Correct Errors Research Articles

ACHIEVING THE REQUIRED MACHINING ACCURACY BY CORRECTING ERRORS USING VARIABLES OF PARAMETRIC FUNCTIONS

Nowadays, the machine-building complex is actively saturated with high-tech equipment, without which an enterprise cannot enter the global market or keep up with its competitors. And it's not just about using systems with artificial intelligence elements as scientific and technical solutions to improve quality and reduce production costs. It is also about reducing the time required to prepare products for production and bringing them to the consumer ahead of schedule. Today, the most preferred method in the competitive struggle is to equip CAD/CAM production with computer-aided design systems. With their help, engineering and technology services cannot only design models of new products but also generate CNC control software for manufacturing parts on CNC machines. At the same time, program frames composed in G-codes have several drawbacks, the main one being a "rigid" algorithm of action, i.e., the lack of variability for final solutions when the required accuracy is achieved by correcting the numerical values of the coordinates of the points of formation of individual surfaces in the PC. In this case, the operator largely intuitively corrects individual program frames based on his or her own experience, which is almost impossible without appropriate calculations for controllers with linear-circular and angular motions. In addition, after some such changes, the program loses its geometric adequacy to the part drawing, and by correcting one element of the shaped profile, we invariably violate the laws of contact with neighboring elements specified in the drawing. The paper considers the analytical geometry apparatus that allows a line on a double curvature surface, the theoretical trajectory of tool movement, to be represented not as a set of scalar points but in a vector representation, considering its possible torsion. This approach is ensured by parametric programming with computational frames of point coordinates and logical transitions, determining the angles of inclination of the cutter axis relative to the normal to the surface in the case of multi-axis machining. However, the main advantage of this method is the ability to correct processing errors not by local changes in the numerical values of the coordinates, but by introducing correction coefficients into the equation of the shape formation trajectory - reactors for the appearance of errors in the shape or location of surfaces, arising, for example, from elastic movements. The value of the coefficients per group of personnel can be set in the process of research and industrial production and change depending on the properties of the blanks. For example, according to the current standards, variations in hardness of up to 10-12% were allowed for the blanks of rolling mill rolls. The article presents the experimental data of the research of the correction task in a parametric form and the results of their application for machining parts with radius cutters in real production..

An Improved Classification Model for English Syntax Error Correction Design of DL Algorithm

To better respond to the slogan of smart teaching in universities and fully integrate various artificial intelligence technologies with educational learning, many scholars have conducted research on teaching methods and models in universities. Traditional English teaching often uses manual verification to correct grammar errors. In view of the shortcomings of the traditional manual English grammar correction methods, such as low efficiency, time-consuming, this paper combines the deep learning technology to design an English Syntax Error Correction (ESEC) model based on the Transformer structure. The paper first introduces the working principle of traditional neural networks in syntax error correction, and then studies the Generative Adversarial Network (GAN) and Transformer structure. Finally, the Transformer structure is integrated with the GAN and the ESEC model to create the final syntax error correction model. The results of testing the performance of the model showed that the designed model had good performance. The recognition accuracy, recognition recall, and F1 values on the test dataset CoNLL-2020 were 0.98, 0.96, and 0.97, respectively. The three values on the JFLEG test set dataset were 0.96, 0.98, and 0.97, respectively. In conclusion, the English grammar error correction model proposed in this paper demonstrates satisfactory performance, and its implementation in practical English grammar error correction tasks yields similarly positive results

A digital twin-driven ultra-precision machining system

The demand for ultra-precision machining has expanded significantly across industries such as aerospace, automotive, electronics, and medical sectors. These industries require parts manufactured to micrometre tolerances in a timely and cost-effective manner. To address these demands, efforts have been focused on developing digital twin technology for ultra-precision machining, aimed at improving production accuracy and efficiency. One of the primary challenges in ultra-precision machining is time-consuming setup due to manual workpiece handling. Additionally, machining speeds are limited to mitigate dynamic errors, further prolonging production times. This paper proposes a digital twin system designed to automate workpiece handling and correct dynamic errors in real time to tackle these challenges. The proposed digital twin comprises two systems: one for controlling a collaborative robot arm (COBOT) to automate workpiece handling with corrective action, eliminating the need for manual loading and unloading; and another for controlling a hybrid mill to mitigate dynamic errors through real-time machine learning-based prediction of elastic deformation allowing for higher machining speeds. In this paper, the current progress is discussed, and a methodology for validating this digital twin system is proposed. The proposed validation process will involve machining microfluidic devices using the digital twin system, compared to conventional machining methods to assess the effectiveness.

Impact of subjective sleep quality on objective measures of neurocognitive dysfunction in patients with major depressive disorder

Background: Sleep disturbances are prevalent in major depressive disorder (MDD). MDD and sleep disturbances are both linked to cognitive impairments. Studies exploring the mechanisms and impact of sleep disturbances on neurocognitive functioning in depressed patients are lacking and proper assessment and therapeutic interventions for sleep disturbances are not part of clinical management of MDD. Aim: We investigated the association between subjective sleep quality and neurocognitive dysfunction in patients with MDD. Materials and Methods: Patients with moderate MDD episode were matched and assigned to two groups with poor and good sleep quality. We used Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. To measure frontotemporally mediated cognitive functioning, following tests were administered: Wisconsin Card Sorting Test (WCST) and degraded continuous performance test (CPT-DS). Two-tailed independent samples t tests or Mann–Whitney U tests and Pearson’s correlation coefficient were performed for the statistical analysis of sleep latency, sleep duration, overall sleep quality, CPT d’ value, WCST correct answers, errors, and perseverative errors. Results: Participants with MDD and poor sleep quality performed worse on cognitive tests compared to patients with MDD and good sleep quality. Scores of subjective sleep on PSQI positively correlated with WCST errors (r (60) =0.8883 P = .001) and negatively correlated with WCST correct answers (r (60) = -.869 P = .001) and measures of CPT-DS d’ value (r (60) = -.9355 P = .001). Conclusions: Poor sleep quality, notably sleep duration and sleep latency, worsens the neurocognitive impairments of MDD patients. As these impairments are found to be associated with treatment outcomes, sleep disturbances should be additionally assessed and treated in MDD episode.

Understanding English Rhetorical Strategies Based on Neurosemantic Analysis

Abstract This paper designs and implements a model for analyzing rhetorical strategies of English texts to address practical needs. This paper combines the true word confusion set and the N meta-model to correct word errors. In addition, a discourse parser is constructed based on the theory of rhetorical structure, which analyzes the semantics of the English text from the intra-sentence and inter-sentence perspectives to extract the key information of the discourse. The information is inputted into a memory network that resembles a tree, and the memory units and gating mechanism are employed to obtain semantic structural features. Finally, a hierarchical network model based on the attention mechanism is designed, using a bidirectional long short-term memory network to extract the global dependency information of the text and adding the attention mechanism to make it adaptively select important features related to English rhetorical strategies. Combining learning of a tree-based long short-term memory network and a hierarchical network based on an attention mechanism is used to generate a model for semantic analysis of English text. This paper’s model has the highest accuracy, recall, and F1 value among the mentioned models in the 20newsgroups dataset, with 93.77%, 91.23%, and 90.15%, respectively. The performance on the three rhetorical strategies of explicit metaphor, personification, and analogy is excellent, with classification accuracy above 90%, which will accomplish the task of analyzing and understanding English modification strategies.

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding.

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3% ∼ 7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.

Teaching the Analysis of Translation Errors through Collaborative Work: The Case of 1st Year Master Students at the University of Mascara – Algeria

Given the gap noted in the Algerian official syllabus of the translation unit, and though both teachers and students are aware of the importance of translation errors, no lecture is dedicated to this concept. Hence, the present investigation attempts to testify the efficiency of collaborative work in teaching translation errors to M.A. students and thereby apply the theoretical background in real situations. It aims to train students to detect and correct errors, improve the final product, and minimise the different types of errors. The paper adopts a qualitative quantitative-mixed method to scrutinise the phenomenon under investigation. Whereas the theoretical part draws on Delisle's (1999) model of translation errors, the practical part relies on data collected via questionnaires, interviews, and document analysis. The paper's data were obtained from M.A. students' documents at Mascara University (Algeria) to examine the efficiency of collaborative work as a method of teaching translation errors. The study finds out that the error rate was reduced by 24% after adopting collaborative work, including the three types of texts (general, medical, and literary). The study also concludes that collaborative work enhances three central competencies competitiveness, autonomy, and motivation; hence, they acquire certain translation maturity and critical thinking.

Effect of Multimedia as Instructional Materials in Correcting the Wrong Use of French Articles in a Military Institution in Nigeria

This research was carried out to investigate the effect of multimedia instructional materials in correcting the misuse of French articles in a military institution in Nigeria. The research was conducted in Nigerian Defense Academy, Kaduna, Nigeria. Military cadets comprising of 42 cadets of which 22 are boys and 20 are girls were used for the study. T-test method was adopted and was done at a significant level of P ≤ 0.05. The research revealed that multimedia as instructional material in best used to correct the wrong used of French article when compared with lecture method. It was also revealed that there is a significant difference in the performance of male and female cadets in favour of male cadets as revealed in their mean scores. It was recommended among others that French teachers in military institutions in the country should adopt multimedia instructional materials when teaching French article to cadets. Military institutions should however, ensure that adequate and modern multimedia Instructional materials be provided to enable French teachers correct errors of articles made by military learners of French. Keywords: French, Grammar Articles, Errors, Multimedia, Military, Teaching Aids

Performance evaluation of real-time orbit determination for LUTAN-01B satellite using broadcast earth orientation parameters and multi-GNSS combination

Real-time orbit determination (RTOD) for spacecraft using the space-borne GNSS technique needs earth orientation parameters (EOPs) for the required coordinate transformation between earth-fixed and inertial reference frames. GPS and BDS transmit EOPs in modernized navigation messages, which enables GNSS-equipped spacecraft to update EOPs onboard and improves the RTOD automaticity. However, the impact of broadcast EOP errors on RTOD has not yet been evaluated. With the GPS and BDS-3 observations collected from a Chinese low-earth orbit (LEO) mission, the LUTAN-01B satellite, we analyze the LEO RTOD performance with broadcast EOPs and GPS/BDS-3 combination for the first time. In RTOD, we parameterize the ephemeris signal-in-space range error (SISRE) to account for its slow-varying signature and to compensate for the EOP errors. Without estimating SISRE, the RTOD precision using broadcast EOPs is 70.1 cm with only GPS observations and 42.4 cm with only BDS-3, revealing degradations of 5.1 cm (7.8%) and 3.3 cm (8.4%) compared to those using the precise C04 EOPs, respectively. With SISRE estimated, the impact of broadcast EOP error is compensated and the RTOD precisions with broadcast EOPs show identical results to those with C04, reaching 36.3 cm with GPS and 25.8 cm with BDS-3. Combining GPS and BDS-3 further improves the RTOD precision to 23.9 cm. Furthermore, we show that the RTOD precision can be further improved by using GPS and BDS broadcast EOPs’ differences to correct ephemeris rotation errors. With this correction, the precision improvement of BDS-3 independent and GPS/BDS-3 combined RTOD increases significantly when BDS-3 broadcast EOPs suffer large errors due to long update intervals. During the experiment period, their maximum orbital improvements reach 10.2 and 7.7 cm, respectively, while the average improvements are both around 1.5 cm.

COMPARISON OF ALGORITHMS FOR FUNDAMENTAL FREQUENCY DETECTION IN THE CONTEXT OF AUDIO PLUG-INS

This scientific article presents a comprehensive study of audio signals' fundamental frequency detection methods, focusing on both time-domain and frequency-based approaches and audio file processing since it is crucial for the post-processing part of the audio plug-ins for which this study is intended. Additionally, the article introduces self-repairing algorithms that adaptively identify and correct errors in the detected signals, ensuring robustness and accuracy in signal processing tasks and enhancing the overall detection performance. The findings from this study offer valuable insights into advancing signal processing techniques with broader implications across various domains.

APPLICATION OF BAGGING CART IN THE CLASSIFICATION OF ON-TIME GRADUATION OF STUDENTS IN THE STATISTICS STUDY PROGRAM OF TANJUNGPURA UNIVERSITY

The timeliness of graduation is used as the success of students in pursuing education which can be seen from the time taken and measured by the predicate of graduation obtained. The characteristics of students who tend to graduate not or on time can be analyzed using classification techniques. Classification and Regression Tree (CART) is one of the classification tree methods. There is a weakness in CART, which is less stable in predicting a single classification tree. The weaknesses in CART can be improved by using Ensemble methods, one of which is Bootstrap Aggregating (Bagging) which can reduce classification errors and increase accuracy in a single classification model. This study aims to classify and determine the accuracy of Bagging CART in the case of the accuracy of student graduation classification. The number of samples used is 140 data on the graduation status of Untan Statistics Study Program students from Period I of the 2017/2018 academic year to Period II of the 2022/2023 academic year. The variables used are the timeliness of graduation which is categorized into two namely Not and On Time, Gender, Semester 1 GPA, Semester 2 GPA, Semester 3 GPA, Semester 4 GPA, Region of Origin Domicile, High School Accreditation, Entry Path, Scholarship, and first TUTEP. A good classification can be seen from the accuracy value. The CART method obtained an accuracy value of 70%. While using the CART Bagging method obtained an accuracy value of 85.71%. Based on the accuracy value obtained, the application of the CART Bagging method can increase accuracy and correct classification errors on a single CART classification tree by 15.71% by resampling 25 times.

Spectrum Extension of a Real-Aperture Microwave Radiometer Using a Spectrum Extension Convolutional Neural Network for Spatial Resolution Enhancement

Enhancing the spatial resolution of real-aperture microwave radiometers is an essential research topic. The accuracy of the numerical values of brightness temperatures (BTs) observed using microwave radiometers directly affects the precision of the retrieval of marine environmental parameters. Hence, ensuring the accuracy of the enhanced brightness temperature values is of paramount importance when striving to enhance spatial resolution. A spectrum extension (SE) method is proposed in this paper, which restores the suppressed high-frequency components in the scene BT spectrum through frequency domain transformation and calculations, specifically, dividing the observed BT spectrum by the conjugate of the antenna pattern spectrum and applying a Taylor approximation to suppress error amplification, thereby extending the observed BT spectrum. By using a convolutional neural network to correct errors in the calculated spectrum and then reconstructing the BT through inverse fast Fourier transform (IFFT), the enhanced BTs are obtained. Since the extended BT spectrum contains more high-frequency components, namely, the spectrum is closer to that of the original scene BT, the reconstructed BT not only achieves an enhancement in spatial resolution, but also an improvement in the accuracy of BT values. Both the results from simulated data and satellite-measured data processing illustrate that the SE method is able to enhance the spatial resolution of real-aperture microwave radiometers and concurrently improve the accuracy of BT values.

Analisis Kesalahan Siswa dalam Menyesaikan Soal pada Materi Statistika Kelas XII IPS SMA Negeri 1 Kota Tambolaka

Student error in learning mathematics is valid in presenting data in the form of frequency distribution tables in statistical material at the high school level. These mistakes can be in the form of errors in identifying the problem, understanding the meaning of the problem, and the purpose of the problem, errors in writing the correct formula and errors in calculating the result. The purpose of this study is to describe the mistakes made by students in solving problems in statistical material. This research is a type of qualitative descriptive research. The subject of this study is students of grade XII IPS SMAN 1 Tambolaka City, and the object of research Analysis of student errors in solving problems on statistical material. The data analysis technique in research is in several steps, namely: the first step students are asked to do the problem, the second step of student work results is analyzed with the aim of finding out what mistakes students make in solving the problem, and the last step is to identify the type of student error to find out the factors causing the error. From the results of the study showed that there were three types of mistakes made by students, namely concept errors, errors in writing the correct formula and errors in calculating the result. These errors are caused by students not being careful in reading the questions, not understanding the steps of presenting data in the form of frequency distribution tables

Notes on the Cambridge translation of Kant’s Religion within the Boundaries of Mere Reason

This article mainly discusses translation issues in the two Cambridge editions of Kant’s Religion, while engaging in a briefer discussion of Pluhar’s translation of the same work. The aim is to correct single errors as well as patterns of questionable translation, and to do so guided by criteria of consistency in relation to Kant’s use of key terms. I argue that the English translations have contributed to misinterpretations of Kant’s philosophy of religion, although I make only passing references to the secondary scholarship.

Peran Analisis Kesalahan Berbahasa Dalam Meningkatkan Kemampuan Menulis

This article discusses the important role of language error analysis in improving one's writing skills. Language error analysis plays a crucial role in the language learning process, especially in the development of writing skills. By understanding the errors made in writing, one can identify frequently occurring error patterns and address them. The language error analysis method not only helps correct grammatical errors, but also opens up a deeper understanding of better language structure. With a better understanding of mistakes made, one can improve clarity, coherence, and cohesion in one's writing. This article also highlights how the use of language error analysis techniques in the learning process can provide constructive feedback to writers. Thus, this article emphasizes the importance of language error analysis in supporting the improvement of one's writing ability.

Benchmarking software tools for trimming adapters and merging next-generation sequencing data for ancient DNA.

Ancient DNA is highly degraded, resulting in very short sequences. Reads generated with modern high-throughput sequencing machines are generally longer than ancient DNA molecules, therefore the reads often contain some portion of the sequencing adaptors. It is crucial to remove those adaptors, as they can interfere with downstream analysis. Furthermore, overlapping portions when DNA has been read forward and backward (paired-end) can be merged to correct sequencing errors and improve read quality. Several tools have been developed for adapter trimming and read merging, however, no one has attempted to evaluate their accuracy and evaluate their potential impact on downstream analyses. Through the simulation of sequencing data, seven commonly used tools were analyzed in their ability to reconstruct ancient DNA sequences through read merging. The analyzed tools exhibit notable differences in their abilities to correct sequence errors and identify the correct read overlap, but the most substantial difference is observed in their ability to calculate quality scores for merged bases. Selecting the most appropriate tool for a given project depends on several factors, although some tools such as fastp have some shortcomings, whereas others like leeHom outperform the other tools in most aspects. While the choice of tool did not result in a measurable difference when analyzing population genetics using principal component analysis, it is important to note that downstream analyses that are sensitive to wrongly merged reads or that rely on quality scores can be significantly impacted by the choice of tool.

AI chatbot-based learning: alleviating students' anxiety in english writing classroom

In the ever-evolving landscape of education, integrating innovative technologies can enhance the learning experience for students. ChatGPT, a cutting-edge language processing tool developed by OpenAI, offers exciting possibilities for teaching writing. This advanced AI model can be a powerful asset in the classroom, providing students with valuable resources and support as they develop their writing skills. Seventy-three college students participated in the quasi-experiment. The findings demonstrate that AI chatbot-based instruction reduces students' anxiety about learning English writing. AI chatbots offer instant feedback, allowing students to correct errors immediately. This quick feedback loop can prevent students from ruminating over their mistakes, thus reducing anxiety. With AI chatbot, students can learn at their own pace. They can take time to understand concepts, practice writing, and receive feedback without feeling rushed. This flexibility can alleviate the pressure of strict deadlines in traditional classroom settings. The findings imply teachers to implement chatbot-based learning in the classroom.

Analysis of Methods to Correct Indonesian Language Spelling Errors in Thesis Writing Among Students of State University of Medan

This text discusses the editing of Indonesian language manuscripts using various linguistic techniques, encompassing on-screen and manual editing. Academic and developer-created editing applications for Android-based apps and web pages facilitate on-screen editing. Notable programs include jasa.id, lektur.id, typoonline.com, typograp.com, and SIPEBI (Indonesian Spelling Editing). These applications provide features to enhance the editing process for Indonesian language users, each having distinct strengths and weaknesses. They detect elements like standardized and non-standardized words, spelling variations, including capitalization, prepositions, numbers, and typing errors. Generalizing these aspects across sections is challenging, necessitating varied handling and corrections for each text or document. While these programs streamline editing, manual editing remains crucial due to unknown factors. Despite its time-consuming nature, manual editing excels in accuracy and coverage. In summary, these applications play a valuable role, but manual editing is indispensable for comprehensive and precise language refinement.

Design of soft error correction flip-flop cells for highly reliable applications

With the development of technology, the vulnerability of integrated circuits to Single Event Effect (SEE) increases, and the sensitivity of flip-flops to soft errors induced by Single Event Transient (SET) and Single Event Upset (SEU) increases. Fault tolerant design is necessary for high reliability applications. In this work, a Soft Error Correction Flip-Flop 1(SEC FF1) is proposed, which can correct soft errors induced by SET and SEU at low clock phase without additional clock cycle. It has the lowest area and power consumption overhead and the lowest Power-Delay Product (PDP) compared with other radiation hardened flip-flops. In order to correct soft errors earlier, a variant of SEC FF1, SECFF2, is proposed at the cost of power consumption and switching delay. As the feature size of transistors shrinks, Double Node Upset (DNU) induced by single-event charge sharing is becoming an emerging reliability challenge. In order tolerate DNU, variant of SEC FFs, DNU Correction (DNUC) FFs, are proposed. In addition, the delay element used in the radiation-hardened flip-flops is also introduced.

Alterations in sperm RNAs persist after alcohol cessation and correlate with epididymal mitochondrial dysfunction.

Chronic preconception paternal alcohol use adversely modifies the sperm epigenome, inducing fetoplacental and craniofacial growth defects in the offspring of exposed males. A crucial outstanding question in the field of paternal epigenetic inheritance concerns the resilience of the male germline and its capacity to recover and correct sperm-inherited epigenetic errors after stressor withdrawal. We set out to determine if measures of the sperm-inherited epigenetic program revert to match the control treatment 1 month after withdrawing the daily alcohol treatments. Using a voluntary access model, we exposed C57BL/6J males to 6% or 10% alcohol for 10 weeks, withdrew the alcohol treatments for 4 weeks, and used RNA sequencing to examine gene expression patterns in the caput section of the epididymis. We then compared the abundance of sperm small RNA species between treatments. In the caput section of the epididymis, chronic alcohol exposure induced changes in the transcriptional control of genetic pathways related to the mitochondrial function, oxidative phosphorylation, and the generalized stress response (EIF2 signaling). Subsequent analysis identified region-specific, alcohol-induced changes in mitochondrial DNA copy number across the epididymis, which correlated with increases in the mitochondrial DNA content of alcohol-exposed sperm. Notably, in the corpus section of the epididymis, increases in mitochondrial DNA copy number persisted 1 month after alcohol cessation. Analysis of sperm noncoding RNAs between control and alcohol-exposed males 1 month after alcohol withdrawal revealed a ∼100-fold increase in mir-196a, a microRNA induced as part of the nuclear factor erythroid 2-related factor 2 (Nrf2)-driven cellular antioxidant response. Our data reveal that alcohol-induced epididymal mitochondrial dysfunction and differences in sperm noncoding RNA content persist after alcohol withdrawal. Further, differences in mir-196a and sperm mitochondrial DNA copy number may serve as viable biomarkers of adverse alterations in the sperm-inherited epigenetic program.