Effective Technique Research Articles

Leveraging Partially Context-Sensitive Profiles for Enhanced AOT Compilation: A Review

In the realm of compiler optimization, just-in-time (JIT) compilation dynamically adjusts code execution based on runtime profiling, contrasting with the static approach of ahead-of-time (AOT) compilation. While JIT benefits from real-time profiling data, AOT lacks this advantage, necessitating innovative strategies to enhance performance without runtime feedback. This review article explores the integration of partially context-sensitive profiles into AOT compilation, offering insights into optimizing statically compiled programs through advanced profiling techniques. Also, it explores the utilization of partially context-sensitive profiles in ahead-of-time (AOT) compilation to enhance program performance. It delves into the challenges of AOT optimization without runtime profiling, contrasting it with the dynamic optimization capabilities of just-in-time (JIT) compilation. The proposed algorithm strategically leverages partial profiles to identify and optimize hot code segments, presenting a promising avenue for improving AOT compilation efficiency. Through empirical evaluation of diverse benchmarks, the article validates the technique's effectiveness, underscoring its significance in advancing compiler optimization strategies for statically compiled programs.

Role of Endoscopic Ultrasound-Guided Radiofrequency Ablation in Pancreatic Lesions: Where Are We Now and What Does the Future Hold?

Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) has emerged as an effective and minimally invasive treatment for pancreatic lesions, particularly in patients at high surgical risk. Utilizing thermal energy, RFA induces the coagulative necrosis of the tissue and potentially triggers immunomodulation by releasing intracellular antigens. Numerous studies have confirmed the technical feasibility, safety, and efficacy of EUS-RFA in pancreatic neuroendocrine tumors and premalignant cystic lesions, with an acceptable profile of adverse events. The technique's potential immunomodulatory effects offer intriguing implications for the treatment of advanced pancreatic malignancies, encouraging further evaluation. This review paper aims to highlight the EUS-RFA principles, technology, and clinical applications in various pancreatic lesions and safety, and the future research directions.

Chemical neurolysis of genicular nerves for chronic non-cancer knee pain: A scoping review.

Chemical neurolysis of the genicular nerves is a treatment option for intractable non-cancer knee pain. This scoping review synthesizes the available literature on the effectiveness, adverse effects and procedural techniques of chemical neurolysis of genicular nerves for the management of knee pain. Scoping review. All clinical and research settings. Adult participants with chronic non-cancer knee pain undergoing chemical neurolysis of genicular nerves. A literature search in MEDLINE, EMBASE and Cochrane Library was conducted up to 4 September 2023. Articles were searched via terms and keywords relating to "knee", "pain", "knee osteoarthritis", "ablation", "alcohol", "phenol" and "chemical neurolysis". Included articles were full text primary studies and in English. Data was extracted using an electronic database by 2 independent reviewers. There were 8 studies included in this review (including 1 randomized controlled trial), comprising 192 patients. There were 4 studies that used phenol, 3 studies that used alcohol, and 1 study that used either alcohol or phenol for chemical neurolysis. Both fluoroscopy and/or ultrasound guidance were utilized for nerve target identification. All studies demonstrated that chemical neurolysis resulted in improved pain and/or functional outcomes, with no serious adverse events reported. Chemical neurolysis of the genicular nerves is a promising treatment strategy for chronic knee pain. Interpretation of the available studies are limited by study heterogeneity and small sample sizes. High-quality randomized controlled trials are required to clarify the selection of appropriate nerve targets, choice of image guidance, and to compare with other ablative modalities.

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules. It is critical to accurately identify solid lung nodules with lobulation and spiculation signs before surgery; however, traditional diagnostic imaging is prone to misdiagnosis, and studies on artificial intelligence-assisted diagnosis are few. Therefore, we introduce a volumetric SWIN Transformer-based method. It is a multi-scale, multi-task, and highly interpretable model for distinguishing between benign solid lung nodules with lobulation and spiculation signs, lung adenocarcinomas, and lung squamous cell carcinoma. The technique's effectiveness was improved by using 3-dimensional (3D) computed tomography (CT) images instead of conventional 2-dimensional (2D) images to combine as much information as possible. The model was trained using 352 of the 441 CT image sequences and validated using the rest. The experimental results showed that our model could accurately differentiate between benign lung nodules with lobulation and spiculation signs, lung adenocarcinoma, and squamous cell carcinoma. On the test set, our model achieves an accuracy of 0.9888, precision of 0.9892, recall of 0.9888, and an F1-score of 0.9888, along with a class activation mapping (CAM) visualization of the 3D model. Consequently, our method could be used as a preoperative tool to assist in diagnosing solitary solid lung nodules with lobulation and spiculation signs accurately and provide a theoretical basis for developing appropriate clinical diagnosis and treatment plans for the patients.

Innovation in sustainable composite research: Investigating graphene-reinforced MMCs for liquid hydrogen storage tanks in aerospace and space exploration

The demand for lightweight materials in space exploration applications has seen a significant rise. This study presents a novel approach by integrating graphene with Aluminium alloy (AA) 2900 powder, synthesized through High energy ball milling technique. The resulting powder was used to reinforce Aluminium alloy 2195, creating metal matrix composites (MMCs) via squeeze casting. The findings demonstrate that incorporating 0.5 wt % 2D graphene nanoplatelets (2D-Grnp) in AA 2195 achieved density match, enabling homogeneous dispersion, addressing composite casting challenges. Consequently, the AA 2900 alloy embedded with 2D-Grnp facilitated the homogeneous dispersion of reinforcements during the squeeze casting process, yielding MMCs Ideal for potential use in lightweight liquid hydrogen fuel tank structures for launch vehicles. Following T8 heat treatment, the final casted composite plate exhibited significant enhancements in ultimate tensile strength, with a 4.5% increase over monolithic Al 2195-T8, exhibiting nominal reduction in elongation behavior and higher hardness. Additionally, scanning and transmission electron microscopy (SEM, TEM), Small Angle Neutron Scattering (SANS) and Electron Backscatter Diffraction (EBSD) revealed the squeeze casting technique's effectiveness by exhibiting enhanced bonding among homogeneously reinforced 2D-Grnp, T1/θ’ precipitates, and AA 2195.

Validation of an absorption coefficient measurement technique using two cardioid microphones in any impedance tube

We have proposed a technique for measuring sound pressure and particle velocity using a combination of cardioid microphones and then applied it to obtain the acoustic absorption coefficient in a tube. So far, we have revealed that either using one cardioid microphone measured twice or using a pair of cardioid microphones measured once are both suitable for the measurement and that the calibration of the cardioid microphones is required. If the technique works correctly, this technique holds promise for measuring the acoustic absorption coefficient of various specimens in any tube and with any cardioid microphones. To validate the technique's effectiveness, we conducted experiments in multiple square-section wooden or round-section steel tubes, with specimens of glass wool and polyester terephthalate (PET) wool. As a result, this technique can provide accurate absorption coefficient measurements in any tube configuration.

Evaluating EHS parastomal hernia classification for surgical planning: a retrospective analysis of 160 consecutive cases in a single center.

Parastomal hernia (PH) is a prevalent complication following ostomy formation, presenting significant challenges in surgical management. This study aims to validate the European Hernia Society classification for PH through the application of the Hybrid Parastomal Endoscopic Repair (HyPER) method. The study focuses on establishing the practical utility of the European Hernia Society classification in a clinical setting, particularly in guiding surgical approaches and improving patient outcomes. This retrospective observational study aimed to assess the utility of the European Hernia Society classification in planning surgical strategies for parastomal hernias. The validation of the classification of PH was based on the experience involving 160 patients in single center. Patients were classified according to the European Hernia Society criteria, and data were collected on patient demographics, clinical presentations, and surgical outcomes. Main goal was to assess the consistency and applicability of the European Hernia Society classification in predicting surgical challenges and outcomes. The study found a predominance of complex Type III and IV hernias. The European Hernia Society classification was effective in categorizing PH, aiding in surgical planning and highlighting the increased complication rates associated with more complex hernia types. This study represents the largest single-center cohort treated for PH by a single team, providing a controlled evaluation of the HyPER technique's effectiveness. The validation of the European Hernia Society classification in this study is a significant advancement in the standardization of PH management. The findings demonstrate the classification's utility in enhancing surgical planning and patient-centered care. The study also opens avenues for further research into standardized approaches and techniques in PH treatment.

Painless Stochastic Conjugate Gradient for Large-Scale Machine Learning.

Conjugate gradient (CG), as an effective technique to speed up gradient descent algorithms, has shown great potential and has widely been used for large-scale machine-learning problems. However, CG and its variants have not been devised for the stochastic setting, which makes them extremely unstable, and even leads to divergence when using noisy gradients. This article develops a novel class of stable stochastic CG (SCG) algorithms with a faster convergence rate via the variance-reduced technique and an adaptive step size rule in the mini-batch setting. Actually, replacing the use of a line search in the CG-type approaches which is time-consuming, or even fails for SCG, this article considers using the random stabilized Barzilai-Borwein (RSBB) method to obtain an online step size. We rigorously analyze the convergence properties of the proposed algorithms and show that the proposed algorithms attain a linear convergence rate for both the strongly convex and nonconvex settings. Also, we show that the total complexity of the proposed algorithms matches that of modern stochastic optimization algorithms under different cases. Scores of numerical experiments on machine-learning problems demonstrate that the proposed algorithms outperform state-of-the-art stochastic optimization algorithms.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Apple fruits categorizing based on deep convolutional neural network techniques

<p>For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.</p>

Brightfield Multiplex Immunohistochemistry Assay for PD-L1 Evaluation in Challenging Melanoma Samples.

Targeting the PD1/PD-L1 immune checkpoint pathway has rapidly become a therapeutic strategy for melanoma patients. Indeed, the quantification of PD-L1 expression by immunohistochemistry (IHC) in melanoma samples is already required, in some contexts, to allow access to anti-PD-1/PD-L1 immunotherapy. Despite a rising demand for PD-L1 testing, paralleling increasing cumulative experience in its assessment and quantification, it is fair to recognize that PD-L1 evaluation in melanoma samples still presents some critical issues. The aim of this technical report is to develop and validate a multiplex double staining protocol for PD-L1/SOX10 in Ventana Benchmark Ultra for routine practice. Our results show that double labeling provides the necessary tools to identify PD-L1 + melanoma cells clearly. The simultaneous visualization of 2 different proteins targets allows the topographical relationship between the 2 labeling to be evaluated within the context of the tissue morphology. Future studies are needed to test this technique's real-world applicability and effectiveness in implementing interpathologist agreement.

Numerical solution for the interior electric displacement of the moving DBY‐PS model for semi‐permeable cracked piezoelectric material

AbstractIn this study, we analysed a moving crack at the interface of an infinitely long piezoelectric bilayer using the Dugdale–Barenblatt yield (DBY) model and the polarisation saturation (PS) model. To model the moving crack problem, a Yoffe‐type crack moves at a constant subsonic speed on the interface of an infinitely long piezoelectric bilayer. The crack faces are assumed to be semi‐permeable, and at the boundary of the bilayer, in‐plane electrical and out‐of‐plane mechanical stresses are applied. Due to the application of electro‐mechanical loads, cracks propagate, mechanical yielding zones and electric saturation zones are developed. To arrest the crack from further propagation, mechanical yield stress and saturation electric displacement are applied at the developed zones. To address this problem analytically and numerically, the mixed boundary value problem is transformed into a set of coupled Fredholm integral equations (FIEs) of the second kind using the Fourier transform and the Copson method. The closed‐form analytical expressions for the length of the electrical saturation zone (ESZ), whether longer, shorter or equal to the mechanical yielding zone (MYZ), show dependence on external electro‐mechanical loads under semi‐permeable crack conditions. The algorithm to solve the electric crack condition parameter (ECCP) has been defined using numerical discretization and the bisection method. Illustrative examples demonstrate the proposed technique's effectiveness and suitability for Yoffe‐type moving cracks. The numerical results show the convergence of the ECCP. Furthermore, the numerical results show how mechanical and electrical zone lengths and energy release rate (ERR) are affected by electrical and mechanical loads, strip thickness and crack velocity. In addition, the size of the mechanical yielding zone is consistently promoted by electrical load, while the promotion or prevention of the electrical saturation zone by mechanical load depends on the relative sizes of the nonlinear zones.

Abdominoplasty Combined with Hip Expansion by Fat Grafting: An Evolution in Waistline Contouring.

Recent sociocultural trends show numerous patients requesting more curvaceous profiles. Abdominoplasty techniques had evolved into a combination of fascial plication with liposuction of the lateral torso, but often left patients with "boxy" profiles. The senior author performs 360-degree liposuction of abdomen and back, hip expansion with structural fat grafting, excision of redundant soft tissue, and wide plication of abdominal fascia to create the desired profile. Retrospective review of patient charts and CosmetAssure claims of female patients treated from January 2014 through May 2022 identified 1125 patients with a minimum 6-month follow-up who underwent abdominoplasty using 360-degree liposuction of waist, back, and flanks; wide plication of the rectus abdominis muscle; and hip expansion with fat grafting. Authors reviewed pre- and postoperative photographs to evaluate the technique's effectiveness. Hip expansion with fat grafting combined with abdominoplasty was successfully achieved in 1125 cases. Average age was 38 years. Average body mass index was 29 kg per m2. Average amount of aspirated fat was 1896 mL. Average amount of fat injected into the bilateral hips was 493 mL. Complication rates were comparable to those observed in similar abdominoplasty series reported in the literature. Abdominoplasty combining liposuction of the waist, back, abdomen, and flanks followed by wide fascial plication and expansion of the hips with fat grafting is a safe, reproducible technique for female patients. This technique prioritizes the hip anatomical area as an aesthetic consideration in abdominoplasty and facilitates creating a harmonious hip-to-waist ratio characteristic of a feminine figure.

Learning Accurate Label-Specific Features From Partially Multilabeled Data.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.

Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data.

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

Quality of Life of Patients Using Esophageal Speech after Total Laryngectomy: A Systematic Review Study.

(1) Background: The present systematic study aimed to assess whether using esophageal speech (ES) as a method of vocal rehabilitation in patients after total laryngectomy enhances their quality of life (QoL) and vocal functionality based on patients' reports. (2) Methods: Data collection was conducted from PubMed, Google Scholar, and Speech Bite, and the PRISMA Flow Diagram tool was used to record different stages of the literature search process. In the review, nine studies were included, while a bias check was carried out using the Critical Appraisal Skills Programme (CASP) checklists. Survey analysis incorporated quantitative and qualitative data, including standardized questionnaires and audio analyses. (3) Results: A technique's effectiveness depends on the method's functionality and the patient's abilities. Furthermore, the findings revealed that ES use unexpectedly affects quality of life regarding patients. While statistical analysis of the studies showed that some patients reported improvement in quality of life and vocal functionality, others faced challenges such as difficulty in learning the technique, long-term intervention, and unsatisfactory phonetic performance. Some studies observed quantitative measures, such as improved Voice Impairment Index (VHI) scores and Voice-Related Quality of Life (V-RQOL) scores. However, results were not uniformly positive across studies, with a subset of patients reporting minimal improvement. (4) Conclusions: The limited literature on the effect of ES on patients' QoL appears to influence the results in different ways. However, research data support that patients' communication and psychological state seem to improve significantly compared to patients who have not been rehabilitated. The final assessment of the technique's effectiveness on quality of life must depend on many factors.

Utilizing Question-and-Answer Technique to Boost Students’ English-Speaking Skills

A question-and-answer technique is one of the strategies to learn English-speaking skills. It invites the class to ask the concerned student questions and avoids a monologue where all the focus is on one person, which helps the teacher reduce tension. The purpose of this study was to confirm the impact of question-and-answer sessions on the speaking proficiency of first-grade senior high school students in Malang, Indonesia. A Quasi-experimental research design was employed since the present study utilized two intact groups. To find out the result, the researchers conducted a pre-test to measure the homogeneity of the group and a post-test to verify the technique's effectiveness. Based on the pre-test result, the groups were claimed to be homogenous before treatment was done since the significant value (.53) was greater than 0.05 (.53> 0.05). However, the post-test result after the treatment indicated that the two groups differed significantly at 0.05 levels as the sig. of the test results was less than 0.05 (.001<0.05). Furthermore, the descriptive statistics revealed better performance in the experimental than in the control group (76.56 > 66.22). It is summarized that the students who were taught using question-and-answer techniques had better speaking ability than those who were not. Therefore, the researchers recommended teachers apply this technique to their students to develop their speaking ability.

Fast, universal scheme for calibrating microwave crosstalk in superconducting circuits

A challenge in building large-scale superconducting quantum processors is the precise control and manipulation of the qubit state. However, the crosstalk between the microwave control lines impedes the parallel execution of high-fidelity digital and analog quantum operations. Here, we propose and demonstrate a universal compensation protocol for calibrating the microwave signal crosstalk. We also introduce amplified error sequences to optimize accuracy. Furthermore, we show a definitive improvement in parallel gate operations with crosstalk cancellation, demonstrating the technique's effectiveness. This work paves the way for superconducting hardware that features the automated calibration of microwave crosstalk, leading to enhanced fidelities in multiqubit circuits.

Evaluating the Effectiveness of the High-SMAS Facelift Technique: Objective Clinical Outcomes at 1-year.

The primary goal of the facelift is to rejuvenate the face without changing its natural features. This involves working on the deeper layers to preserve the surface look while adjusting the fat pads beneath. Thus, we often use a modified High-SMAS facelift method, following this deep-layer approach. The study evaluates whether the high-SMAS facelift, focusing on optimal repositioning, can effectively reduce aging signs in the mid to lower face and neck. Conducted from 2018 to 2022, this retrospective cohort study included demographic data, relevant comorbidities, and operative details of patients undergoing lateral extended high-SMAS facial lifting, excluding revision cases. Complications were recorded, and a minimum one-year follow-up was ensured. The technique's effectiveness was assessed using pre- and one-year post-surgery images analyzed by three masked examiners with a validated scoring method by La Padula et al. RESULTS: The study included 325 patients, with no major complications reported. Significant improvements were noted in post-operative High-SMAS visual scores compared to pre-operative scores (p<0.0001), particularly in cheek fullness, jawline, and cervical angle definition. The High-SMAS facelift technique, evaluated using the Face- and Neck-Lift Objective Photo-Numerical Assessment Scale, demonstrated significant cosmetic enhancements. This technique, focusing on optimal flap draping vectors, effectively restores a youthful appearance by improving facial contours. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Exploration of Skillification and Its Use in Awarding Credit for Prior Learning

In applications for prior learning credit, learners with professional experience describe competencies gained during their employment history or past study, explaining how these competencies correspond to what is taught in a course sufficiently well to award them credit for prior learning. Comparing skills that are extracted algorithmically from artifacts describing a learner’s prior experience to skills similarly extracted from syllabi offers a potential solution. Current research indicates that algorithmically extracted skills can be used to identify relevance between artifacts and groups of courses but that connecting an artifact to a specific course requires guidance to learners on the artifact creation, careful use of analytical techniques, and faculty involvement. The guidance includes setting expectations for language volume and creating focused text, like an essay, rather than more general descriptive artifacts, like résumés. Effective analytical techniques accommodate observed variation in skill volume between different artifacts, optimizing the potential identification of appropriate relationships. Basing decisions on the degree of overlap in the total artifact-course skill set appears more effective than considering the number of matches to program skills. Deterministic matching is insufficient for identifying course-level correspondence, while approaches that capture information about the covariation of skills among courses offer promise.