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This study analyzes coupled atmosphere-ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959-2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest-northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño-Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantics Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

North Sumatra is one of the provinces that contributes the most prominent agricultural commodities of red chilies and cayenne peppers in Indonesia. This study aims to determine the outcomes of price forecasts for cayenne and red chilies in the province of North Sumatra. The method used is multivariate singular spectrum analysis. Results were grouped into six groups based on 12 eigenvectors with a forecast length of 9 monthly periods. Further, the level of accuracy was obtained from MAPE for each variable, with the highest MAPE being 30% for the curly red chili, 27.55% for the big red chili, and the lowest at 23.44% for mixed cayenne pepper. So, the price forecast for red chilies and cayenne peppers in North Sumatra Province for October 2023 to June 2024 using the Multivariate Singular Spectrum Analysis model is included in the forecast category with reasonable capabilities.

Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline's wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring. [ABSTRACT FROM AUTHOR]

Rainfall forecasting is pivotal for issuing flood warnings and flood management. Machine learning (ML) models are popular as they can effectively manage extensive data and non-stationarity of the data series with improved performance and cost-effective solutions. However, more studies are required to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall forecast. Singular spectrum analysis (SSA) was used as a data pre-processing technique (successfully removing and identifying the nature of noise) and coupled with ML models (artificial neural network (ANN) and support vector machine (SVM)) improving daily scale forecast. Since the current response of the hydrological system depends on previous responses, rainfall at the next time step was derived with the previous 2-, 3-, 5- and 7-day rainfall. Study shows that the first eigen vector derived through SSA is the trend component which has a maximum contribution of 18.75%, suggesting it can explain 18.75% of the given rainfall series. The 16.42% (eigen vector 2-9) contributes to periodicity, with period of 1 year, 6 months, and 4 months within the data. Conclusively, the hybrid SSA-ML model outperformed the single model for daily rainfall forecasts. HIGHLIGHTS A hybrid SSA–ML model is proposed for daily rainfall forecast.; Red noise is detected and removed from rainfall series.; Periodic components with period of 1 year, 6 months, and 4 months are identified.; Pre-processing enhances SSA–ML model, with R2 values of (0.65–0.72), (0.76–0.81), (0.61–0.73), (0.77–0.82) for ANN, SSA-ANN, MA-SVM, and SSA-SVM, respectively.; Previous 2 days of rainfall strongly influences next day rainfall.;

Feature extraction has a valuable role in hyperspectral images processing. In recent years, various methods have been presented to extract efficient features of hyperspectral images. Recent studies have successfully used conventional singular spectrum analysis in the spectral domain and two-dimensional singular spectrum analysis in the spatial domain for feature extraction in hyperspectral images. However, a lack of success in joint spectral-spatial feature extraction is a problem with both algorithms. This study uses a three-dimensional singular spectrum analysis extension to overcome this problem. The implementation of proposal model on hyperspectral images removes the noise components during spectral-spatial feature extraction process and significantly improves features identification capability. This study conducts experiments using two publically available datasets. Experimental results show that our proposed method has a promising performance so that it has obtained a classification accuracy of at least 1.93% and 1.27% respectively on the hyperspectral dataset of Indian Pines and Pavia University compared to other recent methods.

Singular spectrum analysis is widely used in geodetic time series analysis. However, when extracting time-varying periodic signals from a large number of Global Navigation Satellite System (GNSS) time series, the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient. To improve the efficiency and accuracy of singular spectrum analysis, this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix. The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series, and the extraction efficiency of a single time series is six times that of singular spectrum analysis. The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

(Multichannel) Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression. It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem. However, in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers. The presence of this kind of noise, called erratic noise, makes singular spectrum analysis (SSA) reconstruction unstable and has undesirable effects on the final results. We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA (RD-SSA) method. It incorporates the damped SSA, an improved version of SSA, into a reweighted framework. The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise. The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations. The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance. The feasibility of RD-SSA is validated via both synthetic and field data examples. [ABSTRACT FROM AUTHOR]

Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On the other hand, time-series data often exhibit multiple frequency components, such as trends, seasonality, cycles, and noise. These frequency components can be optimized in forecasting using Singular Spectrum Analysis (SSA). Furthermore, the two most widely used approaches in SSA are Linear Recurrent Formula (SSAR) and Vector (SSAV). SSAV has better accuracy and robustness than SSAR, especially in handling structural breaks. Therefore, this research proposes modeling the SSAV coefficient with an SDM approach to take structural breaks called SDM-SSAV. SDM recursively updates the SSAV coefficient to adapt over time and between states using an Extended Kalman Filter (EKF). Empirical results with Indonesian Export data and simulation studies show that the accuracy of SDM-SSAV outperforms SSAR, SSAV, SDM-SSAR, hybrid ARIMA-LSTM, and VARI.

The study was aimed to evaluate the performance of a newly developed spectroscopy-based non-invasive and noncontact device (SAMIRA) for the simultaneous measurement of hemoglobin, bilirubin and oxygen saturation as an alternative to the invasive biochemical method of blood sampling. The accuracy of the device was assessed in 4318 neonates having incidences of either anemia, jaundice, or hypoxia. Transcutaneous bilirubin, hemoglobin and blood saturation values were obtained by the newly developed instrument which was corroborated with the biochemical blood tests by expert clinicians. The instrument is trained using Artificial Neural Network Analysis to increase the acceptability of the data. The artificial intelligence incorporated within the instrument determines the disease condition of the neonate. The Pearson's correlation coefficient, r was found to be 0.987 for hemoglobin estimation and 0.988 for bilirubin and blood gas saturation respectively. The bias and the limits of agreement for the measurement of all the three parameters were within the clinically acceptance limit.

The Singular Spectrum Analysis (SSA) is a useful tool for extracting signals from noisy time series. However, the structural insights provided by SSA are significantly influenced by the choice of window length. While the conventional approach, recommending a larger window length, excels with short to moderately-sized time series, it becomes computationally burdensome for longer time series, potentially amplifying mean squared reconstruction errors. This study addresses this methodological gap by introducing an adaptive sequential SSA method that iteratively selects an optimal window length for efficient extraction of essential eigen-sequences (signals) with minimal reconstruction error. This proposed method is versatile, catering to both short-moderate and lengthy time series. Simulation studies demonstrate its efficacy in scenarios where observed data stem from the sum of two sinusoidal functions and noise. Real data analysis on 6-day heart rate data from a young adult e-cigarette user reveals a distinct clustering of vaping events in the scatter plot of the first and third eigen-sequences, indicating the potential of developing “digital biomarkers” for vaping behavior based on extracted eigen-sequences in future studies. In conclusion, the adaptive sequential SSA method offers a robust and flexible approach for signal extraction in diverse time series applications.

Abstract Industrial IoT (IIoT) applications are widely used in multiple use cases to automate the industrial environment. Industry 4.0 presents challenges in numerous areas, including heterogeneous data, efficient data sensing and collection, real‐time data processing, and higher request arrival rates, due to the massive amount of industrial data. Building a time‐sensitive network that supports the voluminous and dynamic IoT traffic from heterogeneous applications is complex. Therefore, the authors provide insights into the challenges of industrial networks and propose a strategy for enhanced traffic management. An efficient multivariate forecasting model that adapts the Multivariate Singular Spectrum Analysis is employed for an SDN‐based IIoT network. The proposed method considers multiple traffic flow parameters, such as packet sent and received, flow bytes sent and received, source rate, round trip time, jitter, packet arrival rate and flow duration to predict future flows. The experimental results show that the proposed method can effectively predict by contemplating every possible variation in the observed samples and predict average load, delay, inter‐packet arrival rate and source sending rate with improved accuracy. The forecast results shows reduced error estimation when compared with existing methods with Mean Absolute Percentage Error of 1.64%, Mean Squared Error of 11.99, Root Mean Squared Error of 3.46 and Mean Absolute Error of 2.63.

Abstract This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic’s Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

Abstract The precise prediction of polar motion parameters is needed for the astrogeodynamics, navigation and positioning of the deep space probe. However, the current prediction methods are limited to predicting polar motion for specific periods, either short- or long-term. In this study, a sliding multilayer perceptron (MLP) method combined singular spectrum analysis (SSA) and autoregressive moving average (ARMA) for short- and long-term polar motion prediction was proposed. MLP was introduced into PM prediction due to its automatic learning characteristics and its ability to effectively process nonlinear and multi-dimensional data. The SSA was used to extract and predict the principal components of polar motion, while the remaining components were predicted using ARMA. In the meantime, SSA and ARMA were used to provide training data and target learning data for the MLP model. MLP input data were constructed by sliding processing with a window of 7 days, composed of n series of the same length (18 years). Finally, MLP was employed to predict the residuals generated during SSA and ARMA prediction. To evaluate the accuracy of the proposed method, the polar motion prediction was applied for a 364-day lead time based on the IERS EOP 14C04 product. The method outperformed the IERS Bulletin A, as demonstrated by the mean-absolute errors of the x and y components of polar motion on the 30th day, which were lower (5.14 mas and 3.37 mas, respectively) than those predicted by IERS Bulletin A (6.66 mas and 3.94 mas). Similarly, the mean-absolute errors on the 364th day were 17.79 mas and 16.29 mas, respectively, compared to the 19.24 mas and 18.81 mas predicted by IERS Bulletin A. Graphical Abstract

Profile Analysis (PA) is a research approach developed by David M. Green and his associates, aimed at measuring listeners’ ability to discern changes in the spectral envelope shape of complex tones. PA introduces an innovative method by separating across-frequency spectral envelope shape comparisons within a complex tone and across-time level comparisons within specific frequency channels. This paper revisits the outcomes of several PA studies and examines the correlations between PA and the results obtained from experiments involving harmonic signals and listening tests designed to enhance timbre evaluation skills among sound engineering students at the Chopin University of Music in Warsaw.

Considering the nonlinear and non-stationary characteristics of sea-level-change time series, this study focuses on enhancing the predictive accuracy of sea level change. The adjacent seas of China are selected as the research area, and the study integrates singular spectrum analysis (SSA) with long short-term memory (LSTM) neural networks to establish an SSA-LSTM hybrid model for predicting sea level change based on sea level anomaly datasets from 1993 to 2021. Comparative analyses are conducted between the SSA-LSTM hybrid model and singular LSTM neural network model, as well as (empirical mode decomposition) EMD-LSTM and (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) CEEMDAN-LSTM hybrid models. Evaluation metrics, including the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), are employed for the accuracy assessment. The results demonstrate a significant improvement in prediction accuracy using the SSA-LSTM hybrid model, with an RMSE of 5.26 mm, MAE of 4.27 mm, and R2 of 0.98, all surpassing those of the other models. Therefore, it is reasonable to conclude that the SSA-LSTM hybrid model can more accurately predict sea level change.

The wholesale price index (WPI) is an index used to measure the average price change over time of a package of goods in wholesale trade. This price index is an indicator to view general economic developments and material for market and monetary analysis. Related to WPI as material for market and monetary analysis, forecasting of WPI is very much needed, especially in agricultural commodities, which are a vital sector. The WPI in the agricultural commodity group in this study was forecasted using the singular spectrum analysis (SSA) method. The data used in forecasting is WPI data from January 2005 to December 2022. Forecasting accuracy level is determined by using vector singular spectrum analysis (VSSA) method. Forecasting results show that the best SSA model is obtained with the window length value and MAPE value is 1,9%. This shows that the SSA method can predict the WPI of agricultural commodities very accurately.

Different tectonic characteristics are exhibited among Maluku Sea and Banggai-Sula Microcontinent which are located side by side to each other. However, the northern part of the Banggai-Sula Microcontinent, positioned along the Maluku Sea margin, has a complex geological structure to be observed. Therefore, this study aimed to analyze the subsurface structure and sediment thickness due to extensional mechanisms in the area, using the gravity method. The investigation was executed through power spectrum analysis and three-dimensional (3D) modeling of complete Bouguer anomaly data. Eleven x-direction profiles were employed to determine the depth of the contact boundaries between the shallow and the deep discontinuities. The results showed that the deep and shallow discontinuities obtained from the 3D modeling and spectrum analysis have similar values, with depths ranging between 14.52 km and 15.14 km, as well as 1.23 km and 1.39 km, respectively. The shallow discontinuity area was dominated by sediment rocks with a 2.6 g/cm3 to 2.8 g/cm3 density. The area is interpreted as the Buya Formation superimposed by the Tanamu Formation. The deep discontinuity area with a depth of up to 16 km. We found areas of high density (density value is >3 g/cm3) adjacent to low rock density (density value is 2 g/cm3 to 2.3 g/cm3). The observed are interpreted as fault-bounded formations of terrestrial conglomerates and sandstones from the Bobong Formation. These formations are located within a half-graben and are found to rest unconformably on the basement towards the southern onshore region.

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed. [ABSTRACT FROM AUTHOR]

As computer science and technology continue to evolve and become more pervasive, their application in analyzing the audio spectrum of vocalizations offers valuable insights for vocal music education. This study introduces a method utilizing Fourier transform analysis to examine time-frequency domain signals in vocal teaching. Initially, voice frequencies are collected during vocal music instruction. Subsequently, these frequencies are processed to extract characteristic sequences, which are then reduced in scale to develop a model for voice spectrum recognition tailored to vocal music education. This model facilitates detailed spectral analysis, enabling the investigation of its auxiliary benefits in vocal music teaching, particularly in identifying prevalent instructional challenges. Our findings indicate that during training on vowels “a” and “i,” professional singers’ pitch at 4kHz declined to between −15 and −18 dB, whereas students’ pitch varied around ±6dB, trending upwards. In cases of air leakage, significant gaps were observed at frequencies of 5500Hz, 10500Hz, and 14500Hz. At the same time, students exhibited missing frequencies at 7kHz, 12kHz, and 14kHz during glottal tone production, with pronounced, abrupt peaks occurring when vocal folds were tightly constricted and devoid of excessive links. This research substantiates the theoretical and practical benefits of digital spectrum technology in enhancing vocal music education, thereby providing a scientific and supportive role.

AstraZeneca (AZ) vaccine is one of the most common vaccines against COVID-19 used globally. However, adverse reactions post-vaccination have been reported, including severe symptoms and cases of sudden death within several hours. Therefore, this study aimed to establish a database of spectral characteristics of blood pressure waveforms (BPWs) for the AZ vaccine and analyze reactions after vaccine administration using objective physiological signal and symptom analyses for identifying potential differences between heavy and slight groups defined in the study. In total, 24 participants were enrolled in the case-control study. BPW measurements were acquired pre- and post-vaccination. A questionnaire survey on side effects was conducted 5 days after vaccination. The related spectral characteristics of heavy and slight groups were acquired after Fourier transform analysis. Four types of harmonic indexes from BPW signals, including amplitude proportion (Cn), coefficient of variation of Cn (CVn), phase angle (Pn), and standard deviation of Pn (Pn_SD), were derived. The characteristics of harmonic indexes of arterial BPW for the AZ vaccine were in C6 (P = .011), CV2 (P = .027), P5 (P = .009), and P2_SD (P = .027) on the radial pulse. C5 (P = .037), C8 (P = .007), C9 (P = .037), CV5 (P = .015), CV8 (P = .005), and CV9 (P = .028) were significantly different at posttest between heavy and slight groups. In both pretest or posttest, C8 was almost significantly different between slight and heavy groups. More parameters changed significantly post-vaccination, with more severe side effects. Most average values of posttest/pretest of CVn and Pn_SD in the slight group exceeded 100%. All average values of posttest/pretest of CVn and Pn_SD in the heavy group were smaller than 100%. This approach may enable prediction of the risk of reactions post-vaccination to determine suitability of the AZ vaccine and evaluation of side effect severity in vaccinated individuals using pulse analysis to ensure relevant precautions are taken., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Renewable energy sources have a significant approach to eco-friendly systems with economically cost-effective and efficient use of biodiesel. The creation of innovative catalysts is attracting the attention and concern of the chemical and manufacturing sectors to be used in the production of biodiesel. The main objective of this work is to produce the optimum biodiesel yield using BBD design and the base-catalyzed method. The production of biodiesel was achieved through experimental investigation (alkali-catalyzed transesterification) and optimized using the BBD design method. The most important parameters such as methanol to oil ratios, KOH as catalysts, and reaction times were provided with variable conditions. The variation of methanol to oil ratios has significant relationships and is highly correlated in findings. The physicochemical properties of cottonseed biodiesel were carried out according to ASTM and EN standards. The maximum yield was achieved at 96.708 at a molar ratio of 6:1, reaction time of 60 min, and catalyst concentration of 1 wt%. The strong correlation between actual and predicted values of COSME was analyzed using ANOVA variances. The F-value and P-value validated the outcomes of produced biodiesel having a model value of 43.68 and 0.00, respectively. The coefficient of determinations and adjusted R2 of CSOME were achieved at 98.7% and 98.6%, respectively. The gas chromatogram mass spectrum analysis was also obtained to identify the acid profile present in COSME. The major percentage of ester available in COSME has been determined using GC–MS analysis and presented by its weight percentages. The main esters have been including myristic acid methyl ester (1.7%), palmitic acid methyl ester (26.65%), methyl 9-cis,11-trans-octadecadienoate(44.05%), ethyl 9. Cis.11.trans octadecadienoate(9.67%), methyl 20-methyl-heneicosanoate(6.83%), 9,12-Octadecadienoic acid (Z,Z)-, 2-hydroxy-1-(hydroxymethyl)ethyl ester(1.93%), and Pentacosanoic acid, methyl ester(7.14%).