Fitting Candidate Research Articles

High-Performance Telescope System Design for Space-Based Gravitational Waves Detection.

Space-based gravitational wave (GW) detection employs the Michelson interferometry principle to construct ultra-long baseline laser interferometers in space for detecting GW signals with a frequency band of 10-4-1 Hz. The spaceborne telescope, as a core component directly integrated into the laser link, comes in various configurations, with the off-axis four-mirror design being the most prevalent. In this paper, we present a high-performance design based on this configuration, which exhibits a stable structure, ultra-low wavefront aberration, and high-level stray light suppression capabilities, effectively eliminating background noise. Also, a scientifically justified positioning of the entrance and exit pupils has been implemented, thereby paving adequate spatial provision for the integration of subsequent optical systems. The final design realizes a wavefront error of less than λ/500 in the science field of view, and after tolerance allocation and Monte Carlo analysis, a wavefront error of less than λ/30 can be achieved with a probability of 92%. The chief ray spot diagram dimensions are significantly small, indicating excellent control of pupil aberrations. Additionally, the tilt-to-length (TTL) noise and stray light meet the stringent requirements for space-based gravitational wave detection. The refined design presented in this paper proves to be a more fitting candidate for GW detection projects, offering more accurate and rational guidance.

Development of a recombinant non-replicating Newcastle disease virus

ABSTRACT Newcastle disease virus (NDV) is an avian orthoavulavirus I that causes high morbidity and mortality in birds, depending on the pathotype of the virus. Several clinical studies have shown that NDV is a promising oncolytic virus for cancer therapies, yet its application to clinics is restricted due to the pathogenicity of this virus in poultry. Hence, it is important to produce a recombinant NDV (rNDV) with reduced virulence to make it a fit candidate as an oncolytic virus. In this study, we aimed to produce a rNDV with a truncated ant-igenome via reverse genetics to render it non-replicating (nr). The genes responsible for replication were removed from the NDV genome. The rescued nr-NDV was verified, propagated, and purified from tissue culture medium. The virus failed to propagate in embryonated eggs, confirming its non-replicating property. The virus retained its oncolytic activities similar to that of the replicating rAF-GFP as observed in a cell viability assay when a breast cancer cell line, MCF-7, was treated with the virus. In conclusion, this study has successfully generated a system to produce a non-replicating NDV which can be used as a viral vector for heterologous protein delivery, as a safer alternative compared to the virulent strains as an oncolytic virus. RESEARCH HIGHLIGHTS Development of nr-NDV. Reverse transfection was applied for the recovery of nr-NDV. Propagation of nr-NDV was done by sub-passaging transfected BSR T7/5 cells. Safety profile was done to prove that the nr-NDV is non-replicating.

Single-drive electro-optic frequency comb source on a photonic-wire-bonded thin-film lithium niobate platform.

Stable pulse and flat-top frequency comb generation are an indispensable component of many photonic applications, from ranging to communications. Lithium niobate on insulator is an excellent electro-optic (EO) platform, exhibiting high modulation efficiency and low optical loss, making it a fitting candidate for pulse generation through electro-optic modulation of continuous-wave (CW) light, a commonly utilized method for generating ultrashort pulses. Here, we demonstrate an on-chip electro-optic comb generation module on thin-film lithium niobate (TFLN) consisting of a Mach-Zehnder interferometer (MZI) amplitude modulator (AM) and a cascaded phase modulator (PM) system driven by a single-electrode drive. We show that when operated in the correct regime, the lithium niobate chips can generate frequency combs with excellent spectral power flatness. In addition, we optically package one of the pulse generator chips via photonic wire bonding. The pulses generated by the photonic-wire-bonded device are compressed to 840 fs pulse duration using an optical fiber and show extremely stable operation.

Rigidity of pressures of Hölder potentials and the fitting of analytic functions through them

AbstractThe first part of this work is devoted to the study of higher derivatives of pressure functions of Hölder potentials on shift spaces with finitely many symbols. By describing the derivatives of pressure functions via the central limit theorem for the associated random processes, we discover some rigid relationships between derivatives of various orders. The rigidity imposes obstructions on fitting candidate convex analytic functions by pressure functions of Hölder potentials globally, which answers a question of Kucherenko and Quas. In the second part of the work, we consider fitting candidate analytic germs by pressure functions of locally constant potentials. We prove that all 1-level candidate germs can be realised by pressures of some locally constant potentials, as long as the number of symbols in the symbolic set is large enough. There are also some results on fitting 2-level germs by pressures of locally constant potentials obtained in the work.

Efficient microwave-assisted synthesis of 1-hexylpyridin-1-ium bromide: Anion exchange to acetate and fundamentals tools to probe its interaction with RuCl3

The study presents a robust and efficient method for synthesizing high-purity 1-hexylpyridin-1-ium acetate (1-HP-Ac), a room-temperature ionic liquid (RTIL), using microwave synthesis. The study also explores its interaction with ruthenium trichloride (RuCl3). Initially, we employed classical solvothermal methods to obtain 1-hexypyridin-1-ium bromide (1-HP-Br), a precursor for 1-HP-Ac. Issues related to impurity and time constraints necessitated a transition to microwave-assisted synthesis, which emerged as an eco-friendly, energy-efficient approach. Microwave synthesis proved superior by delivering shorter reaction times (30 min), higher yields, and eliminating the need for solvents or inert atmospheres. Various characterization techniques (1H NMR, 13C NMR, FTIR, TGA, and HRMS) and matching quantum mechanical NMR predictions confirmed the successful synthesis of the hexylpyridinium cation and anion exchange transforming 1-HP-Br into its acetate form. The interaction between the synthesized RTIL and RuCl3 was monitored through NMR-based titrations and molecular dynamics (MD) modeling. The study explored the effects of increasing amounts of either RTIL or RuCl3 with the critical inflection point at a 1:2 ratio of 1-HP-Ac to RuCl3 for effective interactions and potential changes in average molecular conformation. Specifically, examining Karplus J-coupling constants provided information about dihedral angles within 1-HP-Ac. The data suggested that RuCl3 has a discernible influence on these angles, particularly for aliphatic protons. With thermal stability up to 200 °C, 1-HP-Ac is a fitting candidate for high-temperature reactions. MD simulation quantifies the solvation environment created by the IL as well as elucidates interactions of 1-HP-Ac with the RuCl3 crystal structure. The insights gained from our research into 1-HP-Ac's interaction with RuCl3, combined with the efficiency of microwave-assisted synthesis, open new avenues for further studies in catalysis, material science, and related fields..

Increment of Academic Performance Prediction of At‐Risk Student by Dealing With Data Imbalance Problem

Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. This approach has a fatal weakness when it is used for disadvantaged groups, such as those with academic warnings or who have dropped out, because these groups are often much smaller than other common groups in number. The imbalanced data that have class distribution skew create a big challenge to training good classification models. The significant approach to tackle this challenge is applying oversampling methods to increase the number of minor classes; however, generating good new samples from the existing instances of a minor class is still a hard issue and requires new investigation. This study presents two new methods of handling data imbalance based on the original algorithms SMOTE and adaptive synthetic sampling (ADASYN), called Improved SMOTE (I_SMOTE) and Improved ADASYN (I_ADASYN). These modifications involve a new selecting fit candidate method based on a new similarity measurement and a roulette wheel selection to generate synthetic data samples. The aim is to rebalance data and therefore improve the prediction accuracy of minor groups. The proposal methods were designed and applied to education datasets, and they were tested on public datasets and a dataset collected from a Vietnamese university for evaluation. The experimental results on learning datasets showed the high potential of novel algorithms, I_SMOTE and I_ADASYN, for student academic performance problems in general and at‐risk student groups especially. Empirical results proved that the recall, precision, and F1‐score of the minority class of I_SMOTE and I_ADASYN are strongly better than the original balancing algorithms. Besides, the I_SMOTE and I_ADASYN also improve relatively by 6.6% and 8.0% of the ROC area compared to the original SMOTE and ADASYN, respectively.

Selection and thermophysical assessment of phase change materials (PCMs) for space cooling applications in buildings

Phase change materials are gaining more attention in building comfort applications because of their latent heat thermal energy storage (TES) capacity. Identifying the best PCM candidate is a pivotal factor that significantly impacts the thermal performance of a building. This study delves into assessing prospective PCM candidates for potential utilization in cooling systems within buildings in hot subtropical climates. Extending the application of multicriteria decision-making (MCDM) methodologies, this research scrutinizes various commercial PCMs by conducting an exhaustive analysis of pertinent literature in the context of this specific application objective. Thermophysical properties are integrated into the MCDM framework to identify the most fitting candidates with optimal properties and attributes. The properties and attributes of PCMs are subjected to scoring and weighting through the AHP based on their significance in the given application context. TOPSIS and VIKOR techniques select the suitable PCM from the identified PCMs. The results showed that latent heat receives the highest weightage, amounting to 50.2%, due to its paramount importance. Furthermore, OM37 emerged as a promising choice for building space cooling, considering the prescreened temperature range of 30 °C–40 °C and for the specific location under consideration. Following PCM selection, thorough characterization was performed to evaluate the suitability of the chosen PCM for integration within building envelopes. The thermal conductivity of OM37 was determined to be 0.20 W/mK, while the morphology by a Field Emission Scanning Electron Microscope (Fe-SEM) intricate porous structure. Furthermore, a differential scanning calorimeter (DSC) suggests melting and solidification temperatures of 40 °C and 34 °C with a total enthalpy of 197.04 j/g, and a Thermogravimetry Differential Thermal Analysis (TG/DTA) showed a substantial weight loss of approximately 98.9% at around 247.3 °C. The presented experimental test results agree with the literature data and other researchers’ results.

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.

Impact of organic dye-photosensitizer on TiO2 nanocrystalline surface for high performance organic electronic devices: A computational insight

Using computational modelling methods as density functional theory (DFT) and time-dependent density functional theory (TD-DFT), the optoelectronic properties of different compounds from Thiophene-Cyanoacrylic-Acid derivatives have been performed for organic electronic devices. New challenges in specific properties of organic-π-conjugated system of Thiophene-Cyanoacrylic-Acid derivatives are of great significance since they have become the most encouraging for the organic electronic device technology such as solar cells. The use of electronic excitations and reorganization energies (λ) are viable methods for increasing its efficiency and enhanced harvesting of the solar spectrum. Different Donor parts are anchoring to the Thiophene-Cyanoacrylic-Acid Acceptor part for their high electron extraction to semi-conductor devices. The impact of Acceptor parts in each π-conjugated systems, were investigated in their isolated form as well as upon adsorption on TiO2 cluster surface in chelating state [dye-(TiO2)16]. For the three series of dyes studied in this investigation: D–A–π–A, D–π–π–A and D–A-π–π–A, the adsorptions were effective on the TiO2 surface. The calculated results suggest that the HOMO-LUMO energy gap (ΔEgap), ionization potential (IP), electron affinity (EA), reorganization energies (electron and hole) (λ), open-circuit voltage (Voc), fill factor (FF), short-circuit current density (Jsc), light-harvesting efficiency (LHE), dye regeneration driving force (ΔGregn), electron injection driving force (ΔGinjec), and density of state (DOS) are affected by Acceptor introducing groups in different Thiophene-Cyanoacrylic-Acid Derivatives. Moreover, our investigation shows that these considered novel optoelectronic Photosensitizer dyes can exhibit enhanced organic electronic parameters and are fitting candidates for preparation of high-performance charge-transport materials.

Back to the features: assessing the discriminating power of future CMB missions on inflationary models

Future Cosmic Microwave Background (CMB) experiments will deliver extremely accurate measurements of the E-modes pattern of the CMB polarization field. Given the sharpness of the E-modes transfer functions, such surveys make for a powerful detector of high-frequency signals from primordial features that may be lurking in current data sets. With a handful of toy models that increase the fit to the latest Planck data, but are of marginal statistical significance, we use a state-of-the-art forecast pipeline to illustrate the promising prospects to test primordial features in the next decade. Not only will future experiments allow us to detect such features in data, but they will also be able to discriminate between models and narrow down the physical mechanism originating them with high statistical significance. On the other hand, if the anomalies in the currently measured CMB spectra are just statistical fluctuations, all the current feature best fit candidates will be ruled out. Either way, our results show that primordial features are a clear target of forthcoming CMB surveys beyond the detection of tensor modes.

InLiquido Computation with Electrochemical Transistors and Mixed Conductors for Intelligent Bioelectronics.

Next-generation implantable computational devices require long-term-stable electronic components capable of operating in, and interacting with, electrolytic surroundings without being damaged. Organic electrochemical transistors (OECTs) emerged as fitting candidates. However, while single devices feature impressive figuresof merit, integrated circuits (ICs) immersed in common electrolytes are hard to realize using electrochemical transistors, and there is no clear path forward for optimal top-down circuit design and high-density integration. The simple observation that two OECTs immersed in the same electrolytic medium will inevitably interact hampers their implementation in complex circuitry. The electrolyte's ionic conductivity connects all the devices in the liquid, producing unwanted and often unforeseeable dynamics. Minimizing or harnessing this crosstalk has been the focus of very recent studies. Herein, the main challenges, trends, and opportunities for realizing OECT-based circuitry in a liquid environment that could circumnavigate the hard limits of engineering and human physiology, are discussed. The most successful approaches in autonomous bioelectronics and information processing are analyzed. Elaborating on the strategies to circumvent and harness device crosstalk proves that platforms capable of complex computation and even machine learning (ML) can be realized inliquido using mixed ionic-electronic conductors (OMIECs).

Р. В. Кунья. Буду ли я когда-то киборгом? / пер. с англ. Р. Л. Кочнева

Eric Olson’s animalist view relies on the premise that person is not a fit candidate to be a substance concept, in Wiggins’s terminology. Instead, he claims, animal is what best serves as the answer to what we most fundamentally are and what determines our persistence conditions. Pro-posing a thought experiment concerning inorganic replacement, I aim to show that Olson’s animalist view cannot accommodate our very strong intuitions about such cases. My claim is then that animalism either fails on its own grounds or requires some tuning regarding what exactly an organism is and its persistence conditions. I will examine Matthew Liao’s attempt to accommodate such intuitions within an animalist view. I will also describe the basics about Olson’s animalist view but let us first look at the rough outline of my thought experiment. The essentials are as follows, the rest I will provide later, as we go along.

Topology Identification of Radial Distribution Networks Using Smart Meter Data

This article proposes a novel approach for identifying the topology of radial distribution networks. It uses smart meter measurements from loads (e.g., houses) of the network to estimate the voltage sensitivity coefficients with respect to changes in the loads currents. These coefficients are then used to reconstruct the network topology. An enhanced graph learning algorithm with backtracking is proposed to generate a cluster of graphs, from which the best fitting candidate is selected using a set of optimization criteria. The performance of the proposed method is first assessed using the European Low-Voltage IEEE test case, followed by an extensive performance evaluation in noisy conditions on randomly generated topologies. Finally, measurements from a real low-voltage network in Australia are used to validate the method.

Novel Therapies for Unmet Clinical Needs in Myelodysplastic Syndromes.

Simple SummarySeveral novel therapies are being developed to improve the management of patients with myelodysplastic syndromes. They include drugs aimed at improving hematopoiesis and differentiation of myeloid precursors, hypomethylating agents, several compounds that target intracellular molecular pathways, and immunotherapies. In this review article, we discuss how the novel drugs may address the several unmet needs of lower- and higher-risk patients.Myelodysplastic syndromes (MDS) are a very heterogeneous disease, with extremely variable clinical features and outcomes. Current management relies on risk stratification based on IPSS and IPSS-R, which categorizes patients into low (LR-) and high-risk (HR-) MDS. Therapeutic strategies in LR-MDS patients mainly consist of erythropoiesis stimulating agents (ESAs), transfusion support, and luspatercept or lenalidomide for selected patients. Current unmet needs include the limited options available after treatment failure, and the consequent transfusion burden with several hospital admissions and poor quality of life. Therapeutic approaches in HR-MDS patients are aimed at changing the natural course of the disease and hypometylating agents (HMA) are the first choice. The only potentially curative treatment is allogeneic stem cell transplant (allo-HCT), restricted to a minority of young and fit candidates. Patients unfit for or those that relapse after the abovementioned options harbor an adverse prognosis, with limited overall survival and frequent leukemic evolution. Recent advances in genetic mutations and intracellular pathways that are relevant for MDS pathogenesis are improving disease risk stratification and highlighting therapeutic targets addressed by novel agents. Several drugs are under evaluation for LR and HR patients, which differ by their mechanism of action, reported efficacy, and phase of development. This review analyzes the current unmet clinical needs for MDS patients and provides a critical overview of the novel agents under development in this setting.

Geodesic deviation in Sáez–Ballester theory

We study the geodesic deviation (GD) equation in a generalized version of the Sáez–Ballester (SB) theory in arbitrary dimensions. We first establish a general formalism and then restrict to particular cases, where (i) the matter-energy distribution is that of a perfect fluid, and (ii) the spacetime geometry is described by a vanishing Weyl tensor. Furthermore, we consider the spatially flat FLRW universe as the background geometry. Based on this setup, we compute the GD equation as well as the convergence condition associated with fundamental observers and past directed null vector fields. Moreover, we extend that framework and extract the corresponding geodesic deviation in the modified Sáez–Ballester theory (MSBT), where the energy–momentum tensor and potential emerge strictly from the geometry of the extra dimensions. In order to examine our herein GD equations, we consider two novel cosmological models within the SB framework. Moreover, we discuss a few quintessential models and a suitable phantom dark energy scenario within the mentioned SB and MSBT frameworks. Noticing that our herein cosmological models can suitably include the present time of our Universe, we solve the GD equations analytically and/or numerically. By employing the correct energy conditions plus recent observational data, we consistently depict the behavior of the deviation vector η ( z ) and the observer area distance r 0 ( z ) for our models. Concerning the Hubble constant problem, we specifically focus on the observational data reported by the Planck collaboration and the SH0ES collaboration to depict η ( z ) and r 0 ( z ) for our herein phantom model. Subsequently, we contrast our results with those associated with the Λ CDM model. We argue that the MSBT can be considered as a fitting candidate for a proper description of the late evolution of the universe.

Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs

Quantum Error Correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their hardware implementation has not been presented yet. This work presents a space exploration of fully-connected feed-forward NN decoders for small distance surface codes. The goal is to optimize the neural network for high decoding performance, while keeping a minimalistic hardware implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We demonstrate that hardware based NN-decoders can achieve high decoding performance comparable to other state-of-the-art decoding algorithms whilst being well below the tight delay requirements $(\approx 440\ \mathrm{ns})$ of current solid-state qubit technologies for both ASIC designs $(<30\ \mathrm{ns})$ and FPGA implementations $(<90\ \mathrm{ns})$. These results designates NN-decoders as fitting candidates for an integrated hardware implementation in future large-scale quantum computers.

Inflation story: slow-roll and beyond

We present constraints on inflationary dynamics and features in the primordial power spectrum of scalar perturbations using the Cosmic Microwave Background temperature, polarization data from Planck 2018 data release and updated likelihoods. We constrain the slow-roll dynamics using Hilltop Quartic Potential and Starobinsky R + R 2 model in the Einstein frame using the Planck 2018 binned Plik likelihood. Using the Hilltop as base potential, we construct Whipped Inflation potential to introduce suppression in the scalar power spectrum at large angular scales. We notice marginal (68% C.L.) preference of suppression from the large scale temperature angular power spectrum. However, large-scale E-mode likelihood based on high frequency instrument cross spectrum, does not support this suppression and in the combined data the preference towards the suppression becomes negligible. Based on the Hilltop and Starobinsky model, we construct the Wiggly Whipped Inflation potentials to introduce oscillatory features along with the suppression. We use unbinned data from the recently released CamSpec v12.5 likelihood which updates Planck 2018 results. We compare the Bayesian evidences of the feature models with their baseline slow-roll potentials. We find that the complete slow-roll baseline potential is moderately preferred against potentials which generate features. Compared to Planck 2015 PlikHM bin1 likelihood, we find that the significance of sharp features has decreased owing to the updates in the data analysis pipeline. We also compute the bispectra for the best fit candidates obtained from our analysis.

Fertility Practitioners’ Coping Strategies When Faced with Intra-Role Conflict from Screening Aspiring Single Mothers by Choice

Women without a partner can become single mothers by choice through the use of fertility treatments. In Belgium, the decision to accept a candidate single mother by choice rests with the fertility clinic’s multidisciplinary team of fertility practitioners. As a result, the fertility practitioners fulfil a gatekeeping role. However, this can cause an intra-role conflict as the responsibility to select the best fitting candidates is at odds with the responsibility to help patients. In this explorative study, we examine how fertility practitioners cope with the strain resulting from intra-role conflict in the decision-making process regarding single motherhood by choice in Belgium. The findings showed that practitioners appear to mainly resort to problem-focused coping, by constructing a grassroots criteria list and by shifting their role from screening agent to counsellor. These results are based on ten open in-depth interviews with fertility practitioners employed in the multidisciplinary teams of fertility centers, using a reflexive interview lead.

EP.TU.581Cholecysto-colonic fistula – A rare complication of gallstone

Abstract We present a 90year old male patient presenting to the emergency department with complaints of PR bleed for a month with no associated abdominal pain. He described that the blood was mixed with stools and dark in colour. He has a significant past medical history with multiple comorbidities. The patient is known to have gallstones and had ERCP with sphincterotomy in 2017 as a definitive treatment, as he wasn’t a fit candidate for surgical intervention. On clinical examination, his abdomen was soft and non-tender and per rectal examination showed stools mixed with the blood but no active bleeding or fresh blood. His haemoglobin was 72 g/L, inflammatory markers were significantly elevated with deranged liver enzymes and normal bilirubin. The medical team were involved in the management of this patient because of pneumonia and extensive medical issues. A gastroscopy was performed as there was a suspicion of UGI bleed, which was normal. Given a deranged liver function and there was a suspicion of biliary sepsis patient had MRCP and Computed tomography of the abdomen which confirmed that there is haematoma in the gallbladder with gas in the biliary tree, with possible cholecysto-colonic fistula, with a large gall stone (2.7cm lamellated structure within rectum) in the rectum. As the patient was not a surgical candidate decision was made to manage him conservatively with antibiotics under medical care. The cholecysto-colonic fistula is a rare complication of gallstone disease, and very few cases have been reported in the literature.

Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer

We develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operator regression problem that characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier–Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using these data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.