Double Phenomenon Research Articles

Double Resonance of Electromagnetically Induced Transparency of Rydberg Atom in Counter-Propagating Configuration

The double resonance phenomenon of EIT is studied through the ladder three-level Rydberg system. A probe laser with the wavelength λp=852.35 nm is used to coupling the ground state 6S1/2 to the middle state 6P3/2, and a coupling laser with the wavelength λc=509.08 nm is implemented to couple the state 6P3/2 to the Rydberg state 62D5/2. A special optical scheme is designed, in which the co-propagating and counter-propagating configurations are both used. As a result, the double resonance of electromagnetically induced transparency (EIT) with the Rydberg atom is observed. By comparing the distance between the double peaks, it is found that the double resonance phenomenon comes from the Doppler effect, and the distance between the two resonance peaks in the absorption spectrum is related to the detuning of the resonant lasers.

Sensitivity enhancement of nonlinear micromechanical sensors using parametric symmetry breaking

The working mechanism of resonant sensors is based on tracking the frequency shift in the linear vibration range. Contrary to the conventional paradigm, in this paper, we show that by tracking the dramatic frequency shift of the saddle-node bifurcation on the nonlinear parametric isolated branches in response to external forces, we can dramatically boost the sensitivity of MEMS force sensors. Specifically, we first theoretically and experimentally investigate the double hysteresis phenomena of a parametrically driven micromechanical resonator under the interaction of intrinsic nonlinearities and direct external drive. We demonstrate that the double hysteresis is caused by symmetry breaking in the phase states. The frequency response undergoes an additional amplitude jump from the symmetry-breaking-induced parametric isolated branch to the main branch, resulting in double hysteresis in the frequency domain. We further demonstrate that significant force sensitivity enhancement can be achieved by monitoring the dramatic frequency shift of the saddle-node bifurcations on the parametric isolated branches before the bifurcations annihilate. Based on the sensitivity enhancement effect, we propose a new sensing scheme which employs the frequency of the top saddle-node bifurcation in the parametric isolated branches as an output metric to quantify external forces. The concept is verified on a resonant MEMS charge sensor. A sensitivity of up to 39.5 ppm/fC is achieved, significantly surpassing the state-of-the-art resonant charge sensors. This work provides a new mechanism for developing force sensors of high sensitivity.

Experimental Study on the Combustion Characteristics of Ultralow-Concentration Methane in Divergent Porous Media Burner

ABSTRACT To improve the stable combustion characteristics of ultralow-concentration methane (Concentration of 3.5%-5.0%), a series of combustion experiments were carried out by filling alumina spheres with different particle sizes (i.e. 5 mm, 10 mm and 15 mm) in a divergent burner, and the effects of divergent angle (i.e. 10°, 20° and 30°) and pore distribution on the flame propagation and stable combustion characteristics of ultralow-concentration methane were revealed. The results show that larger particle sizes and methane flow rates lead to the double flame surface phenomenon, and increasing angle and decreasing particle size can help to improve the preheating efficiency of the burner. Moreover, the inlet flow rates corresponding to the steady combustion of ultra-low concentrations methane are between 0.225 m/s and 0.308 m/s, the increase in inlet velocity, particle size, and angle will reduce the stability of the flame and make the shape of the flame surface developed in the order of “V→W→∧.” The burner with 10 mm particles and 30° angle has a wider stable combustion range, and the burner filled with smaller pore distribution help to improve the utilization of the lower concentration methane premixed.

Crystal structure and spectral tuning of Eu2+/Eu3+ co-activated Na2Ba1-xSrxCa(PO4)2 phosphors with high thermal stability

The coupling of UV LED chips with phosphors to produce white or color-tunable light is still a challenge. In this work, we propose a simple rare earth occupation site engineering method to adjust the valence of doped Eu ions. Based on the Ba2+ substituted by Sr2+, new Eu2+/Eu3+ co-activated Na2Ba1-xSrxCa(PO4)2 (NB1-xSxCP) phosphors were designed and successfully synthesized. In NB1-xSxCP, the Ba2+/Sr2+ site is occupied by Eu2+, while the Ca2+ site is occupied by Eu3+. The coupled broad blue 448 nm and sharp red 619 nm luminescence under 321 nm excitation has demonstrated the coexistence of Eu2+ and Eu3+. This double emission phenomenon is studied in terms of the fluorescence lifetime as well as lattice distortion induced by the substitution ions. With the increased substitution of Sr, the luminescence of NB1-xSxCP:0.03Eu2+/3+ can be tuned from blue to red crossing the warm-white region. When x = 0.3, the chromaticity coordinates is located at (0.3779, 0.2495). The integrated intensity of Na2Ba0.7Sr0.3Ca(PO4)2:0.03Eu2+/3+ at 150℃ remains 90.9 % of that at room temperature, while the chromaticity shift (ΔE) can be calculated as 15 ×10−3. The Eu2+/Eu3+ co-activated NB1-xSxCP is a potential candidate for single-phase color-tunable phosphors used for light-tunable applications due to the combining contributions from both Eu3+ and Eu2+ ions.

Commentary on: The Prevalence and Histological Characteristics of the Double Capsule Phenomenon in Breast Augmentation With Implants

Commentary on: The Prevalence and Histological Characteristics of the Double Capsule Phenomenon in Breast Augmentation With Implants

Comparison of the effects on heat transfer through oil oscillation in a steel piston cooling gallery: Friction welding versus laser welding

Piston cooling gallery plays a critical role in managing the temperature of the piston and its performance. This paper aims to study the effect of oscillatory heat transfer within the cooling galleries, focusing on two distinct types: flanged (friction-welded) and smooth structures (laser-welded). To do so, the Eulerian multiphase flow model and the k-ω SST turbulence model were used to simulate the oil and air flow through the cooling gallery. Key parameters such as the oil filling rate, wall heat transfer coefficient, oil outlet flow rate, heat removal rate by the oil, and overall wall heat transfer rate were assessed for both gallery designs at an engine speed of 1800 r/min. The results indicate significant disparities between the two cooling gallery structures: the smooth structure exhibits an oil filling rate that is 5.41% higher and an oil outlet flow rate that is 33.48% greater compared to the flanged structure. The flange structure impedes internal oil movement, which leads to boundary layer separation, secondary wall contact, and the emergence of a double vortex phenomena. Collectively, these factors influence the oil fill ratio. This, in turn, affects the distribution of oil within the gallery and, subsequently, the efficiency of heat transfer. Comparative analyses of heat transfer within both galleries, under the same studied conditions, revealed that the smooth structure outperforms the flanged one. However, the presence of a flange significantly alters the heat contribution from different walls, thereby highlighting the substantial impact of flanges on the overall efficiency of heat transfer.

Quantifying spatial interaction centrality in urban population mobility: A mobility feature- and network topology-based locational measure

Spatial interaction centrality reflects the relative importance of population mobility within a location in urban population mobility. Population mobility networks visually represent urban population mobility, with mobility features and network topology contributing to the quantification of spatial interaction centrality of locations (i.e., geographical nodes). However, existing centrality measures rarely consider mobility features and network topology simultaneously. Centrality quantification also ignores the differences in distance effects between long- and short-distance trips. These factors have led to the inaccurate quantification of centrality. We propose an algorithm called k-dis-weight-shell that quantifies the spatial interaction centrality of geographical nodes at different spatiotemporal scales. Considering the different effects of distance on long- and short-distance trips, we use a spatial continuous wavelet transformation to estimate the radiation radius of geographical nodes. Then, by combining network topology with mobility features (mobility distance and flow), the algorithm transforms them into a ranked order of spatial interaction centrality. Tested in Wuhan and Chengdu, our algorithm outperforms six existing benchmarks. For cases in urban planning and epidemic management, results show that k-dis-weight-shell effectively distinguishes similarities and differences between the distribution of population mobility's spatial interaction centrality and the urban center hierarchy at a coarse spatiotemporal scale. Additionally, it reveals a double wave phenomenon of spatiotemporal correlation between population mobility and COVID-19 transmission before and after lockdown at a fine spatiotemporal scale.

Enhanced Response Envelope via Envelope Regularization

The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the immaterial part. In this article, we propose the enhanced response envelope by incorporating a novel envelope regularization term based on a nonconvex manifold formulation. It is shown that the enhanced response envelope can yield better prediction risk than the original envelope estimator. The enhanced response envelope naturally handles high-dimensional data for which the original response envelope is not serviceable without necessary remedies. In an asymptotic high-dimensional regime where the ratio of the number of predictors over the number of samples converges to a nonzero constant, we characterize the risk function and reveal an interesting double descent phenomenon for the envelope model. A simulation study confirms our main theoretical findings. Simulations and real data applications demonstrate that the enhanced response envelope does have significantly improved prediction performance over the original envelope method, especially when the number of predictors is close to or moderately larger than the number of samples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Enhanced Porous Electrode Theory Based Electrochemical Model for Higher Fidelity Modelling and Deciphering of the EIS Spectra

Electrochemical impedance spectroscopy (EIS) is essential for non-invasive battery characterization. This paper addresses the challenge of adequate interpretation of EIS spectra, which are often complicated by overlapping internal phenomena occurring on similar time scales. We present, for the first time, a high-fidelity numerical time-domain electrochemical model that can virtually replicate experimental EIS spectra with three superimposed high-frequency semicircles, a transition to the diffusion tail at elevated imaginary values, and a tilted diffusion tail at low frequencies. These advanced features were made possible by extending state-of-the-art porous electrode model with innovative sub-models for the double layer phenomenon at the carbon black/electrolyte and metal Li-anode/electrolyte interfaces, and transport phenomena of charged species through the solid electrolyte interphase at the Li-anode interface. Additionally, we modelled the diffusion tail inclination by introducing representative active particles of varying sizes. Results from custom-made half-cells confirm the model’s ability to decipher EIS spectra more accurately compared to existing models. Moreover, innovative physics-based battery model that is capable of accurately modelling intra-cell phenomena can reveal internal states and physical parameters of batteries using measured EIS spectra. The model, therefore, also enables functionality of an advanced virtual sensor, which is an important diagnostics feature in next-generation battery management systems.

High-temperature fracture behavior of an α/β Titanium alloy manufactured using laser powder bed fusion

The room and high temperature (up to 600 °C) mechanical properties, with emphasis on the fracture toughness and fatigue resistance, of an α/β titanium alloy that was additively manufactured using the laser powder bed fusion (LPBF) technique were evaluated in the as-printed (α’ lath martensite with negligible β phase content) and heat-treated (α/β structure with a significant volume fraction of the β phase) states for critically examining the β phase's role in the fracture and fatigue behavior (given the significantly higher diffusion rates of various species in β compared to α). Experimental results reveal that the heat-treated sample exhibits superior ductility and fracture initiation toughness at elevated temperatures, compared to the as-printed sample, due to the presence of β ligaments in the former. They also prevent strain localization along the prior β grain boundaries, and their absence in the as-printed state leads to cracking along those boundaries. While the fatigue crack growth (FCG) rates in both the as-printed and heat-treated samples deteriorated with increasing temperature, the threshold for FCG generally increased, which is attributed to creep-induced crack tip blunting. Moreover, a double Paris exponent phenomenon is observed for the heat-treated sample at 300 °C, attributed to the hydrogen-assisted crack growth, with β phase providing an accelerated diffusion pathway. Overall, the experimental results and their analyses illustrate the intricate relationship between creep, hydrogen diffusion, and the β phase in dictating the fracture behavior of LPBF α/β titanium at elevated temperatures.

The Prevalence and Histological Characteristics of the Double Capsule Phenomenon in Breast Augmentation With Implants.

Double capsule formation around breast implants is associated with implant rotation and seroma. However, the prevalence and histological characteristics remain unclear. To quantify the prevalence of double capsule formation between different implant surface textures and to explore the histological differences between the inner- and outer capsules from breast implant capsule biopsies. The study was performed on data from the Copenhagen Breast Implant (COBI) Biobank comparing the prevalence of double capsule formation around Allergan Biocell implants (Allergan, Dublin, Ireland), Eurosilicone Cristalline implants (GC Aesthetics, Dublin, Ireland), and Mentor Siltex implants (Mentor, Irvine, CA). The histological characteristics of the inner and outer capsules was analyzed using a validated assessment tool. The study included data from 588 patients and 1128 implants. Double capsule formation was found around 25 implants resulting in an overall prevalence of 2.5% for textured implants. Mentor implants with a Siltex surface had a double capsule prevalence of 0.72%, which was significantly lower than the prevalence for Allergan implants with a Biocell surface (7.8%), (P<.001), and Eurosilicone implants with a Cristalline surface (3.4%), (P=.03). Histological analysis showed that inner capsules had lower cellular density (P=.04) and were more calcified (P=.03) compared with outer capsules. The risk of double capsule formation was highly correlated with the roughness of the breast implant texture, with the risk of double capsule formation around Mentor Siltex implants being significantly lower than that of macrotextured implants. The histological analysis implies that loss of vascularization to the inner capsule results in a lower cellular density and more frequently calcification.

Predominance of Cephalic Presentation at Birth: An Oxygen Delivery-Based Concept

Background: Fetuses closer to term present predominantly as cephalic, and by the time of delivery 96-97% of them are born headfirst. To date, no coherent hypothesis has been put forth to explain the phenomenon. Observation: As pregnancy advances, there is a progressive increase in cephalic presentation; by 25 weeks, 60% are cephalic, at 35 weeks 90% cephalic and at term only less than 3-4% remain breech. Hypothesis: Oxygen delivery to the fetus comes from the environment through the parturient and it must traverse a very long passage to get to the fetal tissues making oxygen delivery a very precarious process. The fetus develops several compensatory mechanisms to enhance the delivery of oxygen—higher hemoglobin level, hemoglobin with greater affinity for oxygen, the double Bohr phenomenon to improve the diffusion of oxygen from the parturient and lastly redesign of the fetal circulation to increase flow to the brain whilst in the state of near chronic hypoxia whilst in utero. The better flow of blood to the head and therefore more oxygen to the brain makes this organ the densest causing it to progressively present cephalic as pregnancy advances. Implications: The hypothesis aligns well with not only the observed progressive cephalic phenomenon but also the long-term subtle cognitive deficits and neurological sequelae observed in several studies of those born breech. Conclusion: Cephalic presentation at birth is driven by better blood flow to the brain as another compensatory mechanism to cope with hypoxia.

Design of interconnected graphene loaded thermoplastic elastomeric blend composite films for minimizing electromagnetic radiation and efficient heat management

AbstractIn this work, electrically conductive thermoplastic elastomeric blend composite films based on polystyrene (PS)/ethylene‐co‐methyl acrylate (EMA) filled with functionalized graphene were developed via the solution mixing technique. Morphological analysis revealed that selective localization of amine‐functionalized reduced graphene oxide (G‐ODA) sheets in the EMA phase of co‐continuous binary blend formed a well‐connected dense conductive pathway by graphene sheets ultimately facilitating the double percolation phenomenon. The electrical percolation threshold was achieved at ~2 wt% of G‐ODA loading which was much lower than that for both single polymer composites. An electrical conductivity of 0.9 S/cm was obtained for blend composite film with 10 wt% of graphene concentration whereas for the same filler loading, PS and EMA composites exhibited electrical conductivity of 1.9 × 10−1 and 2.3 × 10−1 S/cm, respectively. The obtained thermal conductivity of the blend composite with 10 wt% of G‐ODA loading was 0.95 W/m K with 400% enhancement compared to the neat blend system. The same composite exhibited increased real and imaginary permittivity of 92 and 83, respectively. The electrical percolation threshold is well‐correlated with the percolation concentration found from storage modulus and thermal conductivity data. The fabricated PS/EMA blend composite film exhibited absorption‐dominant electromagnetic interference SE of −25 and − 35 dB in X‐band frequency (8.2–12.4 GHz) for 10 wt% of graphene loading with a sample thickness of 0.5 and 1 mm, respectively.

Prediction of Tropical Pacific Rain Rates with Overparameterized Neural Networks

Abstract The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces overparameterized neural networks not only to forecast tropical rain rates but also to explain their heavy-tailed distribution. The investigation is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the west and east Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although overparameterized neural networks are well known for their “double descent phenomenon,” little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, overparameterized neural networks accurately estimate the rain-rate distributions and outperform other machine learning methods. Spatial maps show that overparameterized neural networks also successfully describe the spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each overparameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of overparameterized neural networks in predicting the distribution of the rain rate and explaining extreme values. Significance Statement This study aims to introduce the capability of overparameterized neural networks, a type of neural network with more parameters than data points, in predicting the distribution of tropical rain rates from gridscale environmental variables and explaining their tail behavior. Rainfall prediction has been a topic of importance, yet it remains a challenging problem for its heavy-tailed nature. Overparameterized neural networks correctly captured rain-rate distributions and the spatial patterns and heterogeneity of the observed rain rates for multiple rain types, which could not be achieved by any other previous statistical or machine learning frameworks. We find that overparameterized neural networks can play a key role in general prediction tasks, with potential expanded applicability to other domains with heavy-tailed data distribution.

Stratified numerical heat and mass transport mechanism in MHD Maxwell fluid flow with nonuniform heat source/sink

The present boundary layer flow of Maxwell fluid over a stretching sheet has good number of practical applications in metallurgy, drawing of plastics, polymer sheet extrusion including hot rolling extrusion, glass blowing, spinning of fibers, rubber and plastic sheets, crystal growing and many more. Due to these extensive applications of Maxwell fluid in engineering, medicine and manufacturing industries including the above-mentioned applications, authors have motived, inspired and investigated the present problem. However, in this research paper, the influences of Soret and Dufour effects on the steady-state Maxwell fluid flow past a stretching sheet with nonuniform heat source/sink are considered numerically. The thermal and concentration stratifications impacts are also incorporated. The viscous dissipation and magnetic field effects are also included in the respective governing equations. The main novelty and uniqueness of this numerical research is to generalize the earlier studies and describe the salient features of magneto-thermo visco-elastic dissipative Maxwell fluid motion about a stretching sheet with cross-diffusion effects under the influence of double stratification phenomena. To differentiate the non-Newtonian fluids from those of Newtonian fluids, a well-known Maxwell fluid flow model is used in the present study. The investigated physical problem results the highly nonlinear coupled partial differential equations (PDEs) and which are not amenable to any of the direct methods. Suitable similarity transformations are deployed to reduce the highly complex PDEs into the system of ordinary differential equations (ODEs). A robust BVP4C Matlab function is employed to solve the rendered dimensionless system of ODEs. The numerically generated computed data is plotted in terms of velocity, temperature and concentration profiles including engineering quantities of interest such as skin-friction, Nusselt and Sherwood numbers in the flow region. It is evident from the current analysis that, the amplified magnetic field decreases the velocity field and increases the thermal and concentration distribution fields. Accelerating Maxwell’s number diminished the flow velocity and increased the temperature field. Increasing Soret and Dufour numbers enhances the concentration and temperature fields, respectively. Amplifying Maxwell’s number raises the skin-friction coefficient and enlarging the thermal stratification parameter magnifies the thermal transport rate. The mass transport rate amplified with a raise in concentration stratification number in the flow region. Finally, it is provided that, the current results are excellently matching with earlier published results in the literature and it confirms the accuracy of the used numerical method and the produced similarity solutions.

Entropy Analysis for Three-Dimensional Stretched Flow of Viscous Fluid Engaging Radiation Effect and Double Diffusion Phenomenon

Entropy Analysis for Three-Dimensional Stretched Flow of Viscous Fluid Engaging Radiation Effect and Double Diffusion Phenomenon

Conditioning of random Fourier feature matrices: double descent and generalization error

Abstract We provide high-probability bounds on the condition number of random feature matrices. In particular, we show that if the complexity ratio $N/m$, where $N$ is the number of neurons and $m$ is the number of data samples, scales like $\log ^{-1}(N)$ or $\log (m)$, then the random feature matrix is well-conditioned. This result holds without the need of regularization and relies on establishing various concentration bounds between dependent components of the random feature matrix. Additionally, we derive bounds on the restricted isometry constant of the random feature matrix. We also derive an upper bound for the risk associated with regression problems using a random feature matrix. This upper bound exhibits the double descent phenomenon and indicates that this is an effect of the double descent behaviour of the condition number. The risk bounds include the underparameterized setting using the least squares problem and the overparameterized setting where using either the minimum norm interpolation problem or a sparse regression problem. For the noiseless least squares or sparse regression cases, we show that the risk decreases as $m$ and $N$ increase. The risk bound matches the optimal scaling in the literature and the constants in our results are explicit and independent of the dimension of the data.

DSD²: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?

Neoteric works have shown that modern deep learning models can exhibit a sparse double descent phenomenon. Indeed, as the sparsity of the model increases, the test performance first worsens since the model is overfitting the training data; then, the overfitting reduces, leading to an improvement in performance, and finally, the model begins to forget critical information, resulting in underfitting. Such a behavior prevents using traditional early stop criteria. In this work, we have three key contributions. First, we propose a learning framework that avoids such a phenomenon and improves generalization. Second, we introduce an entropy measure providing more insights into the insurgence of this phenomenon and enabling the use of traditional stop criteria. Third, we provide a comprehensive quantitative analysis of contingent factors such as re-initialization methods, model width and depth, and dataset noise. The contributions are supported by empirical evidence in typical setups. Our code is available at https://github.com/VGCQ/DSD2.

Investigation of thermal radiations impacts with double diffusive convection for Prandtl nanofluid with slip in an asymmetric ciliated channel

Thermal radiations are extensively utilized in the medical sciences, and the investigation of thermal radiation combined with double diffusive phenomena is the burning research area because of its enormous applications in treatment of various diseases. Cilia play significant part in many physiological processes of animal and humans. A novel mathematical prodigy for Prandtl nanofluid with thermal radiations and double diffusion convection by implementing slip at boundaries is presented for cilia induce flow in an asymmetric microchannel. Mathematical scheme for the physiological flow is developed and then pertinent equations are designed exploiting low Reynolds number and long wavelength simplifications. Solution for the physiological nanofluid is gathered by emphasizing on the novel BVP4C technique in MATLAB and resulting outcomes are elaborated with aid of graphical illustrations. Pros and cons of various physical flow parameters like thermal slip parameters, Prandtl fluid parameters, Grashof parameter, Prandtl parameter , Brownian motion parameter, thermal radiation parameter, Brinkmann number, Soret parameter are examined on the velocity, magnetic force function, temperature profile, concentration and nanoparticles volume fraction. It is reported that slip parameter really effects the nanofluid transport within the ciliated microchannel, it reduces the fluid flow, diffusion phenomena of the nanoparticles in the ciliated microchannel surges when radiation parameter is strengthened. The reported investigation will be instrumental in heat radiation effect for regulation of blood circulation to cure the cancer tissues in multiple drug delivery systems and will pave the way for the designs of certain diagnostic and pharmacological devices.

Numerical treatment for double diffusion phenomenon with generalized heat flux effects in 3d nanomaterial flow over bi-directional stretched wall

Current study aims to numerically investigate the bi-directional flow of nanofluids in the presence of Cattaneo–Christov double diffusion for two heat sources, namely, prescribed surface temperature (PST) along with prescribed heat flux (PHF). The conventional mathematical model in partial differential equations (PDEs) of the system have been reduced into an equivalent set of ordinary differential equations (ODEs). The system dynamics in the form of approximate solutions of the ODEs is presented by Adams and Explicit Runge Kutta numerical solvers. For better understanding the influence of physical parameter of interest including Brownian movement parameter, thermophoresis parameter, concentration relaxation parameter, Schmidt number, Prandtl number, thermal relaxation parameter, temperature ratio parameter, stretching ratio parameter and Deborah number on temperature, as well as concentration profiles are presented exhaustively. Appropriateness of the scheme is ascertained through close resemblance of results for different state of the art counterparts with negligible error around 10–05 to 10–09.