Disparate Scales Research Articles

Visual Analysis of Anomalous Behavior Patterns in Oil Wells Using Dimensionality Reduction with t-SNE Projection

Anomaly detection in production processes, especially in the oil industry, is of paramount importance due to the complexity and criticality of these systems. Producing oil wells are subject to a series of dynamic and interdependent variables, which can be monitored using sensors installed along the well. Precise analysis of these sensor records is essential for early identification of possible anomalies, as any deviation from the normal pattern can result in serious consequences, from operational failures to environmental and safety risks. Given the multidimensionality and variability of this data, the use of machine learning techniques becomes indispensable for analyzing and classifying the well's situation. However, even for computational software, anomaly identification remains a significant challenge, given the oscillatory behavior and scale disparity of the variables analyzed throughout the well's productive life. To mitigate these challenges, dimensional reduction techniques can be implemented, as they can bring some advantages, such as reducing computational cost, noise reduction, and improving classifier accuracy. A widely used tool for dimensional reduction, especially for visualization, is Distributed Stochastic Neighbor Embedding (t-SNE), which seeks to project high-dimensional data into a low-dimensional space preserving clusters, bringing similar data together by affinity through a t-distribution. For this approach, real records of pressure and temperature sensors from oil wells are used. These sensors are located at eight distinct points in the well, totaling eight dimensions that will be reduced to a two-dimensional plane. Therefore, this study aims to identify behavior patterns in multivariate data from producing oil wells visually using the t-SNE dimensionality reduction method. It is expected that through visualization, it will be possible to identify behavior patterns before and after the occurrence of anomalies and enable the development of appropriate artificial intelligence algorithms for this anomaly detection problem.

Phase-contrast visualization of human tissues using superimposed wavefront imaging of diffraction-enhanced x-rays.

Phase-contrast computed tomography (CT) using high-brilliance, synchrotron-generated x-rays enable three-dimensional (3D) visualization of microanatomical structures within biological specimens, offering exceptionally high-contrast images of soft tissues. Traditional methods for phase-contrast CT; however, necessitate a gap between the subject and the x-ray camera, compromising spatial resolution due to penumbral blurring. Our newly developed technique, Superimposed Wavefront Imaging of Diffraction-enhanced x-rays (SWIDeX), leverages a Laue-case Si angle analyzer affixed to a scintillator to convert x-rays to visible light, capturing second-order differential phase contrast images and effectively eliminating the distance to the x-ray camera. This innovation achieves superior spatial resolution over conventional methods. In this paper, the imaging principle and CT reconstruction algorithm based on SWIDeX are presented in detail and compared with conventional analyzer-based imaging (ABI). It also shows the physical setup of SWIDeX that provides the resolution preserving second-order differential images for reconstruction. We compare the spatial resolution and the sensitivity of SWIDeX to conventional ABI. To demonstrate high-spatial resolution achievable by SWIDeX, the internal structures of four human tissues-ductal carcinoma in situ, normal stomach, normal pancreas, and intraductal papillary mucinous neoplasm of the pancreas-were visualized using an imaging system configured at the Photon Factory's BL14B beamline under the High Energy Accelerator Research Organization (KEK). Each tissue was thinly sliced after imaging, stained with hematoxylin and eosin (H&E) for conventional microscope-based pathology. A comparison of SWIDeX-CT and pathological images visually demonstrates the effectiveness of SWIDeX-CT for biological tissue imaging. SWIDeX could generate clearer 3D images than existing analyzer-based phase-contrast methods and accurately delineate tissue structures, as validated against histopathological images. SWIDeX can visualize important 3D structures in biological soft tissue with high spatial resolution and can be an important tool for providing information between the disparate scales of clinical and pathological imaging.

Outdoor mesoscale fabricated ecosystems: Rationale, design, and application to evapotranspiration

The disparity in scale, complexity, and control level between laboratory experiments and field observational studies has shaped both the methodologies employed and the nature of the research questions pursued in ecology and hydrology. While lysimeters and fabricated ecosystems suitably fit in this gap, their use as mesoscale experimental facilities has not been fully explored because of the limited manipulating capabilities and integration with imaging and monitoring methods, particularly for soil functioning. The proposed fabricated ecosystem (4.7 L × 1.2 W × 1.2 H m) focuses on the spatiotemporal integration of point sensors and imaging methods along the soil-plant-atmosphere continuum. Because energy and water fluxes are key environmental drivers, the designed setup was first applied to a multi-approach evapotranspiration investigation. Below the ground, electrical resistivity tomography (ERT) was combined with soil water sensors and a distributed temperature profiling system. Together, they provided the 3D monitoring of water and temperature changes, and thus an estimation of the evapotranspiration, as well as the interpretation of its below-ground controlling processes. Above-ground sensors supported a classical energy balance investigation that was compared with the lysimeter load changes and the ERT-based ET estimation. Our results provide first experimental evidence of water and temperature spatiotemporal variability at the lysimeter scale, and thus explain the discrepancies among the three estimated evapotranspiration time series and their seasonality. Beyond evapotranspiration, the multi-approach investigation of water and energy fluxes emphasizes how mesoscale setups can further support the development and upscaling of methods and models, as well as their integration and application under expected climate disturbances.

Machine Learning in Fuel Cell Stacks, Systems and Modeling

Machine learning (ML) has become a prominent tool for science and engineering as computing speed has increased over the last 5 to10 years. This paper describes our recent progress in applying ML to polymer electrolyte fuel cells (PEFC) systems prognostics and their research and development.A key requirement for ML is the availability of high quality, labeled data to train models. Plug Power has 60,000 fuel cells in the field for materials handling and small stationary power, a sufficient number of systems from which to collect data. However, cost effective, practical systems have limited on board computing power, and often can be difficult to retrofit with processors that are compatible with ML. Real-time high-speed data collection for 60,000 systems for offboard computing can also become prohibitively expensive. Despite the data challenges, we have been able to use onboard system data to inexpensively predict the failure of valves and coolant pumps from sensors already instrumented on the systems. The valves are solenoids, which fail catastrophically within microseconds. We have found that by training a long-short-term-memory (LSTM) model with 3 to 4 variables, we observe changes in the fuel cell balance of plant that predict an unhealthy valve ahead of valve failure. A similar approach is used for the coolant loop, however although this subsystem has different behavior than the valves, and oscillate between healthy and unhealthy, before gradually becoming too unhealthy to use. For both cases, we had to understand the system operation in combination with training on the system sensors to establish which variables were most relevant to the state of health of the system and component.ML is also becoming invaluable to modeling, particularly for expediting the cost and accuracy of the complex models that correlate the nano- and micro-structures of a fuel cell to its electrochemical performance. Computational simulation of fuel cells at the cell scale or stack level has traditionally required the use of computational fluid dynamics (CFD) models with reduced-order, analytical solutions for the catalyst layer phenomena. Examples include the agglomerate model that is used to model the coupled oxygen transport and oxygen reduction reaction in the catalyst agglomerates. However, as our fundamental understanding of the complex phenomena at the catalyst scale increases, including the impacts of local ionomer resistance, anion adsorption, and capillary condensation in the carbon-supported catalyst, there is an increasing amount of significant phenomena that should be incorporated into the models. The combined phenomena are beyond the capabilities of analytical solutions, and requires computational simulation to evaluate the local reaction rates. A valuable, emerging approach to addressing the significant length scale disparity is to train an ML model on a catalyst-scale simulation and embed the output of that model into a cell-scale model. We will highlight the use of this approach in evaluating the efficacy of high oxygen permeability ionomer (HOPI) with high surface area carbon supports versus low surface area supports.Emerging research is showing how to create the next generation of data-informed electrochemical models. Instead of separating data-driven models from physical models, a new paradigm for embedding data-driven elements within physical models promises increased accuracy and speed relative to standalone physical models, pure “black-box” ML or hybrid approaches that still enforce a strict separation between data-driven and physical elements. Embedded ML has already shown considerable promise in dynamic systems, outperforming state-of-the-art recurrent neural networks such as LSTM and gated recurring units (GRUs) on benchmark problems. Embedded methods require computationally compact data-driven functions such as Karhunen-Loève decomposed Gaussian processes and gradient boosted trees, which represent physically well-defined functions such as rate constants, equilibrium constants and transport coefficients in the context of chemical and electrochemical device-scale models. The connection to physical quantities creates opportunities for independent measurements via targeted experimentation. The combination of speed and accuracy offered by embedded ML is ideal for product and process design as well as control applications.

On the Implications of Ground‐Based High‐Definition Imaging of Io's Surface

AbstractThe capability to observe temporal changes on Io's surface at optical wavelengths has recently been demonstrated by Conrad et al. (2024, https://doi.org/10.1029/2024GL108609) using new instrumentation at the Large Binocular Telescope. Monitoring of Io's surface morphology would be impactful since preexisting metrics of the degree, location and composition of Io's plume activities are severely limited. Relationships with external data sets appear especially fruitful. Comparisons with spatially resolved data on gas distributions and thermal hots spots would better characterize composition and chemistry in Io's volcanic plumes. Comparisons with measurements remote from Io are posed to advance our understanding of atmospheric escape and how eruptions connect to transient enhancements at disparate size and time scales in the extended neutral clouds and plasma torus.

Heterogeneity of population exposure to particulate matter pollution and its socioeconomic driving mechanism in Shaanxi Province, China

The rapid pace of economic and social development has led to a surge of particulate matter (PM) pollution incidents, which is detrimental to human health. Air quality in China has improved in recent years, but differences in PM exposure remain due to spatial heterogeneity in pollution and population. The identification of socioeconomic factors associated with PM pollution has attracted widespread attention. Current research predominantly targets cities, overlooking regional and county scale disparities. An analytical framework is established to evaluate the spatial heterogeneity in PM exposure and to identify the socioeconomic factors contributing to its pollution. The results revealed that: (1) the Theil index increased from 0.06 to 0.12, indicating a gradual increase in the inequality of PM exposure in Shaanxi Province. The differences within the Guanzhong Plain are obvious and the interregional differences substantially outweigh the intraregional variations. (2) Energy transition is pivotal in curbing air pollution in a region and economic growth consistently fuels particulate emissions. (3) For effective pollution mitigation, further emphasis on vegetation preservation and environmental inputs is imperative. The findings of this research provide decision support for optimizing air pollution control measures at the county scale.

Physics-informed neural networks for weakly compressible flows using Galerkin–Boltzmann formulation

In this work, we study the Galerkin–Boltzmann formulation within a physics-informed neural network (PINN) framework to solve flow problems in weakly compressible regimes. The Galerkin–Boltzmann equations are discretized with second-order Hermite polynomials in microscopic velocity space, which leads to a first-order conservation law with six equations. Reducing the output dimension makes this equation system particularly well suited for PINNs compared with the widely used D2Q9 lattice Boltzmann velocity space discretizations. We created two distinct neural networks to overcome the scale disparity between the equilibrium and non-equilibrium states in collision terms of the equations. We test the accuracy and performance of the formulation with benchmark problems and solutions for forward and inverse problems with limited data. We compared our approach with the incompressible Navier–Stokes equation and the D2Q9 formulation. We show that the Galerkin–Boltzmann formulation results in similar L2 errors in velocity predictions in a comparable training time with the Navier–Stokes equation and lower training time than the D2Q9 formulation. We also solve forward and inverse problems for a flow over a square, try to capture an accurate boundary layer, and infer the relaxation time parameter using available data from a high-fidelity solver. Our findings show the potential of utilizing the Galerkin–Boltzmann formulation in PINN for weakly compressible flow problems.

Constructing a picture of fatigue in the context of cancer: assessment of construct overlap in common fatigue scales.

Individuals diagnosed with cancer experience multiple inter-related short- and long-term side effects. Chief among such symptomology is cancer-related fatigue (CRF), which, if left unmanaged, can become chronic and result in increased disability and healthcare utilization. A growing number of self-report scales have been developed to measure CRF symptoms based on various theoretical conceptualizations with the aim of promoting targeted assessment and intervention efforts. It may be, however, unwise to assume that the various measures are conceptually similar (i.e., that they assess for the same constructs). Accordingly, we aimed to characterize item content among nine self-report scales, using a Jaccard index to quantify content overlap among scales. We characterized construct assessment among nine self-report scales recommended to assess CRF by a recent clinical practice guideline, and used a Jaccard index to quantify content overlap among scales. Analysis of 208 items across nine rating scales resulted in 20 distinct symptoms of CRF assessed. The most common symptoms were energy level (captured in all nine scales), cognitive function, impaired task performance (in eight scales), sleepiness, and physical function (in seven scales). Mean overlap among all scales was low (Jaccard index = 0.455). Only one construct (duration of fatigue; 5.0%) was captured by a single scale, and one symptom (energy level; 5.0%) was common across all scales. The PFS, MFSI, and BFI each captured at least one symptom from each of the NCCN domains of CRF. CRF scales are heterogeneousin the content they measure, critically impairing integration of knowledge across studies using disparate scales. Future work is urgently needed to build more integrated theoretical and/or computational models of CRF based on relevant mechanisms.

Worldtube excision method for intermediate-mass-ratio inspirals: Self-consistent evolution in a scalar-charge model

This is a third installment in a program to develop a method for alleviating the scale disparity in binary black hole simulations with mass ratios in the intermediate astrophysical range, where simulation cost is prohibitive while purely perturbative methods may not be adequate. The method is based on excising a “worldtube” around the smaller object, much larger than the object itself, replacing it with an analytical model that approximates a tidally deformed black hole. Previously [N. A. Wittek , Worldtube excision method for intermediate-mass-ratio inspirals: Scalar-field model in 3+1 dimensions, ] we have tested the idea in a toy model of a scalar charge in a fixed circular geodesic orbit around a Schwarzschild black hole, solving for the massless Klein-Gordon field in 3+1 dimensions on the p platform. Here we take the significant further step of allowing the orbit to evolve radiatively, in a self-consistent manner, under the effect of back-reaction from the scalar field. We compute the inspiral orbit and the emitted scalar-field waveform, showing a good agreement with perturbative calculations in the adiabatic approximation. We also demonstrate how our simulations accurately resolve postadiabatic effects (for which we do not have perturbative results). In this work we focus on quasicircular inspirals. Our implementation is publicly accessible in the p numerical relativity code. Published by the American Physical Society 2024

A pseudo-density conjugate heat transfer method and its application to simulating the quasi-steady temperature field of a compressor cylinder

The substantial disparity in time scales between heat conduction and convection requires extremely tiny time steps in simulations, leading to significant computational demands when using current-tightly coupled methods to solve the temperature field of cylinders at the quasi-steady stage. A new pseudo-density method is proposed to accelerate the heat conduction rate. The influence of solid density on transient temperature under the periodic third-type boundary conditions is analyzed in a one-dimensional model by analytical methods and verified in a three-dimensional structure by numerical methods. It is found that reducing solid density can accelerate the heat conduction rate and increase the fluctuation amplitude of solid temperature. Therefore, a variable pseudo density is adopted for solids in the simulation of the transient heat transfer characteristics between the compressed gas and the cylinder. The results show that the pseudo-density method provides similar computational accuracy to the current tightly coupled methods, but at significantly lower computational cost. The fluctuation amplitude of the cylinder's temperature decreases rapidly in the wall thickness direction. The distribution of the convective heat transfer coefficient on the inner wall of the cylinder is extremely transient and highly non-uniform, which is consistent with the distribution of gas velocity near the cylinder wall.

A LiDAR-depth camera information fusion method for human robot collaboration environment

With the evolution of human–robot collaboration in advanced manufacturing, multisensor integration has increasingly become a critical component for ensuring safety during human–robot interactions. Given the disparities in range scales, densities, and arrangement patterns among multisensor data, such as that from depth cameras and LiDAR, accurately fusing information from multiple sources has emerged as a pressing need to safeguard human–robot safety. This paper focuses on LiDAR and depth cameras, addressing the challenges posed by the differences in data collection range, point density, and distribution patterns which complicate information fusion. We propose a heterogeneous sensor information fusion method for human–robot collaborative environments. To solve the problem of substantial differences in point cloud range scales, a moving sphere space coarse localization algorithm is introduced, narrowing down the scale of interest based on similar features. Furthermore, to address the challenge of significant density differences and low overlap rates between point clouds, we present an improved FPFH coarse registration algorithm based on overlap ratio and an enhanced ICP fine registration algorithm based on the generation of corresponding points. The method proposed herein is applied to the fusion of information from a 64-line LiDAR and a depth camera within a human–robot collaboration scene. Experimental results demonstrate an absolute translational accuracy of 4.29 cm and an absolute rotational accuracy of 0.006 rad, meeting the requirements for heterogeneous sensor information fusion in the context of human–robot collaboration.

The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity

Vegetation exerts a significant cooling effect, particularly during the hot summer; however, the spatial scale effects and gender difference among occupants’ subjective thermal comfort remain elusive. Developing a comprehensive model to elucidate the multidimensional relationship between green spaces and thermal experience holds paramount importance. Taking Longzi River Park in Zhengzhou city as a case study, this research examined the influence of vegetation on thermal experience by using structural equation modeling (SEM) from perspectives of fitting scale and gender disparities. It was found that (1) The vegetation environment not only influences thermal sensation, comfort and demand independently, but also influences the pathway between them. These influence paths constitute a complex causal network, functioning as a framework of “sensation → comfort → demand” and its influencing factors. (2) There exists a scaling effect in the pathway framework, which conforms to a threshold of 10 m for the inner radius and 30 m for the outer radius. The goodness of SEM model fit declines with the increase in either the inner radius or the outer radius, or both. (3) Differences in genders are exhibited for the pathway framework, with the vegetation exerting a stronger influence on female sensation and comfort, as well as male demand. The pathway from sensation to comfort to demand is more pronounced in male populations. The research findings contribute to the development of improved and sustainable vegetation distribution in urban parks.

Clinical outcomes of unicompartmental knee arthroplasty and total knee arthroplasty in the same patient.

Osteoarthritis has become the predominant manifestation of arthritic conditions on a worldwide scale and serves as a significant instigator of pain, impairment, and increasing socio-economic strain on a global level. The ongoing discourse on the choice between total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) for patients suffering from anterior medial osteoarthritis continues to ignite scholarly controversy. Our objective was to assess and compare the clinical outcomes of UKA and TKA within the same patient, hereby offering a novel perspective on this topic. Fifty-seven individuals who underwent TKA on one knee and UKA on the other knee at the Department of Orthopaedics, First Hospital of Hebei Medical University between March 2019 and March 2024 were analysed for this retrospective study. We conducted a comprehensive examination and evaluation of perioperative laboratory assessments, radiological examinations, knee functionality, contentment levels, and postoperative complications within the two groups. Following surgical procedures, levels of hemoglobin, red blood cells, and albumin were found to be elevated in the UKA group when compared to the TKA group (hemoglobin: 121.2 ± 12.54 vs. 110.1 ± 13.21g/L; red blood cells: 4.0 ± 0.47 vs. 3.6 ± 0.42 *1012/L; albumin: 37.7 ± 5.66 vs. 35.3 ± 5.23g/L). There is a significant difference in the hip-knee-ankle angles between the postoperative UKA group and the TKA group (5.3 ± 3.46° vs. 4.1 ± 2.86°, p < 0.05). There existed no notable disparity in postoperative visual analog scale, knee society score, and forgotten joint score between the two groups. However, a remarkable variance was observed in postoperative range of motion between the two groups (116.4 ± 5.96° vs. 108.4 ± 5.32°). We found that UKA resulted in less physical strain, less postoperative inflammatory response, improved joint mobility, although with less effective lower limb force line correction compared to TKA. Many patients have shown a preference for UKA and express higher levels of satisfaction with the procedure.

Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition

Over the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts.

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet's superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.

On the three-dimensional structure of instabilities beneath shallow-shoaling internal waves

The stimulation of instability and transport in the bottom boundary layer by internal solitary waves has been documented for over twenty years. However, the challenge of shallow slopes and a disparity of scales between the large-scale wave and the small-scale boundary layer has proven challenging for simulations. We present laboratory scale simulations that resolve the three-dimensionalisation in the boundary layer during the entire shoaling process. We find that the late stage, in which the incoming wave fissions into boluses, provides the most consistent source of three-dimensionalisation. In the early stage of shoaling, three-dimensionalisation occurs not so much due to separation bubble instability, but due to the interaction of vortices shed from the separation bubble with the overlying pycnocline. This interaction overturns the pycnocline, and creates bursts in kinetic energy and viscous dissipation, suggesting that the shed vortices induce turbulent motion and sediment resuspension in the water column above and behind the separation bubble.

A Chimera method for thermal part-scale metal additive manufacturing simulation

This paper presents a Chimera approach for the thermal problems in welding and metallic Additive Manufacturing (AM). In particular, a moving mesh is attached to the moving heat source while a fixed background mesh covers the rest of the computational domain. The thermal field of the moving mesh is solved in the heat source reference frame. The chosen framework to couple the solutions on both meshes is a non-overlapping Domain Decomposition (DD) with Neumann–Dirichlet transmission conditions.Increased steadiness and accuracy within the vicinity of the Heat Affected Zone (HAZ) are the main advantages of this approach. The steadiness gain allows for the use of larger time steps, which is vital in AM applications and, in particular, Laser Powder Bed Fusion (LPBF), where the disparity of time scales represents a major hurdle. Moreover, enhanced accuracy can be observed in the resulting morphology of the melt pool. It will be shown that the method addresses classical shortcomings pointed out by Goldak without requiring the use of an asymmetrical heat source profile.

Kinetic investigation of discharge performance for Xe, Kr, and Ar in a miniature ion thruster using a fast converging PIC-MCC-DSMC model

Abstract The present work develops a full particle-based model that couples the particle-in-cell plus Monte Carlo collision (PIC-MCC) simulation for plasma dynamics and the direct simulation Monte Carlo (DSMC) method for neutral dynamics in a synergistic iterative manner. This new model overcomes the slow convergence issue in the conventional direct coupling approach caused by the disparity of the time scales between the plasma and neutral dynamics. This model is applied to simulate the behavior of xenon (Xe) and its potential alternatives, krypton (Kr) and argon (Ar), in the discharge chamber of a miniature direct current (DC) ion thruster. The results show that a stable discharge is difficult to achieve for Kr and Ar under the operating conditions optimal for Xe. While increasing the discharge voltage can effectively improve the stability of discharge for Kr and Ar, other common strategies such as changing the magnetic field strength, propellant flow rate, and cathode current are not successful. The propellant utilization efficiency and discharge efficiency are affected by both discharge voltage and propellant flow rate. A maximum utilization efficiency and an optimal discharge efficiency are observed for all three propellants, with the values decreasing in the order of Xe, Kr, and Ar. Moreover, the discharge voltage corresponding to the optimal efficiency is inversely proportional to the square root of the propellant mass, indicating that the ion diffusional loss to the wall, rather than the ionization energy, is the dominant factor affecting the discharge performance for alternative propellants in a miniature DC thruster.

Interconnection between Polarization-Detected and Population-Detected Signals: Theoretical Results and Ab Initio Simulations.

Most of spectroscopic signals are specified by the nonlinear laser-induced polarization. In recent years, population-detection of signals becomes a trend in femtosecond spectroscopy. Polarization-detected (PD) and population-detected signals are fundamentally different, because they are determined by photoinduced processes acting on disparate time scales. In this work, we consider the fluorescence-detected (FD) N-wave-mixing (NWM) signal as a representative example of population-detected signals, derive a rigorous expression for this signal, and discuss its approximate variants suitable for numerical simulations. This leads us to the definition of the phenomenological FD (PFD) signal, which contains as a special case all definitions of FD signals available in the literature. Then we formulate and prove the population-polarization equivalence (PPE) theorem, which states that PFD NWM signals produced by (possibly strong) laser pulses can be evaluated as conventional PD signals in which the effective polarization is determined by the PFD transition dipole moment operator. We use the PPE theorem for the construction of the ab initio protocol for the simulation of PFD 4WM signals. As an example, we calculate electronic two-dimensional (2D) PFD spectra of the gas-phase pyrazine and compare them with the corresponding PD 2D spectra.

Hounsfield units of vertebrae as a predictor of cervical deep paraspinal muscles atrophy and neck pain in degenerative cervical myelopathy.

This study investigated the correlation between Hounsfield units (HU) of the cervical vertebrae and atrophy of the cervical deep paraspinal muscles, namely the multifidus and semispinalis cervicis (SCer), in patients diagnosed with degenerative cervical myelopathy (DCM). The authors retrospectively analyzed data from 136 patients aged 50-79 years (81 males and 55 females) who underwent surgical intervention for DCM. HU measurements of the cancellous bone in the C4 vertebra were acquired through standardized techniques. The authors evaluated fatty infiltration (FI); analyzed functional and vertebral cross-sectional area (CSA) of the multifidus and SCer at the C4-5, C5-6, and C6-7 levels; and analyzed the presence of Modic changes (MCs) and the incidence of axial neck pain. Patients were categorized into group A (n = 56) with mean ± SD HU of 293.3 ± 15.6 and group B (n = 80) with mean ± SD HU of 389.5 ± 10.6. Both groups demonstrated significant improvements in postoperative clinical outcomes (p < 0.05); however, no statistically significant difference was observed (p > 0.05). Significant disparities in HU measurements and visual analog scale (VAS) scores for neck pain were observed between the groups (p < 0.05). The highest VAS score correlated with MCs-1 type (i.e., low signal on T1-weighted images and high signal on T2-weighted images). The functional CSA to vertebral CSA ratios of the multifidus and SCer in group A were markedly reduced compared to those of group B (p < 0.05). No significant difference was noted in functional CSA asymmetry between the groups for both muscles (p > 0.05). Lower HU measurements directly correlated with increased FI in the multifidus (p = 0.002) and SCer (p = 0.035). Furthermore, a strong positive association was found between the functional CSA to vertebral CSA ratio of the multifidus and HU values (p = 0.003), whereas HU measurements and VAS scores exhibited a negative correlation (p = 0.020). Among those patients older than 50 years with DCM, those with decreased HU values demonstrated elevated FI levels in the multifidus and SCer muscles. Moreover, these patients presented with pronounced muscle atrophy, which correlated with axial neck pain. A significant relationship was also identified between MCs and diminished HU values.