Correction Ability Research Articles

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

State-of-Charge Estimation Method for Lithium Batteries Based on Adaptive Fusion Factors

Abstract Accurate estimation of the state of charge is significant for battery safety. To improve robustness, computational efficiency, and noise stability in state of charge estimation for lithium batteries, we propose a method based on adaptive fusion factors. Based on the methodological properties of the open circuit voltage method and the ampere hour method, we designed a fusion factor function to combine the strong correction ability of the open circuit voltage method with the smoothing advantages of the ampere hour method. The proposed method utilizes an adaptive forgetting factor recursive least squares approach to address the limitation of the traditional open circuit voltage method, which cannot estimate state of charge online. It corrects the battery capacity using historical data to achieve accurate state of charge estimation. The effectiveness and robustness of the proposed method are validated using self-tests and a public dataset. The results demonstrate that the mean absolute error in state of charge estimation is approximately 1%, even when the initial state of charge value deviates from the actual value and the dataset contains noise.

Application of deep forest algorithm incorporating seasonality and temporal correlation for wind speed prediction in offshore wind farm

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation.

English Grammar Auto-Correction Robot based on Grammatical Error Generation Model

The traditional English grammar error correction system has problems such as poor error recognition precision and error correction success rate to be improved. Therefore, in this study, data augmentation techniques were used to transform and process error correcting English texts, and a rule-based and shallow neural network-based English text grammar correction model was constructed. The experimental results showed that the grammar error generation (GEG), rule-based (RB), classification-based (CB), and recurrent neural network (RNN) models achieved accuracy rates of 93.92%, 82.17%, 79.41%, and 88.09% in correcting grammar errors on 1702641 test sentences in the One Billion word corpus, respectively. The experimental results showed that the English grammar error correction model designed in this study had a strong error correction ability, but the computational efficiency was low. The research results significantly improved the accuracy and generalization ability of English grammar correction, optimized learning costs, and brought positive impacts to educational applications, providing strong support for the development of intelligent English grammar correction.

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.

Bayesian ex Post Evaluation of Recursive Multi-Step-Ahead Density Prediction

This research is focused on a formal Bayesian method of recursive multi-step-ahead density prediction and its ex post evaluation. Our approach remains within the framework of the standard (classical or orthodox) Bayesian paradigm based on the Bayes factor and on the use of the likelihood-based update. We propose a new decomposition of the predictive Bayes factor into the product of partial Bayes factors, for both a finite number of consecutive k-step-ahead forecasts (where k>1) and the recursive updates of the posterior odds ratios based on updated data sets. The first factor in the decomposition is related to the relative k-step-ahead forecasting ability of models, while the second one measures the updating effect. To illustrate the usefulness of the proposed measures, we apply the new decomposed predictive Bayes factors to compare the forecasting ability of models when the true data generating process (DGP) is known, using simulated data sets. Taking into account the effect of updating, the posterior odds ratios leads to the conclusion that the best model coincides with the true DGP. However, the highest k-step-ahead forecasting ability (considered alone) can be achieved by some other, less adequate models. Next, we investigate the predictive ability of different Vector Error Correction (VEC) models with conditional heteroscedasticity, combining three macroeconomic variables: unemployment, inflation and interest rates, separately for the US and Polish economies. The results show that the inference about the models’ predictive performance depends on the forecast horizon as well as on taking into account the updating effect.

Three-dimensional deformity correction in adolescent idiopathic scoliosis patients: what are the benefits of hybrid apical sublaminar bands versus all-pedicle screws?

The amount of three-dimensional (3D) correction with apical sublaminar band (hybrid-SLB) technique has not been compared to all-pedicle screw instrumentation for adolescent idiopathic scoliosis (AIS) using detailed axial correction metrics or comparable rod types. Our purpose is to compare 3D improvement in AIS deformities following posterior spinal instrumentation and fusion (PSIF) with hybrid-SLB and segmental correction to all-pedicle screw correction. Patients ages 10-18 years with AIS who underwent PSIF between 2015 and 2022 and had preoperative and postoperative EOS imaging were included. Electronic medical records were reviewed for demographic, Lenke classification, operative technique, and 3D EOS data. Average changes in major and minor Cobb angle, axial rotation, thoracic kyphosis, and lumbar lordosis were compared. Ninety-five patients met inclusion criteria with 55 in the hybrid-SLB group (mean age 14.9 ± 1.9 years) and 40 in all-pedicle screw (mean age 14.7 ± 2.1 years). While all-pedicle screw demonstrated greater correction of major (45.7 ± 13.4 vs 37.9 ± 14.3 degrees; P = 0.008) and minor (28.7 ± 13.1 vs 17.8 ± 12.5 degrees; P = 0.001) Cobb angles, hybrid-SLB showed greater increase in T4-T12 kyphosis (13.3 ± 15.3 vs 5.6 ± 13.5 degrees; P = 0.01). Correction of T1-T12 kyphosis, axial rotation, and lumbar lordosis was similar between groups. 3D EOS analysis of AIS patients before and after PSIF revealed that all-pedicle screw constructs had greater overall coronal plane correction and hybrid-SLB had greater thoracic sagittal plane correction. Axial corrective abilities were similar. Hybrid-SLB may have advantages for correction of thoracic lordosis or hypokyphosis. Level of evidence: Level III, retrospective cohort study.

Illumination Field Uniformity Correction by Novel Finger Arrays for Lithography Illumination System

In order to correct the integrated nonuniformity of a lithographic illumination field, a high-precision uniformity correction method for an advanced lithographic illumination system is proposed. The method adopts the opaque finger array structure and improves correction ability and accuracy by optimizing the arrangement and structure of the unit without changing the width of each unit. The correction accuracy is expressed as the percentage of the corrected integrated nonuniformity. Through theoretical analysis and simulation, it can be seen that the correction accuracy of a staggered finger array is better than 0.22%. When staggered and layered, the correction accuracy of a finger array is better than 0.14%, which is better than that of a non-layered finger array. When staggered, layered, and chamfered of each unit, the correction accuracy of the finger array structure is better than 0.12%.

Research on business English grammar detection system based on LSTM model

Abstract In order to solve the problems that the current English grammar correction algorithms are not effective, the error correction ability is limited, and the error correction accuracy needs to be improved, this study proposes an automatic grammar correction method for business English writing based on two-way long short-term memory (LSTM) and N-gram. First, this study considers article and preposition errors as a special sequence labeling task, and proposes a Grammar error checking (GEC) method for sequence labeling based on bidirectional LSTM. During training, english as a second language (ESL) corpus and supplementary corpus are used to label specific articles or prepositions. Second, for noun simple-plural errors, verb form errors, and subject-verb inconsistency errors, a large number of news corpora are used to count the frequency of N-gram, and a GEC method based on ESL and news corpora N-gram voting strategy is proposed. Experimental results show that the overall F 1 value of the method designed in this study on the GEC data of CoNLL2013 is 33.87%, which is higher than the F 1 value of UIUC. The F 1 value of article error correction is 38.05%, and the F 1 value of preposition error correction is 28.89%. It is proved that this method can effectively improve the accuracy of grammar error correction and solve the gradient explosion problem of traditional error correction model, which is of great significance to further strengthen the practicality of automatic grammar error correction technology.

Radiologic and Clinical Outcomes of the Dovetailed Notch Scarf Osteotomy for Correcting the First Metatarsal Pronation in Moderate to Severe Hallux Valgus Deformity: A Comparative Study.

The traditional scarf osteotomy (TSO) has limited ability to correct the first metatarsal pronation. A novel modification that we refer to as a "dovetailed notch scarf osteotomy" (DNSO) has been developed to enhance the ability to correct coronal plane pronation. The study aimed to observe and compare TSO to DNSO in the treatment of moderate to severe hallux valgus deformity. This retrospective study included 78 feet that had a TSO and 105 feet that had a DNSO. Minimum follow-up was 24 months. Weightbearing computed tomography (WBCT) and weightbearing anterior-posterior (AP) radiographs were taken preoperatively and at the last follow-up. We measured the intermetatarsal angle (IMA), hallux valgus angle, distal metatarsal articular surface angle on AP radiographs and first metatarsal coronal pronation angle (α angle), tibial sesamoid coronal grading, and first metatarsal length on WBCT. Clinical assessment was done using visual analog scale (VAS), American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot scale, Foot and Ankle Ability Measure (FAAM), and the 36-Item Short Form Health Survey (SF-36). The occurrence of postoperative complications was also documented. The DNSO group exhibited a significantly higher correction amount of α angle and IMA (14.3 ± 9.9 and 10.3 ± 4.6 degrees) than the TSO group (8.6 ± 5.9 and 5.4 ± 5.9 degrees) during the final follow-up assessment (P < .05).The DNSO group (10.1 [8.0-12.0] degrees and 4.8 [3.9-5.6] degrees) demonstrated significantly smaller α angle and IMA compared with the TSO group (4.8 [3.9-5.6] degrees and 9.5 [7.5-11.5] degrees) at 24 months postsurgery (P < .05). The postoperative FAAM activities of daily living and SF-36 physical functioning scores were significantly higher in the DNSO group (97.2 ± 3.3 and 95.7 ± 4.4 points) compared with the TSO group (92.3 ± 3.3 and 87.7 ± 8.7 points) (P < .05). Additionally, hallux varus occurred in 1 case in the DNSO group, whereas 4 cases were observed in the TSO group. Two osteotomy methods can effectively correct moderate to severe hallux valgus deformity. Compared with the TSO, the DNSO has stronger correction ability. The most crucial aspect lies in its controllability when correcting first metatarsal pronation and addressing IMA. Level III, retrospective comparative study.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Canter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

Transfer effects of recursive self-feedback on connected speech production in patients with chronic nonfluent aphasia: Preliminary results

ABSTRACT Background Persons with nonfluent aphasia (PWNA) have deficits with online feedback for speech error detection and correction which have debilitating effects on language relearning in learned and novel contexts. Previous studies have shown that aphasia treatments that combine PWNA’s offline self-feedback and external feedback from speech-language pathologists (SLPs) improve language production in learned and novel contexts. However, it remains unclear whether PWNA can use their offline self-feedback alone during treatments that generalizes to improvement in connected speech production. Here, we examine generalization effects of recursive self-feedback on PWNA’s connected speech production. Recursive self-feedback utilizes offline self-feedback loops alone to help PWNA optimize their error monitoring and correction abilities for language production in learned and novel contexts. Method A crossover design was used to investigate generalization effects of two treatments on connected speech production in four PWNA. These treatments comprised script production with recursive self-feedback and script production with external feedback. Employing a mobile health approach, treatments were remotely administered to participants within the comfort of their homes. Practice of both treatment blocks occurred intensively over two hours per day, five days per week, spanning two to three weeks. Each treatment block featured pretreatment and posttreatment assessment phases, with the sequencing of treatment blocks evenly counterbalanced across all participants. Following the initial treatment block, a two-week period devoid of treatment preceded the commencement of the second treatment block. Untrained narrative elicitation tasks were utilized to prompt connected speech production samples from all participants during the assessment phases of each treatment block. Effect sizes per participant were computed using within-case standard mean difference analysis. Results PWNA exhibited variability in performance, showing improvement in microlinguistic measures of connected speech following both script production with recursive self-feedback and script production with external feedback. Particularly, script production with recursive self-feedback demonstrated marginally superior enhancement in connected speech measures compared to its counterpart. Conclusion The study provides preliminary evidence for generalization effects of recursive self-feedback training and external feedback training on PWNA’s connected speech production. Further research is required to corroborate the findings and to implement recursive self-feedback procedures optimized specifically for spontaneous speech.

Evaluation methods of BDS-3 ground-segment defensive capability oriented to satellite navigation countermeasures

Owing to intentional interference and destructions during satellite navigation countermeasures, the security application of global navigation satellite systems (GNSS) faces immense challenges. To ensure the safe application of the GNSS in complex electromagnetic environment, the system-level defensive capability need be assessed, which can make for maintaining GNSS normal service during countering and further realizing the optimal decision. Therefore, this study used the ground control segment in the GNSS as the evaluation object, the bottom indices related to the criterion layers including service performance and station function as the key evaluation indices, and the technical requirement of each index under a secure situation as the standard, to propose the evaluation system including index system and evaluation methods for the ground-segment defensive capability. As BeiDou navigation satellite system III (BDS-3) is a global navigation satellite system independently built and operated by China, it was taken as a case for method verification. Then, BDS-3 two-way observation data between the satellite and the ground and between stations, which was collected from Beijing flight Control Center, from March 5 to 12 (2022) were selected during tests, and the results were compared with the existing standards contained in BDS open service performance standard to evaluate the system-level defensive capability. Evaluation Results of positioning accuracy and error correction ability for the BDS-3's ground stations determined through seven damage experiments with different interference strength show that: Corresponding horizontal and vertical positioning accuracy of stations under test did not exceed 1 m in the statistical period and their convergence time did not exceed the limit (30 min); thus, the ground segment of the BDS-3 has a good defensive capability. Compared to comprehensive evaluation results of the BDS-3, based on the theoretical threshold (the ground-segment threshold ≤48.40 % and the space-segment threshold ≤51.60 %), we know that its ground-segment defense (14.06 %) was worse than GPS ground-segment defense (14.43 %), whereas the ground-segment defense of the BDS/GPS integrated system (15.29 %) was best and its capability evaluation grade was strong. They indicate system's future improvements should focus more on enhancing the development scale and service efficiency of the BDS-3's ground stations in the Western Hemisphere, and reasonably promote system compatibility and interoperability while considering facility cost and operating efficiency.

Embedded parallel-actuated technology for deformable space segmented mirror

The deployable segmented space imaging system is an important solution for future ultra-large aperture space optical systems. To achieve the imaging capability of an equivalent aperture monolithic mirror, it requires not only to ensure the positional accuracy in the cophasing process, but also to have extremely high surface accuracy and curvature consistency of the sub-mirrors. However, this work is extremely challenging due to the manufacturing error of the sub-mirrors and the complex space environment. Active optical technology can ensure the surface shape accuracy of the spliced mirror by controlling the mirror surface deformation and compensating for the wavefront aberration. This article compares and analyzes the control ability of two types of deformable mirrors actuated by vertical and parallel methods. We explored the characteristics of the influence function mathematical models of the two types of actuation forms and compared the aberration and curvature correction abilities of them through finite element analysis, summarizing the advantages of the parallel actuation forms. Finally, a 300mm aperture embedded parallel-actuated deformable mirror was designed and manufactured, and relevant experiments were conducted to verify its adjustment ability. By comparing and analyzing the experimental results with the design results, the adjustment ability of the embedded parallel-actuated deformable mirror was verified.

Single-voxel delay map from long-axial field-of-view PET scans.

We present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics. Whole-body scans of 15 patients divided equally among three tracers, namely [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution. Our proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays. The algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

Design and Simulation of a 19-Electrode MEMS Piezoelectric Thin-Film Micro-Deformable Mirror for Ophthalmology.

This study presents a numerical simulation-based investigation of a MEMS (micro-electromechanical systems)technology-based deformable mirror employing a piezoelectric film for fundus examination in adaptive optics. Compared to the classical equal-area electrode arrangement model, we optimize the electrode array for higher-order aberrations. The optimized model centralizes electrodes around the mirror center, which realizes low-voltage driving with high-accuracy correction. The optimized models exhibited commendable correction abilities, achieving a unidirectional displacement of 5.74 μm with a driven voltage of 15 V. The voltage-displacement relationship demonstrated high linearity at 0.99. Furthermore, the deformable mirror's influence matrix was computed, aligning with the Zernike standard surface shape of the order 1-3. To quantify aberration correction capabilities, fitting residuals for both models were calculated. The results indicate an average removal of 96.8% of aberrations to the human eye. This underscores that the optimized model outperforms the classical model in correcting high-order aberrations.

Constructing order-2 information granules of linguistic expressions with the aid of the principle of justifiable granularity

To capture collective opinions/evaluations in a collection of individual linguistic expressions, this study proposes an approach to construct order-2 information granules by extending the numerical data-based principles of justifiable granularity to a linguistic data-based one. First, the two key criteria of the principle of justifiable granularity, namely coverage and specificity, are formally defined in the context of order-2 information granules. Second, three order-2 information granules construction models by maximizing the product of coverage and specificity are developed for coping with one-dimensional direct linguistic expressions, linguistic preference relations, and multi-dimensional direct linguistic expressions, respectively. Third, considering that the developed order-2 information granules construction models exhibit a non-linear uncertain objective function and equality constraints, the constrained multi-swarm PSO without velocity is improved with the use of the ε constrained handling technique for effectively solving the models. Case studies on the considered three types of linguistic expressions show the applicability of the proposed models and ensuing algorithm. The superiority of the improved algorithm in handling the proposed models is demonstrated by comparison with the original one. The effectiveness of the proposed models in terms of abnormal corrective ability and balance of coverage and specificity is verified by comparing with a family of Top-n methods. The originality of this study lies in the construction of an operational entity, namely order-2 information granule, that reflects the group opinion without specifying the formalism of individual linguistic expressions, which provides an effective and efficient way to aggregate ubiquitous linguistic information for subsequent computation, reasoning and decision-making.

DNA-QLC: an efficient and reliable image encoding scheme for DNA storage

BackgroundDNA storage has the advantages of large capacity, long-term stability, and low power consumption relative to other storage mediums, making it a promising new storage medium for multimedia information such as images. However, DNA storage has a low coding density and weak error correction ability.ResultsTo achieve more efficient DNA storage image reconstruction, we propose DNA-QLC (QRes-VAE and Levenshtein code (LC)), which uses the quantized ResNet VAE (QRes-VAE) model and LC for image compression and DNA sequence error correction, thus improving both the coding density and error correction ability. Experimental results show that the DNA-QLC encoding method can not only obtain DNA sequences that meet the combinatorial constraints, but also have a net information density that is 2.4 times higher than DNA Fountain. Furthermore, at a higher error rate (2%), DNA-QLC achieved image reconstruction with an SSIM value of 0.917.ConclusionsThe results indicate that the DNA-QLC encoding scheme guarantees the efficiency and reliability of the DNA storage system and improves the application potential of DNA storage for multimedia information such as images.

Feature of the forward error correction constructions for optical transport networks

Due to the rapid increase of network traffic in the last few years, many telecommunication operators have started transitions to 100G optical networks and beyond. However, high speed optical networks need more efficient Forward Error Correction (FEC) codes to deal with the optical impairments. Forward error correction is widely used in optical communications to improve error correction and extend optical transmission distance. It is also used to reduce optical transmitter power and system costs. In response to the rapid growth of optical communications, the ITU-T has started researching FEC coding and has recommended ITU-T G.709 and G.975.1. As optical transmission systems evolve towards longer transmission distance, greater capacity, and speeds of 100G and beyond a variety of unwanted effects are seriously affecting smooth evolution. In optical transport network, FEC is getting more and more important as spectral efficiency is increasing. As 100G system is commercialized, there are some trends in the evolution of FEC, for example overhead is increased from 7% to 20% or even more. High-performance FEC codes are therefore being developed so that higher net coding gain (NCG) and better error correction can be achieved. The Optical Internetworking Forum (OIF) suggests that soft-decision forward-error correction (SD-FEC) with a redundancy of 20% and beyond be used in a 100G DWDM coherent system. The use of advanced modulation formats is a promising solution for attaining such high data rates in optical networks. However, as a signal constellation grows in size, so does the optical signal to signal-to-noise ratio it requires to achieve a certain bit error rate (BER). This will degrade the error correction capability. Decoding is moving from hard to soft. Vendors are developing their own advanced codes instead of using codes recommended by ITU-T. Because different application systems have different decoding performance, latency, power and cost requirements, FEC systems with various transmission overhead, implementation complexity, burst-error correction ability and error floor are available in today’s market. Soft-decision FEC often uses turbo product codes and low density parity check (LDPC) codes. In research area, new codes such as LDPC codes and their different varieties are introduced into optical communication. Optimized codeword structure and decoding algorithm that provides low BER. LDPC codes can be very powerful, but their practical implementation for optical communications links at ultra-high data rates remains a challenge since the decoding of the code requires soft-decision bits about the received bits. A family of structured codes known as quasi-cyclic LDPC codes, whose common structural properties can be exploited by unified encoding and decoding architectures to reduce the complexity in implementation.

Biomechanical effects of Evans versus Hintermann osteotomy for treating adult acquired flatfoot deformity: a patient-specific finite element investigation.

Evans and Hintermann lateral column lengthening (LCL) procedures are both widely used to correct adult acquired flatfoot deformity (AAFD), and have both shown good clinical results. The aim of this study was to compare these two procedures in terms of corrective ability and biomechanics influence on the Chopart and subtalar joints through finite element (FE) analysis. Twelve patient-specific FE models were established and validated. The Hintermann osteotomy was performed between the medial and posterior facets of the subtalar joint; while, the Evans osteotomy was performed on the anterior neck of the calcaneus around 10mm from the calcaneocuboid joint surface. In each procedure, a triangular wedge of varying size was inserted at the lateral edge. The two procedures were then compared based on the measured strains of superomedial calcaneonavicular ligaments and planter facia, the talus-first metatarsal angle, and the contact characteristics of talonavicular, calcaneocuboid and subtalar joints. The Hintermann procedure achieved a greater correction of the talus-first metatarsal angle than Evans when using grafts of the same size, indicating that Hintermann had stronger corrective ability. However, its distributions of von-Mises stress in the subtalar, talonavicular and calcaneocuboid joints were lesshomogeneous than those of Evans. In addition, the strains of superomedial calcaneonavicular ligaments and planter facia of Hintermann were also greater than those of Evans, but both generally within the safe range (less than 6%). This FE analysis study indicates that both Evans and Hintermann procedures have good corrective ability for AAFD. Compared to Evans, Hintermann procedure can provide a stronger corrective effect while causing greater disturbance to the biomechanics of Chopart joints, which may be an important mechanismof arthritis. Nevertheless, it yields a better protection to the subtalar joint than Evans osteotomy. Both Evans and Hintermann LCL surgeries have a considerable impact on adjacent joints and ligament tissues. Such effects alongside the overcorrection problem should be cautiously considered when choosing the specific surgical method. Level III, case-control study.