bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast Complex-Valued CNN for Radar Jamming Signal Recognition
Authors: Zhang Haoyu , Yu Lei , Chen Yushi , Wei Yinsheng
Published: July 2021

Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.
A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth
Authors: Hemati MohammadAli , Hasanlou Mahdi , Mahdianpari Masoud , Mohammadimanesh Fariba
Published: July 2021

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.
Preface: Earth Observations for Environmental Sustainability for the Next Decade
Authors: Liou Yuei-An , Kuleshov Yuriy , Ho Chung-Ru , Nguyen Kim-Anh , Reising Steven C.
Published: July 2021

Evidence of the rapid degradation of the Earth’s natural environment has grown in recent years [...]
Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction
Authors: Zhang Lixian , Dong Runmin , Yuan Shuai , Li Weijia , Zheng Juepeng , Fu Haohuan
Published: July 2021

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g., 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms eight other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20%, and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction.
Effects of Assimilating Clear-Sky FY-3D MWHS2 Radiance on the Numerical Simulation of Tropical Storm Ampil
Authors: Xu Dongmei , Shu Aiqing , Li Hong , Shen Feifei , Li Qiang , Su Hang
Published: July 2021

A new advanced microwave humidity sounder FY-3D MWHS2 radiance has been assimilated under the clear-sky conditions by implementing its data assimilation interface. The case of the tropical storm Ampil in 2018 is selected to address the effectiveness of the new-built module in the initialization and forecast of typhoons. Apart from the experiment assimilating both the Global Telecommunications System (GTS) data and the FY-3D MWHS2 radiance data, an experiment with only GTS data is also conducted for comparison. The results show that the bias correction of this humidity sounder is effective, and the analysis field after assimilating its radiance data matches well with the observation. The increment of specific humidity below the middle layers is evident after the assimilation of the radiance data. Besides, the geopotential height increment and the specific humidity increment at 500 hPa and 850 hPa, respectively, are favorable, resulting in more accurate rain belt distribution and a higher fraction skill score (FSS). In the deterministic forecast, the track error of the FY-3D MWHS2 experiment is consistently less than 90 km.
Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets
Authors: Tao Yu , Xiong Siting , Conway Susan J. , Muller Jan-Peter , Guimpier Anthony , Fawdon Peter , Thomas Nicolas , Cremonese Gabriele
Published: July 2021

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.
Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran
Authors: Andalibi Lida , Ghorbani Ardavan , Moameri Mehdi , Hazbavi Zeinab , Nothdurft Arne , Jafari Reza , Dadjou Farid
Published: July 2021

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.
Using Ground Penetrating Radar and Resistivity Methods to Locate Unmarked Graves: A Review
Authors: Berezowski Victoria , Mallett Xanthé , Ellis Justin , Moffat Ian
Published: July 2021

The location of unmarked graves in forensic and archaeological investigations is legally and culturally important. In a forensic context, locating covert burials of missing persons can provide closure to the family, as well as facilitating the successful prosecution of the individual(s) responsible. Archaeologically, burials provide an important source of information about health, diet, physical anthropology, and culture. Despite the importance of these features, the location of unmarked graves with conventional archaeological and forensic techniques, such as excavation, is difficult and expensive. As a result, geophysical techniques have been widely applied to the location of unmarked graves as they are non-invasive, cost and time effective, and avoid the unnecessary disturbance of human remains. This article brings together the literature on ground penetrating radar (GPR), and two resistivity methods, electrical resistivity tomography (ERT) and fixed probe resistivity (FPR), on their ability to locate burials and reviews their use in forensic and archaeological investigations. This paper aims to provide law enforcement personnel, archaeologists, geophysicists, and interested academics with an overview of how these techniques work, how they have been previously applied to grave detection, and the strengths and weakness of these methods.
Land Use and Degradation in a Desert Margin: The Northern Negev
Authors: Prince Stephen , Safriel Uriel
Published: July 2021

Degradation in a range of land uses was examined across the transition from the arid to the semi-arid zone in the northern Negev desert, representative of developments in land use taking place throughout the West Asia and North Africa region. Primary production was used as an index of an important aspect of dryland degradation. It was derived from data provided by Landsat measurements at 0.1 ha resolution over a 2500 km2 study region—the first assessment of the degradation of a large area of a desert margin at a resolution suitable for interpretation in terms of human activities. The Local NPP Scaling (LNS) method enabled comparisons between the observed NPP and the potential, nondegraded, reference NPP. The potential was calculated by normalizing the actual NPP to remove the effects of environmental conditions that are not related to anthropogenic degradation. Of the entire study area, about 50% was found to have a significantly lower production than its potential. The degree of degradation ranged from small in pasture, around informal settlements, minimally managed dryland cropping, and a pine plantation, to high in commercial cropping and extreme in low-density afforestation. This result was unexpected as degradation in drylands is often attributed to pastoralism, and afforestation is said to offer remediation and prevention of further damage.
Hyperanalytic Wavelet-Based Robust Edge Detection
Authors: Isar Alexandru , Nafornita Corina , Magu Georgiana
Published: July 2021

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.
Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods
Authors: Li Tengteng , Zhang Hongzhen , Fan Hongdong , Zheng Chunliu , Liu Jiuli
Published: July 2021

The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%.
Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7
Authors: Adame Toni , Carrascosa-Zamacois Marc , Bellalta Boris
Published: July 2021

A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.
Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing
Authors: Bellavista Paolo , Esposito Christian , Foschini Luca , Giannelli Carlo , Mazzocca Nicola , Montanari Rebecca
Published: July 2021

Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization; thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead.
Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease
Authors: Bouça-Machado Raquel , Pona-Ferreira Filipa , Leitão Mariana , Clemente Ana , Vila-Viçosa Diogo , Kauppila Linda Azevedo , Costa Rui M. , Matias Ricardo , Ferreira Joaquim J.
Published: July 2021

Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions.
Supervised Learning of Neural Networks for Active Queue Management in the Internet
Authors: Szyguła Jakub , Domański Adam , Domańska Joanna , Marek Dariusz , Filus Katarzyna , Mendla Szymon
Published: July 2021

The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.
Development of Gas Sensor Array for Methane Reforming Process Monitoring
Authors: Dobrzyniewski Dominik , Szulczyński Bartosz , Dymerski Tomasz , Gębicki Jacek
Published: July 2021

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6–1.5, Outlet Molar Ratio (OMR) in the range 0.6–1.0, and Methane Conversion Level (MCL) in the range 80–95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6–1.5. However, in the case of the process range (0.9–1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.
Low-Light Image Enhancement Based on Multi-Path Interaction
Authors: Zhao Bai , Gong Xiaolin , Wang Jian , Zhao Lingchao
Published: July 2021

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding–decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.
Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device”
Authors: Lakoju Mike , Ajienka Nemitari , Khanesar M. Ahmadieh , Burnap Pete , Branson David T.
Published: July 2021

To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and manufacturing processes, aligned to the Industry 4.0 paradigm. In this paper, we propose a model that enables new forms of agile engineering product development via “chatty” products. Products relay their “experiences” from the consumer world back to designers and product engineers through the mediation provided by embedded sensors, IoT, and data-driven design tools. Our model aims to identify product “experiences” to support the insights into product use. To this end, we create an experiment to: (i) collect sensor data at 100 Hz sampling rate from a “Chatty device” (device with sensors) for six common everyday activities that drive produce experience: standing, walking, sitting, dropping and picking up of the device, placing the device stationary on a side table, and a vibrating surface; (ii) pre-process and manually label the product use activity data; (iii) compare a total of four Unsupervised Machine Learning models (three classic and the fuzzy C-means algorithm) for product use activity recognition for each unique sensor; and (iv) present and discuss our findings. The empirical results demonstrate the feasibility of applying unsupervised machine learning algorithms for clustering product use activity. The highest obtained F-measure is 0.87, and MCC of 0.84, when the Fuzzy C-means algorithm is applied for clustering, outperforming the other three algorithms applied.
Polymer Ring Resonator with a Partially Tapered Waveguide for Biomedical Sensing: Computational Study
Authors: Sahraeibelverdi Tayebeh , Guo L. Jay , Veladi Hadi , Malekshahi Mazdak Rad
Published: July 2021

Ring resonators are well-known optical biosensors thanks to their relatively high Q-factor and sensitivity, in addition to their potential to be fabricated in large arrays with a small footprint. Here, we investigated the characteristics of a polymer ring resonator with a partially tapered waveguide for Biomedical Sensing. The goal is to develop a more sensitive biosensor with an improved figure of merit. The concept is more significant field interaction with the sample under test in tapered segments. Waveguide width is hereby gradually reduced to half. Sensitivity improves from 84.6 to 101.74 [nm/RIU] in a relatively small Q-factor reduction from 4.60 × 103 for a strip waveguide to 4.36 × 103 for a π/4 partially tapered one. After the study, the number of tapered parts from zero to fifteen, the obtained figure of merit improves from 497 for a strip ring to 565 for a π/4 tapered ring close to six tapered ones. Considering the fabrication process, the three-tapered one is suggested. The all-polymer material device provides advantages of a low-cost, disposable biosensor with roll-to-roll fabrication compatibility. This design can also be applied on silicon on isolator, or polymer on silicon-based devices, thereby taking advantage of a higher Q-factor and greater sensitivity.
Digital Overload among College Students: Implications for Mental Health App Use
Authors: Smith Arielle C. , Fowler Lauren A. , Graham Andrea K. , Jaworski Beth K. , Firebaugh Marie-Laure , Monterubio Grace E. , Vázquez Melissa M. , DePietro Bianca , Sadeh-Sharvit Shiri , Balantekin Katherine N. , Topooco Naira , Wilfley Denise E. , Taylor C. Barr , Fitzsimmons-Craft Ellen E.
Published: July 2021

Mental health phone applications (apps) provide cost-effective, easily accessible support for college students, yet long-term engagement is often low. Digital overload, defined as information burden from technological devices, may contribute to disengagement from mental health apps. This study aimed to explore the influence of digital overload and phone use preferences on mental health app use among college students, with the goal of informing how notifications could be designed to improve engagement in mental health apps for this population. A semi-structured interview guide was developed to collect quantitative data on phone use and notifications as well as qualitative data on digital overload and preferences for notifications and phone use. Interview transcripts from 12 college students were analyzed using thematic analysis. Participants had high daily phone use and received large quantities of notifications. They employed organization and management strategies to filter information and mitigate the negative effects of digital overload. Digital overload was not cited as a primary barrier to mental health app engagement, but participants ignored notifications for other reasons. Findings suggest that adding notifications to mental health apps may not substantially improve engagement unless additional factors are considered, such as users’ motivation and preferences.
A Review of Monitoring Technologies for Solar PV Systems Using Data Processing Modules and Transmission Protocols: Progress, Challenges and Prospects
Authors: Ansari Shaheer , Ayob Afida , Lipu Molla S. Hossain , Saad Mohamad Hanif Md , Hussain Aini
Published: July 2021

Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications.
The Tragedy of the Commons as a Prisoner’s Dilemma. Its Relevance for Sustainability Games
Authors: Carrozzo Magli Alessio , Della Posta Pompeo , Manfredi Piero
Published: July 2021

In the current battle for sustainability and climate, understanding the nature of sustainability games is of paramount importance, especially to inform appropriate policy actions to contrast the harmful effects of global climate change. Relatedly, there is no consensus in the literature on the proper game-theoretic representation of the so-called Tragedy of the Commons. A number of contributions have questioned the prisoner’s dilemma as an appropriate framework. In this work, we provide a representation that reconciles these two positions, confirming the ultimate nature of the Tragedy as a prisoner’s dilemma, rather than a coordination issue, and discuss the ensuing implications for sustainability policy interventions.
A Method to Include Life Cycle Assessment Results in Choosing by Advantage (CBA) Multicriteria Decision Analysis. A Case Study for Seismic Retrofit in Peruvian Primary Schools
Authors: Vázquez-Rowe Ian , Córdova-Arias Cristina , Brioso Xavier , Santa-Cruz Sandra
Published: July 2021

Building information modeling (BIM) is an emerging technology that improves visualization, understanding, and transparency in construction projects. Its use in Latin America and the Caribbean (LA&C), while still scarce, is developing in combination with multi-criteria decision-making (MCDM) methods, such as the choosing by advantages (CBA) method. Despite the holistic nature of MCDM methods, the inclusion of life cycle environmental metrics is lagging in construction projects in LA&C. However, recent studies point toward the need to optimize the synergies between BIM and life cycle assessment (LCA), in which a method like CBA could allow improving the quality of the decisions. Therefore, the main objective of this study is to integrate LCA and CBA methods to identify the effect that the inclusion of environmental impacts can have on decision-making in public procurement, as well as comparing how this final decision differs from an exclusively LCA-oriented interpretation of the results. Once the LCA was performed, a set of additional criteria for the CBA method were fixed, including transparency, technical, and social indicators. Thereafter, a stakeholder participative workshop was held in order to gather experts to elucidate on the final decision. The methodology was applied to a relevant construction sector problem modelled with BIM in the city of Lima (Peru), which consisted of three different construction techniques needed to retrofit educational institutions. Results from the LCA-oriented assessment, which was supported by Monte Carlo simulation, revealed a situation in which the masonry-based technique showed significantly lower environmental impacts than the remaining two options. However, when a wider range of technical, social, and transparency criteria are added to the environmental indicators, this low-carbon technique only prevailed in those workshop tables in which environmental experts were present and under specific computational assumptions, whereas teams with a higher proportion of government members were inclined to foster alternatives that imply less bureaucratic barriers. Finally, the results constitute an important milestone when it comes to including environmental factors in public procurement in LA&C.
Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism
Authors: Rehman Khan Haseeb Ur , Lim Chen Kim , Ahmed Minhaz Farid , Tan Kian Lam , Bin Mokhtar Mazlin
Published: July 2021

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.
Potential of Biochar Derived from Agricultural Residues for Sustainable Management
Authors: Khawkomol Sasiwimol , Neamchan Rattikan , Thongsamer Thunchanok , Vinitnantharat Soydoa , Panpradit Boonma , Sohsalam Prapa , Werner David , Mrozik Wojciech
Published: July 2021

A horizontal drum kiln is a traditional method widely used in Southeast Asian countries for producing biochar. An understanding of temperature conditions in the kiln and its influence on biochar properties is crucial for identifying suitable biochar applications. In this study, four agricultural residues (corncob, coconut husk, coconut shell, and rice straw) were used for drum kiln biochar production. The agricultural residues were turned into biochar within 100–200 min, depending on their structures. The suitability of biochar for briquette fuels was analyzed using proximate, ultimate, and elemental analysis. The biochar’s physical and chemical properties were characterized via bulk density, iodine number, pHpzc, SEM, and FTIR measurements. All biochars had low O/C and H/C ratios and negative charge from both carbonyl and hydroxyl groups. Coconut husk and shell biochar had desirable properties such as high heating value and a high amount of surface functional groups which can interact with nutrients in soil. These biochars are thus suitable for use for a variety of purposes including as biofuels, adsorbents, and as soil amendments.
Prioritization and Analysis of Watershed: A Study Applied to Municipal Solid Waste
Authors: Araiza-Aguilar Juan Antonio , Rojas-Valencia María Neftalí , Nájera-Aguilar Hugo Alejandro , Gutiérrez-Hernández Rubén Fernando , Martínez-Salinas Rebeca Isabel , García-Lara Carlos Manuel
Published: July 2021

This paper shows a watershed prioritization analysis applied to municipal solid waste. The study area was the macrowatershed “Cañón del Sumidero”, in the state of Chiapas, Mexico. Geographic information systems, multi-criteria evaluation techniques, as well as several geomorphometric, land use, vegetation and waste management variables were used. The results indicate that, of the set of watersheds analyzed (4 subwatersheds and 80 microwatersheds), only 14 (2 subwatersheds and 12 microwatersheds) have high priority, since they are severely affected by the mismanagement of solid waste. This is also due to the major presence of urban settlements, which are places with different dynamics in terms of population growth, migration, as well as access to infrastructure and services, such as collection and final disposal of waste. Additionally, the incidence of certain biophysical and geomorphometric variables, such as steep slopes, high rainfall and high drainage density, among others, exacerbate the waste-related problems. The remaining watersheds (2 subwatersheds and 68 microwatersheds) showed moderate or low prioritization values because of the low amount of solid waste produced there. Finally, this work concludes that the regionalization of municipalities and the management of solid waste through decentralized operating agencies can help solve solid waste management problems since this approach would permit to delegate non-primary activities from watershed operating agencies to other specialized waste agencies.
Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services
Authors: Liu Dunnan , Zhang Tingting , Wang Weiye , Peng Xiaofeng , Liu Mingguang , Jia Heping , Su Shu
Published: July 2021

A large number of renewable energy and EVs (electric vehicles) are connected to the grid, which brings huge peak shaving pressure to the power system. If we can make use of the flexible characteristics of EVs and effectively aggregate the adjustable resources of EVs to participate in power auxiliary services, this situation can be alleviated to a certain extent. In this paper, a two-stage physical and economic adjustable capacity evaluation model of EVs for peak shaving and valley filling ancillary services is constructed. The main steps are as follows: with the help of the deep learning ability of the AC (Actor-Critic) algorithm, the optimal physical charging scheme of EV fleet is determined to minimize the grid fluctuation under the travel constraints of private EVs, and the optimized charging power is transferred to the second stage. In the second stage, load aggregators encourage users to participate in ancillary services by setting subsidy prices. In this stage, the model constructs a user decision model based on a logistic function to describe the probability of users accepting dispatching instructions. With the goal of maximizing the revenue of load aggregators, the wolf colony algorithm is used to solve the optimal solution of the time-sharing subsidy level, and finally the economic adjustable capacity of the EV fleet considering the subjective decision of users is obtained.
An Empirical Study on Dynamic Evolution of Industrial Structure and Green Economic Growth—Based on Data from China’s Underdeveloped Areas
Authors: Liang Gefu , Yu Dajia , Ke Lifei
Published: July 2021

From the experiences of developed countries or areas, advanced industrial structure is an effective way to promote economic transformation and high-quality growth. This paper uses the economic development data of seven underdeveloped provinces in China in 10 years to study the relationship between industrial structure upgrading, industrial structure rationalization and green economic growth. The result shows: (1) The relationship between the upgrading of industrial structure and green total factor productivity (GTFP) is a non-linear relationship that is difficult to fit. (2) There are two turning points in the relationship curve between industrial structure upgrading and green total factor productivity (these can be called “rationalization points”). (3) The “rationalization points” are affected by the rationalization of the industrial structure. (4) The “rationalization point” divides the relationship curve into three intervals. Within the threshold range [0.661, 0.673] of the rationalization of the industrial structure, the upgrading of the industrial structure promotes the increase of green total factor productivity, while outside the range, the upgrading of the industrial structure inhibits the increase of green total factor productivity. Therefore, industrial development in underdeveloped areas should first implement rationalization of industrial structure. After the rational adjustment of the industrial structure, we will then develop a high-level industrial structure to improve the green TFP.
Experimental Investigation of a Pilot-Scale Concerning Ex-Situ Bioremediation of Petroleum Hydrocarbons Contaminated Soils
Authors: Micle Valer , Sur Ioana Monica
Published: July 2021

The soil samples were taken from the site of a former oil products depot from an industrial area (Romania). The soil samples taken were analyzed from a physical and chemical point of view: texture, pH, soil micronutrient content, metals concentration and petroleum hydrocarbon concentration (PHCs). The soil contaminated with total petroleum hydrocarbon (TPH (4280 mg kg−1) was disposed in the form of a pile (L × W × H: 3000 × 1400 × 500 mm). Experiments on a pilot-scale were conducted over 12 weeks at constant pH (7.5–8), temperature (22–32 °C), nutrient contents C/N/P ratio 100/10/1, soil aeration time (8 h/day) and moisture (30%). Samples were taken every two weeks for the monitoring of the TPH and the microorganisms content. During the experiment, microorganisms were added (Pseudomonas and Bacillus) every two weeks. Results of the analyses regarding the concentration of PHCs were revealed a linear decrease of the concentration of PHCs after only two weeks of treatment. This decrease in concentration was also achieved in the following weeks. Following the analysis performed on the model at the pilot scale regarding the depollution process, it can be concluded that a soil contaminated with petroleum hydrocarbons can be efficiently depolluted by performing an aeration of 8 h/day, adding microorganisms Pseudomonas and Bacillus to ensure the conditions for increasing in the total number of germs (colony forming units–CFU) from 151 × 105 to 213 × 107 CFU g−1 soil, after 12 weeks of soil treatment—the depollution efficiency achieved is 83%.