Optimal design of protective clothing based on difference equation

The temperature distribution and thickness design of high temperature protective clothing are studied in this paper. Based on the data provided by China mathematical modeling competition in 2018. We establish the temperature distribution model and skin layer heat conduction and burn model. The interface continuous conditional difference method, differential iterative method, least squares method and the chasing method are used to solve the given temperature distribution on the protective clothing in the environment, and analyze protective clothing meeting the actual needs

Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoomin-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. 2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets that captures diverse behaviors. Recently, studies in computer vision and natural language processing have shown that leveraging massive amounts of unlabeled data enables performance on par with state-of-the-art supervised models. In this work, we present SelfHAR, a semi-supervised model that effectively learns to leverage unlabeled mobile sensing datasets to complement small labeled datasets. Our approach combines teacher-student self-training, which distills the knowledge of unlabeled and labeled datasets while allowing for data augmentation, and multi-task self-supervision, which learns robust signal-level representations by predicting distorted versions of the input. We evaluated SelfHAR on various HAR datasets and showed state-of-the-art performance over supervised and previous semi-supervised approaches, with up to 12% increase in F1 score using the same number of model parameters at inference. Furthermore, SelfHAR is data-efficient, reaching similar performance using up to 10 times less labeled data compared to supervised approaches. Our work not only achieves state-of-the-art performance in a diverse set of HAR datasets, but also sheds light on how pre-training tasks may affect downstream performance

UniHCP: A Unified Model for Human-Centric Perceptions

Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models. While specific human-centric tasks have their own relevant semantic aspect to focus on, they also share the same underlying semantic structure of the human body. However, few works have attempted to exploit such homogeneity and design a general-propose model for human-centric tasks. In this work, we revisit a broad range of human-centric tasks and unify them in a minimalist manner. We propose UniHCP, a Unified Model for Human-Centric Perceptions, which unifies a wide range of human-centric tasks in a simplified end-to-end manner with the plain vision transformer architecture. With large-scale joint training on 33 human-centric datasets, UniHCP can outperform strong baselines on several in-domain and downstream tasks by direct evaluation. When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e.g., 69.8 mIoU on CIHP for human parsing, 86.18 mA on PA-100K for attribute prediction, 90.3 mAP on Market1501 for ReID, and 85.8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task.Comment: Accepted for publication at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023

A Sandwich Electrochemical Immunosensor Using Magnetic DNA Nanoprobes for Carcinoembryonic Antigen

A novel magnetic nanoparticle-based electrochemical immunoassay of carcinoembryonic antigen (CEA) was designed as a model using CEA antibody-functionalized magnetic beads [DNA/Fe3O4/ZrO2; Fe3O4 (core)/ZrO2 (shell) nano particles (ZMPs)] as immunosensing probes. To design the immunoassay, the CEA antibody and O-phenylenediamine (OPD) were initially immobilized on a chitosan/nano gold composite membrane on a glassy carbon electrode (GCE/CS-nano Au), which was used for CEA recognition. Then, horseradish peroxidase (HRP)-labeled anti-CEA antibodies (HRP-CEA Ab2) were bound to the surface of the synthesized magnetic ZMP nanoparticles as signal tag. Thus, the sandwich-type immune complex could be formed between secondary antibody (Ab2) modified DNA/ZMPs nanochains tagged by HRP and GCE/CS-nano Au. Unlike conventional nanoparticle-based electrochemical immunoassays, the recognition elements of this immunoassay included both electron mediators and enzyme labels, which obviously simplifies the electrochemical measurement process. The sandwich-type immunoassay format was used for online formation of the immunocomplex of CEA captured in the detection cell with an external magnet. The electrochemical signals derived from HRP during the reduction of H2O2 with OPD as electron mediator were measured. The method displayed a high sensitivity for CEA detection in the range of 0.008–200 ng/mL, with a detection limit of 5 pg/mL (estimated at a signal-to-noise ratio of 3). The precision, reproducibility, and stability of the immunoassay were good. The use of the assay was evaluated with clinical serum samples, and the results were in excellent accordance with those obtained using the standard enzyme-linked immunosorbent assay (ELISA) method. Thus, the magnetic nanoparticle-based assay format is a promising approach for clinical applications, and it could be further developed for the detection of other biomarkers in cancer diagnosis

A retrospective study of surgical treatment and outcome among women with adnexal torsion in eastern Taiwan from 2010 to 2015

Background Adnexal torsion is a gynecologic emergency that requires surgical treatment. In this study, we reviewed the surgical outcomes of women with adnexal torsion in eastern Taiwan (Hualien county, area 4,629 km2, 330,000 residents). Methods This retrospective study included 42 women diagnosed with surgically-proven adnexal torsion from January 1, 2010, to September 31, 2015. We compared the symptoms, objective findings, and surgical outcomes of patients who underwent laparotomy or laparoscopy. Results The laparoscopy and laparotomy groups included 27 and 15 patients, respectively. The most common symptom and sign was abdominal pain, followed by nausea and vomiting. In all patients, an adnexal tumor was detected through ultrasound. The median and range of time from admission to surgery was 1.5 (1–11.5) and 1.0 (1–11) hours in the laparotomy and laparoscopy groups, respectively. Compared with those undergoing laparotomy, the smaller tumor size [7 (4.2–10) vs. 10 (7–17) cm] and shorter hospital stay [4 (2–8) vs. 6 (3–9) days] in patients undergoing laparoscopy were significantly noted, respectively (P < 0.01). No differences were observed in age, operative time, and blood loss between both groups. The surgeries performed were mostly detorsion with cystectomy and adnexectomy. The most common pathology was a simple ovarian cyst, followed by teratoma. Regarding the surgical types, older age is the only risk factor for radical surgery. Discussion Acute onset of abdominal pain with a presenting ovarian tumor is the most common feature of adnexal torsion. Laparoscopic surgical group showed a small tumor size and a short ER hospital stay than laparotomy. Older age is the risk factor for radical surgery

HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining

Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting. To learn both coarse-grained and fine-grained knowledge in human bodies, we further propose a \textbf{P}rojector \textbf{A}ssis\textbf{T}ed \textbf{H}ierarchical pretraining method (\textbf{PATH}) to learn diverse knowledge at different granularity levels. Comprehensive evaluations on HumanBench show that our PATH achieves new state-of-the-art results on 17 downstream datasets and on-par results on the other 2 datasets. The code will be publicly at \href{https://github.com/OpenGVLab/HumanBench}{https://github.com/OpenGVLab/HumanBench}.Comment: Accepted to CVPR202

Carotid and cerebrovascular disease in symptomatic patients with type 2 diabetes: assessment of prevalence and plaque morphology by dual-source computed tomography angiography

<p>Abstract</p> <p>Background</p> <p>Plaque morphology directly correlates with risk of embolism and the recently developed dual-source computed tomography angiography (DSCTA) may help to detect plaques more precisely. The aim of our study was to evaluate the prevalence and morphology of carotid and cerebrovascular atherosclerotic plaques in patients with symptomatic type 2 diabetes mellitus (DM) by DSCTA.</p> <p>Methods</p> <p>From July 2009 to August 2010, DSCTA was prospectively performed in 125 consecutive patients with symptomatic type 2 DM. We retrospectively analyzed plaque type, distribution, and extensive and obstructive natures were determined for each segment for all patients.</p> <p>Results</p> <p>Atherosclerotic plaques were detected in 114 (91.2%) patients. Relatively more noncalcified (45%) and calcified (39%) plaques and less mixed (16%) plaques were observed (p < 0.001). Noncalcified plaques were found mainly in the intracranial arteries (81.8%), mixed plaques in the intracranial arteries (25.2%) and intracranial internal carotid artery (ICA) (56.1%). Calcified plaques were found mainly in the intracranial ICA (65.9%) and extracranial arteries (28.2%) (for all, p < 0.001). Extension of plaques from the 1^stto 5^thsegments was observed in 67 (58.8%) patients and from the 6^thto 10^thsegments in 35 (30.7%) patients. The most common site of all detected plaques was the cavernous segment. Regarding stenosis, there were significantly more nonobstructive than obstructive stenosis (91% vs. 9%, p < 0.001).</p> <p>Conclusion</p> <p>DSCTA detected a high prevalence of plaques in patients with symptomatic type 2 DM. A relatively high proportion of plaques were noncalcified, as well as with nonobstructive stenosis. The distribution of plaques was extensive, with the cavernous portion of ICA being the most common site.</p