Average Prediction Research Articles

Application of autoregressive integrated moving average and vector autoregression prediction models for stock and price stabilization at perum bulog

This study implements the ARIMA (Autoregressive Integrated Moving Average) method to predict rice stock levels at Perum Bulog in Lhokseumawe City. The objective of this research is to forecast rice stock levels for the upcoming year, 2024, and provide Perum Bulog Lhokseumawe with a more accurate reference for future stock planning. The benefit of this research lies in its potential to enable preventive measures to minimize rice stock shortages and support Perum Bulog in formulating policies to reduce the risk of future shortages. Error testing for rice prices with the ARIMA model shows MAPE values of ARIMA (1,0,0) = 21,256,076,432,276.10%, ARIMA (0,0,2) = 3.54%, and ARIMA (1,0,2) = 21,305,440,935,436.50%. Rice stock testing with MAPE error values yielded ARIMA (2,0,0) = 13.49%, ARIMA (0,0,1) = 13.65%, and ARIMA (2,0,1) = 10.27%. The error measurement results for prediction using MAPE are 1.5% for rice prices and 58.9% for rice stock. Based on these error measurements, the VAR model appears sufficiently accurate for predicting rice prices, as the MAPE value is at a low level.

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies

The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled time-series data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over time. Longitudinal studies commonly track several biomarkers, which are likely governed by nonlinear dynamics that might have subject-specific idiosyncrasies and exhibit both direct and indirect causes. Furthermore, real-world longitudinal data often suffer from widespread missingness. GC methods are not well-suited to handle these issues. In this paper, we propose an approach named GLACIAL (Granger and LeArning-based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model. GLACIAL treats subjects as independent samples and uses the model’s average prediction accuracy on hold-out subjects to probe causal links. Input dropout and model interpolation are used to efficiently learn nonlinear dynamic relationships between a large number of variables and to handle missing values respectively. Extensive simulations and experiments on a real longitudinal medical imaging dataset show GLACIAL beating competitive baselines and confirm its utility. Our code is available at <a href='https://github.com/mnhng/GLACIAL'>https://github.com/mnhng/GLACIAL</a>

A Multimodal Machine Learning Model in Pneumonia Patients Hospital Length of Stay Prediction

Hospital overcrowding, driven by both structural management challenges and widespread medical emergencies, has prompted extensive research into machine learning (ML) solutions for predicting patient length of stay (LOS) to optimize bed allocation. While many existing models simplify the LOS prediction problem to a classification task, predicting broad ranges of hospital days, an exact day-based regression model is often crucial for precise planning. Additionally, available data are typically limited and heterogeneous, often collected from a small patient cohort. To address these challenges, we present a novel multimodal ML framework that combines imaging and clinical data to enhance LOS prediction accuracy. Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. As a case study, we applied this framework to pneumonia patient data collected during the COVID-19 pandemic at two hospitals in Naples, Italy—one specializing in infectious diseases and the other general-purpose. Under our experimental setup, the proposed system achieved an average prediction error of only three days, demonstrating its potential to improve patient flow management in critical care environments.

Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

This study presents an energy consumption (EC) forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning (FTL). To predict the EC of manufacturing products, particularly from scale-down to scale-up, a general paradigm was first developed by categorizing the overall process into three main sub-steps. The operating electrical power was further formulated as a combinatorial function, based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC. Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power. Considering the interconnections among these factors, the outputs of the neural networks were blended and fused to jointly predict the electrical power. Most innovatively, large artifacts can be decomposed into time-dependent laser-scanning trajectories, which can be further transformed into fusionable information via neural networks, inspired by large language model. Accordingly, transfer learning can deal with either scale-down or scale-up forecasting, namely, FTL with scalability within artifact structures. The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion. The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%. The melting fusion quality was examined using metallographic diagrams. The proposed FTL framework can forecast the EC of scaled structures, which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

An improved body force model based on a data-driven region-segmentation combinational loss model for a transonic fan rotor under uniform and non-uniform inflow

Boundary layer ingestion (BLI) fans are required to continuously operate under distorted inflow conditions, which severely reduces the fan's efficiency. Therefore, it is necessary to focus efforts on designing a high-performance distortion-tolerant fan. During the preliminary design stage of the fan, it is inevitable to repeatedly evaluate the aerodynamic performances of different design cases through low-fidelity computational approaches. However, the predictive accuracy of these low-fidelity computational methods generally depends on the precision of the loss models integrated into them. These loss models are built using simplified physics or human experience, making it difficult to depict the complex non-linear mapping relationships between losses and their influencing factors under BLI inflow distortion. In this work, a high-accuracy data-driven based region-segmentation combinational loss model is used for the first time to attempt to improve the overall prediction accuracy of the body force model. As a low-fidelity computational approach, the body force model has the ability to predict the spatial distribution of three-dimensional, non-uniform flow under inflow distortion conditions. Various operating conditions, including different rotational speeds, radial inflow, and BLI-distorted inflow, are selected to test the predictive accuracy of this improved body force model based on a region-segmentation combinational loss model. The results show that, compared with the one based on traditional loss prediction approach, the improved body force model in this work has shown higher prediction accuracy for the fan adiabatic efficiency characteristic curve. In addition, further evaluations of the flow loss spatial distributions under BLI inflow distortion conditions indicate that the body force model based on region-segmentation combinational loss model can more accurately capture the radial loss distributions at different circumferential locations. Specifically, for the loss distribution in the tip area, it can capture the sharp increase in variation trend near the end wall, and the average prediction errors can be reduced by more than 10% at different annulus locations under various BLI inflow distortions. Meanwhile, this improved body force model also enhances the accuracy of predicting the loss distribution in the circumferential direction at different span positions, including more accurately capturing the circumferential positions corresponding to the maximum and minimum flow losses.

A modified Johnson-Cook constitutive model of Inconel 690 weld overlay taking into account the strain rate softening effect

Precise material constitutive models are essential for finite element (FE) cutting simulation and analytical calculations of cutting mechanics. In this work, a split Hopkinson pressure bar (SHPB) equipment was employed to investigate the dynamic mechanical behavior of Inconel 690 weld overlay with a wide range of temperatures (20℃, 200℃, 400℃, 600℃, and 800℃) and strain rates (5000s-1, 7500s-1, 10,000s-1, and 12,500s-1). Based on the material flow characteristics at different loading conditions, a modified Johnson-Cook (JC) constitutive model was established, which takes into account the strain rate softening effect at high strain rates and the softening behavior induced by the competition between work hardening and dynamic recovery at large strains. Compared with the original JC constitutive model, the modified constitutive model can provide a closer correspondence between the predicted flow stresses and experimental data. The modified material constitutive model was introduced into FE simulation for orthogonal cutting of Inconel 690 weld overlay. The results indicate that the average prediction errors for the main cutting force and thrust force are 6.38% and 11.38%, respectively, validating the applicability and reliability of the modified JC constitutive model for cutting simulation applications. This study can provide a mechanical foundation for FE analysis in high-speed cutting of Inconel 690 weld overlay, which is conducive to optimizing cutting parameters, reducing machining costs, and enhancing production efficiency in practical industrial manufacturing.

Power quality disturbances categorization using Identity Feature Vector and Extreme Learning Machine

Power quality disturbances are variations or anomalies in the voltage, current, or frequency of electrical power that can affect the proper operation of electrical equipment. These disturbances are usually classified into different categories based on their attributes and effects. This article presents an intelligent technique based on an Identity Feature Vector and an Extreme Learning Machine (ELM). This study first derives a constant length vector for each disturbance signal. A wavelet transform is applied to derive attributes from the input disturbance signal, and the identity vector is formed using the approximation coefficients. After the required normalization procedures, the normalized identity vector is classified using an ELM. To assess the productivity of the suggested approach, 12 types of disturbances, single and combined, are generated, and the system's efficiency is studied. The results indicate that ten out of 12 combinations, including Harmonic, Sag, and Flicker, were detected with 100% accuracy. Additionally, the combination "Harmonic + Swell" exhibited the lowest accuracy, identified with 98% accuracy. The total average accuracy of this method is 99.75%. The outcomes demonstrate the highly favorable performance of this approach. This study evaluated the analyzed algorithm under noisy conditions with three different noise levels: 30 dB, 40 dB, and 50 dB, respectively. The average prediction accuracy for these three noise levels is 99.16%, 99.25%, and 98.91%. The outcomes demonstrate that the evaluated algorithm accurately detects power quality disturbances across various noisy conditions.

Classification of Defect Photovoltaic Panel Images Using Matrox Imaging Library for Machine Vision Application

The maintenance of large-scale photovoltaic (PV) power plants has long been a challenging task. Currently, monitoring is carried out using electrical performance measurements or image processing, which have limited ability to detect faults, are time-consuming and costly, and cannot pinpoint the defect's precise location quickly. To address these challenges, this research focused on using deep learning techniques to classify defect and non-defect PV panels. The application provided deep learning algorithms capable of image classification in various classifiers. The image dataset was carefully curated and split into training and development datasets during the training model to ensure the highest accuracy for the prediction of the presence or absence of defects on the PV panel. Statistical measures, which are the average accuracy for the training model and average prediction, were employed to evaluate the classification performance of the defect PV panel model. The results demonstrated a remarkable total accuracy of model 99.9% for each class, and prediction results showed that almost 70% of defect PV panels were detected from the testing dataset. Furthermore, a comparative analysis was conducted to benchmark the findings against other algorithms. The practical implications of this research are significant, showcasing the effectiveness of deep learning algorithms and their compatibility with machine vision applications for the classification of defect PV panel images. By leveraging these techniques, solar farm operators can significantly improve maintenance management, thereby enhancing the efficiency and reliability of solar power generation and potentially saving significant costs.

Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China

Genomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China.

Using anthropometry-based scaling to predict responses across sex in reclined frontal impact sled tests

Objective Field data studies have suggested greater injury risk in motor vehicle crashes for females compared to males. Data on female responses may provide more insight into these sex-based differences in injury risk, but few sled tests have been conducted with female subjects, with most cases being of either small or obese anthropometries. Since numerous sled tests have been conducted using mid-size male post mortem human subjects, anthropometry-based scaling of mid-size male responses is a potential approach to leverage this mid-size male data for a broader range of applicability. This study aimed to determine if scaling to predict female responses from male responses is suitable for a reclined condition and evaluate the effectiveness of varying anthropometry-based scaling techniques in predicting female responses from male responses. Methods Data from frontal impact sled tests conducted on four mid-size male and three mid-size female post mortem human subjects in a reclined posture were used. Geometric scaling using dimensional analysis (equal stress-equal velocity approach) was then implemented on the male data to test the effect various scale factors had on predicting female response. These scaling factors were based on mass, stature, torso height, torso volume, and torso volume at torso height. Results Predictions results varied, with lap belt force and forward excursions of bony landmarks yielding high overall prediction scores and vertical excursions of bony landmarks yielding lower overall prediction scores. Generally, the unscaled responses also yielded high prediction scores, and while scaling offered an improvement to the overall prediction score, this suggested that anthropometry-based differences may not fully explain male-female differences in reclined occupant response. Torso volume yielded the highest overall prediction score for the greatest number of responses, but the anthropometry measure that yielded the highest average overall prediction score across all responses varied. Conclusions Marginal differences in the predictive capabilities of the different anthropometry-based measures were found, but, in general, anthropometry was not able to fully explain differences in the responses observed between sex. Overall, this study highlighted the need for further whole body sled tests with mid-size female PMHS to further investigate male-female differences in occupant kinematics.

Prediction of Protein Secondary Structures Based on Substructural Descriptors of Molecular Fragments.

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence-structure-property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed.

Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models

This study focuses on advanced landslide susceptibility mapping in Bertioga, utilizing ensemble machine-learning models to identify and predict landslide-prone regions. Four algorithms—Adaptive Boosting (AdaBoost), Gradient-Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), and Random Forest (RF)—were employed to model landslide susceptibility at a 30-m spatial scale, using thirteen landslide conditioning factors (LCFs). These LCFs include topographical, geological, and environmental variables significantly influencing landslide occurrence. The performance of each model was evaluated based on precision (P), recall (R), F1-Score, and area under the curve (AUC), ensuring a robust assessment of prediction accuracy. Furthermore, the models demonstrated varying area coverage in terms of susceptibility classes. For instance, MLP identified 15% of the study area as very low susceptibility, 22% as low, 18% as moderate, 18% as high, and 27% as very high. RF predicted 40% of the region as very low susceptibility, whereas GBDT indicated a substantial 45% of the area as very high risk. AdaBoost, on the other hand, assigned the highest moderate risk coverage at 31%. These results provide a comprehensive understanding of the spatial distribution of landslide risks. Results show that MLP and GBDT achieved higher accuracies in the susceptibility mapping, with AUC values ranging from 0.85 to 0.96. For instance, in Parque CauiBura, MLP and GBDT performed exceptionally well with AUC scores of 0.98 and 0.97, respectively. The average prediction for all cities yielded a high accuracy of 0.95 in Parque CauiBura, followed by 0.92 in Centro. These findings highlight the importance of using ensemble machine learning techniques in regional landslide susceptibility mapping, offering valuable insights for risk management and mitigation strategies in landslide-prone areas such as Bertioga.

Gadolinium oxide modified molecular imprinted polymer electrode for the electrochemical detection of theophylline in black tea

The present study reported a facile approach to fabricate a molecular imprinted polymer electrode modified with nano-composites of gadolinium oxide (Gd2O3@TH-MIP-CPE) to detect theophylline in variants of black tea samples. The synthesized electrode material was characterized by scanning electron microscope, fourier transform infrared spectroscopy and UV–visible spectroscopy. The electroanalytic performance of the Gd2O3@TH-MIP-CPE electrode was studied using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The electrode exhibits a linear response from 50 µM to 900 µM with limit of detection being 2.004 µM. The electrode showed significant repeatability (%RSD=1.19), reproducibility (%RSD=0.92) and stability (%RSD=1.015). To evaluate the predictive ability of the electrode, partial least square regression (PLSR) model has been developed by correlating the high-performance liquid chromatography (HPLC) values with the voltammogram signals recorded using the electrode. The average prediction accuracy obtained from the PLSR model is 91.3 % with root mean square error of calibration being as low as 0.73.

Water Use Attribution Analysis and Prediction Based on the VIKOR Method and Grey Neural Network Model: A Case Study of Zhangye City

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.

An aluminum agglomeration model based on realistic pocket distribution via microcomputed tomography

The condensed combustion products (CCPs) resulted from aluminum agglomeration significantly affect the operation and safety of solid rocket motor, so it is essential to create a precise agglomeration model to predict the size of the CCPs of aluminized solid propellant. To accomplish this, high-precision microcomputed tomography (micro-CT) scanning was employed to obtain the quasi-three-dimensional structure of the propellant. The distribution of the ammonium perchlorate (AP) pockets was recognized via artificial intelligence (AI) method. The results revealed that the pocket size was mainly influenced by the AP grade. As the fraction of the coarse AP fraction declined from 43.1 % to 23.1 %, the average pocket diameter reduced from 388.91 to 68.23 μm. Size distribution predictions were subsequently performed for the agglomerations based on the pocket model theory. The equation relationship between agglomeration coefficient and propellant formulation was presented and corrected through mathematical fitting. The proposed agglomeration model was validated using the CCPs collection experiments of six aluminized propellants at 7 MPa. The agglomeration model produced an average particle size prediction error of <9.8 %, and the goodness of fit of the agglomerate distribution was >0.85. Subsequent analysis indicated that the agglomerate size mainly depended on the percentage of coarse AP, the burn rate, and the size distribution of raw aluminum particles. The present model is expected to offer a new way to achieve the accurate prediction of solid propellant agglomeration behaviors.

A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

Accurate prediction of multiple orders remaining completion time (MORCT) is crucial in the make-to-order production model. It enables managers to keep track of production status, make timely decisions, and ensure on-time delivery of orders. However, dynamic production environments, characterized by constantly changing order quantities and relationships, as well as the special temporal features of the production process, pose challenges to existing prediction methods. To address these issues, this paper proposes a novel framework based on multi-target regression. First, production data are collected and standardized from various sources using multiple data transfer protocols. The input dataset is then constructed and dynamically adjusted to accommodate changes in order quantities and priorities. Finally, a prediction model named DMTR-LSA is developed to effectively handle the specific temporal relationships in the production data by integrating long short-term memory (LSTM) and self-attention mechanisms. A case study in a real production workshop demonstrates that the proposed method supports simultaneous prediction of multiple orders. It outperforms existing methods on several evaluation metrics, reducing the average prediction error by more than 8.9%. These results highlight the practical value of the proposed method for predicting MORCT in dynamic production environments and its potential impact to enhance the production decision-making process.

Memristor Array Based on Wafer-Scale 2D HfS2 for Dual-Mode Physically Unclonable Functions.

Conventional Si-based physically unclonable functions (PUFs) take advantage of the unique variations in the fabrication processes. However, these PUFs are hindered by limited entropy sources and susceptibility to noise interference. Here we present a memristive device based on wafer-scale (2-in.) two-dimensional (2D) hafnium disulfide (HfS2) grown by molecular beam epitaxy (MBE). The polycrystalline HfS2 thin film can offer enhanced entropy sources for PUF applications, such as lattice defects, which can facilitate the random formation of conductive filaments and result in significant device-to-device (D2D) variations. Our proposed PUF design seamlessly integrates two distinct operating modes within a single circuit module. First, a reconfigurable and highly secure mode 1, and second, an ultrareliable mode 2, both with near-ideal Entropy (∼1.0), normalized Hamming distance (∼0.5) and correlation coefficient (∼0.0). Additionally, a predictive Fourier regression model further confirms the unpredictable nature of our dual-mode PUF, with an average prediction accuracy of ∼50%.

EXPRESS: A Framework for Execution Time Prediction of Concurrent CNNs on Xilinx DPU Accelerator

Deep learning Processor Unit (DPU) is a highly configurable CNN accelerator that supports a variety of CNNs and can be implemented with multiple instances on the same FPGA. Many applications deploy concurrent execution of different CNNs and in such a setting, an execution time predictor can help “optimize” the DPU configurations to meet the performance requirements of different tasks. We characterize CNN execution on DPUs and reduce the variability in execution time due to interference from the operating system. Subsequently, we propose a machine learning-based framework (EXPRESS) to predict the execution time of any given CNN on a DPU configuration, considering CNN, DPU, and bus characteristics. We improvise EXPRESS to support heterogeneous CNNs in EXPRESS-2.0 by making features independent of the number of CNNs. Our entire experimentation is based on data from a real FPGA board for 16 standard CNNs. Our frameworks, EXPRESS and EXPRESS-2.0, significantly outperform state-of-the-art by achieving an average execution time prediction error of 2.2% and 0.7%, respectively. We illustrate the effectiveness of this low prediction error for design space exploration, which is very useful for embedded system application developers.

A Semi-Quantitative Approach to Nontarget Compositional Analysis of Complex Samples.

Nontarget analysis (NTA) by liquid chromatography coupled to high-resolution mass spectrometry improves the capacity to comprehend the molecular composition of complex mixtures compared to targeted analysis techniques. However, the detection of unknown compounds means that quantification in NTA is challenging. This study proposes a new semi-quantitative methodology for use in the NTA of organic aerosol. Quantification of unknowns is achieved using the average ionization efficiency of multiple quantification standards which elute within the same retention time window as the unknown analytes. In total, 110 authentic standards constructed 25 retention time windows for the quantification of oxygenated (CHO) and organonitrogen (CHON) species. The method was validated on extracts of biomass burning organic aerosol (BBOA) and compared to quantification with authentic standards and had an average prediction error of 1.52 times. Furthermore, 70% of concentrations were estimated within a factor of 2 (prediction errors between 0.5 and 2 times) from the authentic standard quantification. The semi-quantification method also showed good agreement for the quantification of CHO compounds compared to predictive ionization efficiency-based methods, whereas for CHON species, the prediction error of the semi-quantification method (1.63) was significantly lower than the predictive ionization efficiency approach (14.94). Application to BBOA for the derivation of relative abundances of CHO and CHON species showed that using peak area underestimated the relative abundance of CHO by 19% and overestimated that of CHON by 11% compared to the semi-quantification method. These differences could lead to significant misinterpretations of source apportionment in complex samples, highlighting the need to account for ionization differences in NTA approaches.

Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.