Evaluation Of Algorithms Research Articles

Analysis and Research of Printmaking Teaching Effect Based on Virtual Generation Technology and Neural Network Evaluation Algorithm

Traditional printmaking teaching is often limited by factors such as materials, equipment, and space, making it difficult for every student to fully practice and experience the entire process of printmaking creation. By introducing virtual generation technology, this study aims to create an immersive learning environment that allows students to intuitively observe, operate, and learn printmaking in a virtual environment, thereby stimulating their learning interest and motivation. In this system, students can create and learn prints through virtual reality technology, and at the same time, a neural network evaluation algorithm can evaluate and feedback on students’ works in real time. The combination of virtual generation technology and neural network evaluation algorithms can effectively improve the effect of printmaking teaching. First, virtual reality technology provides students with an immersive learning environment that enables them to more intuitively understand the creative process of printmaking. Second, the neural network evaluation algorithm can evaluate students’ works in real time, provide personalized feedback, and help students better master printmaking skills. The combination of virtual generation technology and neural network evaluation algorithms cannot only improve students’ learning interests but also improve teachers’ teaching efficiency. By using virtual reality technology, teachers can better demonstrate the process of printmaking, while neural network evaluation algorithms can help teachers understand students’ learning more accurately, so as to achieve more targeted teaching.

A knowledge empowered graph learning feature selection method based on variation propagation effect representation and analysis for human-centric manufacturing systems

As the construction goals of manufacturing systems shift from Industry 4.0 to Industry 5.0, the development goals of manufacturing systems have also shifted from technology driven to human-centric. Feature Selection (FS) of manufacturing systems plays an important role in helping human control and upgrade manufacturing system quality. However, due to the propagation of variations in manufacturing systems, traditional FS methods cannot accurately select the key features that have an important disturbance to quality indicator. In this paper, a process knowledge-enabled FS method is proposed to obtain key features of quality indictor (QI) that would cause bias of prediction model accuracy. Firstly, a process representation learning algorithm embedded in the process knowledge graph is proposed, which represents the manufacturing variation propagation process during the manufacturing process. Secondly, a feature importance evaluation algorithm based on disturbance intensity judgment rules is proposed to evaluate the importance of process features. Finally, the top k important features are selected as key features based on feature importance ranking. Experimental results demonstrate the proposed method outperforms state-of-art and classic methods in four evaluated indicators. Moreover, the four indicators of MSE, MAE, RMSE, and R 2 have respectively increased by 18%, 16%, 18%, and 33% with 7 features compared to the cutting-edge method.

Research on prediction and evaluation algorithm of sports athletes performance based on neural network.

The Ultimate Fighting Championship (UFC) stands as a prominent global platform for professional mixed martial arts, captivating audiences worldwide. With its continuous growth and globalization efforts, UFC events have garnered significant attention and achieved commendable results. However, as the scale of development expands, the operational demands on UFC events intensify. At its core, UFC thrives on the exceptional performances of its athletes, which serve as the primary allure for audiences. This study aims to enhance the allure of UFC matches and cultivate exceptional athletes by predicting athlete performance on the field. To achieve this, a recurrent neural network prediction model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed. The model seeks to leverage athlete portraits and characteristics for performance prediction. The proposed methodology involves constructing athlete portraits and analyzing athlete characteristics to develop the prediction model. The BiLSTM-based recurrent neural network is utilized for its ability to capture temporal dependencies in sequential data. The model's performance is assessed through experimental analysis. Experimental results demonstrate that the athlete performance prediction model achieved an overall accuracy of 0.7524. Comparative analysis reveals that the proposed BiLSTM model outperforms traditional methods such as Linear Regression and Multilayer Perceptron (MLP), showcasing superior prediction accuracy. This study introduces a novel approach to predicting athlete performance in UFC matches using a BiLSTM-based recurrent neural network. By leveraging athlete portraits and characteristics, the proposed model offers improved accuracy compared to classical methods. Enhancing the predictive capabilities in UFC not only enriches the viewing experience but also contributes to the development of exceptional athletes in the sport.

Approaches to Pediatric Chest Pain: A Narrative Review

Chest pain in children and adolescents is a common reason for emergency department visits and referrals to pediatric cardiologists, often driven by parental concern about potential cardiac causes. However, the vast majority of pediatric chest pain cases are benign and non-cardiac in origin. This narrative review examines the etiology, evaluation, and management of pediatric chest pain, emphasizing the importance of a thorough clinical history and physical examination in distinguishing between benign and serious conditions. This review also explores the role of diagnostic tests such as electrocardiograms, chest radiography, and echocardiography, highlighting the need to balance the avoidance of unnecessary tests with the imperative to rule out life-threatening cardiac conditions. Despite the low prevalence of cardiac causes, the variability in diagnostic approaches underscores the need for standardized evaluation algorithms. These could streamline care, reduce unnecessary resource utilization, and minimize anxiety for both patients and their families. Future studies should focus on assessing the effectiveness of such algorithms in improving clinical outcomes and resource management. The findings underscore the importance of a careful, evidence-based approach to the management of pediatric chest pain.

Enhanced GDM Algorithms for College Mathematics Teaching Quality Evaluation Under Interval-Valued Intuitionistic Fuzzy Sets

The college mathematics teaching quality evaluation is the multiple-attribute group decision-making (MAGDM). Currently, the Exponential TODIM (ExpTODIM) and MABAC was executed to put forward MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are executed for portraying fuzzy data during the college mathematics teaching quality evaluation. In this study, the interval-valued intuitionistic fuzzy number ExpTODIM-MABAC (IVIFN-ExpTODIM-MABAC) approach is put forward the MAGDM with IVIFSs. At last, numerical example for college mathematics teaching quality evaluation is executed to verify the IVIFN-ExpTODIM-MABAC. The major contributions are executed: (1) the ExpTODIM-MABAC based was extended to IVIFSs along with Entropy model; (2) the entropy is employed to execute the weight under IVIFSs. (3) the IVIFN-ExpTODIM-MABAC approach is put forward MAGDM with IVIFSs; (4) numerical example for college mathematics teaching quality evaluation and comparative analysis is executed to proof the IVIFN-ExpTODIM-MABAC approach.

The rebar corrosion length evaluation technique based on nonlinear ultrasonic guided wave energy flow

In offshore reinforced concrete (RC) structures, phenomena of rebar corrosion are widespread, seriously threatening the durability of the structures. However, the issue of entire rebar corrosion process detection, especially for the evaluation of local corrosion states and the scope, is also challengeable. The paper aims at utilizing the nonlinear ultrasonic guided wave (UGW) energy flow to identify reinforcement corrosion level and length during the entire corrosion process in RC structures and establishing a corresponding corrosion evaluation algorithm. Therefore, a corroded rebar with the surrounding concrete cover is simplified into a cylindrical layered structural model. Meanwhile, piezoelectric transducers are used for the actuation and reception of UGWs, and a granular material contact (GMC) model is applied to illustrate the mechanism for generating surface-contact-induced nonlinear components in scattering waves. Then, the propagation behavior of the nonlinear UGWs is analyzed by regarding the corrosion layer as a secondary sound source changing the wave energy flow. A corrosion evaluation algorithm based on the GMC model is established and validated by both experiment and finite element (FE) analysis. The results show that for a harmonic UGW excitation signal, the periodic relationship between the second-order nonlinear coefficient of received signal and the corrosion length is found in which the period is associated with the difference (kd) of 2 times of fundamental frequency wave number (2 kω) and double frequency wave number (k2ω). Particularly, as kd trends to zero, a degraded linear relationship is found for establishing the corrosion length evaluation algorithm with the relatively high evaluation precision and convenient evaluation technique.

Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry.

Speckle Characteristic Based Auto-focusing Approach for Diffuse Object in Digital Holography

Multi-wavelength digital holography extends the axial measurement range to the order of several tens of microns or more and makes the inspection of optical-rough objects and objects with steep slopes possible. As the optical-rough surface would superimpose speckle field on hologram, the existing focus evaluation criteria frequently fail in indicating correct reconstruction distance. This paper proposes a focus evaluation algorithm for focus detection of diffused surfaces. The average size of the speckle is used as the index and the average size of speckle is evaluated through morphological image processing. Two roughness specimens and a gear tooth are used as test pieces to validate the feasibility of the proposed method. Compared with the existing image focus evaluation method, the proposed morphological average size of speckle approach shows good robustness and sensitivity to various kinds of diffusing surface of mechanical parts.

A Large Model-Derived Algorithm for Complex Organoids with Internal Morphogenesis and Digital Marker Derivation.

Automated segmentation and evaluation algorithms have been demonstrated to enhance the simplicity and translational utility of organoid technology. However, there is a pressing need for the development of complex organoids that possess epithelium environmental elements, dense regional cell aggregation, and intraorganoid morphologies. Nevertheless, there has been limited progress, including both the construction of data sets and the development of algorithms, in the use of user-friendly microscopy to address such complex organoids. In this study, a data set of bright-field and living cell fluorescence images in paired forms and with temporal variance was constructed using droplet-engineered lung organoids. Additionally, a large model-based algorithm was developed. Both the organoid contours and intraorganoid morphologies were included in the data set, and their physical parameters were included and screened to form multiplex digital markers for organoid evaluation. The algorithm has been demonstrated to outperform existing methods and is therefore suitable for the evaluation of complex organoids. It is expected that the algorithm will facilitate the successful demonstration of AI in organoid evaluation and decision-making regarding their status.

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing & Evaluation (NDT & E) approach to monitor the health
of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical
analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight
to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP)
sample. The proposed approach models the observed data covariance into combination of temporal signal covariance
and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by
the presence of heterogeneity in the experimental data obtained from GFRP sample. This novel method is based on the
coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the
negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used
statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect
detection with the proposed technique.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

MECOM Locus classical transcript isoforms affect tumor immune microenvironment and different targets in ovarian cancer

The MECOM locus is a gene frequently amplified in high-grade serous ovarian carcinoma (HGSOC). Nevertheless, the body of research examining the associations among MECOM transcripts, patient prognosis, and their role in modulating the tumor immune microenvironment (TIME) remains sparse, particularly in large cohorts. This study assessed the expression of MECOM transcripts in 352 HGSOC patients and 88 normal ovarian tissues from the combined GTEx/TCGA database. Using resources such as the UCSC Genome Browser, Ensembl, and NextProt, two transcripts corresponding to classical protein isoforms from MECOM were identified. Cox proportional hazards regression analysis, Kaplan-Meier survival curves, and a comprehensive TIME evaluation algorithm were employed to elucidate the connections between the expression levels of these transcripts and both patient prognosis and TIME status. Chromatin Immunoprecipitation sequencing (ChIP-seq) data for the two protein isoforms, as well as RNA sequencing data post-targeted silencing, were analyzed to identify potential regulatory targets of the different transcription factors. Elevated expression of the MECOM isoform transcripts was correlated with poorer survival in HGSOC patients, potentially through the modulation of cancer-associated fibroblasts (CAFs) and immunosuppressive cell populations. In contrast, higher levels of EVI1 isoform transcripts were linked to enhanced survival, possibly due to the regulation of CD8+ T cells, macrophages, and a reduction in the expression of JUN protein, or its DNA-binding activity on downstream genes. Diverse protein isoforms derived from MECOM were found to differentially affect the survival and tumor development in ovarian cancer patients through specific mechanisms. Investigating the molecular mechanisms underlying disease pathogenesis and identifying potential drug target proteins at the level of splice variant isoforms were deemed crucial.

Algorithms and data structures for Cs-smooth RMB-splines of degree 2s + 1

The simple mesh refinement algorithm of Groiss et al. (2023) generates T-meshes admitting Reachable Minimally supported (RM) B-splines that possess the property of local linear independence and form a non-negative partition of unity. The construction was first presented for the bilinear case and has later been extended to Cs-smooth splines of degree p=2s+1. The present paper is devoted to algorithms and data structures for RMB-splines. We prove that the memory consumption of the data structures for representing a T-mesh and the associated RMB-splines is linear with respect to the mesh size, and we describe the details of the underlying refinement algorithm. Moreover, we introduce a novel evaluation algorithm for RMB-spline surfaces, which is based solely on repeated convex combinations of the control points, thereby generalizing de Boor's algorithm for tensor-product splines. Numerical experiments are included to demonstrate the advantageous behavior of the proposed data structures and algorithms with respect to their efficiency. We observe that the total computational time (which includes also error estimation and spline coefficient computation) scales roughly linearly with the number of degrees of freedom for the meshes considered.

Preoperative visualization of parathyroid pathology in patients with primary hyperparathyroidism using PAT/KT: a retrospective study

INTRODUCTION: Topical diagnosis of pathologically altered parathyroid glands in patients with primary hyperparathyroidism is a challenge both for specialists of radial diagnostics and for planning the scope of surgical intervention. The choice of diagnostic modality for localization of parathyroid glands depends on the availability of the method for a particular medical center. PET/CT is a promising method to determine the localization of parathyroid adenomaOBJECTIVE: To analyze the experience of using PET/CT with 11C-methionine and PET/CT with 11C-choline for preoperative diagnosis of parathyroid pathology in patients with primary hyperparathyroidism.MATERIALS AND METHODS: 72 patients diagnosed with PHPT and underwent surgical treatment were included in the study. Before surgical intervention all patients underwent laboratory and instrumental investigations in the scope of: blood tests for PTH, TTH, ionized calcium, calcitonin, 25(OH)D, creatinine, phosphorus, ultrasound of the neck, computer tomography of the neck with intravenous bolus contrasting.RESULTS: PET/CT showed the highest rates of DA in the patient group before primary surgery — 90.9%, compared with ultrasound (89.6%) and CT with IV contrast (84.9%). In the group of solitary adenomas of ectopic MTCS, PET/CT also had a higher DA — 92.8% than the other modalities. In patients with relapsed or persistent disease, the informativity rates of CT with intravenous contrast and PET/CT were similar (Se — 100%, Sp — 94.1%, DT — 96.2%).DISCUSSION: The algorithm for preoperative evaluation of patients to identify the location of pathologic parathyroid glands now includes imaging with two modalities: Neck ultrasound and radionuclide imaging: planar scintigraphy (washout/subtraction), SPECT, SPECT-CT. If the results are questionable, a CT scan of the neck with contrast or PET/CT is performed as a second step. None of the modalities has 100% diagnostic accuracy. Our study demonstrated the superiority of PET/CT over ultrasound and CT w/in contrast in a group of patients scheduled for primary surgical treatment. In a small group of patients, high informative rates of PET/CT and CT with contrast were demonstrated before reoperation, confirming the great potential of these techniques in these patients.CONCLUSION: PET/CT with 11C-methionine and PET/CT with 11C-choline have high diagnostic accuracy in patients with primary hyperparathyroidism before reoperation. PET/CT and CT with contrast showed similarly high diagnostic accuracy for the diagnosis of parathyroid adenoma with persistence or recurrence of the disease in patients with primary hyperparathyroidism.

An evaluation model for an optimal decarbonisation process in the built environment

PurposeThe goal is to deliver a decision-support framework to both public and private entities engaged in energy retrofit investments in the property market.Design/methodology/approachThe evaluation algorithm that is being offered takes an innovative approach to financial and economic analysis. Its foundation is a market-driven/cost-driven method, drawing logic from operational research and goal programming.FindingsThe algorithm is tested to a real estate portfolio yielding an optimal asset retrofitting schedule. The ranking list is determined by taking into consideration a variety of parameters, including investment costs and total CO2 emissions from energy retrofit initiatives. The Carbon Risk Real Estate Monitor’s (CRREM) emission targets for 2030 are employed as a reference point in the process of creating a ranking list of the assets that compose the real estate portfolio under examination.Practical implicationsThe evaluation algorithm will allow to determine, in a real estate portfolio, a priority list of assets to be enhanced. This is accomplished by taking into account the client’s financial resources, the overall cost of the intervention programmes for each asset, and the effects that each asset would have on the environment and the energy once the suggested retrofit programme is put into place.Originality/valueThe study proposes a methodological approach that seeks to balance the optimisation of energy performance, the reduction of environmental effect, the promotion of social well-being and economic sustainability in the context of managing the current property sector.

The Effectiveness of using Artificial Intelligence for Building Professional Competencies of Future Specialists in Art Majors in Higher Education Institutions

The research aims to study the impact of the use of artificial intelligence (AI) tools in higher education institutions (HEIs) on building professional competencies of future art specialists. The research employed quantitative and qualitative methods (in particular, modeling methods, pedagogical experiments, and survey of respondents to assess the impact of AI tools on building professional competencies). The author’s definition of the concept of “professional competencies of art specialists” is proposed. Targeted tools were selected and used for building components of professional competencies. For example, VocalAnalysis AI tools were used to form the perceptual component — for students majoring in Musical Art; Art Vision AI — for students majoring in Fine Arts; ChoreoVision AI — for students majoring in Choreography. The results of the study show that students rated their level of ability to use AI as higher than medium. The questionnaire designed to study the impact of the use of AI on building professional competencies of future specialists in art majors, demonstrated a high level of agreement between the assessment of the impact of the use of AI tools on the formation of various components of professional competencies. Further research can be aimed at the development and testing of an algorithm for objective expert evaluation of specific AI tools for the implementation of art projects by students of the specified art majors.

Making Formulog Fast: An Argument for Unconventional Datalog Evaluation

With its combination of Datalog, SMT solving, and functional programming, the language Formulog provides an appealing mix of features for implementing SMT-based static analyses (e.g., refinement type checking, symbolic execution) in a natural, declarative way. At the same time, the performance of its custom Datalog solver can be an impediment to using Formulog beyond prototyping—a common problem for Datalog variants that aspire to solve large problem instances. In this work we speed up Formulog evaluation, with some surprising results: while 2.2× speedups can be obtained by using the conventional techniques for high-performance Datalog (e.g., compilation, specialized data structures), the big wins come by abandoning the central assumption in modern performant Datalog engines, semi-naive Datalog evaluation. In the place of semi-naive evaluation, we develop eager evaluation, a concurrent Datalog evaluation algorithm that explores the logical inference space via a depth-first traversal order. In practice, eager evaluation leads to an advantageous distribution of Formulog’s SMT workload to external SMT solvers and improved SMT solving times: our eager evaluation extensions to the Formulog interpreter and Soufflé’s code generator achieve mean 5.2× and 7.6× speedups, respectively, over the optimized code generated by off-the-shelf Soufflé on SMT-heavy Formulog benchmarks. All in all, using compilation and eager evaluation (as appropriate), Formulog implementations of refinement type checking, bottom-up pointer analysis, and symbolic execution achieve speedups on 20 out of 23 benchmarks over previously published, hand-tuned analyses written in F ♯ , Java, and C++, providing strong evidence that Formulog can be the basis of a realistic platform for SMT-based static analysis. Moreover, our experience adds nuance to the conventional wisdom that traditional semi-naive evaluation is the one-size-fits-all best Datalog evaluation algorithm for static analysis workloads.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.

Optimization of surgical evaluation algorithms for living donor liver transplantation

BackgroundLiving donor liver transplantation (LDLT) is an established and endorsed alternative for deceased donor liver transplantation with better recipient outcomes. Nevertheless, while extensive evaluation of potential donors is crucial, evaluation algorithms differ between transplant centres and guidelines. MethodsWe included 317 individuals evaluated for LDLT between 07/2007–07/2022 in a retrospective analysis. The evaluation process was analysed to identify the key reasons for declining 77 potential donors. Additionally, 146 donors that underwent LDLT were analysed regarding risk factors for complications. ResultsThe main reasons for donor refusal were liver volumetry (40.3 %) and metabolic factors including obesity or steatotic liver disease (20.8 %). Contrast-enhanced computed tomography (CECT) identified 63.6 % of all declined donors; CECT combined with assessment of medical history, physical examination, blood testing and ultrasonography, identified 87.0 % of declined potential donors. Associated with this selection, complication rates in donors were low (≥II in 17.1 %; none with ≥IVb). Notably, higher age was a risk factor for developing a complication ≥II after hemi-hepatectomy (p = 0.0373). ConclusionsWe propose a progressive 4-step evaluation algorithm that begins with a very basic assessment combined with up-front CECT. This early phase of testing is expected to identify nearly 90 % of ineligible donors, thereby conserving critical resources, time and money, as well as minimising burden for potential donors. FundingJ.M.W. received funding by grant We-4675/6–1 from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany.