Phase Sensitivity Research Articles

Neutrino CP measurement in the presence of RG running with mismatched momentum transfers

The neutrino mixing parameters are expected to have renormalization group (RG) running effect in the presence of new physics. If the momentum transfers at production and detection mismatch with each other, the oscillation probabilities are generally modified and become dependent on not just the neutrino energy but also the momentum transfer. Even in the limit of vanishing baseline, the transition probability for the appearance channel is interestingly not zero. This would significantly affect the sensitivity of the genuine leptonic Dirac CP phase. We further explore the possibility of combining the long- and short-baseline neutrino experiments to constrain such RG running effect for the purpose of guaranteeing the CP measurement. To simulate the double dependence on the neutrino energy and momentum transfer, we extend the usual o simulation of fixed baseline experiments and use a two-dimensional χ2 analysis to obtain sensitivities. Published by the American Physical Society 2024

Investigating pseudo parabolic dynamics through phase portraits, sensitivity, chaos and soliton behavior

This research examines pseudoparabolic nonlinear Oskolkov-Benjamin-Bona-Mahony-Burgers (OBBMB) equation, widely applicable in fields like optical fiber, soil consolidation, thermodynamics, nonlinear networks, wave propagation, and fluid flow in rock discontinuities. Wave transformation and the generalized Kudryashov method is utilized to derive ordinary differential equations (ODE) and obtain analytical solutions, including bright, anti-kink, dark, and kink solitons. The system of ODE, has been then examined by means of bifurcation analysis at the equilibrium points taking parameter variation into account. Furthermore, in order to get insight into the influence of some external force perturbation theory has been employed. For this purpose, a variety of chaos detecting techniques, for instance poincaré diagram, time series profile, 3D phase portraits, multistability investigation, lyapounov exponents and bifurcation diagram are implemented to identify the quasi periodic and chaotic motions of the perturbed dynamical model. These techniques enabled to analyze how perturbed dynamical system behaves chaotically and departs from regular patterns. Moreover, it is observed that the underlying model is quite sensitivity, as it changing dramatically even with slight changes to the initial condition. The findings are intriguing, novel and theoretically useful in mathematical and physical models. These provide a valuable mechanism to scientists and researchers to investigate how these perturbations influence the system’s behavior and the extent to which it deviates from the unperturbed case.

Two-parameter estimation with single squeezed-light interferometer via double homodyne detection

The simultaneous two-parameter estimation problem in a single squeezed-light Mach–Zehnder interferometer with the double-port homodyne detection is investigated in this work. The analytical form of the two-parameter quantum Cramér–Rao bound defined by the quantum Fisher information matrix is presented, which shows the ultimate limit of the phase sensitivity will be further approved by the squeezed vacuum state. It can not only surpass the standard quantum limit, but also can even surpass the Heisenberg limit when half of the input intensity of the interferometer is provided by the coherent state and half by the squeezed light. For the double-port homodyne detection, the classical Fisher information matrix is also obtained. Our results show that although the classical Cramér–Rao bound does not saturate the quantum one, it can still asymptotically approach the quantum Cramér–Rao bound when the intensity of the coherent state is large enough. Our results also indicate that the squeezed vacuum state indeed can further improve the phase sensitivity, similar to the single-parameter estimation. In addition, when half of the input intensity of the interferometer is provided by the coherent state and half by the squeezed light, the phase sensitivity obtained by the double-port homodyne detection can also surpass the Heisenberg limit for a small range of the estimated phases.

Investigating Degradation in Extrusion-Processed Bio-Based Composites Enhanced with Clay Nanofillers

This research investigates the extrusion-based fabrication and characterization of nanocomposites derived from bio-sourced polypropylene (PP) and poly(butylene succinate) (PBS: a biodegradable polymer derived from renewable biomass sources such as corn or sugarcane), incorporating Cloisite 20 (C20) clay nanofillers, with a specific focus on their suitability for electrical insulation applications. The research includes biodegradation tests employing the fungus Phanerochaete chrysosporium to evaluate the impact of composition and extrusion conditions. These tests yield satisfactory results, revealing a progressive disappearance of the PBS phase, as corroborated by scanning electron microscopy (SEM) observations and a reduction in the intensity of Fourier transform infrared spectroscopy (FTIR) peaks associated with C-OH and C-O-C bonds in PBS. Despite positive effects on various properties (i.e., barrier, thermal, electrical, and mechanical properties, etc.), a high clay content (5 wt%) does not seem to enhance biodegradability significantly, highlighting the specific sensitivity of the PBS phase to the addition of clay during this process. This study provides valuable insights into the complex interplay of factors conditioning nanocomposite biodegradation processes and highlights the need for an integrated approach to understanding these processes. This is the first time that research has focused on studying the degradation of nanocomposites for electrical insulation, utilizing partially bio-sourced materials that contain PBS.

Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing

To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa. Not applicable. Centrifuged human sperm samples. Not applicable. Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 μm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 μm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.

Precise refractive index measurement of fused silica optics

Abstract We have developed an experimental platform that non-destructively and precisely measures the Refractive Index (RI) and dispersion of ultra-polished fused silica optics. Using Total Internal Reflection Digital Holographic Microscopy (TIRDHM), we exploit the phase change of reflected light in Total Internal Reflection (TIR) mode. This phase change depends on the incident angle at the TIR interface and the refractive indices of the involved media. We have optimized a combination of higher TIR phase sensitivity, considerable penetration depth, and minimized phase measurement inaccuracies through simulations to design our experiment. Key features include a custom-made precision Right-Angle Prism (RAP) of Astrositall material, a seamless interface with fused silica optics on TIR interface through optical contact, and single-shot measurement. We have demonstrated the accuracy of measuring fused silica optics through proof of concept and experimental results. Our measurements on two different samples show accuracy better than ±3 × 10−4 compared to those obtained using a commercially available critical angle Refractometer (Metricon). Importantly, the setup offers the advantage of spatially mapping the refractive index, unlike point measurements by available Refractometers.

Bridging-Solvent Strategy for Quasi-Single-Crystal Perovskite Films and Stable Solar Cells.

Controllable fabrication of formamidinium (FA)-based perovskite solar cells (PSCs) with both high efficiency and long-term stability is the key to their further commercialization. However, the diversity of PbI2 complexes and perovskite compositions usually leads to light sensitive PbI2 residues and phase impurities in the film, which can accelerate the device degradation. Here, the crystallization kinetics of FA-based perovskite films are studied and a bridging-solvent strategy is proposed to modulate the reaction kinetics between PbI2 and ammonium salts by prohibiting the formation of undesired intermediates. N-methylpyrrolidone (NMP) solvent is introduced into the PbI2 precursor solution to obtain stable and homogeneous PbI2-NMP complex films. The strong interaction between NMP and formamidinium iodide (FAI) molecules promotes the conversion from PbI2-NMP into (001)-oriented quasi-single-crystal perovskite films with negligible impurities, long carrier lifetime of 1.5µs and a large grain size of 3µm. The optimized PSCs exhibit a high power conversion efficiency of 24.1%, as well as superior shelf stability which maintains 95% initial efficiency after storage in air for 1200h (T95=1200h), and operating stability with T96=300h under continuous working at the maximum power point. This work offers a simple and reproducible method for fabricating phase-pure and uniaxially oriented perovskite films.

Anisotropic Sensing Performance in a High-Sensitivity Surface Plasmon Resonance Sensor Based on Few-Layer Black Phosphorus.

Few-layer black phosphorus (FLBP) is a highly promising material for high sensitivity label-free surface plasmon resonance (SPR) sensors due to its exceptional electrical, optical, and mechanical properties. FLBP exhibits inherent anisotropy with different refractive indices along its two main crystal orientations, the zigzag and armchair axes. However, this anisotropic property is often overlooked in FLBP-based sensors. In this study, we conducted a comprehensive investigation of the SPR reflectivity and phase in a BK7-Ag-FLBP structure to understand the influence of the stacking sequence and the number of FLBP layers on the sensing performance. Clear resonant angle shifts caused by different stacking sequences of FLBP could be observed both theoretically and experimentally. In the theoretical study, the highest reflective and phase sensitivities were achieved with a 12-layer black phosphorus (BP) structure. The reflectivity sensitivity reached 287.9°/refractive index units (RIU) with the zz stacking 12-layer BP film exhibiting a sensitivity 76°/RIU higher than the ac stacking structure. Similarly, the phase sensitivity reached 1162°/RIU with the zz stacking 12-layer BP structure showing a sensitivity 276.9°/RIU higher than the ac stacking structure. The electric field distribution of the 12-layer BP structure with four different stacking sequences has also been analyzed. In the experiment study, the well-known Attenuated Total Reflection (ATR) θ-2θ SPR setup is utilized to detect the reflectivity and phase of BK7-Ag-FLBP structures. The FLBP samples with the same thickness but different stacking sequences show significant resonant angle shift (0.275°) and maximum phase difference variation (34.6°). The FLBP sample thickness and crystal orientations have been demonstrated using the angular-resolved polarized Raman spectroscopy (ARPRS). These theoretical and experimental results provide strong evidence that the stacking sequences of FLBP have a significant impact on the sensing performance of SPR sensors. By harnessing the anisotropic properties of materials like FLBP, novel structures of anisotropic-2D material-based SPR sensors could open up exciting possibilities for innovative applications.

Can instability variations warn developers when open-source projects boost?

Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations of the instability of the structure of the classes and packages. Consequently, in this article we analyze the instability in OSS projects, especially during that sensitive phase where they become community-driven. Our results show that instability metrics can be easily obtained in such type of transitions. We also observed from our case studies that instability metrics can help finding out the balance between adding new functionality and performing refactoring. As a conclusions we state that instability metrics offer relevant information in the transition phase from the cathedral to the bazaar.

Pillar[5]arene and Azine Derivative Assembly Improved Dual‐Channel Detection of CN–

Comprehensive SummaryThe cyanide anion (CN–) is known to be one of the most toxic anions. Therefore, there is an urgent need to develop a reliable, sensitive, selective, rapid and effective method for the detection of CN–. Here, a self‐assembly strategy based on pillar[5]arene P5 and azine derivative AZ was used to construct supramolecular sensors, and it was found that the detection effect of CN– was significantly improved by the assembly strategy. The sensitivity of the assembled sensor P5‐AZ to CN– is more than 10 times higher than that of AZ. The detect mechanism was further investigated by theoretical calculations and 1H NMR. The results showed that AZ detects CN– by a deprotonation process with fluorescence enhancement, while P5‐AZ improves the sensitivity of CN– recognition through hydrogen bonding, anion‐π and anion‐dipole interactions, as well as the strong bonding ability of the assembly. Supramolecular assembly P5‐AZ has the advantages of low toxicity, high sensitivity, and more importantly, it provides a method to detect CN– sensitivity in the aqueous phase and organisms by host‐guest assembly.

Phase autoencoder for limit-cycle oscillators.

We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on the mathematical model of the oscillator: first, it can evaluate the asymptotic phase and the phase sensitivity function of the oscillator; second, it can reconstruct the oscillator state on the limit cycle in the original space from the phase value as an input. Using several examples of limit-cycle oscillators, we demonstrate that the asymptotic phase and the phase sensitivity function can be estimated only from time-series data by the trained autoencoder. We also present a simple method for globally synchronizing two oscillators as an application of the trained autoencoder.

Beating the Standard Quantum Limit Electronic Field Sensing by Simultaneously Using Quantum Entanglement and Squeezing.

Quantum entanglement and quantum squeezing are two typical approaches to beat the standard quantum limit (SQL) for the sensitive phase estimations in quantum metrology. Each of them has already been utilized individually and sequentially to improve the sensitivity of electric field sensing with the trapped ion platform. However, the upper bound of the demonstrated sensitivity gain is still limited, i.e., the theoretical 6dB and experimental 3dB over the corresponding SQL, for electric field sensing. By simultaneously using the internal (spin)-external (oscillator) state entanglement and the oscillator squeezing to effectively amplify the accumulation phase, we show here that such a theoretical sensitivity gain upper bound can be significantly surpassed. The proposal provides a novel approach to implement the stronger beat of the SQL and even approach the Heisenberg limit, for the sensitive sensings of the desired electric field and also the other metrologies.

Mechanistic screening of reproductive toxicity in a novel 3D testicular co-culture model shows significant impairments following exposure to low-dibutyl phthalate concentrations

To improve the mechanistic screening of reproductive toxicants in chemical-risk assessment and drug development, we have developed a three-dimensional (3D) heterogenous testicular co-culture model from neonatal mice. Di-n-butyl phthalate (DBP), an environmental contaminant that can affect reproductive health negatively, was used as a model compound to illustrate the utility of the in vitro model. The cells were treated with DBP (1 nM to 100 µM) for 7 days. Automated high-content imaging confirmed the presence of cell-specific markers of Leydig cells (CYP11A1 +), Sertoli cells (SOX9 +), and germ cells (DAZL +). Steroidogenic activity of Leydig cells was demonstrated by analyzing testosterone levels in the culture medium. DBP induced a concentration-dependent reduction in testosterone levels and decreased the number of Leydig cells compared to vehicle control. The levels of steroidogenic regulator StAR and the steroidogenic enzyme CYP11A1 were decreased already at the lowest DBP concentration (1 nM), demonstrating upstream effects in the testosterone biosynthesis pathway. Furthermore, exposure to 10 nM DBP decreased the levels of the germ cell-specific RNA binding protein DAZL, central for the spermatogenesis. The 3D model also captured the development of the Sertoli cell junction proteins, N-cadherin and Zonula occludens protein 1 (ZO-1), critical for the blood–testis barrier. However, DBP exposure did not significantly alter the cadherin and ZO-1 levels. Altogether, this 3D in vitro system models testicular cellular signaling and function, making it a powerful tool for mechanistic screening of developmental testicular toxicity. This can open a new avenue for high throughput screening of chemically-induced reproductive toxicity during sensitive developmental phases.

Super-resolution and super-sensitivity of quantum LiDAR with a multi-photonic state and binary outcome photon counting measurement

Here, we are investigating the enhancement in resolution and phase sensitivity of a Mach-Zehnder interferometer (MZI) based quantum LiDAR. We are using a multi-photonic state (MPS), superposition of four coherent states [Int. J. Quantum Inf. 19, 2150013 (2021)0219-749910.1142/S0219749921500131], as the input state and binary outcome parity photon counting measurement and binary outcome zero non-zero photon counting measurement as the measurement schemes. We have thoroughly investigated the results in lossless as well as lossy cases. We found enhancement in resolution and phase sensitivity in comparison to the coherent state and even coherent superposition state (ECSS) based quantum LiDAR. Our analysis shows that MPS may be an alternative nonclassical resource in the field of quantum imaging and quantum sensing technologies, like in quantum LiDAR.

DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction

Objective. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be challenging or sometimes infeasible to acquire in certain scenarios. The goal is to develop an effective alternative for improved reconstruction quality that does not rely on external training datasets. Approach. We introduce a novel zero-shot dual-domain fusion unsupervised neural network (DFUSNN) for parallel MR imaging reconstruction without any external training datasets. We employ the Noise2Noise (N2N) network for the reconstruction in the k-space domain, integrate phase and coil sensitivity smoothness priors into the k-space N2N network, and use an early stopping criterion to prevent overfitting. Additionally, we propose a dual-domain fusion method based on Bayesian optimization to enhance reconstruction quality efficiently. Results. Simulation experiments conducted on three datasets with different undersampling patterns showed that the DFUSNN outperforms all other competing unsupervised methods and the one-shot Hankel-k-space generative model (HKGM). The DFUSNN also achieves comparable results to the supervised Deep-SLR method. Significance. The novel DFUSNN model offers a viable solution for reconstructing high-quality MR images without the need for external training datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

The expression of mantle seismic anisotropy in the global seismic wavefield

SUMMARY The dependence of seismic wave speeds on propagation or polarization direction, called seismic anisotropy, is a relatively direct indicator of mantle deformation and flow. Mantle seismic anisotropy is often inferred from measurements of shear-wave splitting. A number of standard techniques to measure shear-wave splitting have been applied globally; for example, *KS splitting is often used to measure upper mantle anisotropy. In order to obtain robust constraints on anisotropic geometry, it is necessary to sample seismic anisotropy from different directions, ideally using different seismic phases with different incidence angles. However, many standard analysis techniques can only be applied for certain epicentral distances and source–receiver geometries. To search for new ways to detect mantle anisotropy, instead of focusing on the sensitivity of individual phases, we investigate the wavefield as a whole: we apply a ‘wavefield differencing’ approach to (systematically) understand what parts of the seismic wavefield are most affected by splitting due to seismic anisotropy in the mantle. We analyze differences between synthetic global wavefields calculated for isotropic and anisotropic input models, incorporating seismic anisotropy at different depths. Our results confirm that the seismic phases that are commonly used in splitting techniques are indeed strongly influenced by mantle anisotropy. However, we also identify less commonly used phases whose waveforms reflect the effects of anisotropy. For example, PS is strongly affected by splitting due to seismic anisotropy in the upper mantle. We show that PS can be used to fill in gaps in global coverage in shear-wave splitting data sets (for example, beneath ocean basins). We find that PcS is also a promising phase, and present a proof-of-concept example of PcS splitting analysis across the contiguous United States using an array processing approach. Because PcS is recorded at much shorter distances than *KS phases, PcS splitting can therefore fill in gaps in backazimuthal coverage. Our wavefield differencing results further hint at additional potential novel methods to detect and characterize splitting due to mantle seismic anisotropy.

What are the characteristics and results of Tilt Tests in the elderly?

What are the characteristics and results of Tilt Tests in the elderly?

Qualitative analysis and modulation instability for the extended (3+1)-dimensional nonlinear Schrödinger equation with conformable derivative

The focus of the article studies the qualitative analysis and modulation instability for the extended (3+1)-dimensional nonlinear Schrödinger equation with conformable derivative. Firstly, the extended (3+1)-dimensional nonlinear Schrödinger equation with conformable derivative is transformed into two-dimensional planar dynamic system through complex exponential wave transformation. Secondly, phase portrait and its orbit of the system are analyzed through the theory of dynamical system. By using Maple software the phase portrait, Poincaré section and sensitivity analysis are compared and displayed. Finally, parameter conditions for stable systems are provided through modulation instability.

Metabolic-Associated Fatty Liver Disease is Characterized by a Post-Oral Glucose Load Hyperinsulinemia in Individuals with Mild Metabolic Alterations.

Metabolic-associated fatty liver disease (MAFLD) has been identified as risk factor of incident type 2 diabetes (T2D), but the underlying postprandial mechanisms remains unclear. We compared the glucose metabolism, insulin resistance, insulin secretion and insulin clearance post-oral glucose tolerance test (OGTT) between individuals with and without MAFLD. We included 50 individuals with a BMI between 25-40 kg/m2 and ≥1 metabolic alteration: increased fasting triglycerides or insulin, plasma glucose 5.5-6.9 mmol/L, or glycated hemoglobin 5.7-5.9%. Participants were grouped according to MAFLD status, defined as hepatic fat fraction (HFF) ≥5% on MRI. We used oral minimal model on a frequently sampled 3h 75g-OGTT to estimate insulin sensitivity, insulin secretion, and pancreatic ß-cell function. Fifty percents of participants had MAFLD. Median age (IQR) (57 (45-65) vs 57 (44-63) years) and gender (60% vs 56% female) were comparable between groups. Post-OGTT glucose concentrations did not differ between groups, whereas post-OGTT insulin concentrations were higher in the MAFLD group (p <0.03). Individuals with MAFLD exhibited lower insulin clearance, insulin sensitivity and first phase pancreatic β cell function. In all individuals, increased insulin incremental area under the curve and decreased insulin clearance were associated with HFF after adjusting for age, sex, and BMI (p <0.02). Amongst individuals with metabolic alterations, the presence of MAFLD was characterized mainly by post-OGTT hyperinsulinemia and reduced insulin clearance, while exhibiting lower first phase β-cell function and insulin sensitivity. This suggests that MAFLD is linked with impaired insulin metabolism that may precede type 2 diabetes.