A novel driver emotion recognition system based on deep ensemble classification

Driver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which can recognize faces in real-time and offline video reliably and effectively. The feature-fusing technique is utilized to integrate the features extracted from three CNN models, and the fused features are then used to train the suggested ensemble classification model. To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. The custom-developed dataset has been recorded as the best among all under the simulation environment

A Network Investigation on Idiopathic Hypogonadotropic Hypogonadism in China

Idiopathic hypogonadotropic hypogonadism (IHH) is a rare condition in which puberty does not take place naturally. We aimed to develop and follow an internet-based cohort and to improve our understanding of the disease. We established an internet-based questionnaire survey. A total of 74 male IHH patients were recruited from the Chinese largest IHH network social group. The clinical symptoms before treatment mainly included small testis, underdeveloped secondary sexual characteristics, and sexual dysfunction. After treatment, the penis length, testicular volume, external genital organ development, pubic hair, beard, laryngeal prominence, erection, and spermatorrhea were improved significantly (P<0.001). 18.9% of the patients completed fertility; however, more than half of the patients still complained of poor happiness and low physical strength. In addition, improvements in penis and pubic hair development, testosterone normalization and the physical strength in IHH patients who received gonadotropin and androgen replacement therapy were better than in those who received single gonadotropin therapy (P<0.05 for all). In conclusion, disease-specific network investigation can be used as an alternative method of medical research for rare diseases. The results of our cross-sectional study showed the effectiveness of hormone replacement therapy for IHH and implied that gonadotropin and androgen replacement therapy may be superior to gonadotropin treatment alone

Integrative proteomic and metabonomic profiling elucidates amino acid and lipid metabolism disorder in CA-MRSA-infected breast abscesses

ObjectiveBacterial culture and drug sensitivity testing have been the gold standard for confirming community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) infection in breast abscess with a long history. However, these tests may delay treatment and increase the risk of nosocomial infections. To handle and improve this critical situation, this study aimed to explore biomarkers that could facilitate the rapid diagnosis of CA-MRSA infection.MethodsThis study for the first time applied label-free quantitative proteomics and non-targeted metabonomics to identify potential differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs) in breast abscess infected with CA-MRSA compared to methicillin-susceptible S. aureus (MSSA). The two omics data were integrated and analyzed using bioinformatics, and the results were validated using Parallel Reaction Monitoring (PRM). Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of the identified biomarkers for diagnosing CA-MRSA infection.ResultsAfter using the above-mentioned strategies, 109 DEPs were identified, out of which 86 were upregulated and 23 were downregulated. Additionally, a total of 61 and 26 DEMs were initially screened in the positive and negative ion modes, respectively. A conjoint analysis indicated that the amino acid metabolism, glycosphingolipid biosynthesis, and glycerophospholipid metabolism pathways were co-enriched by the upstream DEPs and downstream DEMs, which may be involved in structuring the related network of CA-MRSA infection. Furthermore, three significant DEMs, namely, indole-3-acetic acid, L-(−)-methionine, and D-sedoheptulose 7-phosphate, displayed good discriminative abilities in early identification of CA-MRSA infection in ROC analysis.ConclusionAs there is limited high-quality evidence and multiple omics research in this field, the explored candidate biomarkers and pathways may provide new insights into the early diagnosis and drug resistance mechanisms of CA-MRSA infection in Chinese women

Local and systemic therapy may be safely de-escalated in elderly breast cancer patients in China: A retrospective cohort study

BackgroundFor elderly patients with breast cancer, the treatment strategy is still controversial. In China, preoperative axillary lymph node needle biopsy is not widely used, resulting in many patients receiving axillary lymph node dissection (ALND) directly. Our study aims to determine whether local and systemic therapy can be safely de-escalated in elderly breast cancer.MethodsPatients aged ≥70 years were retrospectively enrolled from our institution’s medical records between May 2013 and July 2021. Groups were assigned according to local and systemic treatment regimens, and stratified analysis was performed by molecular subtypes. Univariate and multivariate survival analyses were used to compare the effects of different regimens on relapse-free survival (RFS).ResultsA total of 653 patients were enrolled for preliminary data analysis, and 563 patients were screened for survival analysis. The mean follow-up was 19 months (range, 1–82 months). Axillary lymph node metastases were pathologically confirmed in only 2.1% of cN0 cases and up to 97.1% of cN+ cases. In the aspect of breast surgery, RFS showed no significant difference between mastectomy and BCS group (p = 0.3078). As for axillary surgery, patients in the ALND group showed significantly better RFS than those in the sentinel lymph node biopsy (SLNB) group among pN0 patients (p = 0.0128). Among these cases, the proportion of cN+ in ALND was significantly higher than that in SLNB (6.4% vs. 0.4%, p = 0.002), which meant axillary lymph nodes (ALNs) of ALND patients were larger in imaging and more likely to be misdiagnosed as metastatic. With regard to adjuvant therapy, univariate and multivariate analyses showed that RFS in different comprehensive adjuvant regimens were similar especially among hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− subgroup where patients who did not receive any adjuvant therapy accounted for 15.7% (p &gt; 0.05).ConclusionsIt is feasible to reduce some unnecessary local or systemic treatments for elderly breast cancer patients, especially in HR+/HER2− subtype. Multiple patient-related factors should be considered when making treatment plans

Anemia is a risk factor for rapid eGFR decline in type 2 diabetes

ObjectiveTo investigate the association between anemia and progression of diabetic kidney disease (DKD) in type 2 diabetes.MethodsThis was a retrospective study. A total of 2570 in-patients with type 2 diabetes hospitalized in Jinan branch of Huashan hospital from January 2013 to October 2017 were included, among whom 526 patients were hospitalized ≥ 2 times with a median follow-up period of 2.75 years. Annual rate of eGFR decline was calculated in patients with multiple admissions. A rate of eGFR decline exceeding -5 ml/min per 1.73 m2 per year was defined as rapid eGFR decline. The prevalence of DKD and clinical characteristics were compared between anemia and non-anemia patients. Correlation analysis was conducted between anemia and clinical parameters. Comparison of clinical features were carried out between rapid eGFR decline and slow eGFR decline groups. The risk factors for rapid DKD progression were analyzed using logistic regression analysis.ResultsThe prevalence of anemia was 28.2% among the 2570 diabetic patients, while in patients with DKD, the incidence of anemia was 37.8%. Patients with anemia had greater prevalence of DKD, higher levels of urinary albumin-to-creatinine ratio (UACR), serum creatinine, BUN, urine α1-MG, urine β2-MG, urine NAG/Cr, hsCRP, Cystatin C, homocysteine and lower eGFR, as compared to the patients without anemia. Anemia was correlated with age, UACR, eGFR, urinary NAG/Cr, hsCRP and diabetic retinopathy (DR). Logistic regression analysis of 526 patients with type 2 diabetes during the follow-up period showed that anemia was an independent risk factor for rapid eGFR decline.ConclusionAnemia is associated with worse renal function and is an independent risk factor for rapid eGFR decline in type 2 diabetes

Amplitude variation with incident angle inversion for fluid factor in the depth domain

The development of Pre−stack depth migration makes the imaging of the subsurface structure in the depth possible, which set a foun− dation for the study of amplitude variation with incident angle (AVA) inversion. This leads to the increasing demanding of the seismic inversion methods in the depth domain for guiding reservoir characterization. However, the conventional seismic inversion methods in the time domain are not suitable in the depth domain due to the seismic wavelet in the depth domain is depth−variant and depending on medium velocity. To address this issue, we proposed a pragmatic seismic inversion method for fluid factor in the depth domain with amplitude variation with incident angle gathers by using a true−depth wavelet on the process of seismic inversion. This wavelet is es− timated by converting the time wavelet to the depth wavelet with seismic velocity. To guide the fluid discrimination, the proposed method directly estimates the fluid factor from the pre−stack seismic data and all the process of the method is implemented in the depth domain. To deal with the weak nonlinearity induced by the velocity, the Bayesian inference, the prior information and model constraint are in− troduced in this seismic inversion method. Tests on synthetic data show that the fluid factor can be well estimated reasonably even with moderate noise. The field data example illustrates the feasibility and efficiency of the proposed method in application and the estimated fluid factor and shear modulus are in good agreement with the drilling results

Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Crack Grid Detection and Calculation Based on Convolutional Neural Network

This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different, and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realises complete crack detection. The experimental results show that 3030 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways