ALBERT

Description: Cigarette smoking is an important risk factor for hypertension. However, the effects on hypertension of the interaction between smoking and the genotype of the nicotinic acetylcholine receptor gene are unclear. The purpose of this study is to determine whether the CHRNA3 rs6495308 genotype affects the association between daily cigarette consumption and hypertension. We recruited 947 male smokers in southern China and used a questionnaire administered in face to face interviews to obtain information on their socio-demographic characteristics and smoking behavior. Blood samples were collected to test for CHRNA3 rs6495308 genotype variations. Three blood-pressure measurements were taken for each participant, and the average values recorded. We found that, compared with light smoking (〈15 cigarettes per day), heavy smoking (≥15 cigarettes per day) yielded a greater risk of hypertension. We also observed that the interaction between daily cigarette consumption and the CHRNA3 rs6495308 genotype may affect hypertension. Heavy smokers with the homozygous mutant CHRNA3 rs6495308 genotype exhibited a significantly greater risk of hypertension than light smokers with wild-type CHRNA3 rs6495308 genotypes. The positive interaction between heavy smoking and the homozygous mutant CHRNA3 rs6495308 genotype was found to affect the likelihood of hypertension in Chinese male smokers.

Description: Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain.

Description: This paper focuses on developing a novel technique based on machine vision for detection of foreign substances in injections. Mechanical control yields spin/stop movement of injections which helps to cause relative movement between foreign substances in liquid and an ampoule bottle. Foreign substances are classified into two categories: subsiding-slowly object and subsiding-fast object. A sequence of frames are captured by a camera and used to recognize foreign substances. After image preprocessing like noise reduction and motion detection, two different methods, Moving-object Clustering (MC) and Frame Difference, are proposed to detect the two categories respectively. MC is operated to cluster subsiding-slowly foreign substances, based on the invariant features of those objects. Frame Difference is defined to calculate the difference between two frames due to the change of subsiding-fast objects. 200 ampoule samples filled with injection are tested and the experimental result indicates that the approach can detect the visible foreign substances effectively.

Description: In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones.

Description: Sensors, Vol. 18, Pages 2657: A Non-Contact Measuring System for In-Situ Surface Characterization Based on Laser Confocal Microscopy Sensors doi: 10.3390/s18082657 Authors: Shaowei Fu Fang Cheng Tegoeh Tjahjowidodo Yu Zhou David Butler The characterization of surface topographic features on a component is typically quantified using two-dimensional roughness descriptors which are captured by off-line desktop instruments. Ideally any measurement system should be integrated into the manufacturing process to provide in-situ measurement and real-time feedback. A non-contact in-situ surface topography measuring system is proposed in this paper. The proposed system utilizes a laser confocal sensor in both lateral and vertical scanning modes to measure the height of the target features. The roughness parameters are calculated in the developed data processing software according to ISO 4287. To reduce the inherent disadvantage of confocal microscopy, e.g., scattering noise at steep angles and background noise from specular reflection from the optical elements, the developed system has been calibrated and a linear correction factor has been applied in this study. A particular challenge identified for this work is the in-situ measurement of features generated by a robotized surface finishing system. The proposed system was integrated onto a robotic arm with the measuring distance and angle adjusted during measurement based on a CAD model of the component in question. Experimental data confirms the capability of this system to measure the surface roughness within the Ra range of 0.2–7 μm (bandwidth λc/λs of 300), with a relative accuracy of 5%.

Description: Energies, Vol. 11, Pages 985: Hydrodynamic Investigation of a Concentric Cylindrical OWC Wave Energy Converter Energies doi: 10.3390/en11040985 Authors: Yu Zhou Chongwei Zhang Dezhi Ning A fixed, concentric, cylindrical oscillating water column (OWC) wave energy converter (WEC) is proposed for shallow offshore sites. Compared with the existing shoreline OWC device, this wave energy device is not restricted by the wave directions and coastline geography conditions. Analytical solutions are derived based on the linear potential-flow theory and eigen-function expansion technique to investigate hydrodynamic properties of the device. Three typical free-surface oscillation modes in the chamber are discussed, of which the piston-type mode makes the main contribution to the energy conversion. The effects of the geometrical parameters on the hydrodynamic properties are further investigated. The resonance frequency of the chamber, the power extraction efficiency, and the effective frequency bandwidth of the device is discussed, amongst other topics. It is found that the proposed OWC-WEC device with a lower draft and wider chamber breadth has better power extraction ability.

Description: Sustainability, Vol. 10, Pages 1752: Data-Driven Method to Estimate the Maximum Likelihood Space–Time Trajectory in an Urban Rail Transit System Sustainability doi: 10.3390/su10061752 Authors: Xing Chen Leishan Zhou Yixiang Yue Yu Zhou Liwen Liu The Urban Rail Transit (URT) passenger travel space–time trajectory reflects a passenger’s path-choice and the components of URT network passenger flow. This paper proposes a model to estimate a passenger’s maximum-likelihood space–time trajectory using Automatic Fare Collection (AFC) transaction data, which contain the passenger’s entry and exit information. First, a method is presented to construct a space–time trajectory within a tap in/out constraint. Then, a maximum likelihood space–time trajectory estimation model is developed to achieve two goals: (1) to minimize the variance in a passenger’s walk time, including the access walk time, egress walk time and transfer walk time when a transfer is included; and (2) to minimize the variance between a passenger’s actual walk time and the expected value obtained by manual survey observation. Considering the computational efficiency and the characteristics of the model, we decompose the passenger’s travel links and convert the maximum likelihood space–time trajectory estimation problem into a single-quadratic programming problem. Real-world AFC transaction data and train timetable data from the Beijing URT network are used to test the proposed model and algorithm. The estimation results are consistent with the clearing results obtained from the authorities, and this finding verifies the feasibility of our approach.

Description: As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoothing necessary. This study aims to use spectral smoothing as a pretreatment and to test the applicability of the spectral derivative algorithm for Chl-a estimation in Taihu Lake, China, based on the in situ hyperspectral reflectance. Data from July–August of 2004 were used to build the model, and data from July–August of 2005 and March of 2011 were used to validate the model, with Chl-a ranges of 5.0–156.0 mg/m3, 4.0–98.0 mg/m3 and 11.4–35.8 mg/m3, respectively. The derivative model was first used and then compared with the band ratio, three-band and four-band models. The results show that the first-order derivative model at 699 nm had satisfactory accuracy (R2 = 0.75) after kernel regression smoothing and had smaller validation root mean square errors of 15.21 mg/m3 in 2005 and 5.85 mg/m3 in 2011. The distribution map of Chl-a in Taihu Lake based on the HJ1/HSI image showed the actual distribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.

Description: Remote Sensing, Vol. 9, Pages 955: The Performance of Airborne C-Band PolInSAR Data on Forest Growth Stage Types Classification Remote Sensing doi: 10.3390/rs9090955 Authors: Qi Feng Liangjiang Zhou Erxue Chen Xingdong Liang Lei Zhao Yu Zhou In this paper, we propose a classification scheme for forest growth stage types and other cover types using a support vector machine (SVM) based on the Polarimetric SAR Interferometric (PolInSAR) data acquired by Chinese Multidimensional Space Joint-observation SAR (MSJosSAR) system. Firstly, polarimetric, texture, and coherence features were calculated from the PolInSAR data. Secondly, the capabilities of the polarimetric, texture, and coherence features in land use/cover classification were quantified independently through histograms. Following this, the polarimetric features were used for the classification of land use/cover types, followed by a combination of texture and coherence features. Finally, the three classification results were validated against test samples using the confusion matrix. It was shown that, with the integration of texture and coherence features, the producer’s accuracy for afforested land, young forest land, medium forest land, and near-mature forest land improved by 6%, 31%, 11%, and 6%, respectively, compared with the former experiment using solely polarimetric features. Our study indicates that the forest and non-forest lands can be discriminated by the polarimetric features, which also play an important role in the separation between afforested land and other forest types as well as medium forest land and near-mature forest land. The texture features further discriminate afforested land and other forest types, while the coherence features obviously improved the separation of young forest land and medium forest land. This paper provides an effective way of identifying various land use/cover types, especially for distinguishing forest growth stages with SAR data. It would be of great interest in regions with frequent cloud coverage and limited optical data for the monitoring of land use/cover types.

Description: Energies, Vol. 11, Pages 1157: Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions Energies doi: 10.3390/en11051157 Authors: Yong Wang Yu Zhou Lin Zhu Fei Zhang Yingchun Zhang Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.