bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Sheep Wool-Based Composites for Building Insulation
Authors: Dénes Tünde-Orsolya , Iştoan Raluca , Tǎmaş-Gavrea Daniela Roxana , Manea Daniela Lucia , Hegyi Andreea , Popa Florin , Vasile Ovidiu
Published: May 2022

The aim of this paper is to propose ecological thermal insulation materials that meet the goals of sustainability but also fulfill the imposed thermal performance requirements. This paper studies new composite materials based on sheep wool from the perspective of thermal conductivity. The composites were prepared using two types of binder: acrylic-polyurethane resin and natural rubber latex, which were applied to the wool fibres through different methods and percentages. Based on the obtained results of thermal conductivity, two types of samples were selected for further analysis, which aimed to determine the microstructure, chemical composition, water absorption, attack of microorganisms, water vapour permeability, hygrothermal adsorption characteristics and sound absorption of the samples. In order to analyse the variation of thermal conductivity, the following parameters were taken into account: thickness, density, type of binder and percentage of binder. Following the obtained results, it was observed that the value of the thermal conductivity of the samples varies between 0.0324 and 0.0436 W/mK. It was found that all the samples prepared and analysed in this study fulfil the national criteria for the thermal performance of thermal insulation material. After conducting the in-depth analysis of the two selected sample types, it was concluded that both materials have good sound absorption characteristics over the considered frequency range. In addition, as it was expected from the natural fibres, the samples had low resistance against the attack of microorganisms and water-related tests.
A U-Shaped Optical Fiber Temperature Sensor Coated with Electrospinning Polyvinyl Alcohol Nanofibers: Simulation and Experiment
Authors: Chou Yen-Lung , Wen Hsin-Yi , Weng Yu-Qiao , Liu Yi-Ching , Wu Chao-Wei , Hsu Hsiang-Cheng , Chiang Chia-Chin
Published: May 2022

This study describes the fabrication of an electrospun, U-shaped optical fiber sensor for temperature measurements. The sensor is based on single mode fibers and was fabricated into a U-shaped optical fiber sensor through flame heating. This study applied electrospinning to coat PVA, a polymer, onto the sensor layer to reduce its sensitivity to humidity. The sensor is used to measure temperature variations ranging from 30 °C to 100 °C. The objectives of this study were to analyze the sensitivity variation of the sensor with different sensor layer thicknesses resulting from different electrospinning durations, as well as to simulate the wavelength signals generated at different electrospinning durations using COMSOL. The results revealed that the maximum wavelength sensitivity, transmission loss sensitivity, and linearity of the sensor were 25 dBm/°C, 70 pm/°C, and 0.956, respectively. Longer electrospinning durations resulted in thicker sensor layers and higher sensor sensitivity, that wavelength sensitivity of the sensor increased by 42%.
Fast-Growing Magnetic Wood Synthesis by an In-Situ Method
Authors: Rahayu Istie , Prihatini Esti , Ismail Rohmat , Darmawan Wayan , Karlinasari Lina , Laksono Gilang Dwi
Published: May 2022

Jabon (Anthocephalus cadamba) is a fast-growing wood with low quality due to its low density and strength. The quality can be increased by modifying the wood through impregnation with various chemical compounds. In this study, jabon was impregnated with a solution of Fe and immersed in a strong base (NaOH) or a weak base (NH4OH) to form magnetite (Fe3O4) in-situ. This study analysed the use of NaOH and NH4OH in synthesising magnetic jabon wood and evaluated the wood’s characteristics. The impregnation process began with a vacuum of −0.5 bar for 0.5 h and then a pressure of 1 bar for 2 h. The samples subsequently underwent assessment of their dimensional stability, density, and characteristics. The results showed that impregnation with Fe solution followed by NaOH or NH4OH significantly affected the density and dimensional stability of the wood. The polymer weight gain was higher with NaOH, while the anti-swelling efficiency was higher with NH4OH. The density and bulking effect were increased, but the water uptake was decreased. Fourier transform infrared analysis showed the successful synthesis of magnetite. Scanning electron microscopy–energy-dispersive X-ray spectroscopy analysis revealed that magnetite covered the vessel fibre cell walls, and vibrating sample magnetometry analysis showed significant magnetic properties of the wood.
Structural Performance of Lightweight Aggregate Concrete Reinforced by Glass or Basalt Fiber Reinforced Polymer Bars
Authors: Abed Mohammed A. , Anagreh Aysha , Tošić Nikola , Alkhabbaz Ola , Alshwaiki Majd Eddin , Černý Robert
Published: May 2022

Lightweight aggregate concrete (LWC) and fiber reinforced polymer (FRP) reinforcement are potentially more sustainable alternatives to traditional steel-reinforced concrete structures, offering several important benefits. To further the knowledge in this area, the physical–mechanical properties of LWC produced with 0%, 50%, and 100% expanded clay aggregate were assessed. Subsequently, the flexural behavior of LWC beams reinforced with steel reinforcement and glass and basalt FRP bars was tested. The results of the experimental program allowed quantifying of the effect of expanded clay aggregate incorporation on LWC properties. The use of FRP reinforcement was also compared to steel-reinforced concrete beam behavior. The results of this study can provide additional support for the use of innovative materials such as LWA and FRP reinforcement.
Laboratory Research on Design of Three-Phase AC Arc Plasma Pyrolysis Device for Recycling of Waste Printed Circuit Boards
Authors: Bai Liuyang , Sun Wenbin , Yang Zhao , Ouyang Yuge , Wang Min , Yuan Fangli
Published: May 2022

Accumulation of electronic waste (e-waste) will place a heavy burden on the environment without proper treatment; however, most ingredients contained in it are useful, and it could bring great economic benefits when recycled. A three-phase alternating current (AC) arc plasma pyrolysis device was designed for resourcing treatment of waste printed circuit boards (WPCBs). This paper focuses on the analysis of plasma pyrolysis gas products, and the results showed that the plasma could operate stably, and overcame the problems of the poor continuity and low energy of single-arc discharge. Air-plasma would generate NOx contaminants, burn the organics, and oxidize the metals; therefore, air had not been selected as a working gas. Ar-plasma can break the long chains of organic macromolecules to make a combustible gas. Moreover, the strong adhesion between the metals and fiberglass boards would be destroyed, which facilitates subsequent separation. Ar/H2-plasma promoted the decrease of carbon dioxide and the increase of combustible small molecular hydrocarbons in the pyrolysis product compared with Ar-plasma, and the increase of the H2 flow rate or plasma power intensified that promotion effect. The percentage of other components, except the hydrogen of CO2, CO, CH4, C2H4, and C3H6, accounted for 55.7%, 34.2%, 5.6%, 4.5%, and 0% in Ar-plasma, and changed to 35.0%, 29.0%, 11.2%, 24.3%, and 0.5% in Ar/H2-plasma. Ar/H2-plasma could provide a highly chemically active species and break chemical bonds in organic macromolecules to produce small molecules of combustible gas. This laboratory work presents a novel three-phase AC arc plasma device and a new way for recycling WPCBs with high value.
Nitric Oxide Generation in N2-Diluted H2–N2O Flames: A Computational Study
Authors: Razus Domnina , Giurcan Venera , Movileanu Codina , Mitu Maria
Published: May 2022

A computational study was conducted on the addition of nitrogen (0–60 vol%) to stoichiometric hydrogen–nitrous oxide flames at various initial pressures (1–10 bar) and temperatures (300–500 K), with a special emphasis on NO, which is a major pollutant that is generated by these flames. The detailed kinetic modeling of H2–N2O–N2 flames, which was performed using the GRI 3.0 mechanism, produced the structure of the H2–N2O–N2 flames for the temperature and mass fraction profiles of 53 species, which participated in 325 elementary reactions. The NO profiles that were computed by the detailed kinetic modeling followed the trend of experimental NO profiles that have been reported in the literature for laminar premixed flames at sub-atmospheric pressures. For the examined H2–N2O–N2 flames, an increase in initial pressure resulted in a significant reduction in the NO mass fraction of the burned gas. The increase in initial temperature was also accompanied by an increase in the mass fractions of the generated NO, as well as the flame temperature.
Wheat Proteomics for Abiotic Stress Tolerance and Root System Architecture: Current Status and Future Prospects
Authors: Halder Tanushree , Choudhary Mukesh , Liu Hui , Chen Yinglong , Yan Guijun , Siddique Kadambot H. M.
Published: May 2022

Wheat is an important staple cereal for global food security. However, climate change is hampering wheat production due to abiotic stresses, such as heat, salinity, and drought. Besides shoot architectural traits, improving root system architecture (RSA) traits have the potential to improve yields under normal and stressed environments. RSA growth and development and other stress responses involve the expression of proteins encoded by the trait controlling gene/genes. Hence, mining the key proteins associated with abiotic stress responses and RSA is important for improving sustainable yields in wheat. Proteomic studies in wheat started in the early 21st century using the two-dimensional (2-DE) gel technique and have extensively improved over time with advancements in mass spectrometry. The availability of the wheat reference genome has allowed the exploration of proteomics to identify differentially expressed or abundant proteins (DEPs or DAPs) for abiotic stress tolerance and RSA improvement. Proteomics contributed significantly to identifying key proteins imparting abiotic stress tolerance, primarily related to photosynthesis, protein synthesis, carbon metabolism, redox homeostasis, defense response, energy metabolism and signal transduction. However, the use of proteomics to improve RSA traits in wheat is in its infancy. Proteins related to cell wall biogenesis, carbohydrate metabolism, brassinosteroid biosynthesis, and transportation are involved in the growth and development of several RSA traits. This review covers advances in quantification techniques of proteomics, progress in identifying DEPs and/or DAPs for heat, salinity, and drought stresses, and RSA traits, and the limitations and future directions for harnessing proteomics in wheat improvement.
Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice
Authors: Rapp Paul E. , Cellucci Christopher , Darmon David , Keyser David
Published: May 2022

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.
Mary Astell on Neighborly Love
Authors: Yenter Timothy
Published: May 2022

In discussing the obligation to love everyone, Mary Astell (1666–1731) recognizes and responds to what I call the theocentric challenge: if humans are required to love God entirely, then they cannot fulfill the second requirement to love their neighbor. In exploring how Astell responds to this challenge, I argue that Astell is an astute metaphysician who does not endorse the metaphysical views she praises. This viewpoint helps us to understand the complicated relationship between her views and those of Descartes, Malebranche, Henry More, and John Norris, as well as her sophisticated approach to biblical interpretation and theology. Attending to theocentrism opens up new avenues of research in the study of early modern philosophy. It also helps us to see connections between Astell and other theocentric philosophers such as Spinoza and Anne Conway.
Tsinghua Scientific Satellite Precise Orbit Determination Using Onboard GNSS Observations with Antenna Center Modeling
Authors: Shao Kai , Wei Chunbo , Gu Defeng , Wang Zhaokui , Wang Kai , Cai Yingkai , Peng Dachen
Published: May 2022

The Tsinghua scientific satellite is a Chinese spherical micro satellite for Earth gravity and atmospheric scientific measurements. The accurate orbits of this satellite are the prerequisites to satisfy the mission objectives. A commercial off-the-shelf dual-frequency GNSS receiver is equipped on the satellite for precise orbit determination (POD). The in-flight performances of the receiver are assessed. Regular long-duration gaps up to 50 min are observed in GNSS data, and the typical data availability is about 60–70% each day. The RMS of code noises is 0.24 m and 0.30 m for C1 and P2 codes, respectively. The RMS of fitting residuals of the carrier phase geometry-free L1–L2 combination is 2.4 mm. The GNSS receiver antenna center offsets (ACOs) and antenna center variations (ACVs) maps are estimated using in-flight data for both dual-frequency and single-frequency POD. Significant improvements in POD performances are obtained when the measurement models are updated by using the ACO and ACV maps’ corrections. With the updated measurement model, the RMS of the orbit overlap differences is 1.23 cm in three dimensions for dual-frequency POD, which is reduced by 27%. Meanwhile, two different empirical acceleration types are employed and compared for dual-frequency POD, and the results show that consistency on the 5 cm level is demonstrated for orbit solutions obtained with the updated measurement model. After correcting the ACO and ACV maps, the precision of single-frequency orbit solutions is better than 10 cm, which is improved by 32%. The results indicate that the antenna center modeling can significantly improve the consistency of Tsinghua scientific satellite precise orbits, which will be conducive to the realization of the mission objectives.
Flash Flood Risk Assessment and Mitigation in Digital-Era Governance Using Unmanned Aerial Vehicle and GIS Spatial Analyses Case Study: Small River Basins
Authors: Bilașco Ștefan , Hognogi Gheorghe-Gavrilă , Roșca Sanda , Pop Ana-Maria , Iuliu Vescan , Fodorean Ioan , Marian-Potra Alexandra-Camelia , Sestras Paul
Published: May 2022

Watercourses act like a magnet for human communities and were always a deciding factor when choosing settlements. The reverse of these services is a potential hazard in the form of flash flooding, for which human society has various management strategies. These strategies prove to be increasingly necessary in the context of increased anthropic pressure on the floodable areas. One of these strategies, Strategic Flood Management (SFM), a continuous cycle of planning, acting, monitoring, reviewing and adapting, seems to have better chances to succeed than other previous strategies, in the context of the Digital-Era Governance (DEG). These derive, among others, from the technological and methodological advantages of DEG. Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAV) stand out among the most revolutionary tools for data acquisition and processing of data in the last decade, both in qualitative and quantitative terms. In this context, this study presents a hybrid risk assessment methodology for buildings in case of floods. The methodology is based on detailed information on the terrestrial surface—digital surface model (DSM) and measurements of the last historical flash flood level (occurred on 20 June 2012)—that enabled post-flood peak discharge estimation. Based on this methodology, two other parameters were calculated together with water height (depth): shear stress and velocity. These calculations enabled the modelling of the hazard and risk map, taking into account the objective value of buildings. The two components were integrated in a portal available for the authorities and inhabitants. Both the methodology and the portal are perfectible, but the value of this material consists of the detailing and replicability potential of the data that can be made available to administration and local community. Conceptually, the following are relevant (a) the framing of the SFM concept in the DEG framework and (b) the possibility to highlight the involvement and contribution of the citizens in mapping the risks and their adaptation to climate changes. The subsequent version of the portal is thus improved by further contributions and the participatory approach of the citizens.
Evapotranspiration Seasonality over Tropical Ecosystems in Mato Grosso, Brazil
Authors: Biudes Marcelo Sacardi , Geli Hatim M. E. , Vourlitis George Louis , Machado Nadja Gomes , Pavão Vagner Marques , dos Santos Luiz Octávio Fabrício , Querino Carlos Alexandre Santos
Published: May 2022

Brazilian tropical ecosystems in the state of Mato Grosso have experienced significant land use and cover changes during the past few decades due to deforestation and wildfire. These changes can directly affect the mass and energy exchange near the surface and, consequently, evapotranspiration (ET). Characterization of the seasonal patterns of ET can help in understanding how these tropical ecosystems function with a changing climate. The goal of this study was to characterize temporal (seasonal-to-decadal) and spatial patterns in ET over Mato Grosso using remotely sensed products. Ecosystems over areas with limited to no flux towers can be performed using remote sensing products such as NASA’s MOD16A2 ET (MOD16 ET). As the accuracy of this product in tropical ecosystems is unknown, a secondary objective of this study was to evaluate the ability of the MOD16 ET (ETMODIS) to appropriately represent the spatial and seasonal ET patterns in Mato Grosso, Brazil. Actual ET was measured (ETMeasured) using eight flux towers, three in the Amazon, three in the Cerrado, and two in the Pantanal of Mato Grosso. In general, the ETMODIS of all sites had no significant difference from ETMeasured during all analyzed periods, and ETMODIS had a significant moderate to strong correlation with the ETMeasured. The spatial variation of ET had some similarity to the climatology of Mato Grosso, with higher ET in the mid to southern parts of Mato Grosso (Cerrado and Pantanal) during the wet period compared to the dry period. The ET in the Amazon had three seasonal patterns, a higher and lower ET in the wet season compared to the dry season, and minimal to insignificant variation in ET during the wet and dry seasons. The wet season ET in Amazon decreased from the first and second decades, but the ET during the wet and dry season increased in Cerrado and Pantanal in the same period. This study highlights the importance of deepening the study of ET in the state of Mato Grosso due to the land cover and climate change.
Divergent Climate Sensitivities of the Alpine Grasslands to Early Growing Season Precipitation on the Tibetan Plateau
Authors: Wang Zhipeng , Zhang Xianzhou , Niu Ben , Zheng Yunpu , He Yongtao , Cao Yanan , Feng Yunfei , Wu Jianshuang
Published: May 2022

Warming is expected to intensify hydrological processes and reshape precipitation regimes, which is closely related to water availability for terrestrial ecosystems. Effects of the inter-annual precipitation changes on plant growth are widely concerned. However, it is not well-known how plant growth responds to intra-annual precipitation regime changes. Here, we compiled reanalysis climate data (ERA5) and four satellite-based vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Solar-induced Chlorophyll Fluorescence (SIF), and the Modified Triangular Vegetation Index (MTVI2), to evaluate the response of alpine grasslands (including alpine meadow and alpine steppe) to the change of precipitation regimes, especially to the intra-annual precipitation regimes on the Tibetan Plateau. We found monthly precipitation over the alpine steppe significantly increased in the growing season (May–September), but precipitation over the alpine meadow significantly increased only in the early growing season (May–June) (MJP) during the past four decades (1979–2019). The inter-annual plant growth (vegetation indices changes) on the alpine meadow was dominated by temperature, but it was driven by precipitation for the alpine steppe. On the intra-annual scale, the temperature sensitivity of the vegetation indices generally decreased but precipitation sensitivity increased during the growing season for both the alpine meadow and steppe. In response to the increase in MJP, we found the temperature sensitivity of the vegetation indices during the mid-growing season (July–August) (MGNDVI, MGEVI, MGSIF, and MGMTVI2) in the alpine meadow significantly increased (p < 0.01) while its precipitation sensitivity significantly decreased (p < 0.01). We infer that more MJP over the meadow may be the result of enhanced evapotranspiration, which is at the expense of soil moisture and even induces soil “drought” in the early growing season. This may be to elevate community water acquisition capacity through altering root mass allocation and community composition, consequently regulating the divergent climate sensitivities of vegetation growth in the mid-growing season. Our findings highlight that it is inadequate to regard precipitation as an indicator of water availability conditions for plant growth, which may limit our understanding of the response and acclimatization of plants to climate change.
The Relative Roles of Climate Variation and Human Activities in Vegetation Dynamics in Coastal China from 2000 to 2019
Authors: Jiang Honglei , Xu Xia , Zhang Tong , Xia Haoyu , Huang Yiqin , Qiao Shirong
Published: May 2022

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies.
A GIS-Based Spatiotemporal Impact Assessment of Droughts in the Hyper-Saline Urmia Lake Basin on the Hydro-Geochemical Quality of Nearby Aquifers
Authors: Feizizadeh Bakhtiar , Abdollahi Zahra , Shokati Behzad
Published: May 2022

Urmia Lake is a hyper-saline lake in northwestern Iran that has been drying up since 2005. The main objective of this study was to evaluate the water quality in aquifers that are the main source of fresh water for the eastern plains Urmia Lake, which has been drying up due to intensive land use/cover changes and climate change. We evaluated hydro-geochemical data and factors contributing to aquifer pollution and quality variation for nine aquifers in the vicinity of Urmia Lake during the dry and wet seasons from 2000–2020. Our methodology was based on the analysis of 10 years of data from 356 deep and semi-deep wells using GIS spatial analysis, multivariate statistical analysis, and agglomerative hierarchical clustering. We developed a Water Quality Index (WQI) for spatiotemporal assessment of the status of the aquifers. In doing so, we highlighted the value of combining Principal Component Analysis (PCA), WQI, and GIS to determine the hydro-geochemical attributes of the aquifers. We found that the groundwater in central parts of the study area was unsuitable for potable supplies. Anthropogenic sources of contamination, such as chemical fertilizers, industrial waste, and untreated sewage water, might be the key factors causing excessive concentrations of contaminants affecting the water quality. The PCA results showed that over 80% of the total variance could be attributed to two principal factors for most aquifers and three principal factors for two of the aquifers. We employed GIS-based spatial analysis to map groundwater quality in the study area. Based on the WQI values, approximately 48% of groundwater samples were identified as poor to unsuitable for drinking purposes. Results of this study provide a better hydro-geochemical understanding of the multiple aquifers that require preventive action against groundwater damage. We conclude that the combined approach of using a multivariate statistical technique and spatial analysis is effective for determining the factors controlling groundwater quality.
Beijing Land Subsidence Revealed Using PS-InSAR with Long Time Series TerraSAR-X SAR Data
Authors: Bai Zechao , Wang Yanping , Balz Timo
Published: May 2022

Beijing is a major city suffering from land subsidence due to long-term over-exploitation of groundwater. The South-to-North Water Diversion Project (SNWDP), however, has had a significant impact on the structure of water consumption since the end of 2014, and it is changing the status of land subsidence in Beijing. In this study, we employed Persistent Scatterers Synthetic Aperture Radar Interferometric (PS-InSAR) to investigate the decadal evolution of land subsidence in Beijing with 100 TerraSAR-X stripmap images collected from April 2010 to December 2019. The water resources, historic climate and urban construction data were compiled for the years of 2010 to 2019 to analyze changes in groundwater level, human activity, surface geology, active faults and land subsidence patterns. The results show that the changes in the water supply structure are correlated to a rise in groundwater level after 2015. These changes include an increase in the water supply from the SNWDP, a reduction in groundwater exploitation, the optimization of water consumption, replacing recycled water for environmental water and a reduction in the use of water for agriculture. Land subsidence in the study area was concentrated in the eastern regions, trending towards a decreasing velocity starting about two years after the commencement of SNWDP in 2015. Uneven subsidence in the land subsidence area was related to excavations of underground soil, and the construction of Line 6 and Line 7 led to rapid nonlinear subsidence. Our results have scientific significance for reducing subsidence hazards in the context of SNWDP and urban expansion.
A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation
Authors: Balivada Srinivasa , Grant Gregory , Zhang Xufeng , Ghosh Monisha , Guha Supratik , Matamala Roser
Published: May 2022

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.
An Innovative Huffman Forest-Based Method to Detected Railroad Station Anomalies
Authors: Wang Yuan , Li Xiaopeng
Published: May 2022

Detecting railroad station anomalies is a critical task prior to segmentation and making optimization decisions for each cluster. Three types of anomalies (local clustered, axis paralleled, and surrounded by normal instances) caused by the specialty of railroad operations bring the existing methods non-trivial challenges in detecting them accurately and efficiently. To tackle this limitation of existing methods, this paper proposes a novel anomaly detection method named Huffman Anomaly Detection Forest (HuffForest) to detect station anomalies, which leverages Huffman encoding to measure abnormalities in certain railroad scenarios with high accuracy. The proposed method establishes a Huffman forest by constructing trees from the perspective of data points and subsequently computes anomaly scores of instances considering both local and global information. A sampling-based version is also developed to improve scalability for large datasets. Taking advantage of the encoding mechanism, the proposed method can effectively recognize the underlying patterns of railroad stations and detect outliers in various complicated scenarios where the conventional methods are not reliable. Experiment results on both synthesized and public benchmarks are demonstrated to show the advances of the proposed method compared to the state-of-the-art isolation forest (iForest) and local outlier factor (LOF) methods on detection accuracy with an acceptable computational complexity.
Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges
Authors: Tronci Eleonora Maria , Nagabuko Sakie , Hieda Hiroyuki , Feng Maria Qing
Published: May 2022

Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations.
GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System
Authors: Bo Zhao , Atif Othmane , Lee Jonguk , Park Daihee , Chung Yongwha
Published: May 2022

Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.
Titanium and Silicon Dioxide-Coated Fabrics for Management and Tuning of Infrared Radiation
Authors: Yuce Ismail , Canoglu Suat , Yukseloglu Sevhan Muge , Li Voti Roberto , Cesarini Gianmario , Sibilia Concita , Larciprete Maria Cristina
Published: May 2022

Far infrared radiation (FIR) is emitted by every body at a given temperature, including the human body. FIR ranging between 4–14 μm is considered useful for cell growth, and the human body emits a maximum of infrared (IR) radiation at the wavelength of approximately 9.3 µm. In the present study, fabrics based on five different raw textiles having the same yarn count as well as the same weaving patterns were designed and created. Some of them were subjected to a coating process. The fabrics to be tested were as follows: coated with TiO2 nanoparticles, coated with SiO2 nanoparticles, coated fabric that does not contain bioceramic nanoparticle (BNFC), and non-coated fabrics (NCF). The structural characterization of the resulting samples was performed using scanning electron microscopy (SEM), abrasion tests, and air permeability. Following the structural characterization, the infrared emissivity properties were investigated using infrared thermography as well as attenuated total reflectance Fourier-transform infrared spectroscopy in the 8–14 IR range. According to the experimental findings, the fabrics coated with TiO2 and SiO2 displayed increased infrared emissivity values compared to the uncoated ones. In addition, it was observed that the use of bioceramic powders had no effect on air permeability and abrasion properties.
Acoustic, Phononic, Brillouin Light Scattering and Faraday Wave-Based Frequency Combs: Physical Foundations and Applications
Authors: Maksymov Ivan S. , Huy Nguyen Bui Quoc , Pototsky Andrey , Suslov Sergey
Published: May 2022

Frequency combs (FCs)—spectra containing equidistant coherent peaks—have enabled researchers and engineers to measure the frequencies of complex signals with high precision, thereby revolutionising the areas of sensing, metrology and communications and also benefiting the fundamental science. Although mostly optical FCs have found widespread applications thus far, in general FCs can be generated using waves other than light. Here, we review and summarise recent achievements in the emergent field of acoustic frequency combs (AFCs), including phononic FCs and relevant acousto-optical, Brillouin light scattering and Faraday wave-based techniques that have enabled the development of phonon lasers, quantum computers and advanced vibration sensors. In particular, our discussion is centred around potential applications of AFCs in precision measurements in various physical, chemical and biological systems in conditions where using light, and hence optical FCs, faces technical and fundamental limitations, which is, for example, the case in underwater distance measurements and biomedical imaging applications. This review article will also be of interest to readers seeking a discussion of specific theoretical aspects of different classes of AFCs. To that end, we support the mainstream discussion by the results of our original analysis and numerical simulations that can be used to design the spectra of AFCs generated using oscillations of gas bubbles in liquids, vibrations of liquid drops and plasmonic enhancement of Brillouin light scattering in metal nanostructures. We also discuss the application of non-toxic room-temperature liquid–metal alloys in the field of AFC generation.
Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset
Authors: Khemmar Redouane , Mauri Antoine , Dulompont Camille , Gajula Jayadeep , Vauchey Vincent , Haddad Madjid , Boutteau Rémi
Published: May 2022

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed.
A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
Authors: Wang Mingchang , Ding Wen , Wang Fengyan , Song Yulian , Chen Xueye , Liu Ziwei
Published: May 2022

Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality.
Microclimatic Evaluation of Five Types of Colombian Greenhouses Using Geostatistical Techniques
Authors: Villagrán Edwin , Flores-Velazquez Jorge , Akrami Mohammad , Bojacá Carlos
Published: May 2022

In Colombia, the second-largest exporter of cut flowers worldwide and one of the South American countries with the largest area of crops under cover, passive or naturally ventilated greenhouses predominate. Locally, there are several types of greenhouses that differ in architecture, size, height, shape of roof and ventilation surfaces, of which many characteristics of the microclimate generated in their interior environment are unknown. This generates productive limitations that in some way may be limiting the yield, quality and health of the final products harvested; in addition, Colombian producers do not have the ability to monitor the microclimate of their farms, much less to correlate microclimate data with data on crop production and yield. Therefore, there is a need for the Colombian grower to know the most relevant microclimate characteristics generated in the main greenhouses used locally. The objective of this work was to carry out a microclimatic characterization of the five most used types of greenhouses in Colombia. The main results allowed determining that in these structures, there are conditions of high humidity and low vapor pressure for several hours of the day, which affects the physiological processes of growth and development of the plants. It was also identified that for each type of greenhouse, depending on the level of radiation, there is a significant microclimatic heterogeneity that may be the cause of the heterogeneity in plant growth, which is a common characteristic observed by the technical cultivation personnel. Therefore, it can be concluded that it is urgent to propose microclimatic optimization strategies to help ensure the sustainability of the most important production systems in the country.
Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application
Authors: Mao Wei-Lung , Chiu Yu-Ying , Lin Bing-Hong , Wang Chun-Chi , Wu Yi-Ting , You Cheng-Yu , Chien Ying-Ren
Published: May 2022

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.