Original Time Research Articles

Investigating the Effects of Distillation System, Geographical Origin, and Aging Time on Aroma Characteristics in Brandy Using an Untargeted Metabonomic Approach.

In this study, the influence of the distillation system, geographical origin, and aging time on the volatiles of brandy was investigated. An untargeted metabolomics approach was used to classify the volatile profiles of brandies based on the presence of different distillation systems and geographical origins. Through the predictive ability of PLS-DA models, it was found that higher alcohols, C13-norisopenoids, and furans could serve as key markers to discriminate between continuous stills and pot stills, and the contents of C6/C9 compounds, C13-norisoprenoids, and sesquiterpenoids were significantly affected by brandy origin. A network analysis illustrated that straight-chain fatty acid ethyl esters gradually accumulated during aging, and several higher alcohols, furfural, 5-methylfurfural, 4-ethylphenol, TDN, β-damascenone, naphthalene, styrene, and decanal were also positively correlated with aging time. This study provides effective methods for distinguishing brandies collected from different distillation systems and geographical origins and summarizes an overview of the changes in volatile compounds during the aging process.

Multifractal perturbations to multiplicative cascades promote multifractal nonlinearity with asymmetric spectra.

Biological and psychological processes have been conceptualized as emerging from intricate multiplicative interactions among component processes across various spatial and temporal scales. Among the statistical models employed to approximate these intricate nonlinear interactions across scales, one prominent framework is that of cascades. Despite decades of empirical work using multifractal formalisms, several fundamental questions persist concerning the proper interpretations of multifractal evidence of nonlinear cross-scale interactivity. Does multifractal spectrum width depend on multiplicative interactions, constituent noise processes participating in those interactions, or both? We conducted numerical simulations of cascade time series featuring component noise processes characterizing a range of nonlinear temporal correlations: nonlinearly multifractal, linearly multifractal (obtained via the iterative amplitude adjusted wavelet transform of nonlinearly multifractal), phase-randomized linearity (obtained via the iterative amplitude adjustment Fourier transform of nonlinearly multifractal), and phase and amplitude randomized (obtained via shuffling of nonlinearly multifractal). Our findings show that the multiplicative interactions coordinate with the nonlinear temporal correlations of noise components to dictate emergent multifractal properties. Multiplicative cascades with stronger nonlinear temporal correlations make multifractal spectra more asymmetric with wider left sides. However, when considering multifractal spectral differences between the original and surrogate time series, even multiplicative cascades produce multifractality greater than in surrogate time series, even with linearized multifractal noise components. In contrast, additivity among component processes leads to a linear outcome. These findings provide a robust framework for generating multifractal expectations for biological and psychological models in which cascade dynamics flow from one part of an organism to another.

Identification of a new HIV-1 circulating recombinant form (CRF159_01103) derived from CRF103_01B and CRF01_AE in Hebei Province, China

Recombinant HIV-1 genomes identified in three or more epidemiological unrelated individuals are defined as circulating recombinant forms (CRFs). CRFs can further recombine with other pure subtypes or recombinants to produce secondary recombinants. In this study, a new HIV-1 intersubtype CRF, designated CRF159_01103, isolated from three men who have sex with men with no epidemiological linkage, was identified in Baoding city, Hebei Province, China. CRF159_01103 was derived from CRF103_01B and CRF01_AE. Bayesian molecular clock analysis was performed on the CRF01-AE and CRF103_01B regions of CRF159_01103. The time of origin of CRF159_01103 was predicted to be 2018–2019, indicating that it is a recent recombinant virus. The emergence of CRF159_01103 has increased the complexity of the HIV-1 epidemic in Hebei Province.

Temporal upscaling of solar radiation components using an analytical model for variability modeling

To properly design systems that harness the solar resource, reliable measurements or estimates of its availability are needed. Often, this data is only available at temporal resolutions that are not sufficiently fine to capture the short-term fluctuations of the solar resource, creating uncertainty in the design and performance assessment of solar-powered systems. Synthetic irradiance generation techniques aim to create high temporal resolution estimates starting from lower resolution data, however, a literature review on the topic shows opportunities for improvement regarding the generality of the generation and evaluation approaches. This work proposes novel methods to estimate a quantifiable indicator of short-term variability from hourly data, a new temporal upscaling methodology for time series of arbitrary origin and destination temporal resolution, based on explicit mathematical functions and sufficient generality for application in different contexts, and an improved assessment methodology. The method was applied tor ground and satellite upscaling up to 1 min, resulting in time series with characteristics consistent with those of a higher temporal resolution and exhibit minimum deviations against the original time series characteristics. This method has applications not only for upscaling of low temporal resolution data, but also for gap-filling techniques and the upscaling of hourly solar radiation forecasts.

Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms

Abstract To enhance the efficiency and sustainability, technical preparations were made for eliminating the Temperature, Sample, Oxygen test of basic oxygen furnace (BOF) steelmaking process in this work. Utilizing data from 13,528 heats and state-of-the-art (SOTA) machine learning (ML) and deep learning algorithms, data-driven models with different types of inputs were developed, marking the first use of time series data (off-gas profiles and blowing practice related curves) for BOF steelmaking’s endpoint prediction, and the tabular features were expanded to 45. The prediction targets are molten steel’s concentrations of phosphorus (Endpoint [P], %) and carbon (Endpoint [C], %), and temperature (Endpoint-Temp, °C). The optimal models for each target were implemented at a Hesteel Group’s BOF steelmaking facility. Initially, SOTA ML models (XGBoost, LightGBM, Catboost, TabNet) were employed to predict Endpoint [P]/[C]/Temp with tabular data. The best mean absolute errors (MAE) achieved were 2.276 × 10−3% (Catboost), 6.916 × 10−3% (Catboost), and 7.955°C (LightGBM), respectively, which surpassed the conventional models’ performance. The prediction MAEs of the conventional models with the same inputs for Endpoint [P]/[C]/Temp were 3.158 × 10−3%, 7.534 × 10−3%, and 9.150°C (Back Propagation neural network) and 2.710 × 10−3%, 7.316 × 10−3%, and 8.310°C (Support Vector Regression). Subsequently, predictions were explored to be made using SOTA time series analysis models (1D ResCNN, TCN, OmniScaleCNN, eXplainable Convolutional neural network (XCM), Time-Series Transformer, LSTM-FCN, D-linear) with the original time series data and SOTA image analysis models (Pre-activation ResNet, DenseNet, DLA, Dual path networks (DPN), GoogleNet, Vision Transformer) with resized time series data. Finally, the concat-model and the paral-model architectures were designed for making predictions with both tabular data and time series data. It was determined that the concat-Model with TCN and ResCNN as the backbone exhibited the highest accuracy. It’s MAE for predicting Endpoint [P]/[C]/Temp reaches 2.153 × 10−3%, 6.413 × 10−3%, and 5.780°C, respectively, with field test’s MAE at 2.394 × 10−3%, 6.231 × 10−3%, and 7.679°C. Detailed results of the importance analysis for tabular data and time series are provided.

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model.

Geochemical and Isotopic Techniques Constraints on the Origin, Evolution, and Residence Time of Low‐enthalpy Geothermal Water in Western Wugongshan, SE China

AbstractGeothermal resources are increasingly gaining attention as a competitive, clean energy source to address the energy crisis and mitigate climate change. The Wugongshan area, situated in the southeast coast geothermal belt of China, is a typical geothermal anomaly and contains abundant medium‐ and low‐temperature geothermal resources. This study employed hydrogeochemical and isotopic techniques to explore the cyclic evolution of geothermal water in the western Wugongshan region, encompassing the recharge origin, water–rock interaction mechanisms, and residence time. The results show that the geothermal water in the western region of Wugongshan is weakly alkaline, with low enthalpy and mineralization levels. The hydrochemistry of geothermal waters is dominated by Na‐HCO3 and Na‐SO4, while the hydrochemistry types of cold springs are all Na‐HCO3. The hydrochemistry types of surface waters and rain waters are Na‐HCO3 or Ca‐HCO3. The δD and δ18O values reveal that the geothermal waters are recharged by atmospheric precipitation at an altitude between 550.0 and 1218.6 m. Molar ratios of major solutes and isotopic compositions of 87Sr/86Sr underscore the significant role of silicate weathering, dissolution, and cation exchange in controlling geothermal water chemistry. Additionally, geothermal waters experienced varying degrees of mixing with cold water during their ascent. The δ13C values suggest that the primary sources of carbon in the geothermal waters were biogenic and organic. The δ34S value suggests that the sulfates in geothermal water originate from sulfide minerals in the surrounding rock. Age dating using 3H and 14C isotopes suggests that geothermal waters have a residence time exceeding 1 kaBP and undergo a long‐distance cycling process.

Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

The time-varying periodic variations in Global Navigation Satellite System (GNSS) stations affect the reliable time series analysis and appropriate geophysical interpretation. In this study, we apply the singular spectrum analysis (SSA) method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets. These include 23-year observations from the Crustal Movement Observation Network of China (CMONOC), displacements inferred from the Gravity Recovery and Climate Experiment (GRACE), and loadings derived from Geophysical models (GM). The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations, and the SSA method outperforms the traditional least squares fitting (LSF) method in extracting and interpreting the time-varying seasonal signals from the original time series. The decrease in the root mean square (RMS) correlates well with the annual cycle variance estimated by the SSA method, and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method. With SSA analysis, the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues. Moreover, both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations, and the geophysical effects can account for 71% of the GNSS annual amplitudes, and the average RMS reduction is 15%. The SSA method has proved to be useful for investigating the GNSS time-varying seasonal signals. It could be applicable as an auxiliary tool in the improvement of non-linear variations investigations.

Firefly toxin lucibufagins evolved after the origin of bioluminescence.

Fireflies were believed to originally evolve their novel bioluminescence as warning signals to advertise their toxicity to predators, which was later adopted in adult mating. Although the evolution of bioluminescence has been investigated extensively, the warning signal hypothesis of its origin has not been tested. In this study, we test this hypothesis by systematically determining the presence or absence of firefly toxin lucibufagins (LBGs) across firefly species and inferring the time of origin of LBGs. We confirm the presence of LBGs in the subfamily Lampyrinae, but more importantly, we reveal the absence of LBGs in other lineages, including the subfamilies of Luciolinae, Ototretinae, and Psilocladinae, two incertae sedis lineages, and the Rhagophthalmidae family. Ancestral state reconstructions for LBGs based on firefly phylogeny constructed using genomic data suggest that the presence of LBGs in the common ancestor of the Lampyrinae subfamily is highly supported but unsupported in more ancient nodes, including firefly common ancestors. Our results suggest that firefly LBGs probably evolved much later than the evolution of bioluminescence. We thus conclude that firefly bioluminescence did not originally evolve as direct warning signals for toxic LBGs and advise that future studies should focus on other hypotheses. Moreover, LBG toxins are known to directly target and inhibit the α subunit of Na+, K+-ATPase (ATPα). We further examine the effects of amino acid substitutions in firefly ATPα on its interactions with LBGs. We find that ATPα in LBG-containing fireflies is relatively insensitive to LBGs, which suggests that target-site insensitivity contributes to LBG-containing fireflies' ability to deal with their own toxins.

(Nonequilibrium) Dynamics of Diffusion Processes with Non-conservative Drifts

The nonequilibrium Fokker–Planck dynamics with a non-conservative drift field, in dimension N≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\ge 2$$\\end{document}, can be related with the non-Hermitian quantum mechanics in a real scalar potential Vand in a purely imaginary vector potential -iAof real amplitude A (Mazzolo and Monthus in Phys Rev E 107:014101, 2023). Since Fokker–Planck probability density functions may be obtained by means of Feynman’s path integrals, the previous observation points towards a general issue of “magnetically affine” propagators, possibly of quantum origin, in real and Euclidean time. In below we shall follow the N=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=3$$\\end{document} “magnetic thread”, within which one may keep under a computational control formally and conceptually different implementations of magnetism (or surrogate magnetism) in the dynamics of diffusion processes. We shall focus on interrelations (with due precaution to varied, not evidently compatible, notational conventions) of: (i) the pertinent non-conservatively drifted diffusions, (ii) the classic Brownian motion of charged particles in the (electro)magnetic field, (iii) diffusion processes arising within so-called Euclidean quantum mechanics (which from the outset employs non-Hermitian “magnetic” Hamiltonians), (iv) limitations of the usefulness of the Euclidean map exp(-itHquant)→exp(-tHEucl)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\exp (-itH_{quant}) \\rightarrow \\exp (-tH_{Eucl})$$\\end{document}, regarding the probabilistic significance of inferred (path) integral kernels in the description of diffusion processes.

Estimation of Frequencies and Splitting of <sub>2</sub>S<sub>1</sub> and <sub>3</sub>S<sub>1</sub> Modes from the IGETS Superconducting Gravimeter Network Data after the 2013 Sea of Okhotsk Earthquake

For the first time, the 2S1 and 3S1 modes of the Earth’s free oscillations following the largest deep-focus earthquake in the Sea of Okhotsk on May 24, 2013, have been analyzed using the maximum likelihood method. Data from 14 superconducting gravimeters of the IGETS network deployed in the central part of Europe were used for the analysis. Estimates of the time of mode excitation after the earthquake origin time, degenerate frequencies, and mode splitting parameters are obtained.

Data-driven analysis of annual rain distributions

Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. Published by the American Physical Society 2024

Comparative Mitogenome of Phylogenetic Relationships and Divergence Time Analysis within Potamanthidae (Insecta: Ephemeroptera).

Potamanthidae belongs to the superfamily Ephemeroidea but has no complete mt genome released in the NCBI (except for two unchecked and one partial mt genome). Since the sister clade to Potamanthidae has always been controversial, we sequenced seven mt genomes of Potamanthidae (two species from Rhoenanthus and five species from Potamanthus) in order to rebuild the phylogenetic relationships of Potamanthidae in this study. The divergence time of Potamanthidae was also investigated by utilizing five fossil calibration points because of the indeterminate origin time. In addition, because Rhoenanthus coreanus and Potamanthus luteus are always in low-temperature environments, we aimed to explore whether these two species were under positive selection at the mt genome level. Amongst the 13 PCGs, CGA was used as the start codon in COX1, whereas other genes conformed to initiating with an ATN start codon. From this analysis, UUA (L), AUU (I), and UUU (F) had the highest usage. Furthermore, the DHU arm was absent in the secondary structure of S1 in all species. By combining the 13 PCGs and 2 rRNAs, we reconstructed the phylogenetic relationship of Potamanthidae within Ephemeroptera. The monophyly of Potamanthidae and the monophyly of Rhoenanthus and Potamanthus were supported in the results. The phylogenetic relationship of Potamanthidae + (Ephemeridae + Polymitarcyidae) was also recovered with a high prior probability. The divergence times of Potamanthidae were traced to be 90.44 Mya (95% HPD, 62.80-121.74 Mya), and the divergence times of Rhoenanthus and Potamanthus originated at approximately 64.77 Mya (95% HPD, 43.82-88.68 Mya), thus belonging to the late Pliocene Epoch or early Miocene Epoch. In addition, the data indicated that R. coreanus was under negative selection and that ATP8 and ND2 in Potamanthidae had a high evolutionary rate.

Gendered labor: Appearance management and the unequal extraction of effort and time among ambassadors

This article examines the gendered appearance management ambassadors do to properly represent states. Drawing on feminist sociologist theorizations of labor, and attentive to intersections with sexuality, class, and Western standards, the study shows the unequal extraction of time and effort from men and women as they manage their appearance in diplomacy. Theoretically, I argue that the gendered management of dress, hair, and adornments are better conceived of as labor than as habitus or capital. Empirically, the study focuses on appearance management among ambassadors posted to Washington D.C. 2018–2020. Relying on a wealth of data—an original time survey, ambassador interviews, and observations—the study makes three empirical claims about the diplomatic embodiment of states: (a) diplomatic appearance standards are largely gender binary, with male uniformity and female variation; (b) more time and effort is demanded from female than male ambassadors; and (c) diplomatic appearance standards demand gender labor that is inextricably linked with the management of sexuality, upper class conventions, and Western physical appearance norms for representing states. In short, individuals presenting as men or women (although apparently not nonbinary) may now represent states as ambassadors, but more labor is extracted from women in doing so.

From simple to complex: a sequential method for enhancing time series forecasting with deep learning

Abstract Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing.

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator's cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Predicting software aging in cloud servers is essential for preventing cloud system breakdown and unexpected failures. However, the aging time series data in cloud servers often exhibits non-linearity and stochastic fluctuations, posing challenges for accurate prediction. To address this issue, this study proposes a novel multi-step ahead approach for cloud server aging prediction. Firstly, the original aging time series data is denoised using discrete wavelet transformation (DWT) and then reconstructed into multi-dimensional data by phase state reconstruction (PSR) to capture potential aging patterns. Subsequently, a deep long short-term memory network (DLSTM) is developed to predict the aging trend. Additionally, a stacked auto-encoder LSTM (SAE-LSTM) is designed to optimize the initial weight matrix of DLSTM to enhance prediction abilities. Finally, multi-step prediction experiments are conducted on our time series datasets from OpenStack cloud servers and a public dataset from Alibaba Cluster Trace Program. The comparative analysis of the proposed method with five baseline methods confirms its superiority in terms of prediction accuracy. For response time and free memory test sets, the range of root mean square error (RMSE) values are produced by the proposed method distributed in [0.1688–0.1931] and [139.05–146.05] respectively, with respect to the multi-step ahead prediction horizon from 1 up to 10. These results demonstrate that the proposed method can tackle the limitations of one-step ahead prediction, effectively extracting relevant aging features, and achieving low prediction accuracy. The study provides valuable insights into revealing future aging trends in cloud servers and offers a reference for implementing preventive maintenance and prognostics in cloud servers.

Energy Prices and Their Impact on US Stock Indices: A Wavelet- based Quantile-on-Quantile Regression Approach

This study delves into the effects of crude oil and gas prices on the United States’ (US) conventional, Islamic, and environmental, social, and governance (ESG) stock indices from January 2013 to December 2022. Decomposing original time series data to minimise inherent fluctuations and using the Quantile-on-Quantile (QQ) regression approach presents a nuanced view of how these energy prices impact different stock indices. The findings reveal that crude oil prices have a variable impact on the indices: high prices negatively influence the indices, low prices have a positive effect, and moderate prices yield a moderate positive impact. After data decomposition, this positive influence diminishes in higher quantiles, indicating an emerging neutral effect in stabilised conditions. In contrast, gas prices show a limited impact, with high prices slightly benefiting conventional and ESG indices but less so for the Islamic index. This suggests a more pronounced influence of oil prices on the indices, likely due to the dependence of many listed companies on oil. The study emphasises the importance of considering oil-related risks in investment strategies and highlights the asymmetric impact of crude oil prices on the US stock indices. These findings have significant implications for investors and policymakers. They underscore the need for careful consideration of oil price dynamics in investment decisions and the importance of staying vigilant against shifts in oil prices that could lead to market instability.

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets.

Inferring the Focal Depths of Small Earthquakes in Southern California Using Physics-Based Waveform Features

ABSTRACT Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ∼8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2<ML<4) recorded at local distances (<150 km), for which depth estimation is especially challenging. We find that differential magnitudes (Mw/ML–Mc) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡log10(M0)+3log10(fc), and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. We also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.