bluuebunny/crossref_metadata_2025 huggingface dataset:
Pretreatments of Non-Woody Cellulosic Feedstocks for Bacterial Cellulose Synthesis
Authors: Kashcheyeva Ekaterina I. , Gismatulina Yulia A. , Budaeva Vera V.
Published: October 2019

Pretreatment of biomass is a key step in the production of valuable products, including high-tech bacterial cellulose. The efficiency of five different pretreatment methods of Miscanthus and oat hulls for enzymatic hydrolysis and subsequent synthesis of bacterial cellulose (BC) was evaluated herein: Hydrothermobaric treatment, single-stage treatments with dilute HNO3 or dilute NaOH solution, and two-stage combined treatment with dilute HNO3 and NaOH solutions in direct and reverse order. The performance of enzymatic hydrolysis of pretreatment products was found to increase by a factor of 4−7. All the resultant hydrolyzates were composed chiefly of glucose, as the xylose percentage in total reducing sugars (RS) was 1−9%. The test synthesis of BC demonstrated good quality of nutrient media prepared from all the enzymatic hydrolyzates, except the hydrothermobaric treatment hydrolyzate. For biosynthesis of BC, single-stage pretreatments with either dilute HNO3 or dilute NaOH are advised due their simplicity and the high performance of enzymatic hydrolysis of pretreatment products (RS yield 79.7−83.4%).
Stabilization of Silver Nanoparticles by Cationic Aminoethyl Methacrylate Copolymers in Aqueous Media—Effects of Component Ratios and Molar Masses of Copolymers
Authors: Mikhailova Mariya E. , Senchukova Anna S. , Lezov Alexey A. , Gubarev Alexander S. , Trützschler Anne -K. , Schubert Ulrich S. , Tsvetkov Nikolay V.
Published: October 2019

The ability of aminoethyl methacrylate cationic copolymers to stabilize silver nanoparticles in water was investigated. Sodium borohydride (NaBH4) was employed as a reducing agent for the preparation of silver nanoparticles. The objects were studied by ultraviolet-visible (UV-vis) spectroscopy, dynamic light scattering (DLS), analytical ultracentrifugation (AUC) and scanning electron microscopy (SEM). Formation of nanoparticles in different conditions was investigated by varying ratios between components (silver salt, reducing agent and polymer) and molar masses of copolymers. As a result, we were successful in obtaining nanoparticles with a relatively narrow size distribution that were stable for more than six months. Consistent information on nanoparticle size was obtained. The holding capacity of the copolymer was studied.
Superharmonic Resonance in Carbon-Black-Filled Rubber by High-Frequency DMA
Authors: Syed Imran Hussain , Lacayo-Pineda Jorge
Published: October 2019

A systematic study of several SBR compounds filled with carbon black of various grades were analysed with the high-frequency Dynamic Mechanical Analyzer (HF DMA) in order to quantify the degree of nonlinearity induced by fillers in rubber compounds. These filler grades indirectly reflect different degrees of microdispersion, which seems to be the main influence on the superharmonic resonance phenomenon observed in HF DMA. This statement arises from the comparison of the microdispersion observed in TEM images. In the second part of the paper, a model compound filled with carbon black is enhanced with a standard reinforcing resin, which leads to a more compact filler network. This induces a higher superharmonic resonance response as well as a higher transmissibility behaviour.
Forest Connectivity, Host Assemblage Characteristics of Local and Neighboring Counties, and Temperature Jointly Shape the Spatial Expansion of Lyme Disease in United States
Authors: Wang Yingying X. G. , Matson Kevin D. , Xu Yanjie , Prins Herbert H. T. , Huang Zheng Y. X. , de Boer Willem F.
Published: October 2019

Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors.
Autonomous Airborne 3D SAR Imaging System for Subsurface Sensing: UWB-GPR on Board a UAV for Landmine and IED Detection
Authors: Garcia-Fernandez Maria , Alvarez-Lopez Yuri , Las Heras Fernando
Published: October 2019

This work presents an enhanced autonomous airborne Synthetic Aperture Radar (SAR) imaging system able to provide full 3D radar images from the subsurface. The proposed prototype and methodology allow the safe detection of both metallic and non-metallic buried targets even in difficult-to-access scenarios without interacting with the ground. Thus, they are particularly suitable for detecting dangerous targets, such as landmines and Improvised Explosive Devices (IEDs). The prototype is mainly composed by an Ultra-Wide-Band (UWB) radar module working from Ultra-High-Frequency (UHF) band and a high accuracy dual-band Real Time Kinematic (RTK) positioning system mounted on board an Unmanned Aerial Vehicle (UAV). The UAV autonomously flies over the region of interest, gathering radar measurements. These measurements are accurately geo-referred so as to enable their coherent combination to obtain a well-focused SAR image. Improvements in the processing chain are also presented in order to deal with some issues associated to UAV-based measurements (such as non-uniform acquisition grids) as well as to enhance the resolution and the signal to clutter ratio of the image. Both the prototype and the methodology were validated with measurements, showing their capability to provide high-resolution 3D SAR images.
Distributed Acoustic Sensing Using Chirped-Pulse Phase-Sensitive OTDR Technology
Authors: R. Fernández-Ruiz María , Costa Luis , F. Martins Hugo
Published: October 2019

In 2016, a novel interrogation technique for phase-sensitive (Φ)OTDR was mathematically formalized and experimentally demonstrated, based on the use of a chirped-pulse as a probe, in an otherwise direct-detection-based standard setup: chirped-pulse (CP-)ΦOTDR. Despite its short lifetime, this methodology has now become a reference for distributed acoustic sensing (DAS) due to its valuable advantages with respect to conventional (i.e., coherent-detection or frequency sweeping-based) interrogation strategies. Presenting intrinsic immunity to fading points and using direct detection, CP-ΦOTDR presents reliable high sensitivity measurements while keeping the cost and complexity of the setup bounded. Numerous technique analyses and contributions to study/improve its performance have been recently published, leading to a solid, highly competitive and extraordinarily simple method for distributed fibre sensing. The interesting sensing features achieved in these last years CP-ΦOTDR have motivated the use of this technology in diverse applications, such as seismology or civil engineering (monitoring of pipelines, train rails, etc.). Besides, new areas of application of this distributed sensor have been explored, based on distributed chemical (refractive index) and temperature-based transducer sensors. In this review, the principle of operation of CP-ΦOTDR is revisited, highlighting the particular performance characteristics of the technique and offering a comparison with alternative distributed sensing methods (with focus on coherent-detection-based ΦOTDR). The sensor is also characterized for operation in up to 100 km with a low cost-setup, showing performances close to the attainable limits for a given set of signal parameters [≈tens-hundreds of pe/sqrt(Hz)]. The areas of application of this sensing technology employed so far are briefly outlined in order to frame the technology.
Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors
Authors: Chen Liping , Zhangzhong Lili , Zheng Wengang , Yu JingXin , Wang Zehan , Wang Long , Huang Chao
Published: October 2019

Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.
Correction: Suwannapong, C. et al. Congestion Control in CoAP Observe Group Communication. Sensors 2019, 19, 3433
Authors: Suwannapong Chanwit , Khunboa Chatchai
Published: October 2019

The authors wish to make the following corrections to this paper [...]
Switchable Piezoresistive SmS Thin Films on Large Area
Authors: Sousanis Andreas , Poelman Dirk , Detavernier Christophe , Smet Philippe F.
Published: October 2019

Samarium monosulfide (SmS) is a switchable material, showing a pressure-induced semiconductor to metal transition. As such, it can be used in different applications such as piezoresistive sensors and memory devices. In this work, we present how e-beam sublimation of samarium metal in a reactive atmosphere can be used for the deposition of semiconducting SmS thin films on 150 mm diameter silicon wafers. The deposition parameters influencing the composition and properties of the thin films are evaluated, such as the deposition rate of Sm metal, the substrate temperature and the H2S partial pressure. We then present the changes in the optical, structural and electrical properties of this compound after the pressure-induced switching to the metallic state. The back-switching and stability of SmS thin films are studied as a function of temperature and atmosphere via in-situ X-ray diffraction. The thermally induced back switching initiates at 250 °C, while above 500 °C, Sm2O2S is formed. Lastly, we explore the possibility to determine the valence state of the samarium ions by means of X-ray photoelectron spectroscopy.
Labyrinth Metasurface for Biosensing Applications: Numerical Study on the New Paradigm of Metageometries
Authors: Jáuregui-López Irati , Rodríguez-Ulibarri Pablo , Kuznetsov Sergei A. , Quemada Carlos , Beruete Miguel
Published: October 2019

The use of metasurfaces operating in the terahertz regime as biosensor devices has attracted increased interest in recent years due to their enhanced sensitivity and more accurate detection capability. Typical designs are based on the replica of relatively simple unit cells, usually called metaatoms. In a previous paper, we proposed a new paradigm for ultrasensitive thin-film sensors based on complex unit cells, called generically metageometries or labyrinth metasurfaces. Here, we extend this concept towards biosensing, evaluating the performance of the labyrinth as a fungi detector. The sensing capabilities are numerically evaluated and a comparison with previous works in this field is performed, showing that metageometries improve the performance compared to metaatoms both in sensitivity and figure of merit, by a factor of more than four. In particular, we find that it is able to detect five fungi elements scattered on the unit cell, equivalent to a concentration of only 0.004/µm2.
Explicit and Implicit Responses to Tasting Drinks Associated with Different Tasting Experiences
Authors: Kaneko Daisuke , Hogervorst Maarten , Toet Alexander , van Erp Jan B. F. , Kallen Victor , Brouwer Anne-Marie
Published: October 2019

Probing food experience or liking through verbal ratings has its shortcomings. We compare explicit ratings to a range of (neuro)physiological and behavioral measures with respect to their performance in distinguishing drinks associated with different emotional experience. Seventy participants tasted and rated the valence and arousal of eight regular drinks and a “ground truth” high-arousal, low-valence vinegar solution. The discriminative power for distinguishing between the vinegar solution and the regular drinks was highest for sip size, followed by valence ratings, arousal ratings, heart rate, skin conductance level, facial expression of “disgust,” pupil diameter, and Electroencephalogram (EEG) frontal alpha asymmetry. Within the regular drinks, a positive correlation was found between rated arousal and heart rate, and a negative correlation between rated arousal and Heart Rate Variability (HRV). Most physiological measures showed consistent temporal patterns over time following the announcement of the drink and taking a sip. This was consistent over all nine drinks, but the peaks were substantially higher for the vinegar solution than for the regular drinks, likely caused by emotion. Our results indicate that implicit variables have the potential to differentiate between drinks associated with different emotional experiences. In addition, this study gives us insight into the physiological temporal response patterns associated with taking a sip.
PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data
Authors: Tang Yan , Wang Jianwu , Nguyen Mai , Altintas Ilkay
Published: October 2019

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints. Second, it is quite difficult to select a learner from numerous BN structure learning algorithms to consistently achieve good learning accuracy. Lastly, there is a lack of an intelligent method that merges separately learned BN structures into a well structured BN network. To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning). PEnBayes starts with an adaptive data preprocessing phase that calculates the Appropriate Learning Size and intelligently divides a big dataset for fast distributed local structure learning. Then, PEnBayes learns a collection of local BN Structures in parallel using a two-layered weighted adjacent matrix-based structure ensemble method. Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer. For the experiment, we generate big data sets by simulating sensor data from patient monitoring, transportation, and disease diagnosis domains. The Experimental results show that PEnBayes achieves a significantly improved execution performance with more consistent and stable results compared with three baseline learning algorithms.
A Low-Profile High-Gain and Wideband Log-Periodic Meandered Dipole Array Antenna with a Cascaded Multi-Section Artificial Magnetic Conductor Structure
Authors: Trinh-Van Son , Kwon Oh Heon , Jung Euntae , Park Jinwoo , Yu Byunggil , Kim Kichul , Seo Jongwoo , Hwang Keum Cheol
Published: October 2019

This paper presents a low-profile log-periodic meandered dipole array (LPMDA) antenna with wideband and high gain characteristics. The antenna consists of 14 dipole elements. For compactness, a meander line structure is applied to each dipole element to reduce its physical length. As a result, a compact and wideband LPMDA antenna is realized, exhibiting a wide impedance bandwidth of 1.04–5.22 GHz (ratio bandwidth of 5.02:1) for | S 11| < −10 dB. To enhance the antenna gain performance while maintaining the wideband behavior, the LPMDA antenna is integrated with a new design of an artificial magnetic conductor (AMC) structure. The designed AMC is realized by combining three AMC structures of different sizes to form a cascaded multi-section AMC structure, of which its overall operating bandwidth can continuously cover the entire impedance bandwidth of the LPMDA antenna. The proposed AMC-backed LPMDA antenna is experimentally verified and its measured −10 dB reflection bandwidth is found to be in the range of 0.84–5.15 GHz (6.13:1). At the main beam direction within the operating frequency bandwidth, the gain of the proposed AMC-backed LPMDA antenna ranges from 7.15–11.43 dBi, which is approximately 4 dBi higher than that of an LPMDA antenna without an AMC. Moreover, the proposed antenna has a low profile of only 0.138 λ L. ( λ L is the free-space wavelength at the lowest operating frequency).
DCS: Distributed Caching Strategy at the Edge of Vehicular Sensor Networks in Information-Centric Networking
Authors: Meng Yahui , Naeem Muhammad Ali , Ali Rashid , Zikria Yousaf Bin , Kim Sung Won
Published: October 2019

Information dissemination in current Vehicular Sensor Networks (VSN) depends on the physical location in which similar data is transmitted multiple times across the network. This data replication has led to several problems, among which resource consumption (memory), stretch, and communication latency due to the lake of data availability are the most crucial. Information-Centric Networking (ICN) provides an enhanced version of the internet that is capable of resolving such issues efficiently. ICN is the new internet paradigm that supports innovative communication systems with location-independent data dissemination. The emergence of ICN with VSNs can handle the massive amount of data generated from heterogeneous mobile sensors in surrounding smart environments. The ICN paradigm offers an in-network cache, which is the most effective means to reduce the number of complications of the receiver-driven content retrieval process. However, due to the non-linearity of the Quality-of-Experience (QoE) in VSN systems, efficient content management within the context of ICN is needed. For this purpose, this paper implements a new distributed caching strategy (DCS) at the edge of the network in VSN environments to reduce the number of overall data dissemination problems. The proposed DCS mechanism is studied comparatively against existing caching strategies to check its performance in terms of memory consumption, path stretch ratio, cache hit ratio, and content eviction ratio. Extensive simulation results have shown that the proposed strategy outperforms these benchmark caching strategies.
Modeling and Optimal Design for a High Stability 2D Optoelectronic Angle Sensor
Authors: Cheng Zhenying , Liu Liying , Xu Peng , Li Ruijun , Fan Kuang-Chao , Li Hongli , Wei Yongqing
Published: October 2019

The structural deformations caused by environmental changes in temperature, vibration, and other factors are harmful to the stability of high precision measurement equipment. The stability and optimal design method of a 2D optoelectronic angle sensor have been investigated in this study. The drift caused by structural deformations of the angle sensor has been studied and a drift error model has been achieved. Key components sensitive to thermal and vibrational effects were identified by error sensitivity analysis and simulation. The mounts of key components were analyzed using finite element analysis software and optimized based on the concept of symmetric structures. Stability experiments for the original and optimized angle sensors have been carried out for contrast. As a result, the stability of the optimized angle sensor has been improved by more than 63%. It is verified that the modeling and optimal design method is effective and low-cost, which can also be applied to improve the stability of other sensors with much more complex principles and structures.
Urban Systems Design Case Study: Tokyo’s Sumida Ward
Authors: Tobey Michael B. , Binder Robert B. , Yoshida Takahiro , Yamagata Yoshiki
Published: October 2019

Meeting the needs of increasing environmental and systematic pressures in urban settlements requires the use of integrated and wholistic approaches. The Urban Systems Design (USD) Conceptual Framework joins the metric-based modeling of rationalized methods with human-driven goals to form a combined iterative design and analysis loop. The framework processes information for the fundamental element of cities—humans—to large-scale modeling and decision-making occurring in district- and ward-level planning. There is a need in the planning and design profession to better integrate these efforts at a greater scale to create smart communities that are inclusive and comprehensive in aspects from data management to energy and transportation networks. The purpose of this study is to examine the applicability of this method as it pertains to a model and design integrated approach. Northern Sumida Ward, located in Tokyo, exemplifies the contextualized needs of Tokyo, and Japan, while forming a coherent internal community. Focusing on methodology, our process explores the creation of typologies, metric-based analysis, and design-based approaches that are integrated into modeling. The results of the analyses provide initial evidence regarding the validity of the USD approach in modeling changes to complex systems at differing design scales, connecting various qualities of the built environment, building and urban forms, and diagnostic comparisons between baseline and change conditions. Because of some inconsistencies and the need for further evidence gathering, the methods and processes show that there is much work to be done to strengthen the model and to continue building a more productive field of USD. However, in this framework’s continuing evolution, there is increasing evidence that combining the planning and design of urban systems creates a more resilient, economically viable, sustainable, and comfortable city.
Gender Concerns When Noah the Economist Ranks Biodiversity Protection Policies
Authors: Martin David W.
Published: October 2019

Because the funds to protect biodiversity are very limited, biodiversity protection policies are prioritized using the Noah’s Ark perspective. I discuss how gender affects Noah’s assessment of key elements of his ranking: Discounting, changes in total economic value, marginal costs, changes in ecological value, and the probability of policy success. This incremental approach makes visible the breadth of the mechanisms by which gender differentiated social constructs interact to affect Noah in a richly complex manner.
Exploring the Relationship between Farmers’ Innovativeness and Their Values and Aims
Authors: Walder Peter , Sinabell Franz , Unterlass Fabian , Niedermayr Andreas , Fulgeanu Denisa , Kapfer Martin , Melcher Michael , Kantelhardt Jochen
Published: October 2019

To meet global demands towards food security, safety as well as sustainable agriculture and food systems innovative approaches are inevitable. Despite the growing body of literature in both innovation research and in values and aims, what has been explored to a lesser extent is the bridging link between these areas. This study represents a first step in addressing this relationship. Policy- and decision-makers foster sustainable innovation in agriculture, since on-farm innovation and innovation adoption have attracted their attention as a means of enhancing competitiveness as well as socially and environmentally benign farming also benefiting rural areas. By using a negative binomial model we explore the relationship between farmers’ innovativeness and those values and aims which guide farmers’ farm-management decisions as well as other farm/farmer characteristics. Based on a sample of 174 Austrian farmers agricultural education is found to be an essential driver of innovativeness. Regarding the different values we find that self-direction and hedonistic values, in contrast to achievement and economic, are associated with more innovative capabilities. In conclusion, we see a need to foster self-direction and hedonistic narratives in policy and extension service, together with reducing the focus on an economic angle to promote farmers’ innovation capabilities.
Willingness to Pay for Energy Efficient Appliances: The Case of Lebanese Consumers
Authors: Harajli Hassan , Chalak Ali
Published: October 2019

A contingent valuation study was conducted to elicit the willingness to pay (WTP) of Lebanese citizens for five types of energy-efficient home appliances: washing machines, air conditioners, televisions, light bulbs, and refrigerators. A face-to-face survey is designed to this end, with data collection achieving a sample of 605 households geographically representative of the Lebanese population. A multivariate Tobit model was used to gauge the effects of socio-demographic characteristics, attitudes, perceptions, behavior, and experience on the WTP across all these appliances, while accounting for potential correlations between these WTP values. Findings provide evidence of the importance of several socio–demographics in determining WTP for more energy efficient appliances, such as income, education, and age, and the importance of perceptions and experience covariates, not least the implementation of energy labelling and the cost of electricity. In terms of policy implications, this study highlights the need to legally enforce a shift in payment mode from renting per capacity (kW) to purchasing per power (kWh) consumed for electricity in the back-up power sector, and the immediate introduction of a local labelling scheme for home appliances.
Design and Evaluation of Double-Stage Energy Harvesting Floor Tile
Authors: Isarakorn Don , Jayasvasti Subhawat , Panthongsy Phosy , Janphuang Pattanaphong , Hamamoto Kazuhiko
Published: October 2019

This paper introduces the design and characterization of a double-stage energy harvesting floor tile that uses a piezoelectric cantilever to generate electricity from human footsteps. A frequency up-conversion principle, in the form of an overshooting piezoelectric cantilever, plucked with a proof mass is utilized to increase energy conversion efficiency. The overshoot of the proof mass is implemented by a mechanical impact between a moving cover plate and a stopper to prevent damage to the plucked piezoelectric element. In an experiment, the piezoelectric cantilever of a floor tile prototype was excited by a pneumatic actuator that simulated human footsteps. The key parameters affecting the electrical power and energy outputs were investigated by actuating the prototype with a few kinds of excitation input. It was found that, when actuated by a single simulated footstep, the prototype was able to produce electrical power and energy in two stages. The cantilever resonated at a frequency of 14.08 Hz. The output electricity was directly proportional to the acceleration of the moving cover plate and the gap between the cover plate and the stopper. An average power of 0.82 mW and a total energy of 2.40 mJ were obtained at an acceleration of 0.93 g and a gap of 4 mm. The prototype had a simple structure and was able to operate over a wide range of frequencies.
Does Herding Bias Drive the Firm Value? Evidence from the Chinese Equity Market
Authors: Shah Sayyed Sadaqat Hussain , Khan Muhammad Asif , Meyer Natanya , Meyer Daniel F. , Oláh Judit
Published: October 2019

Equity markets play a pivotal role in the sustainability of developing countries, such as China. The literature on the detection of herding biases is confined to the aggregate level (firms, sector/industry and market). The present study adds to the behavioral finance literature by addressing the surprisingly unnoticed phenomena of the behavioral impact of herding bias on firm value (FV) at the firm level, using the sample of A-Shares listed firms at the Shanghai and Shenzhen Stock Exchanges (SSE and SZSE) under panel fixed effect specification. Initially, we detect the existence of investors and managers herding (IHR and MHR) biases at firm-level, and later, we examine their impact (distinct and interactive) upon the FV. The empirical results document the presence of IHR and MHR bias at market, sector and firm-level in both equity markets, which potentially drive the FV, while the impact is more pronounced during the extreme trading period. The findings are robust under different time intervals, and industry classification, therefore, offers useful policy implications to understand the behavioral dynamics of investors and managers.
What Drives Green Innovation? A Game Theoretic Analysis of Government Subsidy and Cooperation Contract
Authors: Ma Weimin , Zhang Ranran , Chai Shiwei
Published: October 2019

Green innovation, implemented by enterprises, contributes to sustainable development and environmental protection. However, because of the high cost and high risk of green innovation, enterprises are reluctant to step into green innovation activities in practice. Government subsidies are conducive to promoting green innovation in enterprises. To investigate firms’ preferences for green innovation, we consider a three-player game in a supply chain where a government offers subsidies (price, innovation, or both subsidies) to a manufacturer and a retailer, while the latter two players cooperate with each other through contracts (revenue-sharing and cost-sharing contracts). By exploring the impacts of government subsidies and cooperative contracts on the optimal level of green innovation efforts and profits of participants, we find that: (1) for green innovation that leads to increased production costs, the government should subsidize both the retailer and the manufacturer to improve the level of green innovation; (2) the revenue-sharing contract is more effective than the cost-sharing contract under the premise of government subsidies; and (3) the revenue-sharing ratio decreases in production and innovation costs, while the cost-sharing ratio increases in these two costs.
Improving Firms’ Performance and Sustainability: The Case of Eco-Innovation in the Agri-Food Industry
Authors: Rabadán Adrián , González-Moreno Ángela , Sáez-Martínez Francisco J.
Published: October 2019

Companies’ environmental responsibility has significantly increased in the last decade. However, the question about the benefits that this responsible decision has on the company’s performance in the market remains. In this scenario, the main goal of this study is to analyze the conditions that improve the performance of companies in the agri-food industry, paying specific attention to technological eco-innovation and different types of cooperation (in the use and in the development of eco-innovations). Our initial sample contains data of agri-food companies operating in Spain. The Qualitative Comparative Analysis (QCA) has been used as a new path for the analysis of firm’s data. Company performance has been considered by using a construct including three variables (increase in the sales, company profitability and cost reduction). Results show that the conditions that largely benefit company performance are R&D spending and the development of technological eco-innovation. Cooperation in the development and use of eco-innovations are especially important for the smaller companies, with the larger companies in the sector relying both in cooperation and in their own resources. The different recipes which improve the performance, as well as the sustainability of the sector, are presented in this study.
Visualizing Sustainability Research in Business and Management (1990–2019) and Emerging Topics: A Large-Scale Bibliometric Analysis
Authors: Jia Qiong , Wei Liyuan , Li Xiaotong
Published: October 2019

While researchers from many disciplines are increasingly interested in studying issues related to sustainability, few studies have presented a holistic view of sustainability from the perspectives of business and management. This bibliometric study quantitatively analyzed a big data set of 30 years of sustainability research (1990–2019), consisting of 37,322 publications and 1,199,398 cited references, visualizing major topics, dynamic evolution, and emerging development. The decade-by-decade in-depth analysis shows a clear shift from a nearly exclusive focus on economic growth and consumption to all three pillars of sustainability, i.e., economic growth, social development, and environmental protection. Highlighting the differences between United Nations’ Sustainable Development Goals and the popular research topics from academia, our analysis uncovers research gaps and suggests future research directions for sustainability researchers and practitioners.
Does Urban Sprawl Inhibit Urban Eco-Efficiency? Empirical Studies of Super-Efficiency and Threshold Regression Models
Authors: Zhang Qian , Zhang Huaxing , Zhao Dan , Cheng Baodong , Yu Chang , Yang Yanli
Published: October 2019

With rapid urbanization in China, the phenomenon of urban sprawl has become prominent and has severely challenged sustainable urbanization and ecological civilization. Aiming to understand the impact of urban sprawl on the urban environment, this study calculates the eco-efficiency of 264 prefecture-level cities in China from 2003 to 2016 by using a super-efficiency data envelopment analysis model. Then, we establish a panel Tobit model and threshold regression model to empirically test the impact of urban sprawl on eco-efficiency and the threshold effect of the urban scale. The results show that urban sprawl hinders the improvement of urban eco-efficiency, especially in Eastern China, but relatively weak or even insignificant effects are observed in Central and Western China. Additionally, a threshold effect of urban sprawl on eco-efficiency can be found. When the city scale is small, urban sprawl seriously hinders the improvement of eco-efficiency. As the city scale gradually expands, the negative effect of urban sprawl on eco-efficiency first decreases, then the restraining effect is gradually strengthened. Our research findings can aid urban development in cities with different scales to reduce the negative effect of urban sprawl on the urban environment.
Assessing Investor Belief: An Analysis of Trading for Sustainable Growth of Stock Markets
Authors: Han Yan , Shao Xue-Feng , Cui Xin , Yue Xiao-Guang , Bwalya Kelvin Joseph , Manta Otilia
Published: October 2019

Investors’ beliefs are the driving force behind the trading of stocks and, hence, sustainable stock returns. Although investors’ beliefs are usually unobservable, this study develops a new approach to estimate investors beliefs. Following well-established rational learning and market microstructure models, it is assumed that informed traders submit market orders according to their beliefs, whereas market makers/uninformed traders make Bayesian inferences about the informed traders’ private signals after observing the total order flows. By fitting intraday transaction data to this model, we can estimate the daily belief uncertainties of informed and uninformed investors; this estimation is performed on S&P 500 stocks. The belief parameters estimated by this approach have incremental explanatory power to bid-ask spreads. The findings show that market makers’ belief uncertainty plays a more important role in determining sustainable stock returns than informed traders’. Implications of these findings include: (a) the larger market maker group is influencing the market trends; (b) this dominant group is making decisions based on diverse types of data; and (c) increased understanding of the diversity of belief parameters may facilitate strategies to enhance sustainable returns, however, stock trading is still significantly influenced by emotive factors worthy of further research.
Female Executives in the Service Sector: The Case of Small and Medium-Sized Enterprises in Spain
Authors: Esteban-Salvador Luisa , Gargallo-Castel Ana F.
Published: October 2019

Small and medium-sized enterprises (SMEs) and women are important agents for economic and social development. This study attempts to understand how the economic crisis has affected the behavior of SMEs with high female participation in the decision-making process. To that end, the quantitative research focused on a sample of 425 small and medium-sized enterprises in Spain relying on a questionnaire-based survey. The results of the empirical study offer new evidence on the effects of the recession on decision-making female-dominated bodies in organizations and illustrate the importance of corporate governance issues. In addition, the research provides greater knowledge of female entrepreneurs in a country severely affected by the global economic crisis. Several recommendations are proposed to provide a theoretical basis for governments to reduce glass ceiling and promote women’s empowerment policies.
EPC Green Premium in Two Different European Climate Zones: A Comparative Study between Barcelona and Turin
Authors: Dell’Anna Federico , Bravi Marina , Marmolejo-Duarte Carlos , Bottero Marta Carla , Chen Ai
Published: October 2019

Energy performance certificates (EPCs) are important tools aimed at improving buildings’ energy performance. They play a central role in the context of the Energy Performance of Buildings Directive (EPBD) which asks member states (MS) to take the necessary measures to establish a complete certification system. In this study, an application of the hedonic price method (HPM) assessing the effect of energy labels derived from the EPC on real estate market value is presented. The estimation methodology was applied to two European cities characterized by different climate conditions. The analysis was based on two datasets of listing prices referring to multi-family residential markets in Turin (Italy) and Barcelona (Spain). Four models for each dataset were applied to capture the marginal price of green attributes, but also to control for the spatial autocorrelation among values. The findings showed how the EPC has been applied in the two countries and how it has influenced the real estate market. Turin’s buyers pay more attention to the EPC label, while in Barcelona, they value much more single characteristics, such as air conditioning and a swimming pool, considered popular attributes among contemporary buildings in this climate zone. From the results, it is possible to deduce that the implementation of the EPC schemes is still irregular in EU countries and must be strengthened through a standardized rating model.
Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features
Authors: Hachaj Tomasz
Published: October 2019

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.
A Fast Approach for Generating Efficient Parsers on FPGAs
Authors: Cao Zhuang , Zhang Huiguo , Li Junnan , Wen Mei , Zhang Chunyuan
Published: October 2019

The development of modern networking requires that high-performance network processors be designed quickly and efficiently to support new protocols. As a very important part of the processor, the parser parses the headers of the packets—this is the precondition for further processing and finally forwarding these packets. This paper presents a framework designed to transform P4 programs to VHDL and to generate parsers on Field Programmable Gate Arrays (FPGAs). The framework includes a pipeline-based hardware architecture and a back-end compiler. The hardware architecture comprises many components with varying functionality, each of which has its own optimized VHDL template. By using the output of a standard frontend P4 compiler, our proposed compiler extracts the parameters and relationships from within the used components, which can then be mapped to corresponding templates by configuring, optimizing, and instantiating them. Finally, these templates are connected to output VHDL code. When a prototype of this framework is implemented and evaluated, the results demonstrate that the throughputs of the generated parsers achieve nearly 320 Gbps at a clock rate of around 300 MHz. Compared with state-of-the-art solutions, our proposed parsers achieve an average of twice the throughput when similar amounts of resources are being used.
Topological Design Methods for Mecanum Wheel Configurations of an Omnidirectional Mobile Robot
Authors: Li Yunwang , Dai Sumei , Zhao Lala , Yan Xucong , Shi Yong
Published: October 2019

A simple and efficient bottom-roller axle intersections approach for judging the omnidirectional mobility of the Mecanum wheel configuration is proposed and proved theoretically. Based on this approach, a sub-configuration judgment method is derived. Using these methods, on the basis of analyzing the possible configurations of three and four Mecanum wheels and existing Mecanum wheel configurations of robots in practical applications, the law determining wheel configuration is elucidated. Then, the topological design methods of the Mecanum wheel configurations are summarized and refined, including the basic configuration array method, multiple wheels replacement method, and combination method. The first two methods can be used to create suitable multiple-Mecanum-wheel configurations for a single mobile robot based on the basic Mecanum wheel configuration. Multiple single robots can be arranged by combination methods including end-to-end connection, side-by-side connection, symmetrical rectangular connection, and distributed combination, and then, the abundant combination configurations of robots can be obtained. Examples of Mecanum wheel configurations design based on a symmetrical four-Mecanum-wheel configuration and three centripetal configurations using these topological design methods are presented. This work can provide methods and a reference for Mecanum wheel configurations design.
A Unified Proximity Algorithm with Adaptive Penalty for Nuclear Norm Minimization
Authors: Hu Wenyu , Zheng Weidong , Yu Gaohang
Published: October 2019

The nuclear norm minimization (NNM) problem is to recover a matrix that minimizes the sum of its singular values and satisfies some linear constraints simultaneously. The alternating direction method (ADM) has been used to solve this problem recently. However, the subproblems in ADM are usually not easily solvable when the linear mappings in the constraints are not identities. In this paper, we propose a proximity algorithm with adaptive penalty (PA-AP). First, we formulate the nuclear norm minimization problems into a unified model. To solve this model, we improve the ADM by adding a proximal term to the subproblems that are difficult to solve. An adaptive tactic on the proximity parameters is also put forward for acceleration. By employing subdifferentials and proximity operators, an equivalent fixed-point equation system is constructed, and we use this system to further prove the convergence of the proposed algorithm under certain conditions, e.g., the precondition matrix is symmetric positive definite. Finally, experimental results and comparisons with state-of-the-art methods, e.g., ADM, IADM-CG and IADM-BB, show that the proposed algorithm is effective.
An Antiviral Peptide from Alopecosa nagpag Spider Targets NS2B–NS3 Protease of Flaviviruses
Authors: Ji Mengyao , Zhu Tengyu , Xing Meichen , Luan Ning , Mwangi James , Yan Xiuwen , Mo Guoxiang , Rong Mingqiang , Li Bowen , Lai Ren , Jin Lin
Published: October 2019

Flaviviruses are single-stranded RNA viruses predominantly transmitted by the widely distributed Aedes mosquitoes in nature. As important human pathogens, the geographic reach of Flaviviruses and their threats to public health are increasing, but there is currently no approved specific drug for treatment. In recent years, the development of peptide antivirals has gained much attention. Natural host defense peptides which uniquely evolved to protect the hosts have been shown to have antiviral properties. In this study, we firstly collected the venom of the Alopecosa nagpag spider from Shangri-La County, Yunnan Province. A defense peptide named Av-LCTX-An1a (Antiviral-Lycotoxin-An1a) was identified from the spider venom, and its anti-dengue serotype-2 virus (DENV2) activity was verified in vitro. Moreover, a real-time fluorescence-based protease inhibition assay showed that An1a functions as a DENV2 NS2B–NS3 protease inhibitor. Furthermore, we also found that An1a restricts zika virus (ZIKV) infection by inhibiting the ZIKV NS2B–NS3 protease. Together, our findings not only demonstrate that An1a might be a candidate for anti-flavivirus drug but also indicate that spider venom is a potential resource library rich in antiviral precursor molecules.