bluuebunny/crossref_metadata_2025 huggingface dataset:
A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography
Authors: Gradolewski Dawid , Magenes Giovanni , Johansson Sven , Kulesza Wlodek J.
Published: February 2019

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.
IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things
Authors: Qin Tao , Wang Bo , Chen Ruoya , Qin Zunying , Wang Lei
Published: February 2019

System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, unlike the traditional system use static agents, we employ the mobile agent to perform data collection and analysis, which can automatically transfer to other nodes according to the pre-set monitoring task. The mobility is handled by the mobile agent running platform, which is irrelevant with the node or its operation system. Combined with this technology, we can greatly reduce the number of agents running in the system while increasing the system stability and scalability. Secondly, we design different methods for node level and system level security monitoring. For the node level security monitoring, we develop a lightweight data collection and analysis method which only occupy little local computing resources. For the system level security monitoring, we proposed a parameter calculation method based on sketch, whose computational complexity is constant and irrelevant with the system scale. Finally, we design agents to perform suitable response policies for system maintenance and abnormal behavior control based on the anomaly mining results. The experimental results based on the platform constructed show that the proposed method has lower computational complexity and higher detection accuracy. For the node level monitoring, the time complexity is reduced by 50% with high detection accuracy. For the system level monitoring, the time complexity is about 1 s for parameter calculation in a middle scale IoT network.
An Insulated Flexible Sensor for Stable Electromyography Detection: Application to Prosthesis Control
Authors: Roland Theresa , Wimberger Kerstin , Amsuess Sebastian , Russold Michael Friedrich , Baumgartner Werner
Published: February 2019

Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due to their insulating layer between skin and sensor area, capacitive sensors are insensitive to the skin condition, they require neither conductive connection to the skin nor electrolytic paste or skin preparation. Here, we describe a highly stable, low-power capacitive EMG measurement set-up that is suitable for real-world application. Various flexible multi-layer sensor set-ups made of copper and insulating foils, flex print and textiles were compared. These flexible sensor set-ups adapt to the anatomy of the human forearm, therefore they provide high wearing comfort and ensure stability against motion artifacts. The influence of the materials used in the sensor set-up on the magnitude of the coupled signal was demonstrated based on both theoretical analysis and measurement.The amplifier circuit was optimized for high signal quality, low power consumption and mobile application. Different shielding and guarding concepts were compared, leading to high SNR.
A New Self-Powered Sensor Using the Radial Field Piezoelectric Diaphragm in d33 Mode for Detecting Underwater Disturbances
Authors: Zhang Xingxu , Shan Xiaobiao , Shen Zhiyuan , Xie Tao , Miao Jianmin
Published: February 2019

This paper presents a new sensor based on a radial field bulk piezoelectric diaphragm to provide energy-efficient and high-performance situational sensing for autonomous underwater vehicles (AUVs). This sensor is self-powered, does not need an external power supply, and works efficiently in d33 mode by using inter-circulating electrodes to release the radial in-plane poling. Finite element analysis was conducted to estimate the sensor behavior. Sensor prototypes were fabricated by microfabrication technology. The dynamic behaviors of the piezoelectric diaphragm were examined by the impedance spectrum. By imitating the underwater disturbance and generating the oscillatory flow velocities with a vibrating sphere, the performance of the sensor in detecting the oscillatory flow was tested. Experimental results show that the sensitivity of the sensor is up to 1.16 mV/(mm/s), and the detectable oscillatory flow velocity is as low as 4 mm/s. Further, this sensor can work well under a disturbance with low frequency. The present work provides a good application prospect for the underwater sensing of AUVs.
Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor
Authors: Wang You , Wang Zhan , Diao Junwei , Sun Xiyang , Luo Zhiyuan , Li Guang
Published: February 2019

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.
Privacy-Preserving Vehicular Rogue Node Detection Scheme for Fog Computing
Authors: Al-Otaibi Basmah , Al-Nabhan Najla , Tian Yuan
Published: February 2019

In the last few decades, urban areas across the world have experienced rapid growth in transportation technology with a subsequent increase in transport-related challenges. These challenges have increased our need to employ technology for creating more intelligent solutions. One of the essential tools used to address challenges in traffic is providing vehicles with information about traffic conditions in nearby areas. Vehicle ad-hoc networks (VANETs) allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication with the aim of providing safe and efficient transportation. Since drivers might make life-critical decisions based on information provided by other vehicles, dealing with rogue vehicles that send invalid data or breach users’ privacy is an essential security issue in VANETs. This paper proposes a novel privacy-preserving vehicular rogue node detection scheme using fog computing. The proposed scheme improves vehicle privacy, communication between vehicles, and computation efficiency by avoiding the exchange of traffic data between vehicles, allowing communication only through roadside units (RSUs). This scheme also proposes an RSU authentication mechanism, along with a mechanism that would allow RSUs to detect and eliminate vehicles providing false traffic data, which will improve the accuracy and efficiency of VANETs. The proposed scheme is analyzed and evaluated using simulation, which presents significant improvements for data processing, accurately detecting rogue vehicles, minimizing overhead, and immunizing the system against colluding vehicles.
A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
Authors: Naziha Ahras , Fu Li , Mohamed Elamine Galloua , Wang Lingling
Published: February 2019

Wireless Sensors Networks (WSNs) are currently receiving much research interest due to their wide-ranging use is a number of different fields. In the current study, a system based on a WSN is proposed that can monitor indoor air pollution in several public spaces, such as subway stations, offices, schools, and hospitals. The proposed system uses integrated sensors in mobile phones, moving from a stationary nodes model to a mobile nodes model. The main objective of building this system is to provide full coverage of the target area. To achieve this goal, the system is simulated by MATLAB and the following algorithms are applied: Particle Swarm Optimization (PSO) to maximize the coverage in the region of interest (RoI), Voronoi Diagram (VD) to detect holes in the coverage, and finally the Point in Polygon (PiP) algorithm to heal the holes in the coverage. The application of the algorithms mentioned above has been very effective as PSO has increased the coverage rate of the monitoring area to 100%. The VD allowed us to define the exact location of coverage holes whilew the Point in Polygon algorithm allowed us to heal the holes and find the remaining sensors in order to improve network coverage. This enabled us to achieve full coverage of the monitoring area.
Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model
Authors: Oh JongGeun , Hong Min-Cheol
Published: February 2019

This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods.
Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network
Authors: Liu Xingchen , Zhou Qicai , Zhao Jiong , Shen Hehong , Xiong Xiaolei
Published: February 2019

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.
Adversarial Samples on Android Malware Detection Systems for IoT Systems
Authors: Liu Xiaolei , Du Xiaojiang , Zhang Xiaosong , Zhu Qingxin , Wang Hao , Guizani Mohsen
Published: February 2019

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
Remote Control System for Battery-Assisted Devices with 16 nW Standby Consumption
Authors: Ferdik Manuel , Saxl Georg , Jesacher Erwin , Ussmueller Thomas
Published: February 2019

One of the biggest impacts of the vision ‘Internet of Things’ is the massive number of connected devices, where billions of nodes will exchange data, information and commands. While wireless systems offer advantages such as increased flexibility, they also introduce one major challenge: how to power each individual node. In many cases, there is no way around the use of batteries. To minimize the environmental impact, increasing the battery’s longevity is the most important factor. This paper introduces a wireless battery-assisted node that has a drastically reduced energy consumption in the standby mode. The state (on/off) will be changed by harvesting a radiofrequency signal. A latching switch connects or disconnects the load—for example, a microcontroller—and the battery. The switch is connected to a charge pump which converts an AC (alternating current) signal into a usable DC (direct current) control signal. An antenna is mounted to the charge pump via a matching network. An electromagnetic wave is emitted by a remote control switch that switches the system on and off. The used frequency is 868 MHz and therefore in the UHF RFID (ultra high frequency radio frequency identification) band. The measurement results show that the wireless node consumes less than 16 nW in the standby mode. The remote controlling is possible from a distance of more than 12 m . The presented system can be integrated in further work on a UHF RFID tag. Thus, the existing protocol standard can be used to identify the object to be switched. By custom commands, the switching request can be transmitted from the remote control (UHF RFID reader) to the switching node.
DOA Estimation and Self-Calibration under Unknown Mutual Coupling
Authors: Qi Dong , Tang Min , Chen Shiwen , Liu Zhixin , Zhao Yongjun
Published: February 2019

In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions (TFDs) in the presence of direction-dependent MC. Firstly, the time-frequency (TF) transformation is used to calculate the space-time-frequency distributions (STFDs) matrix of received signals. After that, the estimated steering vector and corresponding noise subspace are estimated by the steps of noise removing, single-source TF points extracting and clustering. Then according to the transformation relationship between the MC coefficients, steering vector and MC matrix, we deduce a set of linear equations. Finally, with two-step alternating iteration, the equations are solved by least square method in order to estimate DOA and MC coefficients. Simulations results show that the proposed algorithm can achieve direction-dependent MC self-calibration and outperforms the existing algorithms.
A New Sustainable Model for Risk Management—RiMM
Authors: Tiganoaia Bogdan , Niculescu Andrei , Negoita Olivia , Popescu Mirona
Published: February 2019

All activities of a company involve risk. In order to achieve its objectives, an organization must identify, analyze, evaluate and then treat all significant risks. According to ISO international standards in the risk field, risk management can be applied to an entire organization, at its many areas and levels, at any time, as well as to specific functions, projects and activities. An effective risk management helps top management of an organization to make optimum decisions and to prevent losses. This paper proposes a new sustainable model for risk management—RiMM. The Sustainable Risk Management model is based on the Monte Carlo method (adapted for risk management process) that is known in the literature but not or very rare applied for this issue of controlling the risks in an organization. There are proposed aspects regarding the design of the model (in five detailed steps—every step with sub steps), a software implementation and an example of a case study that emphasizes the way the model can be used (also to demonstrate its efficiency) for managing risks in an organization. At the end, on conclusions section, the most important points and the contributions of the paper are clearly presented.
Using Causal Maps to Analyse the Major Root Causes of Household Food Waste: Results of a Survey among People from Central and Southern Italy
Authors: Fanelli Rosa Maria
Published: February 2019

Food waste is an important sustainability issue that needs to be addressed. Consumer behaviour is one of the biggest sources of food waste in developed countries. To successfully reduce consumer-related food waste, it is necessary to have a clear understanding of its major root causes at the level of the individual. The present manuscript presents the results of an exploratory on-line survey that was made available through Google Drive and conducted among a representative sample of 1058 Italian individuals. The information contained in the questionnaire related to the characteristics of the individual respondents, their attitudes to expenditure and food, and their opinions of measures to reduce or prevent food waste. Data analysis was conducted in three phases. The first phase allowed for the identification, with the application of descriptive statistics, of the socio-demographic characteristics of the respondents and why, what, and how much they wasted. In the second phase, linear regression analysis and causal maps were used to both measure the statistical dependence between variables and to identify the main root causes of food waste in the phase of individual consumption. As expected, the perceived quantity of food waste that was declared by respondents was very low. Among the major root causes identified, the socio-demographic characteristics of consumers, types of food shopping purchases, and consumer behaviour played a key role. A causal map was drawn, which offers an immediate vision of the major root causes and can be a useful tool for policymakers who intend to introduce measures to combat food waste. Finally, participants’ responses showed that the main initiatives needed to eliminate waste are the separate collection and dissemination of more information on the impact that waste has on the environment. For these reasons, information and education policies are crucial for changing consumer lifestyles and raising awareness of the value of food.
A Transdisciplinary Approach to Identifying Transboundary Tipping Points in a Contentious Area: Experiences from across the Jordan River Region
Authors: Rodriguez Lopez Juan Miguel , Tielbörger Katja , Claus Cornelia , Fröhlich Christiane , Gramberger Marc , Scheffran Jürgen
Published: February 2019

Transdisciplinary research is urgently needed but has rarely been applied in sustainability science. Here, we present a case study for a transdisciplinary approach to identify tipping points in the Jordan River region, an area of severe conflicts and environmental problems. The aim was to identify and prioritize different ecological and social tipping points within a framework of transdisciplinary assessment, using the Jordan River Region as a case study. The methodology included an iterative process in which a multidisciplinary group of scientists and regional stakeholders evaluated emerging tipping points involving professionally facilitated workshops, a correlation analysis, and an anonymous ranking based on specific criteria (practical relevance, scientific novelty, transboundary elements, and feasibility of intervention). During the process, the similarities and differences in the perceptions of the potential case studies by stakeholders from the three countries (Israel, Palestine and Jordan) involved were analyzed. The results found key elements of the term tipping point (abrupt, difficult to predict, irreversible regime shift, difficult management), and showed local and regional differences in evaluating potential case studies, where degradation or desertification of rangelands emerged as a final case study of common concern. The lessons learnt show the potential and challenges of transboundary cooperation, and the possibility of the co-creation of research with stakeholders to build knowledge about the diversity of tipping points. The study concludes that common ground related to the sustainable management of natural resources exists even in highly contentious areas, and among highly different cultural, political, and socio-economic conditions. The approach presented here may thus inform practitioners in prioritizing areas of transboundary sustainability research in other regions.
Blockchain Technology Implementation in Logistics
Authors: Tijan Edvard , Aksentijević Saša , Ivanić Katarina , Jardas Mladen
Published: February 2019

This paper researches decentralized data storage represented by blockchain technology and the possibility of its development in sustainable logistics and supply chain management. Although the benefits of blockchain technology have been most widely researched in the financial sector, major challenges in logistics, such as order delay, damage to goods, errors, and multiple data entry can also be minimized by introducing blockchain technology. This paper presents a comprehensive review of the current and rising trends of blockchain technology usage in logistics and supply chain management.
The Collaborative Management of Sustained Unsustainability: On the Performance of Participatory Forms of Environmental Governance
Authors: Blühdorn Ingolfur , Deflorian Michael
Published: February 2019

In modern democratic consumer societies, decentralized, participative, and consensus-oriented forms of multi-stakeholder governance are supplementing, and often replacing, conventional forms of state-centered environmental government. The engagement in all phases of the policy process of diverse social actors has become a hallmark of environmental good governance. This does not mean to say, however, that these modes of policy-making have proved particularly successful in resolving the widely debated multiple sustainability crisis. In fact, they have been found wanting in terms of their ability to respond to democratic needs and their capacity to resolve environmental problems. So why have these participatory forms of environmental governance become so prominent? What exactly is their appeal? What do they deliver? Exploring these questions from the perspective of eco-political and sociological theory, this article suggests that these forms of environmental governance represent a performative kind of eco-politics that helps liberal consumer societies to manage their inability and unwillingness to achieve the socio-ecological transformation that scientists and environmental activists say is urgently required. This reading of the prevailing policy approaches as the collaborative management of sustained unsustainability adds an important dimension to the understanding of environmental governance and contemporary eco-politics more generally.
Harnessing Insights from Social-Ecological Systems Research for Monitoring Sustainable Development
Authors: Selomane Odirilwe , Reyers Belinda , Biggs Reinette , Hamann Maike
Published: February 2019

The United Nations’ Agenda 2030 marks significant progress towards sustainable development by making explicit the intention to integrate previously separate social, economic and environmental agendas. Despite this intention, the Sustainable Development Goals (SDGs) which were adopted to implement the agenda, are fragmented in their formulation and largely sectoral. We contend that while the design of the SDG monitoring is based on a systems approach, it still misses most of the dynamics and complexity relevant to sustainability outcomes. We propose that insights from the study of social-ecological systems offer a more integrated approach to the implementation of Agenda 2030, particularly the monitoring of progress towards sustainable development outcomes. Using five key features highlighted by the study of social-ecological systems (SESs) relevant to sustainable development: (1) social-ecological feedbacks, (2) resilience, (3) heterogeneity, (4) nonlinearity, and (5) cross-scale dynamics. We analyze the current set of SDG indicators based on these features to explore current progress in making them operational. Our analysis finds that 59% of the indicators account for heterogeneity, 33% for cross-scale dynamics, 23% for nonlinearities, and 18% and 17%, respectively, for social-ecological feedbacks and resilience. Our findings suggest limited use of complex SES science in the current design of SDG monitoring, but combining our findings with recent studies of methods to operationalize SES features suggests future directions for sustainable development monitoring for the current as well as post 2030 set of indicators.
How Does Green Product Knowledge Effectively Promote Green Purchase Intention?
Authors: Wang Hong , Ma Baolong , Bai Rubing
Published: February 2019

Knowledge can influence the whole decision-making process of consumers. While green product knowledge is often conceptualized as a direct antecedent of green purchase intention, empirical findings in support of this relationship are inconsistent. Based on 236 samples, this study investigates how green product knowledge promotes consumers’ green purchase intention. Results show that green trust and perceived consumer effectiveness partly mediate the relationship between green product knowledge and green purchase intention. The relationship between green trust and green purchase intention is positively moderated by perceived price. Compared with low perceived price, green trust has a greater effect on green purchase intention in the case of high perceived price. However, the relationship between perceived consumer effectiveness and green purchase intention is not moderated by perceived price. This study provides a new insight into green product knowledge and how to promote green purchase intention, and the findings help government and enterprises to formulate strategies to promote consumers’ green purchase intention.
Examining the Factors Behind the Success and Sustainability of China’s Creative Research Group: An Extension of the Teamwork Quality Model
Authors: Gao Jie , Huang Cui , Su Jun , Xie Qijun
Published: February 2019

The Creative Research Group (CRG) is the special high-level scientific and innovation team funded by the National Natural Science Foundation of China to promote basic research at the frontiers of science. In general, there are problems of “structure anomie” and “cooperation inefficiency” in the operations and teamwork dynamics within the nationwide CRG project. By extending and developing the two-stage teamwork quality model, this study aims to depict and analyze the impact factors behind the success and sustainability of the Creative Research Group (SSCRG), and reveal the relationships among them. Herein, rationality of team structure (ROTS) is used to describe the rationality of team formation and structure collocation, teamwork quality (TWQ) is used to describe the process of team members’ integration and cooperation, and SSCRG includes team performance, personal success and team comprehensive impacts. The results show that ROTS and TWQ have different influences on SSCRG, while TWQ is the key mediation factor between ROTS and SSCRG. In summary, the model built describes the complex phenomena and relationships in the teamwork of the CRG, and helps us to understand and solve the problems of structure and cooperation. Although the limitations lie in the specific samples and research methods, the extension and migration of classical models and theories would help to further deepen such research and contribute to the governance and development of such innovation teams.
Impact of Building Environment on Residential Satisfaction: A Case Study of Ningbo
Authors: Yin Yanhong , He Yuanwen , Zhang Lei , Zhao Dan
Published: February 2019

Population distribution has a huge influence on the development of port economic circle (PEC) in Ningbo. Residential satisfaction is one of key elements that determine housing location choice and, thus, the success of population distribution policy. To provide suggestions for the development of PEC, this study conducted a survey to investigate residential satisfaction and related factors in 11 port communities, which are located in the harbour area of Beilun, Zhenhai and Meishan in Ningbo. A total of 403 valid samples were collected through face-to-face interviews. The Likert scale was used to express satisfaction levels, with the highest level of 5 and the lowest level of 1. The survey data indicate that the mean value of residential satisfaction is 3.41, 3.40 and 3.49 in Beilun, Zhenhai and Meishan, respectively. Middle level residential satisfaction is noted in the port communities of Ningbo. A linear regression was performed to analyse the relationship between residential satisfaction and influence factors in terms of the demographic and socioeconomic features of the respondents and attributes of the house and neighbourhood. The results of the regression analysis show that household income, design of housing, type of community, population density and distance to the district commercial centre are the most influential factors of residential satisfaction. The findings suggest making regulations to enforce the design standards of new buildings. Moreover, measures to improve the accessibility and quality of public facilities and services in the communities should be encouraged to increase the attractiveness of neighbourhoods in the port area. Promoting land use with mixed commercial and residential functions is also suggested for the development of PEC.
Healthcare and SDGs Governance in Light of the Sustainability Helix Model: Evidence from the African Continent
Authors: Saviano Marialuisa , Sciarelli Fabiana , Rinaldi Azzurra , Alowanou Georcelin G.
Published: February 2019

This work is based on a view of healthcare as a fundamental Sustainable Development Goal (SDG) to share globally to be effective at local level. On this basis, the paper analyzes the health programs in less-favored areas with the aim of understanding why the United Nations SDGs are so difficult to reach in some countries. A brief review of the main literature and research on the health governance systems in three countries of the African continent have been conducted to this aim. The results are interpreted through the sustainability helix model (SHM). Key roles and conditions of effectiveness of the health sustainable development governance approach in the investigated countries are discussed. The main findings reveal that the analyzed governance systems lack implementation plans. By discussing the observed problem in the light of the sustainability helix model, fundamental elements of a health sustainable development helix model have been identified in less favored countries where key actors and roles are identified. The study highlights, in particular, the relevance of ‘interface’ roles played by non-governmental actors (NGA) and international actors (IA) in the health governance system of less-favored countries. These actors and roles allow connections between the global and the local levels of action favoring interaction among actors institutionally devoted to governing development.
Research on an Improved Economic Value Estimation Model for Crop Irrigation Water in Arid Areas: From the Perspective of Water-Crop Sustainable Development
Authors: Ma Miaosen , Zhao Min
Published: February 2019

This paper aimed to construct an improved economic value estimation model (EVIW model) to assess the economic value of water, which plays an important role in the sustainable development of crop planting and irrigation design, especially in arid areas lacking water resources. Firstly, the current EVIW model was based upon improvements and adjustments to the cost-benefit analysis models of previous researchers. Then, to elaborate the whole process of estimation, an empirical study based on the data of Yanqi Basin was conducted. Subsequently, in order to verify the accuracy of the EVIW model, the economic value of irrigation water in this study area was estimated for a second time using the benefit sharing coefficient method. It was concluded that the estimated results of the current EVIW model are in good agreement with those of the traditional benefit sharing coefficient model. The estimation results of the economic value of irrigation water were found to be highly acceptable in terms of accuracy and scientific rigor.
User Acceptance of Mobile Apps for Restaurants: An Expanded and Extended UTAUT-2
Authors: Palau-Saumell Ramon , Forgas-Coll Santiago , Sánchez-García Javier , Robres Emilio
Published: February 2019

This paper examines the adoption of mobile applications for restaurant searches and/or reservations (MARSR) by users, as part of their experiential quality. Following an extended and expanded version of UTAUT-2, this research proposes eight determinants of intentions to use: performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price-saving orientation, habit, social influence, and perceived credibility. The data were collected from Spanish users of MARSR applications (n = 1200), and analyzed using structural equation modeling (SEM). The findings confirm the need to extend and expand UTAUT-2 by incorporating perceived credibility and the social norm approach. The results gathered from SEM indicate that the drivers of intentions to use MARSR are, in order of impact: habit, perceived credibility, hedonic motivation, price-saving orientation, effort expectancy, performance expectancy, social influence, and facilitating conditions. Habit, facilitating conditions, and intentions to use are significantly related to use. Additionally, the moderating effects of gender, age, and experience were tested by means of a multi-group analysis. The users’ experience was seen to exert a moderating effect in some of the relationships hypothesized in the model, while gender and age did not play a significant role. The findings have both research and practical implications.
Energy and Environmental Efficiency in Different Chinese Regions
Authors: Li Ying , Chiu Yung-ho , Lin Tai-Yu
Published: February 2019

China has become the second-largest economy in the world; however, the price of its rapid economic development has been a rise in serious environmental pollution, with air quality being a major public issue in many regions. However, few previous energy and environmental sustainability studies have included the Air Quality Index (AOI) and in particular CO2 and PM2.5 emissions in their calculations and few have included regional differences, as these are difficult to describe using radial and non-radial methods. In this paper, DEA (Data Envelopment Analysis) is used to assess the energy and economic efficiencies of Chinese provinces and cities, in which the environmental pollution source variable is CO2, and the main methods applied are radial (CCR or BCC) and non-radial SBM (Slacks Based Measures). Different from past studies, this study used both a Meta Undesirable EBM (Epsilon-Based measure) method to overcome the radial and non-radial errors and geographical differences and AQI environmental pollution indicators to accurately assess the economic, energy, and environmental efficiencies. It was found that: (1) Guangzhou and Shanghai had the best four-year efficiencies, (2) the energy efficiency differences in each city were large and there was a significant need for improvements, (3) the GDP efficiencies in each city were high, indicating that all cities had strong economic development, (4) the CO2 efficiencies indicated that around half the cities had had sustained improvements, (5) the AQI efficiencies in each city were low and there was a significant need for improvement, and (6) the technological differences between the cities were large, with the efficiencies in the high-income cities being much higher than in the low-income cities.
Fixed-Discs in Rectangular Metric Spaces
Authors: Aydi Hassen , Taş Nihal , Özgür Nihal Yılmaz , Mlaiki Nabil
Published: February 2019

In this manuscript, we present some results related to fixed-discs of self-mappings in rectangular metric spaces. To do this, we give new techniques modifying some classical notions such as Banach contraction principle, α-admissible mappings and Brianciari type contractions. We give necessary illustrative examples to show the validity of our obtained theoretical theorems. Our results are generalizations of some fixed-circle results existing in the literature.
Immune System Modulation and Viral Persistence in Bats: Understanding Viral Spillover
Authors: Subudhi Sonu , Rapin Noreen , Misra Vikram
Published: February 2019

Bats harbor a myriad of viruses and some of these viruses may have spilled over to other species including humans. Spillover events are rare and several factors must align to create the “perfect storm” that would ultimately lead to a spillover. One of these factors is the increased shedding of virus by bats. Several studies have indicated that bats have unique defense mechanisms that allow them to be persistently or latently infected with viruses. Factors leading to an increase in the viral load of persistently infected bats would facilitate shedding of virus. This article reviews the unique nature of bat immune defenses that regulate virus replication and the various molecular mechanisms that play a role in altering the balanced bat–virus relationship.
In Vivo Characterization of Avian Influenza A (H5N1) and (H7N9) Viruses Isolated from Canadian Travelers
Authors: Lu Yao , Landreth Shelby , Gaba Amit , Hlasny Magda , Liu Guanqun , Huang Yanyun , Zhou Yan
Published: February 2019

Highly pathogenic avian influenza (HPAI) H5N1 and low pathogenic avian influenza (LPAI) H7N9 viruses pose a severe threat to public health through zoonotic infection, causing severe respiratory disease in humans. While HPAI H5N1 human infections have typically been reported in Asian countries, avian H7N9 human infections have been reported mainly in China. However, Canada reported a case of fatal human infection by the HPAI H5N1 virus in 2014, and two cases of human illness associated with avian H7N9 virus infection in 2015. While the genomes of the causative viruses A/Alberta/01/2014 (H5N1) (AB14 (H5N1)) and A/British Columbia/1/2015 (H7N9) (BC15 (H7N9)) are reported, the isolates had not been evaluated for their pathogenicity in animal models. In this study, we characterized the pathogenicity of AB14 (H5N1) and BC15 (H7N9) and found that both strain isolates are highly lethal in mice. AB14 (H5N1) caused systemic viral infection and erratic proinflammatory cytokine gene expression in different organs. In contrast, BC15 (H7N9) replicated efficiently only in the respiratory tract, and was a potent inducer for proinflammatory cytokine genes in the lungs. Our study provides experimental evidence to complement the specific human case reports and animal models for evaluating vaccine and antiviral candidates against potential influenza pandemics.
Antiviral Drug Discovery: Norovirus Proteases and Development of Inhibitors
Authors: Chang Kyeong-Ok , Kim Yunjeong , Lovell Scott , Rathnayake Athri D. , Groutas William C.
Published: February 2019

Proteases are a major enzyme group playing important roles in a wide variety of biological processes in life forms ranging from viruses to mammalians. The aberrant activity of proteases can lead to various diseases; consequently, host proteases have been the focus of intense investigation as potential therapeutic targets. A wide range of viruses encode proteases which play an essential role in viral replication and, therefore, constitute attractive targets for the development of antiviral therapeutics. There are numerous examples of successful drug development targeting cellular and viral proteases, including antivirals against human immunodeficiency virus and hepatitis C virus. Most FDA-approved antiviral agents are peptidomimetics and macrocyclic compounds that interact with the active site of a targeted protease. Norovirus proteases are cysteine proteases that contain a chymotrypsin-like fold in their 3D structures. This review focuses on our group’s efforts related to the development of norovirus protease inhibitors as potential anti-norovirus therapeutics. These protease inhibitors are rationally designed transition-state inhibitors encompassing dipeptidyl, tripeptidyl and macrocyclic compounds. Highly effective inhibitors validated in X-ray co-crystallization, enzyme and cell-based assays, as well as an animal model, were generated by launching an optimization campaign utilizing the initial hit compounds. A prodrug approach was also explored to improve the pharmacokinetics (PK) of the identified inhibitors.
Multi-Level Energy Management for Hybrid Electric Vehicles—Part I
Authors: van Reeven Vital , Hofman Theo
Published: February 2019

The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.
Electric vehicles in Danish Municipalities: An Understanding of Motivations, Barriers, and the Future of Sustainable Mobility
Authors: Anderson Tessa Kate
Published: February 2019

This paper explores the procurement, use, and experience of plug-in electric vehicles (PEVs) in Danish municipalities in relation to the notion of early adopters and socio-technical theory. Denmark has been one of the most ambitious countries in terms of electric vehicle adoption and use. This study used a combination of in-depth surveys and interviews with all 61 Danish municipalities on their fleet PEV experience and use. By building on the literature, the paper offers a deeper understanding of decision-making pathways for the procurement of PEVs. PEVs were found to be most suited to certain departments and the acceptance and uptake of PEVs was found to be complex and not straightforward.
Green Smart Technology for Water (GST4Water): Life Cycle Analysis of Urban Water Consumption
Authors: Bonoli Alessandra , Di Fusco Emanuela , Zanni Sara , Lauriola Ilaria , Ciriello Valentina , Di Federico Vittorio
Published: February 2019

The increasing scarcity of water is encouraging strategies in water saving and urban water management systems devoted to reducing natural resource consumption and environmental impact. At household and urban scales, there is an increasing interest in onsite greywater and non-potable water reuse systems in order to improve water availability. In this framework, the project GST4Water funded by the European Union (EU) under the POR-FESR 2014–2020 Program of Emilia-Romagna Region, has been developed with the aim to implement water consumption monitoring systems, to define solutions for greywater reuse, and to develop tools for environmental sustainability evaluation applied to water systems. The present study focuses on this last goal, performing a life cycle assessment of the solutions optimized at a district level. In particular, six different scenarios are compared, starting from two models considering traditional water supply together with or without energy consumption related to hot water generation, and five additional models related with different assumptions in terms of greywater recovery systems, and energy and hot water production, at varying percentages of renewable and photovoltaic energy supply. Finally, an evaluation of the return time of environmental investment is carried out, based on the results obtained through the scenario analysis.