Error Correction Method Research Articles

Role of institutional quality in debt-growth relationship in Pakistan: An econometric inquiry

This study attempts to investigate the mediating role of institutional quality on the relationship between public debt and economic growth in Pakistan spanning 1996–2020. Time series data on all six World Bank World Governance indicators of institutional quality is used in the empirical analysis. Findings of the autoregressive distributed lag (ARDL) bounds testing technique and error correction method (ECM) confirmed the existence of cointegration among variables of interest. The short-run results indicate that public debt has a favorable association with economic growth, while the relationship is found to be detrimental in the long run. Furthermore, the combined effect of public debt and institutional quality indicators revealed the significant positive association with economic growth, suggesting that better institutional quality can contribute to mitigate the negative impact of public debt on economic growth in Pakistan.

Study on the Error Correction Method of Three-axis Fluxgate Sensor

Three-axis fluxgate sensors are quite widely used in various industries. However, due to the inherent limitations of their manufacturing process, various deviations can occur. In this paper, we study and analyze the sources of errors of three-axis fluxgate sensors, and construct a corresponding error model. The three-axis fluxgate sensor is successfully calibrated by the extended Kalman filter algorithm. Both simulations and experiments demonstrate that the method is effective in correcting the three-axis fluxgate sensor. In the experimental stage, the error was reduced by two orders of magnitude by applying the extended Kalman filter.

A camera-based vehicular visible light communication system using modulated taillights in public road scenarios

This paper proposes a camera-based vehicular visible light communication system using modulated LED taillights for vehicle-to-vehicle communication. The taillights are modulated using on-off keying with a carrier frequency above the critical flicker frequency threshold to appear steady to the human eye and only a camera with a short exposure time can perceive the signal. For receiving the signal, a common camera with a recording frame rate of 30 frames per second was used for the experiments. This limits the throughput of the communication system as only one bit per frame and per individually modulated light source can be transmitted, thus the system is not suitable for transmitting time-critical data. However, the main advantage is that the transmitted data as well as the transmitting vehicle are visible in the camera image, hence we can associate the message and its origin.For the communication between two cooperative autonomous vehicles, e.g., members of a platoon, a secure wireless channel with low latency is needed. To establish an encrypted connection in this main channel our system can be used as an out-of-band channel to verify the identity of the vehicle in front by transmitting a verification code.In this paper we evaluate how the communication quality in terms of bit error rate of the raw data transmission without any error correction methods is influenced by various environmental factors, e.g., communication distance, traveling speed, weather, brightness, etc. This is done by testing our system in controlled scenarios as well as on public roads. The results show that our system is capable of transmitting data with a raw bit error rate of 1.59% on average in platooning relevant situations on the highway in dry conditions. Rain affects the system due to raindrops on the windshield, spray thrown up by vehicles in front, and the windshield wipers occluding the modulated taillights of the transmitting car. In light to medium rain conditions the mean bit error rate is between 2.7% and 6.2%, depending on the intensity.

Effect of deformation on crack extension measurement for compact tension specimen with the DCPD technique

Direct current potential drop (DCPD) technique is a crack growth monitoring technique widely used in the crack growth test of specimens in laboratory environment. Deformation of the specimen during test is one of the important factors that affect the accuracy and resolution. Because the effect of deformation on measurement is usually mixed with other factors, quantitative evaluation is difficult through experiments method only. In this work a novel quantitative estimation method combining experiment and finite element analysis has been proposed, and the effect of deformation on measurement for 316 L stainless steel compact tension specimen has been quantitatively evaluated. Through the tensile test and DCPD monitoring test of the non-cracked 316 L stainless steel plate-shaped specimen, the material mechanical property parameters and the change of potential drop caused by deformation were obtained. Then the slow tensile tests have been performed on compact tension specimens. Combined with the FE simulation of the experimental process, the effect of deformation on the potential drop under different crack length has been evaluated. The results show that the influence of deformation on the potential drop is a factor that must be considered for the crack growth monitoring experiment with high crack growth resolution. The proposed method can be used to quantitatively evaluate the influence of deformation, a factor that was usually ignored in the past, on the measurement of crack growth, which provides a method and basis for correction of crack measurement errors caused by deformation. In addition, the proposed method can be used to obtain a more accurate calibration function, which is of great significance to the improvement of the accuracy and resolution of crack growth monitoring.

A Parallel Solution of Timing Synchronization in High-Speed Remote Sensing Data Transmission

Considering the problem that the timing synchronization calculation in high-speed remote sensing signal reception is complex and it is difficult for it to be parallel, this paper deduces and designs a parallel timing error estimation and correction scheme. This paper presents the design of polyphase DFT filter banks with non-maximum decimation. The feedforward timing error estimation and correction method is then improved to enhance synchronization performance. Finally, an implementation scheme for parallel timing error estimation and correction is proposed using the polyphase filter bank time domain decomposition method and the filter polyphase model. In the estimation module, the parallel implementation structure of the joint second-order and fourth-order cyclic statistics methods is designed, which improves the estimation accuracy. In the correction module, a fractional delay filtering method with higher accuracy is adopted in order to improve the calibration accuracy and reduce the computational complexity. The timing synchronization of a high-speed remote sensing signal with timing error is simulated and verified. The experimental results show that the parallel method proposed in this paper greatly reduces the processing speed of subband data, and has a good synchronization performance, which is close to the theoretical limit in the demodulation error rate. This paper utilizes a multi-phase DFT filter bank architecture to achieve parallel timing synchronization, which presents a novel approach for the future parallel reception of high-speed remote sensing signals.

Impact of Economic Growth and FDI on Indonesia Environmental Degradation: EKC and Pollution Hypothesis Testing

This study examines the impact of economic growth and FDI on environmental degradation in Indonesia through a two-hypotheses model. Environmental degradation is a crucial matter that needs attention and is often associated with economic growth and the FDI required by developing countries. Based on the externality theory, this study uses the EKC hypothesis, followed by pollution haven vs. pollution halo. With time series data from 1980–2021, two hypothesis models were estimated using the error correction method. This study's results support the EKC and pollution haven hypotheses. First, for the EKC hypothesis model, this study found that economic growth at an early stage increases environmental degradation, but economic growth beyond the turning point would reduce ecological degradation. Besides testing the EKC hypothesis, renewable energy consumption can reduce environmental degradation caused by carbon emissions. Second, for the pollution hypothesis model, it's been found that increased FDI can increase ecological degradation, thereby supporting a pollution haven. This research suggests that policymakers should require foreign investors to apply the green economy concept supported by a green taxonomy and then provide fiscal incentives to those who do so.

A Novel Error-Correcting Particle Swarm Optimization Back Propagation Fault Diagnosis Method for Microgrid

Compared to traditional power grids, microgrids have a more flexible operating mode. There are various distributed power sources within the microgrid, and different types of distributed power sources have different control methods. Once a short-circuit fault occurs in the microgrid, these characteristics will increase the difficulty of microgrid fault diagnosis and reduce the accuracy of microgrid fault diagnosis. This paper proposes an error-correcting particle swarm optimization back propagation microgrid fault diagnosis method for the diagnosis of short-circuit faults in microgrids that identifies the accuracy of alarm signals, corrects unreasonable signals, and obtains the correct fault set of the microgrid through the temporal logic relationship between each protection. Using the particle swarm optimization back propagation (PSO-BP) neural network algorithm to train fault alarm signals, fast convergence can be achieved, and accurate diagnostic results can be obtained after the sixth generation training is completed. As this fault diagnosis algorithm is applied to line protection equipment, it can be used to diagnose all types of short-circuit faults. This algorithm is easy to implement and has a small data scale, which is conducive to efficient and concise fault diagnoses in microgrids.

Integration of hybrid and self-correction method improves the quality of long-read sequencing data.

Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.

The Impact of COVID-19 Policy Response on Food Prices: A Case Study in China

The COVID-19 pandemic has threatened human lives and caused an unprecedented impact on the global economy, notably on the global food system. COVID-19 itself and its policy response have severely affected food prices. This paper analyzes the short-term and long-term impacts of COVID-19 cases and policy responses (government response index (GRI), economic support index (ESI), stringency index (SI)) on food prices (Chinese cabbage price (CP), pork price (PP), flour price (FP), rice price (RP)) in China’s major food producing areas and major food selling areas through the autoregressive distribution lag error correction (ARDL-ECM) method and graphical analysis. as the purpose is to provide evidence for food security policy regulation to stabilize food prices and improve the food system’s ability to withstand similar emergencies in the future. This study finds that the short-term impact of COVID-19 policy responses on food prices is larger, while the long-term impact is smaller. The impacts of COVID-19 itself and COVID-19 policy responses on food prices vary by food type and food production and consumption region. For food type, the Chinese cabbage price was most affected by the COVID-19 policy response, followed by pork, and the staple food prices (flour price and rice price) were least affected. For regions, the Chinese cabbage price in food producing areas was more influenced by COVID-19 and the pork price in food selling areas was more influenced by COVID-19. For the single indexes, GRI and SI had uneven effects on different food prices. However, ESI had a significant positive impact on the Chinese cabbage price (CP) and pork price(PP), except in Guangdong and Hubei. Staple food prices were almost unaffected by COVID-19 confirmed cases and policy responses. Then, taking the Chinese cabbage price in Beijing, Shandong, and Hubei as an example, we find that “dynamic clearance” influenced the Chinese cabbage price in the short term, and Chinese cabbage price increased largely in the first stage. Finally, the reasons behind the research results of this paper are analyzed, and scientific suggestions are put forward for the implementation of food supply policies to ensure food price stability and food security in the face of similar pandemics in the future.

Downscaling of GPM satellite precipitation products based on machine learning method in complex terrain and limited observation area

Precipitation products with high spatial and temporal resolutions are crucial in hydrological monitoring, meteorological forecasting, and disaster warning. However, obtaining such products in regions with complex terrain and limited observation conditions is a challenging task. To address this issue, a statistical downscaling model for satellite precipitation products using an optimized Back Propagation (BP) neural network was developed. The model employed key environmental variables selected by the Mean Impact Value (MIV) index, including enhanced vegetation (EVI), land surface temperature (LST_D, LST_N), evapotranspiration (ET), digital elevation model (DEM), slope, and latitude and longitude (Lon, Lat). The downscaled precipitation products were corrected twice using a combination of the model error correction and observation data merging methods. Finally, the satellite precipitation products with a resolution of 0.1° (∼10 km) were downscaled to 1 km. A comprehensive evaluation of the results showed that: (1) the accuracy of the precipitation products obtained using this method was better than that of the original satellite precipitation products; (2) the method effectively supplemented precipitation data in areas with few ground stations, with better results in areas with a dense distribution of ground stations; (3) this method demonstrated the efficacy of satellite precipitation products for downscaling in areas with complex terrain.

Design of quantum error correcting code for biased error on heavy-hexagon structure

Surface code is an error-correcting method that can be applied to the implementation of a usable quantum computer. At present, a promising candidate for a usable quantum computer is based on superconductor-specifically transmon. Because errors in transmon-based quantum computers appear biasedly as Z type errors, tailored surface and XZZX codes have been developed to deal with the type errors. Even though these surface codes have been suggested for lattice structures, since transmons-based quantum computers, developed by IBM, have a heavy-hexagon structure, it is natural to ask how tailored surface code and XZZX code can be implemented on the heavy-hexagon structure. In this study, we provide a method for implementing tailored surface code and XZZX code on a heavy-hexagon structure. Even when there is no bias, we obtain $$ 0.231 \%$$ as the threshold of the tailored surface code, which is much better than $$ 0.21 \%$$ and $$ 0.209 \%$$ as the thresholds of the surface code and XZZX code, respectively. Furthermore, we can see that even though a decoder, which is not the best of the syndromes, is used, the thresholds of the tailored surface code and XZZX code increase as the bias of the Z error increases. Finally, we show that in the case of infinite bias, the threshold of the surface code is $$ 0.264\%$$ , but the thresholds of the tailored surface code and XZZX code are $$ 0.296 \% $$ and $$ 0.328 \%$$ respectively.

In-Home Monitoring Sleep Turnover Activities and Breath Rate via WiFi Signals

In-home sleep monitoring is essential for evaluating sleep quality of individuals. Although many sleep monitoring systems have been developed recently, they have limitations in achieving a good performance at low cost. To address this issue, this article proposes a new system based on channel-state information of domestic WiFi network to monitor both turnover activities and breathing rate of sleepers. Unlike recent approaches placing receiving antennas close to each other, scattered placement is adopted to fully exploit spatial diversity of receiving antennas. More importantly, a new error correction method is proposed to accurately recognize turnover activities. Based on the interrelation between consecutive activities, the proposed method can effectively correct the recognition errors of existing methods including convolutional neural network. Then, for accurately estimating breathing rate, both a new subcarrier selection method and a new peak identification method are proposed. Experiment results show that our system can significantly improve the recognition accuracy of eight typical sleep turnover activities and four typical sleep postures. We can achieve the mean accuracy of 94.59% and 95.83% on the recognition of turnover activities and sleep postures, respectively. Besides, our system can also significantly improve the estimation accuracy of breathing rate especially in tough scenarios, such as prone and side-lying positions.

Enhanced ctDNA Profiling Reveals Molecular Determinants of Response and Resistance in Relapsed and Refractory Mantle Cell Lymphoma (NLG‐MCL7‐VALERIA)

Introduction: Deciphering molecular determinants of response to targeted treatments and early detection of refractoriness in patients with relapsed and refractory (R/R) mantle cell lymphoma (MCL) could individualize treatment, improve outcomes and limit unnecessary toxicities. Circulating tumor DNA (ctDNA) allows non-invasive profiling and measurement of treatment responses. Yet, increased sensitivity for detection and distinction from confounding biological signals are warranted. Enhanced ctDNA detection can be achieved using duplex sequencing or tracing of phased variants. Poor recovery of complementary strands and low number of phased variants limit the feasibility of these approaches separately in MCL. Methods: Thus, we developed and optimized a hybrid ctDNA sequencing platform, which combines strand- and phasing-aware error correction methods for improved specificity and sensitivity of non-invasive B-cell lymphoma profiling, quantification and detection (Figure 1A). We applied this platform to profile ctDNA in serial plasma samples from 59 patients with R/R MCL treated with lenalidomide, venetoclax and rituximab in the Nordic phase II trial (NLG-MCL7; VALERIA). We compared the results with outcomes and real-time quantified PCR (RQ-PCR, ‘Euro-Minimal Residual Disease (MRD’)) data from bone marrow (BM) and peripheral blood (PB) samples that guided therapy in the trial (Figure 1B). Results: Joint analysis of targeted sequences from pretreatment tumor, PB, BM, and plasma samples revealed MCL genotypes for ctDNA tracing even in the patients who failed primer designs for Euro-MRD (Figure 1C). Comparison between genotyping of samples from different compartments from individual patients mitigated confounding clonal hematopoiesis background and revealed spatially-confined MCL mutations. TP53 mutated MCL conferred resistance to therapy, whereas SMARCA4 mutations translated into durable molecular remission (Figure 1D-E). Elevated pretreatment ctDNA concentration was associated with multiple clinical risk factors, leukemic disease, and poor outcome (Figure 1F). After six months of therapy, ctDNA levels dropped in all studied patients. The responses in the ctDNA were highly concordant with concomitant Euro-MRD and extended to patients not evaluable by RQ-PCR. Notably, contrasting ctDNA clearance, the analysis of the post-therapy cell-free DNA revealed positive selection of clonal hematopoiesis drivers following therapy exposure, such as TP53 mutations in 77% of the patients. The research was funded by: Academy of Finland, iCAN Flagship, University of Helsinki, Helsinki University Hospital, Finnish Cancer Organisation, Sigrid Juselius Foundation and Abbvie. Keywords: diagnostic and prognostic biomarkers, liquid biopsy, molecular targeted therapies No conflicts of interests pertinent to the abstract.

Simulated annealing‐based high‐level synthesis methodology for reliable and energy‐aware application specific integrated circuit designs with multiple supply voltages

SummaryIntegrated circuits have become more vulnerable to soft errors due to smaller transistor sizes and lower threshold voltage levels. Energy reduction methods make circuits more error‐prone since even the smallest amounts of environmental radiation can cause a bit flip. Furthermore, redundancy‐based error detection and correction methods induce higher costs and area overhead. Thus, several conflicting parameters may need to be considered during embedded system design (e.g., area, performance, energy, and reliability). High‐level synthesis (HLS) is the most practical design step to consider all these parameters as complexity increases at lower levels. HLS can be viewed as a multi‐objective optimization problem of finding a set of Pareto‐optimal designs, allowing designers to choose the ones that best fit the requirements. Moreover, the number of synthesis options superlinearly affects the search space growth, necessitating efficient optimization methods. In this study, we propose simulated annealing (SA)‐based HLS methods for multi‐Vcc application‐specific integrated circuit design, aiming to optimize energy consumption and reliability under the area and latency constraints. Furthermore, we use duplication to improve design reliability as much as the constraints allow. We compared our methods against genetic algorithm (GA)‐based and integer linear programming (ILP)‐based methods and showed their effectiveness in finding optimum or near‐optimum results in a short running time. SA‐based methods achieved up to 21.20% reliability improvement on average and up to 38% energy reduction on average, while preserving the reliability value against the GA‐based metaheuristic counterpart under joint reliability and energy optimization on four HLS benchmarks.

Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China’s regional carbon price.

"Correcting" Gene Trees to be More Like Species Trees Frequently Increases Topological Error.

The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce the distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix. We found that gene tree error correction frequently increases the level of error in gene tree topologies by "correcting" them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Future gene tree correction approaches and methods should incorporate an adequately realistic model of evolution instead of relying on oversimplified heuristics.

ZUR ROLLE DER FEHLERKORREKTUR IM PROZESS DER WEITERENTWICKLUNG DER SPRACHKOMPETENZ

As part of the practical language lessons, German language lessons mostly placed great value on speaking and oral communication. In the courses dedicated to the phenomenon of communicative competence and oral error correction, fluency in communication, expressiveness and correct grammatical or lexical-phraseological competence are primarily promoted. It is primarily about the description of the phenomenon of communicative competence, the diagnosis of errors and methods of verbal error correction. This article addresses the question of which foreign language skills should be promoted and developed in German lessons in order to bring the language skills of the learners to a higher level. In addition, it is primarily about the question of which main goals the German lessons should be pursued by teachers and how the corrections regarding mistakes should be made. In surveys and discussions with students, it has often been stated and our teaching experience proves that, in addition to language skills, students should also acquire basic knowledge of interculturality, media, politics, social sciences and others during their studies. These findings show that learners are interested in multidimensional German lessons. It is a primary task of teachers, in addition to expanding foreign language vocabulary, to develop all the necessary skills of their learners in the process of acquiring foreign languages. In addition to the areas mentioned, the article also focuses on an important didactic phenomenon - the language mistakes. The teachers should be able to recognize statements made by their students and to detect their mistakes.

LCAT: an isoform-sensitive error correction for transcriptome sequencing long reads.

As the carrier of genetic information, RNA carries the information from genes to proteins. Transcriptome sequencing technology is an important way to obtain transcriptome sequences, and it is also the basis for transcriptome research. With the development of third-generation sequencing, long reads can cover full-length transcripts and reflect the composition of different isoforms. However, the high error rate of third-generation sequencing affects the accuracy of long reads and downstream analysis. The current error correction methods seldom consider the existence of different isoforms in RNA, which makes the diversity of isoforms a serious loss. Here, we introduce LCAT (long-read error correction algorithm for transcriptome sequencing data), a wrapper algorithm of MECAT, to reduce the loss of isoform diversity while keeping MECAT's error correction performance. The experimental results show that LCAT can not only improve the quality of transcriptome sequencing long reads but also retain the diversity of isoforms.

A comparison of point-tracking algorithms in ultrasound videos from the upper limb

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive video frames, such as variations of Optical Flow and Lucas–Kanade (LK), exploit frame-to-frame temporal information to track regions of interest. In contrast, convolutional neural-network (CNN) models process each video frame independently of neighboring frames. In this paper, we show that frame-to-frame trackers accumulate error over time. We propose three interpolation-like methods to combat error accumulation and show that all three methods reduce tracking errors in frame-to-frame trackers. On the neural-network end, we show that a CNN-based tracker, DeepLabCut (DLC), outperforms all four frame-to-frame trackers when tracking tissues in motion. DLC is more accurate than the frame-to-frame trackers and less sensitive to variations in types of tissue movement. The only caveat found with DLC comes from its non-temporal tracking strategy, leading to jitter between consecutive frames. Overall, when tracking points in videos of moving tissue, we recommend using DLC when prioritizing accuracy and robustness across movements in videos, and using LK with the proposed error-correction methods for small movements when tracking jitter is unacceptable.

Method of unwrapped phase error correction in phase-measuring profilometry based on adaptive local window

Phase measurement profilometry (PMP) is widely used to reconstruct three-dimensional (3D) shape of objects. However, errors are introduced during standard phase unwrapping process, which considerably brings down measurement accuracy and lowers method reliability. This paper proposes a novel method to identify the errors, and subsequently correct these errors through erecting adaptive local windows. Under proposal, two robust algorithms are developed to correct these errors in initial rudimentary correction as well as in subsequent refined error removal. The adaptive local window is designed as such that the fringe order corresponding to each period of the wrapping phase is independent of each other. Therefore, it avoids the error transmission in the correction process. Experiments are carried out on absolute phase error correction and 3D point cloud reconstruction. Experimental results verified the developed method in terms of reducing the stratification of 3D point cloud and improving the quality of 3D measurement.