Density Of Scatterers Research Articles

Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient (R2) of 0.99. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to −2% and 9%, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R2=0.95, with an MBE of around 2% and an SD of approximately 26%. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99, and the MBE and SD are about 3% and 11%, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.

Correlating scatterer concentration and intensity threshold of random laser in niobium oxide particle colloids with dye solution

Our study examines how the concentration of scatterers affects the intensity threshold of random laser (RL) action in colloids made of niobium oxide particles and Rhodamine B dye. This is the first reported instance of RL action in niobium oxide. We kept the dye (gain medium) concentration constant and quantified how varying concentrations of niobium oxide particles (scatterers) affect the RL intensity threshold. Our findings indicate an inverse relationship between the concentration of niobium oxide particles and the RL threshold. Increasing the niobium oxide concentration from 0.32 mM to 2.50 mM decreased the RL threshold by up to 70 %, suggesting that higher scatterer densities lead to enhanced efficiency of the lasing process. We noticed that the RL intensity thresholds decrease concerning scatterer concentration, highlighting the significant impact of scatterer density on lasing efficiency. These results could provide a better comprehension of the mechanics of RL in disordered media and emphasize the essential role of scatterer density in determining RL efficiency. Our work deepens the understanding of RL dynamics and lays the groundwork for designing and optimizing colloidal RL systems for various applications such as imaging and optical sensing.

Engineering transmitted light speckle statistics with field-tunable magnetic nanofluid scatterers

The active manipulation of electromagnetic wave scattering cross-sections within disordered media is crucial for understanding mesoscopic physics. An adaptable strategy for exploring the parameter space in order to control the scattering cross-section of such systems involves employing external field-tunable disorder. In this study, we utilize magnetic nanofluid as an in-situ tunable scattering medium, whose scattering parameters like scatterer number, density, size, and shape can be adjusted by an external magnetic field which is administered either constantly or with varying ramp rates. Our study on the transmitted light and the associated speckle statistics reveals intriguing behavior in response to changes in the strength and duration of the applied magnetic field. We observe that this tunable system remains within the weak scattering regime across various scattering scenarios: Rayleigh (for scatterer sizes smaller than the light wavelength, λ), Mie (when scatterer size approaches λ), and Geometric (for scatterer sizes exceeding λ) under both constant and ramped magnetic field application. Further, the transmitted speckle patterns deviate from Rayleigh behavior, with speckle contrast values increasing over time until it reaches a saturation point. However, the speckle patterns display intriguing dynamics with faster ramp rates resulting in larger speckle contrast values. This phenomenon arises from the inability of equilibrium field-induced structures to form rapidly under faster ramp rates, leading to a broader size distribution of scatterers and consequently a wider range of phase differences among scattered light components. Our study illuminates the dynamic behavior of wave transport through a tunable disordered media and underscores the potential of magnetic nanofluid as a versatile tool for controlling mesoscopic transport.

Research on the spatial quantity distribution of fibers in ultra high performance concrete slabs based on image recognition method

In this paper, the detection of fiber distribution in six UHPC slabs with fiber content of 1%, 2%, 3%, and horizontal pouring and vertical pouring is studied by image recognition method. The results show that: firstly, the fiber distribution can be identified by a series of processing steps, such as grayscale and binarization of fiber source image, extraction of initial detection area, extraction of fiber contour and segmentation of fiber adhesion area. Furthermore, it is proposed that the scaling curve of n-e detection area per pixel of fiber number can anchor the effective recognition area range of fiber. Secondly, the statistical analysis of the identification results can visually draw the fiber distribution maps of different types of UHPC slabs in different directions, and quantitatively characterize the fiber distribution uniformity of UHPC slabs by quoting the evaluation index of fiber distribution coefficient. Finally, the cross-sectional fiber Scatter Density Plot can perceptually show the degree of fiber dispersion and its position distribution. The more uniform the color, the more uniform the fiber distribution in the cross-section of the specimen.

A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering

In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller’s rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE’s performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

Verification of effect of interference between multiple scatterers on the evaluation of backscattering coefficient

Backscatter coefficient analysis methods for biological tissues have been clinically applied but assume a homogeneous scattering medium. In addition, there are few examples of actual measurement studies in the HF band, and the consistency with theory has not been sufficiently confirmed. In this paper, the effect of correlations among scatterer positions on backscattering was investigated by performing experiments on inhomogeneous media having two types of scattering source with different structural and acoustic properties. In the echo data of phantoms containing two types of scatterer acquired by multiple sensors, the power and frequency dependence of the backscatter coefficient were different from theoretical calculations due to the interference effects of each scatterer. The effect of interference between the two types of scatterer was confirmed to be particularly strong for echoes acquired by the sensor at high intensity and HF, or for a higher number density of strong scatterers.

Nanofiber films based on anthocyanins for quantitative detection of pathogenic bacteria

Pathogenic microbial infections have been regarded as one of the serious factors triggering serious food poisoning and infectious disease outbreaks. Therefore, early detection of pathogenic bacteria is of great importance for food safety, environmental monitoring, and clinical diagnosis. Here, nanofiber sensing films for the visual detection of S. aureus concentrations using polyvinyl alcohol (PVA) as a substrate and anthocyanin as an indicator material were prepared by electrostatic spinning technology. To avoid the loss of dye migration and side effects on the bacteria during the contact detection method, the headspace method was used to detect the concentration of bacteria. The results show that the fiber films can monitor S. aureus at 2 × 102 cfu/mL (ΔE = 7.6) by headspace. Scatter density matrix and correlation analysis yielded that the b* value of the film was the main factor in the variation of bacterial concentrations. Combined with a support vector machine (SVM) supervised learning algorithm for non-linear data, the model was trained to accurately classify S. aureus concentrations with an accuracy of 91%, which is important for accurately controlling pathogenic bacteria and safeguarding human health.

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Kinetic Theory for the Low-Density Lorentz Gas

The Lorentz gas is one of the simplest and most widely-studied models for particle transport in matter. It describes a cloud of non-interacting gas particles in an infinitely extended array of identical spherical scatterers. The model was introduced by Lorentz in 1905 who, following the pioneering ideas of Maxwell and Boltzmann, postulated that in the limit of low scatterer density, the macroscopic transport properties of the model should be governed by a linear Boltzmann equation. The linear Boltzmann equation has since proved a useful tool in the description of various phenomena, including semiconductor physics and radiative transfer. A rigorous derivation of the linear Boltzmann equation from the underlying particle dynamics was given, for random scatterer configurations, in three seminal papers by Gallavotti, Spohn and Boldrighini-Bunimovich-Sinai. The objective of the present study is to develop an approach for a large class of deterministic scatterer configurations, including various types of quasicrystals. We prove the convergence of the particle dynamics to transport processes that are in general (depending on the scatterer configuration) not described by the linear Boltzmann equation. This was previously understood only in the case of the periodic Lorentz gas through work of Caglioti-Golse and Marklof-Strömbergsson. Our results extend beyond the classical Lorentz gas with hard sphere scatterers, and in particular hold for general classes of spherically symmetric finite-range potentials. We employ a rescaling technique that randomises the point configuration given by the scatterers’ centers. The limiting transport process is then expressed in terms of a point process that arises as the limit of the randomised point configuration under a certain volume-preserving one-parameter linear group action.

Parametric analysis: Compressibility of rubber on bandgap for phononic crystals

The capacity of Phononic crystals (PCs) to form bandgaps (BGs) that limit the transmission of elastic/acoustic waves is a key property that is particularly beneficial for vibration/sound isolation and signal processing. In this work, a parametric analysis of Poisson’s ratio of rubber, and the density, geometry and size of scatterer on the BGs of porous, solid/solid, fluid/solid and solid/fluid PCs is presented. Based on the simulation results, it is found that the width of the first absolute bandgaps (FABGs) of porous PCs is not necessarily proportional to the porosity due to the pore shape; when Poisson’s ratio of compressible and incompressible rubber is increased, the FABG width of porous PC decreases dramatically. In addition, the FABGs of solid/solid PCs are strongly dependent on whether the rubber is a matrix or scatterer; the fluctuation of the FABGs is also highly related to the density of the solid. Fluid–structure PCs have smaller FABGs than porous and solid/solid PCs, and these FABGs usually occur within higher-order energy bands. Rubber compressibility significantly affects the FABGs of porous and solid/solid PCs, but almost not fluid-structural PCs. The results presented in this work offer guidance to tune the BG and design acoustic devices in various practical applications such as noise and vibration insulators.

Upgrading interferometric scattering microscopy with ensemble statistical analysis

AbstractInterferometric scattering (iSCAT) microscopy, label‐free high‐speed (up to ~1000 frames per second) imaging and tracking technique, has proven to be a versatile tool by measuring the mass and 3D position of nanoparticles and biomolecules as well as visualizing real‐time dynamics of nanoscale events in complex cellular environments. However, the quantification of iSCAT signals has not been straightforwardly defined in practical terms. We delve into several issues associated with signal processing in iSCAT: error‐prone post‐processing routine and lack of statistical reliability in the convention of iSCAT contrast. After providing a brief account of concepts and principles of correlation spectroscopy, we here discuss an alternative ensemble (higher number density of scatterers) statistical analysis that can be used to extract the dynamic information of scattering particles from fluctuating iSCAT signals. Finally, our perspective on the correlation approach toward time‐correlated iSCAT technique will be presented.

A Non-Stationary Cluster-Based Channel Model for Low-Altitude Unmanned-Aerial-Vehicle-to-Vehicle Communications

Under the framework of sixth-generation (6G) wireless communications, the unmanned aerial vehicle (UAV) plays an irreplaceable role in a number of communication systems. In this paper, a novel cluster-based low-altitude UAV-to-vehicle (U2V) non-stationary channel model with uniform planar antenna arrays (UPAs) is proposed. In order to comprehensively model the scattering environment, both single and twin clusters are taken into account. A novel continuous cluster evolution algorithm that integrates time evolution and array evolution is developed to capture channel non-stationarity. In the proposed algorithm, the link between the time evolution of twin clusters and that of single clusters is established to regulate the temporal evolution trend. Moreover, an improved observable radius method is applied to UPAs for the first time to describe array evolution. Based on the combination of cluster evolution and time-variant channel parameters, some vital statistical properties are derived and analyzed, including space–time correlation function (ST-CF), angular power spectrum density (PSD), Doppler PSD, Doppler spread (DS), frequency correlation function (FCF), and delay spread (RS). The non-stationarity in the time, space, and frequency domain is captured. It demonstrates that the airspeed, density of scatterers within clusters, and carrier frequency have an impact on statistical properties. Furthermore, twin clusters have more flexible spatial characteristics with lower power than single clusters. These conclusions can provide assistance and reference for the design and deployment of 6G UAV communication systems.

A PEEK into carbon fiber: A practical guide for high performance composite polymeric implants for orthopaedic oncology

IntroductionThe use of carbon fiber implants in orthopaedic oncology has increased within recent years. The most widely used type of polymer is carbon fiber polyether ether ketone (CF-PEEK). Its radiolucency enables targeted radiotherapy and artifact-free tumor surveillance, which provides major advantages over metallic hardware. We aim to summarize the unique benefits within orthopaedic oncology, clinical pitfalls, and recent advancements. MethodsFour representative patient cases from a single tertiary academic medical center were treated with carbon fiber implants (n = 2 nails, n = 2 plates) from 2021 to 2022. ResultsThere were no adverse events noted during intraoperative implantation or postoperative follow up. All patients reported improvements in pain and no difficulties in ambulation. There were no instances of catastrophic failure or implant loosening. ConclusionCF implants offer a diverse array of advantages regarding its radiolucency, low scatter density, and bioinert profile. Nonetheless, further research is required to understand the long-term surgical outcomes and robustness of CF implants. Multi institutional trials could address important aspects of durability and stability over extended periods, feasibility and ease-of-use for different anatomical sites and bone quality, as well as cost-effectiveness in post-operative imaging, healthcare resource utilization, and revision rates. Providing orthopaedic surgeons with valuable insight will enable thorough clinically supported, informed decision making regarding optimal use of implants.

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Jones-matrix optical coherence tomography (JM-OCT) is an extension of OCT that provides multiple types of optical contrasts of biological and clinical samples. JM-OCT measures the spatial distribution of the Jones matrix of the sample and also its time sequence. All contrasts (i.e., multi-contrast OCT images) are then computed from the Jones matrix. The contrasts obtained from the Jones matrix include the conventional and polarization-insensitive OCT intensity, cumulative and local phase retardation (birefringence), degree-of-polarization uniformity quantifying the polarization randomness of the sample, signal attenuation coefficient, sample scatterer density, Doppler OCT, OCT angiography, and dynamic OCT. JM-OCT is a generalized version of OCT because it measures the generalized form of the sample information; i.e., the Jones matrix sequence. This review summarizes the basic conception, mathematical principle, hardware implementation, signal and image processing, and biological and clinical applications of JM-OCT. Advanced technical topics, including JM-OCT-specific noise correction and quantity estimation and JM-OCT's self-calibration nature, are also described.

2D tunable all-solid-state random laser in the visible

A two-dimensional (2D) solid-state random laser emitting in the visible is demonstrated, in which optical feedback is provided by a controlled disordered arrangement of air-holes in a dye-doped polymer film. We find an optimal scatterer density for which threshold is minimum and scattering is the strongest. We show that the laser emission can be red-shifted by either decreasing scatterer density or increasing pump area. We show that spatial coherence is easily controlled by varying pump area. Such a 2D random laser provides with a compact on-chip tunable laser source and a unique platform to explore non-Hermitian photonics in the visible.

Skeletal Muscle Assessment Using Quantitative Ultrasound: A Narrative Review.

Ultrasound (US) is an important imaging tool for skeletal muscle analysis. The advantages of US include point-of-care access, real-time imaging, cost-effectiveness, and absence of ionizing radiation. However, US can be highly dependent on the operator and/or US system, and a portion of the potentially useful information carried by raw sonographic data is discarded in image formation for routine qualitative US. Quantitative ultrasound (QUS) methods provide analysis of the raw or post-processed data, revealing additional information about normal tissue structure and disease status. There are four QUS categories that can be used on muscle and are important to review. First, quantitative data derived from B-mode images can help determine the macrostructural anatomy and microstructural morphology of muscle tissues. Second, US elastography can provide information about muscle elasticity or stiffness through strain elastography or shear wave elastography (SWE). Strain elastography measures the induced tissue strain caused either by internal or external compression by tracking tissue displacement with detectable speckle in B-mode images of the examined tissue. SWE measures the speed of induced shear waves traveling through the tissue to estimate the tissue elasticity. These shear waves may be produced using external mechanical vibrations or internal "push pulse" ultrasound stimuli. Third, raw radiofrequency signal analyses provide estimates of fundamental tissue parameters, such as the speed of sound, attenuation coefficient, and backscatter coefficient, which correspond to information about muscle tissue microstructure and composition. Lastly, envelope statistical analyses apply various probability distributions to estimate the number density of scatterers and quantify coherent to incoherent signals, thus providing information about microstructural properties of muscle tissue. This review will examine these QUS techniques, published results on QUS evaluation of skeletal muscles, and the strengths and limitations of QUS in skeletal muscle analysis.

Long-range order Bragg scattering and its effect on the dynamic response of a Penrose-like phononic crystal plate

In this article, we present scattering and localization phenomena in a thin elastic plate comprising an aperiodic arrangement of scatterers. By analyzing the form factor of the scattering cluster, we sample the reciprocal space which shows strong scattering points in reciprocal space associated with nontrivial dynamic response. Specifically, we identify attenuating frequency regimes and bands where strong localization is predicted and experimentally observed. We show that both localization and attenuation are Bragg scattering phenomena, induced by the long-range aperiodic patterns. Illustrative comparisons are drawn with a periodic counterpart having the same density of scatterers. The novel findings are corroborated by analytical estimates, numerical finite-element predictions, and vibrometric experiments. The results are relevant for the research community interested in extending phononic crystal phenomena to lower frequencies.

Magic angle in thermal conductivity of twisted bilayer graphene

We report a local minimum in thermal conductivity in twisted bilayer graphene (TBG) at the angle of 1.08°, which corresponds to the ‘magic angle’ in the transition of several other reported properties. Within the supercell of a moiré lattice, different stacking modes generate phonon scattering sites which reduce the thermal conductivity of TBG. The thermal magic angle arises from the competition between the delocalization of atomic vibrational amplitudes and stresses on one hand, and the increased AA stacking density on the other hand. The former effect weakens the scattering strength of a single scatterer while the latter one increases the density of scatterers. The combination of these two effects eventually leads to the apparition of the highlighted irregularity in heat conduction. The manifestation of a magic angle, disclosing new thermal mechanisms at nanoscale, further uncovers the unique physics of two-dimensional materials.