In advanced semiconductor packaging, products often exhibit high unit cost, complex structures, and stringent precision requirements. Inspection systems such as Scanning Acoustic Microscopy (SAM) are employed to detect internal defects, particularly within the Epoxy Molding Compound (EMC) layer. However, ultrasound images are susceptible to various noise sources that degrade image quality and hinder defect identification. This study proposes a self-supervised image denoising framework called Multi-Branch Task U-Net (MBT-UNet), which is a U-Net backbone with a multi-branch decoder. The model enables multi-task learning without requiring paired clean-noisy data or prior knowledge of noise characteristics. An empirical study of semiconductor packaging is conducted to validate the proposed MBT-UNet by comparing it with several benchmark methods (e.g., paired supervised learning approaches such as SC-UNet and RIDNet). The results show that MBT-UNet achieves competitive performance, as evaluated by the feature similarity (FSIM) and learned perceptual image patch similarity (LPIPS) metrics, and is comparable to supervised models despite being trained without paired data.

Nonalcoholic fatty liver disease represents a major global burden of liver-related illnesses. Its progressive form, known as nonalcoholic steatohepatitis (Progressive NASH), is closely associated with moderate to advanced liver fibrosis. The FibroScan-AST score has been proposed as a non-invasive assessment tool to effectively identify high-risk patients who meet the criteria for progressive NASH. However, as the FAST score relies on blood test data, its applicability for real-time risk prediction remains limited.This study focuses on the high-precision segmentation of liver parenchyma in ultrasound images and proposes a modified U-Net architecture integrated with channel and spatial attention modules. The model is designed to accurately extract the liver parenchyma region while mitigating interference from rib shadows, intestinal gas, and background noise. To verify the practical applicability of the segmentation model, the FibroScan-AST classification task was incorporated. A weighted Dice loss was employed to address the class imbalance caused by the relatively small target area, and various data augmentation strategies were utilized to enhance generalization. The segmentation model achieved an average Dice score of 0.9 on the test set, demonstrating high accuracy in delineating liver parenchyma. Visual inspection further confirms that the predicted liver boundaries are clearer, with non-liver tissue effectively excluded, resulting in clean and semantically meaningful inputs for the downstream classification model.To validate the utility of the segmentation results, we further constructed a FAST classification model and compared the performance of three strategies: using raw images, using segmented ROIs, and an end-to-end integration of segmentation and classification. Experimental results show that using the segmented ROI as input significantly improves the F1-score (from 0.47 to 0.61), confirming that accurate liver segmentation can enhance downstream task performance and hold strong clinical application potential. This study verifies the necessity of high-quality liver parenchyma segmentation in ultrasound imaging and provides an image-driven framework for progressive NASH screening.

Most traditional industries or emerging countries may not be capable of directly transiting to Industry 4.0. To fill the gap between as-is Industry 3.0 and to-be Industry 4.0, some disruptive innovations from automation and industrial engineering identify best practice with adopting cost-effective semi-automated systems to manage the potential socio-economic impacts of infrastructure disruptions, while considering total resource management for sustainability. This is the so-called “hybrid strategy (HS),” or “Industry 3.5.” On the other hand, the current Industry 4.0-related technologies should also emphasize quality enhancement to achieve “zero-defect manufacturing (ZDM),” also referred to as “Industry 4.1.” ZDM is a systematic strategy to realize the goal of Zero Defects, which includes two phases. Phase I: accomplish Zero Defects of all thedeliverablesby applying efficient and economical total-quality-inspection techniques; and Phase II: further ensure Zero Defects of all theproductsgradually by improving the yield with big data analytics and continuous improvement. Both the challenges and opportunities from HS to ZDM have significantly expanded the scope of traditional automation science and engineering.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Process monitoring, which is used to ensure the product quality in semiconductor manufacturing, develops a control chart and alerts engineers whenever a control limit is exceeded. However, a late alarm could generate defects or scraps, and thus, a prealarm is urgent to be developed. This study proposes an in-line predictive monitoring (ILPM) framework that uses process parameter monitoring (PPM) in the first phase and equipment parameter monitoring (EPM) in the second phase. PPM includes off-line training and in-line prediction, where we collect the first half of time-series data and predict the second half for in-line quality control. To maintain robustness and accuracy, the concept drift is used to update the ILPM model in real time; specifically, the EPM detects the loss of prediction by cumulative sum (CUSUM) control chart or detects the change of equipment parameters by Bayesian approach for developing retraining mechanism. An empirical study of a semiconductor manufacturer indicates that the proposed ILPM framework improved both the quality control and production capacity.Note to Practitioners—Although some in-line monitoring approaches have been proposed for specific conditions, little research has been done to develop a prealarm method; in particular, this study proposes predictive monitoring by using data from multiple sensors (or status variable identifications, SVIDs) to predict one sensor in one process step of semiconductor manufacturer. The prealarm benefits the early troubleshooting and equipment capacity. This study also applies concept drift and equipment parameter monitoring (EPM) to identify the prediction model misalignment or equipment misalignment. In practice, root cause identification of the prediction error is critical and benefits the model retraining and equipment maintenance.

Bipolar disorder I (BD-I) and bipolar disorder II (BD-II) have specific characteristics and clear diagnostic criteria, but quite different treatment guidelines. In clinical practice, BD-II is commonly mistaken as a mild form of BD-I. This study uses data science technique to identify the important Single Nucleotide Polymorphisms (SNPs) significantly affecting the classifications of BD-I and BD-II, and develops a set of complementary diagnostic classifiers to enhance the diagnostic process. Screening assessments and SNP genotypes of 316 Han Chinese were performed with the Affymetrix Axiom Genome-Wide TWB Array Plate. The results show that the classifier constructed by 23 SNPs reached the area under curve of ROC (AUC) level of 0.939, while the classifier constructed by 42 SNPs reached the AUC level of 0.9574, which is a mere addition of 1.84 percent. The accuracy rate of classification increased by 3.46 percent. This study also uses Gene Ontology (GO) and Pathway to conduct a functional analysis and identify significant items, including calcium ion binding, GABA-A receptor activity, Rap1 signaling pathway, ECM proteoglycans, IL12-mediated signaling events, Nicotine addiction), and PI3K-Akt signaling pathway. The study can address time-consuming SNPs identification and also quantify the effect of SNP-SNP interactions.

Popularity prediction of social media becomes a more attractive issue in recent years. It consists of multi-type data sources such as image, meta-data, and text information. In order to effectively predict the popularity of a specified post in the social network, fusing multi-feature from heterogeneous data is required. In this paper, a popularity prediction framework for social media based on multi-modal feature mining is presented. First, we discover image semantic features by extracting their image descriptions generated by image captioning. Second, an effective text-based feature engineering is used to construct an effective word-to-vector model. The trained word-to-vector model is used to encode the text information and the semantic image features. Finally, an ensemble regression approach is proposed to aggregate these encoded features and learn the final regressor. Extensive experiments show that the proposed method significantly outperforms other state-of-the-art regression models. We also show that the multi-modal approach could effectively improve the performance in the social media prediction challenge.

In this study, we propose a novel iterative refinement approach to predict the popularity score of the social media meta-data effectively. With the rapid growth of the social media on the Internet, how to adequately forecast the view count or popularity becomes more important. Conventionally, the ensemble approach such as random forest regression achieves high and stable performance on various prediction tasks. However, most of the regression methods may not precisely predict the extreme high or low values. To address this issue, we first predict the initial popularity score and retrieve their residues. In order to correctly compensate those extreme values, we adopt an ensemble regressor to compensate the residues to further improve the prediction performance. Comprehensive experiments are conducted to demonstrate the proposed iterative refinement approach outperforms the state-of-the-art regression approach.