bluuebunny/crossref_metadata_2025 huggingface dataset:
Physicochemical Studies on the Surface of Polyamide 6.6 Fabrics Functionalized by DBD Plasmas Operated at Atmospheric and Sub-Atmospheric Pressures
Authors: Nascimento Larissa , Gasi Fernando , Landers Richard , da Silva Sobrinho Argemiro , Aragão Eduardo , Fraga Mariana , Petraconi Gilberto , Chiappim William , Pessoa Rodrigo
Published: September 2020

This work proposes the use of a dielectric barrier discharge (DBD) reactor operating at atmospheric pressure (AP) using air and sub-atmospheric pressure (SAP) using air or argon to treat polyamide 6.6 (PA6.6) fabrics. Here, plasma dosages corresponding to 37.5 kW·min·m−2 for AP and 7.5 kW·min·m−2 for SAP in air or argon were used. The hydrophilicity aging effect property of untreated and DBD-treated PA6.6 samples was evaluated from the apparent contact angle. The surface changes in physical microstructure were studied by field emission scanning electron microscopy (FE-SEM). To prove the changes in chemical functional groups in the fibers, Fourier transform infrared spectroscopy (FTIR) was used, and the change in surface bonds was evaluated by energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). In addition, the whiteness effect was investigated by the color spectrophotometry (Datacolor) technique. The results showed that the increase in surface roughness by the SAP DBD treatment contributed to a decrease in and maintenance of the hydrophilicity of PA6.6 fabrics for longer. The SAP DBD in air treatment promoted an enhancement of the aging effect with a low plasma dosage (5-fold reduction compared with AP DBD treatment). Finally, the SAP DBD treatment using argon functionalizes the fabric surface more efficiently than DBD treatments in air.
End-of-Life Recycling Options of (Nano)Enhanced CFRP Composite Prototypes Waste—A Life Cycle Perspective
Authors: Petrakli Fotini , Gkika Anastasia , Bonou Alexandra , Karayannis Panagiotis , Koumoulos Elias P. , Semitekolos Dionisis , Trompeta Aikaterini-Flora , Rocha Nuno , Santos Raquel M. , Simmonds Guy , Monaghan Glen , Valota Giorgio , Gong Guan , Charitidis Costas A.
Published: September 2020

Life cycle assessment is a methodology to assess environmental impacts associated with a product or system/process by accounting resource requirements and emissions over its life cycle. The life cycle consists of four stages: material production, manufacturing, use, and end-of-life. This study highlights the need to conduct life cycle assessment (LCA) early in the new product development process, as a means to assess and evaluate the environmental impacts of (nano)enhanced carbon fibre-reinforced polymer (CFRP) prototypes over their entire life cycle. These prototypes, namely SleekFast sailing boat and handbrake lever, were manufactured by functionalized carbon fibre fabric and modified epoxy resin with multi-walled carbon nanotubes (MWCNTs). The environmental impacts of both have been assessed via LCA with a functional unit of ‘1 product piece’. Climate change has been selected as the key impact indicator for hotspot identification (kg CO2 eq). Significant focus has been given to the end-of-life phase by assessing different recycling scenarios. In addition, the respective life cycle inventories (LCIs) are provided, enabling the identification of resource hot spots and quantifying the environmental benefits of end-of-life options.
Design of an Interpenetrating Polymeric Network Hydrogel Made of Calcium-Alginate from a Thermos-Sensitive Pluronic Template as a Thermal-Ionic Reversible Wound Dressing
Authors: Chou Hsiao-Ying , Weng Chang-Chih , Lai Juin-Yih , Lin Shuian-Yin , Tsai Hsieh-Chih
Published: September 2020

Polymer-based hydrogels demonstrate superior performance when used as wound dressing. An ideal dressing should possess an active healing function, absorb wound exudates, and provide a moist interface on the wound for rapid injury repair and the prevention of pain and injury during replacement of the dressing. Thus, the aim of this study was to develop a novel, reversible, smart, interpenetrating polymeric network (IPN) by utilizing the thermosensitive network of pluronic F127 (PF127) as a template to regulate the conformation of calcium-ion-crosslinked alginate. We found that the IPN hydrogels formed soft and elastic thermosensitive networks, retaining their form even after absorbing a large amount of wound exudate. The exterior of the hydrogels was made up of a rigid calcium alginate network that supported the entire hydrogel, promoting the stability of the vascular endothelial growth factor (VEGF) payload and controlling its release when the hydrogel was applied topically to wounds. Raman spectroscopy confirmed the layered structure of the hydrogel, which was found to easily disintegrate even after moderate rinsing of the wound with cold phosphate-buffered saline. Taken together, these results show that the IPN hydrogel developed in this study could be a promising delivery platform for growth factors to accelerate wound healing.
Antibacterial Activity of Chitosan Nanoparticles: A Review
Authors: Chandrasekaran Murugesan , Kim Ki , Chun Se
Published: September 2020

In recent years, nanotechnology has attracted attention in many fields because it has several up-and-coming novel uses. Many researchers have suggested that chitosan nanoparticles (CS-NPs) and their derivatives are one of the best nanomaterials for delivering antibacterial activity. CS-NPs have a broad spectrum of antibacterial activity, but they manifest different inhibitory efficacy against gram-negative (G−) and gram-positive (G+) bacterial species. The mechanism of antibacterial action is an intricate process that varies between G− and G+ bacteria as a result of the differences in cell wall and cell membrane chemistry. In previous studies, greater antibacterial activity was more evident against G− bacteria than G+ bacteria, whereas in some studies G+ bacteria were more sensitive. Researchers predicted that the varied responses of bacteria are caused by the mixed hydrophilicity and negative charge distribution on the bacterial surface. Moreover, its activity depends on a number of variables including bacterial target (i.e., G− or G+ bacteria) and bacterial growth, as well as its concentration, pH, zeta-potential, molecular weight, and degree of acetylation. Therefore, this review examines current research on the mechanisms and factors affecting antibacterial activity, and application of CS-NPs specifically against animal and plant pathogenic bacteria.
Preparation and Characterization of New Electrically Conductive Composites Based on Expanded Graphite with Potential Use as Remote Environmental Detectors
Authors: Prostredný Martin , Krupa Igor , Špitalský Zdenko
Published: September 2020

The presented paper is focused on studying electrically conductive composites based on an elastomeric matrix and expanded graphite as the filler. A potential application as an environmental remote detector was studied. The influence of filler particle size, film thickness, detector length, temperature, and the amount of oil on the detector response rate were explored. Peel tests were performed in order to investigate the adhesion of prepared detector films to different materials. Expanded graphite with average particle size 5 µm was chosen for the experiments due to its fastest response. Decreasing the detector film thickness has caused an increase in the response rate but also a decrease in the signal measured. The response rate of the detector system was in a practical range even for lower temperatures. From the obtained data, the proposed detector seems to be suitable for a practical application.
Does Religiosity Affect Attitudes toward the Ethics of Tax Evasion? The Case of Turkey
Authors: McGee Robert W. , Benk Serkan , Yüzbaşı Bahadır , Budak Tamer
Published: September 2020

This study surveys the opinion of a wide segment of Turkish society on the ethics of tax evasion. The survey instrument includes 18 statements used to justify tax evasion in the past. The research also finds that some reasons to justify tax evasion proved more attractive to participants than others. In our survey, the strongest support for tax evasion was in cases where the government abused human rights, where the government was corrupt or wasted tax funds, or where the taxpayer did not benefit from the tax expenditures. Conversely, the weakest arguments were in cases where the taxpayer did benefit from the tax expenditures or where the tax funds were spent wisely. What separates this study from others on the ethics of tax evasion is that it addresses interpersonal and intrapersonal religiosity. Its finding confirms the existence of an important relationship between both interpersonal and intrapersonal religiosity and the view toward the ethics of tax evasion.
Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text
Authors: Aldana-Bobadilla Edwin , Molina-Villegas Alejandro , Lopez-Arevalo Ivan , Reyes-Palacios Shanel , Muñiz-Sanchez Victor , Arreola-Trapala Jean
Published: September 2020

The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task could be approached through a machine learning approach, in which case a model is trained to recognize a sequence of characters (words) corresponding to geographic entities. The second task consists of assigning such entities to their most likely coordinates. Frequently, the latter process involves solving referential ambiguities. In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation. Once place names are recognized in an input text, they are solved using a grammar, in which a set of rules specifies how ambiguities could be solved, in a similar way to that which a person would utilize, considering the context. As a result, we have an assignment of the most likely geographic properties of the recognized places. We propose an assessment measure based on a ranking of closeness relative to the predicted and actual locations of a place name. Regarding this measure, our method outperforms OpenStreetMap Nominatim. We include other assessment measures to assess the recognition ability of place names and the prediction of what we called geographic levels (administrative jurisdiction of places).
Automatic Grapevine Trunk Detection on UAV-Based Point Cloud
Authors: Jurado Juan M. , Pádua Luís , Feito Francisco R. , Sousa Joaquim J.
Published: September 2020

The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.
A Novel Method for Mitigating the GPS Multipath Effect Based on a Multi-Point Hemispherical Grid Model
Authors: Wang Yawei , Zou Xuan , Deng Chenlong , Tang Weiming , Li Yangyang , Zhang Yongfeng , Feng Jin
Published: September 2020

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources.
Development of a Land Surface Temperature Retrieval Algorithm from GK2A/AMI
Authors: Choi Youn-Young , Suh Myoung-Seok
Published: September 2020

Land surface temperature (LST) is an important geophysical element for understanding Earth systems and land–atmosphere interactions. In this study, we developed a nonlinear split-window LST retrieval algorithm for the observation area of GEO-KOMPSAT-2A (GK2A), the next-generation geostationary satellite in Korea. To develop the GK2A LST retrieval algorithm, radiative transfer model simulation data, considering various impacting factors, were constructed. The LST retrieval algorithm was developed with a total of six equations as per day/night and atmospheric conditions (dry/normal/wet), considering the effects of diurnal variation of LST and atmospheric conditions on LST retrieval. The emissivity of each channel required for LST retrieval was calculated in real-time with the vegetation cover method using the consecutive 8-day cycle vegetation index provided by GK2A. The indirect validation of the results of GK2A LST with Moderate Resolution Imaging Spectroradiometer (MODIS) LST Collection 6 showed a high correlation coefficient (0.969), slightly warm bias (+1.227 K), and root mean square error (RMSE) (2.281 K). Compared to the MODIS LST, the GK2A LST showed a warm bias greater than +1.8 K during the day, but a relatively small bias (<+0.7 K) at night. Based on the results of the validation with in situ measurements from the Tateno station of the Baseline Surface Radiation Network, the correlation coefficient was 0.95, bias was +0.523 K, and RMSE was 2.021 K.
Water Volume Variations Estimation and Analysis Using Multisource Satellite Data: A Case Study of Lake Victoria
Authors: Lin Yi , Li Xin , Zhang Tinghui , Chao Nengfang , Yu Jie , Cai Jianqing , Sneeuw Nico
Published: September 2020

The spatiotemporal changes of lake water resources objectively reflect not only the process of the water resources balance, but also the ecological environment changes in the lake area. In recent decades, climate changes and human activities have caused great impacts on the spatial distribution of the earth’s water resources and the spatiotemporal process of the surface water cycle, which has caused a series of ecological crises and environmental problems, such as the drying-up of inland lakes, the disappearance of the oasis, water shortage or flooding and water pollution. Therefore, monitoring and fully understanding the dynamic changes of lakes is of great scientific significance for grasping regional water balance, water resources management, and sustainable development of the ecological environment. In this study, we focus on using multi-source satellite data on the estimation of water volume and multi-timescale variations analysis for large scale lakes. This study combines the problems in the practical application of “African Water Action”, taking the largest lake in Africa, Lake Victoria, as the study area, and utilizes long-term serial multi-source satellite data of the past 15 years (2003–2017), including Moderate-resolution Imaging Spectroradiometer (MODIS), Jason-1/-2/-3 and Gravity Recovery and Climate Experiment (GRACE) to perform the comprehensive analysis on the water volume change estimation. Firstly, the satellite altimetry data of Jason-1/-2/-3 and MODIS imagery was used to calculate series of water level, and to extract series of water surface area, respectively. On this basis, a more accurate regression model between the area and water level variation (ΔH) was constructed. Then, the model between water volume variation (ΔV) and ΔH, derived from area-ΔH model, was applied to calculate the relative water volume of Lake Victoria. Meanwhile, terrestrial water storage (TWS) changes between 2003 and 2016, derived from GRACE data, were also used for a comparative verification of the ΔV results. The results show the long-term series change trends of ΔV and the TWS are the same. Finally, the multi-timescale analysis of water volume changes was carried out on different time scales, such as the inter-annual, inter-monthly, and variation period.
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications
Authors: Hoeser Thorsten , Bachofer Felix , Kuenzer Claudia
Published: September 2020

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.
Surgical Guidance for Removal of Cholesteatoma Using a Multispectral 3D-Endoscope
Authors: Wisotzky Eric L. , Rosenthal Jean-Claude , Wege Ulla , Hilsmann Anna , Eisert Peter , Uecker Florian C.
Published: September 2020

We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, even for experienced surgeons. It is very difficult to distinguish between bone and cholesteatoma. In addition, it can even reoccur if not all tissue particles of the cholesteatoma are removed, which leads to undesirable follow-up operations. Therefore, we propose an image-based method that combines multispectral tissue classification and 3D reconstruction to identify all parts of the removed tissue and determine their metric dimensions intraoperatively. The designed multispectral filter-wheel 3D-endoscope prototype can switch between narrow-band spectral and broad-band white illumination, which is technically evaluated in terms of optical system properties. Further, it is tested and evaluated on three patients. The wavelengths 400 nm and 420 nm are identified as most suitable for the differentiation task. The stereoscopic image acquisition allows accurate 3D surface reconstruction of the enhanced image information. The first results are promising, as the cholesteatoma can be easily highlighted, correctly identified, and visualized as a true-to-scale 3D model showing the patient-specific anatomy.
Natural Frequency Response Evaluation for RC Beams Affected by Steel Corrosion Using Acceleration Sensors
Authors: Zhang Yuwei , Cheng Yongchun , Tan Guojin , Lyu Xiang , Sun Xun , Bai Yunshuo , Yang Shuting
Published: September 2020

This paper presented a laboratory investigation for analyzing the natural frequency response of reinforced concrete (RC) beams affected by steel corrosion. The electrochemical acceleration technique induced the corroded RC beams until the predetermined value of the steel corrosion ratio was achieved. Then, the natural frequency responses of the corroded beams were tested utilizing piezoelectric acceleration sensors. The damage states of the corroded beams were assessed through the measurement of crack parameters and the equivalent elastic modulus of the beams, which aims to clarify the fundamental characteristics of the dynamic response for the corroded RC beam with the increased steel corrosion ratio. The results revealed that steel corrosion reduces the bending stiffness of the RC beams and, thus, reduces the modal frequency. The variation of natural frequency can identify the corrosion damage even if no surface cracking of the RC beam, and the second-order frequency should be more indicative of the damage scenario. The degradations of stiffness and the natural frequency were estimated in this study by the free vibration equation of a simply supported beam, and a prediction method for the RC beam’s residual service life was established. This study supports the use of variations in natural frequency as one diagnostic indicator to evaluate the health of RC bridge structures.
Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes
Authors: Loss Theresa , Bergmann Alexander
Published: September 2020

Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in operation is needed to optimise turbine settings and, consequently, to reduce alternate bending. In our approach, an acceleration model was used to analyse periodically occurring deviations from uniform bending. By using hierarchical clustering, significant bending patterns could be extracted and patterns were analysed with regard to reference data. In a simulation of alternate bending effects, various effects were successfully represented by different bending patterns. A real data experiment with accelerometers mounted at the blade tip of turbine blades demonstrated a clear relation between the rotation frequency and the resulting bending patterns. Additionally, the markedness of bending shapes could be used to assess the amount of alternate bending of the blade in both simulations and experiments. The results demonstrate that model-based bending shapes provide a strong indication for alternate bending and, consequently, can be used to optimise turbine settings.
Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion
Authors: Xu Haocheng , Li Shenghong , Lee Caroline , Ni Wei , Abbott David , Johnson Mark , Lea Jim M. , Yuan Jinhong , Campbell Dana L. M.
Published: September 2020

Understanding social interactions in livestock groups could improve management practices, but this can be difficult and time-consuming using traditional methods of live observations and video recordings. Sensor technologies and machine learning techniques could provide insight not previously possible. In this study, based on the animals’ location information acquired by a new cooperative wireless localisation system, unsupervised machine learning approaches were performed to identify the social structure of a small group of cattle yearlings (n=10) and the social behaviour of an individual. The paper first defined the affinity between an animal pair based on the ranks of their distance. Unsupervised clustering algorithms were then performed, including K-means clustering and agglomerative hierarchical clustering. In particular, K-means clustering was applied based on logical and physical distance. By comparing the clustering result based on logical distance and physical distance, the leader animals and the influence of an individual in a herd of cattle were identified, which provides valuable information for studying the behaviour of animal herds. Improvements in device robustness and replication of this work would confirm the practical application of this technology and analysis methodologies.
Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones
Authors: Opiela Miroslav , Galčík František
Published: September 2020

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.
Generation of Vortex Optical Beams Based on Chiral Fiber-Optic Periodic Structures
Authors: Gizatulin Azat , Meshkov Ivan , Vinogradova Irina , Bagmanov Valery , Grakhova Elizaveta , Sultanov Albert
Published: September 2020

In this paper, we consider the process of fiber vortex modes generation using chiral periodic structures that include both chiral optical fibers and chiral (vortex) fiber Bragg gratings (ChFBGs). A generalized theoretical model of the ChFBG is developed including an arbitrary function of apodization and chirping, which provides a way to calculate gratings that generate vortex modes with a given state for the required frequency band and reflection coefficient. In addition, a matrix method for describing the ChFBG is proposed, based on the mathematical apparatus of the coupled modes theory and scattering matrices. Simulation modeling of the fiber structures considered is carried out. Chiral optical fibers maintaining optical vortex propagation are also described. It is also proposed to use chiral fiber-optic periodic structures as sensors of physical fields (temperature, strain, etc.), which can be applied to address multi-sensor monitoring systems due to a unique address parameter—the orbital angular momentum of optical radiation.
A Wearable Device for Breathing Frequency Monitoring: A Pilot Study on Patients with Muscular Dystrophy
Authors: Cesareo Ambra , Nido Santa Aurelia , Biffi Emilia , Gandossini Sandra , D’Angelo Maria Grazia , Aliverti Andrea
Published: September 2020

Patients at risk of developing respiratory dysfunctions, such as patients with severe forms of muscular dystrophy, need a careful respiratory assessment, and periodic follow-up visits to monitor the progression of the disease. In these patients, at-home continuous monitoring of respiratory activity patterns could provide additional understanding about disease progression, allowing prompt clinical intervention. The core aim of the present study is thus to investigate the feasibility of using an innovative wearable device for respiratory monitoring, particularly breathing frequency variation assessment, in patients with muscular dystrophy. A comparison of measurements of breathing frequency with gold standard methods showed that the device based on the inertial measurement units (IMU-based device) provided optimal results in terms of accuracy errors, correlation, and agreement. Participants positively evaluated the device for ease of use, comfort, usability, and wearability. Moreover, preliminary results confirmed that breathing frequency is a valuable breathing parameter to monitor, at the clinic and at home, because it strongly correlates with the main indexes of respiratory function.
Experimental Study on Whole Wind Power Structure with Innovative Open-Ended Pile Foundation under Long-Term Horizontal Loading
Authors: Liu Junwei , Wan Zhipeng , Dai Xingke , Jeng Dongsheng , Zhao Yanping
Published: September 2020

The offshore wind energy (OWE) pile foundation is mainly a large diameter open-ended single pile in shallow water, which has to bear long-term horizontal cyclic loads such as wind and waves during OWE project lifetime. Under the complex cyclic loads, the stress and displacement fields of the pile-soil system change continuously, which affects the dynamic characteristics of the pile foundation. Within the service life of the pile foundation, the pile-soil system has irreversible cumulative deformation, which further causes damage to the whole structure. Therefore, it is important to examine the overall dynamic characteristics of wind power foundation under high cycle. In this paper, in the dry sand foundation, taking the Burbo Bank 3.6 MW offshore turbine-foundation structure as the prototype, the horizontal cyclic loading model tests of the wind power pile foundation with the scale of 1:50 were carried out. Considering the factors such as loading frequency and cyclic load ratio, the horizontal dynamic characteristics of the whole OWE pile foundation are studied. The comparison results between the maximum bending moment of pile and the fitting formula are discussed. In conclusion, moment of OWE pile shaft is corresponding to the loading frequency (f = 9 HZ) and loading cycles by fitting formulas. The fatigue damage of the OWE pile does not occurs with low frequencies in high cycles.
Modelling a Spectral Index to Detect Dispersed Oil in a Seawater Column Depending on the Viewing Angle: Gulf of Gdańsk Case Study
Authors: Baszanowska Emilia , Otremba Zbigniew , Piskozub Jacek
Published: September 2020

This paper analyzes the digital modelling of radiance reflectance of the sea surface when the water column is polluted by oil-in-water emulsion. A method tracking the fate of two billion virtual solar photons was applied to obtain the angular distribution of bottom-up radiance for a plane of sunlight striking the sea surface. For the calculations, the inherent optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea) were used. The analyses were performed for two types of oils with extremely different optical properties for an oil concentration of 10 ppm and for a roughened sea surface with a wind speed of 2 m/s. The spectral index for oil detection in seawater for different viewing angles was determined based on the results obtained for reflectance at eight wavelengths in the range of 412–676 nm for viewing angle in the range from 80° to 0°, both on the side of incidence of direct sunlight and on the opposite side. The resulting calculated spectral indexes for different wavelength combinations indicated significant dependence on the viewing angle.
New Insights into the Design and Application of a Passive Acoustic Monitoring System for the Assessment of the Good Environmental Status in Spanish Marine Waters
Authors: Lara Guillermo , Miralles Ramón , Bou-Cabo Manuel , Esteban José Antonio , Espinosa Víctor
Published: September 2020

Passive acoustic monitoring systems allow for non-invasive monitoring of underwater species and anthropogenic noise. One of these systems has been developed keeping in mind the need to create a user-friendly tool to obtain the ambient noise indicators, while at the same time providing a powerful tool for marine scientists and biologists to progress in studying the effect of human activities on species and ecosystems. The device is based on a low-power processor with ad-hoc electronics, ensuring that the system has efficient energy management, and that the storage capacity is large enough to allow deployments for long periods. An application is presented using data from an acoustic campaign done in 2018 at El Gorguel (Cartagena, Spain). The results show a good agreement between theoretical maps created using AIS data and the ambient noise level indicators measured in the frequency bands of 63 Hz and 125 Hz specified in the directive 11 of the EU Marine Strategy Framework Directive. Using a 2D representation, these ambient noise indicators have enabled repetitive events and daily variations in boat traffic to be identified. The ship noise registered can also be used to track ships by using the acoustic signatures of the engine propellers’ noise.
Design of a Small Unmanned Aircraft System for Bridge Inspections
Authors: Whitley Travis , Tomiczek Andy , Tripp Chad , Ortega Andrew , Mennu Matlock , Bridge Jennifer , Ifju Peter
Published: September 2020

Bridge inspections are an important procedure for maintaining the infrastructure vital to our economy and well-being. The current methodology of utilizing specialized equipment such as snooper trucks and scaffolding to support manned-inspections poses a significant financial cost, disrupts traffic, and is dangerous to the inspectors and public. The advent of unmanned aerial systems (UAS), more commonly called drones, presents a practical solution that promises reduced cost, enhanced safety, and is significantly less intrusive than previous methodologies. Current limitations in the implementation of UAS include the reliance on a skilled operator and/or the requirement for a UAS to operate in a cluttered, GPS-denied environment. A solution to these challenges is presented in this paper by utilizing commercial off-the-shelf (COTS) hardware including laser rangefinders, optical flow sensors, and live video telemetry. Included in the system is the obstacle avoidance equipped drone and a ground station intended to be manned by a pilot and bridge inspector. The proposed custom-fabricated UAS was implemented during eight inspections of Florida Department of Transportation (FDOT) bridges. The UAS was able to navigate under GPS-denied and obstacle-laden bridge decks with position-hold performance comparable to, if not better than, a COTS unit in an unobstructed environment. The position hold capability maintained an altitude of ±12.8 cm with a horizontal hold of ±435 cm. Details of the hardware, algorithm development, and suggestions for future research are discussed in this paper.
Using Velocity to Predict the Maximum Dynamic Strength in the Power Clean
Authors: Haff G. Gregory , Garcia-Ramos Amador , James Lachlan P.
Published: September 2020

The primary aim of the present study was to examine the commonly performed training exercise for athlete preparation. Twenty-two recreationally trained males (age: 26.3 ± 4.1 y, height: 1.80 ± 0.07 m; body mass (BM): 87.01 ± 13.75 kg, 1-repetitoon maximum(1-RM)/BM: 0.90 ± 0.19 kg) participated in the present study. All subjects had their 1-RM power clean tested with standard procedures. On a separate testing day, subjects performed three repetitions at 30% and 45%, and two repetitions at 70% and 80% of their 1-RM power clean. During all trials during both sessions, peak velocity (PV) and mean velocity (MV) were measured with the use of a GymAware device. There were no significant differences between the actual and estimated 1-RM power clean (p = 0.37, ES = −0.11) when the load-PV profile was utilized. There was a large typical error (TE) present for the load-PV- and load-MV-estimated 1-RM values. Additionally, the raw TE exceeded the smallest worthwhile change for both load-PV and load-MV profile results. Based upon the results of this study, the load-velocity profile is not an acceptable tool for monitoring power clean strength.
Citizen Science in the Field: Co-experimentation at Pilot Scale for Sustainable Use of Natural Resources
Authors: Kral Rosana Maria , Maftukhah Rizki , Mentler Axel , Murtiningrum Murtiningrum , Ngadisih Ngadisih , Keiblinger Katharina Maria
Published: September 2020

Natural resource management is a cross-sectoral topic, as reflected by its inclusion in several of the United Nations Sustainable Development Goals (e.g., SDGs 2, 6, 12, 15). In the study area on Bangka Island, Indonesia, agriculture is a pillar of local food security and livelihoods, making restoration of degraded lands also a local issue. The present study aims at restoring degraded land after tin mining and at restoring the natural soil base rendering it more suitable for agriculture. We use co-experimentation with citizens as a tool to develop options for re-habilitation at a pilot scale. The recruitment process in this study was reversed insofar as local citizens were at the origin of the project idea. Consequently, buy-in was high among local stakeholders. This set to increase the probability of successfully scaling up effective and actionable practices that were developed during co-experimentation at both local and regional levels. Co-experimentation provided a platform for exchange between local citizens and scientists. Citizens did not need to learn new skills to be able to participate in the scientific process and could autonomously evaluate results. We see involvement of citizens in this type of scientific projects not only as feasible, but as rewarding for all involved partners and as beneficial for the project outcomes. In light of the call for partnerships to achieve the Sustainable Development Goals, we can only recommend investing in communication and relationship building to work together on better solutions to the challenges we face.
Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma
Authors: Yum Sang-Guk , Son Kiyoung , Son Seunghyun , Kim Ji-Myong
Published: September 2020

Extensive use has been made of lifecycle-cost assessment to enhance the cost-effectiveness and resilience of facilities management. However, if such assessments are to be truly effective, supplemental information will be needed on the major costs to be expected over buildings’ entire lives. Electricity generation and distribution systems, for example, are absolutely indispensable to industry and human society, not least in the operation of buildings and other infrastructure as networks. The widespread disruption that ensues when such power systems are damaged often carries considerable repair costs. Natural disasters likewise can cause extensive societal, economic, and environmental damage. Such damage is often associated with lengthy power outages that, as well as being directly harmful, can hinder emergency response and recovery. Accordingly, the present study investigated the correlations of natural hazard indicators such as wind speed and rainfall, along with environmental data regarding the power failure in Florida caused by Hurricane Irma in 2017 utilizing multiple regression analysis. The environmental data in question, selected on the basis of a thorough literature review, was tree density. Our analysis indicated that the independent variables, maximum wind speed, total rainfall, and tree density, were all significantly correlated with the dependent variable, power failure. Among these, rainfall was the least significant. Despite there being only three independent variables in the model, its adjusted coefficient of determination (0.512) indicated its effectiveness as a predictor of the power outages caused by Hurricane Irma. As such, our results can serve the construction industry’s establishment of advanced safety guidelines and structural designs power transmission systems in regions at risk of hurricanes and typhoons. Additionally, insurance companies’ loss-assessment modeling for power-system facilities would benefit from incorporating the three identified risk indicators. Finally, our findings can serve as a useful reference to policymakers tasked with mitigating power outages’ effects on infrastructure in hurricane-prone areas. It is hoped that this work will be extended, facilitating infrastructure restoration planning and making societies and economies more sustainable.
Rye and Oat Agricultural Wastes as Substrate Candidates for Biomass Production of the Non-Conventional Yeast Yarrowia lipolytica
Authors: Drzymała Katarzyna , Mirończuk Aleksandra Maria , Pietrzak Witold , Dobrowolski Adam
Published: September 2020

The aim of this study was to test rye straw, rye bran and oat bran hydrolysates as substrates for growth of the yeast Yarrowia lipolytica, a microorganism known to have large biotechnological potential. First, after the combined process of acid-enzymatic hydrolysis, the concentration and composition of fermentable monosaccharides in the obtained hydrolysates were analyzed. Glucose was the main sugar, followed by xylose and arabinose. Rye bran hydrolysate had the highest sugar content—80.8 g/L. The results showed that this yeast was able to grow on low-cost medium and produce biomass that could be used as a feed in the form of single cell protein. The biomass of yeast grown in oat bran hydrolysate was over 9 g/L after 120 h, with the biomass total yield and total productivity values of 0.141 g/g and 0.078 g/h, respectively. The protein contents in yeast biomass were in the range of 30.5–44.5% of dry weight. Results obtained from Y. lipolytica cultivated in rye bran showed high content of exogenous amino acid (leucine 3.38 g, lysine 2.93 g, threonine 2.31 g/100 g of dry mass) and spectrum of unsaturated fatty acid with predominantly oleic acid—59.28%. In conclusion, these results demonstrate that lignocellulosic agricultural waste, after hydrolysis, could be efficiently converted to feed-related yeast biomass.
Sustainability Management and Performance in the Urban Corporate Economy: A Systematic Literature Review
Authors: Lăzăroiu George , Ionescu Luminița , Andronie Mihai , Dijmărescu Irina
Published: September 2020

In this article, we cumulate previous research findings indicating that organizations advance to superior phases of environmental management development in order to attain corporate sustainability by the use of participative decision-making. We contribute to the literature on corporate sustainability management and performance by showing that the correlation between sustainable development governance, organizational knowledge, sustainable organizational development, and corporate sustainability, which shapes corporate environmental and sustainability management. Throughout June 2020, we conducted a quantitative literature review of ProQuest, Scopus, and the Web of Science databases, with search terms including “corporate sustainability”, “corporate sustainability management”, “corporate sustainability performance”, “sustainability reporting”, “sustainable supply chain management”, “sustainable corporate development”, and “environmental management systems”. As we inspected research published exclusively in the past two years, only 338 articles met the eligibility criteria. By eliminating the findings that were questionable, unsubstantiated by replication, or too general, and due to space limitations, we selected 93, mainly empirical, sources. Future research should investigate whether corporate governance systems, through organizational sustainability practices and performance reporting, can shape operational environmental sustainability and sustainable organizational culture.
Fluctuations in Hong Kong Hotel Industry Room Rates under the 2019 Novel Coronavirus (COVID-19) Outbreak: Evidence from Big Data on OTA Channels
Authors: Wu Feiran , Zhang Qianxi , Law Rob , Zheng Tianxiang
Published: September 2020

The infectious pneumonia caused by the 2019 novel coronavirus (COVID-19) has spread rapidly worldwide, crippling the global tourism industry’s development and operations. In Hong Kong, where tourism is a pillar industry, the hotel industry is essential to maintaining a stable economy. Facing multiple forms of pressure, the industry’s status deserves close attention. More than 200 hotels in Hong Kong were taken as the research set in this study. A Python-based web crawler was used to collect daily hotel prices from various online travel agencies. Repeated-measures analysis of variance (ANOVA), correlation analysis, and descriptive analysis were employed to study hotels’ room rate fluctuations over time. Results indicated that room rates across hotels in Hong Kong were primarily influenced by holidays and festivities prior to COVID-19, whereas rates tended to decline after the outbreak. Data analysis based on hotels’ star ratings revealed that 5-star hotels were relatively less affected by COVID-19 while 4- and 4.5-star hotels were most seriously affected. District-level analysis also showed that hotel room rates were differentially influenced by the virus: Hong Kong’s Islands district was hit hardest, followed by Kowloon. These findings offer valuable implications for hotel managers and relevant government departments in making rational decisions based on the current market state.