bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Strength of Triply Periodic Minimal Surface Lattices Subjected to Three-Point Bending
Authors: Lin Zo-Han , Pan Jyun-Hong , Li Hung-Yuan
Published: July 2022

Sandwich panel structures (SPSs) with lattice cores can considerably lower material consumption while simultaneously maintaining adequate mechanical properties. Compared with extruded lattice types, triply periodic minimal surface (TPMS) lattices have light weight but better controllable mechanical properties. In this study, the different types of TPMS lattices inside an SPS were analysed comprehensively. Each SPS comprised two face sheets and a core filled with 20×5×1 TPMS lattices. The types of TPMS lattices considered included the Schwarz primitive (SP), Scherk’s surface type 2 (S2), Schoen I-graph-wrapped package (I-WP), and Schoen face-centred cubic rhombic dodecahedron (F-RD). The finite element method was applied to determine the mechanical performance of different TPMS lattices at different relative densities inside the SPS under a three-point bending test, and the results were compared with the values calculated from analytical equations. The results showed a difference of less than 21% between the analytical and numerical results for the deformation. SP had the smallest deformation among the TPMS lattices, and F-RD can withstand the highest allowable load. Different failure modes were proposed to predict potential failure mechanisms. The results indicated that the mechanical performances of the TPMS lattices were mainly influenced by the lattice geometry and relative density.
Microscopic and Spectroscopic Imaging and Thermal Analysis of Acrylates, Silicones and Active Pharmaceutical Ingredients in Adhesive Transdermal Patches
Authors: Mikolaszek Barbara , Jamrógiewicz Marzena , Mojsiewicz-Pieńkowska Krystyna , Sznitowska Małgorzata
Published: July 2022

Dermal or transdermal patches are increasingly becoming a noteworthy alternative as carriers for active pharmaceutical ingredients (APIs), which makes their detailed physicochemical evaluation essential for pharmaceutical development. This paper demonstrates mid-infrared (FTIR) and Raman spectroscopy with complementary microscopic methods (SEM, optical and confocal Raman microscopy) and differential scanning calorimetry (DSC) as tools for the identification of the state of model API (testosterone TST, cytisine CYT or indomethacin IND) in selected adhesive matrices. Among the employed spectroscopic techniques, FTIR and Raman may be used not only as standard methods for API identification in the matrix, but also as a means of distinguishing commercially available polymeric materials of a similar chemical structures. A novel approach for the preparation of adhesive polymers for the FTIR analysis was introduced. In silicone matrices, all three APIs were suspended, whereas in the case of the acrylic PSA, Raman microscopy confirmed that only IND was dissolved in all three acrylic matrices, and the dissolved fraction of the CYT differed depending on the matrix type. Moreover, the recrystallization of TST was observed in one of the acrylates. Interestingly, a DSC analysis of the acrylic patches did not confirm the presence of the API even if the microscopic images showed suspended particles.
Removal and Mechanism of Cadmium, Lead and Copper in Water by Functional Modification of Silkworm Excrement Biochar
Authors: Bian Pengyang , Liu Yixuan , Zheng Xiaoqin , Shen Weibo
Published: July 2022

A new type of biochar, called GBC, was prepared from silkworm excrement, and then modified by chitosan combined with pyromellitic dianhydride. The removal of mono-metal and polymetals (Pb, Cd and Cu) from an aqueous solution by GBC was investigated in this research. Compared to unmodified biochar, the removal rate of Pb and Cd by GBC was about 12% higher, while that of Cu was about 94.6% higher. It also shows the types of functional groups in biochar have a great impact on their adsorption. The removal of Pb is mainly involved in the N-C=O functional group, the removal of Cd is mainly involved in N-containing functional group and C=C bond, and that of Cu is mainly involved in N-containing functional group, carboxyl group, hydroxyl group, and a carbonyl group. Five adsorption–desorption cycles of GBC were carried out, and it was found that the adsorption capacities of GBC for Pb, Cd and Cu decreased by 7.28%, 10.78% and 6.07%, respectively, indicating that GBC had a good renewable performance. The adsorption capacity of GBC for Cu in different water samples is between 89.62 and 93.47 mg·g−1, indicating that GBC has great application potential for the removal of Cu in wastewater.
Simultaneous Removal of Cr(VI) and Phenol from Water Using Silica-di-Block Polymer Hybrids: Adsorption Kinetics and Thermodynamics
Authors: Qu Jia , Yang Qiang , Gong Wei , Li Meilan , Cao Baoyue
Published: July 2022

Heavy metal ions and organic pollutants often coexist in industrial effluents. In this work, silica-di-block polymer hybrids (SiO2-g-PBA-b-PDMAEMA) with two ratios (SiO2/BA/DMAEMA = 1/50/250 and 1/60/240) were designed and prepared for the simultaneous removal of Cr(VI) and phenol via a surface-initiated atom-transfer radical polymerization process using butyl methacrylate (BA) as a hydrophobic monomer and 2-(Dimethylamino)ethylmethacrylate (DMAEMA) as a hydrophilic monomer. The removal efficiency of Cr(VI) and phenol by the hybrids reached 88.25% and 88.17%, respectively. The sample with a larger proportion of hydrophilic PDMAEMA showed better adsorption of Cr(VI), and the sample with a larger proportion of hydrophobic PBA showed better adsorption of phenol. In binary systems, the presence of Cr(VI) inhibited the adsorption of phenol, yet the presence of phenol had a negligible effect on the adsorption of Cr(VI). Kinetics studies showed that the adsorption of Cr(VI) and phenol fitted the pseudo-second-order model well. Thermodynamic studies showed that the adsorption behavior of Cr(VI) and phenol were better described by the Langmuir adsorption isotherm equation, and the adsorption of Cr(VI) and phenol were all spontaneous adsorptions driven by enthalpy. The adsorbent still possessed good adsorption capacity for Cr(VI) and phenol after six adsorption–desorption cycles. These findings show that SiO2-g-PBA-b-PDMAEMA hybrids represent a satisfying adsorption material for the simultaneous removal of heavy metal ions and organic pollutants.
Extraction, Structure and Immunoregulatory Activity of Low Molecular Weight Polysaccharide from Dendrobium officinale
Authors: Sun Su-Jun , Deng Peng , Peng Chun-E , Ji Hai-Yu , Mao Long-Fei , Peng Li-Zeng
Published: July 2022

The ethanol precipitation method has been widely-used for Dendrobium officinale polysaccharides preparation. However, the alcohol-soluble fractions have always been ignored, which causes significant wastes of resources and energies. In this study, the extraction, physicochemical properties, and immune regulation activity of an edible D. officinale polysaccharide (DOPs) isolated from the supernatant after 75% ethanol precipitation were systematically investigated. The structural characteristics determination results showed that DOPs was mainly composed of glucose and mannose at a molar ratio of 1.00:5.78 with an average molecular weight of 4.56 × 103 Da, which was made up of α-(1,3)-Glcp as the main skeleton, and the α-(1,4)-Glcp and β-(1,4)-Manp as the branches. Subsequently, the cyclophosphamide (CTX)-induced immunosuppressive mice model was established, and the results demonstrated that DOPs could dose-dependently protect the immune organs against CTX damage, improve the immune cells activities, and promote the immune-related cytokines (IL-2, IFN-γ and TNF-α) secretions. Furthermore, DOPs treatment also effectively enhanced the antioxidant enzymes levels (SOD, GSH-Px) in sera and livers, therefore weakening the oxidative damage of CTX-treated mice. Considering these above data, DOPs presented great potential to be explored as a natural antioxidant and supplement for functional foods.
Effect of Bleaching and Hot-Pressing Conditions on Mechanical Properties of Compressed Wood
Authors: Van Hai Le , Pham Duc Hoa , Kim Jaehwan
Published: July 2022

This paper reports on multiple stage bleaching and its effect on the mechanical and swelling properties of compressed wood (CW). The natural wood specimen was bleached with NaClO2 in five steps and three hot-pressing conditions. Their effects were investigated in morphologies: lignin content, alpha-cellulose content, compression ratio, mechanical properties, swelling and, water contact angle. After compression, the wood specimens became dense and the most porous structures collapsed. The lignin content decreased as the bleaching steps progressed, and the highest alpha-cellulose content was observed at the third bleaching step. This CW showed the best mechanical properties: bending strength was 240.1 ± 35.7 MPa, and Young’s modulus was 23.08 ± 0.89 Gpa. The CW swelling decreased as the bleaching step progressed, and was associated with the density decrease and the compression ratio increase with the bleaching step. The B3 is an optimum bleaching step that accounts for the best mechanical properties, which might be associated with the highest alpha-cellulose content.
Extraction and Characterization of Microplastics from Portuguese Industrial Effluents
Authors: Magalhães Solange , Alves Luís , Romano Anabela , Medronho Bruno , Rasteiro Maria da Graça
Published: July 2022

Microplastics (MPs) are contaminants present in the environment. The current study evaluates the contribution of different well-established industrial sectors in Portugal regarding their release of MPs and potential contamination of the aquifers. For each type of industry, samples were collected from wastewater treatment plants (WWTP), and different parameters were evaluated, such as the potential contamination sources, the concentration, and the composition of the MPs, in both the incoming and outcoming effluents. The procedures to extract and identify MPs in the streams entering or leaving the WWTPs were optimized. All industrial effluents analysed were found to contribute to the increase of MPs in the environment. However, the paint and pharmaceutical activities were the ones showing higher impact. Contrary to many reports, the textile industry contribution to aquifers contamination was not found to be particularly relevant. Its main impact is suggested to come from the numerous washing cycles that textiles suffer during their lifetime, which is expected to strongly contribute to a continuous release of MPs. The predominant chemical composition of the isolated MPs was found to be polyethylene terephthalate (PET). In 2020, the global need for PET was 27 million tons and by 2030, global PET demand is expected to be 42 million tons. Awareness campaigns are recommended to mitigate MPs release to the environment and its potential negative impact on ecosystems and biodiversity.
An Investigation for Skin Tissue Regeneration Enhancement/Augmentation by Curcumin-Loaded Self-Emulsifying Drug Delivery System (SEDDS)
Authors: Mahmood Saima , Bhattarai Prapanna , Khan Nauman Rahim , Subhan Zakia , Razaque Ghulam , Albarqi Hassan A. , Alqahtani Abdulsalam A. , Alasiri Ali , Zhu Lin
Published: July 2022

Diabetes, one of the global metabolic disorders, is often associated with delayed wound healing due to the elevated level of free radicals at the wound site, which hampers skin regeneration. This study aimed at developing a curcumin-loaded self-emulsifying drug delivery system (SEDDS) for diabetic wound healing and skin tissue regeneration. For this purpose, various curcumin-loaded SEDDS formulations were prepared and optimized. Then, the SEDDS formulations were characterized by the emulsion droplet size, surface charge, drug content/entrapment efficiency, drug release, and stability. In vitro, the formulations were assessed for the cellular uptake, cytotoxicity, cell migration, and inhibition of the intracellular ROS production in the NIH3T3 fibroblasts. In vivo, the formulations’ wound healing and skin regeneration potential were evaluated on the induced diabetic rats. The results indicated that, after being dispersed in the aqueous medium, the optimized SEDDS formulation was readily emulsified and formed a homogenous dispersion with a droplet size of 37.29 ± 3.47 nm, surface charge of −20.75 ± 0.07 mV, and PDI value of less than 0.3. The drug content in the optimized formulation was found to be 70.51% ± 2.31%, with an encapsulation efficiency of 87.36% ± 0.61%. The SEDDS showed a delayed drug release pattern compared to the pure drug solution, and the drug release rate followed the Fickian diffusion kinetically. In the cell culture, the formulations showed lower cytotoxicity, higher cellular uptake, and increased ROS production inhibition, and promoted the cell migration in the scratch assay compared to the pure drug. The in vivo data indicated that the curcumin-loaded SEDDS-treated diabetic rats had significantly faster-wound healing and re-epithelialization compared with the untreated and pure drug-treated groups. Our findings in this work suggest that the curcumin-loaded SEDDS might have great potential in facilitating diabetic wound healing and skin tissue regeneration.
Coordinative Chain Transfer Polymerization of Sustainable Terpene Monomers Using a Neodymium-Based Catalyst System
Authors: Córdova Teresa , Enríquez-Medrano Francisco Javier , Cartagena Eduardo Martínez , Villanueva Arnulfo Banda , Valencia Luis , Álvarez Edgar Nazareo Cabrera , González Ricardo López , Díaz-de-León Ramón
Published: July 2022

The present investigation involves the coordinative chain transfer polymerization (CCTP) of biobased terpenes in order to obtain sustainable polymers from myrcene (My) and farnesene (Fa), using the ternary Ziegler–Natta catalyst system comprising [NdV3]/[Al(i-Bu)2H]/[Me2SiCl2] and Al(i-Bu)2H, which acts as cocatalyst and chain transfer agent (CTA). The polymers were produced with a yield above 85% according to the monomeric consumption at the end of the reaction, and the kinetic examination revealed that the catalyst system proceeded with a reversible chain transfer mechanism in the presence of 15–30 equiv. of CTA. The resulting polyterpenes showed narrow molecular weight distributions (Mw/Mn = 1.4–2.5) and a high percent of 1,4-cis microstructure in the presence of 1 equiv. of Me2SiCl2, having control of the molecular weight distribution in Ziegler–Natta catalytic systems that maintain a high generation of 1,4-cis microstructure.
Utilization of Sulfonated Waste Polystyrene-Based Cobalt Ferrite Magnetic Nanocomposites for Efficient Degradation of Calcon Dye
Authors: Srinivasan Vennila , Sumalatha Vasam , Prasannan Adhimoorthy , Govindarajan Sankar
Published: July 2022

We presented a simple and efficient method for making a polymer–metal nanocomposite using various amounts of cobalt ferrite magnetic nanoparticles (CoFe2O4 MNp) with sulfonated waste polystyrene (SWPS) and utilized for Calcon dye degradation. The MNp was encapsulated with SWPS to avoid agglomeration and maintain its smaller size. ATR-FTIR, Raman spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), dynamic light scattering (DLS), field emission scanning electron microscopy (FESEM), high-resolution transmittance electron microscopy (HR-TEM), atomic force microscopy (AFM) and solid UV were used to analyze the produced polymeric magnetic nanoparticles (SWPS/MNp). As the MNp loading increases, the average particle size decreases. For Calcon dye degradation, SWPS/MNp (20 wt%) was utilized with a smaller average particle size, and the structural changes were detected using a UV-Vis spectrophotometer. As a result, the Calcon dye’s characteristic absorbance peak at 515 nm was red-shifted to 536 and 565 nm after 5 min, resulting in a color shift from dark brown to light blue that could be seen with the naked eye. A strong linear correlation was found between the red-shifted absorbance and the concentration of dye solution over the range of 10–100 ppm under optimal conditions. The proposed dye degradation process is simple, efficient, and environmentally friendly and has been successfully used to purify organic azo-dye-containing water.
Improving the Wet-Spinning and Drawing Processes of Poly(lactide)/Poly(ethylene furanoate) and Polylactide/Poly(dodecamethylene furanoate) Fiber Blends
Authors: Fabris Claudia , Perin Davide , Fredi Giulia , Rigotti Daniele , Bortolotti Mauro , Pegoretti Alessandro , Xanthopoulou Eleftheria , Bikiaris Dimitrios N. , Dorigato Andrea
Published: July 2022

This work aims to produce poly(lactic acid) (PLA)/poly(alkylene furanoate)s (PAF)s fiber blends for textile applications and evaluates their microstructural, chemical, thermal, and mechanical properties. The work focuses on two PAFs with very different alkyl chain lengths, i.e., poly(ethylene 2,5−furandicarboxylate) (PEF) and poly(dodecamethylene 2,5−furandicarboxylate) (PDoF), which were blended in solution at various concentrations (in the range 2.5–10 wt %) with PLA, wet spun, and subsequently drawn. Light optical micrographs highlight that PLA/PEF blends present large and concentrate PEF domains, whereas PLA/PDoF blends show small and homogeneously distributed PDoF domains. The blends appear to be immiscible, which is confirmed also by scanning electron microscopy (SEM), Fourier−Transform Infrared (FT−IR) spectroscopy, and differential scanning calorimetry (DSC). Thermogravimetric analysis (TGA) highlights that the addition of the PAFs improves the thermal stability of the fibers. The drawing process, which was carried out at 80 °C with a heat setting step at 95 °C and at three draw ratios, improves the mechanical properties of the fibers upon the addition of the PAFs. The results obtained in this study are promising and may serve as a basis for future investigations on these novel bio−based fiber blends, which can contribute to increase the environmental sustainability of industrial textiles.
The COVID-19 Pandemic and the Interest in Prayer and Spirituality in Poland According to Google Trends Data in the CONTEXT of the Mediatisation of Religion Processes
Authors: Stańdo Jacek , Piechnik-Czyż Gabriela , Adamski Andrzej , Fechner Żywilla
Published: July 2022

The research undertaken in this article uses the Google Trends tool to study the degree of interest in prayer and general spirituality during the initial phase of the COVID-19 pandemic in Poland and Europe. The authors assumed that for people interested in prayer during the COVID-19 pandemic, the Internet served as a virtual prayer book. The main research questions addressed the frequency of typed queries, referring not only to the word “prayer” but also to specific types of prayer. In addition, interest in prayer was compared with interest in the word “prophecy” to explore the relationship between religiosity and interest in the supernatural sphere in its broadest sense. The analysis shows that there is distinct recurrence regarding the terms searched, with some of them noticeably intensifying with the outbreak of the COVID-19 pandemic. The findings also show that keywords related to prophecies were searched more frequently at significant moments in Polish history (2005—the death of John Paul II, 2010—the plane crash in which the President of Poland died) than in the months of 2020 when the pandemic struck and escalated. At that time, searches related to religion were more frequent. It can also be concluded that the outbreak of the pandemic contributed to an increase in the religious activity of Poles. The article is interdisciplinary in nature, referring primarily to Religion Studies and Mass Media and Communication Studies.
A View of Champā Sites in Phú Yên Province, Vietnam: Toward a Longue Durée of Socio-Religious Context
Authors: Quang Van Son , Noseworthy William
Published: July 2022

Champā sites in Phú Yên province, Vietnam, were in what historians have typically called the polity of ‘Kauṭhāra’. Among these, the Hồ Citadel was mentioned in recent studies of Champā citadels and Champā archaeology, but the region of ‘Kauṭhāra’ has yet to be analyzed with a vision for the Longue durée of cultural history. Drawing on the study of maps, historical documents, and archaeological evidence, we provide a more coherent understanding of Hồ Citadel in the socio-religious context of Champā, including the incorporation of Champā’s Hindu–Buddhist polities of Kauṭhāra and Vijaya into what is now Vietnam; historical evidence that suggests follow up archaeological research could fruitfully focus on the Early Modern period of history. Our findings suggest the region was one of the longest-occupied Champā regions, despite a comparative lack of focus on archaeological studies in the area versus Champā sites further northward. Future archaeological work should not only focus on the very earliest finds but rather the Longue durée of persistent settlement patterns. Thus, we hope to inspire continued and more direct collaborations between historians and archaeologists for the benefit of advancing research in the study of local and transregional understandings of Asia in the Humanities and Social Sciences.
Making, Using, Disposing, Remaking…: Sacred Arts of Re-Creation in Southern Asia
Authors: Bean Susan S.
Published: July 2022

For centuries, in the eastern Indian subcontinent, areas now in Bangladesh and the Indian states of West Bengal, Odisha, and Bihar, temporary polychrome terracruda (air-dried clay) figural images have been created for periodic pujas (rituals of worship) and immersed in nearby rivers or ponds at the event’s close. This essay explores how the perennial re-creation of terracruda ritual images supported the rise of goddess worship, stimulated the expansion of the annual cycle of religious festivals, and contributed to a modernizing cosmopolitan public culture. Drawing on recent reconsiderations of materiality that recognize the active roles of inanimate objects and substances, terracruda sacred sculpture is approached through the medium to consider the distinctive contributions that clay makes in interactions with artists, patrons, devotees, and the public. This essay focuses on how the transformational character of air-dried clay enables practices of making, worshipping, and disposing that evoke cosmic cycles, harness potencies that inhere in earth, and realign religious practices in changing times.
A Survey on Recent Advances in Social Robotics
Authors: Youssef Karim , Said Sherif , Alkork Samer , Beyrouthy Taha
Published: July 2022

Over decades, social robotics has evolved as a concept that presently covers different areas of application, and interacts with different domains in technology, education, medicine and others. Today, it is possible to envision social robots in tasks that were not expected years ago, and that is not only due to the evolution of social robots, but also to the evolution of the vision humans have for them. This survey addresses recent advances in social robotics from different perspectives. Different contexts and areas of application of social robots are addressed, as well as modalities of interaction with humans. Different robotic platforms used in social contexts are shown and discussed. Relationships of social robotics with advances in other technological areas are surveyed, and methods and metrics used for the human evaluation of the interaction with robots are presented. The future of social robotics is also envisioned based on surveyed works and from different points of view.
Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy
Authors: Zhang Yi-Wei , Wang Tiejun , Guo Yanpei , Skidmore Andrew , Zhang Zhenhua , Tang Rong , Song Shanshan , Tang Zhiyao
Published: July 2022

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.
Kinematic Galileo and GPS Performances in Aerial, Terrestrial, and Maritime Environments
Authors: Bastos Luisa , Buist Peter , Cefalo Raffaela , Goncalves Jose Alberto , Ivan Antonia , Magalhaes Americo , Pandele Alexandru , Porretta Marco , Radutu Alina , Sluga Tatiana , Snider Paolo
Published: July 2022

On 15 December 2016, the European Commission (EC) declared the provision of the Galileo Initial Services (IS). This marked a historical milestone in the Galileo program, towards the reaching of its Full Operational Capability. This allows users to navigate with performance-accuracy levels either matching or exceeding those obtained with other GNSS. Under the delegation of the EC, the European Union Agency for the Space Programme (EUSPA) has assumed the role of the Galileo Service Provider. As part of this service provision, the primary mission of the Galileo Reference Centre (GRC) is to provide the EUSPA and the EC with independent means for monitoring and evaluating the performance of the Galileo services, the quality of the signals in space, and the performance of other GNSS. This mission includes significant contributions from cooperating entities in the European Union (EU) Member States (MS), Norway and Switzerland. In particular, for a detailed assessment of the Galileo performance, these contributions include (but are not limited to) periodic dynamic campaigns in three different environments (aerial, terrestrial, and maritime). These campaigns were executed in the frame of the GRC-MS Project and use multi-constellation receivers to compare the navigation performance obtained with different GNSS. The objective of this paper is to present the numerical results obtained from these campaigns, together with several considerations about the experimental setup, the methodology for the estimation of the reference («actual») trajectory, and the reasons for possible performance degradations.
Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization
Authors: Wamriew Daniel , Dorhjie Desmond Batsa , Bogoedov Daniil , Pevzner Roman , Maltsev Evgenii , Charara Marwan , Pissarenko Dimitri , Koroteev Dmitry
Published: July 2022

Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning.
Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning
Authors: Armitage Sophie , Awty-Carroll Katie , Clewley Daniel , Martinez-Vicente Victor
Published: July 2022

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution.
Localization of Dielectric Anomalies with Multi-Monostatic S11 Using 2D MUSIC Algorithm with Spatial Smoothing
Authors: Bilal Ahmad , Cho Choon Sik
Published: July 2022

This article demonstrates that the complex value of S11 of an antenna, acquired in a multi-monostatic configuration, can be used for localization of a dielectric anomaly hidden inside a dielectric background medium when the antenna is placed close (~5 mm) to the geometry. It uses an Inverse Synthetic Aperture Radar (ISAR) imaging framework where data is acquired at multiple frequencies and look-angles. Initially, near-field scattering data are used for simulation to validate this methodology since the basic derivation of the Multiple Signal Classification (MUSIC) algorithm is based on the plain wave assumption. Later on, from an applications perspective, data acquisition is performed using an antipodal Vivaldi antenna that has eight constant-width slots on each arm. This antenna operates in a frequency range of 1 to 8.5 GHz and its S11 is fed to the 2D MUSIC algorithm with spatial smoothing whereas the antenna artifact and background effect are removed by subtracting the average S11 at each antenna location. Measurements reveal that this methodology gives accurate results with both homogeneous and inhomogeneous backgrounds because the size of data sub-arrays trades between the image noise and resolution, hence reducing the effect of inhomogeneity in the background. In addition to near-field ISAR imaging, this study can be used in the ongoing research on breast tumors and brain stroke detection, among others.
Dynamic Calibration of a Thin-Film Heat-Flux Sensor in Time and Frequency Domains
Authors: Li Zhiling , Yin Jianping , Wang Gao , Liang Haijian , Zhang Congchun , Huang Manguo , Liu Yundong , Zhang Jie
Published: July 2022

This paper mainly studies the model design of a thin-film heat-flux sensor (TFHFS), and focuses on the comparison of three dynamic calibration methods. The primary motivation for studying this came from the urgent need for heat-flux dynamic measurements in extreme environments, and the one-sidedness of the dynamic performance evaluation of the corresponding TFHFS. The dynamic theoretical model of the TFHFS was originally established on the principle of a temperature gradient on the basis of a thermal radiation boundary. Then, a novel TFHFS sensor was developed, which can be used at temperatures above 880 °C and has a high sensitivity of 2.0 × 10−5 mV/(W/m2). It can function stably for long durations under a heat-flux density of 3 MW/m2. The steady-state, transient, and frequency calibration of a TFHFS were compared to comprehensively analyze the dynamic characteristics of the TFHFS. The steady-state response time measured by the step excitation method was found to be 0.978 s. The QR decomposition method was applied to the steady-state response experimental model construction, and the fitting degree of a second-order transfer function model obtained was 98.61%. Secondly, the transient response time of the TFHFS was 0.31 ms based on the pulse-excitation method. The transient relationship between the surface temperature and the heat flux, and the pulse-width dependence of the TFHFS transient response time were established. Surprisingly, the response frequency of the TFHFS, about 3000 Hz, was efficiently tested using the frequency response function (FRF), which benefitted from the harmonic characteristics of a periodic square-wave excitation signal. Finally, a comprehensive evaluation of the dynamic performance of the TFHFS was realized.
LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers
Authors: Fu Xinhua , Yang Kejun , Liu Min , Xing Tianzhang , Wu Chase
Published: July 2022

Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.
In-Flight Electromagnetic Compatibility of Airborne Vertical VLF Antennas
Authors: Miś Tomasz Aleksander , Modelski Józef
Published: July 2022

Long-wire very low frequency antennas, when lifted up on high altitudes by an aerostat, move through different atmospheric layers and interact with them electrically in a more intense way in comparison with aircraft flights. Such interactions manifest themselves in the form of electrical changes in the clouds and corona discharges excited on the antenna wire, which may increase the risk of mechanical damages and transmitter overload. In order to investigate the interactions between the different types of clouds and a long balloon-borne antenna wire, two theoretical models were developed and compared with results from an experimental balloon flight directly through a storm front. Based on the theoretical and experimental results, the most accurate model proposed was chosen, as well as a set of basic requirements for the balloon-borne VLF antenna system, reducing the risk of failure during operation in highly electrically active atmospheric environments.
Data-Driven Machine-Learning Methods for Diabetes Risk Prediction
Authors: Dritsas Elias , Trigka Maria
Published: July 2022

Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.
On the Identification of Orthotropic Elastic Stiffness Using 3D Guided Wavefield Data
Authors: Orta Adil Han , Kersemans Mathias , Van Den Abeele Koen
Published: July 2022

Scanning laser Doppler vibrometry is a widely adopted method to measure the full-field out-of-plane vibrational response of materials in view of detecting defects or estimating stiffness parameters. Recent technological developments have led to performant 3D scanning laser Doppler vibrometers, which give access to both out-of-plane and in-plane vibrational velocity components. In the present study, the effect of using (i) the in-plane component; (ii) the out-of-plane component; and (iii) both the in-plane and out-of-plane components of the recorded vibration velocity on the inverse determination of the stiffness parameters is studied. Input data were gathered from a series of numerical simulations using a finite element model (COMSOL), as well as from broadband experimental measurements by means of a 3D infrared scanning laser Doppler vibrometer. Various materials were studied, including carbon epoxy composite and wood materials. The full-field vibrational velocity response is converted to the frequency-wavenumber domain by means of Fourier transform, from which complex wavenumbers are extracted using the matrix pencil decomposition method. To infer the orthotropic elastic stiffness tensor, an inversion procedure is developed by coupling the semi-analytical finite element (SAFE) as a forward method to the particle swarm optimizer. It is shown that accounting for the in-plane velocity component leads to a more accurate and robust determination of the orthotropic elastic stiffness parameters.
Power Line Communication and Sensing Using Time Series Forecasting
Authors: Huo Yinjia , Prasad Gautham , Lampe Lutz , Leung Victor
Published: July 2022

Smart electrical grids rely on data communication to support their operation and on sensing for diagnostics and maintenance. Usually, the roles of communication and sensing equipment are different, i.e., communication equipment does not participate in sensing tasks and vice versa. Power line communication (PLC) offers a cost-effective solution for joint communication and sensing for smart grids. This is because the high-frequency PLC signals used for data communication also reveal detailed information regarding the health of the power lines that they travel through. Traditional PLC-based power line or cable diagnostic solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In this paper, we develop a power line sensing technique that can detect various types of cable anomalies without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the approximation that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to build an anomaly detector which identifies the occurrence of a cable fault. We demonstrate the effectiveness and universality of our sensing solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.
Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images
Authors: Xiong Kangni , Jiang Jinbao , Pan Yingyang , Yang Yande , Chen Xuhui , Yu Zijian
Published: July 2022

The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.
A Novel Virtual Optical Image Encryption Scheme Created by Combining Chaotic S-Box with Double Random Phase Encoding
Authors: Tian Peiran , Su Runzhou
Published: July 2022

The double random phase encoding (DRPE) system plays a significant role in encrypted systems. However, it is a linear system that leads to security holes in encrypted systems. To tackle this issue, this paper proposes a novel optical image encryption scheme that combines a chaotic S-box, DRPE, and an improved Arnold transformation (IAT). In particular, the encryption scheme designs a chaotic S-box to substitute an image. The chaotic S-box has the characteristics of high nonlinearity and low differential uniformity and is then introduced to enhance the security of the DRPE system. Chaotic S-boxes are resistant to algebraic attacks. An IAT is used to scramble an image encoded by the DRPE system. Meanwhile, three chaotic sequences are obtained by a nonlinear chaotic map in the proposed encryption scheme. One of them is used for XOR operation, and the other two chaotic sequences are explored to generate two random masks in the DRPE system. Simulation results and performance analysis show that the proposed encryption scheme is efficient and secure.
Torsional Low-Strain Test for Nondestructive Integrity Examination of Existing High-Pile Foundation
Authors: Zhang Yunpeng , El Naggar M. Hesham , Wu Wenbing , Wang Zongqin
Published: July 2022

Low-strain tests are widely utilized as a nondestructive approach to assess the integrity of newly piled foundations. So far, the examination of existing pile foundations is becoming an indispensable protocol for pile recycling or post-disaster safety assessment. However, the present low-strain test is not capable of testing existing pile foundations. In this paper, the torsional low-strain test (TLST) is proposed to overcome this drawback. Both the upward and downward waves are considered in the TLST wave propagation model established in this paper so that a firm theoretical basis is grounded for the test signal interpretations. A concise semi-analytical solution is derived and its rationality is verified by comparisons with the existing solutions for newly piled foundations and the finite element results. The main conclusions of this study can be drawn as follows: (1). by placing the sensors where the incident wave is applied, the number of reflected signals can be minimized; (2). the defects can be more evidently identified if the incident wave/sensors are input/installed close to the superstructure/pile head.