bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature
Authors: Bastos Leonardo M. , Froes de Borja Reis Andre , Sharda Ajay , Wright Yancy , Ciampitti Ignacio A.
Published: December 2021

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer.
Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China
Authors: Yang Lixiao , Horion Stéphanie , He Chansheng , Fensholt Rasmus
Published: December 2021

Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000–2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and ecological-restoration-related policy making.
Monitoring the Characteristics of Ecological Cumulative Effect Due to Mining Disturbance Utilizing Remote Sensing
Authors: Li Quansheng , Guo Junting , Wang Fei , Song Ziheng
Published: December 2021

This study conducted land cover classification and inversion analysis to estimate land surface temperature, soil moisture, specific humidity, atmospheric water vapor density, and relative humidity using remote sensing and multi-source mining data. Using 1990–2020 data from the Shendong mining area in Inner Mongolia, China, the eco-environmental evolution and the ecological cumulative effects (ECE) of mining operations were characterized and analyzed at a long-term scale. The results show that while the eco-environment was generally stable, mining activities affected the eco-environment at the initial stage (1990–2000) to a certain degree. During the rapid development stage of coal mining, the eco-environment was severely damaged, and the ECE were significant at the temporal scale. The absolute value of the change rate of ecological parameters was increasing. Due to an increased focus on ecological restoration, starting in 2010, the environmental indicators gradually stabilized and the eco-environment improved considerably, ushering in a period of stability for coal mining activities. The absolute value of the change rate of ecological parameters became stable. Analysis of the change in eco-environmental indicators with distance and comparison to the contrast area showed the ECE characteristics from mining disturbance at the spatial scale. This study shows that remote sensing technology can be used to characterize the ECE from mining operations and analyze eco-environmental indicators, providing crucial information in support of ecological protection and restoration, particularly in coal mining areas.
AFGL-Net: Attentive Fusion of Global and Local Deep Features for Building Façades Parsing
Authors: Chen Dong , Xiang Guiqiu , Peethambaran Jiju , Zhang Liqiang , Li Jing , Hu Fan
Published: December 2021

In this paper, we propose a deep learning framework, namely AFGL-Net to achieve building façade parsing, i.e., obtaining the semantics of small components of building façade, such as windows and doors. To this end, we present an autoencoder embedding position and direction encoding for local feature encoding. The autoencoder enhances the local feature aggregation and augments the representation of skeleton features of windows and doors. We also integrate the Transformer into AFGL-Net to infer the geometric shapes and structural arrangements of façade components and capture the global contextual features. These global features can help recognize inapparent windows/doors from the façade points corrupted with noise, outliers, occlusions, and irregularities. The attention-based feature fusion mechanism is finally employed to obtain more informative features by simultaneously considering local geometric details and the global contexts. The proposed AFGL-Net is comprehensively evaluated on Dublin and RueMonge2014 benchmarks, achieving 67.02% and 59.80% mIoU, respectively. We also demonstrate the superiority of the proposed AFGL-Net by comparing with the state-of-the-art methods and various ablation studies.
Evaluation of Ecological Stability in Semi-Arid Open-Pit Coal Mining Area Based on Structure and Function Coupling during 2002–2017
Authors: Li Xinhui , Lei Shaogang , Liu Ying , Chen Hang , Zhao Yibo , Gong Chuangang , Bian Zhengfu , Lu Xiaoguang
Published: December 2021

Open-pit coal mining plays an important role in supporting national economic development; however, it has caused ecological problems and even seriously impacted regional ecological stability. Given the importance of maintaining ecological stability in semi-arid coal mining areas, this study used a coupling coordination degree approach based on the structural and functional state transition model (SFSTM) to evaluate the spatio–temporal variation of ecological stability from 2002 to 2017 by using MODIS and Landsat datasets in the semi-arid open-pit coal mining area. Besides, random points were created for different ecological stability levels (containing natural areas, coal mining areas, and reclamation areas) and segment linear regression was conducted to determine the structural change threshold for negative state transitions based on mining and positive state transitions based on reclamation. Furthermore, the impact factors of ecological stability were analyzed. Results showed that ecological stability fluctuated significantly over 16 years, showing a trend of first increasing and then decreasing. It was found that precipitation and temperature were the key natural factors affecting ecological stability, and mining activities constituted the dominant factor. The average perturbation distances to ecological stability from mining activities in the west, southwest, and east mining groups were 7500, 5500, and 8000 m, respectively. SFSTM is appliable to the coal mining ecosystem. Quantitative models of ecological stability response can help resolve ambiguity about management efficacy and the ecological stability results facilitate iterative updating of knowledge by using monitoring data from coal mining areas. Moreover, the proposed ecological structural threshold provides a useful early warning tool, which can aid in the reduction of ecosystem uncertainty and avoid reverse transformations of the positive state in the coal mining areas.
MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data
Authors: Jamaluddin Ilham , Thaipisutikul Tipajin , Chen Ying-Nong , Chuang Chi-Hung , Hu Chih-Lin
Published: December 2021

Mangroves are grown in intertidal zones along tropical and subtropical climate areas, which have many benefits for humans and ecosystems. The knowledge of mangrove conditions is essential to know the statuses of mangroves. Recently, satellite imagery has been widely used to generate mangrove and degradation mapping. Sentinel-2 is a volume of free satellite image data that has a temporal resolution of 5 days. When Hurricane Irma hit the southwest Florida coastal zone in 2017, it caused mangrove degradation. The relationship of satellite images between pre and post-hurricane events can provide a deeper understanding of the degraded mangrove areas that were affected by Hurricane Irma. This study proposed an MDPrePost-Net that considers images before and after hurricanes to classify non-mangrove, intact/healthy mangroves, and degraded mangroves classes affected by Hurricane Irma in southwest Florida using Sentinel-2 data. MDPrePost-Net is an end-to-end fully convolutional network (FCN) that consists of two main sub-models. The first sub-model is a pre-post deep feature extractor used to extract the spatial–spectral–temporal relationship between the pre, post, and mangrove conditions after the hurricane from the satellite images and the second sub-model is an FCN classifier as the classification part from extracted spatial–spectral–temporal deep features. Experimental results show that the accuracy and Intersection over Union (IoU) score by the proposed MDPrePost-Net for degraded mangrove are 98.25% and 96.82%, respectively. Based on the experimental results, MDPrePost-Net outperforms the state-of-the-art FCN models (e.g., U-Net, LinkNet, FPN, and FC-DenseNet) in terms of accuracy metrics. In addition, this study found that 26.64% (41,008.66 Ha) of the mangrove area was degraded due to Hurricane Irma along the southwest Florida coastal zone and the other 73.36% (112,924.70 Ha) mangrove area remained intact.
Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments
Authors: Pu Ruiliang , Bonafoni Stefania
Published: December 2021

The literature review indicates that a scaling effect does exist in downscaling land surface temperature (DLST) processes, and no substantial methods were specially developed for addressing it. In this research, the main aim is to develop a new method to reduce the scaling effect on DLST maps at high resolutions. A thermal component-based thermal spectral unmixing (TSU) model was modified and a multiple regression (REG) model was adopted to create DLST maps at high resolutions. A combined variance of red and NIR bands at a very high resolution with a difference image between upscaled LST and DLST was used to develop a new method. With two case data sets, LSTs at coarse resolutions were downscaled by using the modified TSU model and the REG model to create DLST results. The new method with a correction term expression (a linear model created by using a semi-empirical approach) was used to improve the DLST maps in the two case study areas. The experimental results indicate that the new method could reduce the root mean square error and the mean absolute error >30% and >33%, respectively, and thus demonstrate that the proposed method was effective and significant, especially reducing the scaling effect on DLST results at very high resolutions. The novel significance for the new method is directly reducing the scaling effect on DLST maps at high resolutions.
Comparison of Remote Sensing Techniques for Geostructural Analysis and Cliff Monitoring in Coastal Areas of High Tourist Attraction: The Case Study of Polignano a Mare (Southern Italy)
Authors: Loiotine Lidia , Andriani Gioacchino Francesco , Jaboyedoff Michel , Parise Mario , Derron Marc-Henri
Published: December 2021

Rock slope failures in urban areas may represent a serious hazard for human life, as well as private and public property, even on the occasion of sporadic episodes. Prevention and mitigation measures indispensably require a proper rock mass characterization, which is often achieved by means of time-consuming, costly and dangerous field surveys. In the last decades, remote sensing devices such as high-resolution digital cameras, laser scanners and drones have been widely used as supplementary techniques for rock slope analysis and monitoring, especially in poorly accessible areas, or in sites of large extension. Although several methods for rock mass characterization by means of remote sensing techniques have been reported in specific studies, there are very few contributions that focused on comparing the different methods in an attempt to establish their advantages and limitations. With this study, we performed digital photogrammetry, Terrestrial Laser Scanning and Unmanned Aerial Vehicle surveys on a cliff located in a popular tourist attraction site, characterized by complex geological and geomorphological settings, as well as by disturbance elements such as vegetation and human activities. For each point cloud, we applied geostructural analysis by means of semi-automatic methods, and then compared multi-temporal acquisitions for cliff monitoring. By quantitative comparison of the results and validation by means of conventional geostructural field surveys, the pros and cons of each method were outlined in attempt to depict the conditions and goals the different techniques seem to be more suitable for.
Spatial and Temporal Analyses of Vegetation Changes at Multiple Time Scales in the Qilian Mountains
Authors: Zhang Lifeng , Yan Haowen , Qiu Lisha , Cao Shengpeng , He Yi , Pang Guojin
Published: December 2021

The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10−3, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.
Reproduction of the Marine Debris Distribution in the Seto Inland Sea Immediately after the July 2018 Heavy Rains in Western Japan Using Multidate Landsat-8 Data
Authors: Song Shilin , Sakuno Yuji , Taniguchi Naokazu , Iwashita Hidetsugu
Published: December 2021

Understanding the spatiotemporal environment of the ocean after a heavy rain disaster is critical for satellite remote sensing research and disaster prevention. We attempted to reproduce changes in marine debris distributions using multidate data of Landsat-8 spectral reflectance acquired immediately after a heavy rain disaster in western Japan in July 2018. Data from cleaning ships were used for screening the marine debris area. As most of the target marine debris consisted of plant fragments, a method based on the corrected floating algae index (cFAI) was applied to Landsat-8 data. Data from cleaning ships clarify that most of the marine debris accumulated in the waters in the northern part of Aki Nada, a part of the Seto Inland Sea. The spectral characteristics of the corresponding marine debris spectral reflectance obtained from the Landsat-8 data were explained by the FAI with band 5 (central wavelength: 865 nm) as the maximum value. Unlike traditional FAI, cFAI eliminated the effect of background water turbidity. The Otsu method was effective for the automatic threshold determination for cFAI. Although Landsat-8 data have limited spatial resolution and observation frequency, these data were useful for understanding marine debris distribution after a heavy rain disaster.
An Energy Efficient UAV-Based Edge Computing System with Reliability Guarantee for Mobile Ground Nodes
Authors: Kim Seung-Yeon , Kim Yi-Kang
Published: December 2021

An edge computing system is a distributed computing framework that provides execution resources such as computation and storage for applications involving networking close to the end nodes. An unmanned aerial vehicle (UAV)-aided edge computing system can provide a flexible configuration for mobile ground nodes (MGN). However, edge computing systems still require higher guaranteed reliability for computational task completion and more efficient energy management before their widespread usage. To solve these problems, we propose an energy efficient UAV-based edge computing system with energy harvesting capability. In this system, the MGN makes requests for computing service from multiple UAVs, and geographically proximate UAVs determine whether or not to conduct the data processing in a distributed manner. To minimize the energy consumption of UAVs while maintaining a guaranteed level of reliability for task completion, we propose a stochastic game model with constraints for our proposed system. We apply a best response algorithm to obtain a multi-policy constrained Nash equilibrium. The results show that our system can achieve an improved life cycle compared to the individual computing scheme while maintaining a sufficient successful complete computation probability.
Energy Management Strategy Based on a Novel Speed Prediction Method
Authors: Xing Jiaming , Chu Liang , Hou Zhuoran , Sun Wen , Zhang Yuanjian
Published: December 2021

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.
A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users
Authors: Fu Jicheng , Zhang Shuai , Wang Hongwu , Zhao Yan Daniel , Qian Gang
Published: December 2021

This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices.
The Celestial Frame and the Weighting of the Celestial Pole Offsets in the Computation of VLBI-Based Corrections for the Main Lunisolar Nutation Terms
Authors: Puente Víctor , Folgueira Marta
Published: December 2021

Very long baseline interferometry (VLBI) is the only technique in space geodesy that can determine directly the celestial pole offsets (CPO). In this paper, we make use of the CPO derived from global VLBI solutions to estimate empirical corrections to the main lunisolar nutation terms included in the IAU 2006/2000A precession–nutation model. In particular, we pay attention to two factors that affect the estimation of such corrections: the celestial reference frame used in the production of the global VLBI solutions and the stochastic model employed in the least-squares adjustment of the corrections. In both cases, we have found that the choice of these aspects has an effect of a few μas in the estimated corrections.
Soft Self-Healing Fluidic Tactile Sensors with Damage Detection and Localization Abilities
Authors: George Thuruthel Thomas , Bosman Anton W. , Hughes Josie , Iida Fumiya
Published: December 2021

Self-healing sensors have the potential to increase the lifespan of existing sensing technologies, especially in soft robotic and wearable applications. Furthermore, they could bestow additional functionality to the sensing system because of their self-healing ability. This paper presents the design for a self-healing sensor that can be used for damage detection and localization in a continuous manner. The soft sensor can recover full functionality almost instantaneously at room temperature, making the healing process fully autonomous. The working principle of the sensor is based on the measurement of air pressure inside enclosed chambers, making the fabrication and the modeling of the sensors easy. We characterize the force sensing abilities of the proposed sensor and perform damage detection and localization over a one-dimensional and two-dimensional surface using multilateration techniques. The proposed solution is highly scalable, easy-to-build, cheap and even applicable for multi-damage detection.
Design of Acoustic Bifocal Lenses Using a Fourier-Based Algorithm
Authors: Fuster José Miguel , Pérez-López Sergio , Candelas Pilar
Published: December 2021

In this work, we develop a new design method based on fast Fourier transform (FFT) for implementing zone plates (ZPs) with bifocal focusing profiles. We show that the FFT of the governing binary sequence provides a discrete sequence of the same length, which indicates the location of the main foci at the ZP focusing profile. Then, using reverse engineering and establishing a target focusing profile, we are capable of generating a binary sequence that provides a ZP with the desired focusing profile. We show that this design method, based on the inverse fast Fourier transform (IFFT), is very flexible and powerful and allows to tailor the design of bifocal ZPs to achieve focusing profiles with the desired foci locations and resolutions. The key advantage of our design algorithm, compared to other alternatives presented in previous works, is that our method provides bifocal focusing profiles with an absolute control of the foci locations. Moreover, although we analyze the performance of this novel design algorithm for underwater ultrasonics, it can also be successfully extended to different fields of physics, such as optics or microwaves, where ZPs are widely employed.
An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series
Authors: Xu Huaqing , Lu Tieding , Montillet Jean-Philippe , He Xiaoxing
Published: December 2021

To improve the reliability of Global Positioning System (GPS) signal extraction, the traditional variational mode decomposition (VMD) method cannot determine the number of intrinsic modal functions or the value of the penalty factor in the process of noise reduction, which leads to inadequate or over-decomposition in time series analysis and will cause problems. Therefore, in this paper, a new approach using improved variational mode decomposition and wavelet packet transform (IVMD-WPT) was proposed, which takes the energy entropy mutual information as the objective function and uses the grasshopper optimisation algorithm to optimise the objective function to adaptively determine the number of modal decompositions and the value of the penalty factor to verify the validity of the IVMD-WPT algorithm. We performed a test experiment with two groups of simulation time series and three indicators: root mean square error (RMSE), correlation coefficient (CC) and signal-to-noise ratio (SNR). These indicators were used to evaluate the noise reduction effect. The simulation results showed that IVMD-WPT was better than the traditional empirical mode decomposition and improved variational mode decomposition (IVMD) methods and that the RMSE decreased by 0.084 and 0.0715 mm; CC and SNR increased by 0.0005 and 0.0004 dB, and 862.28 and 6.17 dB, respectively. The simulation experiments verify the effectiveness of the proposed algorithm. Finally, we performed an analysis with 100 real GPS height time series from the Crustal Movement Observation Network of China (CMONOC). The results showed that the RMSE decreased by 11.4648 and 6.7322 mm, and CC and SNR increased by 0.1458 and 0.0588 dB, and 32.6773 and 26.3918 dB, respectively. In summary, the IVMD-WPT algorithm can adaptively determine the number of decomposition modal functions of VMD and the optimal combination of penalty factors; it helps to further extract effective information for noise and can perfectly retain useful information in the original time series.
Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning
Authors: Barzegar Sima , Ruiz Marc , Velasco Luis
Published: December 2021

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes.
Review on Sensing Technology Adoption in the Construction Industry
Authors: Arabshahi Mona , Wang Di , Sun Junbo , Rahnamayiezekavat Payam , Tang Weichen , Wang Yufei , Wang Xiangyu
Published: December 2021

Sensing technologies demonstrate promising potential in providing the construction industry with a safe, productive, and high-quality process. The majority of sensing technologies in the construction research area have been focused on construction automation research in prefabrication, on-site operation, and logistics. However, most of these technologies are either not implemented in real construction projects or are at the very early stages in practice. The corresponding applications are far behind, even in extensively researched aspects such as Radio Frequency Identification, ultra-wideband technology, and Fiber Optic Sensing technology. This review systematically investigates the current status of sensing technologies in construction from 187 articles and explores the reasons responsible for their slow adoption from 69 articles. First, this paper identifies common sensing technologies and investigates their implementation extent. Second, contributions and limitations of sensing technologies are elaborated to understand the current status. Third, key factors influencing the adoption of sensing technologies are extracted from construction stakeholders’ experience. Demand towards sensing technologies, benefits and suitability of them, and barriers to their adoption are reviewed. Lastly, the governance framework is determined as the research tendency facilitating sensing technologies adoption. This paper provides a theoretical basis for the governance framework development. It will promote the sensing technologies adoption and improve construction performance including safety, productivity, and quality.
Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds
Authors: Filko Damir , Marijanović Domagoj , Nyarko Emmanuel Karlo
Published: December 2021

Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. This paper describes an automatic wound recording system build upon 7 DoF robot arm with attached RGB-D camera and high precision 3D scanner. The developed system presents a novel NBV algorithm that utilizes surface-based approach based on surface point density and discontinuity detection. The system was evaluated on multiple wounds located on medical models as well as on real patents recorded in clinical medical center.
Distribution Characteristics of Ground Echo Amplitude and Recognition of Signal Grazing Angle
Authors: Zhang Guangwei , Li Ping , Li Guolin , Jia Ruili
Published: December 2021

With the continuous advancement of electronic technology, terahertz technology has gradually been applied on radar. Since short wavelength causes severe ground clutter, this paper studies the amplitude distribution statistical characteristics of the terahertz radar clutter based on the measured data, and provides technical support for the radar clutter suppression. Clutter distribution is the function of the radar glancing angle. In order to achieve targeted suppression, in this paper, selected axial integral bispectrum (selected AIB) feature is selected as deep belief network (DBN)input to complete the radar glancing angle recognition and the network structure, network training method, robustness are analyzed also. The ground clutter amplitude distribution can follow normal distribution at 0~45° grazing angles. The Weibull distribution and G0 distribution can describe the amplitude probability density function of ground clutter at grazing angles 85° and 65°. The recognition rate of different signal grazing angles can reach 91% on three different terrains. At the same time, the wide applicability of the selected AIB feature is verified. The analysis results of ground clutter amplitude characteristics play an important role in the suppression of radar ground clutter.
Biotic and Abiotic Interactions Shape Seed Germination of a Fire-Prone Species
Authors: Fagundes Marcilio , dos Santos Henrique Tadeu , Cuevas-Reyes Pablo , Cornelissen Tatiana
Published: December 2021

Both biotic and abiotic environmental filters drive the occurrence, distribution, and persistence of plant species. Amongst drivers that influence the distribution of plants in harsh environments, seed predation and temperature are particularly important in habitats that are prone to fire. In this study, we highlight the combined effects of predation and high temperature simulating fire to understand its effects on the germination percentage and germination speed of the fire prone species Copaifera oblongifolia. Groups of seeds attacked by the beetles Rhinochenus brevicollis and Apion sp., seeds manipulated by the ant Atta laevigata, and seeds left intact were put to germinate in controlled environments. To evaluate the effects of abiotic filters, seeds with intact elaiosomes and seeds with elaiosomes removed by the ant Atta laevigata were exposed to temperatures of 27, 60, 100, and 200 °C. The results showed that only 2.8% of the seeds attacked by R. brevicollis germinated. Seeds attacked by Apion sp. germinated faster, followed by seeds with their elaiosomes removed and seeds with intact elaiosomes. Seeds attacked by Apion sp. had the lowest germination percentage. The temperature of 200 °C killed seed embryos, whereas seeds exposed to 100 °C took longer to germinate than seeds exposed to other temperatures. Our results reveal that fire intensity and seed damage are important drivers of seed germination of C. oblongifolia.
Systematically Understanding Cybersecurity Economics: A Survey
Authors: Kianpour Mazaher , Kowalski Stewart J. , Øverby Harald
Published: December 2021

Insights in the field of cybersecurity economics empower decision makers to make informed decisions that improve their evaluation and management of situations that may lead to catastrophic consequences and threaten the sustainability of digital ecosystems. By drawing on these insights, cybersecurity practitioners have been able to respond to many complex problems that have emerged within the context of cybersecurity over the last two decades. The academic field of cybersecurity economics is highly interdisciplinary since it combines core findings and tools from disciplines such as sociology, psychology, law, political science, and computer science. This study aims to develop an extensive and consistent survey based on a literature review and publicly available reports. This review contributes by aggregating the available knowledge from 28 studies, out of a collection of 628 scholarly articles, to answer five specific research questions. The focus is how identified topics have been conceptualized and studied variously. This review shows that most of the cybersecurity economics models are transitioning from unrealistic, unverifiable, or highly simplified fundamental premises toward dynamic, stochastic, and generalizable models.
Low Carbon Diet: Integrating Gastronomy Service Emissions into the Carbon Management of the University of Graz
Authors: Harrer Melanie , Danzer Julia , Aschemann Ralf , Hölbling Stefanie
Published: December 2021

To avert the upcoming crisis of climate change, significant changes on different scales and sectors are necessary. The knowledge and research of the higher education sector is an essential part in the fight against climate change already. Many universities admit the urgency of acting within their institution as well and have started to measure their impact on the environment to formulate emission-reduction goals. As part of its sustainability strategy, the University of Graz launched the Institutional Carbon Management (ICM) project to calculate its emissions via a greenhouse gas emissions inventory. In comparison to other inventories, the ICM also includes the gastronomy services on and around the campus of the University of Graz, which is also the focus of this paper. It was found that especially energy- and carbon-intensive food products such as meat and dairy contribute to the emissions of a gastronomy service. In total, the gastronomy service emissions contribute 1.1% to the total emissions inventory of the university. Although the contribution is a rather small portion, the University of Graz sees itself responsible for all its emissions and therefore also aims to gain comprehensive insights into all sub-areas of its institution to formulate validated reduction pathways. The changes to a more sustainable gastronomy and low-emission diets can therefore be seen as part of a wider change towards more environmentally friendly behaviour in general with the overall aim to meet the Paris climate goal.
Rural Tourism Destination: The Ligurian Farmers’ Perspective
Authors: Peira Giovanni , Longo Davide , Pucciarelli Francesca , Bonadonna Alessandro
Published: December 2021

Rural tourism is considered a high potential form of tourism, enhanced by the demand for more sustainable and nature-based solutions, and able to contribute to territory resilience. A rural area is not necessarily a tourist destination, but it might become one, if agricultural enterprises are willing to diversify their economic activities by investing in rural tourism, and local actors provide active support and co-participation. This research focuses on the development of rural tourism in hinterland, mountainous, and hilly areas of the province of Savona in Liguria (North-West of Italy) in order to gather the farmers’ perspectives about local rural tourism destination development. Liguria is known above all as a seaside tourist destination. In recent years, policy makers have initiated a debate with local actors to relaunch Ligurian tourism by trying to develop alternative forms of tourism, such as rural tourism. A sample of 32 farmers already proposing rural tourism activities such as agritourism were involved in a mixed methodological approach aimed at validating local interest toward rural tourism and collecting information for designing future local development policies. At first, a questionnaire set up by a panel of experts was carried out, followed by one-to-one semi-structured interviews, and finally the Nominal Group Technique (NGT) was used to identify local priorities, strategies, and tools. The results highlight the willingness of farmers to invest in the rural tourism sector, the presence of heterogeneous interests, and the complexity of management of the relationship among the various stakeholders. Findings are partially explained by the early stage of development of rural tourism in the analyzed area, a phase in which starting a constructive dialogue on objective and project ideas among all stakeholders seems to be a crucial priority among the farmers taking part in the study.
Life Cycle Sustainability Assessment—A Survey Based Potential Future Development for Implementation and Interpretation
Authors: Backes Jana Gerta , Traverso Marzia
Published: December 2021

The aim of this study is to define, via an online expert survey, current challenges and possible future approaches in and for the implementation, application, and interpretation of the Life Cycle Sustainability Assessment (LCSA). Using an online survey, sustainability experts from around the world were surveyed over a period of five weeks, resulting in 71 experts answering 25 questions. The experts were invited by e-mail and through networks; the online questionnaire was the preferred survey choice particularly for reasons of time, cost, and the pandemic. The survey evaluation shows that no change in LCSA is needed. Nevertheless, (1) a detailed optional baseline LCSA framework, with pre-selected fixed indicator sets, (2) a supporting optional but unified visualization tool, (3) a clear and transparent communication on assumptions, targets and system boundaries and (4) early defined stakeholders were identified as relevant for further LCSA implementation and interpretation. Due to natural subjectivity, the results of this written survey are to be understood as recommendations for action and orientation, not explicitly as a prediction. Finally, an action outlook for future LCSA-development is given.
Corporate Social Responsibility and Football Clubs: The Value of Environmental Sustainability as a Basis for the Rebranding of Real Betis Balompié in Spain
Authors: Lobillo Mora Gema , Ginesta Xavier , de San Eugenio Vela Jordi
Published: December 2021

On October 2020, Real Betis Balompié, a football club located in Seville (Andalucia), presented the Forever Green programme, a global programme of Corporate Social Responsibility (CSR) that, through the club’s foundation, enables the entity to position itself in areas of sustainable development and environment at a global level. This project was preceded by a sponsorship initiative with the Green Earth project, as well as having been the first football club to sign the United Nations’ Climate Change Now initiative. This article aims to explain the rebranding process of a sports brand based on values linked to sustainability and to assess the impact this rebranding process has had on the fans’ perception of the brand. The methodology used was a combination of in-depth interviews with the executives charged with leading the rebranding process and a convenience survey given to 100 fans to assess the impact of the initiative. The results show that both the entity and the fans agree on a holistic vision of what the CSR is, altruistic in nature, and that its initiatives should be applicable across the entire organisation. However, although the club’s link with values of the environment and sustainability is highly valued by the fans, they still do not feel directly called upon to take part in actions that the club organises.
Modern Details in Meaningful Architecture
Authors: Gawell Ewelina , Grabowiecki Konrad
Published: December 2021

Architecture is the art of shaping space, system, and technology. A close relationship is established between the building and its user, as the facility provides shelter and communicates with the inhabitants by meanings encoded in the form. The reception of architecture occurs through an ideological narrative and the quality of construction and material solutions. Contemporary pro-environmental postulates exert an increasingly clear influence on how architecture is shaped, especially on its aesthetic and semantic solutions. In this context, the article refers to the interdependence between art and technology in shaping the architecture of meanings through detail. The work aims to expand qualitative research on shaping contemporary detail in the context of pro-environmental trends in architecture. The detail was selected based on its clear message—its meaning provides the leading feature of the structure, both in technical (engineering solutions) and semantic (narrative) terms. The article provides an attempt to answer the question of how a semantic detail should be shaped, with the account to contemporary concepts on sustainable development architecture. A synthetic-comparative methodology was adopted; specific groups of completed objects were analyzed in the context of the indicated topics. The conclusions from this part of the work constitute case study guidelines, which was conducted on the example of an original project.