Distribution Of Scatterers Research Articles

Wear of mold surfaces: Interfacial adhesion in precision glass molding

Micro-optical components, such as glass-based microlens arrays, have become essential in applications relying on advanced optical systems such as 3D displays, optical fiber coupling, and unmanned vehicles. Precision glass molding (PGM) has emerged as a promising method for fabricating optical components based on the formability of glasses beyond their glass transition temperature (Tg), while adhesion wear strength and mechanism of mold-glass interfaces remain major obstacles. This paper aims to explore the adhesion wear mechanisms between mold surfaces and optical glass D-FK95 in PGM. The applicability of WC molds with coatings of Ta–C, AlCrN, and AlTiN was investigated respectively considering their thermodynamic properties and surface energy characteristics. The study identified three adhesion wear mechanisms in the open-air atmosphere with WO3 oxidation on mold surfaces and four adhesion mechanisms featured by scattered distribution, island aggregation, dispersed flow, and planar coverage in an inert atmosphere. It was also found that when the temperature was close to Tg, the WC-glass adhesion force was negligible. The adhesion stress increased to 0.80 MPa with an increase in the applied temperature and pressure. With coating, however, the adhesion stress reduced significantly to 0.03 MPa. The study also concluded that when paired with the D-FK95 glass, the WC mold coated with Ta–C provides the best anti-adhesive performance in comparison to those with AlCrN and AlTiN coatings.

Ultrasonic envelope statistical analysis in motion compensated images during temperature change

Motion compensation was applied in the envelope statistical analysis to develop robust thermometry for in vivo situations. Numerical data were reproduced with the temporal change in the scatterer distribution due to thermal expansion and arbitrary translational motions, and experimental phantom data were obtained under heating with the probe motion. Additionally, the case under respiratory motion was evaluated using the data collected from in vivo rat tumor. Ultrasound signals were acquired using a 7.5 MHz linear probe, and their misalignments of envelopes due to the motions were compensated by a block-matching-based motion estimator. The absolute change in the Nakagami shape parameter, in each frame was calculated by normalizing with the initial frame to indicate the temperature change. The uncompensated with physical motions deviated from that without motion, whereas the motion-compensated numerical and experimental data suppressed the rapid or gradual change and showed the same trend of without motion.

Investigating pulmonary edema in rat lungs using separation of multiple scattering and single scattering contribution

Lung ultrasound imaging is challenging due to multiple scattering (MS) from alveoli. Conventional B-mode does not provide lung microstructure images. However, MS signals can provide valuable information about structure and alveolar distribution and investigating conditions such as pulmonary edema. Lung edema results in fluid buildup in interstitial spaces and alveoli, affecting density of alveoli. Previously, we demonstrated that changes in the distribution of scatterers due to edema result in changes in the wave diffusion regime and the scattering mean free path was sensitive to lung injury due to induced edema in rodents. In the present study, we introduce a novel way of quantifying MS in lungs by isolating the single scattering (SS) and MS contributions and processing them separately. After inducing different severity of edema using ischemia reperfusion injury in 18 rats, full synthetic aperture transmit sequences were used to acquire backscattered signals. The SS/MS contributions were separated using singular value decomposition. The separated SS/MS intensities were calculated and a new parameter defined as the rate of decay in intensity with depth. To assess edema severity and method validation, ex vivo CT lung images were assigned scores compared with this novel biomarker (R = 0.47, p = 0.021). Lung wet/dry ratio was also compared (R = 0.52, p = 0.009).

Combining Multi-Dimensional SAR Parameters to Improve RVoG Model for Coniferous Forest Height Inversion Using ALOS-2 Data

This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses the InSAR phase center height histogram and Gaussian function to fit the normalized extinction coefficient curve so as to reflect the vertical structure of the heterogeneous forest. Combining polarization decomposition based on the physical model and the PolInSAR parameter inversion method, the ground and volume coherence matrices can be separated based on the polarization characteristics and interference coherence diversity. By combining the new abovementioned parameters, the semi-empirical improved RVoG inversion model can be used to both quantify the effects of temporal decorrelation on coherence and phase errors and avoid the effects of small vertical wavenumbers on the large temporal baseline of spaceborne data. The model provided robust inversion for the height of the coniferous forest and enhanced the parameter estimation of the forest structure. This study addressed the influence of vertical structure differences on the extinction coefficient, though the coherence of the ground and volume in sparse vegetation areas could not be accurately estimated, and the oversensitivity of temporal decorrelation caused by inappropriate vertical wavenumbers. According to this method we used spaceborne L-band ALOS-2 PALSAR data on the Saihanba forest in Hebei Province acquired in 2020 for the purpose of height inversion, with a temporal baseline range of 14–70 days and the vertical wavenumber range of 0.01–0.03 rad/m. The results are further validated using sample data, with R2 reaching 0.67.

A Prototype Software System for Intra-procedural Staff Dose Monitoring and Virtual Reality Training for Fluoroscopically Guided Interventional Procedures.

Staff dose management in fluoroscopically guided interventional procedures is a continuing problem. The scattered radiation display system (SDS), which our group has developed, provides in-room visual feedback of scatter dose to staff members during fluoroscopically guided interventional (FGI) procedures as well as extra-procedure staff and resident training. There have been a number of virtual safety training systems developed that provide detailed feedback for staff, but utilize expensive graphics processing units (GPUs) and dosimeter systems, or interaction with the x-ray system in a manner which entails additional radiation exposure and is not compatible with the As Low as Reasonably Achievable paradigm. The SDS, on the other hand, incorporates a library of look-up-table (LUT) room scatter distributions determined using the EGSnrc Monte Carlo software, which facilitates accurate and rapid system update without the need for GPUs. Real-time display of these distributions is provided for feedback to staff during a procedure. After a procedure is completed, machine parameter and staff position log files are stored, retaining all of the exposure and geometric information for future review. A graphic user interface (GUI) in Unity3D enables procedure playback and interactive virtual-reality (VR) staff and resident training with virtual control of exposure conditions using an Oculus headset and controller. Improved staff and resident awareness using this system should lead to increased safety and reduced occupational dose.

Nakagami parametric image for evaluation of the degree of thermal denaturation inside porcine liver induced ex vivo by radiofrequency ablation

In an RF ablation procedure for treating hepatocellular carcinoma and metastatic liver cancer, plucking out an ablation antenna from incompletely cauterized tumor tissues can cause neoplastic seeding; hence, it is demanded to properly evaluate the thermal denaturation of tumor tissue before plucking out the antenna. In the present ex vivo study, we show that the degree of thermal denaturation inside porcine liver tissue induced with RF current heating can be evaluated by binarized two-dimensional Nakagami parametric image indicating the weighted average of the Nakagami shape parameter m. Moreover, we present that the pre-Rayleigh distribution is dominant in ultrasonic backscattered envelopes obtained from incompletely cauterized liver tissue, whereas the Rayleigh distribution is dominant in those obtained from completely cauterized liver tissue which has coagulative necrosis region. The result indicates that the change in scatterer distribution would be occurred inside liver tissue by coagulative necrosis caused by RF current heating in this study.

A comprehensive study and tri-objective optimization for an efficient waste heat recovery from solid oxide fuel cell

Since the solid oxide fuel cell (SOFC) has fuel flexibility, high electrical efficiency, and environmental benefits, it is considered a promising electrochemical energy-conversion device. However, it is important to include a waste heat recovery (WHR) unit in SOFC due the high operating temperature. In the present work, the integration of SOFC and WHR is developed. Accordingly, a parametric evaluation of the critical variables from energy, exergy, exergoeconomic, and environmental (4E) viewpoints is carried out. To determine the optimal decision variables, a tri-objective optimization is performed on the novel integrated SOFC-WHR system; the Pareto frontier solution is also provided. On the Pareto curve, the ultimate solution is selected employing the TOPSIS method. The objective functions are cost rate, output power, and CO2 emission, and the corresponding optimal values are identified as 28.5 $/GJ, 1368 kW, and 0.205 kg/kWh, respectively. Moreover, the scattered distribution of nine design variables is explored, and the behavior of decision variables is obtained.

An essay on the differences and linkages between data science and information science

When there are differences in research objects and methodology between data science and information science, there are also linkages between data science and information science, based on the DIKW hierarchy to the concept chain, namely data – information – knowledge – wisdom. While knowledge metrics provides a quantitative linkage of data – information – knowledge – wisdom, information is the logarithm of data and knowledge is the logarithm of information, on which the mechanism of Brookes’ basic equation of information science is revealed. We suggest to maintain similar principles of data science and information science, including the principle of order, the principle of correlation, the principle of reorganized transformation, the principle of scatter distribution, the principle of logarithmic perspective, and the principle of least effort. Also, we extend to discuss a few issues on knowledge science.

On the Design and Evaluation of an Optimal Security-and-Time Cognizant Data Placement for Dynamic Fog Environments

Fog Computing usefully extends Cloud to the edge of the network for the sake of meeting users’ expanding demand for low latency. However, due to its scattered distribution and open architecture, fog nodes are highly vulnerable to security threats, resulting in an inevitable sharp conflict between quick response time and high data security. This conflict motivates the need for effective data placement among fog nodes towards a trade-off between security and time. Existing studies merely offer independent solutions by considering either security or response time. By contrast, we establish a dynamic multi-objective optimization model in this article by optimizing security and response time simultaneously. With this model, we propose an efficient evolutionary algorithm, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Interactive Security-and-Time cognizant algorithm</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIST</i> ), to obtain optimal data placement strategies under Fog environments. To improve efficiency, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIST</i> allows users to gradually incorporate their preference information into the search process so as to find their most preferred solutions without exploring the whole search space. We demonstrate the superiority of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIST</i> by rigorous comparison with the most state-of-art data placement strategy and other well-applied strategies. Experimental results manifest that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIST</i> outperforms other strategies in obtaining solutions with higher data security and shorter response time. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIST</i> is capable of efficiently and continuously tracking the Pareto optimal solution under dynamically changing Fog environments while other existing strategies cannot.

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.

Insight into quantities, flows, and recycling technology of E-waste in China for resource sustainable society

The difficulty of tracking the metabolism of e-waste throughout its life cycle caused by scattered distribution has long constrained the development of the recycling industry and further policy making. In this study, the monitoring of Chinese e-waste, including sources, flows, scrap, and recycling potentials of electronic products, was measured and predicted based on data from both the government and 109 formal e-waste recycling enterprises. From 2009 to 2030, the inventory of electronic products is expected to increase due to overcapacity and near saturation of household possession. Meanwhile, the total amount of e-waste scrapping is predicted to increase from 1.77 million tons to 18.86 million tons, and the amount of generated e-waste will also increase, reaching 240 million tons by 2030. Currently, e-waste recycling is gradually becoming the official waste disposal approach. However, this newly emerged industry has numerous problems, such as lower recycling efficiency and scale, lack of advanced technologies, and corresponding laws and regulations. Hence, the data collected and analyzed in this study shed light on the e-waste market size in China. Meanwhile, to establish an ideal resource-sustainable society, suggestions regarding the optimization of the e-waste recycling industry through legal, reasonable, and efficient methods are put forward from our point of view.

New insights into the distribution, ecology, and systematic position of the rare water mite Rutripalpus limicola Sokolow, 1934 (Acari: Rutripalpidae)

The water mite Rutripalpus limicola Sokolow, 1934, can be considered exceptionally rare. It shows a scattered distribution range and, until recently, was known from six sites across Europe only. This strictly spring-dwelling species has a highly localised distribution, presumably due to its specific ecological requirements. We present the first records of R. limicola in the Netherlands and review the current state of knowledge on its distribution and biology. In addition to the previously existing morphology-based investigations, we provide a phylogenetic placement based on 28S rRNA gene data and shed light on the controversial systematic position of R. limicola: In contrast to the previously hypothesised Lebertioidea-relatedness of the isolated, monotypic family Rutripalpidae, our results reveal a putative Hydryphantoidea association. Moreover, we discuss the host-parasite association of R. limicola larvae and the dipteran family Ptychopteridae. Combined with additional information derived from the new records from the Netherlands, we contribute to a better understanding of this elusive species' biology and phylogenetic position.

Identification and Risk Assessment of Priority Control Organic Pollutants in Groundwater in the Junggar Basin in Xinjiang, P.R. China.

The Junggar Basin in Xinjiang is located in the hinterland of Eurasia, where the groundwater is a significant resource and has important ecological functions. The introduction of harmful organic pollutants into groundwater from increasing human activities and rapid socioeconomic development may lead to groundwater pollution at various levels. Therefore, to develop an effective regulatory framework, establishing a list of priority control organic pollutants (PCOPs) is in urgent need. In this study, a method of ranking the priority of pollutants based on their prevalence (Pv), occurrence (O) and persistent bioaccumulative toxicity (PBT) has been developed. PvOPBT in the environment was applied in the screening of PCOPs among 34 organic pollutants and the risk assessment of screened PCOPs in groundwater in the Junggar Basin. The results show that the PCOPs in groundwater were benzo[a]pyrene, 1,2-dichloroethane, trichloromethane and DDT. Among the pollutants, benzo[a]pyrene, 1,2-dichloroethane and DDT showed high potential ecological risk, whilst trichloromethane represented low potential ecological risk. With the exception of benzo[a]pyrene, which had high potential health risks, the other screened PCOPs had low potential health risks. Unlike the scatter distribution of groundwater benzo[a]pyrene, the 1,2-dichloroethane and trichloromethane in groundwater were mainly concentrated in the central part of the southern margin and the northern margin of the Junggar Basin, while the DDT in groundwater was only distributed in Jinghe County (in the southwest) and Beitun City (in the north). Industrial and agricultural activities were the main controlling factors that affected the distribution of PCOPs.

A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

Do Ultrasound Based Quantitative Hepatic Fat Content Measurements Have Differences Between Respiratory Phases?

The recently developed ultrasound based tools using attenuation coefficient (AC) and scatter distribution coefficient (SDC) values can be used to quantify hepatic fat content in patients with non-alcoholic fatty liver disease (NAFLD). However, currently the impact of respiratory phase on these measurements is not known. The purpose of this study is to compare AC and SDC measurements acquired at peak inspiration and end expiration phases. AC and SDC measurements were obtained in 50 patients with NAFLD. Tissue Attenuation Imaging (TAI) and Tissue Scatter Distribution Imaging (TSI) tools were utilized to measure AC and SDC values, respectively. Five measurements were performed at respiratory phases using TAI and TSI tools and the median values were noted. Subgroup analyses were performed and Wilcoxon signed rank test was used for comparison of the measurements. The median values of the AC measurements at peak inspiration and end expiration phases were 0.87 dB/cm/MHz and 0.89 dB/cm/MHz, respectively. The median values of the SDC measurements at peak inspiration and end expiration phases were 97.91 and 96.62, respectively. There were no statistically significant differences in AC and SDC measurements between the respiratory phases except for AC measurements in BMI <30 kg/m2 subgroup. Our results revealed that respiratory phases have no impact on SDC measurements. However, while the AC measurements in BMI ≥30 kg/m2 subgroup showed no significant difference, there was a significant difference in AC measurements in BMI <30 kg/m2 subgroup between the respiratory phases.

Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method

Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping.

Preliminary Study for Smoke Color Classification of Combustibles Using the Distribution of Light Scattering by Smoke Particles

Photoelectric smoke detectors are used for early detection of building fires, and sensitivity adjustment is generally performed using white smoke generated by the burning of filter paper. Therefore, when black smoke of the same concentration is introduced, the detector is often not activated. To address this problem, differences in the distribution of light scattered by smoke of various colors were analyzed. A light-scattering chamber with a light-receiving unit for various scattering angles was constructed to measure the scattered light generated inside the chamber of the smoke detector. The light scattering distribution was measured for smoke generated from three combustibles—filter paper (white smoke), kerosene (black smoke), and polyurethane (gray-black smoke)—and three analysis criteria were applied. By identifying a section where the measured values were concentrated for a specific analysis criterion and scattering angle, it was confirmed that some combustibles can be distinguished. Specifically, criterion III, a probabilistic section, was presented to determine which combustible smoke particles were close by applying the proposed section in a complex manner. A preliminary study was conducted to evaluate a methodology for the color classification of smoke particles flowing into a smoke detector chamber; this can be utilized as a foundation for determining optical properties.

A Unified Bidirectional Scattering Distribution Function for Convex Quadric Surface

A Unified Bidirectional Scattering Distribution Function for Convex Quadric Surface

An Image-Domain Signal Model for Azimuth Multichannel Reconstruction and Its Applications

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) may benefit from reconstructing signal in image domain, for taking local characteristics of the scene into account. An image-domain signal model for HRWS SAR to reconstruct non-ambiguous image is presented in this paper. This model makes the idea of controlling the regional reconstruction performance and taking the advantage of nonuniform scatter distribution possible. Resolution is also preserved perfectly by the proposed model despite of whatever tradeoff is made between azimuth-ambiguity-to-signal-ratio (AASR) and signal-to-noise-ratio (SNR). The model is derived on back projection (BP) image and can deal with different squint angles and beam steering strategies thanks to BP algorithm. Although the reconstruction is done pixel by pixel, computational burden, which depends on beam steering and the specific constraints in reconstruction, may not severely increase. Two methods for Spotlight SAR and two methods for Stripmap SAR are also presented based on the proposed model to verify the model and demonstrate the potential of it. Simulations on both point target and extended target with different squint angles are carried out to verify the proposed methods, which also verify the proposed model indirectly.

A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.