A feature optimization study based on a diabetes risk questionnaire

IntroductionThe prevalence of diabetes, a common chronic disease, has shown a gradual increase, posing substantial burdens on both society and individuals. In order to enhance the effectiveness of diabetes risk prediction questionnaires, optimize the selection of characteristic variables, and raise awareness of diabetes risk among residents, this study utilizes survey data obtained from the risk factor monitoring system of the Centers for Disease Control and Prevention in the United States.MethodsFollowing univariate analysis and meticulous screening, a more refined dataset was constructed. This dataset underwent preprocessing steps, including data distribution standardization, the application of the Synthetic Minority Oversampling Technique (SMOTE) in combination with the Round function for equilibration, and data standardization. Subsequently, machine learning (ML) techniques were employed, utilizing enumerated feature variables to evaluate the strength of the correlation among diabetes risk factors.ResultsThe research findings effectively delineated the ranking of characteristic variables that significantly influence the risk of diabetes. Obesity emerges as the most impactful factor, overshadowing other risk factors. Additionally, psychological factors, advanced age, high cholesterol, high blood pressure, alcohol abuse, coronary heart disease or myocardial infarction, mobility difficulties, and low family income exhibit correlations with diabetes risk to varying degrees.DiscussionThe experimental data in this study illustrate that, while maintaining comparable accuracy, optimization of questionnaire variables and the number of questions can significantly enhance efficiency for subsequent follow-up and precise diabetes prevention. Moreover, the research methods employed in this study offer valuable insights into studying the risk correlation of other diseases, while the research results contribute to heightened societal awareness of populations at elevated risk of diabetes

A state-of-the-art survey of deep learning models for automated pavement crack segmentation

Survey of road cracks in a timely, complete, and accurate way is pivotal to pavement maintenance planning. Motivated by the increasingly heavy task of identifying cracks, researchers have developed extensive crack segmentation models based on Deep learning (DL) methods with significantly different levels of accuracy, efficiency, and generalizing capacity. Although many of the models provide satisfying detection performance, why these models work still needs to be determined. The objective of this study is to survey recent advances in automated DL crack recognition and provide evidence for their underlying working mechanism. We first reviewed 54 DL crack recognition methods to summarize critical factors in these models. Then, we conducted a performance evaluation of fourteen famous semantic segmentation models using the quantitative metrics: F-1 score and mIoU. Then, the effective receptive field and class activation map of the included models are visualized to demonstrate the training results as qualitative evaluation. Based on the literature review and comparison results, larger kernel size, feature fusion, and attention module all contribute to the improvement of model performance. Striking a balance between increasing the effective receptive field and computational/memory efficiency is the key to designing DL crack segmentation models. Finally, some potential directions and suggestions for future development are provided, such as developing semi-supervised or unsupervised learning for the high cost of pixel-level labeling

The simulation of localized surface plasmon and surface plasmon polariton in wire grid polarizer integrated on InP substrate for InGaAs sensor

We numerically demonstrate the integration of gold wire grid polarizer on InP substrate for InGaAs polarimetric imaging. The effective spectral range of wire grid polarizer has been designed in 0.8-3 μm according to InGaAs response waveband. The dips in TM transmission are observed due to surface plasmon (SPs) significantly damaging polarization performance. To further understand the coupling mechanism between gold wire grid grating and InP, the different contributions of surface plasmon polariton (SPP) and localized surface plasmon (LSP) to the dips are analyzed. Both transmission and reflectance spectra are simulated at different grating periods and duty cycles by finite-different time-domain (FDTD) method. LSP wavelength is located at around 1 μm and sensitive to the specific shape of metal wire. SPP presents higher resonance wavelength closely related to grating period. The simulations of electric field distribution show the same results

Recover User’s Private Training Image Data by Gradient in Federated Learning

Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that gradient inversion technique can reconstruct the input images on the pixel level. In this study, we review the research work of data leakage by gradient inversion technique and categorize existing works into three groups: (i) Bias Attacks, (ii) Optimization-Based Attacks, and (iii) Linear Equation Solver Attacks. According to the characteristics of these algorithms, we propose one privacy attack system, i.e., Single-Sample Reconstruction Attack System (SSRAS). This system can carry out image reconstruction regardless of whether the label can be determined. It can extends gradient inversion attack from a fully connected layer with bias terms to attack a fully connected layer and convolutional neural network with or without bias terms. We also propose Improved R-GAP Alogrithm, which can utlize DLG algorithm to derive ground truth. Furthermore, we introduce Rank Analysis Index (RA-I) to measure the possible of whether the user’s raw image data can be reconstructed. This rank analysis derive virtual constraints Vi from weights. Compared with the most representative attack algorithms, this reconstruction attack system can recover a user’s private training image with high fidelity and attack success rate. Experimental results also show the superiority of the attack system over some other state-of-the-art attack algorithms

Performance of Dual-Band Short-Wave Infrared InGaAs Focal-Plane Arrays with Interference Narrow-Band Filter

In this work, we fabricated dual-band 800 × 2 short-wave infrared (SWIR) indium gallium arsenide (InGaAs) focal-plane arrays (FPAs) using N-InP/i-In0.53Ga0.47As/N-InP double-heterostructure materials, which are often applied in ocean-color remote sensing. Using narrow-band interference-filter integration, our detector-adopted planner structure produced two detection channels with center wavelengths of 1.24 and 1.64 μm, and a full-width half-maximum (FWHM) of 0.02 μm for both channels. The photoelectric characteristics of the spectral response, modulation transfer function (MTF), and detectability of the detector were further analyzed. Our FPAs showed good MTF uniformity with pixel operability as high as 100% for each 800 × 1 linear array. Peak detectivity reached 4.39 × 1012 and 5.82 × 1012 cm·Hz1/2/W at 278 K, respectively, and response nonuniformity was ideal at 2.48% and 2.61%, respectively. As a final step, dual-band infrared detection imaging was successfully carried out in push-broom mode

Diabetes risk prediction model based on community follow-up data using machine learning

Diabetes is a chronic metabolic disease characterized by hyperglycemia, the follow-up management of diabetes patients is mostly in the community, but the relationship between key lifestyle indicators in community follow-up and the risk of diabetes is unclear. In order to explore the association between key life characteristic indicators of community follow-up and the risk of diabetes, 252,176 follow-up records of people with diabetes patients from 2016 to 2023 were obtained from Haizhu District, Guangzhou. According to the follow-up data, the key life characteristic indicators that affect diabetes are determined, and the optimal feature subset is obtained through feature selection technology to accurately assess the risk of diabetes. A diabetes risk assessment model based on a random forest classifier was designed, which used optimal feature parameter selection and algorithm model comparison, with an accuracy of 91.24% and an AUC corresponding to the ROC curve of 97%. In order to improve the applicability of the model in clinical and real life, a diabetes risk score card was designed and tested using the original data, the accuracy was 95.15%, and the model reliability was high. The diabetes risk prediction model based on community follow-up big data mining can be used for large-scale risk screening and early warning by community doctors based on patient follow-up data, further promoting diabetes prevention and control strategies, and can also be used for wearable devices or intelligent biosensors for individual patient self examination, in order to improve lifestyle and reduce risk factor levels

2.6 μm MBE grown InGaAs detectors with dark current of SRH and TAT

We fabricate 2.6 μm InGaAs photodetectors by MBE technology and study its dark current mechanisms. Deep-level transient spectroscopy (DLTS) demonstrates a deep-level trap located at Ec - 0.25 eV in the absorption layer. Using the trap parameters, a dark current model is constructed and the device simulation generates the dark current characteristic which agrees well with the experimental data. The model suggests that the dark current at low reverse voltage is dominated by the Shockley-Read-Hall (SRH) and trap-assisted tunneling (TAT). Furthermore, it predicts some basic rules for suppressing the dark current in 2.6 μm InGaAs detectors

Effects of substrate temperature on the uniformity of InGaAs epilayers using a dual-zone manipulator

Three-inch InGaAs epilayers are grown by solid source molecular beam epitaxy using the manipulator equipped with dual-zone heaters. The effects of the substrate temperature on the uniformity of material surface morphology, indium composition, photoluminescence, electronic mobility, and background doping are investigated. As the temperature of the outer heater in the range of 625 \ub0C to 655 \ub0C, no dim area is observed on the edge of the material surface. At the same time, the indium composition fluctuation of the high-resolution X-ray diffraction and the photoluminescence wavelength fluctuation are less than \ub10.1% for the epilayers grown at the optimum substrate temperatures