bluuebunny/crossref_metadata_2025 huggingface dataset:
An Electrically Tunable Dual-Wavelength Refractive Index Sensor Based on a Metagrating Structure Integrating Epsilon-Near-Zero Materials
Authors: Meng Zhenya , Cao Hailin , Liu Run , Wu Xiaodong
Published: April 2020

In this paper, a reconfigurable sensing platform based on an asymmetrical metal-insulator-metal stacked structure integrating an indium tin oxide (ITO) ultrathin film is proposed and investigated numerically. The epsilon-near-zero (ENZ) mode and antisymmetric mode can be resonantly excited, generating near-perfect absorption of over 99.7% at 1144 and 1404 nm, respectively. The absorptivity for the ENZ mode can be modulated from 90.2% to 98.0% by varying the ENZ wavelength of ITO by applying different voltages. To obtain a highly sensitive biosensor, we show that the proposed structure has a full-width at half-maximum (FWHM) of 8.65 nm and a figure-of-merit (FOM) of 24.7 with a sensitivity of 213.3 nm/RI (refractive index) for the glucose solution. Our proposed device has potential for developing tunable biosensors for real-time health monitoring.
An Experiment-Based Profile Function for the Calculation of Damage Distribution in Bulk Silicon Induced by a Helium Focused Ion Beam Process
Authors: Chen Qianhuang , Shao Tianyang , Xing Yan
Published: April 2020

The helium focused ion beam (He-FIB) is widely used in the field of nanostructure fabrication due to its high resolution. Complicated forms of processing damage induced by He-FIB can be observed in substrates, and these damages have a severe impact on nanostructure processing. This study experimentally investigated the influence of the beam energy and ion dose of He-FIB on processing damage. Based on the experimental results, a prediction function for the amorphous damage profile of the single-crystalline silicon substrate caused by incident He-FIB was proposed, and a method for calculating the amorphous damage profile by inputting ion dose and beam energy was established. Based on one set of the amorphous damage profiles, the function coefficients were determined using a genetic algorithm. Experiments on single-crystalline silicon scanned by He-FIB under different process parameters were carried out to validate the model. The proposed experiment-based model can accurately predict the amorphous damage profile induced by He-FIB under a wide range of different ion doses and beam energies.
A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming
Authors: Tian Yifei , Song Wei , Chen Long , Sung Yunsick , Kwak Jeonghoon , Sun Su
Published: April 2020

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.
LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
Authors: López-Matencio Pablo , Vales-Alonso Javier , Alcaraz Juan J.
Published: April 2020

Radio Frequency Identification (RFID) is considered one of the pioneering technologies of the Internet of Things (IoT). It allows to bind physical environments to information processing systems, adding new capabilities like automatic inventorying, location, or sensing with batteryless tags. Indeed, many data flows of physical objects can be tracked using this technology, and it is common to find heterogeneous traffics present in the same facility, each managed by different sets of readers. For example, in a grocery store, typically we have two kinds of readers: those carrying out a continuous inventory, whose goal is knowing the contents of the shelves as accurately as possible; and a set of checking-out readers at exit gates for the billing process that has to minimize the waiting time of customers. Another example of multiclass traffic is a hospital, where new families of sensing tags allow staff to wirelessly monitor patients—which obviously must be done as a priority—and coexist with other readers aimed at precisely knowing the location of equipment or drugs. Even with the same goal, there could be readers requiring different setups, for example in the hospital case, readers located at doors for inventorying purposes have a short time available to identify passing-by objects or people, and thus they have to work with a higher priority than regular readers performing inventorying tasks. In this work, we investigate a modification of the standard listen-before-talk (LBT) protocol for RFID networks which can support this kind of multipriority environment, by offering different qualities of service to each traffic. Results demonstrate that by tuning the protocol setup, it is possible to establish a trade-off between the performance of each traffic. This is shown for the two cited examples, the grocery shop and the hospital, using a simulation tool allowing us to implement a full-scale RFID model. In addition, we present a greedy mechanism for online reader setup. Instead of selecting offline a hard priority level, this greedy algorithm is able to adapt the priority to achieve the required quality-of-service (QoS) level.
Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features
Authors: Ni Peishuang , Miao Chen , Tang Hui , Jiang Mengjie , Wu Wen
Published: April 2020

Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.
Catalytic Hydrolysis of Tricresyl Phosphate by Ruthenium (III) Hydroxide and Iron (III) Hydroxide towards Sensing Application
Authors: Zhou Lang , Chin Bryan , Simonian Alex L.
Published: April 2020

Tricresyl phosphate (TCP) is an organophosphorous neurotoxin that has been detected in water, soil and air. Exposure to TCP in cockpit and cabin air poses a severe threat to flight safety and the health of the aircraft cabin occupants. Conventional methods for the detection of TCP in various samples are gas or liquid chromatography coupled to mass spectrometry, which are complex and expensive. To develop a simple low-cost methodology for the real-time monitoring of TCP in the environment, an effective catalyst is demanded for the hydrolysis of TCP under neutral condition. In this study, Ruthenium (III) hydroxide and Iron (III) hydroxide are found to facilitate the production of the alcoholysis and hydrolysis products of TCP, suggesting their role as a catalyst. With this finding, these metal hydroxides provide new potential to realize not only simple colorimetric or electrochemical detection of TCP, but also a simple detoxication strategy for TCP in environment. In addition, the catalytic capability of Ru (III) or Fe (III) hydroxide for TCP gives a hint that they can potentially serve as catalysts for the hydrolysis of alcolyolysis of many other organophosphate compounds.
Evaluation of the Georeferencing Accuracy of a Photogrammetric Model Using a Quadrocopter with Onboard GNSS RTK
Authors: Štroner Martin , Urban Rudolf , Reindl Tomáš , Seidl Jan , Brouček Josef
Published: April 2020

Using a GNSS RTK (Global Navigation Satellite System Real Time Kinematic) -equipped unmanned aerial vehicle (UAV) could greatly simplify the construction of highly accurate digital models through SfM (Structure from Motion) photogrammetry, possibly even avoiding the need for ground control points (GCPs). As previous studies on this topic were mostly performed using fixed-wing UAVs, this study aimed to investigate the results achievable by a quadrocopter (DJI Phantom 4 RTK). Three image acquisition flights were performed for two sites of a different character (urban and rural) along with three calculation variants for each flight: georeferencing using ground-surveyed GCPs only, onboard GNSS RTK only, and a combination thereof. The combined and GNSS RTK methods provided the best results (at the expected level of accuracy of 1–2 GSD (Ground Sample Distance)) for both the vertical and horizontal components. The horizontal positioning was also accurate when georeferencing directly based on the onboard GNSS RTK; the vertical component, however, can be (especially where the terrain is difficult for SfM evaluation) burdened with relatively high systematic errors. This problem was caused by the incorrect identification of the interior orientation parameters calculated, as is customary for non-metric cameras, together with bundle adjustment. This problem could be resolved by using a small number of GCPs (at least one) or quality camera pre-calibration.
End-to-End QoS “Smart Queue” Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Networks
Authors: Beshley Mykola , Kryvinska Natalia , Seliuchenko Marian , Beshley Halyna , Shakshuki Elhadi M. , Yasar Ansar-Ul-Haque
Published: April 2020

This paper proposes a modified architecture of the Long-Term Evolution (LTE) mobile network to provide services for the Internet of Things (IoT). This is achieved by allocating a narrow bandwidth and transferring the scheduling functions from the eNodeB base station to an NB-IoT controller. A method for allocating uplink and downlink resources of the LTE/NB-IoT hybrid technology is applied to ensure the Quality of Service (QoS) from end-to-end. This method considers scheduling traffic/resources on the NB-IoT controller, which allows eNodeB planning to remain unchanged. This paper also proposes a prioritization approach within the IoT traffic to provide End-to-End (E2E) QoS in the integrated LTE/NB-IoT network. Further, we develop “smart queue” management algorithms for the IoT traffic prioritization. To demonstrate the feasibility of our approach, we performed a number of experiments using simulations. We concluded that our proposed approach ensures high end-to-end QoS of the real-time traffic by reducing the average end-to-end transmission delay.
A Fine-Tuned Positioning Algorithm for Space-Borne GNSS Timing Receivers
Authors: Chen Xi , Wei QiHui , Zhan YaFeng , Ma TianYi
Published: April 2020

To maximize the usage of limited transmission power and wireless spectrum, more communication satellites are adopting precise space–ground beam-forming, which poses a rigorous positioning and timing requirement of the satellite. To fulfill this requirement, a space-borne global navigation satellite system (GNSS) timing receiver with a disciplined high-performance clock is preferable. The space-borne GNSS timing receiver moves with the satellite, in contrast to its stationary counterpart on ground, making it tricky in its positioning algorithm design. Despite abundant existing positioning algorithms, there is a lack of dedicated work that systematically describes the delicate aspects of a space-borne GNSS timing receiver. Based on the experimental work of the LING QIAO (NORAD ID:40136) communication satellite’s GNSS receiver, we propose a fine-tuned positioning algorithm for space-borne GNSS timing receivers. Specifically, the proposed algorithm includes: (1) a filtering architecture that separates the estimation of satellite position and velocity from other unknowns, which allows for a first estimation of satellite position and velocity incorporating any variation of orbit dynamics; (2) a two-threshold robust cubature Kalman filter to counteract the adverse influence of measurement outliers on positioning quality; (3) Reynolds averaging inspired clock and frequency error estimation. Hardware emulation test results show that the proposed algorithm has a performance with a 3D positioning RMS error of 1.2 m, 3D velocity RMS error of 0.02 m/s and a pulse per second (PPS) RMS error of 11.8ns. Simulations with MATLAB show that it can effectively detect and dispose outliers, and further on outperforms other algorithms in comparison.
Precise Loran-C Signal Acquisition Based on Envelope Delay Correlation Method
Authors: Yan Wenhe , Zhao Kunjuan , Li Shifeng , Wang Xinghui , Hua Yu
Published: April 2020

The Loran-C system is an internationally standardized positioning, navigation, and timing service system. It is the most important backup and supplement for the global navigation satellite system (GNSS). However, the existing Loran-C signal acquisition methods are easily affected by noise and cross-rate interference (CRI). Therefore, this article proposes an envelope delay correlation acquisition method that, when combined with linear digital averaging (LDA) technology, can effectively suppress noise and CRI. The selection of key parameters and the performance of the acquisition method are analyzed through a simulation. When the signal-to-noise ratio (SNR) is −16 dB, the acquisition probability is more than 90% and the acquisition error is less than 1 μs. When the signal-to-interference ratio (SIR) of the CRI is −5 dB, the CRI can also be suppressed and the acquisition error is less than 5 μs. These results show that our acquisition method is accurate. The performance of the method is also verified by actual signals emitted by a Loran-C system. These test results show that our method can reliably detect Loran-C pulse group signals over distances up to 1500 km, even at low SNR. This will enable the modern Loran-C system to be a more reliable backup for the GNSS system.
Reconfigurable Diplexer-Based Filtenna for Tx/Rx Operation in Mobile Satellite Terminals
Authors: Rodrigues Luís , Varum Tiago , Matos João N.
Published: April 2020

Large constellations of low-orbit satellites have already been launched with the aim of offering complete worldwide coverage of broadband Internet; however, compact, simple, and low-cost mobile terminals are necessary to establish the communication. This paper describes the design of a reconfigurable and compact filtenna with the ability to switch between the satellites’ uplink and downlink frequencies, 20 GHz and 29 GHz, maintaining an excellent performance. Due to its simplicity, efficiency, and Rx/Tx isolation, this antenna is a relevant candidate to be part of mobile terminals and devices, or even sensors, that communicate with satellites.
A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates
Authors: Khan Asif , Shin Jae Kyoung , Lim Woo Cheol , Kim Na Yeon , Kim Heung Soo
Published: April 2020

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.
Terrain Point Cloud Assisted GB-InSAR Slope and Pavement Deformation Differentiate Method in an Open-Pit Mine
Authors: Zheng Xiangtian , He Xiufeng , Yang Xiaolin , Ma Haitao , Yu Zhengxing , Ren Guiwen , Li Jiang , Zhang Hao , Zhang Jinsong
Published: April 2020

Ground-based synthetic aperture radar interferometry (GB-InSAR) is a valuable tool for deformation monitoring. The 2D interferograms obtained by GB-InSAR can be integrated with a 3D terrain model to visually and accurately locate deformed areas. The process has been preliminarily realized by geometric mapping assisted by terrestrial laser scanning (TLS). However, due to the line-of-sight (LOS) deformation monitoring, shadow and layover often occur in topographically rugged areas, which makes it difficult to distinguish the deformed points on the slope between the ones on the pavement. The extant resampling and interpolation method, which is designed for solving the scale difference between the point cloud and radar pixels, does not consider the local scattering characteristics difference of slope. The scattering difference information of road surface and slope surface in the terrain model is deeply weakened. We propose a differentiated method with integrated GB-InSAR and terrain surface point cloud. Local geometric and scattering characteristics of the slope were extracted, which account for pavement and slope differentiating. The geometric model is based on a GB-InSAR system with linear repeated-pass and the topographic point cloud relative observation geometry. The scattering model is based on k-nearest neighbor (KNN) points in small patches varies as radar micro-wave incident angle changes. Simulation and a field experiment were conducted in an open-pit mine. The results show that the proposed method effectively distinguishes pavement and slope surface deformation and the abnormal area boundary is partially relieved.
End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism
Authors: Eom Heesang , Lee Dongseok , Han Seungwoo , Hariyani Yuli Sun , Lim Yonggyu , Sohn Illsoo , Park Kwangsuk , Park Cheolsoo
Published: April 2020

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.
MSR2N: Multi-Stage Rotational Region Based Network for Arbitrary-Oriented Ship Detection in SAR Images
Authors: Pan Zhenru , Yang Rong , Zhang Zhimin
Published: April 2020

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance.
Folding-Based Electrochemical Aptasensor for the Determination of β-Lactoglobulin on Poly-L-Lysine Modified Graphite Electrodes
Authors: Amor-Gutiérrez Olaya , Selvolini Giulia , Fernández-Abedul M. Teresa , de la Escosura-Muñiz Alfredo , Marrazza Giovanna
Published: April 2020

Nowadays, food allergy is a very important health issue, causing adverse reactions of the immune system when exposed to different allergens present in food. Because of this, the development of point-of-use devices using miniaturized, user-friendly, and low-cost instrumentation has become of outstanding importance. According to this, electrochemical aptasensors have been demonstrated as useful tools to quantify a broad variety of targets. In this work, we develop a simple methodology for the determination of β-lactoglobulin (β-LG) in food samples using a folding-based electrochemical aptasensor built on poly-L-lysine modified graphite screen-printed electrodes (GSPEs) and an anti-β-lactoglobulin aptamer tagged with methylene blue (MB). This aptamer changes its conformation when the sample contains β-LG, and due to this, the spacing between MB and the electrode surface (and therefore the electron transfer efficiency) also changes. The response of this biosensor was linear for concentrations of β-LG within the range 0.1–10 ng·mL−1, with a limit of detection of 0.09 ng·mL−1. The biosensor was satisfactorily employed for the determination of spiked β-LG in real food samples.
‘Warm Eyes’, ‘Warm Breath’, ‘Heart Warmth’: Using Aroha (Love) and Warmth to Reconceptualise and Work towards Best Interests in Child Protection
Authors: Young Susan , McKenzie Margaret , Omre Cecilie , Schjelderup Liv , Walker Shayne
Published: April 2020

The attributes ‘warm eyes’, ‘breathe warm air’, ‘heart warmth’ and aroha (love) guide our work in child protection. These quotes are from a young person from the Change Factory 2020, a MFAMILY student in 2020 and Jan Erik Henricksen Key Note at the 4th International Indigenous Voices in Social Work Conference, Alta, Norway 2017 respectively, to describe the way young people and families want workers to be. We reflect on the child rights and family inclusion provisions of the United Nations Convention on the Rights of the Child (UNCRoC), and the Aotearoa New Zealand (ANZ) legislation Children, Young Persons and their Families Act (1989), in contributing to the best interests of the child. We examine current events in our locations, Aotearoa New Zealand, Norway and Western Australia, as demonstrating that these joint principles are far from universally used in child protection practice. The sole use of Article 3 of the UNCRoC, in particular, often results in excluding families as legitimate stakeholders. In seeking to achieve the best interests of the child, we apply a practice framework to example vignettes. Here, we have added micro-practices to address the identified gaps in relationship building, engagement and enabling practices in working towards the practice of best interests.
Sustainable Policy for Water Pricing in Kuwait
Authors: Aljamal Ali , Speece Mark , Bagnied Mohsen
Published: April 2020

This research investigates consumer willingness to pay (WTP) for water in Kuwait as a foundation for policy decisions on reducing water subsidies. Heavy subsidies have encouraged unsustainable very high consumption, but efforts to cut subsidies can generate strong political opposition. A survey (n = 443) indicates that WTP is greater at lower prices, but resistance is not purely about price. The presence of a continued partial water subsidy for basic household use slightly increases WTP, probably mainly from perceptions of fairness. Information about Kuwait’s water scarcity also has a small impact. All of these effect sizes are small, so we discuss these issues using a nudge framework from behavioral economics. A number of policies can foster small shifts in WTP; collectively they may have larger impact and make subsidy reduction relatively painless.
The Taxation of Agriculture in the Republic of Serbia as a Factor of Development of Organic Agriculture
Authors: Milošević Goran , Kulić Mirko , Đurić Zvezdan , Đurić Olivera
Published: April 2020

Agricultural activity is very important for every country that strives to create a stimulating, stable, abundant, sustainable and equal business environment for all market participants. By striving for sustainable economic development and growth, as well as preservation of the ecosystem, organic agricultural production aims to produce high-quality food. Within the socio-economic space, the role of the state as a regulator of production and market relations is indispensable. The state often uses fiscal policy as an instrument for the regulation of relations in the sphere of production and trade. The level of the tax burden is of vital importance for achieving a sustainable level of agricultural development. From the aspect of the Serbian economy, the taxation of agriculture in the future period must be based on a system of tax incentives for organic production. In the long run, this can increase the volume of organic production and the use of healthy food in human nutrition.
Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods
Authors: Kwag Shinyoung , Hahm Daegi , Kim Minkyu , Eem Seunghyun
Published: April 2020

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.
Sustainable Entrepreneurship in the Agriculture Sector: The Nexus of the Triple Bottom Line Measurement Approach
Authors: Sargani Ghulam Raza , Zhou Deyi , Raza Muhammad Haseeb , Wei Yuzhi
Published: April 2020

The field of entrepreneurship is considered essential for the economy, and many researchers around the world have studied it from diverse perspectives. The outcomes of this research are not yet consensual. Today, it is gaining attention and consensus due to the increasing pressure of sustainable development, so there is a need for academic research to examine this field by incorporating sustainability value creation practices and the efforts of current entrepreneurs towards said goal, especially in the case of the agricultural sector. Accordingly, this study aims to address the problem of what drives students to incorporate sustainable agriculture in their entrepreneurial ventures. Moreover, we aim to determine whether the value placed in the triple bottom line affects the intention to establish sustainable businesses. The study is based on five antecedents of the Theory of Planned Behavior (TBP) and was designed to explore the mechanism underlying the intention to promote sustainable entrepreneurship in agriculture. The primary objective was to collect and analyze the data using the partial least square structural equation model (PLS-SEM) to test the determinants. The results revealed that the indicators of a favorable sustainable attitude, supportive subjective norms, control behaviors, adequate opportunity recognitions, and encouraging the triple bottom line had strong influences on the intention of promoting sustainability in entrepreneurship. Besides, the attitudes, subjective norms, opportunity recognition, and sustainability values can also predict students’ significant positive intentions toward sustainable agriculture in entrepreneurship. The research findings contribute to the literature by providing an empirical basis for the formulation of policies to encourage students to start an agribusiness, thereby improving the effectiveness of entrepreneurship education development programs and bridging the gap between sustainable entrepreneurial intentions and actions. Therefore, the insight into the determinants of sustainable entrepreneurship can be an essential step toward designing a practical and durable policy mechanism for the implementation of the triple bottom line when developing entrepreneurial agriculture education programs.
The Joint Effects of Leader–Member Exchange and Team-Member Exchange in Predicting Job Crafting
Authors: Lee Kihyun
Published: April 2020

In order to better understand the social aspects of job crafting, this study explores the direct and interactive effects of leader–member exchange (LMX) and team-member exchange (TMX) on three types of job crafting (i.e., task, relational, and cognitive crafting). Drawing on both social exchange theory and the job demands–resources model, this study examines the social antecedents of job crafting in a sample of 336 members of three shipbuilding companies. The results indicate that individuals who have high-quality relationships with their leaders engage in more job crafting and that TMX is positively related to job crafting, after controlling for LMX. In addition, the results show that TMX moderates the positive relationship between LMX and job crafting, such that a higher TMX strengthens the LMX–job crafting link. The implications of these findings for job crafting and social antecedents are discussed, and suggestions for future research are presented.
Lender Trust on the P2P Lending: Analysis Based on Sentiment Analysis of Comment Text
Authors: Niu Beibei , Ren Jinzheng , Zhao Ansa , Li Xiaotao
Published: April 2020

Lender trust is important to ensure the sustainability of P2P lending. This paper uses web crawling to collect more than 240,000 unique pieces of comment text data. Based on the mapping relationship between emotion and trust, we use the lexicon-based method and deep learning to check the trust of a given lender in P2P lending. Further, we use the Latent Dirichlet Allocation (LDA) topic model to mine topics concerned with this research. The results show that lenders are positive about P2P lending, though this tendency fluctuates downward with time. The security, rate of return, and compliance of P2P lending are the issues of greatest concern to lenders. This study reveals the core subject areas that influence a lender’s emotions and trusts and provides a theoretical basis and empirical reference for relevant platforms to improve their operational level while enhancing competitiveness. This analytical approach offers insights for researchers to understand the hidden content behind the text data.
Participatory Approach to Gap Analysis between Policy and Practice Regarding Air Pollution in Ger Areas of Ulaanbaatar, Mongolia
Authors: Koo Bonjun , Na Jong-Il , Thorsteinsson Throstur , Cruz Ana Maria
Published: April 2020

Ulaanbaatar, the capital of Mongolia, suffers from severe air pollution during the long winter months, and its air pollution levels are among the highest in the world. Residents in the ger areas of Ulaanbaatar are unable to take advantage of the laws and policy regulations to reduce air pollution despite years of efforts to address this issue by international and local organizations including the government of Mongolia (GoM). Important challenges and barriers that have limited the success of various governmental policies that tackle air pollution problems were identified through participatory approaches. In order to do this, personal interviews were conducted with various stakeholders such as officials from central and local governments, government agencies, non-governmental organizations (NGOs), international organizations, and academia. Moreover, a workshop was conducted to elucidate the views of ger area community members in Ulaanbaatar. Based on the results of these interviews and workshop, the gaps between the policy approaches of the government, the actual conditions, and the desired situations of the residents to improve air quality in Ulaanbaatar were analyzed. The large gaps that were identified between the national and local perspectives on air pollution issues demonstrated that the reduction of air pollution in Ulaanbaatar requires solving fundamental and complex problems based on a better understanding of the specific conditions and needs of the residents of the ger areas. Moreover, active participation and discussion of the residents at the workshop indicated that community-based approaches could be applied in the cultural context of Mongolia with promising results in finding solutions.
Integration of AC/DC Microgrids into Power Grids
Authors: Mohammadi Fazel
Published: April 2020

The Special Issue on “Integration of AC/DC Microgrids into Power Grids” is published. A total of six qualified papers are published in this Special Issue. The topics of the papers are the Optimal Power Flow (OPF), control, protection, and the operation of hybrid AC/DC microgrids. Nine researchers participated in this Special Issue. We hope that this Special Issue is helpful for sustainable energy applications.
Ethical Leadership and Employee Green Behavior: A Multilevel Moderated Mediation Analysis
Authors: Saleem Maria , Qadeer Faisal , Mahmood Faisal , Ariza-Montes Antonio , Han Heesup
Published: April 2020

In today’s globalized world, it has become challenging for organizations to prevent environmental damage and decay as they are the major contributors to these concerns. Researchers in the field of organizational behavior and sustainable development have been concentrating on the role of corporate leaders in deriving employee green behavior (EGB). Despite a few early attempts in this emerging field, no precise antecedents and mechanisms have been established so far. Accordingly, this research contributes to this emerging debate by examining how and under what conditions ethical leadership affects EGB. This study investigates the impact of ethical leadership on EGB through the mediation of green psychological climate (GPC) and the boundary condition of environmental awareness. This research uses social learning theory to derive hypotheses. This study empirically examines the underlying framework by conducting two surveys with time-based breaks to collect multilevel data from 410 employees working in four private and public sector universities and hospitals in Pakistan. We conducted multilevel path analysis through Mplus and confirmed a statistically significant positive effect of ethical leadership on GPC that ultimately translates to EGB. Further, the contingency of environmental awareness strengthens the indirect impact of ethical leadership on EGB through the mediation of GPC. Our findings reveal various ways by which organizations can strategically focus on employee green behavior, such as saving energy, wastage, and recycling.
Decentralized Community Composting: Past, Present and Future Aspects of Italy
Authors: Bruni Cecilia , Akyol Çağrı , Cipolletta Giulia , Eusebi Anna Laura , Caniani Donatella , Masi Salvatore , Colón Joan , Fatone Francesco
Published: April 2020

Italy is among the top biowaste-generating countries in Europe, and has a well-structured waste management framework with quite a number of centralized composting facilities. In recent years, there has also been huge interest from local communities in decentralized composting. Although decentralized community composting is common in some countries, there is still a lack of information on the operative environment together with its potential logistical, environmental, economic, and social impacts. Considering the national Italian legislation on community composting as well as successfully implemented projects at EU level, Italy can set a model especially for Mediterranean countries that intend to build decentralized composting programs. Therefore, in the context of this review paper, a brief overview of the composting process was presented together with main applications in centralized and especially in decentralized composting, while the main focus was kept on the operative and legislative information gathered from Italian community composting. There is a huge difference in the number of composting plants between the regions, and the lack of centralized facilities in the central and southern regions can be supported by decentralized solutions. Decentralizing waste treatment facilities and thus creating local solutions to urban waste management strategies will help to achieve the resource recovery and valorization targets in line with the circular economy.
Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems
Authors: Molina-Gómez Nidia Isabel , Rodríguez-Rojas Karen , Calderón-Rivera Dayam , Díaz-Arévalo José Luis , López-Jiménez P. Amparo
Published: April 2020

Different studies have been carried out to evaluate the progress made by countries and cities towards achieving sustainability to compare its evolution. However, the micro-territorial level, which encompasses a community perspective, has not been examined through a comprehensive forecasting method of sustainability categories with machine learning tools. This study aims to establish a method to forecast the sustainability levels of an urban ecosystem through supervised modeling. To this end, it was necessary to establish a set of indicators that characterize the dimensions of sustainable development, consistent with the Sustainable Development Goals. Using the data normalization technique to process the information and combining it in different dimensions made it possible to identify the sustainability level of the urban zone for each year from 2009 to 2017. The resulting information was the basis for the supervised classification. It was found that the sustainability level in the micro-territory has been improving from a low level in 2009, which increased to a medium level in the subsequent years. Forecasts of the sustainability levels of the zone were possible by using decision trees, neural networks, and support vector machines, in which 70% of the data were used to train the machine learning tools, with the remaining 30% used for validation. According to the performance metrics, decision trees outperformed the other two tools.
A Fuzzy ANP-QFD Methodology for Determining Stakeholders in Product-Service Systems Development from Ecosystem Perspective
Authors: Yin Dao , Ming Xinguo , Liu Zhiwen , Zhang Xianyu
Published: April 2020

Recently the competition between firms is transforming from “firm vs. firm” to “ecosystem vs. ecosystem”. To fulfil the personalized customer requirements for a high-quality product-service in the age of servitization and sustainability, it is difficult for an individual actor to realize it. The product-service system (PSS) is naturally an integration of product and service. The final product-service of PSS that depends on a tangible product and intangible service, calls for value co-creation of multi-stakeholders. However, most existing related works have investigated PSS from the perspective of a supply chain or network, PSS in the view of an ecosystem of multi-stakeholders lacks sufficient exploration, especially the topic on the relationship between product-service and stakeholders. To fill the gap, this paper proposed a framework for the PSS ecosystem with quality function deployment (QFD) and fuzzy analytic network process (fuzzy ANP) to determine the stakeholders by clarifying the relationship between the final integrated product-service and stakeholders. Firstly, the PSS ecosystem structure was presented, including the stakeholders. Secondly, a model with the three-stage fuzzy ANP-QFD approach to determine stakeholders was employed. Thirdly, the specific process of the three-stage approach was presented. An illustrative case study of the automobile aftermarket was presented to verify the proposed model and approach. Discussions and future directions concluded this paper.
A Comparative Study on the Performance of a Horizontal Axis Ocean Current Turbine Considering Deflector and Operating Depths
Authors: Maldar Nauman Riyaz , Ng Cheng Yee , Ean Lee Woen , Oguz Elif , Fitriadhy Ahmad , Kang Hooi Siang
Published: April 2020

Several different designs and prototypes of ocean current turbines have been tested over recent years. For every design test, emphasis is given to achieving an optimum power output from the flow. In this study, the performance of a Horizontal Axis Ocean Current Turbine (HAOCT) has been investigated using three-dimensional Computational Fluid Dynamics (CFD) simulations for three cases, namely, (1) a turbine without a deflector, (2) a turbine with a deflector, and (3) a turbine with a deflector operating at a higher fluid depth. The turbine design was modeled in DesignModeler software and simulations were carried out in commercial CFD software Flow-3D. The Torque Coefficient (Cm) and Power Coefficient (Cp) for the turbine have been investigated for a certain range of Tip-Speed Ratios (TSRs) in a flow velocity of 0.7 m/s. Furthermore, comparisons have been made to demonstrate the effect of the deflector on the performance of the turbine and the influence of a higher fluid pressure on the same. The results from the simulations indicate that the higher value of Cp was achieved for Case 2 as compared to the other two cases. The findings from the study indicate that the use of the deflector enhances the performance of the turbine. Furthermore, a higher fluid pressure acting on the turbine has a significant effect on its performance.
Spatial Effects of Urban Agglomeration on Energy Efficiency: Evidence from China
Authors: Du Jiang , Zhao Mengqin , Zeng Ming , Han Kezhen , Sun Huaping
Published: April 2020

The rapid expansion of large cities in China has substantially increased energy consumption. With ever stringent environmental policy in force, energy efficiency becomes an important issue. As the emergence of these urban agglomerations (UAs) is usually due to externality effects of spatially concentrated factors, this paper investigates how these factors can affect energy efficiency. Based on mono index, which is used to describe the spatial location information, we have constructed the spatial-structure index of UAs. Using panel data on ten major UAs in China from 2008 to 2017, we find that, in the whole sample, there is an inverse relationship between the spatial structure of UAs and energy efficiency: The higher the concentration degree of factors of UAs, the lower the energy efficiency. Across different regions, however, the relationship between spatial structure and energy efficiency is heterogeneous. The concentration degree of factors in the eastern and central regions of China is relatively high, and the spatial structure there does lead to a decrease in energy efficiency. By contrast, UAs in China’s western region are in a period of factor concentration, with spatial structure playing, in that region, a positive role in improving energy efficiency.
Assessing the Effects of Higher-Education Factors on the Job Satisfaction of Engineering Graduates in Korea
Authors: Yoon Jiyoung , Park Soojung , Kim Dae-Jin
Published: April 2020

This paper investigates the relationship between the influential factors related to higher education and job satisfaction of engineering graduates in Korea. The education factors considered in this study can be categorized into three groups: those related to personal information, satisfaction with higher-education quality, and participation in career development programs. The Graduates Occupational Mobility Survey data collected in 2016 and provided by the Korea Employment Information Service were used to evaluate the job satisfaction of Korean engineering graduates, which was in turn analyzed using the hierarchical multiple regression approach. The verification of the statistical analysis results indicates the importance of school location and high satisfaction with overall school quality in determining the job satisfaction of engineering graduates in Korea. It also reveals that internship is more efficient in enhancing job satisfaction than relatively short-term programs such as interviews, resume coaching, and job camps.