bluuebunny/crossref_metadata_2025 huggingface dataset:
Chiral Bifunctional Thioureas and Squaramides Grafted into Old Polymers of Intrinsic Microporosity for Novel Applications
Authors: Valle María , Martín Laura , Maestro Alicia , Andrés José M. , Pedrosa Rafael
Published: December 2018

We have prepared different polymeric chiral bifunctional thioureas and squaramides by modification of the very well-known polymers of intrinsic microporosity (PIM), specifically PIM-1 and PIM-CO-1, to be used as recoverable organocatalysts. The installation of the chiral structures into the polymers has been done in two or three steps in high yields. The catalytic activity of the resulting materials has been proved in the stereoselective nitro-Michael addition and in a cascade process, which allows the synthesis of enantioenriched 4H-chromene derivatives. Squaramide II and thiourea III have been used in six cycles maintaining their activity.
Cold-Cured Epoxy-Based Organic–Inorganic Hybrid Resins Containing Deep Eutectic Solvents
Authors: Lionetto Francesca , Timo Alessia , Frigione Mariaenrica
Published: December 2018

The development of improved cold-cured resins, to be used as either adhesives or matrices for FRP (fiber reinforced polymer) composites employed in the construction industry, has become the focus of several academic and industrial research projects. It is expected that the use of nano-structured organic–inorganic hybrid materials could represent a realistic alternative to commercial epoxy-based resins due to their superior properties, especially in terms of higher durability against: moisture, temperatures, harsh environments, and fire. In this context, organic–inorganic epoxy hybrids were synthesized by a modified sol–gel method without the addition of water. The experimental formulations were prepared starting from a mixture of a silane-functionalized epoxy resin, alkoxysilane components and a deep eutectic solvent (DES) based on a blend of choline chloride and urea. The latter was added in two different loads in order to analyze in depth its effect as a promoter for an effective dispersion of silica nano-phases, formed through hydrolysis and condensation reactions, into the cross-linked epoxy network. The produced formulations were cold-cured for different time spans in the presence of two hardeners, both suitable for a curing process at ambient temperature. In this first part of a wider experimental program, several analyses were carried out on the liquid (rheological and calorimetric) and cold-cured (calorimetric, thermogravimetric, dynamic-mechanical, flexural mechanical, and morphological) systems to evaluate and quantify the improvement in properties brought about by the presence of two different phases (organic and inorganic) in the same epoxy-based hybrid system.
Nanochannel-Confined TAMRA-Polypyrrole Stained DNA Stretching by Varying the Ionic Strength from Micromolar to Millimolar Concentrations
Authors: Lee Seonghyun , Lee Yelin , Kim Yongkyun , Wang Cong , Park Jungyul , Jung Gun Young , Chen Yenglong , Chang Rakwoo , Ikeda Shuji , Sugiyama Hiroshi , Jo Kyubong
Published: December 2018

Large DNA molecules have been utilized as a model system to investigate polymer physics. However, DNA visualization via intercalating dyes has generated equivocal results due to dye-induced structural deformation, particularly unwanted unwinding of the double helix. Thus, the contour length increases and the persistence length changes so unpredictably that there has been a controversy. In this paper, we used TAMRA-polypyrrole to stain single DNA molecules. Since this staining did not change the contour length of B-form DNA, we utilized TAMRA-polypyrrole stained DNA as a tool to measure the persistence length by changing the ionic strength. Then, we investigated DNA stretching in nanochannels by varying the ionic strength from 0.06 mM to 47 mM to evaluate several polymer physics theories proposed by Odijk, de Gennes and recent papers to deal with these regimes.
Machine-Washable Smart Textiles with Photothermal and Antibacterial Activities from Nanocomposite Fibers of Conjugated Polymer Nanoparticles and Polyacrylonitrile
Authors: Lee Dabin , Sang Jeong Seon , Yoo Pil J. , Shin Tae Joo , Oh Kyung Wha , Park Juhyun
Published: December 2018

Smart textiles based on conjugated polymers have been highlighted as promising fabrics that can intelligently respond to environmental stimuli based on the electrical properties of polymer semiconductors. However, there has been limited interest in the photothermal properties of conjugated polymers that can be applied to smart textiles. We prepared nanoparticles by assembling a conjugated polymer with a fatty acid via an emulsion process and nanocomposite fibers by distributing the conjugated polymer nanoparticles in a polyacrylonitrile matrix. We then fabricated the textiles using the fibers. The resulting fabrics based on nanocomposite fibers show a temperature increase to 50 °C in 10 min under white light irradiation because of efficient photothermal conversion by the conjugated polymer light harvester, while the temperature of a pristine polyacrylonitrile fabric increases to only 35 °C. In addition, excellent antimicrobial activity was confirmed by a 99.9% decrease in the populations of Staphylococcus aureus and Escherichia coli over 24 h because of the effect of the fatty acid in the nanocomposite films and fabrics. Furthermore, the fabric showed efficient durability after a laundry test, suggesting the usefulness of these smart textiles based on conjugated polymer nanoparticles for practical applications.
Peer Review of Reviewers: The Author’s Perspective
Authors: Drvenica Ivana , Bravo Giangiacomo , Vejmelka Lucija , Dekanski Aleksandar , Nedić Olgica
Published: December 2018

The aim of this study was to investigate the opinion of authors on the overall quality and effectiveness of reviewers’ contributions to reviewed papers. We employed an on-line survey of thirteen journals which publish articles in the field of life, social or technological sciences. Responses received from 193 authors were analysed using a mixed-effects model in order to determine factors deemed the most important in the authors’ evaluation of the reviewers. Qualitative content analysis of the responses to open questions was performed as well. The mixed-effects model revealed that the authors’ assessment of the competence of referees strongly depended on the final editorial decision and that the speed of the review process was influential as well. In Ordinary Least Squares (OLS) analysis on seven questions detailing authors’ opinions, perception of review speed remained a significant predictor of the assessment. In addition, both the perceived competence and helpfulness of the reviewers significantly and positively affected the authors’ evaluation. New models were used to re-check the value of these two factors and it was confirmed that the assessment of the competence of reviewers strongly depended on the final editorial decision.
Corporate Bodies in Early South Asian Buddhism: Some Relics and Their Sponsors According to Epigraphy
Authors: Milligan Matthew D.
Published: December 2018

Some of the earliest South Asian Buddhist historical records pertain to the enshrinement of relics, some of which were linked to the Buddha and others associated with prominent monastic teachers and their pupils. Who were the people primarily responsible for these enshrinements? How did the social status of these people represent Buddhism as a burgeoning institution? This paper utilizes early Prakrit inscriptions from India and Sri Lanka to reconsider who was interested in enshrining these relics and what, if any, connection they made have had with each other. Traditional accounts of reliquary enshrinement suggest that king Aśoka began the enterprise of setting up the Buddha’s corporeal body for worship but his own inscriptions cast doubt as to the importance he may have placed in the construction of stūpa-s and the widespread distribution of relics. Instead, as evidenced in epigraphy, inclusive corporations of individuals may have instigated, or, at the very least, became the torchbearers for, reliquary enshrinement as a salvific enterprise. Such corporations comprised of monastics as well as non-monastics and seemed to increasingly become more managerial over time. Eventually, culminating at places like Sanchi, the enshrinement of the corporeal remains of regionally famous monks partially supplanted the corporeal remains of the Buddha. Those interested in funding this new endeavor were corporations of relatives, monastic brethren, and others who were likely friends and immediate acquaintances. In the end, the social and corporate collectivity of early Buddhism may have outshined some textual monastic ideals of social isolation as it pertained to the planning, carrying out, and physical enshrinement of corporeal remains for worship, thus evoking an inclusive sentiment with the monastic institution rather than disassociation.
Interaction Effects of Religiosity Level on the Relationship between Religion and Willingness to Donate Organs
Authors: Yoon Sung Man
Published: December 2018

This study aims to investigate the interaction effect of religiosity level on the relationship between religion and willingness to donate organs. Prior studies have suggested that a high level of religiosity indicates a high level of willingness to donate organs. However, these previous works ignore the interaction effect of the level of religiosity and the doctrinal characteristics of each religion regarding one’s own body preservation. Organ donation is an act of transplanting part of one’s own body after death to another person and is influenced by the viewpoint of the post-mortem world and the attitude toward the preservation of the body. Therefore, this study analyzes the effects of religious characteristics and belief levels on the relationship between religion and organ donation. Results show that Christianity, such as Catholicism and Protestantism, positively affects the willingness to donate organs as compared with Buddhism. Religiosity level also exerts an interaction effect that strengthens the relationship between Christianity and willingness to donate organs.
Geopolitics and Identity-Making in US Diasporic Chinese Churches
Authors: Lung Shirley
Published: December 2018

Using ethnographic and interview data, my paper analyzes how geopolitical relationship manifest at the community level in Chinese America Responding to Lien Pei-Te’s call to meaningfully disaggregate among the commonly “lumped together Chinese Americans”, I draw upon the experiences of specific groups of Chinese immigrants to the US, post-1949 migrants to Taiwan, pre-1949 migrants to Taiwan, and the People’s Republic of China (PRC) Chinese, in order to understand how boundary drawing occurs in their various communities but also consider how the act of being “lumped together” itself in the US context complicates identity formation. The year 1949 marks the communist victory in the PRC as well as the inaugural year of the Kuomingdang (KMT)-led Republic of China (ROC) in Taiwan. Carved out of these historical events, the contemporary social relations among these groups persist after their migration to the US, but they manifest differently in various domains of practice, including religious ones. As political relationships among states reorganizes their social relations, the religious site offers what Carolyn Chen calls a “moral vocabulary” to articulate, contemplate, and, in some cases, justify these divides. Even within a Christian context, messages of inclusivity are not universal but redefined according to the political and social contexts. By not assigning a singular definition to Christian thought, my paper makes way for a theorization of an intersectional Christian identity.
Representation of Multiple Acoustic Sources in a Virtual Image of the Field of Audition from Binaural Synthetic Aperture Processing as the Head is Turned
Authors: Tamsett Duncan
Published: December 2018

The representation of multiple acoustic sources in a virtual image of the field of audition based on binaural synthetic-aperture computation (SAC) is described through use of simulated inter-aural time delay (ITD) data. Directions to the acoustic sources may be extracted from the image. ITDs for multiple acoustic sources at an effective instant in time are implied for example by multiple peaks in the coefficients of a short-time base (≈2.25 ms for an antennae separation of 0.15 m) cross correlation function (CCF) of acoustic signals received at the antennae. The CCF coefficients for such peaks at the time delays measured for a given orientation of the head are then distended over lambda circles in a short-time base instantaneous acoustic image of the field of audition. Numerous successive short-time base images of the field of audition generated as the head is turned are integrated into a mid-time base (up to say 0.5 s) acoustic image of the field of audition. This integration as the head turns constitutes a SAC. The intersections of many lambda circles at points in the SAC acoustic image generate maxima in the integrated CCF coefficient values recorded in the image. The positions of the maxima represent the directions to acoustic sources. The locations of acoustic sources so derived provide input for a process managing the long-time base (>10s of seconds) acoustic image of the field of audition representing the robot’s persistent acoustic environmental world view. The virtual images could optionally be displayed on monitors external to the robot to assist system debugging and inspire ongoing development.
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble
Authors: Kim Jiwon , Kim Kwangjin , Cho Jaeil , Kang Yong Q. , Yoon Hong-Joo , Lee Yang-Won
Published: December 2018

Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.
Utilization of a C-band Polarimetric Radar for Severe Rainfall Event Analysis in Complex Terrain over Eastern China
Authors: Gou Yabin , Ma Yingzhao , Chen Haonan , Yin Jiapeng
Published: December 2018

Polarimetric radar measurements and products perform as the cornerstones of modern severe weather warning and nowcast systems. Two radar quantitative precipitation estimation (QPE) frameworks, one based on a radar-gauge feedback mechanism and the other based on standard rain drop size distribution (DSD)-derived rainfall retrieval relationships, are both evaluated and investigated through an extreme severe convective rainfall event that occurred on 23 June 2015 in the mountainous region over eastern China, using the first routinely operational C-band polarimetric radar in China. Complex rainstorm characteristics, as indicated by polarimetric radar observables, are also presented to account for the severe rainfall field center located in the gap between gauge stations. Our results show that (i) the improvements of the gauge-feedback-derived radar QPE estimator can be attributed to the attenuation correction technique and dynamically adjusted Z–R relationships, but it greatly relies on the gauge measurement accuracy. (ii) A DSD-derived radar QPE estimator based on the specific differential phase (KDP) performs best among all rainfall estimators, and the interaction between the mesocyclone and the windward slope of the mountainous terrain can account for its apparent overestimation. (iii) The rainstorm is mainly dominated by small-sized and moderate-sized raindrops, with the mean volume diameter being less than 2 mm, but its KDP column (KDP > 3°·km−1) has a liquid water content that is higher than 2.4815 g·m−3, and a high raindrop concentration (Nw) with log10(Nw) exceeding 5.1 mm−1m−3. In addition, small hailstones falling and melting are also found in this event, which further aggregates Nw upon the severe rainfall center in the gap between gauge stations.
Tension Force Estimation in Axially Loaded Members Using Wearable Piezoelectric Interface Technique
Authors: Ryu Joo-Young , Huynh Thanh-Canh , Kim Jeong-Tae
Published: December 2018

Force changes in axially loaded members can be monitored by quantifying variations in impedance signatures. However, statistical damage metrics, which are not physically related to the axial load, often lead to difficulties in accurately estimating the amount of axial force changes. Inspired by the wearable technology, this study proposes a novel wearable piezoelectric interface that can be used to monitor and quantitatively estimate the force changes in axial members. Firstly, an impedance-based force estimation method was developed for axially loaded members. The estimation was based on the relationship between the axial force level and the peak frequencies of impedance signatures, which were obtained from the wearable piezoelectric interface. The estimation of the load transfer capability from the axial member to the wearable interface was found to be an important factor for the accurate prediction of axial force. Secondly, a prototype of the wearable piezoelectric interface was designed to be easily fitted into existing axial members. Finally, the feasibility of the proposed technique was established by assessing tension force changes in a numerical model of an axially loaded cylindrical member and a lab-scale model of a prestressed cable structure.
An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
Authors: Hou Xiaolan , Wu Muqing , Zhao Min
Published: December 2018

Software-defined networks (SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. Segment routing (SR) exploits an end-to-end logical path and is composed of a sequence of segments as an effective routing strategy. Each segment is represented by a middle point. The combination of SR and SDN can meet the differentiated business needs of users and can quickly deploy applications. In this paper, we propose two routing algorithms based on SR in SDN. The algorithms aim to save the cost of the path, alleviate the congestion of networks, and formulate the selection strategy by comprehensively evaluating the value of paths. The simulation results show that compared with existing algorithms, the two proposed algorithms can effectively reduce the consumption of paths and better balance the load of the network. Furthermore, the proposed algorithms take into account the preferences of users, actualize differentiated business networks, and achieve a larger comprehensive evaluation value of the path compared with other algorithms.
Cooperative Spectrum Sensing with Coded and Uncoded Decision Fusion under Correlated Shadowed Fading Report Channels
Authors: Costa Lucas dos Santos , Guimarães Dayan Adionel , De Souza Rausley Adriano Amaral , Bomfin Roberto César Dias Vilela
Published: December 2018

This article addresses the impact of forward error correction when applied to the report channel transmissions of a centralized decision fusion cooperative spectrum sensing scheme designed to detect idle orthogonal frequency division multiple access (OFDMA) subchannels. The OFDMA signal is transmitted over slow frequency-selective multipath Rayleigh fading channels and sensed using the maximum eigenvalue detection test statistic. The decisions on the OFDMA subchannel occupancy are transmitted to a fusion center over report channels represented by a shadowed fading model combining a three-dimensional spatially correlated shadowing with a slow and flat multipath Rayleigh fading. Binary Bose-Chaudhuri-Hochquenghem (BCH) and Repetition codes are used to protect these decisions. Results show that shadowing correlation severely deteriorates the overall spectrum sensing performance and that error correction may not be able to protect the report channel transmissions. It can be even worse with respect to the system performance especially at low signal-to-noise regimes. In the situations in which error correction is effective, the Repetition code is capable of outperforming the BCH, meaning that the diversity gain may be more relevant than the coding gain when the spectrum sensing decisions are subjected to correlated shadowing.
Depth from a Motion Algorithm and a Hardware Architecture for Smart Cameras
Authors: Aguilar-González Abiel , Arias-Estrada Miguel , Berry François
Published: December 2018

Applications such as autonomous navigation, robot vision, and autonomous flying require depth map information of a scene. Depth can be estimated by using a single moving camera (depth from motion). However, the traditional depth from motion algorithms have low processing speeds and high hardware requirements that limit the embedded capabilities. In this work, we propose a hardware architecture for depth from motion that consists of a flow/depth transformation and a new optical flow algorithm. Our optical flow formulation consists in an extension of the stereo matching problem. A pixel-parallel/window-parallel approach where a correlation function based on the sum of absolute difference (SAD) computes the optical flow is proposed. Further, in order to improve the SAD, the curl of the intensity gradient as a preprocessing step is proposed. Experimental results demonstrated that it is possible to reach higher accuracy (90% of accuracy) compared with previous Field Programmable Gate Array (FPGA)-based optical flow algorithms. For the depth estimation, our algorithm delivers dense maps with motion and depth information on all image pixels, with a processing speed up to 128 times faster than that of previous work, making it possible to achieve high performance in the context of embedded applications.
Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras
Authors: Zengeler Nico , Kopinski Thomas , Handmann Uwe
Published: December 2018

In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes
Authors: Wang Junyuan , Han Xiaofeng , Wang Zhijian , Du Wenhua , Zhou Jie , Zhang Jiping , He Huihui , Guo Xiaoming
Published: December 2018

Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.
Hydrophilic Monomethyl Auristatin E Derivatives as Novel Candidates for the Design of Antibody-Drug Conjugates
Authors: Ekholm Filip S. , Ruokonen Suvi-Katriina , Redón Marina , Pitkänen Virve , Vilkman Anja , Saarinen Juhani , Helin Jari , Satomaa Tero , Wiedmer Susanne K.
Published: December 2018

Antibody-drug conjugates (ADCs) are promising state-of-the-art biopharmaceutical drugs for selective drug-delivery applications and the treatment of diseases such as cancer. The idea behind the ADC technology is remarkable as it combines the highly selective targeting capacity of monoclonal antibodies with the cancer-killing ability of potent cytotoxic agents. The continuous development of improved ADCs requires systematic studies on the nature and effects of warhead modification. Recently, we focused on the hydrophilic modification of monomethyl auristatin E (MMAE), the most widely used cytotoxic agent in current clinical trial ADCs. Herein, we report on the use of micellar electrokinetic chromatography (MEKC) for studying the hydrophobic character of modified MMAE derivatives. Our data reveal a connection between the hydrophobicity of the modified warheads as free molecules and their cytotoxic activity. In addition, MMAE-trastuzumab ADCs were constructed and evaluated in preliminary cytotoxic assays.
Corporate Propensity for Long-Term Donations to Non-Profit Organisations: An Exploratory Study in Portugal
Authors: Casais Beatriz , Santos Sara
Published: December 2018

The motivations for individual and corporate donations to non-profit organisations (NPOs) have been vastly researched. However, companies tend to diversify their donations towards different institutions, hindering the development of substantial and continuous social interventions by charities. Considering the importance of regular support to guarantee NPOs’ sustainability, this study analyses the corporate motivations and the conditions that might favour their propensity for long-term donations. Researchers have conducted twenty semistructured interviews in companies with social responsibility policies and concluded that motivations to donate goes beyond visibility or recognition the donation might bring to the business. Interviewees highlighted the sense of altruism and the perceived importance of their donation for the well-being of people the institutions are supporting. However, managers admitted they diversify their partnerships rather than donate regularly to the same institution and consider that a relationship and proactive marketing approach by NPOs would be a key point to activate long-term donations.
Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration
Authors: Osório Gerardo J. , Lotfi Mohamed , Shafie-khah Miadreza , Campos Vasco M. A. , Catalão João P. S.
Published: December 2018

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.
Assessing the Impacts of Higher Education Institutions on Sustainable Development—An Analysis of Tools and Indicators
Authors: Findler Florian , Schönherr Norma , Lozano Rodrigo , Stacherl Barbara
Published: December 2018

Many higher education institutions (HEIs) have started to incorporate sustainable development (SD) into their system. A variety of sustainability assessment tools (SATs) have been developed to support HEIs to systematically measure, audit, benchmark, and communicate SD efforts. In recent years, stakeholders have increasingly asked HEIs to demonstrate their impacts on SD. These impacts are the direct and indirect effects an HEI has outside of its organizational boundaries on society, the natural environment, and the economy. This study analyzes to what extent SATs are capable of measuring the impacts that HEIs have on SD. A mixed-method approach, using descriptive statistics and an inductive content analysis, was used to examine 1134 indicators for sustainability assessment derived from 19 SATs explicitly designed for application by HEIs. The findings reveal that SATs largely neglect the impacts HEIs have outside their organizational boundaries. SATs primarily use proxy indicators based on internally available data to assess impacts and thus tend to focus on themes concerning the natural environment and the contribution to the local economy. Updating existing SATs and developing new ones may enable HEIs to fully realize their potential to contribute to SD.
Impact of Corporate Political Activity on the Relationship Between Corporate Social Responsibility and Financial Performance: A Dynamic Panel Data Approach
Authors: Lin Woon Leong , Ho Jo Ann , Sambasivan Murali
Published: December 2018

As corporate social responsibility (CSR) gains momentum in the business world, it is imperative to comprehend the relationship between CSR and corporate financial performance (CFP). While there is prior research looking at this relationship, scholars have proposed a contingency view that is meant to determine the situational contexts in which critical associations between CFP and CSR activities will arise. This study provides further insight into the moderating effects of corporate political activity, specifying the ways in which different arrangements of corporate CSR and CPA might align or otherwise, thus influencing CFP beyond associated dissimilar effects on corporate performance. The data for this study was obtained for the periods 2007–2016 from the samples selected from the list of Fortune’s World’s Most Admired Companies. The dynamic panel data was analyzed using the System Generalized Method of Moment estimation. The main findings are that CSR does not significantly influence CFP. However, CPA does negatively moderate the relationship between CSR and CFP. This indicates that high political expenditures worsen a firm’s financial position compared to the financial position of firms with less spending on CPA.
Disclosure of Intellectual Capital Components in Integrated Reporting: An Empirical Analysis
Authors: Santis Serena , Bianchi Michela , Incollingo Alberto , Bisogno Marco
Published: December 2018

The purpose of the study is to investigate how firms disclose information in their integrated report (IR) on intellectual capital (IC), regarding its components and their link with the value creation process. Therefore, by adopting a content analysis methodology, the study, which covers three years (2014–2016), is focused on IC. A sample of firms belonging to the financial services sector is investigated by analysing 135 integrated reports. The main findings show that firms, on the one hand, provide information on IC by adopting a classification close to those outlined by IC scholars; on the other hand, the vast majority of the investigated firms tends to adopt a superficial approach. More specifically, firms disclose a low amount of information about the link between IC and the value creation process, even though they are aware of its importance.
The Value of Business–Government Ties for Manufacturing Firms’ Product Innovation during Institutional Transition in China
Authors: Yang Chun , Bossink Bart , Peverelli Peter
Published: December 2018

This study investigates how firms invest in building and maintaining business–government (B–G) ties when they aim to innovate in regions where, due to institutional transitions, institutional contexts differ remarkably. Using data from the China Enterprise Survey of the World Bank, empirical findings suggest that the influence of B–G ties on Chinese firms’ product innovation is different in distinctive institutional contexts in China. More specifically, during institutional transition, B–G ties become less efficient for facilitating product innovation when regional legal institutions and infrastructural supporting systems in a region are more stable, fair, and efficient. By contrast, during institutional transition, a positive effect of B–G ties on firm product innovation in a region becomes more significant when financial systems are relatively advanced. In addition to this, the value of B–G ties for firm product innovation appears to be more stable when business regulation develops within subnational regions.
Predicting Corporate Financial Sustainability Using Novel Business Analytics
Authors: Kim Kyoung-jae , Lee Kichun , Ahn Hyunchul
Published: December 2018

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.
Transport Infrastructure Development, Public Performance and Long-Run Economic Growth: A Case Study for the Eu-28 Countries
Authors: Cigu Elena , Agheorghiesei Daniela Tatiana , Gavriluță (Vatamanu) Anca Florentina , Toader Elena
Published: December 2018

This paper examines the link between the transport infrastructure and the economic performance in the EU-28 countries, over the period of time 2000–2014, using panel data methods. Firstly, we aim to provide the theoretical background of the transport infrastructure development, public sector performance and economic growth. The paper’s key point is the detailed look at the components of transport infrastructure, analyzing the implications of the policy-makers based on a production function and in order to test the policy implication, factor analysis is also employed. The results show significant effects from transport infrastructure components even after institutional and other factors are controlled for. From the path analysis results, the study confirm the alternative hypothesis, outlining the unidirectional long-run causality relationship between growth, transport infrastructure and Public Sector Performance. Transport infrastructure status (measured thought index of transport) has significant impact on economic development with coefficient estimate. The public performance indicators influence the way to economic growth. On the one hand, the corruption, the regulatory environment, size shadow economy, infant mortality, income inequality, inflation and unemployment rate negatively affect the economic growth, and on the other hand there is a strong positive relationship between the quality of the judiciary, education achievement, life expectancy and economic growth.
How Social Status Contributes to Sustainable Livelihoods? An Empirical Analysis in Ethiopia
Authors: Gebrekidan Abbay Aradom , Rutten Roel , Azadi Hossein , Witlox Frank
Published: December 2018

This paper scrutinized the links between social status and income of rural households to provide insight into how social status is indicated and used as a strategy for improving livelihood income. It also provides a brief look into some selected key determinants of livelihood income. We applied a two-stage least-squares estimation to household-level data from rural areas in the Tigray regional state of Ethiopia. We also proposed the latent class analysis model to identify the number of classes for the variable “social status”. The results indicate that livelihood income is significantly affected by households’ social status, indicating that high status household heads tend to enhance their participation in different social networks with the intention of strengthening the social bonds that they have and improving their status in the community, which in turn has an economic payback. Apart from this, household heads’ access to off-farm work, size of owned land, exposure to multimedia, livestock ownership and spatial proximity to towns were the variables that have significant positive effects on livelihood income.
A Reflection of the Use of the Life Cycle Assessment Tool for Agri-Food Sustainability
Authors: Gava Oriana , Bartolini Fabio , Venturi Francesca , Brunori Gianluca , Zinnai Angela , Pardossi Alberto
Published: December 2018

In pursuit of agricultural sustainability and food security, research should contribute to policy-making by providing scientifically robust evidence. Life cycle assessment (LCA) is an excellent candidate for generating that evidence, thereby helping the selection of interventions towards more sustainable agri-food. The purpose of this article is proposing a basis for discussion on the use of the LCA tool for targeting and monitoring of environmental policy interventions in agri-food. The problem of reducing the environmental burden in agri-food can be tackled by acting on the supply and/or demand sides and may benefit from the collaboration of supply chain stakeholders. Agri-food policies that most benefit from LCA-based data concern cross-border pollution, transaction costs following the adoption of environmental standards, adoption of less polluting practices and/or technologies, and business-to-consumer information asymmetry. The choice between the methodological options available for LCA studies (attributional, consequential, or hybrid models) depends on the purpose and scope of the study. The possibility of integrating the LCA with economic and social impact assessments—e.g., under the life cycle sustainability assessment framework—makes LCA an excellent tool for monitoring business or sectoral-level achievements with respect to UN 2030 Sustainable Development Goals.
Floating Charge Method Based on Shared Parking
Authors: Hao Jun , Chen Jun , Chen Qin
Published: December 2018

With the cultivation of the shared economy, shared parking provides a new solution to the urban parking resource shortage problem. In this paper, the shared parking mode is taken as the research object to improve the utilization efficiency of parking spaces. The stated preference (SP) survey is used to collect the intention of sharing parking behavior in a typical shared parking mode situation. The behavior selection characteristics of the person sharing parking are analyzed and a binary logit model is used to establish the parking behavior selection model. The key parameters of a floating charge are proposed. Based on the above research, a dynamic balance adjustment method for shared parking floating charges is proposed and an empirical analysis is carried out. The research results showed that compared with fixed fees, the floating charge method can improve the utilization rate of idle spaces by more than 60% and control the occupancy rate of spaces by 60–80%. The floating charge method not only guarantees its own parking demand but also exploits the potential of shard parking facilities, which is good for promoting the sustainable and healthy development of urban transportation.
Airbnb Branding: Heritage as a Branding Element in the Sharing Economy
Authors: Fierro Alvaro , Aranburu Ibon
Published: December 2018

The cultural heritage of cities has been proven to be a generator of economic activity and a singular place branding/marketing tool for territories. In addition, in recent times, there has been a boom in the collaborative economy in urban destinations, epitomized by accommodation companies, such as Airbnb. The main objective of this article was to analyze how cultural heritage adds value to the collaborative economy. To this end, a method was proposed that analyzed Airbnb host descriptions, detecting the cultural and heritage goods mentioned, therein, which are used as marketing elements. From these cultural assets, various econometric models were generated that aimed to assess the number of guest reviews, which were used as a proxy for the number of guests. To evaluate this method, a case study was developed in the city of Bilbao. In view of the results, it can be concluded that cultural heritage and spatial location positively influence the activity of the collaborative economy, increasing the number of guests and their economic value.
Sunflower Value Chain Enhancements for the Rural Economy in Tanzania: A Village Computable General Equilibrium-CGE Approach
Authors: Mgeni Charles Peter , Müller Klaus , Sieber Stefan
Published: December 2018

Poverty is prevalent and widespread in rural Tanzania, where agriculture is the main activity. The government is making significant public investments intended to speed the growth of agriculture as a means to accelerate inclusive economic growth. In line with public investments, the government is promoting public–private partnerships by encouraging the use of improved agricultural innovations and linking farmers to markets, seeking to increase their yields and income. However, there is a paucity of empirical evidence using multipliers analysis about the extent of how gains in agricultural productivity and market linkages for farmers in rural areas help improve the economy at the household level. This paper assesses the welfare effects of the sunflower value chain for a rural economy in Tanzania using a computable general equilibrium (CGE) model for the selected village, which has a high potential for sunflower. Findings highlight the use of the CGE model, first, for analyzing and understanding the economic sectors at a village level. Second, the effects of various upgrading strategies promoted for improving rural farming communities by the government and non-governmental development partners at the micro-scale are analyzed and potential agricultural commodity value chains identified. The multiplier analysis provided insights regarding the potential of sunflower crops for the village economy.
Family Business Owner as a Central Figure in Customer Relationship Management
Authors: Arsić Siniša , Banjević Koviljka , Nastasić Aleksandra , Rošulj Dragana , Arsić Miloš
Published: December 2018

This paper presents theoretical and empirical research on the activities and attitudes of a family business owner regarding marketing as a business function. The development of successful business relationships of a family business is tightly connected with the activities of the business owner. The theoretical review examined numerous family and non-family business studies by analyzing the existing paradigms of marketing management as a whole. The empirical research, surveying 420 family businesses in Serbia, defined the overall role of the family business owner in customer relationship management regarding business-to-business (B2B) and business-to-consumer (B2C) relationships. Key findings suggest that the main difference in customer relationship management (CRM) between family and non-family companies is related to B2B relationships, which the family business owner is heavily involved in managing, in terms of invested time and responsibility. Future research should cover aspects of internationalization (to regional markets) because it is essential to cross-examine regional with local contacts of a family business owner, to provide full comprehension of the complexity of market relationships in a family business environment.