bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Functional Bio-Based Coatings Including Chitin Nanofibrils or Polyphenols on Mechanical Properties of Paper Tissues
Authors: Panariello Luca , Coltelli Maria-Beatrice , Giangrandi Simone , Garrigós María Carmen , Hadrich Ahdi , Lazzeri Andrea , Cinelli Patrizia
Published: June 2022

The paper tissue industry is a constantly evolving sector that supplies markets that require products with different specific properties. In order to meet the demand of functional properties, ensuring a green approach at the same time, research on bio-coatings has been very active in recent decades. The attention dedicated to research on functional properties has not been given to the study of the morphological and mechanical properties of the final products. This paper studied the effect of two representative bio-based coatings on paper tissue. Coatings based on chitin nanofibrils or polyphenols were sprayed on paper tissues to provide them, respectively, with antibacterial and antioxidant activity. The chemical structure of the obtained samples was preliminarily compared by ATR-FTIR before and after their application. Coatings were applied on paper tissues and, after drying, their homogeneity was investigated by ATR-FTIR on different surface areas. Antimicrobial and antioxidant properties were found for chitin nanofibrils- and polyphenols-treated paper tissues, respectively. The mechanical properties of treated and untreated paper tissues were studied, considering as a reference the same tissue paper sample treated only with water. Different mechanical tests were performed on tissues, including penetration, tensile, and tearing tests in two perpendicular directions, to consider the anisotropy of the produced tissues for industrial applications. The morphology of uncoated and coated paper tissues was analysed by field emission scanning electron microscopy. Results from mechanical properties evidenced a correlation between morphological and mechanical changes. The addition of polyphenols resulted in a reduction in mechanical resistance, while the addition of chitin enhanced this property. This study evidenced the different effects produced by two novel coatings on paper tissues for personal care in terms of properties and structure.
Relationship between Neuroticism, Spiritual Well-Being, and Subjective Well-Being in Korean University Students
Authors: Yoo Jieun , You Sukkyung , Lee June
Published: June 2022

Previous studies on mental health and quality of life have revealed that religiosity/spirituality was positively associated with indicators of well-being and personality factors. However, limited research has examined the relationship between spiritual well-being, the subfactors of the personality factor Neuroticism (i.e., anxiety, hostility, depression, self-consciousness, impulsiveness, and vulnerability), and subjective well-being in a non-Western sample. The present findings revealed that the five subfactors of neuroticism did not have an equally negative or positive effect on spiritual and subjective well-being among Korean undergraduate University students. Regarding its subdimensions, vulnerability was strongly associated with spiritual well-being, while depression was closely linked to subjective well-being. Moreover, we found that spiritual well-being exerted significant effects on subjective well-being above personality factors. The significance of the findings and directions for further research have been discussed.
Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers
Authors: Medeiros Rodolpho , Andrade João , Ramos Desirée , Moura Magna , Pérez-Marin Aldrin , dos Santos Carlos , da Silva Bernardo , Cunha John
Published: June 2022

The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall.
Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast
Authors: Xue Pengfei , Wagh Aditya , Ma Gangfeng , Wang Yilin , Yang Yongchao , Liu Tao , Huang Chenfu
Published: June 2022

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems.
Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem
Authors: Guo Jianhua , Xu Qingsong , Zeng Yue , Liu Zhiheng , Zhu Xiaoxiang
Published: June 2022

In terms of semi-supervised cloud detection work, efforts are being made to learn a promising cloud detection model via a limited number of pixel-wise labeled images and a large number of unlabeled ones. However, remote sensing images obtained from the same satellite sensor often show a data distribution drift problem due to the different cloud shapes and land-cover types on the Earth’s surface. Therefore, there are domain distribution gaps between labeled and unlabeled satellite images. To solve this problem, we take the domain shift problem into account for the semi-supervised learning (SSL) network. Feature-level and output-level domain adaptations are applied to reduce the domain distribution gaps between labeled and unlabeled images, thus improving predicted results accuracy of the SSL network. Experimental results on Landsat-8 OLI and GF-1 WFV multispectral images demonstrate that the proposed semi-supervised cloud detection network (SSCDnet) is able to achieve promising cloud detection performance when using a limited number of labeled samples and outperforms several state-of-the-art SSL methods.
Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River
Authors: Zhao Hongbo , Liu Yaxin , Gu Tianshun , Zheng Hui , Wang Zheye , Yang Dongyang
Published: June 2022

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.
SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning
Authors: Senogles Andrew , Olsen Michael J. , Leshchinsky Ben
Published: June 2022

Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches.
A Nyström-Based Low-Complexity Algorithm with Improved Effective Array Aperture for Coherent DOA Estimation in Monostatic MIMO Radar
Authors: Ma Teng , Du Jiang , Shao Huaizong
Published: June 2022

In this paper, we propose a computationally efficient algorithm with improved effective aperture for coherent angle estimation in a monostatic multiple-input multiple-output (MIMO) radar. First, the direction matrix of MIMO radar is mapped into a low-dimensional matrix of virtual uniform linear array (ULA). Then, an augmented data expansion matrix with improved effective aperture is obtained by exploiting the Vandermonde-like structure of the low-dimensional direction matrix and radar cross section (RCS) matrix to enlarge the aperture of the array. Next, a unitary transformation is used to transform the augmented matrix into a real value and the approximate signal subspace of the augmented matrix is obtained by the Nyström method, which can reduce the computational complexity. The eigenvectors of the approximate signal subspace are used to reconstruct the matrix for direct decorrelation processing. Finally, direction of arrivals (DOAs) can be estimated faster by utilizing the unitary ESPRIT algorithm since the rotation invariance of the extended reconstruction matrix still exists. The proposed algorithm has a lower total computational complexity, and the estimation accuracy is improved by utilizing real values and enlarging the array aperture for estimation. Several theoretical analyses and simulation results confirm the effectiveness and advantages of the proposed method.
Deep Learning Models for Passive Sonar Signal Classification of Military Data
Authors: Fernandes Júlio de Castro Vargas , de Moura Junior Natanael Nunes , de Seixas José Manoel
Published: June 2022

The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as 99.0±0.4% was achieved using experimental data, which improves upon previous machine learning designs in the field.
Worldwide Statistical Correlation of Eight Years of Swarm Satellite Data with M5.5+ Earthquakes: New Hints about the Preseismic Phenomena from Space
Authors: Marchetti Dedalo , De Santis Angelo , Campuzano Saioa A. , Zhu Kaiguang , Soldani Maurizio , D’Arcangelo Serena , Orlando Martina , Wang Ting , Cianchini Gianfranco , Di Mauro Domenico , Ippolito Alessandro , Nardi Adriano , Sabbagh Dario , Chen Wenqi , He Xiaodan , Shen Xuhui , Wen Jiami , Zhang Donghua , Zhang Hanshuo , Zhang Yiqun , Zeren Zhima
Published: June 2022

Nowadays, the possibility that medium-large earthquakes could produce some electromagnetic ionospheric disturbances during their preparatory phase is controversial in the scientific community. Some previous works using satellite data from DEMETER, Swarm and, recently, CSES provided several pieces of evidence supporting the existence of such precursory phenomena in terms of single case studies and statical analyses. In this work, we applied a Worldwide Statistical Correlation approach to M5.5+ shallow earthquakes using the first 8 years of Swarm (i.e., from November 2013 to November 2021) magnetic field and electron density signals in order to improve the significance of previous statistical studies and provide some new results on how earthquake features could influence ionospheric electromagnetic disturbances. We implemented new methodologies based on the hypothesis that the anticipation time of anomalies of larger earthquakes is usually longer than that of anomalies of smaller magnitude. We also considered the signal’s frequency to introduce a new identification criterion for the anomalies. We find that taking into account the frequency can improve the statistical significance (up to 25% for magnetic data and up to 100% for electron density). Furthermore, we noted that the frequency of the Swarm magnetic field signal of possible precursor anomalies seems to slightly increase as the earthquake is approaching. Finally, we checked a possible relationship between the frequency of the detected anomalies and earthquake features. The earthquake focal mechanism seems to have a low or null influence on the frequency of the detected anomalies, while the epicenter location appears to play an important role. In fact, land earthquakes are more likely to be preceded by slower (lower frequency) magnetic field signals, whereas sea seismic events show a higher probability of being preceded by faster (higher frequency) magnetic field signals.
Assessing the Volume of Defensive Structures for Architectural Energetics Analysis Using 3D Electrical Resistivity Tomography
Authors: Klanica Radek , Grison Hana , Šteffl Jindřich , Beránek Roman
Published: June 2022

Architectural energetics is a methodology that translates architectural objects into a quantitative time-labor equivalent, from which information about past societies, labor organizations, or political relations can be inferred. Preceding such study, the volume of every architectural structure must be determined. This is usually done by in situ measurements and computing of volume by mathematical formulae or using UAV-based photogrammetry processed into digital surface model. However, both of these methods are impracticable in the case of buried or semi-buried monuments where the only remaining option is direct excavation. Hence, we introduce a new method for the determination of volumetric information based on the electrical resistivity tomography (ERT) geophysical method. We conducted our study at defensive lines (ramparts/ditches) within two hillforts of different ages, constructed from different building materials, in the Czech Republic. ERT surveys performed in 3D can differentiate ramparts/ditches in detail from the surrounding environment based on resistivity. Compared to previous excavations, the 3D inversion results show that ERT can obtain meaningful volumes based on the chosen resistivity threshold. The best results were achieved on homogeneous semi-buried ramparts and the ditch. ERT can be performed at a fraction of the cost of direct excavation. This method also leaves an intact site for future generations.
Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model
Authors: Huang Da , He Jun , Song Yixiang , Guo Zizheng , Huang Xiaocheng , Guo Yingquan
Published: June 2022

Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement.
Interdisciplinary Investigations of the Neolithic Circular Ditch Enclosure of Velm (Lower Austria)
Authors: Wallner Mario , Doneus Michael , Kowatschek Ingrid , Hinterleitner Alois , Köstelbauer Felix , Neubauer Wolfgang
Published: June 2022

Middle Neolithic circular enclosures, known as Kreisgrabenanlage (KGA), are the oldest known monumental sites in Central Europe, dating roughly to 4850–4600/4500 BC. These large prehistoric monuments are mainly discovered by aerial archaeology and have been investigated by geophysical prospection and archaeological excavations since the 1960s. The site of Velm (Lower Austria) was discovered by aerial photographs in 2001. Due to its unusual location on a flat gravel plateau, the enclosure has become the object of intensive interdisciplinary research in recent years. In 2016, two motorized ground-penetrating radar (GPR) surveys were conducted, resulting in a detailed three-dimensional dataset visualizing the circular ditches, palisades and dwellings of an adjacent settlement. The high contrast between the gravel sediments and the humic earthen backfill of the ditches, palisades and individual postholes resulted in a highly detailed visualization of the Middle Neolithic monument. Based on this survey, selected structures were investigated by targeted archaeological excavations to evaluate the GPR results and to take samples for radiocarbon dating. This paper presents a synopsis of all the methods used. An integrated interpretation of aerial photo information, magnetometry and GPR is conducted, and it is shown to what extent these could be verified by the targeted archaeological excavation. By a detailed analysis of all applied archaeological prospection methods, it is now possible to interpret the monument in its entirety and confirm its dating to the Middle Neolithic Lengyel cultural complex.
Flat-Top Line-Shaped Beam Shaping and System Design
Authors: Liu Che , Guo Yanling
Published: June 2022

In this study, the circular Gaussian spot emitted by a laser light source is shaped into a rectangular flat-top beam to improve the scanning efficiency of a selective laser sintering scanning system. A CO2 laser with a power of 200 W, wavelength of 10.6 μm, and spot diameter of 9 mm is shaped into a flat-top spot with a length and width of 0.5 × 0.1 mm, and the mapping function and flat-top Lorentzian function are calculated. We utilize ZEMAX to optimize the aspherical cylindrical lens of the shaping system and the cylindrical lens of the focusing system. We then calculate the energy uniformity of the flat-top line-shaped beam at distances from 500 to 535 mm and study the zoom displacement of the focusing lens system. The results indicated that the energy uniformity of the flat-top beam was greater than 80% at the distances considered, and the focusing system must precisely control the displacement of the cylindrical lens in the Y-direction to achieve precise zooming.
Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation
Authors: Zimermann Rastislav , Mohseni Ehsan , Vasilev Momchil , Loukas Charalampos , Vithanage Randika K. W. , Macleod Charles N. , Lines David , Javadi Yashar , Espirindio E Silva Misael Pimentel , Fitzpatrick Stephen , Halavage Steven , Mckegney Scott , Pierce Stephen Gareth , Williams Stewart , Ding Jialuo
Published: June 2022

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.
Multi-Swarm Algorithm for Extreme Learning Machine Optimization
Authors: Bacanin Nebojsa , Stoean Catalin , Zivkovic Miodrag , Jovanovic Dijana , Antonijevic Milos , Mladenovic Djordje
Published: June 2022

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.
Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter
Authors: Yang Ning , Li Jun , Xu Mingqiang , Wang Shuqing
Published: June 2022

The real-time identification of time-varying cable force is critical for accurately evaluating the fatigue damage of cables and assessing the safety condition of bridges. In the context of unknown wind excitations and only one available accelerometer, this paper proposes a novel cable force identification method based on an improved adaptive extended Kalman filter (IAEKF). Firstly, the governing equation of the stay cable motion, which includes the cable force variation coefficient, is expressed in the modal domain. It is transformed into a state equation by defining an augmented Kalman state vector with the cable force variation coefficient concerned. The cable force variation coefficient is then recursively estimated and closely tracked in real time by the proposed IAEKF. The contribution of this paper is that an updated fading-factor matrix is considered in the IAEKF, and the adaptive noise error covariance matrices are determined via an optimization procedure rather than by experience. The effectiveness of the proposed method is demonstrated by the numerical model of a real-world cable-supported bridge and an experimental scaled steel stay cable. Results indicate that the proposed method can identify the time-varying cable force in real time when the cable acceleration of only one measurement point is available.
Using Conventional Cameras as Sensors for Estimating Confidence Intervals for the Speed of Vessels from Single Images
Authors: Huillca Jose L. , Fernandes Leandro A. F.
Published: June 2022

In this paper, we describe an image-based approach for estimating the speed of a moving vessel using the wakes that remain on the surface of water after the vessel has passed. The proposed method calculates the speed of the vessel using only one RGB image. In this study, we used the vanishing line of the mean water plane, the camera height concerning the level of the tide, and the intrinsic parameters of the camera to perform geometric rectification on the surface plane of the water. We detected the location of troughs on one of the wake arms and computed the distance between them in the rectified image to estimate the speed of the vessel as a so-called inverse ship wake problem. We used a radar that was designed to monitor ships to validate the proposed method. We used statistical studies to determine the reliability and error propagation of the estimated values throughout the calculation process. The experiments showed that the proposed method produced precise and accurate results that agreed with the actual radar data when using a simple capture device, such as a conventional camera.
Editorial: Special Issue “Satellite Networks for Massive IoT Communication”
Authors: De Gaudenzi Riccardo , Soret Beatriz
Published: June 2022

The growing need to interconnect sensors and devices anytime, anywhere is stimulating the design and development of innovative solutions for efficiently supporting a wide variety of applications: from simple low-duty cycle sensors for which energy efficiency and low-cost are of paramount importance, to more demanding platforms (e [...]
A Review of Capabilities and Scope for Hybrid Integration Offered by Silicon-Nitride-Based Photonic Integrated Circuits
Authors: Gardes Frederic , Shooa Afrooz , De Paoli Greta , Skandalos Ilias , Ilie Stefan , Rutirawut Teerapat , Talataisong Wanvisa , Faneca Joaquín , Vitali Valerio , Hou Yaonan , Bucio Thalía Domínguez , Zeimpekis Ioannis , Lacava Cosimo , Petropoulos Periklis
Published: June 2022

In this review we present some of the recent advances in the field of silicon nitride photonic integrated circuits. The review focuses on the material deposition techniques currently available, illustrating the capabilities of each technique. The review then expands on the functionalisation of the platform to achieve nonlinear processing, optical modulation, nonvolatile optical memories and integration with III-V materials to obtain lasing or gain capabilities.
Pulse Oximeter Performance during Rapid Desaturation
Authors: Horakova Lenka , Roubik Karel
Published: June 2022

The reliability of pulse oximetry is crucial, especially in cases of rapid changes in body oxygenation. In order to evaluate the performance of pulse oximeters during rapidly developing short periods of concurrent hypoxemia and hypercapnia, 13 healthy volunteers underwent 3 breathing phases during outdoor experiments (39 phases in total), monitored simultaneously by five different pulse oximeters. A significant incongruity in values displayed by the tested pulse oximeters was observed, even when the accuracy declared by the manufacturers were considered. In 28.2% of breathing phases, the five used devices did not show any congruent values. The longest uninterrupted congruent period formed 74.4% of total recorded time. Moreover, the congruent periods were rarely observed during the critical desaturation phase of the experiment. The time difference between the moments when the first and the last pulse oximeter showed the typical study endpoint values of SpO2 85% and 75% was 32.1 ± 23.6 s and 24.7 ± 19.3 s, respectively. These results suggest that SpO2 might not be a reliable parameter as a study endpoint, or more importantly as a safety limit in outdoor experiments. In the design of future studies, more parameters and continuous clinical assessment should be included.
Exploring the Capabilities of a Piezoresistive Graphene-Loaded Waterborne Paint for Discrete Strain and Spatial Sensing
Authors: Tamburrano Alessio , Proietti Alessandro , Fortunato Marco , Pesce Nicola , Sarto Maria Sabrina
Published: June 2022

The development of a piezoresistive coating produced from dispersing graphene nanoplatelets (GNPs) inside a commercial water-based polyurethane paint is presented. The feasibility of its exploitation for realizing highly sensitive discrete strain sensors and to measure spatial strain distribution using linear and two-dimensional depositions was investigated. Firstly, the production process was optimized to achieve the best electromechanical response. The obtained materials were then subjected to different characterizations for structural and functional investigations. Morphological analyses showed a homogenous dispersion of GNPs within the host matrix and an average thickness of about 75 µm of the obtained nanostructured films. By several adhesion tests, it was demonstrated that the presence of the nanostructures inside the paint film lowered the adhesion strength by only 20% in respect to neat paint. Through electrical tests, the percolation curve of the nanomaterial was acquired, showing an effective electrical conductivity ranging from about 10−4 S/m to 3.5 S/m in relation to the different amounts of filler dispersed in the neat paint: in particular, samples with weight fractions of 2, 2.5, 3, 3.5, 4, 5 and 6 wt% of GNPs were produced and characterized. Next, the sensitivity to flexural strain of small piezoresistive sensors deposited by a spray-coating technique on a fiberglass-reinforced epoxy laminate beam was measured: a high gauge factor of 33 was obtained at a maximum strain of 1%. Thus, the sensitivity curve of the piezoresistive material was successively adopted to predict the strain along a multicontact painted strip on the same beam. Finally, for a painted laminate plate subjected to a mechanical flexural load, we demonstrated, through an electrical resistance tomography technique, the feasibility to map the electrical conductivity variations, which are strictly related to the induced strain/stress field. As a further example, we also showed the possibility of using the coating to detect the presence of conducting objects and damage.
An Empirical Study on Ensemble of Segmentation Approaches
Authors: Nanni Loris , Lumini Alessandra , Loreggia Andrea , Formaggio Alberto , Cuza Daniela
Published: June 2022

Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting of dangerous pathologies and thus mitigates the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors; one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentations. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results.
Integrated Sustainability Planning and Local Food Systems: Examining Areas of and Gaps in Food Systems Integration in Community Sustainability Plans for Municipalities across British Columbia
Authors: Issac Jofri , Newell Robert , Dring Colin , White Charmaine , Ghadiri Mohaddese , Pizzirani Stefania , Newman Lenore
Published: June 2022

No “one size fits all” approach exists for local sustainability and food systems planning. Such planning must balance needs for being both comprehensive and place-based. The current study explores this tension by examining Integrated Community Sustainability Plans (ICSP) developed by municipalities in British Columbia (BC), Canada. The research examines items and actions related to food systems (focusing on agriculture and food production) in the ICSPs of municipalities in different regions across BC to (1) identify how municipalities “integrate” food systems with other sustainability objectives, (2) elucidate how place and geography influence integrated planning, and (3) reveal gaps in integrated approaches to developing local food systems. The study employs document analysis and thematic coding methodology. The results indicate that common areas of food systems integration in ICSPs include local economy and education. Many plans outline goals for bolstering local food economies and building local food capacity through community participation and engagement. Findings also show how foci and approaches for developing sustainable food systems vary by region. The study elucidates how food systems are integrated within place-based sustainability plans as well as reveals gaps that local governments can address when adopting and implementing integrated sustainability plans for improving food systems.
Sustainable Use of Legume Residues: Effect on Nutritive Value and Ensiling Characteristics of Maize Straw Silage
Authors: Hawu Onke , Ravhuhali Khuliso Emmanuel , Mokoboki Hilda Kwena , Lebopa Cornelia Kedidimetse , Sipango Nkosomzi
Published: June 2022

The objective of this study was to investigate the nutritive value, in vitro dry matter degradability (IVDMD), and ensiling characteristics of legume foliage–maize straw silages. For silage, the proportion of legume to maize was 20%:80% (maize–lablab (ML), maize–indigenous cowpea (MI), maize–Betswit (MB), and maize–Dr Saunders (MD)). Maize alone (M) was used as a control, making up five treatments. Silages were opened after 45 days, and samples were taken for chemical composition, IVDMD, and fermentation characteristics analysis. Other samples were put through a seven-day aerobic stability test. All data were subjected to a one-way analysis of variance, in a completely randomized design (CRD). For the nutritive value of the silage, the ML had the highest (p < 0.05) crude protein (CP) content, when compared with other silages. Maize–Betswit had the lowest (p < 0.05) neutral detergent fibre (NDF), acid detergent fibre (ADF), and acid detergent lignin (ADL) content, when compared with other silages. The lowest (p < 0.05) ether-extract (EE) and hemicellulose content was recorded for MI silage. The lowest (p < 0.05) pH was recorded for maize silage. All silages had similar (p > 0.05) amounts of lactic acid (LA), water-soluble carbohydrates (WSC), yeast, and mould counts. The MI had the lowest (p < 0.05) carbon dioxide (CO2) concentration, when compared with other silages. The highest (p < 0.05) IVDMD was recorded for ML silage, from 24 h up to 72 h. The addition of legumes in maize straw resulted in improved silage quality and fermentation characteristics.
Unravelling the Paradoxical Seasonal Food Scarcity in a Peasant Microregion of Mexico
Authors: Rivera-Núñez Tlacaelel , García-Barrios Luis , Benítez Mariana , Rosell Julieta A. , García-Herrera Rodrigo , Estrada-Lugo Erin
Published: June 2022

Seasonal food scarcity during pre-harvest months is, widely, considered to be the principal manifestation of food insecurity, for some 600 million members of smallholder families, who rely on a variety of coping strategies. This paper analyses both the peasant-economy variables that explain the presence and intensity of seasonal food scarcity, and the coping strategies of 120 rural households in a microregion of southern Mexico. We, also, examine how supply networks for six archetypical foods of the peasant diet express robustness or vulnerability during seasons of abundance and scarcity. The method combines surveys, ethnographic fieldwork, statistical models and social network analyses. Results show that 74% of households experience at least one month of food scarcity annually, and 34% of shortages last more than six months. In total, 29% of affected households gather wild foods, and 14% use intense coping strategies, such as international migration, taking out rural loans, and parental food buffering. During scarce seasons, self-sufficiency networks for maize and beans contract, but still maintain the food supply of peasant households, while cash-consumption networks such as those of beef become accessible only to a small sector of economically differentiated households. In contrast to the vast majority of research, which simply reports the presence of seasonal food shortages and describes the coping strategies of rural households, this paper provides an in-depth analysis—based upon a novel methodological integration—of the socioeconomic, agrifood, and land tenure conditions that may determine why many peasant territories in the Global South face the “farmer–food-scarcity paradox”.
Food Waste Diversion from Landfills: A Cost–Benefit Analysis of Existing Technological Solutions Based on Greenhouse Gas Emissions
Authors: Sanciolo Peter , Rivera Eduardo , Navaratna Dimuth , Duke Mikel C.
Published: June 2022

Landfill disposals of food result in fugitive emissions of methane—a powerful greenhouse gas (GHG). This desktop study focuses on the cost and GHG emissions associated with food waste diversion from landfills using aerobic digesters with liquid outputs (ADLO). Despite the emerging popularity of ADLO units for food waste disposal, their cost and the GHG emissions associated with their use have not been independently quantified and compared to those of other food waste management options. This study compared landfill disposals, the currently available composting services, electric food dehydrators, and in-sink waste disposal units (garbage grinders). For a food waste production rate of 30 kg d−1, the landfill base case showed the lowest cost at USD 23 week−1. The modeled ADLO cost ranged from USD 20–42 week−1, depending on performance. Dehydrator costs were high at USD 29 week−1, largely due to the high energy intensity of the process. The cost of the current centralized composting was USD 51 week−1. The ADLO option with good performance was estimated to produce 5% of the GHG emissions of a landfill. This study showed that well-performing ADLO technology can be economically competitive with landfills and centralized composting and can markedly reduce GHG emissions.