bluuebunny/crossref_metadata_2025 huggingface dataset:
Carbon Dynamics in a Human-Modified Tropical Forest: A Case Study Using Multi-Temporal LiDAR Data
Authors: Moura Yhasmin Mendes de , Balzter Heiko , Galvão Lênio S. , Dalagnol Ricardo , Espírito-Santo Fernando , Santos Erone G. , Garcia Mariano , Bispo Polyanna da Conceição , Oliveira Raimundo C. , Shimabukuro Yosio E.
Published: January 2020

Tropical forests hold significant amounts of carbon and play a critical role on Earth´s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance (e.g., selective logging, understory fires, and fragmentation). Understanding the multi-temporal dynamics of carbon stocks over human-modified tropical forests (HMTF) is crucial to close the carbon cycle balance in the tropics. Here, we used multi-temporal and high-spatial resolution airborne LiDAR data to quantify rates of carbon dynamics over a large patch of HMTF in eastern Amazon, Brazil. We described a robust approach to monitor changes in aboveground forest carbon stocks between 2012 and 2018. Our results showed that this particular HMTF lost 0.57 m·yr−1 in mean forest canopy height and 1.38 Mg·C·ha−1·yr−1 of forest carbon between 2012 and 2018. LiDAR-based estimates of Aboveground Carbon Density (ACD) showed progressive loss through the years, from 77.9 Mg·C·ha−1 in 2012 to 53.1 Mg·C·ha−1 in 2018, thus a decrease of 31.8%. Rates of carbon stock changes were negative for all time intervals analyzed, yielding average annual carbon loss rates of −1.34 Mg·C·ha−1·yr−1. This suggests that this HMTF is acting more as a source of carbon than a sink, having great negative implications for carbon emission scenarios in tropical forests. Although more studies of forest dynamics in HMTFs are necessary to reduce the current remaining uncertainties in the carbon cycle, our results highlight the persistent effects of carbon losses for the study area. HMTFs are likely to expand across the Amazon in the near future. The resultant carbon source conditions, directly associated with disturbances, may be essential when considering climate projections and carbon accounting methods.
Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau
Authors: An Shuai , Chen Xiaoqiu , Zhang Xiaoyang , Lang Weiguang , Ren Shilong , Xu Lin
Published: January 2020

Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.
A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography
Authors: Jia Wen , Pang Yong , Tortini Riccardo , Schläpfer Daniel , Li Zengyuan , Roujean Jean-Louis
Published: January 2020

Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.
A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests
Authors: Shirvani Zeinab
Published: January 2020

Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.
Temperature and Humidity Profiles Retrieval in a Plain Area from Fengyun-3D/HIRAS Sensor Using a 1D-VAR Assimilation Scheme
Authors: Zhu Liuhua , Bao Yansong , Petropoulos George P. , Zhang Peng , Lu Feng , Lu Qifeng , Wu Ying , Xu Dan
Published: January 2020

In this study, a one-dimensional variational (1D-VAR) retrieval system is proposed to simultaneously retrieve temperature and humidity atmospheric profiles under clear-sky conditions. Our technique requires observations from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) satellite combined with the Weather Research and Forecast (WRF) model. In the method, the radiative transfer for the TIROS Operational Vertical Sounder (TOVS (RTTOV) model is also used as a forward observation operator. The accuracy of our approach was evaluated using as a case study the region of Beijing in China. Predicted temperature and humidity profiles were compared against ERA-Interim data, which was used as reference. Mean bias (MB) of the temperature profiles varied between −0.8 K to 0.9 K, while the root-mean-square error (RMSE) ranged from 0.5 K to 2.6 K. In the boundary layer, the 1D-VAR algorithm performed better compared with the first guess. In the middle troposphere, the retrievals were more dependent on the first guess. With respect to relative humidity predictions, the accuracy of the evaluation of the whole troposphere was improved with the inclusion of the satellite observations, reporting an MB varying from −5.68% to 2.83%. Compared with Atmospheric Infrared Sounder’s (AIRS’) products, our predicted temperature profiles showed a very good consistency and the humidity predictions were also of an acceptable prediction accuracy. All in all, results clearly evidenced the promising potential of our proposed approach for retrieving temperature and humidity profiles under clear-sky conditions.
A Self-Powered and Low Pressure Loss Gas Flowmeter Based on Fluid-Elastic Flutter Driven Triboelectric Nanogenerator
Authors: Phan Trung Kien , Wang Song , Wang Yan , Wang He , Xiao Xiu , Pan Xinxiang , Xu Minyi , Mi Jianchun
Published: January 2020

A self-powered and low pressure loss gas flowmeter is presently proposed and developed based on a membrane’s flutter driven triboelectric nanogenerator (TENG). Such a flowmeter, herein named “TENG flowmeter”, is made of a circular pipe in which two copper electrodes are symmetrically fixed and a nonconductive, thin membrane is placed in the middle plane of the pipe. When a gas flows through the pipe at a sufficiently high speed, the membrane will continuously oscillate between the two electrodes, generating a periodically fluctuating electric voltage whose frequency can be easily measured. As demonstrated experimentally, the fluctuation frequency (fF) relates linearly with the pipe flow mean velocity (Um), i.e., fF ∝ Um; therefore, the volume flow rate Q (=Um × A) = C1fF + C2, where C1 and C2 are experimental constants and A is the pipe cross-sectional area. That is, by the TENG flowmeter, the pipe flow rate Q can be obtained by measuring the frequency fF. Notably, the TENG flowmeter has several advantages over some commercial flowmeters (e.g., vortex flowmeter), such as considerable lower pressure loss, higher sensitiveness of the measured flow rate, and self-powering. In addition, the effects of membrane material and geometry as well as flow moisture on the flowmeter are investigated. Finally, the performance of the TENG flowmeter is demonstrated.
Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography
Authors: Zhou Bo , Ghose Tandra , Lukowicz Paul
Published: January 2020

We investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the system’s ability to detect well-defined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chance-level) and 0.381 among seven full-face expressions (0.143 chance-level). A second experiment was conducted with 20 participants to induce cognitive loads with N-back tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the N-back score. From the 10 most facially expressive participants, our approach can predict whether the N-back score is above or below the average with 0.767 accuracy.
Discovering Suspicious APT Behaviors by Analyzing DNS Activities
Authors: Yan Guanghua , Li Qiang , Guo Dong , Meng Xiangyu
Published: January 2020

As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and real-time evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Pre-processing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.
Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications
Authors: Tibaduiza Burgos Diego A. , Gomez Vargas Ricardo C. , Pedraza Cesar , Agis David , Pozo Francesc
Published: January 2020

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.
An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data
Authors: Hendry Danica , Leadbetter Ryan , McKee Kristoffer , Hopper Luke , Wild Catherine , O’Sullivan Peter , Straker Leon , Campbell Amity
Published: January 2020

This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland–Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network
Authors: Emperuman Malathy , Chandrasekaran Srimathi
Published: January 2020

Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.
Real-Time Compliant Stream Processing Agents for Physical Rehabilitation
Authors: Calvaresi Davide , Calbimonte Jean-Paul
Published: January 2020

Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines.
A Novel Position and Orientation Sensor for Indoor Navigation Based on Linear CCDs
Authors: Wang Chuang , Xing Li , Tu Xiaowei
Published: January 2020

The position and orientation of a mobile agent, such as robot or drone, etc., should be estimated in a timely way during operation in the structured indoor environment, so as to ensure the security and efficiency of task execution. Concerning the problem that the position and orientation are often estimated separately by different kinds of sensors in the off-the-shelf methods, we design a novel position orientation sensor (POS). The POS consists of four pairs of linear charge-coupled devices (CCDs) and cylindrical lenses, which can estimate the 3D coordinate of the anchor in the POS’s field of view. After detecting at least three anchors in its field of vision sequentially, the Rodrigues coordinate transformation algorithm is utilized to estimate the position and orientation of POS simultaneously. Meanwhile, the position and orientation are estimated at the receiver side. Hence there is no privacy concern associated with this system. The architecture of the proposed POS is symmetrical and redundant, even if one of the linear CCDs or cylindrical lens malfunctions, the whole system could still work normally. The proposed method is cost-effective and easily extends to a wide range. The numerical simulation demonstrates the feasibility and high accuracy of the proposed method, and it outperforms the off-the-shelf methods.
Stray Flux Sensor Core Impact on the Condition Monitoring of Electrical Machines
Authors: Tian Pengfei , Platero Carlos A. , Gyftakis Konstantinos N. , Guerrero Jose Manuel
Published: January 2020

The analysis of the stray flux for electrical machine condition monitoring is a very modern and active research topic. Thanks to this technique, it is possible to detect several types of failures, including stator and rotor inter-turn faults, broken rotor bars and mechanical faults, among others. The main advantages are that it involves a non-invasive technique and low-cost monitoring equipment. The standard practice is to use coreless flux sensors, with which the stray flux of the machine is not perturbed and there are no problems due to saturation or nonlinear behavior of the iron. However, the induced voltage in the coreless coil sensor may be very low and even, in some cases, have a similar amplitude to the noise floor. This paper studies the use of iron core stray flux sensors for condition monitoring of electrical machines. The main advantage of iron core flux sensors is that the measured electromotive force is stronger. In the case of large machines in noisy environments, this can be crucial. Two different types of iron core stray flux sensors and a coreless flux sensor are tested. A comparison of the three sensors is presented. Extensive experimental testing with all sensors shows the superiority and greater sensitivity of sensors with core versus the coreless ones.
Reduction of the Multipath Propagation Effect in a Hydroacoustic Channel Using Filtration in Cepstrum
Authors: Czapiewska Agnieszka , Luksza Andrzej , Studanski Ryszard , Zak Andrzej
Published: January 2020

During data transmission in a hydroacoustic channel, one of the problems is the multipath propagation effect, which leads to a decrease in the transmission parameters and sometimes completely prevents it. Therefore, we have attempted to develop a method, which is based on a recorded hydroacoustic signal, that allows us to recreate the original (generated) signal by eliminating the multipath effect. In our method, we use cepstral analysis to eliminate replicas of the generated signal. The method has been tested in simulation and during measurements in a real environment. Additionally, the influence of the method on data transmission in the hydroacoustic channel was tested. The obtained results confirmed the usefulness of the application of the developed method and improved the quality of data transmission by reducing the multipath propagation effect.
Developing a Secure Low-Cost Radon Monitoring System
Authors: Alvarellos Alberto , Gestal Marcos , Dorado Julián , Rabuñal Juan Ramón
Published: January 2020

Radon gas has been declared a human carcinogen by the United States Environmental Protection Agency (USEPA) and the International Agency for Research on Cancer (IARC). Several studies carried out in Spain highlighted the high radon concentrations in several regions, with Galicia (northwestern Spain) being one of the regions with the highest radon concentrations. The objective of this work was to create a safe and low-cost radon monitoring and alert system, based on open source technologies. To achieve this objective, the system uses devices, a collection of sensors with a processing unit and a communication module, and a backend, responsible for managing all the information, predicting radon levels and issuing alerts using open source technologies. Security is one of the largest challenges for the internet of things, and it is utterly important in the current scenario, given that high radon concentrations pose a health risk. For this reason, this work focuses on securing the entire end-to-end communication path to avoid data forging. The results of this work indicate that the development of a low-cost, yet secured, radon monitoring system is feasible, allowing one to create a network of sensors that can help mitigate the health hazards that high radon concentrations pose.
Accurate and Practical Energy Detection over α-μ Fading Channels
Authors: Cao Kaitian , Qian Ping , An Jing , Wang Li
Published: January 2020

In this study, a novel and exact closed-form expression for detection probability of energy detection (ED) in terms of Meijer’s G-function over α-μ generalized fading channels was derived. It is more accurate and practical than the existing exact expressions and has wide application prospects in the performance evaluations in various areas of wireless communications, especially in the wireless sensor network (WSN) and the cognitive radio network (CRN). Furthermore, an exact and simple analytical solution for the sample size meeting the desired detection performance in terms of the probability mass function of a Poisson distribution was also solved. Simulations verified the detection performance and accuracy of our derived expressions with a small sample size compared to the existing exact expressions and approximations.
Green Awareness in Action—How Energy Conservation Action Forces on Environmental Knowledge, Values and Behaviour in Adolescents’ School Life
Authors: Maurer Michaela , Koulouris Pavlos , Bogner Franz X.
Published: January 2020

Affordable, reliable, sustainable and modern energy consumption is a crucial goal of the Agenda 2030. To raise each citizen’s awareness for more effective energy consumptions, proper education is necessary. The classroom project GAIA (Green Awareness in Action) was designed to change energy consumption patterns to pursue green behaviour. The class-wise aim was to improve schools’ CO2-balance and to promote environmentally sustainable behaviour without impacting school life quality. Our target group were sixth graders (N = 132, M = 11.03, SD ± 0.23, 53.4% = girls) of one Greek school. To monitor the project’s effect, a pre- and post-test design was applied to measure environmental literacy regarding environmental knowledge, attitudes/values and behaviour. A regression analysis revealed that students with poor previous knowledge reached higher learning effects compared to those with good previous knowledge. Related to the environmental knowledge types, an ANCOVA analysis revealed a knowledge gain in action-related and effectiveness knowledge. The overall learning effect correlates positively with pro-environmental preference (high scores in preservation, low scores in utilisation) and negatively with weak pro-environmental preferences. Anthropocentric (utilitarian) preferences primarily focussing on nature exploitation have considerably decreased. The project illustrates how far individual behaviour can be targeted in green educational initiatives.
Ethnic and Cultural Diversity amongst Yak Herding Communities in the Asian Highlands
Authors: Joshi Srijana , Shrestha Lily , Bisht Neha , Wu Ning , Ismail Muhammad , Dorji Tashi , Dangol Gauri , Long Ruijun
Published: January 2020

Yak (Bos grunniens L.) herding plays an important role in the domestic economy throughout much of the Asian highlands. Yak represents a major mammal species of the rangelands found across the Asian highlands from Russia and Kyrgyzstan in the west to the Hengduan Mountains of China in the east. Yak also has great cultural significance to the people of the Asian highlands and is closely interlinked to the traditions, cultures, and rituals of the herding communities. However, increasing issues like poverty, environmental degradation, and climate change have changed the traditional practices of pastoralism, isolating and fragmenting herders and the pastures they have been using for many years. Local cultures of people rooted in the practice of yak herding are disappearing. Therefore, it is very important to document the socioeconomic and cultural aspects of yak herding. The broad aim of this paper was to provide a brief overview on the geographical distribution of yak in the Asian highlands and to provide in-depth information on yak-herding ethnic communities, the sociocultural aspect associated with yak herding, and challenges and emerging opportunities for yak herding in the Asian highlands. Altogether, 31 ethnic groups in 10 different countries of Asia and their cultures are documented herein. Yak was found to be utilized for many different household purposes, and to have cultural and religious aspects. Unfortunately, yak rearing and related traditions have been losing their charm in recent years due to modernization and several other environmental issues. Lastly, we suggest that there is an urgent need to take action to minimize the challenges faced by yak-herding mountain communities to conserve the traditional pastoral system and associated cultures of these ethnic communities.
A Framework to Evaluate the Effects of Organizational Resilience on Service Quality
Authors: Annarelli Alessandro , Battistella Cinzia , Nonino Fabio
Published: January 2020

Disruptions and unexpected turbulences characterize the contemporary service industry and pose greater criticalities to organizations that have concerns about both their survival and their business sustainability. The answer to this challenge is to design an organizational system aimed at enhancing so-called organizational resilience. The aim of this work is to present and test an assessment methodology based on a framework that links key static and dynamic characteristics of firms as resilient systems and relates these characteristics with service quality preservation after disruptions. The framework adopts a set of indicators, namely resilience dimensions, to comprehend the real effects of resilience characteristics with service quality dimensions. Through the analysis of a real case, we provided evidence of how the model can be applied in a backward perspective to understand fault modes of specific events. Results show how inadequacies in terms of resilience characteristics determine losses of service quality, and also how the model can be used as a predictive tool to determine in which area companies should intervene in order to improve resilience and service quality.
Structural Change for a Post-Growth Economy: Investigating the Relationship between Embodied Energy Intensity and Labour Productivity
Authors: Hardt Lukas , Barrett John , Taylor Peter G. , Foxon Timothy J.
Published: January 2020

Post-growth economists propose structural changes towards labour-intensive services, such as care or education, to make our economy more sustainable by providing meaningful work and reducing the environmentally damaging production of material goods. Our study investigates the assumption underlying such proposals. Using a multi-regional input-output model we compare the embodied energy intensity and embodied labour productivity across economic sectors in the UK and Germany between 1995 and 2011. We identify five labour-intensive service sectors, which combine low embodied energy intensity with low growth in embodied labour productivity. However, despite their lower embodied energy intensities, our results indicate that large structural changes towards these sectors would only lead to small reductions in energy footprints. Our results also suggest that labour-intensive service sectors in the UK have been characterised by higher rates of price inflation than other sectors. This supports suggestions from the literature that labour-intensive services face challenges from increasing relative prices and costs. We do not find similar results for Germany, which is the result of low overall growth in embodied labour productivity and prices. This highlights that structural change is closely associated with economic growth, which raises the question of how structural changes can be achieved in a non-growing economy.
Using Mobile Phones at Work in Personal and Professional Information Processes
Authors: Neștian Ștefan Andrei , Tiță Silviu Mihail , Turnea Elena-Sabina
Published: January 2020

Employees use mobile phones for several information processes at work, either in relation to their work or their personal life. This study uses descriptive statistics and regression analysis in order to identify which information processes are accessed by employees to reach their personal and professional goals when using mobile phones at work, using a sample of 368 professionals from different fields in Romania. The results of the study show that messaging applications (e-mail, sms, Messenger, Whatsapp, etc.) and searches for information on the Internet are the biggest time consumers for personal purposes, while telephone conversations (voice only) and searches for useful information on the Internet are the biggest consumers of working time for professional purposes. Based on the opinions of employees, we found that the most important effect of mobile phone use at work for personal purposes lies in the fact that it helps employees be more productive through a better work–life balance. The mobile phone was originally designed as a personal device, but today, since employers are allowing or requiring its use, during work, it is also used for professional purposes.
Development of an Optimal Model for the Xiluodu-Xiangjiaba Cascade Reservoir System Considering the Downstream Environmental Flow
Authors: Dai Lingquan , Dai Huichao , Liu Haibo , Wang Yu , Guo Jiali , Cai Zhuosen , Mi Chenxi
Published: January 2020

To explore the influence of the Xiluodu-Xiangjiaba cascade reservoir system on the appropriate environmental flow (AEF) of the Jinsha River, a multiobjective optimal cascade reservoir model was established with the aim of maximizing power generation while minimizing the downstream degree of AEF alteration. The AEF was determined using the range of variability approach (RVA). The optimal model was solved using an improved version of NSGA-II called INSGA2-DS. Inflows in typical normal and dry years were selected for optimization. The results show that in a normal year, power generation can be increased by 1.28% compared with that under the current regular operation conditions by prioritizing the maximization of power generation, in which case the degree of AEF alteration will increase by 13.86%. In contrast, the degree of AEF alteration will decrease by 22.53% if ecological protection is prioritized, but power generation will decrease by 0.62%. Similarly, in a dry year, power generation can be increased by 1.76% compared with that under the current regular operation conditions to maximize economic benefit, in which case, the degree of AEF alteration will increase by 4.95%. By contrast, the degree of AEF alteration can be decreased by 13.70% if the objective is AEF minimization, but power generation will decrease by 0.48%. These research results provide useful information for the formulation of ecological operation schemes involving cascade reservoirs on the Jinsha River.
Analyzing the Employees’ New Media Use in the Energy Industry：The Role of Creative Self-Efficacy, Perceived Usefulness and Leaders’ Use
Authors: Zhang Yuting , Yu Xiaofen , Cai Ning , Li Yong
Published: January 2020

Good management of companies in the energy industry is critical to the world’s sustainable development as they are the guarantor of social lives and the cornerstone of economic development. This study tries to figure out how to improve the management of energy companies from the angle of new media use. It is widely known that the emergence of new media has provided more opportunities for the development of various entities in society, especially in improving the interaction mode of “enterprise–employee” and “enterprise–consumer”, and boosting the efficiency of information transmission. However, the technical and cutting-edge features of those new media have also resulted in some obstacles in their promotion among employees. To address this problem, this paper took the perspective of the social influence theory and the technological acceptance model (TAM), and sampled 20,161 employees from the energy industry in China to explore the effects of creative self-efficacy, perceived usefulness in information acquisition and perceived usefulness in communication on employees’ use of new media. Results have shown that both creative self-efficacy and perceived usefulness have positive effects on new media use of employees, and perceived usefulness mediates the relationship between creative self-efficacy and new media use. Leaders’ use moderates the mediation relationship, while it weakens the relationship between perceived usefulness and new media use. Our study enriches the knowledge regarding new media promotion and thereby offers insightful and practical implications for corporate managers.
Communicating Renewable Energy in the National Action Plans of the Member States of the European Union
Authors: Vincze András , Hegedűsné Baranyai Nóra , Zsiborács Henrik , Csányi Szilvia , Háber István , Pintér Gábor
Published: January 2020

For implementing renewable energy policies it is necessary to convince the population of the appropriateness and usefulness of the ideas. The information provision needed for that requires the communication to be accessible and understandable to the target audience. This research examined the national renewable energy action plans of the member states of the European Union. The online accessibility of these documents and their readability were examined using a simple accessibility test and the Flesch and Flesch–Kincaid readability tests. The paper also addresses the relationship between the culturally-determined power distance indices and the economic and developmental characteristics of the countries and the readability values of their samples. The results showed that the examined documents could be easily accessed, but the action plans of all the member states were difficult to read. The emerging pattern suggested that the text samples of the countries with higher power distances were harder to interpret. Nevertheless, no significant relationship was detected by the correlation analyses between the power distance indices and the Flesch and Flesch–Kincaid readability scores, while strong relationships were detected between the Human Development Index (HDI) data and the results of both readability tests in the group of countries with the highest power distance values.
Sustainable Strategy in Housing Renovation: Moving from a Technology-and-Engineering-Focused Model to a User-Oriented Model
Authors: Staffansson Pauli Karin , Liu Ju , Bengtsson Bo
Published: January 2020

Housing renovation, in contrast to new construction projects, has to take good care of the tenants who are already living in the building. What are the theoretical and practical implications concerning the transformation from a technology-and-engineering-focused renovation approach to a more user-oriented one? What are the mechanisms of strategy change? Based on our case we argue that the mechanisms of strategy change are based on the interplay between external disturbance and internal renewal. External disturbance is the trigger of strategy change, but it does not, in itself, necessarily lead to strategy change, and particularly not for an innovative new strategy. The internal new competence is the source of changing from an old strategy to an innovative new strategy. The real estate industry needs to undergo a transformation from the rationalistic technology- and engineering-focused renovation model (TEF model) to a more inclusive approach. We suggest a user-oriented model (UO model) where user involvement is seen as integrated in the whole process of renovation.
Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia
Authors: Pavić Gordana , Hadzima-Nyarko Marijana , Bulajić Borko , Jurković Željka
Published: January 2020

Assessing earthquake risk and building vulnerability requires an exposure model. These exposure models quantify the building stock in terms of structural characteristics, spatial location, and occupancy. The most significant exposure parameters are the structural characteristics of buildings, which must be uniformly covered by structural typologies. Structural typologies that take into account the regional specificities of design and construction provide more accurate and reliable exposure models. Despite the long history of earthquake engineering in the Republic of Croatia, the assessment of exposure and vulnerability of buildings is a rather new concept, hindered by the fact that no city in the Republic of Croatia has a database on the number, types, and characteristics of existing buildings. The article presents the creation of a building exposure model for the city of Osijek, points out the problems and concerns that the realization process brings, and details the practical solutions and strategies that have been used to achieve the set goals.
Experimental Study to Design Warm Mix Asphalts and Recycled Warm Mix Asphalts Using Natural Zeolite as Additive for Sustainable Pavements
Authors: Sanchez-Alonso Elsa , Valdes-Vidal Gonzalo , Calabi-Floody Alejandra
Published: January 2020

There are currently various technologies for the manufacture of warm mix asphalts (WMA). This paper presents the possibility of using a natural zeolite to manufacture WMA as an alternative to existing synthetic products for the manufacture of this type of mixture. Moreover, the possibility of manufacturing WMA with the addition of recycled asphalt pavement (RAP) using natural zeolite as the basis of a warm mix technology was evaluated. Firstly, asphalt mixtures were manufactured at three different temperatures (145 °C, 135 °C, and 125 °C) with different percentages of natural zeolite to determine the temperature and the optimum content for the manufacture of WMA. Then, the zeolite moisture content and its release over time were determined at different temperatures, and its distribution in the binder was checked at different concentrations by scanning electron microscopy and fluorescence. Next, with the optimum zeolite content, the addition of RAP between 10–30% in the WMA at the same three manufacturing temperatures was evaluated. Two types of compaction were used: the impact and gyratory compactions. The Marshall parameters were evaluated for all the designed mixtures. The results indicated that the manufacture of WMA with the addition of natural zeolite is feasible, and depending on the required mixing temperature, recycled WMA with different percentages of RAP can be obtained.