ALBERT

Description: Water, Vol. 10, Pages 1234: Evaluation of the Non-Darcy Effect of Water Inrush from Karst Collapse Columns by Means of a Nonlinear Flow Model Water doi: 10.3390/w10091234 Authors: Yi Xue Teng Teng Lin Zhu Mingming He Jie Ren Xun Dong Fei Liu Karst collapse columns (KCCs) are naturally formed geological structures that are widely observed in North China. Given their influence on normal mining operations and the progress of mining work, collapse columns pose a hidden danger in coal mining under the influence of manual mining. By communicating often with the aquifer, the water inrush from KCCs poses a serious threat to construction projects. This paper adopts three flow field models, namely, Darcy aquifer laminar flow, Forchheimer flow, and Navier–Stokes turbulent flow, based on the changes in the water inrush flow pattern in the aquifer and laneway, and uses COMSOL Multiphysics software to produce the numerical solutions of these models. As the water inrush flow velocity increases, the Forchheimer flow shows the effect of additional force (inertial resistance) on flow in KCCs, in addition to the effect of viscous resistance. After the joint action of viscous resistance and inertial resistance, the inertial resistance ultimately dominates and gradually changes the water inrush from the KCCs to fluid seepage. Forchheimer flow can comprehensively reflect the nonlinear flow process in the broken rock mass of KCCs, demonstrate the dynamic process from the Darcy aquifer to the final tunnel turbulence layer, and quantitatively show the changes in the flow patterns of the water inrush from KCCs.

Description: Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Description: The growth morphology and structure of ceria nano-islands on a stepped Au(788) surface has been investigated by scanning tunneling microscopy (STM) and low-energy electron diffraction (LEED). Within the concept of physical vapor deposition, different kinetic routes have been employed to design ceria-Au inverse model catalysts with different ceria nanoparticle shapes and arrangements. A two-dimensional superlattice of ceria nano-islands with a relatively narrow size distribution (5 ± 2 nm2) has been generated on the Au(788) surface by the postoxidation method. This reflects the periodic anisotropy of the template surface and has been ascribed to the pinning of ceria clusters and thus nucleation on the fcc domains of the herringbone reconstruction on the Au terraces. In contrast, the reactive evaporation method yields ceria islands elongated in [01-1] direction, i.e., parallel to the step edges, with high aspect ratios (~6). Diffusion along the Au step edges of ceria clusters and their limited step crossing in conjunction with a growth front perpendicular to the step edges is tentatively proposed to control the ceria growth under reactive evaporation conditions. Both deposition recipes generate two-dimensional islands of CeO2(111)-type O–Ce–O single and double trilayer structures for submonolayer coverages.

Description: In this study, a multiple linear regression model to evaluate the risk of morbidity and mortality of ischemic cardiovascular disease is demonstrated. In this model, predictor variables are selected from physiological chemicals in a blood test of the subjects. Meanwhile, the calculated risk score is selected as a response variable. Four major latent variables including hepatic, nephric, metabolic, and BMI (Body Mass Index) are revealed by performing statistical and principal component analysis for the collected survey data. The analyzed result also shows that the cardiac disorder is correlated with symptoms of abnormal BMI, hepatic disorder, nephric disorder, and metabolic disorder. Thus, the risk of morbidity and mortality of ischemic cardiovascular disease can be assessed from the proposed multiple regression model.

Description: Regular monitoring of blood α-fetoprotein (AFP) and/or carcino-embryonic antigen (CEA) levels is important for the routine screening of liver cancer. However, AFP and CEA have a much lower specificity than des-γ-carboxyprothrombin (DCP) to detect liver cancer. Therefore, the study reported here was designed, to develop a screen-printed DCP immunosensor incorporating zinc oxide nanoparticles, for accurate determination of DCP. The designed immunosensor shows low detection limits for the detection of DCP: 0.440 ng/mL (based on impedance measurement), 0.081 ng/mL (based on real part of impedance measurement) and 0.078 ng/mL (based on imaginary part of impedance measurement), within the range of 3.125 ng/mL to 2000 ng/mL. In addition, there was little interference to DCP determination by molecules such as Na+, K+, Ca2+, Cl−, glucose, urea, and uric acid. It is therefore concluded that the DCP immunosensor developed and reported here is simple, inexpensive and effective, and shows promise in the rapid screening of early-stage liver cancer at home with a point-of-care approach.

Description: Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

Description: Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

Description: Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to maintain physical stillness. In this article, wireless wearable accelerometers were used to detect physical stillness, and the created physical stillness index (PSI) was also shown. Ninety college students participated in this study. Primarily, accelerometers used on the arms and chest were examined. The results showed that the PSI values on the arms were higher than that of the chest, when participants moved their bodies in three different ways, left-right, anterior-posterior, and hand, movements with natural breathing. Then, they were divided into three groups to practice Chan Ding for approximately thirty minutes. Participants without any Chan Ding experience were in Group I. Participants with one year of Chan Ding experience were in Group II, and participants with over three year of experience were in Group III. The Chinese Happiness Inventory (CHI) was also conducted. Results showed that the PSI of the three groups measured during 20–30 min were 0.123 ± 0.155, 0.012 ± 0.013, and 0.001 ± 0.0003, respectively (p 〈 0.001 ***). The averaged CHI scores of the three groups were 10.13, 17.17, and 25.53, respectively (p 〈 0.001 ***). Correlation coefficients between PSI and CHI of the three groups were −0.440, −0.369, and −0.537, respectively (p 〈 0.01 **). PSI value and the wearable accelerometer that are presently available on the market could be used to evaluate the quality of the physical stillness of the participants during Chan Ding practice.

Description: Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning.

Description: Nanoindentation-induced multiple pop-ins were observed in the load-displacement curves when the mechanical responses of AlN films grown on c-plane sapphire substrates were investigated by using Berkovich indenters. No evidence of phase transformation is revealed by cross-sectional transmission electron microscopy (XTEM) and selected area diffraction (SAD) analyses. Instead XTEM observations suggest that these “instabilities” resulted from the sudden nucleation of dislocations propagating along the slip systems lying on the {0001} basal planes and the  pyramidal planes commonly observed in hexagonal compound semiconductors. Based on this scenario, an energetic estimation of dislocation nucleation is made.