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With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products. Y-MaskNet combines the high efficiency of YOLOv5 in target detection with the fine segmentation capability of Mask R-CNN and optimizes the overall performance of the model through a multi-task learning framework. Experimental results show that Y-MaskNet achieves a significant improvement in detection and segmentation tasks, with mAP@[0.5:0.95] reaching 0.72 (up from 0.62 for YOLOv5 and 0.65 for Mask R-CNN) on the PCB Defect Dataset, and IoU improving by 7% compared to existing methods. These improvements are particularly notable in small object detection and fine-grained defect segmentation, making Y-MaskNet an efficient and accurate solution for defect detection in electronic products, offering strong technical support for future industrial intelligent quality control.

Growing up as youngest of three siblings in a middle class family in India, there was no dearth of role models in my early life. It was probably a combination of my love for biology and a strong desire of the family to have at least one doctor that led me to become a physician. After attending medical school at Bellary, a rural part of southern India, it was increasing clear that pediatrics is what I find most interesting.

Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep learning techniques, particularly in object detection, have shown significant promise for various applications. This paper proposes an improved YOLOv11-based method for surface defect detection in electronic products, aiming to address the limitations of existing YOLO models in handling complex backgrounds and small target defects. By introducing the MD-C2F module, DualConv module, and Inner_MPDIoU loss function, the improved YOLOv11 model has achieved significant improvements in precision, recall rate, detection speed, and other aspects. The improved YOLOv11 model demonstrates notable improvements in performance, with a precision increase from 90.9% to 93.1%, and a recall rate improvement from 77.0% to 84.6%. Furthermore, it shows a 4.6% rise in mAP50, from 84.0% to 88.6%. When compared to earlier YOLO versions such as YOLOv7, YOLOv8, and YOLOv9, the improved YOLOv11 achieves a significantly higher precision of 89.3% in resistor detection, surpassing YOLOv7’s 54.3% and YOLOv9’s 88.0%. In detecting defects like LED lights and capacitors, the improved YOLOv11 reaches mAP50 values of 77.8% and 85.3%, respectively, both outperforming the other models. Additionally, in the generalization tests conducted on the PKU-Market-PCB dataset, the model’s detection accuracy improved from 91.4% to 94.6%, recall from 82.2% to 91.2%, and mAP50 from 91.8% to 95.4%.These findings emphasize that the proposed YOLOv11 model successfully tackles the challenges of detecting small defects in complex backgrounds and across varying scales. It significantly enhances detection accuracy, recall, and generalization ability, offering a dependable automated solution for defect detection in electronic product manufacturing.

The potential for improving the penetration depth of optical coherence tomography systems by using light sources with longer wavelengths has been known since the inception of the technique in the early 1990s. Nevertheless, the development of mid-infrared optical coherence tomography has long been challenged by the maturity and fidelity of optical components in this spectral region, resulting in slow acquisition, low sensitivity, and poor axial resolution. In this work, a mid-infrared spectral-domain optical coherence tomography system operating at a central wavelength of 4 µm and an axial resolution of 8.6 µm is demonstrated. The system produces two-dimensional cross-sectional images in real time enabled by a high-brightness 0.9- to 4.7-µm mid-infrared supercontinuum source with a pulse repetition rate of 1 MHz for illumination and broadband upconversion of more than 1-µm bandwidth from 3.58–4.63 µm to 820–865 nm, where a standard 800-nm spectrometer can be used for fast detection. The images produced by the mid-infrared system are compared with those delivered by a state-of-the-art ultra-high-resolution near-infrared optical coherence tomography system operating at 1.3 μm, and the potential applications and samples suited for this technology are discussed. In doing so, the first practical mid-infrared optical coherence tomography system is demonstrated, with immediate applications in real-time non-destructive testing for the inspection of defects and thickness measurements in samples that exhibit strong scattering at shorter wavelengths.Optical Coherence Tomography: Longer wavelengths improve imaging in depthUsing longer wavelengths of light in Optical Coherence Tomography (OCT) imaging allows deeper penetration in highly scattering materials, offering possibilities for OCT in non-destructive testing and enhanced non-invasive biomedical imaging. OCT images are based on interference patterns generated by combining light reflected from the examined object with reference light that does not encounter the object. It is currently most widely used to examine the retina of the eye. Researchers at the Technical University of Denmark, together with co-workers in Austria and the UK, overcame several technical challenges to obtain images using mid-infrared light to reveal microscopic structures that are not visible using conventional shorter wavelength near-infrared light.The team combined broadband supercontinuum light and frequency upconversion to achieve high-resolution and real-time image acquisition.Promising applications include advances in defect detection and thickness measurements.

BackgroundPreeclampsia (PE) is a pregnancy complication characterized by an anti-angiogenic environment. This can affect fetal pulmonary vascular and alveolar development but data of the impact of PE on respiratory outcome in extremely premature infants are inconclusive. The objective of this study was to determine if PE is associated with an increased risk for severe respiratory distress syndrome (RDS) and bronchopulmonary dysplasia (BPD) in extremely premature infants.MethodsProspectively collected single center data from a cohort of infants born at 23–28 w gestational age between January 2005 and December 2015 were analyzed. Logistic regression analysis and generalized estimating equations were used to model the association between PE and severe RDS (≥30% supplemental oxygen on d1), BPD and severe BPD [supplemental oxygen and ≥30% oxygen at 36 w postmenstrual age (PMA), respectively].ResultsThe cohort included 1218 infants of whom 23% were exposed to PE. PE was associated with increased risk for severe RDS as well as severe BPD among infants alive at 36w PMA.ConclusionExposure to preeclampsia is independently associated with an increased risk for severe RDS and adverse respiratory outcome in extreme premature infants. The mechanisms behind these associations need to be investigated.

We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple output model, thereby compelling the network architecture to learn the geometry of the metamaterial designs from the spectral data in lieu of abstract features.

Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.

In this paper, we use large scale structure observations to test the redshift dependence of cosmic distance duality relation (CDDR), DL(1+z)-2/DA=η(z), with DL and DA, being the luminosity and angular diameter distances, respectively. In order to perform the test, the following data set are considered: strong lensing systems and galaxy cluster measurements (gas mass fractions). No specific cosmological model is adopted, only a flat universe is assumed. By considering two η(z) parametrizations, It is observed that the CDDR remain redshift independent within 1.5σ which is in full agreement with other recent tests involving cosmological data. It is worth to comment that our results are independent of the baryon budget of galaxy clusters.

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection’s severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data. The blockchain is a decentralized digital ledger that ensures data-sharing security, transparency, and traceability through cryptographic technology and consensus algorithms. Consequently, medical blockchain data-sharing methods have garnered significant attention and research efforts. Nevertheless, current methods have different storage and transmission measures for original data in the medical blockchain, resulting in large differences in performance and privacy. Therefore, we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location. Among them, off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is moved. Subsequently, we provide a detailed analysis of basic processes and research content for each method. Finally, we summarize the challenges posed by the current methods and discuss future research directions.