True Peak Research Articles

Non-monotonic effect of differential stress and temperature on mechanical property and rockburst proneness of granite under high-temperature true triaxial compression

Complicated geological conditions such as high geo-stress and high ground temperatures often have a significant impact on the mechanical behavior and failure potential of hard rocks in deep engineering. This study aims to investigate the mechanical behaviors and rockburst proneness of granite under high differential stress and high temperatures in deep-buried high-temperature tunnels. A comprehensive experimental study was conducted on the coupling behavior of granite under high-temperature and high differential stress by true triaxial compression tests. The strength and failure mechanism of granite as well as their relationship with high temperature and high differential stress were revealed. The rockburst proneness of granite from a deep-buried high geothermal tunnel was assessed based on the rock's ultimate energy storage characteristics. It shows the strength, failure mechanisms, and rockburst proneness of granite, are significantly correlated to the granite high-temperature high-stress conditions. The temperatures extend the strengthening range of the intermediate principal stress on the true triaxial peak strength of the granite. With increasing temperature and differential stress, secondary vertical cracks develop quickly to induce the macroscopic failure of granite. Under high temperatures, the failure of granite changes from compressive-shear failure to tensile-shear failure at low differential stress. High temperature coupled with high differential stress strengthens approximately 1.14 times the rockburst proneness of granite. The results are important for the safe construction, and disaster assessment of deep-buried high geothermal tunnels.

Understanding coaxial photodiode-based multispectral pyrometer measurements at the overhang regions in laser powder bed fusion for part qualification

Coaxial photodiode monitoring sensors provide a digital signature at the melt pool level in laser powder bed fusion (L-PBF), facilitating faster part qualification. However, current signatures are plagued by significant noise and signal variation and show counterintuitive trends such as a decrease in measured temperature near overhang surfaces. This paper investigates the behavior of coaxial photodiode-based melt pool monitoring (PD-MPM) thermal measurements at overhang regions by conducting a combination of experiments and multiphysics simulations. High-speed in situ synchrotron X-ray imaging is coupled with a coaxial photodiode system to enable comparison of the observed melt pool phenomena and monitoring signals during double-track AlSi10Mg experiments. A multiphysics model is developed to simulate the melt pool dynamics and concurrent sensor signals throughout the process. We propose a surrogate model that clarifies the correlation between sensor signals and melt pool temperature. Both experimental and simulation results emphasize the significant impacts of laser energy and keyhole formation on solid-liquid interface discontinuities and PD-MPM signals. Results reveal that a boiling region with a relatively smaller projected area, as near an overhang, can lead to a decrease in the measured melt pool temperature, even when the true peak temperature remains constant, meaning that PD-MPM temperature measurements cannot be used to estimate absolute melt pool temperature. Consequently, in overhang regions, interaction between the melt pool and underlying powder results in an abruptly deforming melt pool and a pronounced decrease in both individual photodiode intensities and overall measured melt pool temperature. This work illustrates how to correctly interpret the coaxial photodiode monitoring signals in L-PBF by uncovering the intricate dynamics within the melt pool, particularly in challenging overhang regions, and pave the foundation for data-driven part qualification.

Impacts of distributed thermal and electric contact resistance on performance and geometric optimization of thermoelectric generators

Thermal and electric contact resistance (TCR/ECR) critically impact performance and geometric optimization of thermoelectric generators (TEGs). However, conventional treatments usually ignored or simplified them as lumped variables, neglecting their actual distributions across the TEG system. In this study, we proposed a multi-physical model to characterize TEG performance with explicitly specifying TCRs/ECRs at different TEG interfaces (locations). The numerical results show that the lumped-variabletreatment led to maximal overestimations of 16.9 % and 24.5 % in the TEG output power and efficiency, respectively, compared to the results with distributed TCR in this article. Importantly, it also reveals that the TEG performance was susceptible to the TCR location—the interfaces on the cold side exerted more negative impacts than those on the hot side. Furthermore, reducing both TCR and ECR could improve TEG performance and reducing TCR is more effective. It is shown that an 80 % reduction in TCR increased the maximum TEG output power by 35.6 %, while the same reduction percentage in ECR only improved it by 8.8 %. As to geometric optimization, an optimal TE leg height equal to 0.6 mm was obtained for the maximum output power. This contrasts with previous studies without considering TCR and ECR, which always favoured shorter heights. As for copper electrodes, their optimal heights were in the range of 0.2–0.4 mm corresponding to the maximum efficiency, far smaller than those (0.7–1.2 mm) obtained when TCR/ECR were neglected. The latter even further resulted in a reduction in the maximum efficiency by more than 1 % compared to its true peak. In this study, all these numerical results clearly elucidate the important impacts of distributed TCR and ECR on TEG performance, and provide a comprehensive and balanced guideline for TEG design.

An HST survey of 33 T8 to Y1 brown dwarfs: NIR photometry and multiplicity of the coldest isolated objects

ABSTRACT We present results from a Hubble Space Telescope imaging search for low-mass binary and planetary companions to 33 nearby brown dwarfs with spectral types of T8–Y1. Our survey provides new photometric information for these faint systems, from which we obtained model-derived luminosities, masses, and temperatures. Despite achieving a deep sensitivity to faint companions beyond 0.2–0.5 arcsec, down to mass ratios of 0.4–0.7 outside ∼5 au, we find no companions to our substellar primaries. From our derived survey completeness, we place an upper limit of $f \lt 4.9~{{\ \rm per\ cent}}$ at the 1σ level (<13.0 per cent at the 2σ level) on the binary frequency of these objects over the separation range 1–1000 au and for mass ratios above q = 0.4. Our results confirm that companions are extremely rare around the lowest mass and coldest isolated brown dwarfs, continuing the marginal trend of decreasing binary fraction with primary mass observed throughout the stellar and substellar regimes. These findings support the idea that if a significant population of binaries exist around such low-mass objects, it should lie primarily below 2–3 au separations, with a true peak possibly located at even tighter orbital separations for Y dwarfs.

Radar Maneuvering Target Detection Based on Product Scale Zoom Discrete Chirp Fourier Transform

Long-time coherent integration works to significantly increase the detection probability for maneuvering targets. However, during the observation time, the problems that often tend to occur are range cell migration (RCM) and Doppler frequency cell migration (DFCM), due to the high velocity and acceleration of the maneuvering target, which can reduce the detection of the maneuvering targets. In this regard, we propose a new coherent integration approach, based on the product scale zoom discrete chirp Fourier transform (PSZDCFT). Specifically, by introducing the zoom operation into the modified discrete chirp Fourier transform (MDCFT), the zoom discrete chirp Fourier transform (ZDCFT) can correctly estimate the centroid frequency and chirp rate of the linear frequency-modulated signal (LFM), regardless of whether the parameters of the LFM signal are outside the estimation scopes. Then, the scale operation, combined with ZDCFT, is performed on the radar echo signal in the range frequency slow time domain, to remove the coupling. Thereafter, a product operation is executed along the range frequency to inhibit spurious peaks and reinforce the true peak. Finally, the velocity and acceleration of the target estimated from the true peak position, are used to construct a phase compensation function to eliminate the RCM and DFCM, thus achieving coherent integration. The method is a linear transform without energy loss, and is suitable for low signal-to-noise (SNR) environments. Moreover, the method can be effectively fulfilled based on the chirp-z transform (CZT), which prevents the brute-force search. Thus, the method reaches a favorable tradeoff between anti-noise performance and computational load. Intensive simulations demonstrate the effectiveness of the proposed method.

Observations of GRB 230307A by TESS

We present the Transiting Exoplanet Survey Satellite (TESS) light curve of GRB 230307A. We find two distinct components: a bright, prompt optical component at the time of the Fermi observation that peaked at TESS magnitude 14.49 ± 0.05 (averaged over 200 s), followed by a gradual rise and fall over 0.5 day, likely associated with the afterglow, that peaked at 17.65 ± 0.06 mag. The prompt component is observed in a single 200 s Full Frame Image and was undetectable in the next TESS image (T mag > 17.79). Assuming that the onset of the optical transient was coincident with the gamma-ray emission, the prompt emission lasted less than 73.6 s, which implies the true peak was actually brighter than T mag = 13.40. We also fit parametric models to the afterglow to characterize its shape. The TESS TICA light curve can be retrieved at https://tess.mit.edu/public/tesstransients/light_curves/lc_grb230307A_cleaned.txt.

Instrumentation Techniques for the Measurement of Gunfire

The peak sound pressure from near-field small arms gunfire is commonly used when assessing a shooter's hearing damage risk. Close to a shooter's ear, the peak sound pressure level from a rifle or pistol can be 155-to-160 decibels with a pulse rise time on the order of ten microseconds. Standard dosimeters equipped with conventional measurement microphones significantly under-report the true peak levels from proximate gunfire; hence, the shooter's exposure is either unknown or underestimated. The U.S. Military has established exposure limits for small arms gunfire; however, the specified properties of the measurement instrumentation are more prescriptive than performance-based. The implication is that the practitioner is responsible for pre-qualifying his or her own instrumentation. For near-field gunfire, the leading cause of measurement error occurs when the wide-band signal from the acoustical transient is transduced by a relatively large-diameter measurement microphone having limited bandwidth. Secondary measurement errors include the finite bandwidth of the microphone signal amplifier and its slewing limit, the characteristics of the anti-aliasing filter used, and the sampling rate of the digital recording device. The paper discusses these instrumentation constraints and recommends minimum performance requirements to help attain reproducible measurements of small arms gunfire.

Hot Deformation Analysis of 100MnCrW4 Tool Steel

Analysis of the high temperature plastic behaviour of 100MnCrW4 tool steel was developed in the temperature range of 800 – 1200°C and the deformation rate in the range of 0.001 – 10 s−1 to the maximum value of the true strain 0.8. Microstructural changes were observed using light optical microscopy (LOM). The effect of hot deformation temperature on true stress, peak stress and true strain was evaluated by means of the flow curves. Based on these results, steel transformation was discussed from the dynamic recovery and recrystallization point of view. Furthermore, a present model, taking into account the Zener-Hollomon parameter was applied to predict the true stress and strain over a wide range of temperatures and strain rates. Based on the results, it can be stated that the proposed constitutive model suits our tool steel and can be applied to predict the behavior of the material during hot forming.

Composition design of a novel Ti-6Mo-3.5Cr-1Zr alloy with high-strength and ultrahigh-ductility

• A novel titanium alloy with high strength and ultrahigh ductility was designed to break through the longstanding strength-ductility trade-off. • The strong strain partitioning effect and the dislocation pile-up effect caused by SIω were found and quantitatively analyzed by in situ EBSD/DIC and in situ TEM tests, respectively. • The TRIP/TWIP/DPU coupling effect during plastic deformation was firstly proposed and deciphered. The strength-elongation to fracture ( ε f ) trade-off and low strain hardening rate have been a longstanding dilemma in titanium alloys. In this work, we innovatively manufactured a novel Ti-6Mo-3.5Cr-1Zr alloy via the introduction of a stress-induced strengthening phase into the material. Stress-induced ω (SI ω ) phase transformation was expected to replace the α'' martensitic transformation that resulted in the low yield strength of titanium alloys. The obtained alloy exhibited an extremely high strain hardening rate of up to ∼1820 MPa. The true peak tensile strength and ε f reached ∼1242 MPa and ∼40%, respectively. The intrinsic mechanisms underlying the simultaneous improvement of strength and ductility of the material were systematically investigated via in-situ and ex-situ characterizations. In-situ electron backscatter diffraction (EBSD)/digital image correlation (DIC) results showed that SI ω phase transformation dominated the early stage of plastic deformation (1.5%–3%) and promoted the strain partitioning between the stress-induced bands and β matrix. Subsequently, the formation of {332}<113> β twins and ω twins was observed via ex-situ EBSD. In-situ transmission electron microscopy results revealed that dislocation pile-up (DPU) occurred at the SI ω / β interface. The coupling effects associated with the transformation induced plasticity (TRIP), twinning induced plasticity (TWIP), and DPU mechanisms contributed to the enhanced strength and ε f of the designed titanium alloy.

Successful Desensitization of Severe Alloimmune Platelet Refractoriness and Provision of Permissive Platelet Transfusions with a Novel IgG-Targeted Enzyme Therapeutic

AK and JCS contributed equallyBACKGROUND Alloimmune platelet refractoriness (APR), inadequate response to platelet transfusion secondary to alloantibodies targeting Human Leukocyte Antigens (HLA), affects ~25% of acute myeloid leukemia (AML) patients. For profoundly sensitized thrombocytopenic patients, HLA- or crossmatch-compatible platelet units may be unavailable, increasing the risk of thrombocytopenic hemorrhage. Imlifidase cleaves systemic IgG precluding complement fixation/phagocytosis and has been used to desensitize renal allograft recipients.1 Here we show that desensitization with imlifidase can significantly improve compatible platelet availability in APR and post-hematopoietic stem cell transplant platelet nadir. PATIENT CASE A 43-year-old woman with AML treated with cytarabine and daunorubicin was referred for matched-unrelated donor allogeneic hematopoietic progenitor cell transplantation. Her induction, at an outside institution, was complicated by bilateral retinal and mucosal hemorrhage secondary to APR and profound thrombocytopenia (platelet count to 0/µL). On presentation, her panel reactive antibody (PRA) showed exquisite sensitization (99.9%, i.e. ~99.9% of all donor platelet units predicted incompatible), precluding identification of HLA-compatible platelet units to support the patient through her anticipated platelet nadir. Desensitization included rituximab 375 mg/m2 (transplant D-13 and -6), therapeutic plasma exchange (TPE, D-2 and -1) and FDA-emergency-use-authorized imlifidase 0.25 mg/kg IV (D-1 and D+1). Following imlifidase administration, the number of unacceptable antigens and PRA decreased significantly (Figure) allowing sufficient compatible platelets to be identified and procured. During admission, the patient received 7 total platelet transfusions and experienced no hemorrhage (Table). She was discharged stable, post-platelet engraftment on D+19. STUDY DESIGN With IRB approval, the number of predicted clinically significant alloantibodies (antibodies with ≥ 3000 mean fluorescence intensity, MFI) and PRA (LABScreen single antigen and PRA Class I, One Lambda/ThermoFisher, West Hills, California) were compared at baseline, after rituximab/TPE, and after imlifidase administration. Corrected count increment (CCI) to platelet transfusions administered following imlifidase desensitization was calculated. CCI > 5000 was judged a permissive transfusion. Categorical variables were compared with Chi-squared followed by Fisher's Exact. P < .05 was significant. RESULTS AND CONCLUSION Compared to the baseline, unacceptable antigens (antigens targeted by antibodies at clinically significant levels) decreased significantly after rituximab/TPE (N=53/55 to 44/55 antigens, P = .015) and imlifidase (N = 44/55 to 16/55, P < .0001). PRA did not decrease significantly with rituximab/TPE (99.9 to 99.5%, P = .22) but did following imlifidase (99.5 to 87.2%, P < .0001). Of 7 transfusions given post imlifidase, the median CCI was 6000 (interquartile range: 500-7500), N=5/7 were judged permissive. In summary, the imlifidase-mediated decrease in PRA significantly aided the identification of numerous compatible platelet units for this patient and prevented further thrombocytopenic hemorrhage. Our limited data suggest a potentially novel application of imlifidase for desensitization of highly platelet allo-refractory patients requiring multiple platelet transfusions over a short period. We will perform western blot analysis of antibodies from in vivo samples collected over the treatment course to compare antibody fragmentation over time, as partially cleaved immunoglobulin detected by our microbead-based assays may not be pathologic.2 Additionally, as the patient's initial antibody levels exceeded the range of linearity of the bead-based assay, serial dilution studies of patient serum over the treatment course will more precisely semi-quantitate the true peak antibody levels. REFERENCES 1. Jordan SC, Legendre C, Desai NM, et al. Imlifidase desensitization in crossmatch-positive, highly sensitized kidney transplant recipients: results of an international phase 2 trial (Highdes). Transplantation. 2021;105(8):1808-1817. 2. Bockermann R, Järnum S, Runström A, et al. Imlifidase-generated single-cleaved IgG: implications for transplantation. Transplantation. 2022;106(7):1485-1496. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Modeling COVID-19 dynamics in the sixteen West African countries

The COVID-19 pandemic is currently causing several damages to the world, especially in the public health sector. Due to identifiability problems in parameters’ estimation of complex compartmental models, this study considered a simple deterministic susceptible–infectious–recovered (SIR)-type model to characterize the first wave and predict the future course of the pandemic in the West African countries. We estimated some specific characteristics of the disease’s dynamics, such as its initial conditions, reproduction numbers, true peak and peak of the reported cases, with their corresponding times, final epidemic size and time-varying attack ratio. Our findings revealed a relatively low proportion of susceptible individuals in the region and the different countries (1.2% across West Africa). The detection rate of the disease was also relatively low (0.9% for West Africa as a whole) and <2% for most countries, except for Gambia (12.5 %), Cape-Verde (9.5%), Mauritania (5.9%) and Ghana (4.4%). The reproduction number varied between 1.15 (Burkina-Faso) and 4.45 (Niger), and most countries’ peak time of the first wave of the pandemic was between June and July. Generally, the peak time of the reported cases came a week (7-8 days) after the true peak time. The model predicted for the first wave, 222,100 actual active cases in the region at the peak time, while the final epidemic size accounted for 0.6% of the West African population (2,526,700 individuals). The results showed that COVID-19 has not severely affected West Africa as in other regions. However, current control measures and standard operating procedures should be maintained over time to accelerate a decline in the observed trends of the pandemic.

Contrast media timing optimization for coronary CT angiography: a retrospective validation study in swine

ObjectivesThe objective was to retrospectively develop a protocol in swine for optimal contrast media timing in coronary CT angiography (CCTA).MethodsSeveral dynamic acquisitions were performed in 28 swine (55 ± 24 kg) with cardiac outputs between 1.5 and 5.5 L/min, for 80 total acquisitions. The contrast was injected (1mL/kg, 5mL/s, Isovue 370), followed by dynamic scanning of the entire aortic enhancement curve, from which the true peak time and aortic and coronary enhancements were recorded as the reference standard. Each dataset was then used to simulate two different CCTA protocols—a new optimal protocol and a standard clinical protocol. For the optimal protocol, the CCTA was acquired after bolus tracking-based trigging using a variable time delay of one-half the contrast injection time interval plus 1.5 s. For the standard protocol, the CCTA was acquired after bolus tracking-based triggering using a fixed time delay of 5 s. For both protocols, the CCTA time, aortic enhancement, coronary enhancement, and coronary contrast-to-noise ratio (CNR) were quantitatively compared to the reference standard measurements.ResultsFor the optimal protocol, the angiogram was acquired within −0.15 ± 0.75 s of the true peak time, for a mean coronary CNR within 7% of the peak coronary CNR. Conversely, for the standard CCTA protocol, the angiogram was acquired within −1.82 ± 1.71 s of the true peak time, for a mean coronary CNR that was 23% lower than the peak coronary CNR.ConclusionsThe optimal CCTA protocol improves contrast media timing and coronary CNR by acquiring the angiogram at the true aortic root peak time.Key Points• This study in swine retrospectively developed the mathematical basis of an improved approach for optimal contrast media timing in CCTA.• By combining dynamic bolus tracking with a simple contrast injection timing relation, CCTA can be acquired at the peak of the aortic root enhancement.• CCTA acquisition at the peak of the aortic root enhancement should maximize the coronary enhancement and CNR, potentially improving the accuracy of CT-based assessment of coronary artery disease.

CNN-based Two Step R Peak Detection Method: Combining Segmentation and Regression.

For semantic segmentation, U-Net provides an end-to-end trainable framework to detect multiple class objects from background. Due to its great achievements in computer vision tasks, U-Net has broadened its application to biomedical signal processing, especially, segmentation of waveforms in ECG signal. Despite its superior performance for QRS complex detection to other traditional signal processing methods, direct application of the U-Net to R peak detection has limitation since the U-Net structures tend to predict high probability around true peak. Such multiple detection results require additional process to determine a unique peak location in each QRS complex. In this study, we use a regression process to detect R peak instead of pixel-wise classification. Such regression process guarantees a unique peak location prediction. We collect data from resting ECG systems and wearable ECG devices as well as public ECG databases and the proposed model is trained on various combinations of the data sources. Especially, we investigate the robustness of the model for input data from the wearable devices when the model is trained by data from heterogeneous devices.

Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

BackgroundProspective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics.MethodsAn age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions.ResultsThe prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak.ConclusionsCaution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.

Influence of functionalized carbon flake on phase change characteristics of deionized (DI) water for cool thermal storage system

The current work aims to study the influence of thermal-resistance (R) and the time constant (τ) in phase change properties of carbon flake-based phase change materials (CPCM) experimentally. The CPCMs were prepared using chemically functionalized graphene flake (f-GP) in three various mass fractions (0.25%, 0.50%, and 0.75%) suspended in deionized (DI) water. The phase transition characteristics were recorded using DSC214-polyma with a heat-flow rate of 1 K/min. The remarkable variation in the peak crystallization and melting during charging and discharging processes were recorded with the combined τ -R correction method. The true peak during charging (−12.21 °C) and discharging (3.91 °C) processes is recorded through the correction of time constant for CPCM with 0.75% of f-GP. In addition, the CPCM with 0.75% f-GP has a maximum reduction in melting and freezing time of 36.01% and 27.75%, respectively. The prediction of phase change properties (peak crystallization and melting temperatures) is very important to design a cool thermal energy storage (CTES) system with maximum storage density. The selection of correct operating temperature of the CTES system reduces the energy input to the chiller plant.

Facial Input Decompositions for Robust Peak Estimation under Polyhedral Uncertainty

This work bounds extreme values of state functions for a class of input-affine continuous-time systems that are affected by polyhedral-bounded uncertainty. Instances of these systems may arise in data-driven peak estimation, in which the state function must be bounded for all systems that are consistent with a set of state-derivative data records corrupted under L-infinity bounded noise. Existing occupation measure-based methods form a convergent sequence of outer approximations to the true peak value, given an initial set, by solving a hierarchy of semidefinite programs in increasing size. These techniques scale combinatorially in the number of state variables and uncertain parameters. We present tractable algorithms for peak estimation that scale linearly in the number of faces of the uncertainty-bounding polytope rather than combinatorially in the number of uncertain parameters by leveraging convex duality and a theorem of alternatives (facial decomposition). The sequence of decomposed semidefinite programs will converge to the true peak value under mild assumptions (convergence and smoothness of dynamics).

An Optimization Method of Ambiguity Function Based on Multi-Antenna Constrained and Application in Vehicle Attitude Determination.

The global navigation satellite system (GNSS)-based multi-antenna attitude determination method has the advantages of a simple algorithm and no error accumulation with time in long endurance operation. However, it is sometimes difficult to simultaneous obtain the fixed solutions of all antennas in vehicle attitude determination. If float or incorrect fixed solutions are used, precision and reliability of attitude cannot be guaranteed. Given this fact, a baseline-constrained ambiguity function method (BCAFM) based on a self-built four GNSS antennas hardware platform is proposed. The coordinates obtained by BCAFM can replace the unreliable real-time kinematic (RTK) float or incorrect fixed solutions, so as to assist the direct method for attitude determination. In the proposed BCAFM, the baseline constraint is applied to improve search efficiency (searching time), and the ambiguity function value (AFV) formula is optimized to enhance the discrimination of true peak. The correctness of the proposed method is verified by vehicle attitude determination results and baseline length difference. Experimental results demonstrate that the function values of error peaks are reduced, and the only true peak can be identified accurately. The valid epoch proportion increases by 14.95% after true peak coordinates are used to replace the GNSS-RTK float or incorrect fixed solutions. The precision of the three attitude angles is 0.54°, 1.46°, and 1.15°, respectively. Meanwhile, the RMS of baseline length difference is 3.8 mm.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra.

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Improved Estimation of Glacial‐Earthquake Size Through New Modeling of the Seismic Source

AbstractThe number of gigaton‐sized iceberg‐calving events occurring annually at Greenland glaciers is increasing, part of a larger trend of accelerating mass loss from the Greenland Ice Sheet. Though visual observation of large calving events is rare, ∼60 glacial earthquakes generated by these calving events are currently recorded each year by regional and global seismic stations. An empirical relationship between iceberg size and MCSF, a summary measure of glacial‐earthquake size, was recently demonstrated by Olsen and Nettles (2019), https://doi.org/10.1029/2019JF005054. However, MCSF is known to be sensitive to choices made in modeling the seismic source. We incorporate constraints on the seismic source from laboratory studies of calving and test multiple source time functions using synthetic and observed glacial‐earthquake waveforms. We find that a simple, fixed time function with a shape informed by laboratory results greatly improves estimates of earthquake size. The average ratio of estimated to true peak force values is 1.03 for experiments using our preferred source model, compared with an average of 0.3 for models used in previous studies. We find that maximum‐force values estimated from waveform modeling depend far less on model choices than does MCSF, and therefore prefer maximum force as a measure of glacial‐earthquake size. Using both synthetic and real data, we confirm a correlation between maximum force and iceberg mass. Our results support the possibility of developing useful scaling relationships between seismic observables and physical parameters controlling glacier calving.

Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance