Correct Errors Research Articles

Abstract 4138376: A Machine Learning Approach to Predict Percutaneous Coronary Intervention in Patients with Critical Illness and Signs of Myocardial Injury

Background: Critically ill patients frequently experience multi-organ dysfunction and unstable vital signs, increasing their risk of myocardial injury and elevated serum troponin levels. Myocardial injury from oxygen supply-demand mismatch, whether due to systemic disease or obstructive coronary artery disease (CAD), often leads to patients undergoing an invasive coronary angiogram (ICA) and potentially percutaneous coronary intervention (PCI) if obstructive CAD is identified. However, this procedure carries significant risks for critically ill patients. To address this challenge, we aimed to develop a machine learning (ML) model to predict the need for PCI in critically ill patients with myocardial injury. Methods: The study cohort included critically ill patients with elevated serum troponin levels who underwent ICA during their hospitalization at a single institution from 2012 to 2022. We defined critical illness as a life-threatening condition requiring intensive support for vital sign instability. Patients with atherothrombosis identified during ICA were excluded from the study, focusing only on patients with type 2 MI. We utilized Matplotlib to analyze patient data, including demographics, lab results, vital signs, and comorbidities. We then used XGBoost, a supervised ML algorithm, for binary classification tasks. An ensemble of decision trees was employed, with gradient boosting to correct errors from previous trees. XGBoost iteratively adjusted predictions, creating a model to predict which patients required PCI. Results: Out of 765 patients, 173 (22.6%) underwent PCI. The cohort had a mean age of 60.5 ± 13.5 years, with 41.7% being female. The ML model accurately identified non-PCI candidates at 75% and PCI candidates at 64%, achieving an overall accuracy of 73%. The three most important features identified by the model were a history of diabetes, diastolic heart failure, and whether the patient received a blood transfusion during hospitalization. Conclusion: A ML approach using XGBoost demonstrated moderate accuracy in predicting the need for PCI in critically ill patients with signs of myocardial injury. This prediction holds clinical significance due to the heightened risks associated with ICA in these patients. Given that PCI occurred in only 22.6% of the cohort, future models should include a higher proportion of patients who underwent PCI to improve the robustness and true positive accuracy of the model.

Partisan Punitive Practice in Varėna District (1944–1952)

One of the least analysed aspects of the partisan war in Lithuania between 1944 and 1953 is the punitive practice of partisans. The aim of Total Irreversible Human Loss in Lithuania Between 1944 and 1953, a programme conducted by the Special Investigation Department of the Genocide and Resistance Research Centre of Lithuania, is to explore in detail the relationship between the partisans and the population during the war, to reveal the variety of executions conducted by partisans, and to clarify the decision-making procedures of partisan organisations with respect to the population. The objectives of the programme are to carry out a complex historical-statistical study and to prepare a classification of cases and a statistical data set; to collect data on the relationship of partisan organisations with the population and on the activities of courts-martial and other partisan institutions; and to carry out historical and legal analysis of data and to assess the legitimacy, validity and effects of partisan decisions concerning the population. It is also necessary to clarify, supplement and correct errors in the Soviet era card files that are stored at the Genocide and Resistance Research Centre and serve as the database for people killed and injured by partisans. This article has been prepared on the basis of the study of death sentences, executions, and people killed in action by partisans in the period 1944–1952 in the district of Varėna, the area of former Varėna County. During the archival-historiographic study, cases of killings were examined in detail through the exploration of the circumstances, motives, and other information. An important part of the study was the analysis of partisan documents: court-martial reports, various proclamations, and warnings. The most serious areas of the punitive practices of partisans were also discussed: killings of families or minors, issues of guilt and innocence, and the circumstances of partisan warfare at the time. Between 1944 and 1952, 688 people were killed in the district of Varėna; a total of 858 recorded cases of executions or possible executions were studied. The social status of 497 victims and dates of birth of 443 victims were identified. The average age of victims was 35.6 years. Three main categories of victims can be identified: employees and officers of military and security forces (29.6%), Soviet administrative staff and employees (20.9%), and farmers and workers (a total of 30.6%; 22.1% and 8.5% respectively). Other categories by job, occupation, etc.: family members of employees of military and security organisations (2%), family members of administrative staff (4.8%), settlers (3%), informers and agents (3.2%), Soviet activists (2.8%), family members of activists (0.2%), members of Komsomol (0.8%), students (0.2%), and former partisans and people who were close to them (1.8%). Eighty convictions by partisan courts-martial were identified, and 23 other partisan documents (warnings, proclamations) were left at the places of execution. This accounts for 14.9% of all executions. Most soldiers, employees and officers of military and security forces perished or were executed in an ambush. The justification for killing Soviet administrative officials, employees, and other staff members was usually that those people were carrying out Soviet orders. Usually executions were carried out at people’s homes or outside homes and by giving a warning and providing a charge. Death penalties and executions of farmers and workers pose the most questions. Most are executions for collaboration with the Soviets and the sabotage of the partisan movement. Based on the information collected, it is however usually difficult to fully assess the legitimacy of such killings.

Barriers to opioid use in the Arab world.

The Arab world consists of 22 countries, representing 5.5% of the world's population. Morphine consumption accounts for less than 1% of the world's consumption. This is the first study to identify barriers to opioid use in the Arab world. This study aimed to investigate the barriers to opioid use in pain management in the Arab world. Online survey was developed to investigate barriers to governance, prescribing, distributing, dispensing and administering, as well as educational barriers in the Arab world. The questionnaire was sent via email and a mobile app to one expert physician in pain management and one licensed pharmacist from each Arab country. With the exception of Tunisia, Djibouti and Comoros, 34(77%) participants from 19 Arab countries answered the survey. Most countries lack local opioid production, necessitate special licenses for physicians, and restrict opioid prescribing to medical specialists. Special prescription forms are mandated, and pharmacists lack the authority to correct prescription errors or accept refill or verbal orders on opioids. Storage requirements for empty ampoules and prescriptions are enforced. Nurses are not allowed to carry opioids during home visits, and only first degree relatives can collect opioids for patients. Furthermore, the integration of palliative care and pain management curricula into pharmacies and medical schools is lacking. There is a wide range of regulatory and other barriers to opioid use in the Arab world. There is a substantial need for regulatory review and reform, as well as for educational initiatives, in most Arab countries.

Chanting and meditation: an 8-week intervention to promote executive functions in school-age children

The present study investigated the effectiveness of a newly developed 8-week meditation program, which included chanting and meditation, on executive functions (EFs) in school-age children. Twenty-five students assigned to an experimental group participated in the meditation program, engaging in daily and weekly activities for 8 weeks. Another 25 students served as a control group and participated in regular school activities. Pretest and posttest assessments included the n-back task for working memory, the go/no-go task for inhibition, and the Wisconsin Card Sorting Test (WCST) for cognitive flexibility. A mixed repeated measures ANOVA was conducted to analyze interaction effects, within-group differences, and between-group differences. The experimental group demonstrated greater improvement compared to the control group across several measures: (1) reaction time in the 1-back task, (2) correct responses in the no-go condition of the go/no-go task, and (3) percentage of correct responses, errors, perseverative errors, non-perseverative errors, perseverative responses, and conceptual level responses in the WCST (p < 0.001). These findings underscore the potential benefits of an 8-week school-based meditation program incorporating chanting and meditation for enhancing EFs in school-age children.

Making It Tractable to Detect and Correct Errors in Graphs

This paper develops \(\mathsf {Hercules} \) , a system for entity resolution (ER), conflict resolution (CR), timeliness deduction (TD) and missing value/link imputation (MI) in graphs. It proposes \(\mathsf {GCR^{+}\!s} \) , a class of graph cleaning rules that support not only predicates for ER and CR, but also temporal orders to deduce timeliness and data extraction to impute missing data. As opposed to previous graph rules, \(\mathsf {GCR^{+}\!s} \) are defined with a dual graph pattern to accommodate irregular structures of schemaless graphs, and adopt patterns of a star form to reduce the complexity. We show that while the implication and satisfiability problems are intractable for \(\mathsf {GCR^{+}\!s} \) , it is in PTIME to detect and correct errors with \(\mathsf {GCR^{+}\!s} \) . Underlying \(\mathsf {Hercules} \) , we train a ranking model to predict the temporal orders on attributes, and embed it as a predicate of \(\mathsf {GCR^{+}\!s} \) . We provide an algorithm for discovering \(\mathsf {GCR^{+}\!s} \) by combining the generations of patterns and predicates. We also develop a method for conducting ER, CR, TD and MI in the same process to improve the overall quality of graphs, by leveraging their interactions and chasing with \(\mathsf {GCR^{+}\!s} \) ; we show that the method has the Church-Rosser property under certain conditions. Using real-life and synthetic graphs, we empirically verify that \(\mathsf {Hercules} \) is 53% more accurate than the state-of-the-art graph cleaning systems, and performs comparably in efficiency and scalability.

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

Speech sound errors in adolescents with congenital hearing loss

Children with hearing impairment (HI) often have vowel and consonant errors. Speech-language pathologists need this information to correct articulation errors. Despite the prevalence of research on speech sound error patterns in hearing aid users, there is a lack of literature in the adolescent group. The present study aimed to investigate the common speech sound errors in HI adolescents from the Malayalam-speaking community who have severe to profound hearing loss and are digital hearing aid users. A total of 15 monolingual Malayalam-speaking children between the ages of 9 and 16 years were recruited who had been diagnosed with speech sound disorders secondary to congenital hearing impairment. The minimum language age for inclusion in the study was over three years. Articulatory error analysis was performed for all consonants and vowels of the Malayalam language, and frequently produced vowel and consonant errors were included in the SODA and PMV analyses. The consonants frequently produced speech sound errors and no vowel errors were found. The most commonly produced consonant errors were fricatives, trills, laterals, velars, and alveolars. Substitution and omission errors were the most common type of error. Place and manner errors were more common than other types of errors. The results of this study may help parents and rehabilitation professionals to understand the common errors that persist even after intensive intervention. It may also be a useful guide for AAC therapists and speech-language pathologists in setting appropriate speech production goals for adolescents with HI.

The Effectiveness of Feedback Provided by Middle School Mathematics Teachers and Preservice teachers on Student Solutions: An Evaluative Study

The aim of this study is to reveal the suggestions of middle school mathematics teachers and preservice mathematics teachers that can both correct errors in students’ incorrect solutions and contribute to the advancement of students who have provided correct solutions. The study is based on the case study method. Initially, six problems related to proportional reasoning were posed to middle school students in a public school, and the solutions obtained from the students were classified according to their proportional reasoning strategies. These solutions were then examined by the students' mathematics teachers and by preservice mathematics teachers. Two questions were asked to mathematics teachers and preservice mathematics teachers during these examinations. After the examination, interviews were conducted to discuss the suggestions made to (1) correct the errors in incorrect solutions and (2) advance the proportional reasoning skills of students who obtained correct solutions. The findings indicate that both mathematics teachers and preservice mathematics teachers were adequate in providing suggestions to correct errors in students’ incorrect solutions. However, it was also found that both groups had difficulties in making suggestions to further the advancement of students who provided correct solutions.

Spatiotemporal mapping of auditory onsets during speech production.

The human auditory cortex is organized according to the timing and spectral characteristics of speech sounds during speech perception. During listening, the posterior superior temporal gyrus is organized according to onset responses, which segment acoustic boundaries in speech, and sustained responses, which further process phonological content. When we speak, the auditory system is actively processing the sound of our own voice to detect and correct speech errors in real time. This manifests in neural recordings as suppression of auditory responses during speech production compared to perception, but whether this differentially affects onset and sustained temporal profiles is not known. Here we investigated this question using intracranial EEG recorded from seventeen pediatric, adolescent, and adult patients with medication-resistant epilepsy while they performed a reading/listening task. We identified onset and sustained responses to speech in bilateral auditory cortex and observed a selective suppression of onset responses during speech production. We conclude that onset responses provide a temporal landmark during speech perception that is redundant with forward prediction during speech production and are therefore suppressed. Phonological feature tuning in these "onset suppression" electrodes remained stable between perception and production. Notably, auditory onset responses and phonological feature tuning were present in the posterior insula during both speech perception and production, suggesting an anatomically and functionally separate auditory processing zone that we believe to be involved in multisensory integration during speech perception and feedback control.Significance Statement Specific neural populations in the auditory cortex preferentially respond to the onset of speech sounds. These "onset responses" aid in perceiving boundaries in continuous speech. We recorded neural responses from patients with intracranial electrodes during a speaking and listening task to investigate the role of onset responses in speech production. Onset responses were present in the auditory cortex during listening, but absent during speaking. On the other hand, onset responses were observed in the insula during both conditions, suggesting a different functional role for the insula in auditory feedback processing. These findings extend our knowledge of how different parts of the brain involved in feedback control operate during speech production by identifying two functionally and anatomically distinct patterns of activity.

Optimizing Pronunciation Instruction for Vietnamese Learners of English: Applying Optimality Theory and Phonetic Principles in Language Teaching

This study explores the perceived benefits of using phonetic principles in teaching English pronunciation from both teacher and student perspectives. The research examines how incorporating phonetic instruction and Optimality Theory can improve pronunciation accuracy and phonological awareness. Using a mixed-methods approach, the study collected quantitative data through surveys from 57 participants (6 teachers and 51 students) and qualitative insights from focus group interviews with teachers. The results showed that teachers rated the benefits of phonetic instruction higher (mean score of 4.24) compared to students (mean score of 3.31). Teachers highlighted how phonetic principles offer a systematic way to identify and correct pronunciation errors, making teaching more efficient. Students, while recognizing the advantages, faced challenges in grasping some technical aspects, such as using phonetic symbols. However, they acknowledged that their pronunciation and listening skills improved with practice. The study concludes that while phonetic instruction is valuable, more practical and accessible approaches are needed to better support students&amp;#39; learning. These findings provide useful insights for educators looking to enhance pronunciation teaching and suggest areas for further exploration.

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

Dual frequency composite pattern temporal phase unwrapping for 3D surface measurement

Phase shifting profilometry (PSP) is widely used in three-dimensional (3D) optical metrology applications such as mechanical engineering, industrial monitoring, computer vision, and biomedicine because of its high measurement accuracy. In PSP, multi-frequency temporal phase unwrapping (TPU) plays a dominant role due to its high-accuracy measurement of surfaces with discontinuities and isolated objects. However, it requires a large number of fringe patterns. To reduce the number of required patterns, a new dual-frequency composite-pattern TPU method is developed, which needs only three patterns. The new method combines two fringe patterns of different frequencies into three composite fringe patterns, reconstructs rough 3D points by demodulating the low-frequency fringe pattern using geometric constraints, and reprojects it to obtain a rough low-frequency absolute phase, which is then refined to correct edge errors. Finally, the high-frequency wrapped phase is unwrapped under the guidance of the refined low-frequency phase, thereby the accurate 3D points are reconstructed based on stereo-vision technology. Experimental results demonstrated that the proposed method can achieve 3D surface measurement with only three images, which greatly improves the measurement efficiency. The proposed method has great application potential for the rapid 3D measurement of discontinuous surfaces and isolated objects.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Correction of dynamic magnetic field errors at the rapid cycling synchrotron of China Spallation Neutron Source

At a Rapid Cycling Synchrotron (RCS), dynamic magnetic field errors, such as magnetic field tracking errors and dynamic fringe field effects, can cause time-dependent tune shift during beam acceleration. If the tune shift is significant enough to pass through resonance lines, it can lead to emittance growth and beam losses. Correcting time-dependent tune shift during acceleration is crucial for a RCS. Modulating the exciting current and magnetic field of quadrupole magnets at a RCS, which are powered by resonant circuits, is challenging during the ramping process. Correcting time-dependent tune shift at a RCS poses a significant technical challenge. We have proposed a method for correcting time-dependent tune shift at the rapid cycling synchrotron of China Spallation Neutron Source (CSNS), based on waveform compensation at the quadrupole magnets. This approach involves modulating the magnetic field variation process by injecting time harmonic exciting current into the quadrupole magnets. This method has been validated during the CSNS beam commissioning and has been applied at the RCS of CSNS to correct various dynamic magnetic field errors.

SỬ DỤNG GOOGLE DOCS VÀ MERCI APP TRONG LỚP HỌC VIẾT TIẾNG PHÁP

This study compares the effectiveness of two automated feedback tools, Google Documents, which will be further addressed as Google Docs, and Merci App, in improving writing ability in French as a foreign language. Forty second-year students used these two reviewer tools to correct their B2-level texts. The data includes 80 student drafts edited on two tools and a survey questionnaire after using these two applications. The results show that both tools help students correct many errors at the sentence, word, and fragment levels but cannot correct errors at the inter-sentence, idea, and coherence levels. Google Docs detects more grammatical errors than Merci App, but Merci App is superior when it comes to finding errors in spelling, vocabulary, expression, and punctuation. Students use the application's error correction suggestions to correct their papers at a very high rate, 92.6% with Google Docs and 81.7% with Merci App. The number of students who prefer Google Docs is also higher than the number of students who prefer Merci App. The study proposes to use both tools to correct students' writing errors and to help students become familiar with error explanations in Merci App to help them systematize language knowledge and develop auto-learning capacity.

A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network

Abstract Healthy individuals readily adjust their behaviour in response to errors using learning mechanisms. This raises the question of how error-related neural mechanisms underlie the learning process and its progress. In this study, 21 healthy participants performed a challenging functional magnetic resonance imaging (fMRI) task to answer this question. We assessed the evolution of error-related neural response as a function of learning progress. We tested the hypothesis that the dorsal anterior cingulate cortex (dACC) and anterior insula, key regions of the error monitoring neural circuitry, reflect both the performance of an action and its improvement. Given the nature of trial-and-error learning, we also expected an involvement of the striatum, particularly the putamen. We found that error-related neural activity (in the dACC and anterior insula) was similar following correct responses and errors in an initial learning period. However, as learning progressed, the activity continuously decreased in response to correct events and increased after errors. In opposition, during the initial learning phase, the putamen activity was modulated by errors, but, as it progressed, this region became unaffected by response outcomes. In sum, our study provides neural evidence for an interaction between the mechanisms underlying error monitoring and learning, contributing to clarifying how error-related neural responses evolve with learning.

Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data

The extreme ultraviolet (EUV) photolithography process is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary least squares (OLS) is the most widely used method for parameters tuning of overlay models, but in most cases it fails to compensate for such phenomena. In this paper, we propose the usage of ridge regression, a widely known machine learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method was applied in perturbed data from a 300 mm wafer fab, and the results show reduced residuals when ridge regression is applied instead of OLS.

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

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

High dynamic performance based by sliding mod control of quadrotor MAV

The paper focuses on strong control of high dynamic performance based on the sliding mode in order to control the quadrotor MAV. The model used in the simulation is the Newton-Euler model, and also choose the Mambo Parrot quadrotor parameters, to improve the latter's performance in terms of accuracy and robustness in case the impedance control chosen by the manufacturer needs to be changed. The results of which gave effectiveness and efficiency compared to classical controls PID and sliding mode. High dynamic performance control (HDP) is a type of control system designed to provide the highest level of performance over a wide range of operating conditions. This control is not a regulator but an organizing technique. It uses a prediction-based approach to anticipate rapid variations in system states and disturbances and to generate real-time commands to correct tracking errors and maintain system stability. This approach allows HDP control to achieve high performance while maintaining system stability, even under harsh operating conditions.

A comparative study on automatic treatment planning for online adaptive proton therapy of esophageal cancer: which combination of deformable registration and deep learning planning tools performs the best?

Objective.To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Approach.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan. (1) DEF-INIT combines deformably registered contours with template-based optimization. (2) DL-INIT, (3) DL-DEF, and (4) DL-DL employ a nnU-Net DL network for organ segmentation and a controlling ROIs-guided DIR algorithm for internal clinical target volume (iCTV) segmentation. DL-INIT uses this segmentation alongside template-based optimization, DL-DEF integrates it with a dose-mimicking (DM) step using a reference deformed dose, and DL-DL merges it with DM on a reference DL-predicted dose. All strategies were evaluated on manual contours and contours used for optimization and compared with manually adapted plans. Key dose volume metrics like iCTV D98% are reported.Main results.iCTV D98% was comparable in manually adapted plans and for all strategies in nominal cases but dropped to 20 Gy in worst-case scenarios for a few patients per strategy, highlighting the need to correct segmentation errors in the target volume. Evaluations on optimization contours showed minimal relative error, with some outliers, particularly in template-based strategies (DEF-INIT and DL-INIT). DL-DEF achieves a good trade-off between speed and dosimetric quality, showing a passing rate (iCTV D98% > 94%) of 90% when evaluated against 2, 4 and 5 mm setup error and of 88% when evaluated against 7 mm setup error. While template-based methods are more rigid, DL-DEF and DL-DL have potential for further enhancements with proper DM algorithm tuning.Significance.Among investigated strategies, DL-DEF and DL-DL demonstrated promising within 10 min OAPT implementation results and significant potential for improvements.