Numerical Information Research Articles

Lessons Learned from Information Sources on Building Defect Studies

Building defects are very prevalent and contribute significantly to a building’s economic value. There are numerous information sources on building defects that have significant potential for learning more about building defects. This study aimed to identify the information sources used in previous building defect studies and to identify the motivation for carrying out such studies. To fulfil this aim, a scoping study was carried out. The information sources identified included insurance companies, private databases, questionnaire surveys, lawsuits, building surveys, client complaint forms, and maintenance reports. This study found that insurance company and client complaint forms include the largest collection of real building defect cases, but such databases may lack detailed descriptions of the causes of the defects. The main purposes of the building defects studies identified here included design challenges, identification of defects, building maintenance management, quality management, systematization in data collection, providing an overview of typical defects, and classifying defects. Identification was found to be the most common purpose, indicating that the industry wants to learn more. This study identified research gaps in the climate perspective in relation to building defects. Most of the studies focused on the economical perspective, and none focused on the carbon footprint perspective. This study provides an overview of information sources, which can motivate stakeholders to collaborate in learning about the physical causes of building defects as well as about other key aspects such as fault, finances consequences, and procedural causes.

Duration, numerosity and length processing in healthy ageing and Parkinson’s disease

People constantly process temporal, numerical, and length information in everyday activities and interactions with the environment. However, it is unclear whether quantity perception changes during ageing. Previous studies have provided heterogeneous results, sometimes showing an age-related effect on a particular quantity, and other times reporting no differences between young and elderly samples. However, three dimensions were never compared within the same study. Here, we conducted two experiments with the aim of investigating the processing of duration, numerosity and length in both healthy and pathological ageing. The experimental paradigm consisted of three bisection tasks in which participants were asked to judge whether the presented stimulus (i.e. a time interval, a group of dots, or a line) was more similar to the short/few or long/many standards. The first study recruited healthy young and elderly participants, while the second recruited healthy elderly participants and patients with Parkinson’s disease, a clinical condition commonly associated with temporal impairments. The results of both experiments showed that discrimination precision differed between domains in all groups, with higher precision in the numerosity task and lower sensitivity in judging duration. Furthermore, while discrimination abilities were affected in healthy elderly and, even more so, in Parkinson’s disease group, no domain-specific impairments emerged. According to our research, reduced discrimination precision might be explained by an alteration of a single system for all quantities or by an age-related general cognitive decline.

The Power of Numeric Evidence in Science Communication

Advantages and disadvantages exist for presenting numeric information in science communication. On the one hand, public innumeracy and experts’ concerns about providing numbers suggest not always showing them. On the other hand, people often prefer getting them, and their provision can increase comprehension, trust, and healthy behaviors while reducing risk overestimates and supporting decision-making autonomy. Presenting numeric facts without considering their comprehensibility and usability, however, is like throwing good money after bad. We summarize research concerning three theory-based strategies that improve the understanding and use of numbers by decreasing cognitive effort (e.g., doing the math for the audience), being consistent with principles of numeric cognition, and providing affective meaning.

Language model enhanced surface chloride concentration determination for concrete within splash environment based on limited field records

Chloride ion is severely harmful to reinforced concrete (RC) structures in marine environments. For maintaining the durability and safety of the designed RC structures, the determination of chloride ion concentration on concrete surfaces is critical. Currently, surface chloride ion concentration can be determined using empirical formulas and machine learning (ML) approaches. However, these approaches only rely on the numerical information within field records, disregarding valuable semantic and background information in the records, leading to low accuracy. Meanwhile, in splash environments, it presents a significant challenge to obtain chloride ion concentration records due to the complex environment and high costs involved. Therefore, based on limited field records of surface ion concentrations in splash environments, and utilizing a state-of-the-art language model (LM), this study proposes an LM-based information generation (LMIG) model to improve the accuracy of determination of surface chloride concentrations on RC structures. This paper utilizes the numerical and semantic information in 70 sets of field records to fine-tune the LMIG model and generates 200 sets of high-quality records. These records are then used to train ML algorithms for predicting chloride ion concentrations on concrete surfaces. After conducting comparative research, it was found that incorporating records generated by the LMIG model significantly enhances the accuracy of the ML algorithm. Specifically, the predictive accuracy using the random forest algorithm increased by 33.1%. Furthermore, this paper also conducts a comparative study on the LMIG model and the generative adversarial network (GAN)-assisted data-driven method. The results demonstrate that integrating semantic and numerical information into the LMIG model shows significant advantages in enhancing ML algorithms’ accuracy.

THE IMPACT OF MOBILE ASSISTED LANGUAGE LEARNING (MALL) IN DEVELOPING THE SPEAKING SKILL: AN EMPIRICAL STUDY IN THE UNITED ARAB EMIRATES

THE IMPACT OF MOBILE ASSISTED LANGUAGE LEARNING (MALL) IN DEVELOPING THE SPEAKING SKILL: AN EMPIRICAL STUDY IN THE UNITED ARAB EMIRATES

Floating-Point Embedding: Enhancing the Mathematical Comprehension of Large Language Models

The processing and comprehension of numerical information in natural language represent pivotal focal points of scholarly inquiry. Across diverse applications spanning text analysis to information retrieval, the adept management and understanding of the numerical content within natural language are indispensable in achieving task success. Specialized encoding and embedding techniques tailored to numerical data offer an avenue toward improved performance in tasks involving masked prediction and numerical reasoning, inherently characterized by numerical values. Consequently, treating numbers in text merely as words is inadequate; their numerical semantics must be underscored. Recent years have witnessed the emergence of a range of specific encoding methodologies designed explicitly for numerical content, demonstrating promising outcomes. We observe similarities between the Transformer architecture and CPU architecture, with symmetry playing a crucial role. In light of this observation and drawing inspiration from computer system theory, we introduce a floating-point representation and devise a corresponding embedding module. The numerical representations correspond one-to-one with their semantic vector values, rendering both symmetric regarding intermediate transformation methods. Our proposed methodology facilitates the more comprehensive encoding and embedding of numerical information within a predefined precision range, thereby ensuring a distinctive encoding representation for each numerical entity. Rigorous testing on multiple encoder-only models and datasets yielded results that stand out in terms of competitiveness. In comparison to the default embedding methods employed by models, our approach achieved an improvement of approximately 3.8% in Top-1 accuracy and a reduction in perplexity of approximately 0.43. These outcomes affirm the efficacy of our proposed method. Furthermore, the enrichment of numerical semantics through a more comprehensive embedding contributes to the augmentation of the model’s capacity for semantic understanding.

What makes different number-space mappings interact?

Models of numerical cognition consider a visuo-spatial representation to be at the core of numerical processing, the 'mental number line'. Two main interference effects between number and space have been described: the SNARC effect reflects a small number/left side and large number/right side association (number-location mapping); the size-congruity effect (SCE) reflects a small number/small size and large number/large size association (number-size mapping). Critically, a thorough investigation on the representational source for these two number-space mappings is lacking, leaving open the question of whether the same representation underlies both phenomena. Here, we build on a recent study (Viarouge and de Hevia in Front Hum Neurosci 15:750964, 2021) in order to address this question in three experiments, by systematically manipulating the presence of the two conditions that might elicit an interaction between SNARC and SCE: (i) an implicit task whereby numerical and spatial information are task-irrelevant, (ii) a design in which the number-space congruency relative to both mappings vary at the same level -either both within or between blocks. Experiment 1 replicated the interaction between the two mappings when both factors were present. Experiments 2 and 3 dissociated the two factors by varying the two mappings at the same level but using an explicit comparison task (Experiment 2), or by using an implicit task but with mappings varying at different levels (Experiment 3). We found that both factors, either in combination or used in isolation, drive the interaction between the two number-space mappings. These findings are discussed in terms of the weight given to each mapping, suggesting that a single representation encompassing both number-space mappings is therefore activated whenever both mappings are given equal weight through task requirements.

Does the Textual Tone of Analyst Reports Have Valuable Information? Korean Evidence*

AbstractWe investigate whether the text of analyst reports can provide additional information beyond the recommendation and target price. Positive and negative word lexicons are generated through an automated Bayesian learning method applied to Korean analyst reports spanning from 2016 to 2018. Then, the textual tone of an analyst report is quantified as the difference between the frequencies of positive and negative words in the text. The announcement returns of portfolios sorted by textual tone exhibit significant differences ranging from 1.14% to 2.82% within the same recommendation or target price revision group. Regression analysis also reveals significant association between the textual tone of analyst reports and stock announcement returns, even when controlling for the recommendation and target price. Notably, the text proves to be more informative in negative tones and within firms with limited analyst coverage. Our results indicate that textual analysis can unveil nuanced analyst opinions not captured by numerical information.

Unconscious Integration of Categorical Relationship of Two Subliminal Numbers in Comparison with "5".

Many studies have shown that the brain can process subliminal numerals, i.e., participants can categorize a subliminal number into two categories: greater than 5 or less than 5. In the context of many studies on the unconscious integration of multiple subliminal stimuli, the issue of whether multiple subliminal numbers can be integrated is contentious. The same-different task is regarded as a perfect tool to explore unconscious integration. In the two experiments reported, we used a same-different task in which a pair of masked prime numbers was followed by a pair of target numbers, and participants were asked to decide whether the two target numbers were on the same (both smaller or larger than 5) or different sides (one smaller, the other larger than 5) of 5 in magnitude. The results indicated that the prime numbers could be categorized unconsciously, which was reflected by the category priming effect, and that the unconscious category relationship of the two prime numbers could affect the judgment on the category relationship of the two target numbers, as reflected by the response priming effect. The duration of the prime-to-target interstimulus interval (ISI) was also manipulated, showing a positive compatibility effect (PCE) of category priming and a negative compatibility effect (NCE) of response priming no matter whether the ISI was short (50 ms) or long (150 ms). The NCE, which occurred when the prime-to-target ISI was relatively short in this study, contradicted the conventional view but was consistent with previous results of unconscious integration based on an attention modulation mechanism. Importantly, this study provided evidence for the still-under-debate issue of numerical information integration.

Evaluation of the effectiveness of psychosocial rehabilitation: an innovative approach based on the analytic hierarchy process

IntroductionIn psychosocial rehabilitation (PSR), rank scales are widely used to assess the severity of functional disorders in patients. The main problem of evaluating the effectiveness of PSR is related to the methods of processing data obtained using psychometric scales used to evaluate the effectiveness of interventions within PSR. The proof of the non-numerical nature of ranks was obtained by J. Pfanzagl (1968). Incorrect processing of rank information obtained in the framework of psychosocial research leads to contradictions in the assessment of the patient’s condition. Consequently, rank processing does not allow classical mathematical operations (summation, average), which makes it impossible to correctly estimate the effectiveness of PSR numerically.ObjectivesDevelopment of algorithms for numerical evaluation of PSR efficiency based on rank information processing using the analytic hierarchy process (AHP) [1].MethodsClinical, psychometric, AHP algorithmsResultsThe analysis of the problems of assessing the patient’s conditions on the basis of categorical and psychometric (rank) scales and subscales shows that these problems can be presented in the form of appropriate hierarchies, the structure of which must be taken into account when processing the initial information.According to the results of the analysis of the data of preliminary studies, the main areas of impaired functioning of patients affecting the evaluation of the effectiveness of PSR have been identified. Rank estimates of changes in the relevant areas of the patient’s dysfunction after the PSR program compared to the initial level are the basis for the conclusion about the effectiveness of the PSR components. Algorithms of the AHP normative approach were used to translate rank information into numerical information [2]. The weight of the areas of the patient’s functioning disorders was used in the formation of integral estimates of the effectiveness of PSR.The fundamental difference between AHP-based assessments and rank assessments is due to the fact that numerical estimates of the weight of the criteria and the corresponding changes in the patient’s condition are obtained, which depend on the qualifications of specialists, the characteristics of the scales used to measure violations in the relevant areas and the procedures of the PSR.ConclusionsObtaining the results of processing rank information in a numerical scale allows to obtain the correct integration of the patient’s personal characteristics when considering PSR procedures and to obtain correct prognostic models of the patient’s condition. 1. Saaty T. European Journal of Operational Research.1990; 48(1):9-26. https://doi.org/10.1016/0377-2217(90)90057-I 2. Mitikhin V.G., Solokhina T.A. et al. Psychiatry, 2022; 20(2): 51-59. DOI: 10.30629/2618-6667-2022-20-2-51-59Disclosure of InterestNone Declared

Personalized assessment of the effectiveness of psychosocial rehabilitation: an innovative approach based on the process of analytical hierarchy

IntroductionIn psychosocial rehabilitation (PSR), rank scales are widely used to assess the severity of functional disorders in patients. The main problem of evaluating the effectiveness of PSR is related to the methods of processing data obtained using psychometric scales used to evaluate the effectiveness of interventions within PSR. J. Pfanzagl (1968) obtained the proof of the non-numerical nature of ranks. Incorrect processing of rank information obtained in the framework of psychosocial research leads to contradictions in the assessment of the patient’s condition. Consequently, rank processing does not allow classical mathematical operations (summation, average), which makes it impossible to correctly estimate the effectiveness of PSR numerically.ObjectivesDevelopment of algorithms for numerical evaluation of PSR efficiency based on rank information processing using the analytic hierarchy process (AHP) [1].MethodsClinical, psychometric, AHP algorithms.ResultsThe analysis of the problems of assessing the patient’s conditions on the basis of categorical and psychometric (rank) scales and subscales shows that these problems can be presented in the form of appropriate hierarchies [2], the structure of which must be taken into account when processing the initial information.According to the results of the analysis of the data of preliminary studies, the main areas of impaired functioning of patients affecting the evaluation of the effectiveness of PSR have been identified. Rank estimates of changes in the relevant areas of the patient’s dysfunction after the PSR program compared to the initial level are the basis for the conclusion about the effectiveness of the PSR components. Algorithms of the AHP normative approach were used to translate rank information into numerical information [2]. The weight of the areas of the patient’s functioning disorders was used in the formation of integral estimates of the effectiveness of PSR.The fundamental difference between AHP-based assessments and rank assessments is due to the fact that numerical estimates of the weight of the criteria and the corresponding changes in the patient’s condition are obtained, which depend on the qualifications of specialists, the characteristics of the scales used to measure violations in the relevant areas and the procedures of the PSR.ConclusionsObtaining the results of processing rank information in a numerical scale allows to obtain the correct integration of the patient’s personal characteristics when considering PSR procedures and to obtain correct models of the patient’s state. 1. Saaty T. European Journal of Operational Research.1990; 48(1):9-26. https://doi.org/10.1016/0377-2217(90)90057-I 2. Mitikhin V.G., Solokhina T.A. et al. Psychiatry, 2022; 20(2): 51-59. DOI: 10.30629/2618-6667-2022-20-2-51-59Disclosure of InterestNone Declared

A Tool for Converting Text to Braille Script to Empower Readability for Blind People

The utilization of Braille patterns has long been instrumental in facilitating literacy among visually impaired individuals, enabling them to decipher alphabets and numerals through tactile means. In this project, an innovative integration of Braille and servo motor technology is employed to render electronic text accessible to individuals with visual impairments. Each character within an electronic file is systematically translated into its corresponding Braille representation, with servo motors dynamically facilitating the tactile presentation of these patterns. Upon receiving input, the system activates the necessary servo motors, causing them to rotate 90 degrees above the surface, thereby bringing the Braille representations into physical contact with the user. To read the content, individuals simply touch these servo motors, allowing them to sense and interpret the encoded alphabetic or numeric information. This approach not only enhances accessibility but also promotes independent literacy among visually impaired individuals. By combining Braille with servo motor technology, this project offers a promising solution for overcoming barriers to information access, fostering inclusivity, and empowering individuals with visual impairments to engage with electronic text effectively.

Temporal Implicit Multimodal Networks for Investment and Risk Management

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.

An Expandable and Generalized Method for Equipment Information Reflection in Digital Twin Workshop Systems

A production workshop is equipped with diverse equipment and machinery, and manufacturing data generated by different equipment show certain differences. Therefore, developing a method with an information presentation capability for a digital twin workshop system (DTWS) can be challenging due to the numerous information sources and types. The level of detail provided for a DTWS is directly related to the management comprehension of the production status of a physical workshop. Thus, arranging and presenting different data effectively represents a significant challenge. To address this challenge, this study proposes an equipment information self-reflection method with expandability for DTWSs. First, a generic equipment information model with an expandability is developed. This model encompasses all relevant data types in a workshop. The analysis of these data is conducted from two perspectives: workshop equipment categorization and equipment internal composition. The analysis results are used for the generation of extensible styles for property elements and function components. For the DTWSs, these two perspectives offer a standardized specification for equipment information presentation. In addition, a self-reflection method is introduced to address the demand for generating diverse information types ranging from equipment-based to extensible types. The system framework and operational flow of this method are explained in detail. To verify the proposed method, this paper conducts a practical case study using the DTWorks software development kit to simulate a real workshop. The proposed self-reflection method is implemented into the DTWorks. The disparities in equipment information types are analyzed for both pre- and post-implementation of the proposed method. The obtained results demonstrate the efficiency and advantages of the proposed self-reflection method. Finally, the results show that the proposed method exhibits advantageous performance regarding intricate business contexts.

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning of Large Language Models

Numerical reasoning is a vital capability for natural language processing models to understand and process numerical information in real-world scenarios. Most current methods first generate the Intermediate Meaning Representations (IMRs) of questions and then generate answers. Current SOTA methods generate programs as IMRs with large language models (LLMs). Intuitively, equations have fewer restrictions and closer semantics to the question than programs, leading to higher generation accuracy. However, current LLMs generate equations worse than programs, where we assume that the equation data is rare in pre-training data compared to programs. So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs. For the first problem, we propose and prove a proposition to theoretically compare the generation accuracy of different IMRs. For the second problem, we present a method called Boosting Numerical ReasonIng by Decomposing the Generation of Equations Bridge, which can improve the accuracy of LLMs in generating equations as IMRs by reducing the tendency of generating constant expressions and programs. Our method improves the performance by 2.2%, 0.9%, and 1.7% on GSM8K, SVAMP, and Algebra datasets compared to the previous state-of-the-art methods under the single reasoning path setting. Our code and prompts are available at https://github.com/zirui-HIT/Bridge_for_Numerical_Reasoning}.

Steered or Guided by Numbers?

Concepts like 'the metric society' and 'the tyranny of metrics' suggest that quantitative information increasingly shapes and steers policy and governance. This paper engages critically with such assumptions by using domestication theory to analyse how Norwegian climate and energy policy actors make sense of, assemble, and employ numeric information. Through analysis of interviews with politicians and public employees working with climate and energy policies in the Norwegian government administration, we identified three main categories of narratives of domesticating quantitative information: (1) Numeric engagements, (2) Uncertainty, and (3) Pragmatic information management. Employees in the administration articulated either of the two first categories, while politicians and political advisors performed the third. All interviewees highlighted the need for more cautious and reflexive approaches to numeric information rather than enthusiastically embracing such information. In their decision-making, the policymakers appeared to be guided by numbers rather than steered by them.

Electrophysiological correlates of temporal numerosity adaptation.

Much research has revealed the human visual system is capable to estimate numerical quantities, rapidly and reliably, in both the spatial and the temporal domain. This ability is highly susceptible to short-term plastic phenomena related to previous exposure to visual numerical information (i.e., adaptation). However, while determinants of spatial numerosity adaptation have been widely investigated, little is known about the neural underpinnings of short-term plastic phenomena related to the encoding of temporal numerical information. In the present study we investigated the electrophysiological correlates of temporal numerosity adaptation. Participants were asked to estimate the numerosity of a test sequence of flashes after being exposed to either a high or low numerous adapting sequence. Behavioral results confirmed the expected underestimation of test stimulus when this was preceded by a high numerous sequence as compared to when preceded by a low numerous sequence. Electrophysiological data revealed that this behavior was tightly linked to the amplitude of the steady-state visual evoked (ssVEP) response elicited by the test stimulus. When preceded by a high numerous sequence, the test stimulus elicited larger ssVEP responses as compared to when preceded by a low numerous sequence with this pattern being robustly correlated with behavior. Finally, topographical maps showed that this difference was mostly evident across two antero-posterior distributed clusters of electrodes and correlated with changes in functional connectivity. Taken together, our results suggest that visual plastic phenomena related to the encoding of temporal numerosity information reflect changes in rhythmic evoked activity that are likely related to long range communications between distinct brain regions.

Using natural language processing to increase prediction and reduce subgroup differences in personnel selection decisions.

The purpose of this research is to demonstrate how using natural language processing (NLP) on narrative application data can improve prediction and reduce racial subgroup differences in scores used for selection decisions compared to mental ability test scores and numeric application data. We posit there is uncaptured and job-related constructs that can be gleaned from applicant text data using NLP. We test our hypotheses in an operational context across four samples (total N = 1,828) to predict selection into Officer Training School in the U.S. Air Force. Boards of three senior officers make selection decisions using a highly structured rating process based on mental ability tests, numeric application information (e.g., number of past jobs, college grades), and narrative application information (e.g., past job duties, achievements, interests, statements of objectives). Results showed that NLP scores of the narrative application generally (a) predict Board scores when combined with test scores and numeric application information at a level of correlation equivalent to the correlation between human raters (.60), (b) add incremental prediction of Board scores beyond mental ability tests and numeric application information, and (c) reduce subgroup differences between racial minorities and nonracial minorities in Board scores compared to mental ability tests and numeric application information. Moreover, NLP scores predict (a) job (training) performance, (b) job (training) performance beyond mental ability tests and numeric application information, and (c) even job (training) performance beyond Board scores. Scoring of narrative application data using NLP shows promise in addressing the validity-adverse impact dilemma in selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Insights Into Challenges Faced by Interpreting Trainees and Their Error Patterns

This study explored the challenges faced by trainees in Arabic and English interpreting by analyzing prevalent errors and investigating their underlying causes. A cohort of 31 female students underwent simultaneous interpretation tests in both languages. The data were used to identify challenges encountered during the interpretation process and analyze participants’ performance using Musa and Al-Maryani’s (2021) eclectic model. The study also examined the impact of L2 level on interpreting output. The findings show that omission is the most common error in interpreting both English and Arabic speech and is often employed as a coping technique for challenges such as speech speed, specialized terminology, structural differences, and numerical information. Hesitation was more pronounced when interpreting English into Arabic, indicating difficulties in directionality and language fluency. Regardless of language proficiency or level, all participants made similar errors when interpreting Arabic and English speech. These findings highlight the importance of developing comprehensive interpreter training that focuses not only on L2 proficiency, but also on strategies for managing the challenges encountered during interpretation, such as speech speed, specialized terminology, and directionality.

Evaluation of the trend of set-up errors during the treatment period using set-up margin in prostate radiotherapy

Accurate information on set-up error during radiotherapy is essential for determining the optimal number of treatments in hypofractionated radiotherapy for prostate cancer. This necessitates careful control by the radiotherapy staff to assess the patient's condition. This study aimed to develop an evaluation method of the temporal trends in a patient's specific prostate movement during treatment using image matching and margin values. This study included 65 patients who underwent prostate volumetric modulated arc therapy (mean treatment time, 87.2 s). Set-up errors were assessed using bone, inter-, and intra-fraction marker matching across 39 fractions. The set-up margin was determined by dividing the four periods into 39 fractions using Stroom's formula and correlation coefficient. The intra-fraction set-up error was biased in the anterior-superior (AS) direction during treatment. The temporal trend of set-up errors during radiotherapy slightly increased based on bone matching and inter-fraction marker matching, with a 1.6-mm difference in the set-up margin fractions 11 to 20. The correlation coefficient of the mean prostate movement during treatment significantly decreased in the superior-inferior direction, while remaining high in the left-right and anterior-posterior directions. Image matching contributed significantly to the improvement of set-up errors; however, careful attention is needed for prostate movement in the AS direction, particularly during short treatment times. Understanding the trend of set-up errors during the treatment period is essential in numerical information sharing on patient condition and evaluating the margins for tailored hypo-fractionated radiotherapy, considering the facility's image-guided radiation therapy technology.