Location Of Features Research Articles

Introducing Phylogenetics in Search-based Software Engineering: Phylogenetics-aware SBSE

Phylogenetics studies the relationships, in terms of biological history and kinship, of a set of taxa (e.g., species). We argue that in Search-based Software Engineering (SBSE), the individuals of an evolutionary computation-driven population could be considered as taxa for which the leverage of Phylogenetic Inference might be beneficial. In this work, we present our Phylogenetics-aware SBSE approach. Our approach introduces a novel Phylogenetic Operation to promote results which are sufficiently aligned (in terms of lineage) with a certain reference given by the domain expert. Our approach is evaluated in two heterogeneous industrial case studies: Procedural Content Generation from Game Software Engineering, and Feature Location from Software Maintenance. The results are analyzed using quality-of-the-solution and acceptance-by-developers measurements. We performed a statistical analysis to determine whether the impact on the results is significant compared to baselines that do not leverage Phylogenetics. The results show that our approach significantly outperforms two baselines in both case studies. Furthermore, two focus groups confirmed the acceptance of our approach and stressed that solution acceptance may make the difference in industrial environments. Our work has the potential to motivate a new breed of research work on Phylogenetics awareness to produce better results in Software Engineering.

Making Fault Localization in Online Service Systems More Actionable and Interpretable

Online service systems struggle with accurately and quickly pinpointing and resolving failures within their intricate systems, and it therefore emerges the solutions for fault localization in the code. However, the previous fault localization models suffer from low localization accuracy and poor interpretability due to the complex dependencies among fault characteristics in industrial practice. To address this issue, challenges brought by the long-distance dependencies among fault features and the unbalanced distribution of fault knowledge, and to improve the interpretability of the model, we present a fault localization model in online service systems more actionable and interpretable, named FL-AIer. Specifically, FL-AIer consists of two components: the feature encoding component and the fault localization component. The feature encoding component utilizes graph attention networks to capture the complex spatio-temporal dependencies within fault features. Then, the fault localization component adopts a three-stage approach, leveraging a multi-attention mechanism to identify and prioritize the most relevant fault features for precise localization. Additionally, the Fault Knowledge Balancing module it contains introduces a weighted Kullback-Leibler divergence loss function to ensure that the model pays adequate attention to all fault features, addressing the issue of imbalanced fault knowledge distribution and enhancing localization performance. We conducted extensive experiments on four datasets, and the results demonstrated that FL-AIer effectively addressing the challenges of fault localization in online system environments, and consistently outperforms the state-of-the-art methods across various evaluation metrics such as A@1, A@2, A@3, A@5, and MAR. For instance, FL-AIer achieves significant improvements of 5.82%, 10.77%, 4.20% and 15.56% on the A@1 metric, respectively. These results fully demonstrate the excellent effectiveness of FL-AIer in effectively addressing the challenges of fault localization in online system environments, surpassing the performance of existing state-of-the-art methods.

Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis

Lung ultrasound is an increasingly utilized non-invasive imaging modality for assessing lung condition but interpreting it can be challenging and depends on the operator’s experience. To address these challenges, this work proposes an approach that combines artificial intelligence (AI) with feature-based signal processing algorithms. We introduce a specialized deep learning model designed and trained to facilitate the analysis and interpretation of lung ultrasound images by automating the detection and location of pulmonary features, including the pleura, A-lines, B-lines, and consolidations. Employing Convolutional Neural Networks (CNNs) trained on a semi-automatically annotated dataset, the model delineates these pulmonary patterns with the objective of enhancing diagnostic precision. Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second with accuracy levels of 89% for consolidation, 92% for B-lines, 66% for A-lines, and 92% for detecting normal lungs compared with an expert opinion.

Vynnyky—Lysivka, district Lviv. Comparison of the results of archaeological and geomagnetic research

Archaeological research in 2023 at the Vynnyky-Lysivka site provided the basis for interpreting the results of geomagnetic work carried out in 2017. The excavations in 2016 resulted in the discovery of clusters of fired clay daub fragments, constituting the remains of the Funnel Beaker Culture household. It was assumed that noninvasive research would show the locations of other such features. However, the results of geomagnetic surveys were difficult to interpret- just like in Gordineşti II-Stînca goală. In the course of the investigation, numerous anomalies were identified, mainly of a dipole nature and less numerous point-positive anomalies. A very weak source of anomalies turned out to be a clearly visible Eneolithic ditch cutting of the Funnel Beaker Culture settlement. The discovery in 2023 in the central part of the Vynnyky-Lysivka settlement of a cluster of pits in the place where anomalies were distinguished in non-invasive studies provided the basis for a reinterpretation of geomagnetic surveys.

HDF-Net: Capturing Homogeny Difference Features to Localize the Tampered Image.

Modern image editing software enables anyone to alter the content of an image to deceive the public, which can pose a security hazard to personal privacy and public safety. The detection and localization of image tampering is becoming an urgent issue to be addressed. We have revealed that the tampered region exhibits homogenous differences (the changes in metadata organization form and organization structure of the image) from the real region after manipulations such as splicing, copy-move, and removal. Therefore, we propose a novel end-to-end network named HDF-Net to extract these homogeny difference features for precise localization of tampering artifacts. The HDF-Net is composed of RGB and SRM dual-stream networks, including three complementary modules, namely the suspicious tampering-artifact prominent (STP) module, the fine tampering-artifact salient (FTS) module, and the tampering-artifact edge refined (TER) module. We utilize the fully attentional block (FLA) to enhance the characterization ability of homogeny difference features extracted by each module and preserve the specifics of tampering artifacts. These modules are gradually merged according to the strategy of "coarse-fine-finer", which significantly improves the localization accuracy and edge refinement. Extensive experiments demonstrate that HDF-Net performs better than state-of-the-art tampering localization models on five benchmarks, achieving satisfactory generalization and robustness.

Analysis of Commercial Na-Ion 18650 Cell Performance: Cell-to-Cell Variation and Long-Term Cycling

The development of commercial Na-ion battery cells for various energy storage applications has ramped up rapidly in the last three years. Currently, 18650 format cells from several manufacturers have been released for purchase through wholesalers, indicating that Na-ion cells may soon be ready for adoption into the grid, electric vehicles, and various consumer electronics. The promise of Na-ion is lower cost and only moderately reduced power and energy density compared to Li-ion cells. Thus, they may work as a drop-in replacement for Li-ion technologies depending on the use case.To be adopted for a wide range of energy storage applications, the commercial cell performance needs to be evaluated under a variety of conditions. Sandia National Labs has embarked on a long-term cycling study of Na-ion cells from two different manufacturers to help fill the gaps in performance evaluation. This study covers variations in performance based on temperature, state of charge, cycling rate, and various simulated duty cycles. Initial results from the first few months of testing of these new cells will be presented.In addition to the long-term cycling study, we conducted materials characterization and cell-to-cell cycling variance analysis. This is to understand the fundamentals of the battery mechanics, operation, and manufacturing quality. Initial results showed that the two manufacturers are using similar electrode chemistries, with a positive electrode active material that is likely a sodium iron manganese nickel oxide (Na(FexMnyNiz)O) and a negative electrode that is composed of hard carbon. During initial cell variance analysis, we observed that the different manufacturers are producing cells with similar performance, with incremental capacity curves that match each other in feature location and amplitude. Manufacturer A’s cells have about 10% more capacity than manufacturer B’s cells, which is likely a product of cycling to a voltage limit 100 mV higher than manufacturer B’s. Both manufacturers have cell-to-cell variance of capacity of less than 1% which matches what is observed in Li-ion cell production. We will also detail more analysis of cell variations like temperature and rate-based capacity variations as well as internal resistance values, electrochemical impedance spectrums and voltage relaxation times. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.

Automated segmentation software in optical coherence tomography (OCT) devices is usually developed for and primarily tested on common diseases. Therefore segmentation accuracy of automated software can be limited in eyes with rare pathologies. We sought to develop a semisupervised deep learning segmentation model that segments 10 retinal layers and four retinal features in eyes with Macular Telangiectasia Type II (MacTel) using a small labeled dataset by leveraging unlabeled images. We compared our model against popular supervised and semisupervised models, as well as conducted ablation studies on the model itself. Our model significantly outperformed all other models in terms of intersection over union on the 10 retinal layers and two retinal features in the test dataset. For the remaining two features, the pre-retinal space above the internal limiting membrane and the background below the retinal pigment epithelium, all of the models performed similarly. Furthermore, we showed that using more unlabeled images improved the performance of our semisupervised model. Our model improves segmentation performance over supervised models by leveraging unlabeled data. This approach has the potential to improve segmentation performance for other diseases, where labeled data is limited but unlabeled data abundant. Improving automated segmentation of MacTel pathology on OCT imaging by leveraging unlabeled data may enable more accurate assessment of disease progression, and this approach may be useful for improving feature identification and location on OCT in other rare diseases as well.

Enhancing Feature Location Accuracy Using Textual and Structural Extraction

Enhancing Feature Location Accuracy Using Textual and Structural Extraction

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

Study on Nondestructive Detection Imaging Method of Log Knot Based on Judging the Shortest Path of Stress Wave Propagation

This paper presents an iterative stress wave log beam imaging algorithm based on external shape to predict the size, shape, and position of internal features in logs. The algorithm’s accuracy is analyzed to establish a theoretical and scientific basis for the prediction and evaluation of internal knots in standing trees. Six sample logs with natural knots were selected for study, and cross-sectional stress wave propagation tests were conducted using FAKOPP to collect data. Using the shortest propagation path method, the algorithm iteratively produced fault images of the log cross-sections. While the algorithm can roughly predict the location of internal features, discrepancies between predicted and actual shapes and sizes result in relative errors ranging from 15.66% to 52.08%. Except for sample log 6, the relative error for other logs is mostly within 31%, with logs 1, 2, and 5 showing errors under 20%. However, the imaging accuracy and effectiveness need improvement. Further experimental studies and algorithm enhancements are necessary to improve fault imaging and prediction accuracy, particularly in terms of shape and size precision.

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Comprehensive study on building chiller fault feature and diagnosis using hybrid CNN

Chillers are one of the biggest energy consumption devices in HVAC systems. Abnormal operation may undermine the performance, energy efficiency, and environment. This study comprehensively explores the hybrid applications of deep convolutional neural network (CNN) in chiller fault diagnosis and fault feature extraction. Unlike the computer vision where the feature locations are fixed, for chiller fault, it can be changed. The effect of feature sequence on diagnostic performance is carefully investigated, and found that it depends on the size and number of the convolution kernels. Small and large number of kernels may extract fine and enough features for the model to counter the location change and maintain the basic characteristics of the faults. 1-D CNN is further studied as a hierarchical feature extractor combined with the traditional machine learning, like k-nearest neighbor (KNN), decision tree (DT), and random forest (RF), to build a hybrid strategy. It is found that the highest accuracy of 99.85% is achieved by RF plus 1-D CNN with an accuracy of 100% for refrigerant leakage. Fine and clear features from deeper structure are most favorable for the weak learner like DT, but may harm the information diversity of RF and lower its accuracy.

HAD-Net: An attention U-based network with hyper-scale shifted aggregating and max-diagonal sampling for medical image segmentation

Objectives:Accurate extraction of regions of interest (ROI) with variable shapes and scales is one of the primary challenges in medical image segmentation. Current U-based networks mostly aggregate multi-stage encoding outputs as an improved multi-scale skip connection. Although this design has been proven to provide scale diversity and contextual integrity, there remain several intuitive limits: (i) the encoding outputs are resampled to the same size simply, which destruct the fine-grained information. The advantages of utilization of multiple scales are insufficient. (ii) Certain redundant information proportional to the feature dimension size is introduced and causes multi-stage interference. And (iii) the precision of information delivery relies on the up-sampling and down-sampling layers, but guidance on maintaining consistency in feature locations and trends between them is lacking. Methods:To improve these situations, this paper proposed a U-based CNN network named HAD-Net, by assembling a new hyper-scale shifted aggregating module (HSAM) paradigm and progressive reusing attention (PRA) for skip connections, as well as employing a novel pair of dual-branch parameter-free sampling layers, i.e. max-diagonal pooling (MDP) and max-diagonal un-pooling (MDUP). That is, the aggregating scheme additionally combines five subregions with certain offsets in the shallower stage. Since the lower scale-down ratios of subregions enrich scales and fine-grain context. Then, the attention scheme contains a partial-to-global channel attention (PGCA) and a multi-scale reusing spatial attention (MRSA), it builds reusing connections internally and adjusts the focus on more useful dimensions. Finally, MDP and MDUP are explored in pairs to improve texture delivery and feature consistency, enhancing information retention and avoiding positional confusion. Results:Compared to state-of-the-art networks, HAD-Net has achieved comparable and even better performances with Dice of 90.13%, 81.51%, and 75.43% for each class on BraTS20, 89.59% Dice and 98.56% AUC on Kvasir-SEG, as well as 82.17% Dice and 98.05% AUC on DRIVE. Conclusions:The scheme of HSAM+PRA+MDP+MDUP has been proven to be a remarkable improvement and leaves room for further research.

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

Computer Vision Algorithm Design in Image Processing Based on Projective Geometry

Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.

Rare Germline Variants in the Adenomatous Polyposis Coli Gene Associated with Dental and Osseous Anomalies.

APC is a tumor suppressor gene that exerts its effect through the regulation of the Wnt signaling pathway. Loss of function mutations of the gene are associated with familial adenomatous polyposis (FAP). Early diagnosis in FAP patients is essential to prevent the development of colorectal cancer. Extraintestinal manifestations often precede the formation of the polyposis; therefore, these manifestations may serve as a clinical indicator for the condition. The aim of this study was to assess genotype-phenotype associations between the location of APC mutations and various extraintestinal features, mainly focusing on osseous and dental anomalies. Analyses of our cases and the mutations available in the literature with these manifestations revealed that mutations in the N-terminal region (amino acids 1-~1000) of the protein are more frequently associated with only osseous anomalies, whereas dental manifestations are more prevalent in mutations in the middle region (amino acids 1000-~2100). In addition, supernumerary teeth were found to be the most common dental feature. Since dental abnormalities often precede intestinal polyposis, dentists have a crucial role in the early identification of patients at risk.

Feature Detection of Non-Cooperative and Rotating Space Objects through Bayesian Optimization.

In this paper, we propose a Bayesian Optimization (BO)-based strategy using the Gaussian Process (GP) for feature detection of a known but non-cooperative space object by a chaser with a monocular camera and a single-beam LIDAR in a close-proximity operation. Specifically, the objective of the proposed Space Object Chaser-Resident Assessment Feature Tracking (SOCRAFT) algorithm is to determine the camera directional angles so that the maximum number of features within the camera range is detected while the chaser moves in a predefined orbit around the target. For the chaser-object spatial incentive, rewards are assigned to the chaser states from a combined model with two components: feature detection score and sinusoidal reward. To calculate the sinusoidal reward, estimated feature locations are required, which are predicted by Gaussian Process models. Another Gaussian Process model provides the reward distribution, which is then used by the Bayesian Optimization to determine the camera directional angles. Simulations are conducted in both 2D and 3D domains. The results demonstrate that SOCRAFT can generally detect the maximum number of features within the limited camera range and field of view.

The Effect of Varying the Light Spectrum of a Scene on the Localisation of Photogrammetric Features

In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., demosaicing and conversion to greyscale, among others. These algorithms respond differently to the varying light spectrum of the scene, which consequently results in the feature location changing. In this study, the effect of scene illumination on the localisation of a feature in an image is presented. The demosaicing and greyscale conversion algorithms that produce the largest and smallest deviation of the feature from the reference point were assessed. Twelve different illumination settings from polychromatic light to monochromatic light were developed and performed, and five different demosaicing algorithms and five different methods of converting a colour image to greyscale were analysed. A total of 300 different cases were examined. As the study shows, the lowest deviation in the polychromatic light domain was achieved for light with a colour temperature of 5600 K and 5000 K, while in the monochromatic light domain, it was achieved for light with a green colour. Demosaicing methods have a significant effect on the localisation of a feature, and so the smallest feature deviation was achieved for smooth hue-type demosaicing, while for greyscale conversion, it was achieved for the mean type. Demosaicing and greyscale conversion methods for monochrome light had no effect. The article discusses the problem and concludes with recommendations and suggestions in the area of illuminating the scene with artificial light and the application of the algorithms, in order to achieve the highest accuracy using photogrammetric methods.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.

Tracing participants for longitudinal environmental health research using social networking sites: a pilot study

Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition.