bluuebunny/crossref_metadata_2025 huggingface dataset:
MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images
Authors: Yang Xin , Hu Lei , Zhang Yongmei , Li Yunqing
Published: November 2021

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.
Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
Authors: Wang Shengchao , Han Liguo , Gong Xiangbo , Zhang Shaoyue , Huang Xingguo , Zhang Pan
Published: November 2021

Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area.
Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions
Authors: Lasko Kristofer , Maloney Megan C. , Becker Sarah J. , Griffin Andrew W. H. , Lyon Susan L. , Griffin Sean P.
Published: November 2021

This study presents an automated methodology to generate training data for surface water mapping from a single Sentinel-2 granule at 10 m (4 band, VIS/NIR) or 20 m (9 band, VIS/NIR/SWIR) resolution without the need for ancillary training data layers. The 20 m method incorporates an ensemble of three spectral indexes with optimal band thresholds, whereas the 10 m method achieves similar results using fewer bands and a single spectral index. A spectrally balanced and randomly generated set of training data based on the index values and optimal thresholds is used to fit machine learning classifiers. Statistical validation compares the 20 m ensemble-only method to the 20 m ensemble method with a random forest classifier. Results show the 20 m ensemble-only method had an overall accuracy of 89.5% (±1.7%), whereas the ensemble method combined with the random forest classifier performed better, with a ~4.8% higher overall accuracy: 20 m method (94.3% (±1.3%)) with optimal spectral index and SWIR thresholds of −0.03 and 800, respectively, and 10 m method (93.4% (±1.5%)) with optimal spectral index and NIR thresholds of −0.01 and 800, respectively. Comparison of other supervised classifiers trained automatically with the framework typically resulted in less than 1% accuracy improvement compared with the random forest, suggesting that training data quality is more important than classifier type. This straightforward framework enables accurate surface water classification across diverse geographies, making it ideal for development into a decision support tool for water resource managers.
Spatial Variations of Stochastic Noise Properties in GPS Time Series
Authors: He Xiaoxing , Bos Machiel Simon , Montillet Jean-Philippe , Fernandes Rui , Melbourne Tim , Jiang Weiping , Li Wudong
Published: November 2021

The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well known that in the frequency domain, the noise exhibits a power-law behavior with a spectral index of around −1. By fitting various noise models to the observations and selecting the most likely one, we demonstrate that the spectral index in some regions flattens to zero at long periods while in other regions it is closer to −2. This has a significant impact on the estimated linear rate since flattening of the power spectral density roughly halves the uncertainty of the estimated tectonic rate while random walk doubles it. Our noise model selection is based on the highest log-likelihood value, and the Akaike and Bayesian Information Criteria to reduce the probability of over selecting noise models with many parameters. Finally, the noise in position time series also depends on the stability of the monument on which the GPS antenna is installed. We corroborate previous results that deep-drilled brace monuments produce smaller uncertainties than concrete piers. However, if at each site the optimal noise model is used, the differences become smaller due to the fact that many concrete piers are located in tectonic/seismic quiet areas. Thus, for the predicted performance of a new GPS network, not only the type of monument but also the noise properties of the region need to be taken into account.
Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China
Authors: Guo Jiaqi , Liu Xiaohong , Ge Wensen , Ni Xiaofeng , Ma Wenyuan , Lu Qiangqiang , Xing Xiaoyu
Published: November 2021

Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.
Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model
Authors: Bauer Luise , Knapp Nikolai , Fischer Rico
Published: November 2021

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.
The Biomechanical Mechanism of Upper Airway Collapse in OSAHS Patients Using Clinical Monitoring Data during Natural Sleep
Authors: Chen Liujie , Xiao Tan , Ng Ching Tai
Published: November 2021

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep disorder characterized by repeated pharyngeal collapse with partial or complete obstruction of the upper airway. This study investigates the biomechanics of upper airway collapse of OSASH patients during natural sleep. Computerized tomography (CT) scans and data obtained from a device installed on OSASH patients, which is comprised of micro pressure sensors and temperature sensors, are used to develop a pseudo three-dimensional (3D) finite element (FE) model of the upper airway. With consideration of the gravity effect on the soft palate while patients are in a supine position, a fluid–solid coupling analysis is performed using the FE model for the two respiratory modes, eupnea and apnea. The results of this study show that the FE simulations can provide a satisfactory representation of a patient’s actual respiratory physiological processes during natural sleep. The one-way valve effect of the soft palate is one of the important mechanical factors causing upper airway collapse. The monitoring data and FE simulation results obtained in this study provide a comprehensive understanding of the occurrence of OSAHS and a theoretical basis for the individualized treatment of patients. The study demonstrates that biomechanical simulation is a powerful supplementation to clinical monitoring and evaluation.
Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding
Authors: Xiong Pengbo , Wang Shaokai , Wang Weibo , Ye Qixin , Ye Shujiao
Published: November 2021

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.
Smart Face Mask with an Integrated Heat Flux Sensor for Fast and Remote People’s Healthcare Monitoring
Authors: Lazaro Marc , Lazaro Antonio , Villarino Ramon , Girbau David
Published: November 2021

The COVID-19 pandemic has highlighted a large amount of challenges to address. To combat the spread of the virus, several safety measures, such as wearing face masks, have been taken. Temperature controls at the entrance of public places to prevent the entry of virus carriers have been shown to be inefficient and inaccurate. This paper presents a smart mask that allows to monitor body temperature and breathing rate. Body temperature is measured by a non-invasive dual-heat-flux system, consisting of four sensors separated from each other with an insulating material. Breathing rate is obtained from the temperature changes within the mask, measured with a thermistor located near the nose. The system communicates by means of long-range (LoRa) backscattering, leading to a reduction in average power consumption. It is designed to establish the relative location of the smart mask from the signal received at two LoRa receivers installed inside and outside an access door. Low-cost LoRa transceivers with WiFi capabilities are used in the prototype to collect information and upload it to a server. Accuracy in body temperature measurements is consistent with measurements made with a thermistor located in the armpit. The system allows checking the correct placement of the mask based on the recorded temperatures and the breathing rate measurements. Besides, episodes of cough can be detected by sudden changes in thermistor temperature.
Radar Signal Modulation Recognition Based on Sep-ResNet
Authors: Mao Yongjiang , Ren Wenjuan , Yang Zhanpeng
Published: November 2021

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.
Bioprinting of Stem Cells in Multimaterial Scaffolds and Their Applications in Bone Tissue Engineering
Authors: Tharakan Shebin , Khondkar Shams , Ilyas Azhar
Published: November 2021

Bioprinting stem cells into three-dimensional (3D) scaffolds has emerged as a new avenue for regenerative medicine, bone tissue engineering, and biosensor manufacturing in recent years. Mesenchymal stem cells, such as adipose-derived and bone-marrow-derived stem cells, are capable of multipotent differentiation in a 3D culture. The use of different printing methods results in varying effects on the bioprinted stem cells with the appearance of no general adverse effects. Specifically, extrusion, inkjet, and laser-assisted bioprinting are three methods that impact stem cell viability, proliferation, and differentiation potential. Each printing method confers advantages and disadvantages that directly influence cellular behavior. Additionally, the acquisition of 3D bioprinters has become more prominent with innovative technology and affordability. With accessible technology, custom 3D bioprinters with capabilities to print high-performance bioinks are used for biosensor fabrication. Such 3D printed biosensors are used to control conductivity and electrical transmission in physiological environments. Once printed, the scaffolds containing the aforementioned stem cells have a significant impact on cellular behavior and differentiation. Natural polymer hydrogels and natural composites can impact osteogenic differentiation with some inducing chondrogenesis. Further studies have shown enhanced osteogenesis using cell-laden scaffolds in vivo. Furthermore, selective use of biomaterials can directly influence cell fate and the quantity of osteogenesis. This review evaluates the impact of extrusion, inkjet, and laser-assisted bioprinting on adipose-derived and bone-marrow-derived stem cells along with the effect of incorporating these stem cells into natural and composite biomaterials.
An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm
Authors: Hu Aihua , Deng Zhongliang , Yang Hui , Zhang Yao , Gao Yuhui , Zhao Di
Published: November 2021

In view of the demand of location awareness in a special complex environment, for an unmanned aerial vehicle (UAV) airborne multi base-station semi-passive positioning system, the hybrid positioning solutions and optimized site layout in the positioning system can effectively improve the positioning accuracy for a specific region. In this paper, the geometric dilution of precision (GDOP) formula of a time difference of arrival (TDOA) and angles of arrival (AOA) hybrid location algorithm is deduced. Mayfly optimization algorithm (MOA) which is a new swarm intelligence optimization algorithm is introduced, and a method to find the optimal station of the UAV airborne multiple base station’s semi-passive positioning system using MOA is proposed. The simulation and analysis of the optimization of the different number of base stations, compared with other station layout methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm. MOA is less likely to fall into local optimum, and the error of regional target positioning is reduced. By simulating the deployment of four base stations and five base stations in various situations, MOA can achieve a better deployment effect. The dynamic station configuration capability of the multi-station semi-passive positioning system has been improved with the UAV.
A Bioluminescent Sensor for Rapid Detection of PPEP-1, a Clostridioides difficile Biomarker
Authors: Ng Kevin K. , Reinert Zachary E. , Corver Jeroen , Resurreccion Danica , Hensbergen Paul J. , Prescher Jennifer A.
Published: November 2021

Current assays for Clostridioides difficile in nonhospital settings are outsourced and time-intensive, resulting in both delayed diagnosis and quarantining of infected individuals. We designed a more rapid point-of-care assay featuring a “turn-on” bioluminescent readout of a C. difficile-specific protease, PPEP-1. NanoLuc, a bright and stable luciferase, was “caged” with a PPEP-1-responsive peptide tail that inhibited luminescence. Upon proteolytic cleavage, the peptide was released and NanoLuc activity was restored, providing a visible readout. The bioluminescent sensor detected PPEP-1 concentrations as low as 10 nM. Sensor uncaging was achieved within minutes, and signal was captured using a digital camera. Importantly, the sensor was also functional at ambient temperature and compatible with fecal material, suggesting that it can be readily deployed in a variety of settings.
Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System
Authors: Shahar Roy T. , Agmon Maayan
Published: November 2021

Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent validity of a smartphone placed in a front-facing pocket to perform gait analysis. Sixty community-dwelling healthy adults equipped with a smartphone and an application for gait analysis completed a 2-min walk on a marked path. Concurrent validity was assessed against an APDM mobility lab (APDM Inc.; Portland, OR, USA). Bland–Altman plots and intraclass correlation coefficients (agreement and consistency) for gait speed, cadence, and step length indicate good to excellent agreement (ICC2,1 > 0.8). For right leg stance and swing % of gait cycle and double support % of gait cycle, results were moderate (0.52 < ICC2,1 < 0.62). For left leg stance and swing % of gait cycle left results show poor agreement (ICC2,1 < 0.5). Consistency of results was good to excellent for all tested parameters (ICC3,1 > 0.8). Thus we have a valid and reliable instrument for measuring healthy adults’ spatio-temporal gait parameters in a controlled walking environment.
Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture
Authors: Zhang Rihong , Li Xiaomin
Published: November 2021

In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng’s grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.
Digitalization and Artificial Intelligence in Migration and Mobility: Transnational Implications of the COVID-19 Pandemic
Authors: McAuliffe Marie , Blower Jenna , Beduschi Ana
Published: November 2021

Digitalization and artificial intelligence (AI) technologies in migration and mobility have incrementally expanded over recent years. Iterative approaches to AI deployment experienced a surge during 2020 and into 2021, largely due to COVID-19 forcing greater reliance on advanced digital technology to monitor, inform and respond to the pandemic. This paper critically examines the implications of intensifying digitalization and AI for migration and mobility systems for a post-COVID transnational context. First, it situates digitalization and AI in migration by analyzing its uptake throughout the Migration Cycle. Second, the article evaluates the current challenges and, opportunities to migrants and migration systems brought about by deepening digitalization due to COVID-19, finding that while these expanding technologies can bolster human rights and support international development, potential gains can and are being eroded because of design, development and implementation aspects. Through a critical review of available literature on the subject, this paper argues that recent changes brought about by COVID-19 highlight that computational advances need to incorporate human rights throughout design and development stages, extending well beyond technical feasibility. This also extends beyond tech company references to inclusivity and transparency and requires analysis of systemic risks to migration and mobility regimes arising from advances in AI and related technologies.
Dogs as Therapeutic Partners, Not Therapeutic Tools: Ethical Considerations for AAT in the Correctional Setting
Authors: Collica-Cox Kimberly , Day George J.
Published: November 2021

Although the benefits of animal assisted therapy for people are well established, the ethical considerations for the welfare and safety of the non-human animals involved are not. Without an accrediting body responsible for creating and overseeing national standards, therapy animal organizations are forced to create their own guidelines, creating inconsistencies within the field. Based on interviews conducted with therapy teams who have worked with Parenting, Prison & Pups (PPP), a parenting program provided to incarcerated jailed women that is integrated with the use of animal-assisted therapy (AAT), this article explores the extent of ethics training offered for AAT teams and will examine how agencies and handlers promote and ensure the safety of canine partners, especially in a correctional setting. The research suggests that specific protocols put forth by individual AAT organizations, which can provide for a national model, can afford for the safety and comfortability of canine partners, especially in a corrections environment, but implies that in order to maintain consistency and increase therapy team professionalism, national standards are a necessity. Guidelines are specifically essential for mental health professionals, who lack guidelines from their own accrediting bodies’ code of ethics, and may incorporate non-human therapy partners into their work settings, without proper supervision.
The Role of Perceived Quality on High-Speed Railway Tourists’ Behavioral Intention: An Application of the Extended Theory of Planned Behavior
Authors: Hou Zhiqiang , Liang Lena Jingen , Meng Bo , Choi HwanSuk Chris
Published: November 2021

Since the COVID-19 pandemic, airlines worldwide have enforced strict travel restrictions, driving passengers to seek alternative transportations such as High-Speed Railway (HSR). Nevertheless, the current understanding of HSR travelers’ behavior is scarce. Moreover, despite the extensive application of the Theory of Planned Behavior (TPB) to explain and predict patrons and tourists’ behavior in the tourism and hospitality context, little research has employed this theory to examine HSR travelers’ processes in making travelling decisions. This study developed a research model by integrating perceived quality into the TPB and examined the relation between HSR passengers’ service quality and intention to travel in the future and the mediating effect of TPB attributes. It is found that perceived train service quality and perceived travel quality have a significant influence on the TPB attributes, which further influence HSR travelers’ travel intention. This study contributes practical implications to destinations and the travel industry that they might attract visitors by marketing the corresponding HSR train service quality and travel experience quality. This also provides recovery guidelines for the transportation and travel industry after the COVID-19 pandemic.
A Combined System Using Lagoons and Constructed Wetlands for Swine Wastewater Treatment
Authors: Denisi Pietro , Biondo Nicola , Bombino Giuseppe , Folino Adele , Zema Demetrio Antonio , Zimbone Santo Marcello
Published: November 2021

This study evaluates the depuration efficiency of a combined system consisting of lagoons (with aerated and non-aerated tanks) and CWs (with Typha latifolia L.) working at pilot scale for treating SW under two recirculation rates (RRs, 4:1 and 10:1) of the CW effluent. The combined system removed about 99% of the total suspended solids and organic matter, and from 80% to 95% of the total nitrogen at both tested RRs. The lagoon system was effective as a pre-treatment of SW, particularly for nitrogen removal. It is convenient to adopt the higher RR, since nitrogen removal can be increased by approximately 20%. The irrigation of the CWs with SW did not generally determine the phyto-toxic effects on Typha latifolia L., except at the start of the experiment and under the lower RR. Despite the limited spatial and temporal scale of this investigation, these results provide a starting point for the use of V-SSF CWs to treat livestock wastewater with a high pollution potential (such as SW).
Performance and Efficiency Evaluation of ASR Inference on the Edge
Authors: Gondi Santosh , Pratap Vineel
Published: November 2021

Automatic speech recognition, a process of converting speech signals to text, has improved a great deal in the past decade thanks to the deep learning based systems. With the latest transformer based models, the recognition accuracy measured as word-error-rate (WER), is even below the human annotator error (4%). However, most of these advanced models run on big servers with large amounts of memory, CPU/GPU resources and have huge carbon footprint. This server based architecture of ASR is not viable in the long run given the inherent lack of privacy for user data, reliability and latency issues of the network connection. On the other hand, on-device ASR (meaning, speech to text conversion on the edge device itself) solutions will fix deep-rooted privacy issues while at same time being more reliable and performant by avoiding network connectivity to the back-end server. On-device ASR can also lead to a more sustainable solution by considering the energy vs. accuracy trade-off and choosing right model for specific use cases/applications of the product. Hence, in this paper we evaluate energy-accuracy trade-off of ASR with a typical transformer based speech recognition model on an edge device. We have run evaluations on Raspberry Pi with an off-the-shelf USB meter for measuring energy consumption. We conclude that, in the case of CPU based ASR inference, the energy consumption grows exponentially as the word error rate improves linearly. Additionally, based on our experiment we deduce that, with PyTorch mobile optimization and quantization, the typical transformer based ASR on edge performs reasonably well in terms of accuracy and latency and comes close to the accuracy of server based inference.
Product Attributes, Evaluability, and Consumer Satisfaction
Authors: Antonides Gerrit , Hovestadt Lies
Published: November 2021

We studied the determinants of consumer satisfaction with mobile phones on the basis of their perceived product attribute performance, and the disconfirmation of product attribute expectations. Disconfirmation refers to the discrepancy between the prior expectation about the performance of a product’s attributes, and its perceives realizations after purchase. Evaluability theory assumes that the perceived attribute performance has a larger effect on consumer satisfaction for easy-to-evaluate than for difficult-to-evaluate attributes, after product acquisition. Furthermore, we used predictions of the asymmetric evaluations of gains (product performs better than expected) and losses (product performs worse than expected) from prospect theory, in combination with evaluability theory. We studied how evaluability influences the effects of the asymmetric evaluations of both positive and negative disconfirmation of product attribute expectations on consumer satisfaction. Our empirical study included 3099 participants of Amazon Mechanical Turk. We found that negative attribute disconfirmation had a larger effect on satisfaction than positive attribute disconfirmation, which is in line with loss aversion theory. Although the perceived product attribute performance positively influenced satisfaction, we found little support for the effects of perceived attribute performance being influenced by attribute evaluability. However, our findings indicated that negative attribute disconfirmation influenced satisfaction to a greater extent for relatively difficult-to-evaluate attributes than for relatively easy-to-evaluate attributes. We discuss both theoretical and managerial implications of our findings.
Assessment of the Environmental Impacts of Bridge Designs Involving UHPFRC
Authors: Bertola Numa , Küpfer Célia , Kälin Edgar , Brühwiler Eugen
Published: November 2021

Ultra-High-Performance Fibre-Reinforced Cementitious Composite (UHPFRC) has been developed to design lightweight structures and enhance existing designs. As the environmental footprint of the construction industry must be significantly reduced, the potential to lower environmental impacts of structures using UHPFRC needs to be explored. While the greenhouse gas emissions of a volume of UHPFRC are higher than that of the same volume of concrete, UHPFRC enables the reduction in the amount of material required in structural designs and improves the durability of structures. The environmental impacts of structural designs must thus be compared on the cradle-to-grave use cycle of the design at a project scale. In this study, a methodology is proposed to evaluate the ecological burdens of several bridge designs involving various structural elements in UHPFRC. The method proposes an analysis over three time horizons: first, the construction phase, then including the scheduled maintenance, and finally, adding the elimination. A case study of a short-span bridge in Switzerland is used to assess three alternatives of bridge designs: a conventional reinforced-concrete structure, a composite timber–UHPFRC bridge, and a full-UHPFRC solution. The results show that timber–UHPFRC structures can significantly reduce the environmental impacts of bridge designs, showing promising results in terms of sustainable development. The use of the methodology supports bridge owners in assessing the environmental impacts of structural designs.
Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
Authors: Chen Yuche , Sun Ruixiao , Wu Xuanke
Published: November 2021

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.
Assessing the Contribution of Citrus Orchards in Climate Change Mitigation through Carbon Sequestration in Sargodha District, Pakistan
Authors: Yasin Ghulam , Farrakh Nawaz Muhammad , Zubair Muhammad , Qadir Ihsan , Saleem Aansa Rukya , Ijaz Muhammad , Gul Sadaf , Amjad Bashir Muhammad , Rehim Abdur , Rahman Shafeeq Ur , Du Zhenjie
Published: November 2021

Adopting agroforestry practices in many developing countries is essential to combat climate change and diversify farm incomes. This study investigated the above and below-ground biomass and soil carbon of a citrus-based intercropping system in six sites (subdivisions: Bhalwal, Kot Momin, Sahiwal, Sargodha, Shahpur and Silanwali) of District Sargodha, Southeast Pakistan. Tree biomass production and carbon were assessed by allometric equations through a non-destructive approach whereas, soil carbon was estimated at 0–15 cm and 15–30 cm depths. Above and below-ground biomass differed significantly, and the maximum mean values (16.61 Mg ha−1 & 4.82 Mg ha−1) were computed in Shahpur due to greater tree basal diameter. Tree carbon stock fluctuated from 6.98 Mg C ha−1 to 10.28 Mg C ha−1 among selected study sites. The surface soil (0–15 cm) had greater bulk density, organic carbon, and soil carbon stock than the subsoil (15–30 cm) in the whole study area. The total carbon stock of the ecosystem ranged from 25.07 Mg C ha−1 to 34.50 Mg C ha−1 across all study sites, respectively. The above findings enable us to better understand and predict the carbon storage potential of fruit-based agroforestry systems like citrus. Moreover, measuring carbon with simple techniques can produce trustworthy outcomes that enhance the participation of underdeveloped nations in several payment initiatives such as REDD+.
HSPF-Based Assessment of Inland Nutrient Source Control Strategies to Reduce Algal Blooms in Streams in Response to Future Climate Changes
Authors: Lee Dong Hoon , Fabian Pamela Sofia , Kim Jin Hwi , Kang Joo-Hyon
Published: November 2021

The HSPF model was modified to improve the growth-temperature formulation of phytoplankton and used to simulate Chl-a concentrations at the outlet of the Seom River watershed in Korea from 2025 to 2050 under four climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. The mean and median Chl-a concentrations increased by 5–10% and 23–29%, respectively, and the number of algal outbreak cases per year (defined as a day with Chl-a concentration ≥100 µg/L) decreased by 31–88% relative to the current values (2011–2015). Among the climate change scenarios, RCP 2.6 (stringent) showed the largest number of algal outbreak cases, mainly because of the largest yearly variability of precipitation and TP load. For each climate change scenario, three nutrient load reduction scenarios were in the HSPF simulation, and their efficiencies in reducing algal blooms were determined. Nonpoint source reduction in TP and TN from urban land, agricultural land, and grassland by 50% (S1) and controlling the effluent TP concentration of wastewater treatment plants (WWTPs) to 0.1 mg/L (S2) decreased algal outbreaks by 20–58% and 44–100%, respectively. The combination of effluent TP control of WWTPs during summer and S1 was the most effective management scenario; it could almost completely prevent algal outbreaks. This study demonstrates the cost effectiveness of using a season-based pollutant management strategy for controlling algal blooms.
Motivations and Satisfaction of New Zealand Domestic Tourists to Inform Landscape Design in a Nature-Based Setting
Authors: Lawson Gill , Dean David , He Yuqing , Huang Xinghua
Published: November 2021

Increased tourist pressures can cause the deterioration of nature-based tourist destinations and adversely affect visitor satisfaction. This study aims to identify how public participation using mobile devices on-site can assist in assessing future design scenarios for a popular nature-based destination, within a short day trip from Christchurch in Aotearoa New Zealand. An online survey using participants’ mobile devices at Kura Tāwhiti Castle Hill Rocks identified domestic tourists’ motivational, satisfaction and dissatisfaction factors, as associated with age and visit frequency at the destination. These factors were linked to site experiences, particularly being out in nature, that could be used to design future scenarios for similar nature-based settings in Aotearoa New Zealand. Four future scenarios using 2D photomontages were used to rank domestic visitor preferences for changing paths and tracks, fencing, signage, structures and people. The study found that the low-impact scenario with the least people was the most desirable. This high level of sensitivity of New Zealanders to change in outdoor recreational destinations suggests that nature-based settings must be designed and managed with considerable care to minimize the perception of over-crowding and the deterioration of the site experience, particularly for return visitors.
Collecting Small-Waste Electrical and Electronic Equipment in Poland—How Can Containers Help in Disposal of E-Waste by Individuals?
Authors: Nowakowski Piotr , Kuśnierz Sandra , Płoszaj Julia , Sosna Patrycja
Published: November 2021

The effectiveness of e-waste collection depends on organizational and social factors. Individuals should have easy access to collection points and be aware of proper methods of e-waste disposal. This article investigates the availability, usability, and occurring irregularities of the container collection of small-waste electrical and electronic equipment. The study explores the attitude and behavior of individuals when disposing of small-waste equipment in the South of Poland. The results show that ninety-five percent of supermarkets’ managers responsible for waste management find a growing interest in the disposal of small e-waste by individuals. An increasing number of collection containers encourages residents to dispose of small-waste appliances. More than eighty-three percent of respondents are aware of proper methods of e-waste disposal. Additional information campaigns addressed to residents are required to prevent mixing electrical and electronic waste with other waste and to encourage the disposal of small e-waste. The intention to dispose of small e-waste was declared by only thirty-two percent of respondents. Containers with discarded easy breakable items, such as lamps, should be removed more frequently. The collection containers’ noticeability is evaluated by individuals as of average difficulty. The containers in supermarkets have simple designs and labels indicating the category of waste for disposal. The containers installed in residential areas have attractive designs and advertising slogans. The individuals expect the containers should be available in more locations that are easily accessible from households. The results of this study should be helpful for local authorities, waste collection companies, and waste electrical and electronic organizations, to support design, location selection, and information campaigns for small e-waste container collections.
Light Distribution and Perceived Spaciousness: Light Patterns in Scale Models
Authors: Wänström Lindh Ulrika , Billger Monica
Published: November 2021

Previous research showed that light distribution can affect the perception of spatial size and shape. However, most studies are limited to quantitative assessment of a few scenarios without explaining possible causes behind peoples’ experiences. This exploratory study aimed to reveal complex relationships between light patterns and perceived size, and to investigate how light patterns affect perceived spaciousness. A qualitative approach was used with pair-wise comparisons between systematic visual observations of scale models. The observations confirmed that illuminated walls increase spaciousness. Yet, darkness impacts the perception of spaciousness as well. Both compound and separated light zones can expand depth, height, or width, depending on the interpretation of these patterns of light seen in relation to the whole spatial context. Furthermore, the position of illuminated areas, with placements on edge or in the center, may additionally influence perceived size.
A Field Study on Thermal Comfort and Cooling Load Demand Optimization in a Tropical Climate
Authors: Esfandiari Masoud , Zaid Suzaini Mohamed , Ismail Muhammad Azzam , Hafezi Mohammad Reza , Asadi Iman , Mohammadi Saleh
Published: November 2021

Energy consumption to cool an indoor environment is a substantial part of total energy end-use, particularly in a tropical climate with high energy demand for cooling. To improve energy efficiency, cooling systems can be optimized using a variety of neutral indoor temperatures to maintain a balance between an occupant’s thermal comfort and cooling energy demand. This explanatory study investigated the thermal quality and cooling energy demand of a Platinum-certified office building in the tropical climate of Malaysia. The investigation aimed to suggest a balance between occupant thermal comfort and cooling energy demand. The thermal investigation includes an objective field measurement that implements environmental equipment to monitor thermal quality and a subjective occupant’s thermal feedback using a questionnaire survey. To calculate cooling energy demand, the total equivalent temperature difference method (TETD) is applied. The results suggested an occupant’s cooling sensation of around 24 °C, with no significant difference concerning age and gender. Cooling load calculation indicated a 36% energy reduction by increasing air temperature to 26 °C, for occupants to feel thermally comfortable in a tropical climate. These findings contribute to improving sustainable energy policies, sustainable construction, and thermal comfort improvement for a tropical climate.