bluuebunny/crossref_metadata_2025 huggingface dataset:
Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics
Authors: Chen Shiqi , Zhan Ronghui , Zhang Jun
Published: May 2018

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation. Despite convolutional neural networks (CNNs) having facilitated the development in this domain, the computation efficiency under real-time application and the accurate positioning on relatively small objects in HSR images are two noticeable obstacles which have largely restricted the performance of detection methods. To tackle the above issues, we first introduce semantic segmentation-aware CNN features to activate the detection feature maps from the lowest level layer. In conjunction with this segmentation branch, another module which consists of several global activation blocks is proposed to enrich the semantic information of feature maps from higher level layers. Then, these two parts are integrated and deployed into the original single shot detection framework. Finally, we use the modified multi-scale feature maps with enriched semantics and multi-task training strategy to achieve end-to-end detection with high efficiency. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset have demonstrated the superiority of the presented method.
Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network
Authors: Liu Shuang , Li Mei , Zhang Zhong , Xiao Baihua , Cao Xiaozhong
Published: May 2018

The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.
How Well Does the ‘Small Fire Boost’ Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning?
Authors: Zhang Tianran , Wooster Martin J. , De Jong Mark C. , Xu Weidong
Published: May 2018

The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.
Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice
Authors: Zheng Hengbiao , Cheng Tao , Li Dong , Zhou Xiang , Yao Xia , Tian Yongchao , Cao Weixing , Zhu Yan
Published: May 2018

Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.
Airborne Doppler Wind Lidar Observations of the Tropical Cyclone Boundary Layer
Authors: Zhang Jun , Atlas Robert , Emmitt G. , Bucci Lisa , Ryan Kelly
Published: May 2018

This study presents a verification and an analysis of wind profile data collected during Tropical Storm Erika (2015) by a Doppler Wind Lidar (DWL) instrument aboard a P3 Hurricane Hunter aircraft of the National Oceanic and Atmospheric Administration (NOAA). DWL-measured winds are compared to those from nearly collocated GPS dropsondes, and show good agreement in terms of both the wind magnitude and asymmetric distribution of the wind field. A comparison of the DWL-measured wind speeds versus dropsonde-measured wind speeds yields a reasonably good correlation (r2 = 0.95), with a root mean square error (RMSE) of 1.58 m s−1 and a bias of −0.023 m s−1. Our analysis shows that the DWL complements the existing P3 Doppler radar, in that it collects wind data in rain-free and low-rain regions where Doppler radar is limited for wind observations. The DWL observations also complement dropsonde measurements by significantly enlarging the sampling size and spatial coverage of the boundary layer winds. An analysis of the DWL wind data shows that the boundary layer of Erika was much deeper than that of a typical hurricane-strength storm. Streamline and vorticity analyses based on DWL wind observations explain why Erika maintained intensity in a sheared environment. This study suggests that DWL wind data are valuable for real-time intensity forecasts, basic understanding of the boundary layer structure and dynamics, and offshore wind energy applications under tropical cyclone conditions.
The On-Orbit Non-Uniformity Correction Method with Modulated Internal Calibration Sources for Infrared Remote Sensing Systems
Authors: Sheng Yicheng , Dun Xiong , Jin Weiqi , Zhou Feng , Wang Xia , Mi Fengwen , Xiao Si
Published: May 2018

The scanning infrared focal plane array (IRFPA) suffers from stripe-like non-uniformity due to the usage of many detectors, especially when working with a large time scale. Typical calibration systems tend to block the sensor aperture and expose the detectors to an on-board blackbody calibration source. They may also point at deep space. Full aperture calibration sources of this type tend to be large and expensive. To address these problems, a dynamic non-uniformity correction (NUC) method is proposed based on a modulated internal calibration device. By employing the on-board calibration device to generate a dynamic scene and fully integrating the system characteristics of the scanning IRFPA into the scene-based non-uniformity correction (SBNUC) algorithm, on-orbit high dynamic range NUC is achieved without blocking the field of view. Here we simulate an internal calibration system alternative, where a dynamic calibration signal is superimposed on the normal imagery, thus requiring no mechanisms and a smaller size. This method using this type of calibrator shows that when the sensor is pointing at deep space for calibration, it provides an effective non-uniformity correction of the imagery. After performing the proposed method, the NU of the two evaluation images was reduced from the initial 12.99% and 8.72% to less than 2%. Compared to other on-board NUC methods that require an extended reference blackbody source, this proposed approach has the advantages of miniaturization, a short calibration time, and strong adaptability.
SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band
Authors: Berninger Anna , Lohberger Sandra , Stängel Matthias , Siegert Florian
Published: May 2018

Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite’s Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R2, and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R2, and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R2) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (>500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release.
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier
Authors: El-Sayed Hesham , Sankar Sharmi , Daraghmi Yousef-Awwad , Tiwari Prayag , Rattagan Ekarat , Mohanty Manoranjan , Puthal Deepak , Prasad Mukesh
Published: May 2018

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.
Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator
Authors: Wang Zhiqiang , Li Xiaolong , Xie Yunde , Long Zhiqiang
Published: May 2018

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator’s frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.
Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics
Authors: Karuppiah Ramachandran Vignesh Raja , Alblas Huibert J. , Le Duc V. , Meratnia Nirvana
Published: May 2018

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.
Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements
Authors: Proverbio Marco , Bertola Numa J. , Smith Ian F. C.
Published: May 2018

The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties—including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, the existing signal-processing and statistics-based algorithms provide little support for outlier detection. This paper introduces a new model-population methodology for outlier detection that is based on the expected performance of the as-designed sensor network. Thus, suspicious measurements are identified even when few measurements, collected with a range of sensors, are available. The structural identification of a full-scale bridge in Exeter (UK) is used to demonstrate the applicability of the proposed methodology and to compare its performance with existing algorithms. The results show that outliers, capable of compromising EDMF accuracy, are detected. Moreover, a metric that separates the impact of powerful sensors from the effects of measurement outliers have been included in the framework. Finally, the impact of outlier occurrence on parameter identification and model extrapolation (for example, reserve capacity assessment) is evaluated.
Numerical Simulation of a Novel Sensing Approach Based on Abnormal Blocking by Periodic Grating Strips near the Silicon Wire Waveguide
Authors: Tsarev Andrei , Kolosovsky Eugeny , De Leonardis Francesco , Passaro Vittorio M. N.
Published: May 2018

This paper discusses the physical nature and the numerical modeling of a novel approach of periodic structures for applications as photonic sensors. The sensing is based on the high sensitivity to the cover index change of the notch wavelength. This sensitivity is due to the effect of abnormal blocking of the guided wave propagating along the silicon wire with periodic strips overhead it through the silica buffer. The structure sensing is numerically modeled by 2D and 3D finite difference time domain (FDTD) method, taking into account the waveguide dispersion. The modeling of the long structures (more than 1000 strips) is accomplished by the 2D method of lines (MoL) with a maximal implementation of the analytical feature of the method. It is proved that the effect of abnormal blocking could be used for the construction of novel types of optical sensors.
Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT)
Authors: Moiş George Dan , Sanislav Teodora , Folea Silviu Corneliu , Zeadally Sherali
Published: May 2018

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings.
DE-Sync: A Doppler-Enhanced Time Synchronization for Mobile Underwater Sensor Networks
Authors: Zhou Feng , Wang Qi , Nie DongHu , Qiao Gang
Published: May 2018

Time synchronization is the foundation of cooperative work among nodes of underwater sensor networks; it takes a critical role in the research and application of underwater sensor networks. Although numerous time synchronization protocols have been proposed for terrestrial wireless sensor networks, they cannot be directly applied to underwater sensor networks. This is because most of them typically assume that the propagation delay among sensor nodes is negligible, which is not the case in underwater sensor networks. Time synchronization is mainly affected by a long propagation delay among sensor nodes due to the low propagation speed of acoustic signals. Furthermore, sensor nodes in underwater tend to experience some degree of mobility due to wind or ocean current, or some other nodes are on self-propelled vehicles, such as autonomous underwater vehicles (AUVs). In this paper, we propose a Doppler-enhanced time synchronization scheme for mobile underwater sensor networks, called DE-Sync. Our new scheme considers the effect of the clock skew during the process of estimating the Doppler scale factor and directly substitutes the Doppler scale factor into linear regression to achieve the estimation of the clock skew and offset. Simulation results show that DE-Sync outperforms existing time synchronization protocols in both accuracy and energy efficiency.
Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home
Authors: Khan Nida Saddaf , Ghani Sayeed , Haider Sajjad
Published: May 2018

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.
Large Scale Triboelectric Nanogenerator and Self-Powered Flexible Sensor for Human Sleep Monitoring
Authors: Ding Xiaoheng , Cao Hailin , Zhang Xinghong , Li Mingyu , Liu Yuntian
Published: May 2018

The triboelectric nanogenerator (TENG) and its application as a sensor is a popular research subject. There is demand for self-powered, flexible sensors with high sensitivity and high power-output for the next generation of consumer electronics. In this study, a 300 mm × 300 mm carbon nanotube (CNT)-doped porous PDMS film was successfully fabricated wherein the CNT influenced the micropore structure. A self-powered TENG tactile sensor was established according to triboelectric theory. The CNT-doped porous TENG showed a voltage output seven times higher than undoped porous TENG and 16 times higher than TENG with pure PDMS, respectively. The TENG successfully acquired human motion signals, breath signals, and heartbeat signals during a sleep monitoring experiment. The results presented here may provide an effective approach for fabricating large-scale and low-cost flexible TENG sensors.
Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review
Authors: Kamišalić Aida , Fister Iztok , Turkanović Muhamed , Karakatič Sašo
Published: May 2018

Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.
A Dual-Channel Acquisition Method Based on Extended Replica Folding Algorithm for Long Pseudo-Noise Code in Inter-Satellite Links
Authors: Zhao Hongbo , Chen Yuying , Feng Wenquan , Zhuang Chen
Published: May 2018

Inter-satellite links are an important component of the new generation of satellite navigation systems, characterized by low signal-to-noise ratio (SNR), complex electromagnetic interference and the short time slot of each satellite, which brings difficulties to the acquisition stage. The inter-satellite link in both Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) adopt the long code spread spectrum system. However, long code acquisition is a difficult and time-consuming task due to the long code period. Traditional folding methods such as extended replica folding acquisition search technique (XFAST) and direct average are largely restricted because of code Doppler and additional SNR loss caused by replica folding. The dual folding method (DF-XFAST) and dual-channel method have been proposed to achieve long code acquisition in low SNR and high dynamic situations, respectively, but the former is easily affected by code Doppler and the latter is not fast enough. Considering the environment of inter-satellite links and the problems of existing algorithms, this paper proposes a new long code acquisition algorithm named dual-channel acquisition method based on the extended replica folding algorithm (DC-XFAST). This method employs dual channels for verification. Each channel contains an incoming signal block. Local code samples are folded and zero-padded to the length of the incoming signal block. After a circular FFT operation, the correlation results contain two peaks of the same magnitude and specified relative position. The detection process is eased through finding the two largest values. The verification takes all the full and partial peaks into account. Numerical results reveal that the DC-XFAST method can improve acquisition performance while acquisition speed is guaranteed. The method has a significantly higher acquisition probability than folding methods XFAST and DF-XFAST. Moreover, with the advantage of higher detection probability and lower false alarm probability, it has a lower mean acquisition time than traditional XFAST, DF-XFAST and zero-padding.
Achieving Sustainability of Traditional Wooden Houses in Indonesia by Utilization of Cost-Efficient Waste-Wood Composite
Authors: Tsai Meng-Ting , Sugiharto Wonodihardjo Anthony
Published: May 2018

Although Indonesians have for many years used wood to build traditional houses, currently it is difficult to find new traditional houses made from wood. Since wood is too expensive for local people, concrete becomes the major construction material instead. However, wood is considered a sustainable material that is eco-friendly, recyclable, and has less of an environmental impact than concrete. In this study, an innovative and cost efficient waste-wood composite structure was proposed with the intention of fulfilling local demand for the construction of traditional wooden houses, as well as supplying a sustainable and cost-efficient wooden product in the construction sector. Four small pieces of waste wood connected with steel nails or self-tapping screws were assembled into a rectangular waste-wood composite, serving as secondary beam, column, or brace. These waste-wood composites are considered recyclable and low-cost, and provide an alternative solution for local people that achieves an affordable and sustainable construction system. The assembled wood components were tested under single shear in order to clarify the structural performance of connection and the failure modes. The comparison of the experimental results and predicted results showed that the predicted strength is considered in a conservative manner for further application. In addition, the cost estimation and comparison between a solid wood structure and the waste-wood composite structure indicated that the price of the waste-wood composite structure is potentially competitive and cost-efficient for the local people, which was optimistic for future development.
Achieving Turkey’s INDC Target: Assessments of NCCAP and INDC Documents and Proposing Conceivable Policies
Authors: Alkan Ayla , Oğuş Binatlı Ayla , Değer Çağaçan
Published: May 2018

In 2015, Turkey submitted its Intended Nationally Determined Contribution (INDC) to the United Nations Framework on Climate Change Convention (UNFCCC) before the Paris Conference of the Parties (COP 21), expressing its intention to decrease emissions level at a rate of 21% from business as usual. This emissions reduction target is important as it is the first one for Turkey. However, Turkey faces unemployment problems and needs to sustain its growth. In this study, an Environmentally Extended Social Accounting Matrix (SAM), based on 2012 Input–Output data, was created, emissions reduction potentials of the National Climate Change Action Plan (NCCAP) together with the INDC were calculated, and alternative policies to reduce emissions to the target level and to boost the economy were proposed separately. The study finds that both the preparation and implementation of the previous national documents are problematic, and that Turkey was not meticulous about implementation of the climate mitigation policies in the previous national documents. The study also finds that reaching the emissions target with the INDC policies seems impossible and more conceivable policies are needed, and recommends that the INDC target and document itself should be revised substantially.
Do Shifts in Renewable Energy Operation Policy Affect Efficiency: Korea’s Shift from FIT to RPS and Its Results
Authors: Park Hyungguen , Kim Changhee
Published: May 2018

South Korea’s new and renewable energy (NRE) policy experienced a drastic shift from the Feed-in Tariff (FIT) to the Renewable Portfolio Standard (RPS) in 2012. This study looks at the changes in the efficiency of NRE policy in this transition through DEA (Data Envelopment Analysis) and MI (Malmquist Index) methods, using investment for NRE technology development and for NRE dissemination as input factors and the number of firms, the number of employees, and the volume of NRE power generation as output factors. The results show a temporary drop in efficiency in 2012 during the transition period for the NRE industry as a whole. However, apart from those energy types with ulterior factors, the implementation of RPS increased the technical change (TC) of most NRE types. Furthermore, the findings highlight that, among South Korea’s three focal NRE industries—photovoltaic, wind power, and fuel cell energies—only fuel cell energies showed an increase in efficiency over time. South Korea’s policy shifts from FIT to RPS and the resulting effects on NRE policy’s efficiency provide a useful reference and guideline for government decision-making on NRE policy changes.
Norm Entrepreneurs Sidestep REDD+ in Pursuit of Just and Sustainable Forest Governance
Authors: Dawson Neil M. , Mason Michael , Fisher Janet A. , Mwayafu David Mujasi , Dhungana Hari , Schroeder Heike , Zeitoun Mark
Published: May 2018

This paper explores the dissonance between conceptions of justice among forest-adjacent communities and their representation in global forest policies, a persistent barrier to delivering just sustainability. We empirically track justice claims of rural villagers upwards through specific intermediaries or ‘justice brokers’: civil society, state, or private sector actors operating at local to international levels, who navigate different institutions to advance various social and ecological interests. We draw on interviews with 16 intermediaries in each of Nepal and Uganda and find that recognition of local values and practices such as customary tenure systems are key justice concerns of forest-adjacent communities in each country. However, intermediaries perceive a low likelihood of advancing those claims through national or international climate and forest policy debates, such as REDD+ (Reducing Emissions from Deforestation and forest Degradation), in large part because deliberations on justice are subordinated to concerns such as carbon accounting and arrangements for distributing monetary benefits. This suggests these policy processes must be modified to offer potential for transformational pathways. Intermediaries who pursued recognition justice issues developed innovative tactics in alternative forums. These ‘norm entrepreneurs’ adopted a suite of complementary strategies to attain influence, including: (1) formation of associations at the grassroots level; (2) media and advocacy campaigns through national coalitions to reach powerful international donors, and; (3) drawing on international support networks for advice, training and to influence national government. In both Uganda and Nepal these strategies were evidenced to enhance recognition for local values and practices.
Energy Efficiency in Logistics: An Interactive Approach to Capacity Utilisation
Authors: Wehner Jessica
Published: May 2018

Logistics operations are energy-consuming and impact the environment negatively. Improving energy efficiency in logistics is crucial for environmental sustainability and can be achieved by increasing the utilisation of capacity. This paper takes an interactive approach to capacity utilisation, to contribute to sustainable freight transport and logistics, by identifying its causes and mitigations. From literature, a conceptual framework was developed to highlight different system levels in the logistics system, in which the energy efficiency improvement potential can be found and that are summarised in the categories activities, actors, and areas. Through semi-structured interviews with representatives of nine companies, empirical data was collected to validate the framework of the causes of the unutilised capacity and proposed mitigations. The results suggest that activities, such as inflexibilities and limited information sharing as well as actors’ over-delivery of logistics services, incorrect price setting, and sales campaigns can cause unutilised capacity, and that problem areas include i.a. poor integration of reversed logistics and the last mile. The paper contributes by categorising causes of unutilised capacity and linking them to mitigations in a framework, providing a critical view towards fill rates, highlighting the need for a standardised approach to measure environmental impact that enables comparison between companies and underlining that costs are not an appropriate indicator for measuring environmental impact.
Environmental Warning System Based on the DPSIR Model: A Practical and Concise Method for Environmental Assessment
Authors: Wang Wenqi , Sun Yuhong , Wu Jing
Published: May 2018

Though we are in urgent need of environmental warnings to slow environmental deterioration, currently, there is no internationally concise method for environmental warnings. In addition, the existing approaches do not combine the three aspects of ecology, resources, and environment. At the same time, the three elements of the environment (air, water, and soil) are separated in most environmental warning systems. Thus, the method this paper gives is an innovative attempt and aims to make environmental assessment more practical. This paper establishes the index system of an environmental early warning based on the Driving–Pressure–State–Influence–Response (DPSIR) model. The Analytic Hierarchy Process (AHP) method was used to determine the weights. Next, single and integrated index methods further assess the environmental warning state, in which the weighted summation method is used to summarize the data and results. The case of Tianjin is used to confirm the applicability of this method. In conclusion, the method in this paper is more well-behaved and, therefore, more suitable to assist cities in their environmental assessment.
A Nationwide Survey Evaluating the Environmental Literacy of Undergraduate Students in Taiwan
Authors: Liang Shih-Wu , Fang Wei-Ta , Yeh Shin-Cheng , Liu Shiang-Yao , Tsai Huei-Min , Chou Jui-Yu , Ng Eric
Published: May 2018

The aim of this nationwide survey was to assess undergraduate students’ environmental literacy level in Taiwan. A total of 29,498 valid responses were received from a number of selected colleges and universities in Taiwan, using stratified random sampling method. A total of 70 items were used to assess the environmental literacy and the results revealed that undergraduate students had a relatively low level of environmental knowledge and behavior, while a moderate level of environmental attitudes was attained. The findings also indicated no significant correlations between knowledge and attitudes or between knowledge and behavior. However, a higher level of environmental knowledge correlated significantly with a higher degree of pro-environmental behavior, and a higher level of environmental knowledge correlated with stronger attitudes. The results also suggested that females outperformed the males in all categories. Results from this study could contribute towards further relevant policy discussion and decision-making, curriculum design and development to the improvement of environmental education in the higher education sector.
Vehicle Weight, Modal Split, and Emissions—An Ex-Post Analysis for Sweden
Authors: Vierth Inge , Lindgren Samuel , Lindgren Hanna
Published: May 2018

This study combines official statistics on freight transportation and emissions to present the long-run development of the use of longer and heavier road vehicles (LHVs), modal split, road freight efficiency, and GHG emissions and air pollution following the increase in the maximum permissible vehicle weight in Sweden in 1990 and 1993. We find that LHVs were quickly incorporated in the vehicle fleet and that road freight efficiency of the largest vehicles increased after the reforms. There was no discernable break in modal split trends as the modal share for road continued its long-run development. We show that road transportation contributes by far the most to emission costs. The composition of the emissions from road freight changed after the weight reforms, with an increasing share of GHG-emissions.
The Technological Trajectory of Integrated Pest Management for Rice in Cambodia
Authors: Flor Rica , Chhay Kry , Sorn Vichet , Maat Harro , Hadi Buyung
Published: May 2018

For the past two decades, while the efficacy of Integrated Pest Management (IPM) has been successfully demonstrated in Cambodia, its dissemination and sustained adoption among farmers have not met similar success. This study moves away from simplistic analyses about constraints in extension methods for IPM. Instead, we take a broader look into technological systems and the trajectory for pest management, which affected the spread and uptake of IPM in the country. Through a review of the wider context in policies and programs, and a survey of farmers from five provinces in Cambodia (N = 400), we examined the connections between options for pest management at the farmer level and conditions in the technological system. Using the Cambodian case study, we show that the technological system predisposes to pesticide use and as such hinders a trajectory of IPM. Systemic conditions, including interrelated agronomic practices, ecological conditions in farming communities, governance mechanisms, structures around the spread of knowledge and the industry around the technological options, have created mutual socio-technical dependencies. Although programs targeted change through knowledge of IPM, much of the systemic conditions sustain the trajectory of pesticide reliance. Hence, promoting an innovation environment that is supportive of IPM requires extension beyond knowledge dissemination, addressing these varied elements of the technological system.
Development of a Streamlined Environmental Life Cycle Costing Model for Buildings in South Korea
Authors: Roh Seungjun , Tae Sungho , Kim Rakhyun
Published: May 2018

In the building construction industry, simultaneous and integrated evaluation of a building’s environmental and economic performance in the early planning stage greatly facilitates stakeholders’ decision-making for sustainable building construction. This study aimed to develop a streamlined Environmental Life Cycle Costing (ELCC) model for buildings, applicable to the early planning stage of construction projects. To this end, we selected three of the private cost-related life cycle cost categories that are determinants of stakeholders’ decision-making in the early planning stage of construction and extracted 10 major building materials that account for over 95% of the total direct construction cost. Then, we developed a streamlined ELCC model for buildings by combining the monetary value-based life cycle analysis model, KOLID (Korean Life Cycle Impact Assessment Method Based on Damage-Oriented Modeling), and the present worth method. Finally, we conducted a case study to empirically verify the applicability of the proposed model.
An Analysis of Rural Household Livelihood Change and the Regional Effect in a Western Impoverished Mountainous Area of China
Authors: Wang Chuansheng
Published: May 2018

Taking Longnan, in the western Qinling Mountains region of Gansu province, China, as our study area, and using the Sixth National Population Census alongside household survey data, we analyze changes in household livelihoods, and consequent regional effects, following the instigation of the “Grain for Green” program in 1999. Our results show rural livelihood changes with respect to natural assets (e.g., reduction of arable land, planting structure changes), human assets (e.g., labor quality improvement, fluidity of population), financial assets (e.g., income channels widening, income increasing), physical assets (e.g., optimized production tools), and social assets (e.g., information network development, increased outreach opportunities). We suggest that increased household livelihoods play an important role in improving land space utilization efficiency, resource conservation and use, and the ecological environment. However, owing to the natural environment, there are also some problems, such as “hollows” in rural production and living spaces, as well as local environmental degradation. To address these issues, regions such as the western, mountainous, impoverished area of our study should establish a policy of using ecosystems, as well as agriculture, for development in order to improve household livelihoods, build an efficient spatial structure, and providing support for the creation of a resource-saving societal system.
Removal of Zearalenone and Zearalenols from Aqueous Solutions Using Insoluble Beta-Cyclodextrin Bead Polymer
Authors: Poór Miklós , Faisal Zelma , Zand Afshin , Bencsik Tímea , Lemli Beáta , Kunsági-Máté Sándor , Szente Lajos
Published: May 2018

Zearalenone (ZEN) is a Fusarium-derived mycotoxin, exerting xenoestrogenic effects in animals and humans. ZEN and its derivatives commonly occur in cereals and cereal-based products. During the biotransformation of ZEN, its reduced metabolites, α-zearalenol (α-ZEL) and β-zearalenol (β-ZEL), are formed; α-ZEL is even more toxic than the parent compound ZEN. Since previous studies demonstrated that ZEN and ZELs form stable complexes with β-cyclodextrins, it is reasonable to hypothesize that cyclodextrin polymers may be suitable for mycotoxin removal from aqueous solutions. In this study, the extraction of ZEN and ZELs from water, buffers, and corn beer was investigated, employing insoluble β-cyclodextrin bead polymer (BBP) as a mycotoxin-binder. Our results demonstrate that even relatively small amounts of BBP can strongly decrease the mycotoxin content of aqueous solutions (including beer). After the first application of BBP for mycotoxin binding, BBP could be completely reactivated through the elimination of ZEN from the cyclodextrin cavities by washing with a 50 v/v% ethanol-water mixture. Therefore, our study suggests that insoluble cyclodextrin polymers may be suitable tools in the future to deplete mycotoxins from contaminated drinks.