bluuebunny/crossref_metadata_2025 huggingface dataset:
RETRACTED: Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images
Authors: Zhu Ruixi , Yan Li , Mo Nan , Liu Yi
Published: August 2019

Scene classification of high-resolution remote sensing images (HRRSI) is one of the most important means of land-cover classification. Deep learning techniques, especially the convolutional neural network (CNN) have been widely applied to the scene classification of HRRSI due to the advancement of graphic processing units (GPU). However, they tend to extract features from the whole images rather than discriminative regions. The visual attention mechanism can force the CNN to focus on discriminative regions, but it may suffer from the influence of intra-class diversity and repeated texture. Motivated by these problems, we propose an attention-based deep feature fusion (ADFF) framework that constitutes three parts, namely attention maps generated by Gradient-weighted Class Activation Mapping (Grad-CAM), a multiplicative fusion of deep features and the center-based cross-entropy loss function. First of all, we propose to make attention maps generated by Grad-CAM as an explicit input in order to force the network to concentrate on discriminative regions. Then, deep features derived from original images and attention maps are proposed to be fused by multiplicative fusion in order to consider both improved abilities to distinguish scenes of repeated texture and the salient regions. Finally, the center-based cross-entropy loss function that utilizes both the cross-entropy loss and center loss function is proposed to backpropagate fused features so as to reduce the effect of intra-class diversity on feature representations. The proposed ADFF architecture is tested on three benchmark datasets to show its performance in scene classification. The experiments confirm that the proposed method outperforms most competitive scene classification methods with an average overall accuracy of 94% under different training ratios.
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements
Authors: Li Wenzhao , Ali Elham , Abou El-Magd Islam , Mourad Moustafa Mohamed , El-Askary Hesham
Published: August 2019

This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.
Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data
Authors: Liu Yuling , Yu Yunyue , Yu Peng , Wang Heshun , Rao Yuhan
Published: August 2019

Satellite land surface temperatures (LSTs) have been routinely produced for decades from a variety of polar-orbiting and geostationary satellites, which makes it possible to generate LST climate data globally. However, consistency of the satellite LSTs from different satellite missions is a concern for such purpose; an enterprise satellite LST algorithm is desired for the LST production through different satellite missions, or at the least, through series satellites of a satellite mission. The enterprise LST algorithm employs the split window technique and uses the emissivity explicitly in its formula. This research focuses on the enterprise LST algorithm design, development and its evaluations with the National Oceanic and Atmospheric Administration’s (NOAA) 20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) data available since 5 January 2018. In this study, the enterprise LST algorithm was evaluated using simulation dataset consisting of over 2000 profiles from SeeBor collection and the results show a bias of 0.19 K and 0.34 K and standard deviation of 0.48 K and 0.69 K for nighttime and daytime, respectively. The in situ observations from seven NOAA Surface Radiation budget (SURFRAD) sites and two Baseline Surface Radiation Network (BSRN) sites were used for LST validation. The results indicate a bias of −0.3 K and a root mean square error (RMSE) of 2.06 K for SURFRAD stations and a bias of 0.2 K and a RMSE of ~2 K for BSRN sites. Further, the cross-satellite analysis presents a bias of 0.7 K and an RMSE of 1.9 K for comparisons with AQUA MODIS LST (MYD11_L2, Collection 6). The enterprise N20 VIIRS LST product reached the provisional maturity in February 2019 and is ready for users to use in their applications.
Correction: Fu, N.; et al. The Two-Parts Step-by-Step Ionospheric Assimilation Based on Ground-Based/Spaceborne Observations and Its Verification. Remote Sens. 2019, 11, 1172
Authors: Fu Naifeng , Guo Peng , Wu Mengjie , Huang Yong , Hu Xiaogong , Hong Zhenjie
Published: August 2019

Because our organization pays great attention to the order of the unit names in the signature of the paper, the papers in which the first affiliation of the article is “Shanghai Astronomical Observatory of Chinese Academy of Sciences, Shanghai, 200030, China” are counted as the first condition for the doctoral graduation oral examination [...]
An Effective Multifactor Authentication Mechanism Based on Combiners of Hash Function over Internet of Things
Authors: Ahmed Adel Ali , Ahmed Waleed Ali
Published: August 2019

Internet of Thing (IoT) is the most emerging technology in which all the objects in the real world can use the Internet to communicate with each other as parts of a single unified system. This eventually leads to the development of many smart applications such as smart cities, smart homes, smart healthcare, smart transportation, etc. Due to the fact that the IoT devices have limited resources, the cybersecurity approaches that relied on complex and long processing cryptography are not a good fit for these constrained devices. Moreover, the current IoT systems experience critical security vulnerabilities that include identifying which devices were affected, what data or services were accessed or compromised, and which users were impacted. The cybersecurity challenge in IoT systems is to find a solution for handling the identity of the user, things/objects and devices in a secure manner. This paper proposes an effective multifactor authentication (CMA) solution based on robust combiners of the hash functions implemented in the IoT devices. The proposed CMA solution mitigates the authentication vulnerabilities of IoT and defends against several types of attacks. Also, it achieves multi-property robustness and preserves the collision-resistance, the pseudo-randomness, the message authentication code, and the one-wayness. It also ensures the integrity, authenticity and availability of sensed data for the legitimate IoT devices. The simulation results show that CMA outperforms the TOTP in term of the authentication failure rate. Moreover, the evaluation of CMA shows an acceptable QoS measurement in terms of computation time overhead, throughput, and packet loss ratio.
Evaluation of a Cost-Effective Ammonia Monitoring System for Continuous Real-Time Concentration Measurements in a Fattening Pig Barn
Authors: Zhuang Shaojie , Van Overbeke Philippe , Vangeyte Jürgen , Sonck Bart , Demeyer Peter
Published: August 2019

Ammonia (NH3) emission is one of the major environmental issues in livestock farming. Gas measurements are required to study the emission process, to establish emission factors, and to assess the efficiency of emission reduction techniques. However, the current methods for acquiring reference measurements of NH3 are either high in cost or labor intensive. In this study, a cost-effective ammonia monitoring system (AMS) was constructed from a commercially-available gas analyzing module based on tunable diode laser absorption (TDLA) spectroscopy. To cope with the negative measurement biases caused by differing inlet pressures, a set of correction equations was formulated. Field validation of the AMS on NH3 measurement was conducted in a fattening pig barn, where the system was compared to a Fourier-transform infrared (FTIR) spectroscopy analyzer. Under two test conditions in a fattening pig barn, the absolute error of the AMS measurements with respect to the average obtained values between the AMS and the FTIR was respectively 0.66 and 0.08 ppmv, corresponding to 5.9% and 0.5% relative error. Potential sources of the measurement uncertainties in both the AMS and FTIR were discussed. The test results demonstrated that the AMS was capable of performing high-quality measurement with sub-ppm accuracy, making it a promising cost-effective tool for establishing NH3 emission factors and studying NH3 emission processes in pig houses.
A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles
Authors: Lu Chao , Gong Jianwei , Lv Chen , Chen Xin , Cao Dongpu , Chen Yimin
Published: August 2019

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.
Indirect Time-of-Flight Depth Sensor with Two-Step Comparison Scheme for Depth Frame Difference Detection
Authors: Kim Donguk , Choi Jaehyuk
Published: August 2019

A depth sensor with integrated frame difference detection is proposed. Instead of frame difference detection using light intensity, which is vulnerable to ambient light, the difference in depth between successive frames can be acquired. Because the conventional time-of-flight depth sensor requires two frames of depth-image acquisition with four-phase modulation, it has large power consumption, as well as a large area for external frame memories. Therefore, we propose a simple two-step comparison scheme for generating the depth frame difference in a single frame. With the proposed scheme, only a single frame is needed to obtain the frame difference, with less than half of the power consumption of the conventional depth sensor. Because the frame difference is simply generated by column-parallel circuits, no access of the external frame memory is involved, nor is a digital signal processor. In addition, we used an over-pixel metal–insulator–metal capacitor to store temporary signals for enhancing the area efficiency. A prototype chip was fabricated using a 90 nm backside illumination complementary metal–oxide–semiconductor (CMOS) image sensor process. We measured the depth frame difference in the range of 1–2.5 m. With a 10 MHz modulation frequency, a depth frame difference of >10 cm was successfully detected even for objects with different reflectivity. The maximum relative error from the difference of the reflectivity (white and wooden targets) was <3%.
Mobile Applications in Evaluations of Knee Joint Kinematics: A Pilot Study
Authors: Lisiński Przemysław , Wareńczak Agnieszka , Hejdysz Krystyna , Sip Paweł , Gośliński Jarosław , Owczarek Piotr , Jonak Justyna , Goślińska Jagoda
Published: August 2019

Because medical professionals lack the means to monitor exercises performed by patients in their home environment directly, there is a strong case for introducing technological solutions into this domain. They include methods that use wireless inertial sensors, which emit signals recorded and processed by special applications that work with mobile devices. This paper’s aim is (a) to evaluate whether such sensors are suitable for qualitative and quantitative motion analysis, and (b) to determine the repeatability of results over a few recordings. Knee joint activity was analysed using a system of inertial sensors connected through a Wi-Fi network to mobile devices. The tested individuals did eight different activities, all of which engaged the knee joint. Each excercise was repeated three times. Study results did not reveal any statistically significant differences between the three measurements for most of the studied parameters. Furthermore, in almost every case, there were no statistically significant differences between the results of the right and left lower limb (p > 0.05). This study shows that easy use and repeatability of results combined with the feature of quantitative and qualitative analysis make the examined method useful for functional evaluations of the knee joint.
Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques
Authors: Tien Bui Dieu , Moayedi Hossein , Abdullahi Mu’azu Mohammed , Safuan A Rashid Ahmad , Nguyen Hoang
Published: August 2019

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.
Validation of Glacier Topographic Acquisitions from an Airborne Single-Pass Interferometer
Authors: Moller Delwyn , Hensley Scott , Mouginot Jeremie , Willis Joshua , Wu Xiaoqing , Larsen Christopher , Rignot Eric , Muellerschoen Ronald , Khazendar Ala
Published: August 2019

The airborne glacier and ice surface topography interferometer (GLISTIN-A) is a single-pass radar interferometer developed for accurate high-resolution swath mapping of dynamic ice surfaces. We present the first validation results of the operational sensor, collected in 2013 over glaciers in Alaska and followed by more exhaustive collections from Greenland in 2016 and 2017. In Alaska, overlapping flight-tracks were mosaicked to mitigate potential residual trends across-track and the resultant maps are validated with lidar. Furthermore, repeat acquisitions of Columbia Glacier collected with a three day separation indicate excellent stability and repeatability. Commencing 2016, GLISTIN-A has circumnavigated Greenland for 4 consecutive years. Due to flight hour limitations, overlapping swaths were not flown. In 2016, comparison with airborne lidar data finds that residual systematic errors exhibit evenly distributed small slopes (all less than 10 millidegrees) and nadir biases were typically less than 1 m. Similarly 2017 data exhibited up to meter-scale nadir biases and evenly distributed residual slopes with a standard deviation of ~10 millidegrees). All satisfied the science accuracy requirements of the Greenland campaigns (3 m accuracy across an 8 km swath).
Wasserstein Distance Learns Domain Invariant Feature Representations for Drift Compensation of E-Nose
Authors: Tao Yang , Li Chunyan , Liang Zhifang , Yang Haocheng , Xu Juan
Published: August 2019

Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore, studies on the drift compensation algorithms are receiving increasing attention in the field of the E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations (WDLFR), is put forward for drift compensation, which is based on the domain invariant feature representation learning. It regards a neural network as a domain discriminator to measure the empirical Wasserstein distance between the source domain (data without drift) and target domain (drift data). The WDLFR minimizes Wasserstein distance by optimizing the feature extractor in an adversarial manner. The Wasserstein distance for domain adaption has good gradient and generalization bound. Finally, the experiments are conducted on a real dataset of E-nose from the University of California, San Diego (UCSD). The experimental results demonstrate that the effectiveness of the proposed method outperforms all compared drift compensation methods, and the WDLFR succeeds in significantly reducing the sensor drift.
IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
Authors: Marchese Mario , Moheddine Aya , Patrone Fabio
Published: August 2019

The Fifth Generation of Mobile Communications (5G) will lead to the growth of use cases demanding higher capacity and a enhanced data rate, a lower latency, and a more flexible and scalable network able to offer better user Quality of Experience (QoE). The Internet of Things (IoT) is one of these use cases. It has been spreading in the recent past few years, and it covers a wider range of possible application scenarios, such as smart city, smart factory, and smart agriculture, among many others. However, the limitations of the terrestrial network hinder the deployment of IoT devices and services. Besides, the existence of a plethora of different solutions (short vs. long range, commercialized vs. standardized, etc.), each of them based on different communication protocols and, in some cases, on different access infrastructures, makes the integration among them and with the upcoming 5G infrastructure more difficult. This paper discusses the huge set of IoT solutions available or still under standardization that will need to be integrated in the 5G framework. UAVs and satellites will be proposed as possible solutions to ease this integration, overcoming the limitations of the terrestrial infrastructure, such as the limited covered areas and the densification of the number of IoT devices per square kilometer.
Can Automated Vehicles Improve Cyclist Safety in Urban Areas?
Authors: Tafidis Pavlos , Pirdavani Ali , Brijs Tom , Farah Haneen
Published: August 2019

Automated vehicles (AVs) are expected to assist in decreasing road traffic fatalities, particularly among passenger cars. However, until now limited research has been conducted on how they will impact the safety of vulnerable road users (VRUs) (i.e., cyclists and pedestrians). Therefore, there is a clear need to start taking into account the interactions between AVs and VRUs as an integrated element of the transport network, especially in urban areas where they are dominant. The objective of this study is to verify whether the anticipated implementation of AVs can actually improve cyclists’ safety. For this purpose, the microscopic traffic flow simulation software PTV Vissim combined with the surrogate safety assessment model (SSAM) were utilized. The road network used for this analysis was generated based on a real study case in a medium-sized city in Belgium, where narrow streets in the city center are shared on many occasions between vehicles and cyclists. The findings of the analysis show a notable reduction in the total number of conflicts between cars, but also between cars and cyclists, compared to the current situation, assuming a 100% market penetration scenario for AVs. Moreover, the severity level of conflicts also decreased as a result of the lack of human-driven vehicles in the traffic streams.
Alignment of the Safety Assessment Method with New Zealand Legislative Responsibilities
Authors: Pons Dirk J.
Published: August 2019

Need—National legislative health and safety (H&S) frameworks impose requirements but grant self-management to organisations. Consequently variability arises in management systems, and some organisations struggle to achieve successful implementation. The risk assessment process is key to the H&S management system, and could benefit from greater consistency and better external alignment with the legislative framework of the jurisdiction. Approach—The harm categories in the New Zealand (NZ) Act were adapted into a consequence scale. A non-linear scale was developed for the consequence axis to represent the disproportional nature of catastrophic harm outcomes compared to minor injuries. A hazard assessment process was devised based on systems engineering methods. Organisational decision-criteria were derived from the communications requirement in the Act, and these thresholds linked to expected treatments. Originality—A method is providing for aligning risk assessments with a national legislative framework, and integrating the technical aspects of risk assessment with the management processes. The approach also more explicitly includes recovery actions in contrast to existing methods where prevention dominates. Regarding the management aspects, it shows how thresholds may be defined relative to the legislation, to give clear expectations regarding treatment and internal communication, thereby assisting executives (‘officers’ in terms of the NZ Act) meet their duties.
Sustainability and Environmental Inequality: Effects of Animal Husbandry Pollution in China
Authors: Yang Yujie , Luan Weixin , Xue Yunan
Published: August 2019

Environmental inequality in animal husbandry is an important issue that matters in sustainable livestock production and environmental sustainability. In this context, the objective of this study is to measure and analyze the chemical oxygen demand (COD) discharge inequality from animal husbandry in the Liaoning province in China, as a case study in environmental inequality. Using the pollutant discharge coefficient method and environmental inequality indices, the study looks specifically at the impacts on unequal distribution and unequal economic efficiency in Liaoning in the period 2000 to 2016. The study explores the determinants of COD discharge inequality by constructing panel threshold regression models. The major findings are as follows. First, there is COD discharge inequality in distribution and in economic efficiency in Liaoning, and these were not reduced over time, despite a decline in the absolute quantity of COD discharge after 2013. Second, the impact of COD discharge inequality in economic efficiency lasted longer and was more serious across Liaoning compared with COD discharge inequality in distribution. Third, the quantitative upgrading of the agricultural industrial structure did not significantly reduce COD discharge inequality, and even led to a rise in inequality, especially in terms of economic efficiency. Ultimately, regional economic development turned out to be the key factor in inequality reduction. In addition, improvement in public fiscal support for science and technology also led to greater reduction in inequality.
Environmental and Economic Performance of Yacon (Smallanthus sonchifolius) Cultivated for Fructooligosaccharide Production
Authors: Wagner Moritz , Kamp Larissa , Graeff-Hönninger Simone , Lewandowski Iris
Published: August 2019

As the prevalence of diabetes is predicted to rise globally in the coming decades, the demand for sugar substitutes is expected to increase significantly. In this context, natural sweeteners have been receiving particular attention, as artificial sweeteners have been associated with obesity and cardiovascular disease. One natural sweetener is yacon (Smallanthus sonchifolius) ((Poepp. and Endl.) H. Robinson), which could play a prominent role due to its high fructooligosaccharides yield. Yacon is currently only a minor crop in Europe and there is little information available on the environmental and economic impacts of its various cultivation systems. These are especially affected by nitrogen fertilization levels and genotype selection. Thus, before the crop is introduced on a larger scale, it is expedient to identify the most sustainable cultivation system. The life-cycle assessment (LCA) and life-cycle costing (LCC) analysis of yacon cultivation systems conducted in this study revealed significant differences between yacon genotypes and found that a nitrogen fertilization level of 80 kg N ha−1 significantly decreased production costs and simultaneously led to a comparatively good environmental performance. The results indicated that, for the holistic evaluation of agricultural systems, it is crucial to assess both the economic and environmental performance of new crops.
Development of a Bridge Management System Based on the Building Information Modeling Technology
Authors: Wan Chunfeng , Zhou Zhenwei , Li Siyuan , Ding Youliang , Xu Zhao , Yang Zegang , Xia Yefei , Yin Fangzhou
Published: August 2019

With the development of the Chinese transportation industry, the number of bridges has increased significantly, but this results in high pressure of structural maintenance and management. Bridge management system (BMS) is critical for efficient maintenance and ensured safety of bridge structures during long-term operation. Building information modeling (BIM) is an emerging technology with powerful visualization and informatization capability, making it an ideal tool for developing modern management systems. This paper introduces the development of a bridge management system based on the BIM technology. Industry Foundation Classes (IFC) and International Framework for Dictionaries (IFD) standards are studied and extended, and coding rules are proposed for the Chinese bridge industry. Also, a standard structural modeling method is proposed to fast build the bridge BIM model. Web-BIM oriented bridge management is proposed, and portable devices are introduced into the system. Collaborative management is realized for different users. The BIM-based maintenance management system is designed. Finally, a practical BIM-based BMS is established for a long-span cable-stayed bridge in China. This system integrates the BIM with the geographic information system (GIS) and contains information management, inspection management, technical condition evaluation, and enables users to cooperate with each other. Such a BMS could help to improve the management efficiency and ensure its normal operation, providing a useful platform for the maintenance of massive bridges in China.
Threshold Determination for Sharing Bus Rapid Transit–Exclusive Lanes with Conventional Buses
Authors: Yue Yifan , Chen Jun , Yang Qi , Ye Jiao
Published: August 2019

Sharing bus rapid transit (BRT) exclusive lanes with conventional buses is being considered to solve the problem of low utilization rate of BRT-exclusive lanes. However, the quantitative conditions and threshold that determine when to share need to be study. This paper took the common section of BRT and conventional bus lines as its research object. Practical investigation was conducted to analyze shared characteristics from multiple perspectives and explore influencing factors and mechanisms for sharing implementation. Based on the survey results, analytical models were established to quantify the influencing factors from three perspectives of road section, intersection, and bus stop. We selected departure volume of conventional buses as a threshold index and then summarized the constraints and the calculation process of sharing threshold. Finally, numerical examples of different scenarios were used to verify the feasibility and effectiveness of the method. The operation efficiency of the road section on exclusive lanes was the constraint on the lower limit of the shared threshold, while the upper limit was constrained by queuing probability or bus operation time under different intersections and stop spacing, which can provide reference for the shared setting of exclusive bus lanes.
The Effect of Consumers’ Perceived Value on Acceptance of an Internet-Only Bank Service
Authors: Ahn Sang Joon , Lee Seong Ho
Published: August 2019

This study focuses on perceived value, which is not mentioned in previous internet-only bank studies, to analyze the popularity of internet-only banks. It does this by exploring the relationships between the perceived value and usage intention of customers. The purpose of this study is to help us understand customers’ decision making to accept innovative services by finding factors that affect consumer acceptance of internet-only banks. Using multiple regression, this study analyzes data gathered from college students in their 20s who are familiar with IT services and are interested in internet-only banks. The results show that all three components of perceived value (economical value, convenience value, and emotional value) increase usage intention. Convenience value is the most important factor in the acceptance of internet-only banking services. The findings indicate that perceived value is an attractive factor in using internet-only banks and suggest that managing and developing perceived value is an important marketing strategy.
Performance Evaluation and Investment Analysis for Container Port Sustainable Development in China: An Inverse DEA Approach
Authors: Lin Yang , Yan Longzhong , Wang Ying-Ming
Published: August 2019

Container ports play an important role in international maritime trade. However, the rapid growth of the port and terminal industry has caused many environmental pollution problems. This paper intends to develop an inverse data envelopment analysis (IDEA) model for measuring container ports’ efficiency and analyzing their resource consumption by considering undesirable outputs. Statistical data from 16 main ports are empirically examined using the proposed method in accordance with the 13th Five-Year Plan in China. The results indicate that the proposed IDEA is a feasible approach for performance evaluation, and provides policymakers with insights into resource optimization of container ports. A comparative study with another DEA model is also discussed.
Optimization of Integrated Inventory Routing Problem for Cold Chain Logistics Considering Carbon Footprint and Carbon Regulations
Authors: Li Lixia , Yang Yu , Qin Gaoyuan
Published: August 2019

This paper studies the optimization of cold chain integrated inventory routing problem while considering carbon emissions. First, the carbon footprint in inventory and transportation process for cold chain logistics is accurately identified and quantified. Secondly, based on the carbon regulations, which are carbon cap, carbon cap and offset, carbon cap and trade, and carbon tax regulations, four green cold chain inventory routing optimization models that minimize the total cost are constructed, respectively. Subsequently, a genetic simulated annealing algorithm (GASA) is developed in order to efficiently solve the models, which combines the advantages of the two algorithms. The effectiveness of the algorithm and the models is verified by numerical comparison experiments. Further, a set of numerical experiments is conducted to examine in detail the effectiveness of each regulation with the change of cap, carbon price, and unit fuel price in order to investigate the difference of these regulations’ impacts on the cold chain logistics. The research results show that (a) the cap and price plays a relatively important role, for their value setting may even lead to the invalidation of the regulations and the development of the enterprises; (b) carbon cap and carbon tax regulations are more powerful when compared to the other two regulations, which reduce more carbon emissions, but also pose more challenge to the enterprises’ economic development; (c) overall, cap and trade regulation is better than cap and offset regulation, because, when the cap is not sufficient, the two regulations are almost as good, but when the cap is sufficient, the offset policy is invalid; and, (d) unlike the traditional logistics, the increase of unit fuel price will not reduce carbon emissions. Several practical managerial implications for government and enterprises are also provided based on research results.
A Study on Green Economy Indicators and Modeling: Russian Context
Authors: Vukovic Natalia , Pobedinsky Vladimir , Mityagin Sergey , Drozhzhin Andrei , Mingaleva Zhanna
Published: August 2019

This article aims to assess and forecast the dynamics of a regional green economy. The research relevance is determined by the need to develop theoretical and methodological basis of the green economy for the transition period and to identify criteria basis for assessing the state and regional level of it. The authors applied the modern methods, which allowed to model criteria considering data uncertainty and both static and dynamic criteria. The research process involved the methods of scientific analysis, comparison and synthesis, the theory of fuzzy sets, and fuzzy modeling. The main principles and methodology of the criteria evaluation for a regional green economy are proposed. The principal methodological approach in this research combines the current state and dynamics of the green economy in evaluating and forecasting the conditions of data uncertainty. The research results form a theoretical, methodological, and practical basis for assessing the current state and level of a regional green economy development, determining the effectiveness of environmental and economic programs, optimizing financial management, conducting environmental monitoring, and developing state plans.
A Model for Measuring Fair Labour Justice in Hotels: Design for the Spanish Case
Authors: García Mestanza Josefa , Cerezo Medina Alfonso , Cruz Morato Marco Antonio
Published: August 2019

There is a growing awareness of Corporate Social Responsibility (CSR) and sustainability as a global movement. The hospitality sector is one of the major industries driving socioeconomic development worldwide (especially in economies such as Spain) and it has responded to this need, in the context of a general worsening of labor conditions in this sector. Evidence of this response is the Fair Hotels Project, which is an international collaborative effort aimed at building new partnerships between fair trade movements and trade unions in order to have a positive effect on the labour market in the hotel sector. This article describes the design of Hoteles Justos Laboralmente Responsables (HJLR), a fair labour justice and socially responsible model for hotels oriented to contribute to sustainability and labour justice within the Spanish hotel sector. The HJLR model was created to meet corporate, labour and local development needs. It includes accurate and objective measures—and homogeneous and comparable indicators—to assess the level of fairness and quality of labour practices of hotels. This model would be of great utility in improving the sustainability and quality of life of people working in this economic sector and could be also used by companies to improve their competitive position. The Spanish Government has shown its support for this project as a part of its 2030 sustainable tourism strategy, aimed to get the United Nation Sustainable Development Goals. Furthermore, this is a relevant line for future research, once the implementation phase is completed and quantitative data is available to measure the situation in depth.
Phosphorus Loss through Overland Flow and Interflow from Bare Weathered Granite Slopes in Southeast China
Authors: Deng Longzhou , Fei Kai , Sun Tianyu , Zhang Liping , Fan Xiaojuan , Ni Liang
Published: August 2019

Phosphorus (P) is the key limiting factor for eutrophication, and the mechanism of P loss from hillslopes is complex. Few attempts have been made to study the processes of P loss through overland flow and interflow from bare weathered granite slopes in Southeast China. Therefore, artificial rainfall simulations were performed to evaluate P loss from bare weathered granite slopes with different slope angles (5°, 8°, 15°, 25°) and different rainfall intensities (1.5, 2.0, 2.5 mm/min). The results show that overland flow increased with rainfall intensity, while it declined with slope angle. Interflow exhibited a single-peak curve with time of runoff. The interflow accounted for 28.53–89.12% of the total runoff yield, and the percentage declined with rainfall intensity and increased with slope angle. Both total phosphorus (TP) concentration (CTP) and TP load (LTP) in overland flow increased with rainfall intensity, and the percentages of LTP in each rainfall event ranged from 51% to 92%. CTP in overland flow distinctly fluctuated, with the maximum appearing on the 25° slope, while the maximum in interflow was observed on the 5° slope. LTP in overland flow was the highest on the 8° slope, and was significantly affected by runoff yield and rainfall intensity (p < 0.01). LTP in interflow was small and was significantly affected by rainfall intensity (p < 0.01). Runoff P was mainly lost through overland flow, dominantly in the form of particulate phosphorus (PP), and P loss through interflow was an important supplementation, mainly in the form of dissolved phosphorus (DP). These results provide underlying insights and scientific background for the control of P loss in bare weathered granite areas.
Green Manufacturing Strategy Considering Retailers’ Fairness Concerns
Authors: Zhang Huanyong , Zhang Zhen , Pu Xujin , Li Yuhang
Published: August 2019

This paper addresses the problem of green manufacturing decision making for a green dual-channel supply chain (SC). In the investigated SC, the manufacturer will decide whether to adopt green manufacturing under the influence of the retailer’s fairness concern-based dual-channel. Thus, we discuss two decision scenarios: the no green manufacturing strategy with retailer fairness (NM model), and green manufacturing with retailer fairness (GM model). Our study has several findings: Firstly, adopting a green manufacturing strategy is not always beneficial to supply-chain members when a retailer has fairness. In particular, when fairness is at a relatively high level, the manufacturer will not adopt green manufacturing. Secondly, under green manufacturing, the product’s green degree and subsidies have a positive impact on the price and demand and the members’ profit and utility. Besides, the subsidies and retailer fairness have a counter effect on the optimal decision. Thirdly, comparing the two scenarios (NM & GM), we found that the channel price of the GM model is lower than the NM model. Finally, from the perspective of the supply chain system, the system tends toward the manufacturer adopting green manufacturing and maintaining retailer fairness concerns at a lower level.
What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis
Authors: Meng Ming , Wu Shucheng , Zhou Jin , Wang Xinfang
Published: August 2019

The rapid growth of household electricity consumption is threatening the sustainable development of China’s economy and environment because of its impacts on the operation efficiency of the electric power system. To recognize the driving factors of the consumption growth and offer policy implications, based on the consumption-related data of 2015 and 2016, this research used the rank sum ratio (RSR) method to divide China’s 30 provinces into 5 groups and a logarithmic mean Divisia index (LMDI) algorithm to decompose the composition growth of each group into the quantitative contribution of each driving factor. The following conclusions were drawn from the empirical analysis. (1) The Yangtze basin is the most vigorous region of consumption growth and should be principally monitored. (2) Climate conditions have a remarkable impact on consumption growth and should be a key consideration when making differentiated household electricity policies. (3) The rebound effect has already appeared in a few of the most developed regions. Electricity price is an effective measure in dealing with this effect. (4) The improvement of the income level is the most important driving factor for consumption growth. (5) For provinces with high growth vitality, the change in the burden level of electricity expenditure prompts consumption growth. However, for provinces with low growth vitality, the situations are opposite. (6) The impacts of electricity price and population on consumption growth are negligible.
Assessing the Relevance of Green Banking Practice on Bank Loyalty: The Mediating Effect of Green Image and Bank Trust
Authors: Ibe-enwo Grace , Igbudu Nicholas , Garanti Zanete , Popoola Temitope
Published: August 2019

Studies that examine green banking practice from the relationship marketing perspective are limited. Bearing in mind the importance of loyalty to managers in the banking industry, and the increase in the call for greater sustainability performance from banking firms by customers, our study explore customers’ perception on green banking practice and its effect on bank loyalty. It also investigates the mediating effect of green image and bank trust in the relationship between green banking practice and bank loyalty. Through a quantitative survey, data for this study was adopted from 551 customers of the retail banking sector in North Cyprus. The structural equation modeling technique was used to test the relationship between the study variables. Results from the structural equation modeling analysis conducted indicated a direct and significant influence of green banking practice on green image, bank trust, and bank loyalty. Green image significantly affects bank trust and bank loyalty. No significant relationship exist between bank trust and bank loyalty. Green image mediates the relationship between green banking practice and bank loyalty, while bank trust does not mediate the relationship between green banking practice and bank loyalty.
Study on the Allocation of a Rescue Base in the Arctic
Authors: Shan Yulong , Zhang Ren
Published: August 2019

Risk assessment and emergency responses to ensure the safety of ships crossing the Arctic have gained tremendous attention in recent years. However, asymmetry in the probability that people will receive aid when navigating through the Arctic still exists because of the unsystematic allocation of rescue bases in the Arctic. At the same time, no study has proposed an overall solution to the problem of allocating rescue bases in the Arctic region to safeguard people’s interests. In this paper, we investigated the main natural factors affecting the safety of ship navigation in the Arctic based on the statistics of ship accidents in the Arctic from 1995 to 2004. The navigation risk of the Arctic was then assessed based on these natural factors, reflecting the need for rescue at all locations in the Arctic. Next, 37 cities with good infrastructure were selected among those along the Arctic as candidate locations for rescue bases. Finally, a new model was constructed based on the Set Covering Location Model, Double Covering Location Model, and P-Median Model to determine the optimal allocation of rescue bases in the Arctic. The rescue bases covered all the areas in the Arctic, and minimized cost in terms of distance and other economic factors. In addition, the constructed model ensured that two rescue bases were allocated to the areas with high navigation risk.
Phylogeny-Guided Selection of Priority Groups for Venom Bioprospecting: Harvesting Toxin Sequences in Tarantulas as a Case Study
Authors: Lüddecke Tim , Vilcinskas Andreas , Lemke Sarah
Published: August 2019

Animal venoms are promising sources of novel drug leads, but their translational potential is hampered by the low success rate of earlier biodiscovery programs, in part reflecting the narrow selection of targets for investigation. To increase the number of lead candidates, here we discuss a phylogeny-guided approach for the rational selection of venomous taxa, using tarantulas (family Theraphosidae) as a case study. We found that previous biodiscovery programs have prioritized the three subfamilies Ornithoctoninae, Selenocosmiinae, and Theraphosinae, which provide almost all of the toxin sequences currently available in public databases. The remaining subfamilies are poorly represented, if at all. These overlooked subfamilies include several that form entire clades of the theraphosid life tree, such as the subfamilies Eumenophorinae, Harpactirinae, and Stromatopelminae, indicating that biodiversity space has not been covered effectively for venom biodiscovery in Theraphosidae. Focusing on these underrepresented taxa will increase the likelihood that promising candidates with novel structures and mechanisms of action can be identified in future bioprospecting programs.