanced metaheuristic algorithms for most benchmark functions, demonstrating the characteristics of fast convergence and high stability. Finally, the ability of the MSFWA to solve practical constrained problems is evaluated on six well-known engineering application problems. Compared with other optimization algorithms, the MSFWA is very competitive in its solution of these complex and constrained practical problems. [ABSTRACT FROM AUTHOR]

With the success of the deep residual network for image recognition tasks, the residual connection or skip connection has been widely used in deep learning models for various vision tasks, including single image super-resolution (SISR). Most existing SISR approaches pay particular attention to residual learning, while few studies investigate highway connection for SISR. Although skip connection can help to alleviate the vanishing gradient problem and enable fast training of the deep network, it still provides the coarse level of approximation in both forward and backward propagation paths and thus challenging to recover high-frequency details. To address this issue, we propose a novel model for SISR by using highway connection (HNSR), which composes of a nonlinear gating mechanism to further regulate the information. By using the global residual learning and replacing all local residual learning with designed gate unit in highway connection, HNSR has the capability of efficiently learning different hierarchical features and recovering much more details in image reconstruction. The experimental results have validated that HNSR can provide not only improved quality but also less prone to a few common problems during training. Besides, the more robust and efficient model is suitable for implementation in real-time and mobile systems. [ABSTRACT FROM AUTHOR]

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Superpixel as a fundamental processing unit can significantly reduce the computational complexity of subsequent computer vision tasks. In this paper, an Adaptive High-Precision (AHP) superpixel segmentation algorithm is proposed. Three major schemes are proposed in this algorithm. First, a scheme of generating the initial number of superpixels adaptively is proposed by calculating the histogram peak value of L-component corresponding to the smallest salient region. In addition, we show that the isolated pixels are not only existent but also abundant. Instead of using distance-measurement of color and position space, we reclassify each isolated pixel just relying on the LAB color space. Furthermore, the detection and re-segmentation scheme of the under-segmentation superpixels is adopted according to the standard deviation and mean value of the L-component, which can re-segment each under-segmentation superpixel directly without measuring the distance between each under-segmentation pixel and new centroid. The proposed algorithm has linear computational complexity and the merits of adaptivity and high precision. Experiments on the Berkeley segmentation dataset demonstrate the effectiveness and feasibility of the proposed schemes, and they also prove AHP can achieve high-precision superpixel segmentation results in comparison with the state-of-the-art algorithms on standard metrics. [ABSTRACT FROM AUTHOR]

In order to perform useful tasks in people's daily life, robots must be able to both communicate and understand the sensations they experience and may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose an effective hierarchical extreme learning machine with local-receptive-field architecture, while introducing the local receptive field concept in neuroscience and maintaining ELM's advantages of training efficiency. In this paper, we further extend the LRF-based ELM method to a hierarchical model for haptic classification. Experimental validation on the Penn Haptic Adjective Corpus 2 dataset illustrates that the proposed hierarchical method achieves better recognition performance. [ABSTRACT FROM AUTHOR]

A high precision image segmentation algorithm using SLIC and neighborhood rough set is proposed. The algorithm mainly includes two stages: the stage of superpixel generation and the mergence stage based on neighborhood rough set. In superpixel generation stage, based on L-channel color histogram and its peak, the scheme of initial superpixel number generation is proposed according to the complexity of the image itself. For inaccuracy segmentation edge of SLIC caused by isolated pixels, the compactness factor is appropriately increased before they are generated. After that, the scheme of reclassifying each isolated pixel is proposed just relying on the color space. In superpixel mergence stage based on neighborhood rough set, the texture information using the gray level co-occurrence matrix is introduced into the feature representation of superpixel. It can reduce the dependence of color feature and improve the accuracy of the mergence. By constructing the information table, the neighborhood granule of each superpixel is acquired under the neighborhood threshold. Finally, the superpixels within the neighborhood granule are merged on the basis of the spatial adjacency between superpixels. In Berkeley segmentation data set, compared with the SLIC algorithm, the schemes of initial superpixel number generation and the isolated pixels processing are proved to be effective. Furthermore, the experiments demonstrate that the proposed algorithm can produce high-quality and high-precision image segmentation results in comparison with the SLIC-based image segmentation algorithms on three standard metrics. [ABSTRACT FROM AUTHOR]

Visual, haptic, and auditory modalities can provide different properties about surface materials and therefore form important perception methods for common material. Nevertheless, how to effectively combine various modalities is an extremely challenging problem. To this end, the active multi-modal framework based on extreme learning machine with multi-scale local receptive fields is developed to fuse the information of different modalities. We conduct multi-modal experiments on the TUM haptic texture database. The experimental results show the highly representative characteristics can be extracted from surface material by the proposed architecture and the three modalities fusion achieves the best performance. The proposed active multi-modal fusion framework shows significant performance improvements. [ABSTRACT FROM AUTHOR]

In this paper, we develop a method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects in a dataset. Hierarchical local-receptive-field-based extreme learning machine architecture is developed to jointly learn the state representation and the reinforcement learning strategy. Experimental validation on the publicly available GERMS dataset shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Kukaazi Pb-Zn-Cu-W polymetallic deposit, located in the Western Kunlun orogenic belt, is a newly discovered skarn-type deposit. Ore bodies mainly occur in the forms of lenses and veins along beddings of the Mesoproterozoic metamorphic rocks. Three ore blocks, KI, KII, and KIII, have been outlined in different parts of the Kukaazi deposit in terms of mineral assemblages. The KI ore block is mainly composed of chalcopyrite, scheelite, pyrrhotite, sphalerite, galena and minor pyrite, arsenopyrite, and molybdenite, whereas the other two ore blocks are made up of galena, sphalerite, magnetite and minor arsenopyrite and pyrite. In this study, we obtained a molybdenite isochron Re-Os age of 450.5 ± 6.4 Ma (2σ, MSWD = 0.057) and a scheelite Sm-Nd isochron age of 426 ± 59 Ma (2σ, MSWD = 0.49) for the KI ore block. They are broadly comparable to the ages of granitoid in the region. Scheelite grains from the KI ore block contain high abundances of rare earth elements (REE, 42.0-95.7 ppm) and are enriched in light REE compared to heavy REE, with negative Eu anomalies (δEu = 0.13-0.55). They display similar REE patterns and Sm/Nd ratios to those of the coeval granitoids in the region. Moreover, they also have similar Sr and Nd isotopes [Sr/Sr = 0.7107-0.7118; ε(t) = −4.1 to −4.0] to those of such granitoids, implying that the tungsten-bearing fluids in the Kukaazi deposit probably originate from the granitic magmas. Our results first defined that the Early Paleozoic granitoids could lead to economic Mo-W-(Cu) mineralization at some favorable districts in the Western Kunlun orogenic belt and could be prospecting exploration targets. [ABSTRACT FROM AUTHOR]

In recent years, a great deal of research has focused on the sparse representation for signal. Particularly, a dictionary learning algorithm, K-SVD, is introduced to efficiently learn an redundant dictionary from a set of training signals. Indeed, much progress has been made in different aspects. In addition, there is an interesting technique named extreme learning machine (ELM), which is an single-layer feed-forward neural networks (SLFNs) with a fast learning speed, good generalization and universal classification capability. In this paper, we propose an optimization method about K-SVD, which is an denoising deep extreme learning machines based on autoencoder (DDELM-AE) for sparse representation. In other words, we gain a new learned representation through the DDELM-AE and as the new 'input', it makes the conventional K-SVD algorithm perform better. To verify the classification performance of the new method, we conduct extensive experiments on real-world data sets. The performance of the deep models (i.e., Stacked Autoencoder) is comparable. The experimental results indicate the fact that our proposed method is very efficient in the sight of speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper is concerned with the free boundary value problem (FBVP) for the spherically symmetric barotropic compressible Navier-Stokes equations (CNS) with density-dependent viscosity coefficients in the case that across the free surface stress tensor is balanced by a nonconstant exterior pressure. Under certain assumptions imposed on the initial data, we show that there exists a unique global strong solution which is strictly positive for any finite time and decays pointwise to zero time-asymptotically. [ABSTRACT FROM AUTHOR]

This paper deals with an initial-boundary value problem to the two-dimensional equations of incompressible micropolar fluids. We first prove that as the angular and micro-rotational viscosities go to zero (i.e., $${\gamma, \zeta \to 0}$$ ), the solution converges to a global weak solution of the original equations with zero angular and micro-rotational viscosities. Convergence rates are also obtained. Then, we study the boundary effects and prove that a boundary-layer thickness is of the value $${\delta(\gamma) = \gamma^\alpha}$$ with $${\alpha \in (0, 1/2)}$$ , provided $${\lim_{\gamma \to 0} \zeta \gamma^{1/2} < \infty}$$ . [ABSTRACT FROM AUTHOR]

This paper is concerned with an initial value problem of the compressible full Hall-MHD equations in three-dimensional whole space. Both the global existence and the optimal decay rates of solutions are obtained, when the smooth initial data are sufficiently close to the non-vacuum equilibrium in H 1 . As a by-product of the uniform estimates, the vanishing limit of Hall coefficient is also justified. [ABSTRACT FROM AUTHOR]