bluuebunny/crossref_metadata_2025 huggingface dataset:
Frequency and Spectrum of Radiation-Induced Mutations Revealed by Whole-Genome Sequencing Analyses of Plants
Authors: Jo Yeong Deuk , Kim Jin-Baek
Published: April 2019

Mutation breeding and functional genomics studies of mutant populations have made important contributions to plant research involving the application of radiation. The frequency and spectrum of induced mutations have long been regarded as the crucial determinants of the efficiency of the development and use of mutant populations. Systematic studies regarding the mutation frequency and spectrum, including genetic and genomic analyses, have recently resulted in considerable advances. These studies have consistently shown that the mutation frequency and spectrum are affected by diverse factors, including radiation type, linear energy transfer, and radiation dose, as well as the plant tissue type and condition. Moreover, the whole-genome sequencing of mutant individuals based on next-generation sequencing technologies has enabled the genome-wide quantification of mutation frequencies according to DNA mutation types as well as the elucidation of mutation mechanisms based on sequence characteristics. These studies will contribute to the development of a highly efficient and more controlled mutagenesis method relevant for the customized research of plants. We herein review the characteristics of radiation-induced mutations in plants, mainly focusing on recent whole-genome sequencing analyses as well as factors affecting the mutation frequency and spectrum.
Contingent Convertible Debt: The Impact on Equity Holders
Authors: Boursicot Delphine , Gauthier Geneviève , Pourkalbassi Farhad
Published: April 2019

Contingent Convertible (CoCo) is a hybrid debt issued by banks with a specific feature forcing its conversion to equity in the event of the bank’s financial distress. CoCo carries two major risks: the risk of default, which threatens any type of debt instrument, plus the exclusive risk of mandatory conversion. In this paper, we propose a model to value CoCo debt instruments as a function of the debt ratio. Although the CoCo is a more expensive instrument than traditional debt, its presence in the capital structure lowers the cost of ordinary debt and reduces the total cost of debt. For preliminary equity holders, the presence of CoCo in the bank’s capital structure increases the shareholder’s aggregate value.
Time-Series InSAR Monitoring of Permafrost Freeze-Thaw Seasonal Displacement over Qinghai–Tibetan Plateau Using Sentinel-1 Data
Authors: Zhang Xuefei , Zhang Hong , Wang Chao , Tang Yixian , Zhang Bo , Wu Fan , Wang Jing , Zhang Zhengjia
Published: April 2019

Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze–thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau.
Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix
Authors: Qu Jiahui , Li Yunsong , Du Qian , Dong Wenqian , Xi Bobo
Published: April 2019

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.
GNSS/INS/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization
Authors: Chang Le , Niu Xiaoji , Liu Tianyi , Tang Jian , Qian Chuang
Published: April 2019

A Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Light Detection and Ranging (LiDAR)-Simultaneous Localization and Mapping (SLAM) integrated navigation system based on graph optimization is proposed and implemented in this paper. The navigation results are obtained by the information fusion of the GNSS position, Inertial Measurement Unit (IMU) preintegration result and the relative pose from the 3D probability map matching with graph optimizing. The sliding window method was adopted to ensure that the computational load of the graph optimization does not increase with time. Land vehicle tests were conducted, and the results show that the proposed GNSS/INS/LiDAR-SLAM integrated navigation system can effectively improve the navigation positioning accuracy compared to GNSS/INS and other current GNSS/INS/LiDAR methods. During the simulation of one-minute periods of GNSS outages, compared to the GNSS/INS integrated navigation system, the root mean square (RMS) of the position errors in the North and East directions of the proposed navigation system are reduced by approximately 82.2% and 79.6%, respectively, and the position error in the vertical direction and attitude errors are equivalent. Compared to the benchmark method of GNSS/INS/LiDAR-Google Cartographer, the RMS of the position errors in the North, East and vertical directions decrease by approximately 66.2%, 63.1% and 75.1%, respectively, and the RMS of the roll, pitch and yaw errors are reduced by approximately 89.5%, 92.9% and 88.5%, respectively. Furthermore, the relative position error during the GNSS outage periods is reduced to 0.26% of the travel distance for the proposed method. Therefore, the GNSS/INS/LiDAR-SLAM integrated navigation system proposed in this paper can effectively fuse the information of GNSS, IMU and LiDAR and can significantly mitigate the navigation error, especially for cases of GNSS signal attenuation or interruption.
Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison
Authors: Jiang Tingting , Chen Bin , Chan Karen Kie Yan , Xu Bing
Published: April 2019

The geostationary earth orbit satellite—Himawari-8 loaded with the Advanced Himawari Imager (AHI) has greatly enhanced our capacity of dynamic monitoring in Asia–Pacific area. The Himawari-8/AHI hourly aerosol product is a promising complementary source to the MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol product for near real-time air pollution observations. However, a comprehensive evaluation of AHI aerosol optical depth (AOD) is still limited, and the difference in performances of AHI and MODIS remains uncertain. In this study, we evaluated the Himawari-8/AHI Level 3 Version 3.0 and MODIS Collection 6.1 Deep Blue AOD products over China against AOD measurements from AErosol RObotic NETwork (AERONET) sites in a spatiotemporal comparison of the products from February 2018 to January 2019. Results showed that AHI AOD achieved a moderate agreement with AERONET with a correlation coefficient of 0.75 and a root-mean-square-error of 0.26, which was slightly inferior to MODIS. The retrieval accuracy was spatially and temporally varied in AHI AOD, with higher accuracies for XiangHe and Lulin sites as well as in the morning and during the summer. The dependency analysis further revealed that the bias in AHI AOD was strongly dependent on aerosol loading and influenced by the Ångström Exponent and NDVI while those for MODIS appeared to be independent of all variables. Fortunately, the biases in AHI AOD could be rectified using a random forest model that contained the appropriate variables to produce sufficiently accurate results with cross-validation R of 0.92 and RMSE of 0.15. With these adjustments, AHI AOD will continue to have great potential in characterizing precise dynamic aerosol variations and air quality at a fine temporal resolution.
Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape
Authors: Liu Huimin , Zhan Qingming , Gao Sihang , Yang Chen
Published: April 2019

There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.
A Novel Wearable EEG and ECG Recording System for Stress Assessment
Authors: Ahn Joong Woo , Ku Yunseo , Kim Hee Chan
Published: April 2019

Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent noise performance of 0.12 μVrms. Significant time and frequency features of HRV and EEG were found in two different stressors, namely the Stroop color word and mental arithmetic tests, using 14 young subjects. Stressor situations were classified using various HRV and EEG feature selections and a support vector machine technique. The five-fold cross-validation results obtained when using both EEG and HRV features showed the best performance with an accuracy of 87.5%, which demonstrated the requirement for simultaneous HRV and EEG measurements.
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks
Authors: Kutlu Hüseyin , Avcı Engin
Published: April 2019

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.
A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks
Authors: Liu Jun , Guan Wenxue , Han Guangjie , Cui Jun-Hong , Fiondella Lance , Al-Bzoor Manal
Published: April 2019

Deployment of surface-level gateways holds potential as an effective method to alleviate high-propagation delays and high-error probability in an underwater wireless sensor network (UWSN). This promise comes from reducing distances to underwater nodes and using radio waves to forward information to a control station. In an UWSN, a dynamic energy efficient surface-level gateway deployment is required to cope with the mobility of underwater nodes while considering the remote and three-dimensional nature of marine space. In general, deployment problems are usually modeled as an optimization problem to satisfy multiple constraints given a set of parameters. One previously published static deployment optimization framework makes assumptions about network workload, routing, medium access control performance, and node mobility. However, in real underwater environments, all these parameters are dynamic. Therefore, the accuracy of performance estimates calculated through static UWSN deployment optimization framework tends to be limited by nature. This paper presents the Prediction-Assisted Dynamic Surface Gateway Placement (PADP) algorithm to maximize the coverage and minimize the average end-to-end delay of a mobile underwater sensor network over a specified period. PADP implements the Interacting Multiple Model (IMM) tracking scheme to predict the positions of sensor nodes. The deployment is determined based on both current and predicted positions of sensor nodes, which enables better coverage and shorter end-to-end delay. PADP uses a branch-and-cut approach to solve the optimization problem efficiently, and employs a disjoint-set data structure to ensure connectivity. Simulation results illustrate that PADP significantly outperforms a static gateway deployment scheme.
A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints
Authors: Sun Guibin , Zhou Rui , Di Bin , Dong Zhuoning , Wang Yingxun
Published: April 2019

In this paper, a multi-robot persistent coverage of the region of interest is considered, where persistent coverage and cooperative coverage are addressed simultaneously. Previous works have mainly concentrated on the paths that allow for repeated coverage, but ignored the coverage period requirements of each sub-region. In contrast, this paper presents a combinatorial approach for path planning, which aims to cover mission domains with different task periods while guaranteeing both obstacle avoidance and minimizing the number of robots used. The algorithm first deploys the sensors in the region to satisfy coverage requirements with minimum cost. Then it solves the travelling salesman problem to obtain the frame of the closed path. Finally, the approach partitions the closed path into the fewest segments under the coverage period constraints, and it generates the closed route for each robot on the basis of portioned segments of the closed path. Therefore, each robot can circumnavigate one closed route to cover the different task areas completely and persistently. The numerical simulations show that the proposed approach is feasible to implement the cooperative coverage in consideration of obstacles and coverage period constraints, and the number of robots used is also minimized.
Ground Reaction Forces and Kinematics of Ski Jump Landing Using Wearable Sensors
Authors: Bessone Veronica , Petrat Johannes , Schwirtz Ansgar
Published: April 2019

In the past, technological issues limited research focused on ski jump landing. Today, thanks to the development of wearable sensors, it is possible to analyze the biomechanics of athletes without interfering with their movements. The aims of this study were twofold. Firstly, the quantification of the kinetic magnitude during landing is performed using wireless force insoles while 22 athletes jumped during summer training on the hill. In the second part, the insoles were combined with inertial motion units (IMUs) to determine the possible correlation between kinematics and kinetics during landing. The maximal normal ground reaction force (GRFmax) ranged between 1.1 and 5.3 body weight per foot independently when landing using the telemark or parallel leg technique. The GRFmax and impulse were correlated with flying time (p < 0.001). The hip flexions/extensions and the knee and hip rotations of the telemark front leg correlated with GRFmax (r = 0.689, p = 0.040; r = −0.670, p = 0.048; r = 0.820, p = 0.007; respectively). The force insoles and their combination with IMUs resulted in promising setups to analyze landing biomechanics and to provide in-field feedback to the athletes, being quick to place and light, without limiting movement.
Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting
Authors: Yu Yang , Huang Jifeng , Du Wen , Xiong Naixue
Published: April 2019

Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size. However, earlier methods based on convolutional neural networks (CNN) have focused primarily on improving accuracy while ignoring the complexity of the model. This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints. The network is composed of three components: a basic feature extractor (BFE), a stacked à trous convolution module (SACM), and a context fusion module (CFM). The BFE encodes basic feature information with reduced spatial resolution for further refining. Various pieces of contextual information are generated through a short pipeline in SACM. To generate a context fusion density map, CFM distills feature maps from the above components. The whole network is trained in an end-to-end fashion and uses a compression factor to restrict its size. Experiments on three highly-challenging datasets demonstrate that the proposed method delivers attractive performance.
An Active Power Control Technique for Downlink Interference Management in a Two-Tier Macro–Femto Network
Authors: Hassan Tehseen Ul , Gao Fei
Published: April 2019

The femtocell has evolved as a great solution for improving coverage and traffic offloading from the current LTE cellular networks, and it accomplishes the dreams of the high data rate for indoor mobile users. However, the exponentially expanding LTE femtocells cause interference in the network, as they share the same licensed spectrum with a macrocell. To tackle this issue, numerous interference mitigation techniques have been proposed in the literature. In this paper, we proposed an Active Power Control (APC) technique, which not only reduces Inter-Cell Interference (ICI) in a Macro User Equipment (MUE), generated from the downlink transmission power of an inadequately deployed femtocell, but also reduces unnecessary power consumption to achieve a green femtocell network. The simulation results show that the proposed APC technique effectively reduces ICI and optimizes the throughput performance of the MUE. Compared with the existing power control techniques, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) of the Femto User Equipment (FUE) and reducing ICI to the victim MUE existing in the close proximity of the femtocell.
Rapid Evaluation of Integral Quality and Safety of Surface and Waste Waters by a Multisensor System (Electronic Tongue)
Authors: Legin Evgeny , Zadorozhnaya Olesya , Khaydukova Maria , Kirsanov Dmitry , Rybakin Vladimir , Zagrebin Anatoly , Ignatyeva Natalia , Ashina Julia , Sarkar Subrata , Mukherjee Subhankar , Bhattacharyya Nabarun , Bandyopadhyay Rajib , Legin Andrey
Published: April 2019

The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring.
Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO
Authors: Jiang Hui , Xu Weidong , Chen Quansheng
Published: April 2019

The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.
Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
Authors: Jia Zhixin , Fu Kaiya , Lin Mengxiang
Published: April 2019

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.
Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation
Authors: Guerrero-Villar Francisca , Dorado-Vicente Rubén , Medina-Sánchez Gustavo , Torres-Jiménez Eloísa
Published: April 2019

This study presents a procedure to reduce the uncertainty of wind power density estimations, which is useful to improve the energy production predictions of wind farms. Power density is usually determined from the wind speed measured by a cup anemometer and the air density value (conventional procedure). An alternative procedure based on wind speed and dynamic pressure estimations provided by a cup anemometer is proposed. The dynamic pressure is obtained by means of a calibration curve that relates the anemometer rotation frequency and the dynamic pressure measured by a Pitot tube. The quadratic regression, used to define the calibration curve, and its uncertainty are both detailed. A comparison between the alternative procedure and the conventional one points out the advantage of the proposed alternative since results show a high reduction of the indirect measurement uncertainty of wind power density.
Lockout and Tagout in a Manufacturing Setting from a Situation Awareness Perspective
Authors: Illankoon Prasanna , Manathunge Yamuna , Tretten Phillip , Abeysekara John , Singh Sarbjeet
Published: April 2019

Applying lockouts during maintenance is intended to avoid accidental energy release, whereas tagging them out keeps employees aware of what is going on with the machine. In spite of regulations, serious accidents continue to occur due to lapses during lockout and tagout (LOTO) applications. Few studies have examined LOTO effectiveness from a user perspective. This article studies LOTO processes at a manufacturing organization from a situation awareness (SA) perspective. Technicians and machine operators were interviewed, a focus group discussion was conducted, and operators were observed. Qualitative content analysis revealed perceptual, comprehension and projection challenges associated with different phases of LOTO applications. The findings can help lockout/tagout device manufacturers and organizations that apply LOTO to achieve maximum protection.
Severe Drought in Finland: Modeling Effects on Water Resources and Assessing Climate Change Impacts
Authors: Veijalainen Noora , Ahopelto Lauri , Marttunen Mika , Jääskeläinen Jaakko , Britschgi Ritva , Orvomaa Mirjam , Belinskij Antti , Keskinen Marko
Published: April 2019

Severe droughts cause substantial damage to different socio-economic sectors, and even Finland, which has abundant water resources, is not immune to their impacts. To assess the implications of a severe drought in Finland, we carried out a national scale drought impact analysis. Firstly, we simulated water levels and discharges during the severe drought of 1939–1942 (the reference drought) in present-day Finland with a hydrological model. Secondly, we estimated how climate change would alter droughts. Thirdly, we assessed the impact of drought on key water use sectors, with a focus on hydropower and water supply. The results indicate that the long-lasting reference drought caused the discharges to decrease at most by 80% compared to the average annual minimum discharges. The water levels generally fell to the lowest levels in the largest lakes in Central and South-Eastern Finland. Climate change scenarios project on average a small decrease in the lowest water levels during droughts. Severe drought would have a significant impact on water-related sectors, reducing water supply and hydropower production. In this way drought is a risk multiplier for the water–energy–food security nexus. We suggest that the resilience to droughts could be improved with region-specific drought management plans and by including droughts in existing regional preparedness exercises.
Collaboration with External Organizations and Technological Innovations: Evidence from Spanish Manufacturing Firms
Authors: Radicic Dragana , Pinto Jonathan
Published: April 2019

Based on the two knowledge dimensions of availability and accessibility, this study investigates the influence of cooperation with external organizations on technological, product, and process innovations. Using longitudinal data from Spanish manufacturing firms, we estimate dynamic random-effects probit models and thus take into account that technological innovations exhibit persistent behavior. We find that cooperation with suppliers and universities is positively associated with both product and process innovations. However, sectoral analysis according to technological intensity reveals that cooperation with suppliers increases the propensity to technological innovation in industries with a higher degree of technological intensity, while cooperation with universities increases the likelihood of innovation in industries with a lower degree of technological intensity. Moreover, empirical results indicate a high degree of true or genuine state dependence in both types of innovations. Based on these findings, we discuss the theoretical, managerial, and policy implications of the study.
Study on Structural Characteristics of China’s Passenger Airline Network Based on Network Motifs Analysis
Authors: Jin Ying , Wei Ye , Xiu Chunliang , Song Wei , Yang Kaixian
Published: April 2019

The air passenger transport network system is an important agent of social and economic connections between cities. Studying on the airline network structure and providing optimization strategies can improve the airline industry sustainability evolution. As basic building blocks of broad networks, the concept of network motifs is cited in this paper to apply to the structural characteristic analysis of the passenger airline network. The ENUMERATE SUBGRAPHS (G, k) algorithm is used to identify the motifs and anti-motifs of the passenger airline network in China. A total of 37 airline companies are subjected to motif identification and exploring the structural and functional characteristics of the airline networks corresponding to different motifs. These 37 airline companies are classified according to the motif concentration curves into three development stages, which include mono-centric divergence companies at the low-level development stage, transitional companies at the intermediate development stage, and multi-centric and hierarchical companies at the advanced development stage. Finally, we found that adjusting the number of proper network motifs is useful to optimize the overall structure of airline networks, which is profitable for air transport sustainable development.
Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model
Authors: Mao Xinhua , Yuan Changwei , Gan Jiahua
Published: April 2019

An optimal pavement maintenance strategy can keep the pavement performance at a high level under budget constraint. However, the impact of changes in traffic distribution caused by maintenance actions on user costs is rarely investigated in existing approaches. This research aims to solve the optimization of pavement maintenance strategy using a multi-stage dynamic programming model combined with the stochastic user equilibrium model, which can simulate the dynamic traffic distribution in the life cycle. To deal with the proposed model, a heuristic iterative algorithm is put forward. Ultimately, a hypothetical network is established to test the model and algorithm. The testing results prove that the proposed framework has an advantage in assessing user costs comprehensively and can provide an effective and optimal pavement maintenance strategy in a 30-year life cycle, which improves the efficiency of budget and pavement conditions. Additionally, this research provides quantitative evidence of interdependency in a road network, i.e., pavement maintenance actions on links can interfere with the user costs and traffic flow distribution in the whole network, which should be taken into account in pavement maintenance decision-making.
Retaining Members of Community Supported Agriculture (CSA) in California for Economic Sustainability: What Characteristics Affect Retention Rates?
Authors: Galt Ryan E. , Van Soelen Kim Julia , Munden-Dixon Kate , Christensen Libby O. , Bradley Katharine
Published: April 2019

Community supported agriculture (CSA) is one response to major ecological and social problems in the conventional agrifood system. Here we are concerned with how CSA management can enhance the economic sustainability of CSAs. More specifically, using a survey of 111 CSA farms in California, we analyze how specific variables in five domains—CSA management characteristics, farmer characteristics, farm characteristics, economic characteristics, and region—influence retention rates (the proportion of CSA members continuing from one year to the next). Our analysis involves first conducting bivariate correlations, then building a simple causal model that theorizes the direction of causation, then constructing a series of ordinary least squares (OLS) multiple regression models to hold constant independent variables. Our discussion draws out recommendations from our findings for CSA farmers and organizations that support CSA, including increasing the length of the season, increasing crop type diversity, including fruit in standard shares, bringing farming practices into line with organic standards, working with other CSAs to reduce inter-CSA competition, and changing marketing regions for farms in certain regions that appear to be highly saturated. We conclude by identifying more collective routes that CSAs can take to cultivate “CSA people” for a more sustainable economic dimension of CSAs in the long term.
Spatial Correlation and Convergence Analysis of Eco-Efficiency in China
Authors: Zheng Defeng , Hao Shuai , Sun Caizhi , Lyu Leting
Published: April 2019

In this paper, we first measured the eco-efficiency of 31 provinces in China during 2000–2015 using the SBM (Slack-Based Measure) model, and the spatial character of eco-efficiency was identified based on symmetrical spatial weight matrix. We then proposed a new asymmetrical spatial weight matrix based on the eco-economic transformation index (EETI)-distance reciprocal principle to assess the spatial character of eco-efficiency. Finally, we analyzed the convergence of eco-efficiency’s total factor productivity (EETFPs) in mainland China and in three major regions based on the results of EETFP. The study revealed the following findings: (1) There were some limitations to the spatial autocorrelation of eco-efficiency in mainland China by the symmetrical spatial weight methods based on the spatial proximity principle or spatial distance principle. However, the new spatial weight scheme improved the reliability of the accounting results of the spatial autocorrelation. (2) The clustering effect of eco-efficiency exhibited a downward trend in mainland China during the study period; meanwhile, the significant high-high and low-high clustering areas were located in the eastern, the central, and the western regions. (3) The study of convergence showed that there was a club-convergence phenomenon in mainland China, and except for the western region, all the regions expressed conditional convergence. The results provide a significant reference for ecological-economy management and sustainable development in China.
Exploring Value Creation in Sustainable Entrepreneurship: Insights from the Institutional Logics Perspective and the Business Model Lens
Authors: Gregori Patrick , Wdowiak Malgorzata A. , Schwarz Erich J. , Holzmann Patrick
Published: April 2019

Sustainable entrepreneurs intend to create environmental and social value while they build their financially viable business. With this in mind, they are embedded in multiple institutionalized value systems (i.e., institutional logics) that provide them with different, often contradictory values, beliefs, and guiding principles. Adhering to these value systems and integrating multiple forms of value into a coherent business model is a key task for sustainable entrepreneurs, yet current efforts lack insight into how this can be achieved. To address this, the article utilizes the institutional logic perspective in conjunction with the componential approach to business models. By analyzing a longitudinal in-depth case study, this article develops a novel theoretical model linking shifts in the entrepreneur’s perception of institutional logic to business model alterations, and emphasizes the underlying mechanisms and behavior of the sustainable entrepreneur. Sustainable entrepreneurs integrate and blend institutional logic through multiple business model transitions, which are characterized by a personal reorientation of the entrepreneur and new practices to implement change. Furthermore, our findings show that the entrepreneur’s habitus, the pre-change business model, and the change-specific dominant logic are integral and previously overlooked concepts that contextualize their business model transition. The findings and discussion advance the theoretical and practical understanding of the processes through which sustainable entrepreneurs integrate multiple forms of value into their business models. With that, the article contributes to research on sustainable entrepreneurship, institutional logic and business models.
How Ricin Damages the Ribosome
Authors: Grela Przemysław , Szajwaj Monika , Horbowicz-Drożdżal Patrycja , Tchórzewski Marek
Published: April 2019

Ricin belongs to the group of ribosome-inactivating proteins (RIPs), i.e., toxins that have evolved to provide particular species with an advantage over other competitors in nature. Ricin possesses RNA N-glycosidase activity enabling the toxin to eliminate a single adenine base from the sarcin-ricin RNA loop (SRL), which is a highly conserved structure present on the large ribosomal subunit in all species from the three domains of life. The SRL belongs to the GTPase associated center (GAC), i.e., a ribosomal element involved in conferring unidirectional trajectory for the translational apparatus at the expense of GTP hydrolysis by translational GTPases (trGTPases). The SRL represents a critical element in the GAC, being the main triggering factor of GTP hydrolysis by trGTPases. Enzymatic removal of a single adenine base at the tip of SRL by ricin blocks GTP hydrolysis and, at the same time, impedes functioning of the translational machinery. Here, we discuss the consequences of SRL depurination by ricin for ribosomal performance, with emphasis on the mechanistic model overview of the SRL modus operandi.
The Mechanisms of Social Immunity Against Fungal Infections in Eusocial Insects
Authors: Liu Long , Zhao Xing-Ying , Tang Qing-Bo , Lei Chao-Liang , Huang Qiu-Ying
Published: April 2019

Entomopathogenic fungus as well as their toxins is a natural threat surrounding social insect colonies. To defend against them, social insects have evolved a series of unique disease defenses at the colony level, which consists of behavioral and physiological adaptations. These colony-level defenses can reduce the infection and poisoning risk and improve the survival of societal members, and is known as social immunity. In this review, we discuss how social immunity enables the insect colony to avoid, resist and tolerate fungal pathogens. To understand the molecular basis of social immunity, we highlight several genetic elements and biochemical factors that drive the colony-level defense, which needs further verification. We discuss the chemosensory genes in regulating social behaviors, the antifungal secretions such as some insect venoms in external defense and the immune priming in internal defense. To conclude, we show the possible driving force of the fungal toxins for the evolution of social immunity. Throughout the review, we propose several questions involved in social immunity extended from some phenomena that have been reported. We hope our review about social ‘host–fungal pathogen’ interactions will help us further understand the mechanism of social immunity in eusocial insects.
Sialic Acid-Containing Glycans as Cellular Receptors for Ocular Human Adenoviruses: Implications for Tropism and Treatment
Authors: Chandra Naresh , Frängsmyr Lars , Imhof Sophie , Caraballo Rémi , Elofsson Mikael , Arnberg Niklas
Published: April 2019

Human adenoviruses (HAdV) are the most common cause of ocular infections. Species B human adenovirus type 3 (HAdV-B3) causes pharyngoconjunctival fever (PCF), whereas HAdV-D8, -D37, and -D64 cause epidemic keratoconjunctivitis (EKC). Recently, HAdV-D53, -D54, and -D56 emerged as new EKC-causing agents. HAdV-E4 is associated with both PCF and EKC. We have previously demonstrated that HAdV-D37 uses sialic acid (SA)-containing glycans as cellular receptors on human corneal epithelial (HCE) cells, and the virus interaction with SA is mediated by the knob domain of the viral fiber protein. Here, by means of cell-based assays and using neuraminidase (a SA-cleaving enzyme), we investigated whether ocular HAdVs other than HAdV-D37 also use SA-containing glycans as receptors on HCE cells. We found that HAdV-E4 and -D56 infect HCE cells independent of SAs, whereas HAdV-D53 and -D64 use SAs as cellular receptors. HAdV-D8 and -D54 fiber knobs also bound to cell-surface SAs. Surprisingly, HCE cells were found resistant to HAdV-B3 infection. We also demonstrated that the SA-based molecule i.e., ME0462, designed to bind to SA-binding sites on the HAdV-D37 fiber knob, efficiently prevents binding and infection of several EKC-causing HAdVs. Surface plasmon resonance analysis confirmed a direct interaction between ME0462 and fiber knobs. Altogether, we demonstrate that SA-containing glycans serve as receptors for multiple EKC-causing HAdVs, and, that SA-based compound function as a broad-spectrum antiviral against known and emerging EKC-causing HAdVs.
Study on the Control of Underground Rivers by Reverse Faults in Tunnel Site and Selection of Tunnel Elevation
Authors: Zhang Peixing , Huang Zhen , Liu Shuai , Xu Tiesheng
Published: April 2019

Along with the need for western economic development, the number of long tunnel projects which go through mountains is constantly on the rise. In the process of construction, various disaster-causing structures are frequently exposed, which leads to many geological disasters. The traditional idea is that the reverse fault is not easily developed for an underground river, which means that the tunnel elevation design is not considered adequately. When some tunnels cross the bottom of the river, the fractures near the fault between the underground river and the excavation space may be activated and then evolve into channels, causing serious water inrush accidents during construction and operation processes. Taking the Qiyueshan Tunnel site as an example, on the premise of the anatomy of the control mechanism of the reverse fault on the development of the underground river, based on the multiperiod typical structural traces of the tunnel and surface outcrop, it was found that stratifications, dip joints, transverse joints, and tension joints of good aperture grade are important control factors. The cut block easily loses its stability and provides space for karst development, while intermittent uplifting of regional structures provides hydrodynamic conditions for the development of the underground river, causing the hydraulic gradient to be inconsistent in the overall underground river. Finally, the rainwater dynamic monitoring and tracer connectivity are data that can be fully utilized to demonstrate that a reverse fracture can control the development of the underground river. The authors further considered the effect of the vertical zoning of the fault structure and the excavation disturbance, and, drawing on the experience of the relative location of the same site in the same field, put forward the suggestion that the construction of the follow-up tunnel in the study area should be slightly higher than the elevation of the underground river. The research results can provide useful reference for similar engineering problems in the future.