Empirical Values Research Articles

The CH3D Absorption Spectrum Near 1.58 μm: Extended Line Lists and Rovibrational Assignments

Monodeuterated methane (CH3D) contributes greatly to absorption in the 1.58 μm methane transparency window. The spectrum is dominated by the 3ν2 band near 6430 cm−1, which is observed in natural methane and used for a number of planetary applications, such as the determination of the D/H ratio. In this work, we analyze the CH3D spectrum recorded by high-sensitivity differential absorption spectroscopy in the 6099–6530 cm−1 region, both at room temperature and at 81 K. Following a first contribution to this topic by Lu et al., the room-temperature line list is elaborated (11,189 lines) and combined with the previous 81 K list (8962 lines) in order to derive about 4800 empirical lower-state energy values from the ratio of the line intensities measured at 81 K and 294 K (2T-method). Relying on the position and intensity agreements with the TheoReTS variational line list, about 2890 transitions are rovibrationally assigned to twenty bands, with fifteen of them being newly reported. Variational positions deviate from measurements by up to 2 cm−1, and the band intensities are found to be in good agreement with measurements. All the reported assignments are confirmed by Ground-State Combination Difference (GSCD) relations; i.e., all the upper-state energies (about 1370 in total) have coinciding determinations through several transitions (up to 8). The energy values, determined with a typical uncertainty of 10−3 cm−1, are compared to their empirical and variational counterparts. The intensity sum of the transitions assigned between 6190 and 6530 cm−1 represents 76.9 and 90.0% of the total experimental intensities at 294 K and 81 K, respectively.

Mathematical model of dissolved microbial products in sewage treatment system

Water is the source of life, but all kinds of water resources in the world are suffering from different degrees of pollution. Water pollution leads to a serious shortage of available fresh water resources, and sewage treatment is the main way to solve water pollution. For the sewage after biological treatment of activated sludge, the organic matter contained in the effluent is mainly the dissolved microbial products (SMPs) produced in the process of microbial metabolism. The composition of SMPs is complex, mainly including macromolecular substances such as protein, polysaccharide, humic acid and DNA and cell fragments. The mathematical model of activated sludge is a quantitative description of the mathematical relationship between substrate degradation parameters and microbial growth. Starting from the Monod equation representing the relationship between substrate consumption and microbial growth, it combines the reactor theory and microbiology theory in the chemical field. Based on the principle of conservation of materials and Monod equation, the mathematical expression of organic degradation model was determined according to the collected data and empirical values of parameters. The general idea of activated sludge model No.1 (ASM1) for activated sludge process simulation was introduced, and the influence of sludge concentration on SBR process water treatment was explored. It was found that the removal rate of COD, ammonia nitrogen, total nitrogen and total phosphorus increases with the increase of sludge concentration. When C/N=8, the removal rate of ammonia nitrogen increased from 62% to 81%, and the removal rate of total nitrogen increased from 64% to 82%, with the most obvious effect.

Modification methods of the Stokes’ kernel for determining the (quasi-) geoid with the Remove-Compute-Restore technique

The geoid and quasi-geoid serve as the reference surfaces of the orthometric and normal height systems, respectively. In order to improve the accuracy of the (quasi-) geoid determined by the Stokes integral with use of the Remove-Compute-Restore (RCR) technique, various modification methods for the spherical Stokes’ kernels, including the spheroidal, cosine-, power-, and Molodensky-modified kernels, are studied in this paper. In addition to the traditional Molodensky-modified Stokes’ kernel, a more effective Molodensky-modified Stokes’ kernel is put forward. A general formula for spectral decomposition of the Stokes integral in the RCR mode is derived, followed by the spectral analysis to reveal the transfer principles of gravity data when using different Stokes’ kernels. The spheroidal and modified Stokes integrals can cause spectral leakage phenomenon, and a method to eliminate spectral leakage is presented based on spectral analysis. The research indicates the low truncation degree of the spheroidal Stokes’ kernel and the low modification degrees of the modified Stokes’ kernel affect the accuracy of the (quasi-) geoid significantly. Quantitative methods for estimating the empirical values of the parameters of the low-degree spheroidal and modified Stokes’ kernels are proposed and the effectiveness of the methods is validated through numerical tests.

Effect of Temperature and Electric Field Strength on Carrier Mobility of Oil-Impregnated Pressboard Under DC Voltage

The influence of carrier mobility on the space charge transport behavior inside the oil-impregnated pressboard insulation of converter transformers cannot be neglected. However, at present, current knowledge is usually derived from empirical or theoretical values, lacks experimental studies, and often ignores the effects of temperature and field strength under actual operating conditions. In this paper, based on the variable-temperature surface potential decay (SPD) method, a carrier mobility measurement platform for oil-impregnated pressboard is established, and the carrier mobility values for different combinations of oil and oil-impregnated pressboard are obtained experimentally to analyze and reveal the influence mechanisms of temperature and field strength on the carrier mobility. The results indicate the following: (1) The positive and negative carrier mobilities of oil-impregnated pressboard are in the range of 10−12–10−13 m2·V−1·s−1, and the negative carrier mobility is always higher than the positive carrier mobility. (2) The carrier mobility is positively correlated with the changes of temperature and field strength, and when the temperature increases from 20 °C to 80 °C, the positive and negative carrier mobilities increase by 4.01 times and 4.72 times, respectively; when the field strength increases from 1 kV/mm to 7 kV/mm, the positive and negative carrier mobility increases by 2.53 and 2.72 times, respectively. (3) The carrier mobility of the pressboard with a higher oil absorption rate changes more significantly with temperature; when the field strength is 7 kV/mm and the temperature increases from 20 °C to 80 °C, the positive polarity carrier mobility increases from 3.96 × 10−13 m2·V−1·s−1 to 2.64 × 10−11 m2·V−1·s−1, an increase of 66.67 times, while the increase in the carrier mobility of the pressboard with a lower oil absorption rate is only 1.59 times. (4) The carrier mobility of the naphthenic transformer oil-impregnated pressboard is higher than that of the paraffin-based transformer oil-impregnated pressboard, and the carrier mobility of two kinds of naphthenic transformer oil-impregnated pressboard is 3.16 times and 2.47 times higher than that of the paraffin-based transformer oil-impregnated pressboard, respectively, under the conditions of 60 °C and 7 kV/mm. (5) Utilizing the Darcy model and microscopic scanning results of the pressboard morphology, it was revealed that permeability and fiber structure are key factors influencing the variation in carrier mobility. The research results of this paper can provide theoretical basis for the calibration and optimization of the oil-pressboard insulation structure of converter transformers.

Comparative Analysis of Heat Transfer Coefficient Using Experimental Data and Empirical Expressions

This study aims to determine the heat transfer coefficient by comparing results obtained from both experimental methods and empirical expressions. A controlled experiment involving the flow of water through a polyethylene hose was conducted, and the heat transfer coefficient was calculated based on inlet and outlet temperatures. These findings were then compared with values derived from empirical correlations, specifically the Sieder-Tate equation, to assess accuracy and reliability. The experimental setup maintained consistent boundary conditions, including water inlet temperature, airflow rate, and hose orientation. The results indicated that the experimentally determined heat transfer coefficient was 48.30 W/(m²·K), while the empirical calculation yielded a value of 53.44 W/(m²·K). The slight discrepancy between these values highlights minor experimental errors and assumptions inherent in empirical models. Overall, the close agreement between the experimental and empirical values validates the use of empirical correlations for predicting heat transfer coefficients in similar configurations. This study provides valuable insights for optimizing heat transfer processes in various industrial and engineering applications, emphasizing the importance of experimental validation. Future work could explore extended parameter ranges, advanced measurement techniques, and numerical simulations to further enhance the accuracy and applicability of the findings.

Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload?

The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing.

A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes

IntroductionCluster randomised trials (CRTs) are the gold standard for measuring the community-wide impacts of malaria control tools. CRTs rely on well-defined sample size estimations to detect statistically significant effects of trialled interventions, however these are often predicted poorly by triallists. Here, we review the accuracy of predicted parameters used in sample size calculations for malaria CRTs with epidemiological outcomes.MethodsWe searched for published malaria CRTs using four online databases in March 2022. Eligible trials included those with malaria-specific epidemiological outcomes which randomised at least six geographical clusters to study arms. Predicted and observed sample size parameters were extracted by reviewers for each trial. Pair-wise Spearman’s correlation coefficients (rs) were calculated to assess the correlation between predicted and observed control-arm outcome measures and effect sizes (relative percentage reductions) between arms. Among trials which retrospectively calculated an estimate of heterogeneity in cluster outcomes, we recalculated study power according to observed trial estimates.ResultsOf the 1889 records identified and screened, 108 articles were eligible and comprised of 71 malaria CRTs. Among 91.5% (65/71) of trials that included sample size calculations, most estimated cluster heterogeneity using the coefficient of variation (k) (80%, 52/65) which were often predicted without using prior data (67.7%, 44/65). Predicted control-arm prevalence moderately correlated with observed control-arm prevalence (rs: 0.44, [95%CI: 0.12,0.68], p-value < 0.05], with 61.2% (19/31) of prevalence estimates overestimated. Among the minority of trials that retrospectively calculated cluster heterogeneity (20%, 13/65), empirical values contrasted with those used in sample size estimations and often compromised study power. Observed effect sizes were often smaller than had been predicted at the sample size stage (72.9%, 51/70) and were typically higher in the first, compared to the second, year of trials. Overall, effect sizes achieved by malaria interventions tested in trials decreased between 1995 and 2021.ConclusionsStudy findings reveal sample size parameters in malaria CRTs were often inaccurate and resulted in underpowered studies. Future trials must strive to obtain more representative epidemiological sample size inputs to ensure interventions against malaria are adequately evaluated.RegistrationThis review is registered with PROSPERO (CRD42022315741).

Application of cloud point extraction coupled with derivative spectrophotometry to remove binary mixture of Cresol Red and Methyl Orange dyes from aqueous solutions: Box–behnken design optimization

Cloud point extraction (CPE) was employed to eliminate Cresol Red (CR) and Methyl Orange (MO), as anionic dyes in a binary mixture from aqueous solutions. To remove these dyes Triton X-100 and NaCl at pH 5.7 were utilized. In this vein, wavelengths of 365 nm and 520 nm were respectively selected for CR and MO using the derivative spectrophotometer and first-order derivatives. According to based on the first-order derivative spectrophotometry, the recoveries rised from 94.3 to 99.5 % for CR and from 94.6 to 99.1 % for MO. In the following, the response surface methodology was administered to investigate the effect of surfactant concentration, temperature, and time on the dyes' elimination process. The quadratic mathematical model was obtained from the Box-Behnken design (BBD) matrix and developed to estimate the impact of each variable and its relationship with the elimination parameters. Later, coefficients of determination (R2) ≥0.97 were obtained using model equations and comparison between predicted and empirical values. Analysis of variance estimated the models’ significance and anticipation while processing the study variables. Based on the results, the model of pseudo-first-order in kinetic modelling can best describe dyes adsorption among the studied models. The analyzed dyes adhere to the Langmuir model with correlation values of 0.86 for CR and 0.87 for MO. The monolayer capacity (Qmax) was determined as 0.77 mol/mol for CR and 26.41 mol/mol for MO.

The Impact of Wildfire Smoke on Asthma Control in California: A Microsimulation Approach.

Wildfire smoke exposure leads to poorer health among those with pre-existing conditions such as asthma. Particulate matter in wildfire smoke can worsen asthma control, cause acute exacerbations, and increase health resource utilization (HRU) and costs. Research to date has been retrospective with few opportunities to project changes in underlying asthma control and HRU given exposure to wildfire smoke. Using a microsimulation of 5,000 Californians with asthma, we calculated changes in asthma control distribution, risk of exacerbation, and HRU and cost outcomes in the 16weeks during and after a wildfire. The model was calibrated against empirical values on asthma control distribution and increased HRU after exposure to wildfire smoke. Without smoke exposure, 48% of the cohort exhibited complete or well control of asthma, and 8% required acute healthcare per cycle. Following two consecutive weeks of wildfire smoke, complete or well control of asthma fell to 27%, with an additional 4% HRU. This corresponds to total additional $601,250 in all-cause medical costs and eight fewer quality-adjusted life years over 16weeks of model time. Our model found increased asthma health and cost burden due to wildfire smoke that were aligned with empirical evidence from a historic wildfire event. This study establishes a framework for a more nuanced understanding of asthma impacts from wildfire smoke that can help inform the development of public health policies to mitigate harm and promote resilience among asthma patients in the face of climate change.

Plant functional type and peat properties determine elemental transfer in boreal mire vegetation

Uptake of elements into plants is an integral part of many environmental impact assessments. Typically, the plant uptake is determined using an empirical soil-to-plant transfer factor (CR). The elemental concentrations in plants are expected to vary with plant species and plant functional type (PFT), but also according to soil and element properties. Specifically, the uptake of essential elements is regulated, and likely less related to soil concentrations than the uptake of non-essential elements. In this study, the impact of PFT, species and environmental factors on the CR of mire plants was tested. The plants included in the study were four common boreal peatland species (Andromeda polifolia, Vaccinium oxycoccus, Eriophorum vaginatum and Carex rostrata) sampled from 40 minerogenic mires along an age gradient.The results show that while plant species and PFT (heathers and sedges) are the main determinants of the CR value, also environmental factors, such as peat C:N ratio, are important. Further, concentrations of essential elements in plants were only weakly correlated to peat concentrations, whereas the correlation was stronger for non-essential elements and elements utilized at trace amounts.The results of this study verify that CR values may vary substantially between peatland plant species and PFTs. Further, the results suggest that it is relevant to include effects of PFTs on CR and among-species variation in environmental impact assessments. This is because the PFT may have a large impact on the exposure pathways to humans, which could, for example, be berries or animal feed, and also due to the uncertainties of the composition of the future vegetation communities. Since CR varies systematically with several soil properties, there may be potential for adjusting the CR values for expected environmental changes, and thereby reduce the uncertainties of empirical CR values determined from a broad range of environmental conditions.

A hybrid denoising method for low-field nuclear magnetic resonance data

Low-field nuclear magnetic resonance (NMR) has broad application prospects in the exploration and development of unconventional oil and gas reservoirs. However, NMR instruments tend to acquire echo signals with relatively low signal-to-noise ratio (SNR), resulting in poor accuracy of T2 spectrum inversion. It is crucial to preprocess the low SNR data with denoising methods before inversion. In this paper, a hybrid NMR data denoising method combining empirical mode decomposition-singular value decomposition (EMD-SVD) was proposed. Firstly, the echo data were decomposed with the EMD method to low- and high-frequency intrinsic mode function (IMF) components as well as a residual. Next, the SVD method was employed for the high-frequency IMF components denoising. Finally, the low-frequency IMF components, the denoised high-frequency IMF components, and the residual are summed to form the denoised signal. To validate the effectiveness and feasibility of the EMD-SVD method, numerical simulations, experimental data, and NMR log data processing were conducted. The results indicate that the inverted NMR spectra with the EMD-SVD denoising method exhibit higher quality compared to the EMD method and the SVD method.

Errors in the field reconstruction using CR-39 proton radiographs with high fluence variation.

CR-39 proton radiography is an experimental charged-particle backlighter platform fielded and used at OMEGA and the NIF to image electric and magnetic fields in a subject plasma. Processing a piece of CR-39 involves etching it in hot NaOH, and the etch time can greatly impact the background-to-signal ratio (BSR) in low-fluence (≲4 × 104cm-2) regions and detection efficiency in high-fluence regions (≳7 × 105cm-2). For CR-39 data with high fluence variation, these effects mean that any single etch time will result in ≳15% error in the measured signal in either the high- or low-fluence regions. This study aims to quantify the impact of the etch time on the BSR and efficiency losses and how these affect the field reconstruction. Experiments at the MIT Linear Electrostatic Ion Accelerator provided empirical values of the BSR and efficiency losses as a function of the fluence and etch time for fluences ranging from 3 × 103 to 7 × 105cm-2. Synthetic radiographs were generated with known fields and modulated based on empirical values of BSR and efficiency losses. The fields were reconstructed using a Monge-Ampère code with the modulated radiographs as input. The results indicate that combining short and long etches allows for more accurate analysis of radiographs with high fluence variation, with the mean squared error of the reconstructed fields decreasing by factors of 1.2-7 compared to the reconstructions using only one etch time.

The W2024 database of the water isotopologue H216O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\rm{H}}}_{2}^{\\,16}{\\rm{O}}$$\\end{document}

The rovibrational spectrum of the water molecule is the crown jewel of high-resolution molecular spectroscopy. While its significance in numerous scientific and engineering applications and the challenges behind its interpretation have been well known, the extensive experimental analysis performed for this molecule, from the microwave to the ultraviolet, is admirable. To determine empirical energy levels for H216O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\bf{H}}}_{{\\bf{2}}}^{\\,{\\bf{16}}}{\\bf{O}}$$\\end{document}, this study utilizes an improved version of the MARVEL (Measured Active Rotational-Vibrational Energy Levels) scheme, which now takes into account multiplet constraints and first-principles energy-level splittings. This analysis delivers 19027 empirical energy values, with individual uncertainties and confidence intervals, utilizing 309 290 transition wavenumbers collected from 189 (mostly experimental) data sources. Relying on these empirical, as well as some computed, energies and first-principles intensities, an extensive composite line list, named CW2024, has been assembled. The CW2024 dataset is compared to lines in the canonical HITRAN 2020 spectroscopic database, providing guidance for future experimental investigations.

Reduction characteristics and mechanism of mechanically activated iron ore powder by lignin

As a primary constituent of renewable biomass fuel, lignin can be effectively utilized as a reducing agent in the ironmaking process, thereby significantly mitigating CO2 emissions throughout the procedure. This study meticulously evaluates the impact of lignin on the reduction of iron ore powder across diverse levels of mechanical activation through thermogravimetric analysis. It elucidates the intricate reduction mechanism at play. The reduction process is characterized by two pivotal stages: the initial reduction of pyrolysis gases followed by the reduction of fixed carbon, which predominantly drives the transformation of iron ore powder. While mechanical activation exhibits a negligible influence on the first stage, it exerts a pronounced effect on the second. An extended activation time elevates the depth of reduction in the latter stage. Furthermore, the heating rate plays a crucial role in the reduction process, with slower rates favoring the reduction of pyrolysis gases and faster rates boosting carbon fixation reduction. Enhanced activation durations lead to improve linear regression fits for the reduction reaction data. Utilizing the carbon gasification model for iron ore powder treated with 480 min of activation yields a calibration variance of 0.99, with the derived activation energy of 48.75 kJ/mol aligning closely with empirical values. This research provides a thorough analysis of the reduction kinetics and mechanism, underscoring the transformative potential of lignin as a reducer in ironmaking processes, thus contributing to the development of sustainable, low-carbon technologies.

Bayesian analyses of multiple random change points in survival models with applications to clinical trials

ABSTRACT Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 − α )% highest posterior density (HPD) intervals calculated from Chen–Shao’s algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.

Determination of the load acting on the probe by separating force and torque during FSW of AA 6060 T66

AbstractThe development of suitable welding processes is required to meet the ever-increasing demands on joining processes, particularly for lightweight construction and increasing environmental awareness. Friction stir welding (FSW) represents a promising alternative to conventional fusion welding processes, particularly for the joining of low-melting-point materials such as aluminium and magnesium alloys, which present a number of challenges, including the formation of pores and the occurrence of hot cracks. The central element of the process is the friction stir welding tool, which consists of a shoulder and a probe. The rotation and the simultaneous application of pressure during the joining process create a friction-based heat input through the tool. The excellent mechanical properties resulting from dynamic recrystallisation during the welding process are a major advantage of the process. As a result, strengths comparable to those of the base material can be achieved. However, FSW is subject to process-specific challenges, including high process forces, which result in the fabrication of complex and robust devices. Additionally, high dynamic loads on the friction stir welding tools must be considered. In many cases, the design of friction stir welding tools is based on empirical data. However, these empirical values are machine-, component- and material-specific, which often results in under- or overmatching of friction stir welding tools. Sudden probe failure, component scrap, and low process reliability are the direct consequences of undermatching. Overmatching results in enlarged tools with limited accessibility, high heat input, and high process forces, leading to component deformation. The aim of this study is to determine the load on the probe by separating the forces and torque of the shoulder and the probe in order to be able to make statements about the load acting on the probe and the resulting stress state. The knowledge of the stress state can be employed to design friction stir welding tools, both statically and dynamically, for a specific welding task. A strategy was devised to distribute the load exerted on the shoulder and probe. To this end, the length of the probe was gradually reduced between the welding tests. The investigations were carried out with a force-controlled robotized welding setup in which AA 6060 T66 sheets with a thickness of 5 mm were welded. A Kistler multicomponent dynamometer type 9139AA allows to measure the Cartesian forces to be recorded in the x-, y-, and z-directions with a sampling rate of 80 kHz. The weld seam properties were determined by visual and metallographic inspections as well as tensile and bending tests in accordance with DIN EN ISO 25239–5.

OpenFlume: An accessible and reproducible benchtop flume for research and education

Open-channel flumes are an important tool in fluid mechanics research and education. However, the few commercially available small-scale flumes are generally expensive and lack complete characterization. In this work, we present an open-source, low-cost, modular benchtop laboratory flume that is designed to be accessible and reproducible. The flume is assembled from widely available materials and hardware, and fabricated exclusively using tools and machinery commonly found in workshop spaces. The recirculating water flow through the system is driven by a controllable electric coolant pump designed for automobiles. Our design has a flow cross-section of 5 × 5 cm, adjustable flow velocity, and a modest overall footprint. All design files and build instructions are shared in a digital repository ensuring openness and reproducibility. The flow in the test section is characterized using particle image velocimetry (PIV) and is shown to be of high uniformity with low turbulence intensity. Furthermore, direct measurements of the drag force on a submerged sphere are reported for a range of control parameters, and exhibit good agreement with established empirical values.

Storm-time variability of terrestrial hydrogen exosphere: kinetic simulation results

Recent studies of TWINS Lyman-α observations have reported an increase in geocoronal column brightness during geomagnetic storms, indicating enhanced exospheric hydrogen atom density (NH). This suggests a complex role of exospheric neutrals in determining storm-time magnetosphere dynamics and their energy release through charge-exchange processes. We developed a Model for Analyzing Terrestrial Exosphere (MATE) to investigate storm-time exospheric behaviors and their physical drivers. MATE traces test hydrogen atoms backward in time from locations in the exosphere to a nominal exobase altitude of 500 km, employing Newtonian mechanics with gravitational force. The model then calculates the phase-space densities (PSDs) of test hydrogen atoms at the exobase using the Maxwellian distribution with physics-based exobase conditions from the TIMEGCM upper atmosphere model. MATE maps PSDs at the exobase to the exosphere using Liouville’s Theorem under collisionless assumptions and derives NH by integrating the PSDs across velocity space. We conducted MATE simulation before, during, and after a minor geomagnetic storm from 12 to 18 June 2008, and compared the model results with NH estimates from the TWINS geocorona data. MATE reproduces storm-time density enhancements soon after the minimum Dst is reached, matching well with a general trend of TWINS NH estimates. The results suggest that upper atmospheric heating during a geomagnetic storm increases the number of ballistic and escaping hydrogen atoms entering the exosphere from the exobase, thereby boosting NH. However, the magnitude of modeled NH mismatches the TWINS NH estimates. The potential mechanisms of this density discrepancy include the physics excluded in the MATE model — such as neutral-neutral collisions, neutral-plasma charge exchange, solar radiation pressure, and photoionization — as well as the higher exobase hydrogen density of TIMEGCM compared to typical empirical values, which will be addressed in future.

Desıgn of quantum-dot semiconductor optical amplifiers wıth near-zero linewidth enhancement factor

The linewidth enhancement factor (LWEF) of a semiconductor optical amplifier (SOA) quantifies refractive index fluctuations in the gain medium, which induce phase distortion in the amplified optical signal. Optoelectronic systems employing SOAs with high LWEFs often exhibit poor device stability and beam coherence. Thus, designing SOAs with low LWEF is imperative. Recently, Quantum-Dot (QD) SOAs have emerged as a solution for LWEF suppression due to quantum-confinement effects enabling tunability of the QD carrier density and emission frequency. In this study, we aim to design a composite active region comprised of a host medium and the embodied QDs, to explore the corresponding LWEF variation and propose the ultimate design strategy to achieve near-zero LWEF in QD SOAs for enhancing device stability and beam coherence. Our approach entails modeling the refractive index of the composite active region using effective medium approximation via Maxwell–Garnett mixing formulation. We then extensively tune key SOA parameters, including QD carrier density, QD emission frequency, and the collision-time constant of the carriers to uncover the optimal configuration for minimizing the LWEF. Based on empirical values, we have developed and validated a simple yet effective algorithm that precisely simulates LWEF behavior in response to changes in key QD SOA parameters. This approach offers a straightforward model for estimating LWEF variation, and its corresponding minimization in QD SOAs without requiring complex experimental measurement techniques.

Mixture multigroup structural equation modeling: A novel method for comparing structural relations across many groups.

Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relate to life satisfaction), and structural equation modeling (SEM) is the state-of-the-art for doing so. When comparing these "structural relations" among many groups, they likely differ across the groups. However, it is equally likely that some groups share the same relations so that clusters of groups emerge. Latent variables are measured indirectly by questionnaires and, for validly comparing their relations among groups, the measurement of the latent variables should be invariant across the groups (i.e., measurement invariance). However, across many groups, often at least some measurement parameters differ. Restricting these measurement parameters to be invariant, when they are not, causes the structural relations to be estimated incorrectly and invalidates their comparison. We propose mixture multigroup SEM (MMG-SEM) to gather groups with equivalent structural relations in clusters while accounting for the reality of measurement noninvariance. Specifically, MMG-SEM obtains a clustering of groups focused on the structural relations by making them cluster-specific, while capturing measurement noninvariances with group-specific measurement parameters. In this way, MMG-SEM ensures that the clustering is valid and unaffected by differences in measurement. This article proposes an estimation procedure built around the R package "lavaan" and evaluates MMG-SEM's performance through two simulation studies. The results demonstrate that MMG-SEM successfully recovers the group-clustering as well as the cluster-specific relations and the partially group-specific measurement parameters. To illustrate its empirical value, we apply MMG-SEM to cross-cultural data on the relations between experienced emotions and life satisfaction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).