Error Correction Method Research Articles

Abstract A037: Improved liquid biopsy assay performance using novel highly accurate sequencing-by-binding (SBB) technology

Abstract Liquid biopsy is revolutionizing the field of early cancer detection research through non-invasive detection of tumor DNA in the blood. However, existing liquid biopsy assays are limited in their sensitivity for ctDNA detection at low variant allele frequencies (VAFs), with most relying on extreme sequencing depth and computational error correction to separate the true ctDNA signal from background errors. This limitation is particularly problematic in the area of early cancer detection and minimal residual disease monitoring, in which expected ctDNA allele frequencies are extremely low. Novel strategies are therefore needed to help improve liquid biopsy assay sensitivity and reduce per-sample sequencing requirements. Here we describe PacBio’s application of the Onso short-read sequencing system to enable detection of ctDNA at low VAFs using the SeraCare Complete ctDNA Mutation Mix reference standard. The Onso system makes use of a novel sequencing by binding (SBB) method to achieve 10- to 100-fold greater quality scores, with ≥90% of reads at Q40 or above. We performed targeted capture and sequencing of libraries prepared from the SeraCare reference mix diluted into WT human DNA at the following VAFs: 0.00% (WT), 0.05%, 0.10%, and 0.25%, and compared the sensitivity at each VAF for SBB compared to a competitor method using sequencing by synthesis (SBS) at varying sequencing depths. At equivalent sequencing depth (12,000X) we observed superior recall for low frequency variants (VAF = 0.05%, 0.1%) in the reference sample for Onso compared to SBS. Furthermore, SBB was able to achieve comparable sensitivity results to SBS using four- fold less sequencing (6,000X vs. 24,000X), and without the use of computational error correction. Combining SBB with error correction methods boosted sensitivity even further, suggesting an additive value for these technologies. Comparison of false positive variant calls in the WT samples showed a dramatic decrease in false positives for Onso (n=2) compared to SBS (n=49) across 10 subsampling replicates, and observed allele frequencies for Onso were closer to the expected values. In conclusion, Onso achieved superior sensitivity for ctDNA detection at compared to SBS at equivalent sequencing depths, and achieved similar performance with four- fold less sequencing and without the use of error correction. This improvement was due in part to the reduction in false positive variant calling in the WT sample, which lowers the limit of detection to maximize variant detection while maintaining high specificity. Taken together, our results demonstrate the potential of SBB to improve upon existing methods of liquid biopsy and better enable research on early cancer detection and minimal residual disease. Citation Format: Alexandra Sockell, Dan Nasko, Young Kim, Kristi Kim, Jonas Korlach. Improved liquid biopsy assay performance using novel highly accurate sequencing-by-binding (SBB) technology [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A037.

Analysis of the influence of macroeconomic factor on long-term and short-term fluctuation of the Indonesian stock exchange (BEI) using the error correction method (ECM) approach

This research aims to empirically test the influence of the short-term equilibrium relationship and long-term equilibrium relationship of economic growth, interest rate, inflation, and exchange rate on IHSG (IDX Composite) using empirical Error Correction Model (ECM) data from 2000 to 2023. During the observation period, the macroeconomic variables showed no significant influence on the movement of the IHSG (IDX Composite) in the short-term equilibrium relationship, whereas in the long-term equilibrium relationship almost all variables, namely economic growth, interest rate, and exchange rate, had a significant influence on the IHSG ( IDX Composite), while inflation has no short or long term effect on the IHSG.

An ADCP Attitude Dynamic Errors Correction Method Based on Angular Velocity Tensor and Radius Vector Estimation

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%.

A gas-surface interaction algorithm for discrete velocity methods in predicting rarefied and multi-scale flows: For Maxwell boundary model

The discrete velocity method (DVM) for rarefied flows and the unified methods (based on the DVM framework) for flows in all regimes, from continuum one to free molecular one, have worked well as precise flow solvers over the past decades and have been successfully extended to other important physical fields. Both DVM and unified methods endeavor to model the gas-gas interaction physically. However, for the gas-surface interaction (GSI) at the wall boundary, they have only use the full accommodation boundary up to now, which can be viewed as a rough Maxwell boundary with a fixed accommodation coefficient (AC) at unity, deviating from the real value. For example, the AC for metal materials typically falls in the range of 0.8 to 0.9. To overcome this bottleneck and extend the DVM and unified methods to more physical boundary conditions, an algorithm for Maxwell boundary with an adjustable AC is established into the DVM framework. The Maxwell boundary model splits the distribution of the bounce-back molecules into specular ones and Maxwellian (normal) ones. Since the bounce-back molecules after the spectral reflection does not math with the discrete velocity space (DVS), both macroscopic conservation (from numerical quadrature) and microscopic consistency in the DVS are hard to achieve in the DVM framework. In this work, this problem is addressed by employing a combination of interpolation methods for mismatch points in DVS and an efficient numerical error correction method for micro-macro consistency. On the other hand, the current Maxwell boundary for DVM takes the generality into consideration, accommodating both the recently developed efficient unstructured velocity space and the traditional Cartesian velocity space. Moreover, the proposed algorithm allows for calculations of both monatomic gases and diatomic gases with internal degrees in DVS. Finally, by being integrated with the unified gas-kinetic scheme within the DVM framework, the performance of the present GSI algorithm is validated through a series of benchmark numerical tests across a wide range of Knudsen numbers.

Qubit: The Leap into Quantum Computation

Abstract. This article delves into the fundamental aspects of qubits in quantum computation, emphasizing their coordination, mathematical representation through the Bloch Sphere, and the unique advantages and challenges of superconducting, trapped ion, and photonic qubits. It highlights the transformative potential of quantum algorithms in solving complex problems in cryptography and information security, while addressing key sources of quantum errors such as decoherence and quantum noise. The study discusses advanced error correction methods and underscores the necessity of improving qubit coherence, stability, scalability, and integration for the practical application and commercialization of quantum computers. Ongoing research and collaboration are deemed essential for overcoming technical challenges and unlocking the full potential of quantum computation across various industries.

Inclination information-aided circumferential deformation reconstruction error correction method for CFRP belt integrated with optical fiber sensor

In view of the problem that the distributed optical fiber-based deformation reconstruction method can produce serious accumulated errors, this paper developed a smart fiber optic sensor belt that employs a carbon-fiber composite material as the matrix and strain-sensing fiber optics as the sensing element and is based on the key-point dip angle information acquired by a high-precision tilt sensor. The method of in situ correction for the cumulative error of circumferential deformation was investigated, and the particle swarm optimization algorithm was adopted to perform in situ correction for the reconstructed circumferential deformation field of the sensor belt to reduce the cumulative measurement error of the circumferential deformation field. Simulations and indoor tests were systematically performed. After the correction, the errors of the simulation and indoor tests were reduced from 210.83 and 235.82 mm to 2.17 and 4.04 mm, respectively, which verified the effectiveness and accuracy of the aforementioned method.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Multi-Step-Ahead Time Series Forecasting using Deep Learning and Fuzzy Time Series-based Error Correction Method

Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

A Survey on Quantum Computing Architectures: Implications for Electrical Circuit Design and Optimization

Quantum computing, a paradigm shift with the potential to revolutionize fields ranging from cryptography to optimization, is rapidly evolving. This survey delves into the key quantum computing architectures—gate-based and quantum annealing—and their profound impact on circuit design and optimization. Gate-based systems, such as those utilizing superconducting qubits and trapped ions, offer versatile platforms capable of executing a broad array of quantum algorithms. However, they present unique challenges for circuit designers, including noise sensitivity, scalability, and the need for complex error correction circuits. Quantum annealing, exemplified by D-Wave systems, offers a more specialized approach to solving optimization problems, requiring distinct circuit design considerations focused on energy efficiency and coherence preservation.This paper reviews the foundational research on these architectures, compares their respective design challenges, and examines the optimization techniques that have emerged in response to these challenges. Gate count reduction, error correction, and hybrid quantum-classical methods are highlighted as key approaches for improving circuit efficiency and scalability in quantum systems. Furthermore, this survey emphasizes the importance of interdisciplinary collaboration between quantum physicists, electrical engineers, and computer scientists to address the complex challenges associated with quantum circuit design. By combining expertise from these fields, researchers can develop innovative solutions that bridge the gap between theoretical concepts and practical hardware implementations. Ultimately, this survey underscores the need for continued innovation at the intersection of quantum computing and electrical engineering, as researchers strive to overcome current limitations and develop practical, large-scale quantum systems that can harness the full potential of this revolutionary technology.

Identifying Potential Landslides in Low-Coherence Areas Using SBAS-InSAR: A Case Study of Ninghai County, China

The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards.

Dynamic Assessment-Based Curriculum Learning Method for Chinese Grammatical Error Correction

Dynamic Assessment-Based Curriculum Learning Method for Chinese Grammatical Error Correction

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

In a data stream, errors are quite likely to occur and sometimes this is much more terrible. So, data safety is very important in digital systems, especially in critical and real-time systems, microprocessors, embedded systems, computer memory, and data communication. The probability of soft error increases with the exponential rate of increasing transistor per chip, operational voltage, particle strike, condensation of bit-cell area, etc. To ensure data integrity, safety, and system reliability, error detection, and correction are fundamental components of data transmission and storage systems. Existing error correction techniques can solve several bits of error. However, these existing methods are not fully efficient, as some consume a lot of time, space, and bit overhead. An ideal approach will have the potential to minimize all of these parameters. This research paper proposes a novel error correction approach with horizontal, vertical, diagonal, and knight (HVDK) parity bits. This approach has been taken to correct 5-bit errors in 64 bits of data word using the parity-based technique with less bit overhead. Our research advances the knowledge of error correction methods and sheds light on how to pick and use parity bit schemes that are appropriate for different applications.

Analysis of orientation errors in triaxial fluxgate sensors and research on their calibration methods

Abstract. Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method.

MC-VMD-CNN-BiLSTM short-term wind power prediction considering rolling error correction

Abstract Wind energy is a clean and renewable source that has the potential to alleviate the global fossil fuel crisis and environmental pollution by generating electricity. However, accurately predicting wind energy output remains challenging due to its inherent uncertainty. To enhance the accuracy of wind power prediction, a short-term wind power forecasting method for power systems, MC-VMD-CNN-BiLSTM, is proposed, which considers error rolling correction. The method begins with feature selection and outlier handling using the quadrature method. Then, wind power data is decomposed into multiple sub-sequences using the Variational Mode Decomposition (VMD) technique to reduce the raw volatility of wind power. Then, a Convolutional Neural Network (CNN) followed by a Bidirectional Long Short-Term Memory (BiLSTM) model is used for wind power prediction. Finally, the proposed method utilizes the Monte Carlo method for rolling error correction by using known errors from previous time frames to correct subsequent predictions. The MC-VMD-CNN-BiLSTM proposed in this paper considering error rolling correction is compared with ELM, SVM, PSO-BP and ARIMA models through an example analysis of the data of a city, and the proposed model in this paper reduces 61.78%, 50.35%, 62.30% and 73.05% in the NRMSE index in the spring as an example, respectively. The results show that the prediction model proposed in this paper has higher prediction accuracy compared with the traditional prediction model.

Phase Error Correction in Sparse Linear MIMO Radar Based on the Equivalent Phase Center Principle

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method.

Effectiveness of Fiscal and Economic Policy in Monitoring Inflation in Nigeria

This study investigates the effectiveness of monetary and fiscal policies in controlling inflation in Nigeria from 1993 to 2023. Using co-integration and error correction methods on yearly time series data, the study reveals several key findings. The unit root test indicates that only inflation (INF) is stationary at levels, while money supply (MS), taxation (TAX), and government expenditures (GEX) are stationary at first difference. The results show that an increase in the money supply and government expenditures lead to a rise in the inflation rate, whereas higher taxation reduces it. Based on these findings, the study concludes that MS, TAX, and GEX significantly impact on inflation in Nigeria. The study recommends that policymakers should better manage the money supply to reduce the inflation rate by decreasing the amount of money in the economy. Increasing the tax rates on individuals and companies, especially during periods of high inflation, can lower the amount of spendable money among citizens, thereby reducing inflation. Lastly, the government should control its expenditures to decrease the total amount of money in circulation, effectively reducing the country's inflation rate.

Comparison and Analysis of Three Methods for Dynamic Height Error Correction in GNSS-IR Sea Level Retrievals

Comparison and Analysis of Three Methods for Dynamic Height Error Correction in GNSS-IR Sea Level Retrievals

Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model

Due to the complex runoff and concentration situation, flood forecasting for small and medium-sized catchments is very difficult. To improve the accuracy of flood forecasting, this study constructs a distributed model for flood forecasting based on the Xainanjiang (XAJ) model and the North China (NC) model respectively, and takes the deep learning model including LSTM and transformer to compare. Taking the Podi Basin and Shibazi Basin as study cases. LSTM, Transformer, and LSTPencoder models were used to correct the error of distributed models, and the differential evolution (DE) algorithm was used to optimize the model parameters. Taking the observed rainfall and distributed model results as input, the residuals of the simulation flow are fitted to improve the accuracy of the distributed model. The research results show that the NC model performs better than the XAJ, LSTM, and transformer models. Compared with the XAJ model, the average Nash-Sutcliffe coefficient of the NC model for the two catchments increased by 25.7 % and 5.87 % respectively. The performance of the NC+LSTPencoder model is better than other models. Compared with the NC model, the average Nash-Sutcliffe coefficient of the NC+LSTPencoder model for two catchments increased by 89.7 % and 1.12 % respectively, and the Root Mean Square Errors (RMSE) of the two catchments reduced by 79.1 % and 63.4 % respectively. The model proposed in this paper has strong correction ability, which has important significance for improving the accuracy of flood forecasts.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Advanced error correction method for quantum key distribution in IoT systems

The secure distribution of keys is essential to ensuring the privacy and integrity of data in Internet of Things (IoT) systems. Quantum Key Distribution (QKD) protocols, such as BB84, use principles from quantum mechanics to establish secure communication channels. However, errors that occur during the transmission of qubits can compromise the reliability of the shared key. This paper presents a new method for error correction that integrates Convolutional Neural Networks (CNNs) into the BB84 protocol for QKD in IoT systems. By harnessing the exceptional pattern recognition capabilities of CNNs, our approach learns hierarchical representations from preprocessed quantum channel data to effectively identify and correct errors. Extensive simulations demonstrate the high performance of our CNN-based method in reducing the Quantum Bit Error Rate (QBER). This contribution significantly enhances the security and reliability of key distribution, offering a promising solution for robust quantum communication in IoT environments.