bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Investigation on Automotive Interior Materials Based on Variable Knitted Structural Parameters
Authors: Siyao Mao , Liu Su , Peihua Zhang , Hairu Long
Published: October 2020

With the rapid development of technical textiles, more and more researchers have focused on developing high performance textiles to meet various needs. The automotive industry is a major market for technical textiles. Compared to other types of fabric, weft-knitted fabric has good extensibility and elasticity, as well as a hand-feel, and it is gradually becoming the preferred type of interior fabric for automobiles. This paper aims to develop an automotive fabric with good comfort and durability. Sixteen types of weft-knitted fabrics with eight different structures and two different materials (draw textured polyester and textured polyamide yarn) were fabricated using a computerized flat knitting machine. Their durability and level of comfort were examined by measuring the tensile and tear strengths, abrasion resistance and air permeability. A fuzzy comprehensive evaluation method was employed to compare the comprehensive properties of the fabric. The results indicated that the overall performance of DTPA fabric was better than DTPE fabric, and an optimum structure was selected for an automotive interior. Meanwhile, we found that the air permeability of the fabric could be increased by using tuck stitches and that the strength and dimensional stability of fabric could be increased by adding tuck stitches and weft-insert yarns. The findings contribute to the field of technical textiles and provide ideas for the development of high-performance textiles.
Coupled Simulation of a Vacuum Creation System and a Rectification Column Block
Authors: Osipov Eduard , Telyakov Eduard , Ponikarov Sergey
Published: October 2020

The purpose of this study was the coupling simulation of the vacuum block of the ethanolamine mixture separation unit to determine the optimal layout of the vacuum creation system. For this, a computational model of the vacuum unit, which was identified by comparing the computational data with the data of an industrial study of vacuum rectification columns, was synthesized in the Unisim Design R461 software package. To determine the required load on the vacuum system, a numerical experiment was carried out, during which it was discovered that the load on the system would be 9600 m3/h. It was proposed to replace individual column vacuum pumps with a single vacuum-generating system (VGS) based on a liquid ring vacuum pump (LRVP). When defining the layout, two possible schemes were considered, the models of which were created in Unisim Design R461. The system layout was determined by matching the characteristics of the system elements with the characteristics of the vacuum columns. A technical and economic comparison of the proposed solutions was carried out and the payback period for capital costs was calculated, which for Scheme 1 was 4.14 years, and for Scheme 2–3.59 years.
Let Him Who Is without Sin Cast the First Stone: Religious Struggle among Persons Convicted of Sexually Offending
Authors: Robertson Theresa M. , Magyar-Russell Gina M. , Piedmont Ralph L.
Published: October 2020

Religiousness and spirituality have been identified as important factors in promoting desistance from sexual offending and as helpful coping resources with negative psychological consequences related to public registration. However, the potential mental health benefits, and detriments, of religiousness and spirituality for persons convicted of sexually offending have not been widely examined. Given the moral implications of their behavior and stigmatization by society, including from religious and spiritual communities, this study aimed to examine levels of religious struggle and their associations with symptoms of mental health among 30 men on the Maryland Sex Offense Registry. Relative to the normative sample, the mean level of spiritual transcendence, constructive perceptions of spirituality that develop within social, cultural, and educational contexts, was significantly lower within this sample. Conversely, religious struggle mean scores indicated that the men in this sample experienced significantly greater difficulties relative to God and their faith community. Greater levels of religious struggle were significantly related to higher neuroticism, greater self-reported shame, depression, anxiety, and hopelessness, as well as lower levels of self-compassion. Based on these preliminary findings, religious struggles may adversely influence the mental health of persons convicted of sexually offending. More research is needed to gain a better understanding of the associations between religiousness, spirituality, and mental health in this population. Future directions for research and clinical implications for mental health providers, including spiritually informed treatment approaches with persons convicted of sexually offending, are discussed.
Some Common Patterns of Islamic Revival in Post-Soviet Central Asia and Challenges to Develop Human Rights and Inclusive Society
Authors: Zhussipbek Galym , Achilov Dilshod , Nagayeva Zhanar
Published: October 2020

This paper argues that the following common patterns help explain the ongoing Islamic revival in Central Asia: (a) “de-modern” and “ethnicized” Islam as an enduring legacy of the Soviet period; (b) penetration of Salafism; (c) securitization of “non-official” Islam by state and non-state actors and (d) the rise of conservative Islam which goes hand in hand with retraditionalization. These factors, in their turn, pose serious challenges to developing inclusive society and human rights in Central Asian countries. This paper argues that the Islamic revival in Central Asian countries has come to the point when it can be analyzed under the prism, whether it impedes the development of inclusive society and human rights or not.
Quasi-Active Thermal Imaging of Large Floating Covers Using Ambient Solar Energy
Authors: Ma Yue , Wong Leslie , Vien Benjamin Steven , Kuen Thomas , Kodikara Jayantha , Chiu Wing Kong
Published: October 2020

Melbourne Water Corporation has two large anaerobic lagoons at the Western Treatment Plant (WTP), Werribee, Victoria, Australia. The lagoons are covered using numerous sheets of high-density polyethylene (HDPE) geomembranes to prevent the emission of odorous gases and to harness biogas as a source of renewable energy. Some of the content of raw sewage can accumulate and form into a solid mass (called “scum”). The development of a large body of solid scum that rises to the surface of the lagoon (called “scumbergs”) deforms the covers and may affect its structural integrity. Currently, there is no method able to effectively “see-through” the opaque covers to define the spread of the scum underneath the cover. Hence, this paper investigates a new quasi-active thermal imaging method that uses ambient solar radiation to determine the extent of the solid matter under the geomembrane. This method was devised by using infrared thermography and a pyranometer to constantly monitor the transient temperature response of the HDPE geomembrane using the time varying ambient solar radiation. Newton’s cooling law is implemented to define the resultant cooling constants. The results of laboratory-scale tests demonstrate the capability of the quasi-active thermography to identify the presence and the extent of solid matter under the cover. This paper demonstrates, experimentally, the importance of measuring the surface temperature of the cover and solar intensity profiles to obtain the cooling process when during variations in solar intensity during normal sunrise, sunset, daily transitioning from morning–afternoon–evening and cloud cover events. The timescale associated with these events are different and the results show that these daily transient temperature cycles of the geomembranes can be used to detect the extent of the accumulation of solid matter underneath the geomembrane. The conclusions from this work will be further developed for field trials to practically monitor the growth in the extent of the scum under the floating covers in WTP with the ambient solar energy.
Color and Laser Data as a Complementary Approach for Heritage Documentation
Authors: Alshawabkeh Yahya
Published: October 2020

Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow for new possibilities in terms of data interpretation and completeness of complex site documentation. In the second level, true orthophoto is generated based on laser data, the results are rectified images with a uniform scale representing all objects at their planimetric position. The proposed approaches enable flexible data fusion and allow images to be taken at an optimum time and position for radiometric information. Data fusion usually involves serious distortions in the form of a double mapping of occluded objects that affect the product quality. In order to enhance the efficiency of visibility analysis in complex structures, a proposed visibility algorithm is implemented into the developed methods of texture mapping and true orthophoto generation. The algorithm filters occluded areas based on a patch processing using a grid square unit set around the projected vertices. The depth of the mapped triangular vertices within the patch neighborhood is calculated to assign the visible one. In this contribution, experimental results from different historical sites in Jordan are presented as a validation of the proposed algorithms. Algorithms show satisfactory performance in terms of completeness and correctness of occlusion detection and spectral information mapping. The results indicate that hybrid methods could be used efficiently in the representation of heritage structures.
Assessing Desert Dust Indirect Effects on Cloud Microphysics through a Cloud Nucleation Scheme: A Case Study over the Western Mediterranean
Authors: Tsarpalis Konstantinos , Katsafados Petros , Papadopoulos Anastasios , Mihalopoulos Nikolaos
Published: October 2020

In this study, the performance and characteristics of the advanced cloud nucleation scheme of Fountoukis and Nenes, embedded in the fully coupled Weather Research and Forecasting/Chemistry (WRF/Chem) model, are investigated. Furthermore, the impact of dust particles on the distribution of the cloud condensation nuclei (CCN) and the way they modify the pattern of the precipitation are also examined. For the simulation of dust particle concentration, the Georgia Tech Goddard Global Ozone Chemistry Aerosol Radiation and Transport of Air Force Weather Agency (GOCART-AFWA) is used as it includes components for the representation of dust emission and transport. The aerosol activation parameterization scheme of Fountoukis and Nenes has been implemented in the six-class WRF double-moment (WDM6) microphysics scheme, which treats the CCN distribution as a prognostic variable, but does not take into account the concentration of dust aerosols. Additionally, the presence of dust particles that may facilitate the activation of CCN into cloud or rain droplets has also been incorporated in the cumulus scheme of Grell and Freitas. The embedded scheme is assessed through a case study of significant dust advection over the Western Mediterranean, characterized by severe rainfall. Inclusion of CCN based on prognostic dust particles leads to the suppression of precipitation over hazy areas. On the contrary, precipitation is enhanced over areas away from the dust event. The new prognostic CCN distribution improves in general the forecasting skill of the model as bias scores, the root mean square error (RMSE), false alarm ratio (FAR) and frequencies of missed forecasts (FOM) are limited when modelled data are compared against satellite, LIDAR and aircraft observations.
Automatic Identification and Dynamic Monitoring of Open-Pit Mines Based on Improved Mask R-CNN and Transfer Learning
Authors: Wang Chunsheng , Chang Lili , Zhao Lingran , Niu Ruiqing
Published: October 2020

As the ecological problems caused by mine development become increasingly prominent, the conflict between mining activity and environmental protection is gradually intensifying. There is an urgent problem regarding how to effectively monitor mineral exploitation activities. In order to automatic identify and dynamically monitor open-pit mines of Hubei Province, an open-pit mine extraction model based on Improved Mask R-CNN (Region Convolutional Neural Network) and Transfer learning (IMRT) is proposed, a set of multi-source open-pit mine sample databases consisting of Gaofen-1, Gaofen-2 and Google Earth satellite images with a resolution of two meters is constructed, and an automatic batch production process of open-pit mine targets is designed. In this paper, pixel-based evaluation indexes and object-based evaluation indexes are used to compare the recognition effect of IMRT, faster R-CNN, Maximum Likelihood (MLE) and Support Vector Machine (SVM). The IMRT model has the best performance in Pixel Accuracy (PA), Kappa and MissingAlarm, with values of 0.9718, 0.8251 and 0.0862, respectively, which shows that the IMRT model has a better effect on open-pit mine automatic identification, and the results are also used as evaluation units of the environmental damages of the mines. The evaluation results show that level Ⅰ (serious) land occupation and destruction of key mining areas account for 34.62%, and 36.2% of topographical landscape damage approached level I. This study has great practical significance in terms of realizing the coordinated development of mines and ecological environments.
Individual Palm Tree Detection Using Deep Learning on RGB Imagery to Support Tree Inventory
Authors: Culman María , Delalieux Stephanie , Van Tricht Kristof
Published: October 2020

Phoenix palms cover more than 1.3 million hectares in the Mediterranean, Middle East, and North Africa regions and they represent highly valued assets for economic, environmental, and cultural purposes. Despite their importance, information on the number of palm trees and the palm distribution across different scenes is difficult to obtain and, therefore, limited. In this work, we present the first region-wide spatial inventory of Phoenix dactylifera (date palm) and Phoenix canariensis (canary palm) trees, based on remote imagery from the Alicante province in Spain. A deep learning architecture that was based on convolutional neural networks (CNN) was implemented to generate a detection model able to locate and classify individual palms trees from aerial high-resolution RGB images. When considering that creating large labeled image datasets is a constraint in object detection applied to remote sensing data, as a strategy for pre-training detection models on a similar task, imagery and palm maps from the autonomous community of the Canary Islands were used. Subsequently, these models were transferred for re-training with imagery from Alicante. The best performing model was capable of mapping Phoenix palms in different scenes, with a changeable appearance, and with varied ages, achieving a mean average precision (mAP) value of 0.861. In total, 511,095 Phoenix palms with a probability score above 0.5 were detected over an area of 5816 km2. The detection model, which was obtained from an out-of-the-box object detector, RetinaNet, provides a fast and straightforward method to map isolated and densely distributed date and canary palms—and other Phoenix palms. The inventory of palm trees established here provides quantitative information on Phoenix palms distribution, which could be used as a baseline for long-term monitoring of palms’ conditions. In addition to boosting palm tree inventory across multiple landscapes at a large scale, the detection model demonstrates how image processing techniques that are based on deep learning leverage image understanding from remote sensing data.
Using SPOT Data and FRAGSTAS to Analyze the Relationship between Plant Diversity and Green Space Landscape Patterns in the Tropical Coastal City of Zhanjiang, China
Authors: Cheng Xia-Lan , Nizamani Mir Muhammad , Jim C.Y. , Balfour Kelly , Da Liang-Jun , Qureshi Salman , Zhu Zhi-Xin , Wang Hua-Feng
Published: October 2020

Urban green spaces provide a host of ecosystem services, the quantity and structure of which play an important role in human well-being. Rapid urbanization may modify urban green spaces, having various effects on plant diversity. Tropical coastal cities have urbanized rapidly in recent decades, but few studies have been conducted with a focus on their green spaces. We studied the responses of cultivated and spontaneous plants, both key components of urban flora, to the landscape structure of urban green spaces and possible social drivers. We analyzed existing relationships between plant diversity indices, urban green space landscape metrics (using Systeme Probatoire d’Observation de la Terre (SPOT) data,), and social factors, including the type, population density, construction age, and GPS coordinates of each Urban Functional Unit, or UFU. We found that UFUs with more green space patches had higher cultivated and spontaneous species richness than those with fewer green space patches. Spontaneous species richness decreased when green space patches became fragmented, and it increased when green space patches were more connected (e.g., via land bridges). Conversely, cultivated species richness increased with green space patch fragmentation. The phylogenetic diversity of both cultivated and spontaneous plants were weakly associated with green space structure, which was strongly driven by land use. Old UFUs and those with larger populations had more green space patches overall, although they tended to be small and fragmented. Green space patch density was found to increase as the UFU age increased. From the viewpoint of knowledge transfer, understanding the effects and drivers of landscape patterns of urban green spaces could inform the development of improved policies and management of urban green space areas.
Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots
Authors: Beeri Ofer , Netzer Yishai , Munitz Sarel , Mintz Danielle Ferman , Pelta Ran , Shilo Tal , Horesh Alon , Mey-tal Shay
Published: October 2020

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.
Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset
Authors: Gao Yuan , Liu Liangyun , Zhang Xiao , Chen Xidong , Mi Jun , Xie Shuai
Published: October 2020

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.
SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections
Authors: Boyd Dylan , Kurum Mehmet , Eroglu Orhan , Gurbuz Ali Cafer , Garrison James L. , Nold Benjamin R. , Vega Manuel A. , Piepmeier Jeffrey R. , Bindlish Rajat
Published: October 2020

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.
Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients
Authors: Heller Betty , Siegismund Frank , Pail Roland , Gruber Thomas , Haagmans Roger
Published: October 2020

The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems.
Multi-Source Data Analysis of Mesoscale Eddies and Their Effects on Surface Chlorophyll in the Bay of Bengal
Authors: Yang Xiao , Xu Guangjun , Liu Yu , Sun Wenjin , Xia Changshui , Dong Changming
Published: October 2020

Mesoscale eddies are important to ocean circulation due to their roles in the transport of mass, energy, and heat. This study employs a combination of data sources to initiate a statistical analysis of eddy spatiotemporal characteristics in the Bay of Bengal (BOB) to elucidate the sea surface and vertical structures of the eddies and their impacts on sea surface chlorophyll (Chl) distributions. The results suggest that 1237 cyclonic eddies (CEs) and 1121 anticyclonic eddies (AEs) were detected in 26 years. The number of two eddy polarities was almost the same, and most of them spread to the west or southwest direction. The vertical change of temperature (T) and salinity (S) caused by the eddies is studied and the anomalous eddies, i.e., a CE (AE) eddy with warm (cold) water at the center, are mainly distributed on the northeast side of the Island of Sri Lanka. Furthermore, CEs are found to increase Chl concentration in the surrounding sea by approximately 11.15%, while AEs decrease concentrations also by approximately 11.25%. Changes in Chl concentrations occur most rapidly during the mature and intensification eddy phases. Observations also indicate that the strong local current and wind fields are the primary mechanisms in eddy generation.
An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions
Authors: Klaer Peter , Huang Andi , Sévigny Pascale , Rajan Sreeraman , Pant Shashank , Patnaik Prakash , Balaji Bhashyam
Published: October 2020

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.
Bi-Layer Shortest-Path Network Interdiction Game for Internet of Things
Authors: Yan Jingwen , Xiao Kaiming , Zhu Cheng , Wu Jun , Yang Guoli , Zhang Weiming
Published: October 2020

Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods.
A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
Authors: Li Zhu , Zhou Yisha , Sheng Qinghua , Chen Kunjian , Huang Jian
Published: October 2020

Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters.
Tetramethylbenzidine: An Acoustogenic Photoacoustic Probe for Reactive Oxygen Species Detection
Authors: Bresolí-Obach Roger , Frattini Marcello , Abbruzzetti Stefania , Viappiani Cristiano , Agut Montserrat , Nonell Santi
Published: October 2020

Photoacoustic imaging is attracting a great deal of interest owing to its distinct advantages over other imaging techniques such as fluorescence or magnetic resonance image. The availability of photoacoustic probes for reactive oxygen and nitrogen species (ROS/RNS) could shed light on a plethora of biological processes mediated by these key intermediates. Tetramethylbenzidine (TMB) is a non-toxic and non-mutagenic colorless dye that develops a distinctive blue color upon oxidation. In this work, we have investigated the potential of TMB as an acoustogenic photoacoustic probe for ROS/RNS. Our results indicate that TMB reacts with hypochlorite, hydrogen peroxide, singlet oxygen, and nitrogen dioxide to produce the blue oxidation product, while ROS, such as the superoxide radical anion, sodium peroxide, hydroxyl radical, or peroxynitrite, yield a colorless oxidation product. TMB does not penetrate the Escherichia coli cytoplasm but is capable of detecting singlet oxygen generated in its outer membrane.
Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
Authors: Sfair Palar Piatan , de Vargas Terres Vinícius , Schneider de Oliveira André
Published: October 2020

This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.
Fabricating a Portable ECG Device Using AD823X Analog Front-End Microchips and Open-Source Development Validation
Authors: Bravo-Zanoguera Miguel , Cuevas-González Daniel , Reyna Marco A. , García-Vázquez Juan P. , Avitia Roberto L.
Published: October 2020

Relevant to mobile health, the design of a portable electrocardiograph (ECG) device using AD823X microchips as the analog front-end is presented. Starting with the evaluation board of the chip, open-source hardware and software components were integrated into a breadboard prototype. This required modifying the microchip with the breadboard-friendly Arduino Nano board in addition to a data logger and a Bluetooth breakout board. The digitized ECG signal can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. The data logging shield provides gigabytes of storage, as the signal is recorded to a microSD card adapter. A menu incorporates the device’s several operating modes. Simulation and testing assessed the system stability and performance parameters in terms of not losing any sample data throughout the length of the recording and finding the maximum sampling frequency; and validation determined and resolved problems that arose in open-source development. Ultimately, a custom printed circuit board was produced requiring advanced manufacturing options of 2.5 mils trace widths for the small package components. The fabricated device did not degrade the AD823X noise performance, and an ECG waveform with negligible distortion was obtained. The maximum number of samples/second was 2380 Hz in serial cable transmission, whereas in microSD recording mode, a continuous ECG signal for up to 36 h at 500 Hz was verified. A low-cost, high-quality portable ECG for long-term monitoring prototype that reasonably complies with electrical safety regulations and medical equipment design was realized.
Cylindrical Shell Vibration Gyroscope Excited and Detected by High-Temperature-Sintered Piezoelectric Ceramic Electrodes
Authors: Qu Tianliang , Zhou Guanqing , Xue Xiaoming , Teng Junhua
Published: October 2020

A cylindrical shell piezoelectric vibration gyroscope is a kind of Coriolis vibration gyroscope. Its core components are the cylindrical quartz resonator (CQR) and the piezoelectric ceramic electrodes (PCEs). In order to develop a high-precision Cylindrical shell piezoelectric vibration gyroscope, it is very important to reduce the influence of the PCEs and obtain a high-quality-factor CQR. To achieve this goal, a novel high-temperature sintering method is proposed to combine the CQR and the PCEs, and the corresponding sintered resonators are fabricated. After sintering, results of the acoustic excitation experiment and piezoelectric excitation experiment are tested, and the influence of the sintered PCEs on the CQR is determined. A complete gyroscope is obtained by vacuum packaging the sintered resonator. Through the open-loop and closed-loop tests, the performance parameters of gyroscope are obtained. The feasibility of the high-temperature sintering method is proved by experiments.
Recent Advances in Noninvasive Biosensors for Forensics, Biometrics, and Cybersecurity
Authors: McGoldrick Leif K. , Halámek Jan
Published: October 2020

Recently, biosensors have been used in an increasing number of different fields and disciplines due to their wide applicability, reproducibility, and selectivity. Three large disciplines in which this has become relevant has been the forensic, biometric, and cybersecurity fields. The call for novel noninvasive biosensors for these three applications has been a focus of research in these fields. Recent advances in these three areas has relied on the use of biosensors based on primarily colorimetric assays based on bioaffinity interactions utilizing enzymatic assays. In forensics, the use of different bodily fluids for metabolite analysis provides an alternative to the use of DNA to avoid the backlog that is currently the main issue with DNA analysis by providing worthwhile information about the originator. In biometrics, the use of sweat-based systems for user authentication has been developed as a proof-of-concept design utilizing the levels of different metabolites found in sweat. Lastly, biosensor assays have been developed as a proof-of-concept for combination with cybersecurity, primarily cryptography, for the encryption and protection of data and messages.
A Novel NIR-FRET Biosensor for Reporting PS/γ-Secretase Activity in Live Cells
Authors: Houser Mei CQ , Hou Steven S , Perrin Florian , Turchyna Yuliia , Bacskai Brian J , Berezovska Oksana , Maesako Masato
Published: October 2020

Presenilin (PS)/γ-secretase plays a pivotal role in essential cellular events via proteolytic processing of transmembrane proteins that include APP and Notch receptors. However, how PS/γ-secretase activity is spatiotemporally regulated by other molecular and cellular factors and how the changes in PS/γ-secretase activity influence signaling pathways in live cells are poorly understood. These questions could be addressed by engineering a new tool that enables multiplexed imaging of PS/γ-secretase activity and additional cellular events in real-time. Here, we report the development of a near-infrared (NIR) FRET-based PS/γ-secretase biosensor, C99 720-670 probe, which incorporates an immediate PS/γ-secretase substrate APP C99 with miRFP670 and miRFP720 as the donor and acceptor fluorescent proteins, respectively. Extensive validation demonstrates that the C99 720-670 biosensor enables quantitative monitoring of endogenous PS/γ-secretase activity on a cell-by-cell basis in live cells (720/670 ratio: 2.47 ± 0.66 (vehicle) vs. 3.02 ± 1.17 (DAPT), ** p < 0.01). Importantly, the C99 720-670 and the previously developed APP C99 YPet-Turquoise-GL (C99 Y-T) biosensors simultaneously report PS/γ-secretase activity. This evidences the compatibility of the C99 720-670 biosensor with cyan (CFP)-yellow fluorescent protein (YFP)-based FRET biosensors for reporting other essential cellular events. Multiplexed imaging using the novel NIR biosensor C99 720-670 would open a new avenue to better understand the regulation and consequences of changes in PS/γ-secretase activity.
Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion
Authors: Puchalt Joan Carles , Layana Castro Pablo E. , Sánchez-Salmerón Antonio-José
Published: October 2020

Nowadays, various artificial vision-based machines automate the lifespan assays of C. elegans. These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a “dead or alive criterion” based on nematode position or pose changes, without poking worms. However, worms barely move on their last days of life, even though they are still alive. Therefore, a long monitoring period is necessary to observe motility in order to guarantee worms are actually dead, or a stimulus to prompt worm movement is required to reduce the lifespan variability measure. Here, a new automated vibrotaxis-based method for lifespan machines is proposed as a solution to prompt a motion response in all worms cultured on standard Petri plates in order to better distinguish between live and dead individuals. This simple automated method allows the stimulation of all animals through the whole plate at the same time and intensity, increasing the experiment throughput. The experimental results exhibited improved live-worm detection using this method, and most live nematodes (>93%) reacted to the vibration stimulus. This method increased machine sensitivity by decreasing results variance by approximately one half (from ±1 individual error per plate to ±0.6) and error in lifespan curve was reduced as well (from 2.6% to 1.2%).
Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification
Authors: Doroniewicz Iwona , Ledwoń Daniel J. , Affanasowicz Alicja , Kieszczyńska Katarzyna , Latos Dominika , Matyja Małgorzata , Mitas Andrzej W. , Myśliwiec Andrzej
Published: October 2020

Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectification and computer-aided diagnosis based on video recordings’ analysis. The present study attempts to automatically detect writhing movements, one of the normal general movement categories presented by newborns in the first weeks of life. A set of 31 recordings of newborns on the second and third day of life was divided by five experts into videos containing writhing movements (with occurrence time) and poor repertoire, characterized by a lower quality of movement in relation to the norm. Novel, objective pose-based features describing the scope, nature, and location of each limb’s movement are proposed. Three machine learning algorithms are evaluated in writhing movements’ detection in leave-one-out cross-validation for different feature extraction time windows and overlapping time. The experimental results make it possible to indicate the optimal parameters for which 80% accuracy was achieved. Based on automatically detected writhing movement percent in the video, infant movements are classified as writhing movements or poor repertoire with an area under the ROC (receiver operating characteristics) curve of 0.83.
System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment
Authors: Azam Shoaib , Munir Farzeen , Sheri Ahmad Muqeem , Kim Joonmo , Jeon Moongu
Published: October 2020

In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.
Characterization of Biodegraded Ignitable Liquids by Headspace–Ion Mobility Spectrometry
Authors: P. Calle José Luis , Ferreiro-González Marta , Aliaño-González María José , F. Barbero Gerardo , Palma Miguel
Published: October 2020

The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples’ trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required.