<p>The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.</p><p>The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both</p><p>attention and attribution maps, resulting in a more interpretable model.</p>

An effective lithium-ion battery (LIB) recycling infrastructure is of great importance to alleviate the concerns over the disposal of waste LIBs and the sustainability of critical elements for producing LIB components. The End-of-life (EOL) LIBs are in various sizes and shapes, which create significant challenges to automate a few unit operations (e.g., disassembly at the cell level) of the recycling process. Meanwhile, hazardous and flammable materials are contained in LIBs, posing great threats to the human exposure. Therefore, it is difficult to dismantle the LIBs safely and efficiently to recover critical materials. Automation has become a competitive solution in manufacturing world, which allows for mass production at outstanding speeds and with great repeatability or quality. It is imperative to develop automatic disassembly solution to effectively disassemble the LIBs while safeguarding human workers against the hazards environment. In this work, we demonstrate an automatic battery disassembly platform enhanced by online sensing and machine learning technologies. The computer vision is used to classify different types of batteries based on their brands and sizes. The real-time temperature data is captured from a thermal camera. A data-driven model is built to predict the cutting temperature pattern and the temperature spike can be mitigated by the close-loop control system. Furthermore, quality control is conducted using a neural network model to detect and mitigate the cutting defects. The integrated disassembly platform can realize the real-time diagnosis and closed-loop control of the cutting process to optimize the cutting quality and improve the safety. Supplementary Information The online version contains supplementary material available at 10.1007/s10845-022-01936-x.

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both attention and attribution maps, resulting in a more interpretable model.

Ankylosing spondylitis (AS) is a chronic inflammatory disease. The pathogenesis of AS is poorly understood. However its association with human leukycyte antigen (HLA)-B27 is known. AS causes significant pain, disability, and social burden around the world [1]. Early diagnosis and treatment of AS are necessary in order to prevent or reduce all types of costs associated with loss of function. If the diagnosis is missed, however, the quality of patient's life will degrade. Besides, as a positive family history of AS is a strong risk factor for the disease, this negligence will put other family member's in jeopardy.Nowadays, Expert systems play a big role in diagnosis of patients with different diseases. The application of expert system to diagnose diseases started in the 70s with the development of Mycin. Expert systems in medical diagnosis can help in storing more knowledge than before and make it accessible in absence of a specialist and increase distribution of expertise.Our goal in this paper is to design a type-2 fuzzy rule-based expert system for AS diagnosis where the rules are evidencebased. The basic aim of evidence-based practice is to establish a narrow set of criteria for diagnosis based on research studies. System has mainly two parts. Firstly, the suspicion of disease is assessed for the persons identity according to odds ratio studies and patient's family history. Then the modified New York criteria (1984) for exploring sign and symptoms and the HLA-B27 examination result are considered.The system benefits from fuzzy reasoning and can manage the uncertain inputs through fuzzifying them and making use of type-2 fuzzy rules. Moreover, the system is connected to a spreadsheet for storing the patient's input data and system's final diagnosis. This system can be used by a non-rheumatologist in the diagnosis of AS or by a rheumatologist as an assistant.

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