Evaluation Techniques Research Articles

Analysing the Social Economy Business Survey Index (S-BSI): Development, Features, and Effectiveness in Social Economy Policymaking and Evaluation

This study delves into the Social Economy Business Survey Index (S-BSI), a tool designed to monitor the social and economic value generated within the social economy (SE) to furnish vital insights for timely public policy interventions. The study aims to comprehensively analyze the S-BSI, encompassing its development, distinctive features, and effectiveness as an advanced instrument for policymaking and evaluating the SE policy. The research methodology comprises an in-depth analysis of the S-BSI's development process, followed by multiple comparative analyses of similar surveys on a domestic and international scale. Furthermore, the study utilizes qualitative evaluation techniques grounded in the next-generation public policy framework for the SE sector. This multifaceted research approach aims to offer a holistic understanding of the S-BSI, delivering valuable insights tailored for policymakers and stakeholders engaged in shaping public policies for the SE.

Five guidelines to improve context-aware process selection: an Australian banking perspective

PurposeSelecting which processes to improve plays a critical role in the first phase of the business process management lifecycle, but it is a step with known pitfalls. Decision-makers rely on subjective criteria and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-making and wastes resources. The purpose of this paper is to examine the proposition that decision-makers armed with context-enriched criteria make more logical, better-quality decisions. The context in question is qualitative, sensitive to decision-making bias and politically charged.Design/methodology/approachWe applied a design-science approach, engaging 70 industry decision-makers through a combination of research methods to assess how different contextual configurations, in a hypothetical scenario adapted from the Australian banking industry, influenced and ultimately improved the quality of the process selection step.FindingsThe study highlights the impact of framing effects on context, and the need to adapt framing to decision-maker behavior and provides five guidelines to improve process selection effectiveness.Originality/valueProcess selection research to date has largely focused on quantitative evaluation techniques, with little attention paid to the role of context and the behavioral interplay of decision-making styles in practice.

Exploring the Fundamentals of Bilirubinometry: Methods of Assessment, Timely Monitoring, and Intervention in Neonatal Jaundice with Integrated Phototherapy Solutions—A Review

A frequent ailment in infants, neonatal jaundice is characterized by high bilirubin levels and yellowing of the skin and eyes. It needs to be carefully managed to avoid serious consequences like kernicterus. The principles of bilirubinometry are examined in this review paper, with particular attention paid to different evaluation techniques, the value of prompt monitoring, and modern intervention approaches. Newborns may find it uncomfortable to undergo intrusive blood drawing as part of traditional bilirubin measurement methods. Transcutaneous bilirubinometry (TcB), on the other hand, provides a non-invasive substitute that improves monitoring effectiveness and lessens patient pain. The mainstay of care for newborn jaundice is still phototherapy, which uses light to change bilirubin into a form that is easier to excrete. The efficacy and safety of phototherapy have been enhanced by recent developments, especially about LED-based technology. The evaluation also looks at integrated phototherapy options that optimize therapeutic results and improve neonatal safety by combining automated treatment modifications with real-time bilirubin monitoring. To improve neonatal health outcomes and lessen difficulties related to hyperbilirubinemia, this review emphasizes the significance of timely and effective care of neonatal jaundice by synthesizing existing achievements and highlighting the incorporation of technology innovations

Transferability and Explainability of Deep Learning Emulators for Regional Climate Model Projections: Perspectives for Future Applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global climate models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here, we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as perfect prognosis (PP) and model output statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of explainable artificial intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differs between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications. Significance Statement Regional climate model (RCM) emulators are a cost-effective emerging approach for generating comprehensive ensembles of regional climate projections. Promising results have been recently obtained using deep learning models. However, their potential to capture the regional climate dynamics and to emulate other periods, emission scenarios, or driving global climate models (GCMs) remains an open issue that affects their practical use. This study explores the potential of current emulation approaches incorporating new explainable artificial intelligence (XAI) evaluation techniques to assess the reliability and transferability of the emulators. Our findings show that the different global and regional model biases involved in the different approaches play a key role in transferability. Based on the results obtained, we provide some prospects for potential applications of these models in challenging problems.

S1636 Comparison of Diagnostic Performance of Macroscopic Onsite Evaluation (MOSE) for Tissue Acquisition With EUS-Guided Fine Needle Specimens With Rapid Onsite Evaluation (ROSE) and Conventional Techniques: A Meta-Analysis

S1636 Comparison of Diagnostic Performance of Macroscopic Onsite Evaluation (MOSE) for Tissue Acquisition With EUS-Guided Fine Needle Specimens With Rapid Onsite Evaluation (ROSE) and Conventional Techniques: A Meta-Analysis

Heterodyne background-oriented schlieren for the measurement of thermoacoustic oscillations in flames

In aircraft engines, thermoacoustic oscillations in the combustion chamber contribute significantly to noise emissions, which, like all other emissions, must be drastically reduced. Thermoacoustic oscillations are not only a concern, they can also be beneficial in hydrogen combustion. This work demonstrates that thermoacoustic density oscillations with amplitudes at least an order of magnitude smaller than those resulting from density gradients in a turbulent flame can be detected using laser interferometric vibrometry. This improvement was made possible by heterodyning a carrier fringe system in background-oriented schlieren (BOS) recordings, which were subsequently analyzed using techniques commonly used for holographic interferometry. In comparison with other BOS evaluation techniques, the filtering of the individual frames in the Fourier domain offers a more efficient computational approach, as it allows for phase averaging of a high number of single recordings to reduce noise from turbulence. To address fringe pattern distortions and cross talk in the Fourier domain, which both have been observed by other authors, we propose background subtraction methods and an optimized background pattern. Additionally, the procedure provides a visualization tool for marking the high turbulence regions of heat release by the variations in fringe amplitude. Finally, the line-of-sight data are reconstructed using the inverse Abel transform, with the data calibrated by laser interferometric techniques, resulting in local values for density oscillations.Graphical abstract

Electrolyte engineering for optimizing anode/electrolyte interface towards superior aqueous zinc-ion batteries: A review

Aqueous zinc-ion batteries (AZIBs) are promising candidates for the large-scale energy storage systems due to their high intrinsic safety, cost-effectiveness and environmental friendliness. However, issues such as dendrite growth, hydrogen evolution reaction, and interfacial passivation occurring at the anode/electrolyte interface (AEI) have hindered their practical application. Constructing a stable AEI plays a key role in regulating zinc deposition and improving the cycle life of AZIBs. The fundamentals of AEI and the challenges faced by the Zn anode due to unstable interfaces are discussed. A comprehensive summary of electrolyte regulation strategies by electrolyte engineering to achieve a stable Zn anode is provided. The effectiveness evaluation techniques for stable AEI are also analyzed, including the interfacial chemistry and surface morphology evolution of the Zn anode. Finally, suggestions and perspectives for future research are offered about enabling a durable and stable AEI via electrolyte engineering, which may pave the way for developing high-performance AZIBs.

Nerve conduction velocity studies in diabetic peripheral neuropathy involving sural nerve—A meta-analysis

ABSTRACT Introduction: Diabetes mellitus (DM) poses a significant health burden globally, impacting millions due to its metabolic disruptions. Among its complications, diabetic peripheral neuropathy (DPN) is a prevalent concern, affecting sensory and motor nerves. While various diagnostic methods exist, discrepancies persist in prevalence estimates of DPN among diabetic populations. Objective: This meta-analysis aimed to assess the prevalence of DPN in individuals with DM, focusing on sural nerve conduction velocity (NCV) measurements. Additionally, the study explored how different evaluation techniques influenced prevalence estimates. Methods: A systematic literature search was conducted following PRISMA guidelines across multiple databases. Studies reporting sural nerve NCV measurements in diabetic patients were included. Data extraction, quality assessment, and statistical analyses were performed to synthesize findings. Results: Twenty-six studies met the inclusion criteria, providing insights into sural nerve NCV among diabetic individuals. Pooled analysis revealed a mean sural nerve NCV of 42.12 m/s (95% CI: 39.87-44.36), indicative of reduced conduction velocity in diabetic populations. Furthermore, sural nerve amplitude was significantly lower in diabetic individuals, with a pooled mean of 4.68 μV (95% CI: 3.11-6.25). Conclusion: Individuals with type 2 DM exhibit impaired sural nerve function, as evidenced by reduced NCV and amplitude measurements. Timely NCV assessment, alongside glycemic monitoring, is crucial for identifying and managing DPN to prevent further complications. Interventions addressing glycemic control, dietary regulation, and lifestyle modifications are recommended to mitigate the progression of neuropathy in diabetic populations.

Research Progress and Prospect of Performance Evaluation Techniques for Prestressed Concrete Structures

Research Progress and Prospect of Performance Evaluation Techniques for Prestressed Concrete Structures

The Application of Big Data Technology in the Analysis of Commercial Circulation Data in Emerging Industries

Inside the generation of rapid technological development, rising industries are increasingly counting on big facts to pressure growth and innovation. This examine explores the transformative effect of huge facts generation on the analysis of commercial circulate statistics in those burgeoning sectors. By way of harnessing numerous and voluminous datasets, organisations inside rising industries can uncover crucial insights, optimise delivery chains, are expecting marketplace developments, and enhance patron reviews. The paper begins by using outlining the unique challenges and possibilities that emerging industries face within the virtual landscape, emphasising the want for strong facts-driven techniques. We delve into the methodologies of large facts analytics, together with data acquisition, garage, processing, and visualization strategies tailor-made for the nuanced requirements of these industries. A crucial examination of case research wherein huge facts has been efficaciously carried out offers realistic insights into its effectiveness and barriers. The examine similarly investigates the role of advanced analytics, system studying, and AI in refining data evaluation techniques, providing a complete view of the way those technologies synergistically make contributions to strategic selection-making. Moral issues, especially regarding records of privateness and safety, are also addressed, acknowledging the responsibilities that accompany the utilization of huge information. The paper concludes by projecting future traits in big statistics programs within rising industries, which includes the capacity for predictive analytics and the integration of IoT gadgets. This research now not only underscores the significance of big statistics in revolutionising business flow however also serves as a guiding framework for industry leaders and stakeholders trying to navigate the complexities of the digital age in rising markets.

An automatic workflow for the quantitative evaluation of bit wear based on computer vision

As global oil exploration ventures into deeper and more complex territories, drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety. Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors (IADC), the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers' experience. Consequently, forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging. Therefore, an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database. Initially, a method for acquiring images of worn bits at the drilling site was developed. Subsequently, the wear classification and grading models based on computer vision were established to determine bit status. The wear classification model focuses on the positioning and classification of bit cutters, while the wear grading model quantifies the extent of bit wear. After that, the automatic evaluation method of the bit wear is realized. Additionally, bit wear evaluation software was designed, integrating all necessary functions to assess bit wear in accordance with IADC standards. Finally, a drilling bit database was created by integrating bit wear data, logging data, mud-logging data, and basic drilling bit data.This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites. It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits, marking the inception of intelligent analysis, design, and manufacture of drilling bits, thereby enhancing performance in challenging drilling conditions.

Discussion on the development practice of low permeability reservoir of beach bar in saltwater lake basin—Take Wu5 block of Wunan Oilfield in Qaidam Basin as an example

Abstract Based on the analysis of field outcrops, drilling cores, casting thin sections, logging interpretation, and development characteristics in the southwestern region of the Qaidam Basin, combined with the sedimentary characteristics, development patterns, and injection production well networks of the Neogene saline lake facies beach and dam in the southwestern region of the Qaidam Basin, his study aims to optimize the displacement system and improve the production rate. This paper counters the difficulty of Severely reservoir heterogeneity and interlayer contradictions low producing degree of water drive utilization and unclear understanding of water drive pattern in the study area; the article adopts Reservoir Engineering Methods, applies water injection development displacement and well network optimization evaluation techniques and bases on the geological characteristics of the reservoir to conduct the first time small well spacing pilot test in Block Wu 5, The results confirm that small well spacing encryption is feasible in this block.Therefore, the deployment and adjustment of small well spacing well network encryption in high structural areas of the area. Through implementation in the past two years, The small well spacing encryption in low-permeability sandbar reservoirs can effectively reduce seepage resistance, increase effective driving capacity, improve recovery rate and recovery rate, improve reservoir development efficiency, and increase economic benefits.

Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking.

The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples. The dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models. The performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses. The study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement. This study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

Phytochemicals as Adjuvant Therapies in RND Efflux-mediated Multidrug Resistant Pseudomonas aeruginosa Infections and Evaluation Techniques of Efflux Inhibitory Activities in Bacteria

: One of the top-listed opportunistic pathogens that are frequently found in medical devices such as ventilation systems is Pseudomonas aeruginosa. These bacteria often cause infections in the lungs (pneumonia), blood after surgery, and other parts of the body. Extreme susceptibility to P. aeruginosa infection primarily exists in immunosuppressed individuals, and long-term evolution has led to the development of genetic resistance mechanisms that have high genetic flexibility against damaging antibiotics. Several lines of research evidence point to efflux as the primary reason for the organism's effectiveness against antibiotic treat-ment in infections caused by this bacterium. Drug Efflux pumps play a crucial role in medicine because they expulse a variety of unique and unrelated chemical structures with either antibi-otics or antimicrobials before they reach the concentration necessary to kill bacteria, confer-ring multiple resistance to more than one class of antibiotics. Targeting this mechanism for example by blocking the most active efflux pump MexAB-orpM would probably lead to the discovery of new ways to circumvent the bacterial system of antibiotic resistance and boost treatment effectiveness.

Modern techniques for TMJ evaluation - Modjaw versus Cadiax - a systematic review

The temporomandibular joint (TMJ) plays a crucial role in various functions of the jaw, including chewing, speaking, and swallowing. Disorders of the TMJ can significantly impact a patient's quality of life, causing pain, dysfunction, and other systemic issues. Comprehensive TMJ evaluation is essential for accurately diagnosing and effectively treating these disorders. Two modern techniques for TMJ evaluation, Modjaw and Cadiax, offer advanced tools for detailed assessment. This comparison explores the capabilities, methodologies, and benefits of each system based on the current published studies. The aim is to highlight the novel nature of using digitalized tools for diagnosis and treatment planning, ass well as assessing the reliability of two systems.

Perceptions and impacts of the Happy Program on socio-emotional competencies: a mixed-methods study in primary education

Introduction: The "Happy Program" implemented by AMCO across various schools in Latin America has been the subject of a mixed-methods study assessing its impact on the socio-emotional competencies of primary school students. The study focuses on the perceptions of both teachers and students, integrating quantitative and qualitative techniques for a comprehensive evaluation. Methods: Mixed-methods design to thoroughly assess the impacts of the Happy Program on socio-emotional competencies among primary school students. This design incorporates both quantitative and qualitative approaches to provide a multi-dimensional understanding of the program’s effects. Teacher’s study, this segment involves a survey distributed to 57 teachers, designed to capture their perceptions before and after the implementation of the Happy Program. The survey measures changes in their views on students' socio-emotional skills and the overall classroom environment. Teachers are asked to rate the program’s influence on student behavior and emotional management, and to comment on areas for improvement, such as activity variety and family engagement. Parallel to the teacher survey, a larger scale study surveys 1,526 students. This study aims to gather direct feedback from the students about their experiences with the Happy Program. Students respond to questions about their emotional understanding, how they manage emotions, and their interactions with peers and family members as influenced by the program. This data is crucial for measuring the perceived effectiveness of the program from the students’ perspective. Findings/Results: Teachers reported a generally positive impact of the program on students, noting enhancements in key skills such as self-regulation and empathy. However, they also identified areas for improvement, such as activity diversification and greater family engagement. The results detail an increase in emotional understanding and management skills among students, with 76% of teachers observing significant improvements in these areas following the program's implementation.

Analysis of Nogunt'an (Emerald Cloud Pool) from Chŏng Sŏn’s Album of Views in the Capital Region (Kyŏnggyo myŏngsŭng ch'ŏp) Using Virtual Reality Analysis Tool

The Scenic Views of Kyŏnggyo (Kyŏnggyo myŏngsŭng ch'ŏp 京郊名勝帖) is a two-volume album of poetry and paintings in 1740 by Chŏng Sŏn (sobriquet: Kyŏmjae) during his time serving as magistrate of Yangch'ŏn. It captures scenic views from his travels along the Han River. Among these, three paintings (Nogunt'an 綠雲灘, Tokpaekt'an 獨栢灘, and Uch'ŏn 牛川), feature major viewpoints along the South Han River. Each has encouraged scholarly debates on the exact locations depicted in the album due to the scarcity of historical records and topographical changes of the area’s natural landscape.In particular, there has been no research that identifies the viewpoint and pictorial subject of Nogunt'an. This difficulty arises from two factors: first, the absence of architectural structures depicted in the painting, and second, the submersion of a rock that had once been significant landmark due to the construction of the P'altang Dam. These factors have made it impossible to fully match the painting’s historical landscape with its present-day appearance.This study aims to overcome these limitations by reconstructing the landscape in a virtual space. By analyzing topographical changes and utilizing image similarity evaluation techniques from the field of computer graphics, this study also propose the precise viewpoint of Nogunt'an, which previous studies were unable to clearly identify. Additionally, this research examines the fisheye lens distortion and panoramic composition techniques that Chŏng Sŏn frequently employed in Kyŏnggyo myŏngsŭng ch'ŏp and his other landscape paintings.

Global Trends in Educational Program Evaluation: Bibliometric Analysis for Education Quality Improvement

This study conducts a comprehensive bibliometric analysis to map global trends in educational program evaluation, focusing on research outputs, thematic evolution, and key scholarly networks from 2000 to 2024. Utilizing citation metrics, yearly publication graphs, and VOSviewer visualizations of keyword co-occurrences, the research identifies significant shifts in thematic focus, delineates interconnected research clusters, and highlights the influential contributions within the field. The analysis reveals a notable increase in publications related to educational evaluation during the early 2000s with a peak between 2010 and 2018, indicating heightened research interest coinciding with global educational reforms. Thematic clusters around "educational program," "quality management," and "leadership" underscore a multidisciplinary approach integrating educational theory with business management practices. This study underscores the field's adaptation to evolving educational needs and points towards emerging areas requiring further exploration, such as digital evaluation techniques and program-specific assessments.

Digitalization and tender evaluation in construction projects: a BIM-integrated MCDA approach supported by Macbeth

The tender evaluation process in the procurement for construction works, aligned with regulations such as the European Directive 2014/24/EU, faces various constraints, increasingly acknowledging the impact of digitalization, particularly through Building Information Modeling (BIM). Therefore, a thorough BIM-integrated multi-criteria decision analysis approach is vital to fairly select the best contractor for the job and ensure project success. Despite its significance, the literature remains scarce, particularly in addressing BIM-related and value-focused constraints for construction tender evaluation. This research introduces a comprehensive and value-focused method integrating BIM criteria through Multicriteria Decision Analysis (MCDA) supported by the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), which is applicable to both the public and private sectors, serving as a reference guide for contracting authorities to assess and select the best tender for BIM-based construction works contracts. For this purpose, the following methodology was adopted: 1) conducting an extensive literature review; 2) carrying out interviews with construction experts to identify BIM-integrated evaluation criteria and performance descriptors; 3) creating an adapted MCDA construction tender evaluation framework supported by MACBETH; 4) performing a real-world case study to demonstrate the proposed evaluation framework; 5) discussing its application and limitations, along with considerations for future research. The study demonstrated the practicality of the proposed framework through a case study, showing that a value-based approach can significantly improve project outcomes compared to traditional methods. While acknowledging the potential of the BIM methodology, the impact of BIM-specific criteria on contractor selection was found to be less significant than expected. In conclusion, this work addresses current limitations, such as the absence of BIM-integrated assessment guidelines in the European Directive 2014/24/EU and ISO 19650. It outlines a value-focused MCDA approach for evaluating tenders for construction works contracts, supported by MACBETH for structuring the decision problem and building the evaluation model. The contribution to the body of knowledge lies in providing a robust framework that enhances the evaluation process in BIM-based construction works contracts and offers practical insights for contracting authorities.

Characterization of internal defects in cylindrical components using laser ultrasonic method with a modified SAFT algorithm

The health monitoring of cylindrical elements is particularly important for the safe operation of industrial production, because cylinders bear a lot of support and transmission work. Normal non-destructive evaluation techniques need special designs to suit high curvature surfaces of cylinders. Laser ultrasonic (LU) method can provide a remote and non-destructive inspection solution to solid cylinders due to its flexible adaptability to complex structures and strong penetration depth. However, constrained by the common problems of optical detection systems, the detected ultrasonic signals will suffer low signal-to-noise ratio and bad resolution from poor sample surface quality. Thus, a modified synthetic aperture focusing technique (SAFT) optimized for cylindrical components is proposed to improve the detectability of LU on small and buried defects. Mode conversion wave signals obtained by multiple separate excitations are used in SAFT imaging for the reconstruction of the defects under surface profile correction. To reduce the influence of incident waves such as direct surface acoustic wave, besides common difference method, adjacent wave subtraction algorithm based on cross-correlation is used for signal preprocessing, suppressing the incident waves and exaggerating the mode conversion waves. In numerical simulation, internal defects with diameters from 0.6 to 0.2 mm with various buried depths are visualized and accurately located via the optimized SAFT algorithm using mode conversion waves. For validation, a circumferential scanning system is established in LU experiment and internal defects from 0.8 to 0.4 mm in diameter inside solid cylinders are successfully detected with precise location. The results elucidate the reliability of characterizing the location of small internal buried defects in solid cylinder structures through LU-SAFT imaging.