Precision Index Research Articles

A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.

This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management. This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model's effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI). When including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514). We have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.

Target Tracking Method for Transmission Line Moving Operation Based on Inspection Robot and Edge Computing

Aiming at the problems of low accuracy and high loss rate when the traditional target tracking (TT) method is applied to the TT of the moving operation of the transmission line, a transmission line based on an inspection robot and edge computing (EC) is proposed: the mobile job TT method. First, the basic framework of the TT algorithm is proposed, relying on the edge device to develop the TT system for mobile operations on the transmission line. Video information is collected by an intelligent inspection robot and sent to the target tracking system in the edge device for processing to obtain accurate data. Then, the gradient disappearance and explosion problems caused by the increase of network depth are solved by using the deep residual network. The traditional deep residual network is improved by introducing the improved bidirectional feature reinforcement network and the classification and regression subnet. The loss of position texture information is remedied, and the accurate tracking of the moving target on transmission line is realized. Finally, the real-time data acquisition of mobile operation target is realized by using an intelligent inspection robot, and the experimental verification is conducted. The proposed algorithm is compared and analyzed against the three other algorithms using the same data set through simulation experiments. The results show the precision, recall rate, accuracy, and comprehensive evaluation index F1 value of the proposed algorithm rank highest, reaching 93.8%, 90.2%, 83.8%, and 89.8%, respectively, compared with the other algorithms.

DARU-Net: A dual attention residual U-Net for uterine fibroids segmentation on MRI.

Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images. The proposed method is based on U-Net architecture and integrates two attention mechanisms: channel attention of squeeze-and-excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU-Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI). The average DSC, precision, recall, and JI of DARU-Net reached 0.8066 ± 0.0956, 0.8233 ± 0.1255, 0.7913 ± 0.1304, and 0.6743 ± 0.1317. Compared with U-Net and other deep learning methods, DARU-Net was more accurate and stable. This work proposed an optimized U-Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU-Net was able to accurately segment uterine fibroids from MR images.

Short-Term Power Prediction of Wind Power Generation System Based on Logistic Chaos Atom Search Optimization BP Neural Network

Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of in-depth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model.

Approximating scaffold printability utilizing computational methods

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e. repeatability) and printability across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that a fuzzy inference system (FIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented a FIS model consisting of four inputs (bioink concentration, printing flow rate, speed, and temperature) and two outputs to quantify the precision (scaffold bioprinted linewidth variance) and printability. We validate our use of the bioprinting precision index with both standard and normalized printability factors. Finally, we utilize optimized printing parameters to bioprint scaffolds containing up to 30 × 106 cells ml−1 with high printability and precision. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting.

A calculation method for GNSS positioning precision based on the posteriori unit weight variance

Abstract The precision index is the primary basis for judging the GNSS positioning result, and the positioning mean error is usually used as the precision index in practical applications. In order to solve the problem of mismatch between positioning deviation and mean error in the priori situation, this paper proposed a positioning precision calculation method based on the posteriori unit weight variance and deduced the formulas combining sequential adjustment or Kalman filter, respectively. This method uses the characteristic that there are system errors in error corrections to calculate and screen the posterior unit weight variance. This method introduces the system error’s influence into the mean error, which can improve positioning precision. The application of static difference and RTK proved that this method has remarkable effects, which can significantly alleviate the problem of false high precision and improve the reliability of positioning mean errors.

A Novel Deep Learning Method for Automatic Recognition of Coseismic Landslides

Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to the relatively small size of a landslide and various complicated environmental backgrounds. This work proposes a novel semantic segmentation network, EGCN, to improve the landslide identification accuracy. EGCN conducts coseismic landslide recognition by a recognition index set as the input data, CGBlock as the basic module, and U-Net as the baseline. The CGBlock module can extract the relatively stable global context-dependent features (global context features) and the unstable local features by the GNN Branch and CNN Branch (GNN Branch contains the proposed EISGNN) and integrates them via adaptive weights. This method has four advantages. (1) The recognition indices are established according to the causal mechanism of coseismic landslides. The rationality of the indices guarantees the accuracy of landslide recognition. (2) The module of EISGNN is suggested based on the entropy importance coefficient and GATv2. Owing to the feature aggregation among nodes with high entropy importance, global and useful context dependency can be synthesized and the false alarm of landslide recognition can be reduced. (3) CGBlock automatically integrates context features and local spatial features, and has strong adaptability for the recognition of coseismic landslides located in different environments. (4) Owing to CGBlock being the basic module and U-Net being the baseline, EGCN can integrate the context features and local spatial characteristics at both high and low levels. Thus, the accuracy of landslide recognition can be improved. The meizoseismal region of the Ms 7.0 Jiuzhaigou earthquake is selected as an example to conduct coseismic landslide recognition. The values of the precision indices of Overall Accuracy, mIoU, Kappa, F1-score, Precision, and Recall reached 0.99854, 0.99709, 0.97321, 0.97396, 0.97344, and 0.97422, respectively. The proposed method outperforms the current major deep learning methods.

Scale in Scale for SAR Ship Instance Segmentation

Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID.

A cyclically adjusted spatio-temporal kernel density estimation method for predictive crime hotspot analysis

ABSTRACT This paper presents a new method for predictive crime hotspot analysis that further improves the kernel density estimation (KDE) method and the spatio-temporal kernel density estimation (STKDE) method by accounting for temporal crime cycles and is therefore termed the ‘cyclically adjusted STKDE (cSTKDE) method’. The case study on robbery incidents in Baton Rouge, Louisiana, shows a temporal cycle with a 6-month period of statistical significance from January 2010 to May 2018. This identified period is incorporated into the temporal kernel function of the new cSTKDE method. For validation, the Forecast Accuracy Index (FAI) and Forecast Precision Index (FPI) are used to evaluate the performance across 52 weeks in 2013. For 11 consecutive weeks since the beginning of 2013, the cSTKDE method outperforms the STKDE by 89% lower average abs(1-FAI) and 17% higher average FPI, and outperforms the KDE by 90% lower average abs(1-FAI) and 8% higher average FPI. Overall, the scenario with the best predictive accuracy by the cSTKDE is recommended over the traditional KDE or STKDE method as most feasible and effective in implementation of hotspot policing in practice.

Simultaneous effects of MWCNT and SiO2 on the rheological behavior of cooling oil and sensitivity analysis

Many scholars are attracted to nano-lubricants due to their unique properties. In the present study, the rheological behavior of a new generation of lubricants has been investigated. SiO2 nanoparticles with an average diameter of 20–30 nm along with MWCNT with an internal diameter of 3–5 nm and the external diameter of 5–15 nm has been dispersed in 10W40 engine oil as base-lubricant and MWCNTs-SiO2 (20%–80%)/10W40 hybrid nano-lubricant has been produced. Nano-lubricant behavior agrees with the Herschel-Bulkley model and is of Bingham pseudo-plastic type below 55°C. Also, in the temperature of 55°C, nano-lubricant behavior has changed to Bingham dilatant. The viscosity is increased by 32% in the proposed nano-lubricant compared to base lubricant (Dynamics Viscosity Enhancement). Finally, a new correlation with a precision index of R-squared >0.9800, Adj. R-squared >0.9800, and the maximum margin of deviation of 2.72% has been presented, increasing the applicability of this nano-lubricant. Eventually, the sensitivity analysis of nano-lubricant has been conducted, studying the comparative effect of volume fraction and temperature on viscosity.

Behavior-Dependent Corticocortical Contributions to Imagined Grasping: a BCI-triggered TMS study.

Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting.We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.

O impacto da transmissão na deposição de sementes de milho em dosadores pneumáticos

ABSTRACT The proper distribution of maize seeds in pneumatic dispensers is essential for high-quality sowing operations. To enhance seed distribution accuracy, advanced pneumatic mechanisms utilizing new electrically driven dosing devices have been developed, as mechanical mechanisms often lead to distribution errors. Consequently, it becomes imperative to assess the impact of these mechanisms, particularly concerning operational speed. This study aims to evaluate the transmission performance of pneumatic dispensers when depositing maize seeds at varying operating speeds. The experiment was conducted on a static simulation bench, employing a completely randomized design and assessing two seed distribution mechanisms at speeds of 5.0, 7.0, 9.0, 11.0, and 13.0 km h-1. Parameters analyzed included acceptable spacing, double failure, coefficient of variation, and precision index, gathered from five repetitions of 250 consecutive seed depositions. The results suggest that the independent electrically driven dosing mechanism outperforms the pneumatic dosing mechanism with mechanical transmission in terms of acceptable spacing (1.27), missing spacing (0.85), and accuracy index (1.31). However, it is crucial to note that an increase in operating speed negatively impacts seed quality for both dispenser drive mechanisms.

Personalised recommendation method of college English online teaching resources based on hidden Markov model

Aiming at the problem of the high comprehensive evaluation index of recall and precision in the traditional personalised recommendation methods of college English online teaching resources, a personalised recommendation method of college English online teaching resources based on hidden Markov model is proposed. First, extract the label features, student learning behaviour features, and time weight features. Then, pre-process the extracted college English online teaching resource data, build a recommendation model based on the pre-processing results, and use a hidden Markov model to process the hidden data to obtain the maximum likelihood estimates the parameter. Finally, input the parameter into the recommendation model to obtain the optimal parameter, that is, the optimal recommendation result. The simulation results show that the comprehensive evaluation index values of precision and recall of the recommended results of the proposed method are within 0.9 and 0.5, which has a good recommendation effect and meets the needs of the development of network teaching.

Artificially intelligent differential diagnosis of enlarged lymph nodes with random vector functional link network plus.

Differential diagnosis of enlarged lymph nodes (ELNs) is essential for the treatment of related patients. Though multi-modal ultrasound including B-mode, Doppler ultrasound, elastographyand contrast-enhanced ultrasound (CEUS)can enhance diagnostic performance for ELNs, the scenario of having only single or dual modal data is often encountered. In this study, an artificially intelligent diagnosis model based on the learning using privileged information was proposed to aid in differential diagnosis of ELNs in the case of single or dual modal images. In our model, B-mode, or combined with another modality, was used as the standard information (SI) and other modalities were used as the privileged information (PI). The model was constructed through the combination of the SI and PI in the training stage. By learning from the training samples, a random vector functional link network with privileged information (RVFL+) was obtained, which was used to classify the testing samples of solely the SI. Results showed that the accuracy, precision and Youden's index of the RVFL+ model, using B-mode with elastography as the SI and CEUS as the PI, reached 78.4%, 92.4% and 54.9%, increased by 14.0%, 8.4% and 24.5% compared with the model using B-mode as the SI without the PI. The method based on the LUPI can improve the diagnostic performance for ELNs.

DESIGN OF A PRECISE FSO NETWORK INCLUDING IMPROVED MODIFIED REFRACTIVE INDEX STRUCTURE PROFILING FOR SHIMLA CITY, INDIA

The most commonly used altitude-dependent Hufnagel-Valley (HV) model for Refractive-Index Structure Parameter (C<sub>n</sub><sup>2</sup>) calculation has limited accuracy due to the reason of not considering various meteorological parameters for mountainous locations which have negative influence on optical signals. Although altitude and turbulent winds (due to temperature and pressure variations) are significant parameters giving rise to atmospheric turbulences intensity but the presence of water droplets and snowflakes in humid climate of mountainous locations play major role in reducing optical visibility, so should not be ignored. We have included the effect of snowfall in existing HV model while calculating path loss due to atmospheric turbulences to estimate these effects in Shimla city. The computed results show considerable decrement in Q-factor due to the additional losses imposed by snowflakes to the laser beam. Enlargement in effects of absorption and scattering is also observed in bit error rate (BER) results. So, the proposed modified model presents more accurate estimation of the refractive index profile as it takes more parameters into consideration responsible for atmospheric turbulence.

The Effect of Core Training on Agility, Explosive Strength and Balance in Young Female Volleyball Players

The present study aimed to determine the effect of core training on agility, explosive strength and balance in young female volleyball players. 20 young female volleyball players volunteered to participate in the study. Vertical jump, counter movement jump, pro-agility and dynamic balance tests were applied to all volleyball players before and after the core-training program. The athletes were selected randomly and divided into two groups as study group (n=10) and control group (n=10). The study group performed core exercises added to the beginning of the training sessions for 8 weeks (3days/week) in addition to volleyball training. The control group, on the other hand, continued volleyball training alone. At the end of 8 weeks, the tests applied in the pre-test were repeated and analyzed for all participants in order to determine the effect of core training on agility, explosive strength and balance parameters. In the statistical analysis of the study, Kurtosis-Skewness test was used to determine the normal distribution of the data. Mixed Anova and two-independent independent-samples t-test were used to make in-group and between-group comparisons. During the study, the level of significance was accepted as 0.05. A statistically significant difference was found in the balance precision index, static vertical jump (cm), counter movement jump (cm) and pro-agility agility test (sec) parameters when comparing the study and control groups. As a result, core exercises performed in addition to volleyball training appear to have a positive effect on agility, explosive strength and balance performances.

Burned area detection using Sentinel-1 SAR data: A case study of Kangaroo Island, South Australia

Burned-area maps are useful in disaster management and in response to bushfire events. In this paper, we explored the capabilities of synthetic aperture radar (SAR) Sentinel-1 in detecting and mapping the bushfire-affected areas. Fires in Kangaroo Island, Australia, in 2019–20 known as the “Black Summer” were selected as a case study. We applied a random forest method to the Sentinel-1 image classification to detect the burned areas over Kangaroo Island. Radar burn difference (RBD), radar burn ratio (RBR), and delta modified radar vegetation index (ΔRVI) were calculated and imported as inputs to the random forest classifier. An independent reference map was generated using the difference normalize burn ratio (dNBR) and Sentinel-2 images and was used as the ground truth to evaluate the accuracy of the SAR-based burned-area detection map. Our results show that the SAR-based burned area detection map outperforms the MODIS MCD64. The feature importance in the random forest method indicates that RBDVH is the most important index (importance value of 0.35) followed by RBDVV (0.20), ΔRVI (0.18), RBRVH (0.17), RBRVV (0.10). The random forest method's precision, accuracy and kappa index were 94%, 94%, 0.87, respectively, while corresponding metrics for the MODIS MCD64 products were 92%, 91%, 0.83, respectively.

HOMA-IR mean values in healthy individuals: a population-based study in iranian subjects.

Homeostasis Model Assessment-estimated Insulin Resistance (HOMA-IR) is an important indicator of insulin resistance. In this population-based investigation, we sought to report the mean value of HOMA-IR in different subgroups of a large population-based database of Iranian healthy subjects. This study recruited adult healthy individuals between the ages of 18 to 70 years old to Massoud Medical Laboratory, Tehran, Iran. Fasting insulin was measured by using the Electro Chemiluminescence method using Roche Cobas 6000 e601/602 instrument. The mean ± SD value of the HOMA-IR index in the studied population was 2.11 ± 0.99 (2.5-97.5% percentiles: 0.66-4.50). In addition, the mean ± SD of HOMA-IR index in male and female groups were 2.35 1.0 (2.5-97.5 percentile: 0.57-4.37) and 2.05 ± 1.0 (2.5-97.5 percentiles: 0.53-4.35), respectively. Interestingly, it was observed a significant increment for the HOMA-IR index in the male group compared with the female group in all age subgroups (P < 0.01). Our findings showed the mean value of 2.11 ± 0.99 HOMA-IR in the Iranian healthy population. Considering the large sample size in our study, more clinical investigations in terms of ethnicity should be done to provide a precise standardized HOMA-IR index in the Iranian population. The online version contains supplementary material available at 10.1007/s40200-022-01099-9.

A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor.

We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors of uncertain malignant potential stained with hematoxylin and eosin, respectively). Aperio ImageScope software randomly selected ≥10 areas of the total field of view. Pathologists randomly selected a marked region in each section that was no smaller than the total area of 10 high-power fields in which necrotic, vascular, collagenous, and mitotic areas were labeled. We constructed an automatic identification algorithm for cytological atypia and necrosis by using ResNet and constructed an automatic detection algorithm for mitosis by using YOLOv5. A logical evaluation algorithm was then designed to obtain an automatic UMT diagnostic aid that can "study and synthesize" a pathologist's experience. The precision, recall, and F1 index reached more than 0.920. The detection network could accurately detect the mitoses (0.913 precision, 0.893 recall). For the prediction ability, the AI system had a precision of 0.90. An AI-assisted system for diagnosing UMTs in routine practice scenarios is feasible and can improve the accuracy and efficiency of diagnosis.

A degree-day model for forecasting adult phenology of Popillia japonica (Coleoptera: Scarabaeidae) in a temperate climate.

Japanese beetle, Popillia japonica (Newman), was first detected in the United States in New Jersey in 1916. The beetle gradually spread to the Midwest U.S. region, and was first confirmed in Minnesota in the late 1960's. Popillia japonica has subsequently become a major invasive insect pest in turfgrass and several agricultural crops. As P. japonica continues to spread throughout the U.S., and other countries, it is important to develop efficient ways to monitor adult populations, and where possible, forecast the phenology of adult population dynamics. During 2019-2021, field trials were conducted to develop a degree-day model that can be used to forecast P. japonica adult phenology under Minnesota, and Midwest summer climatic conditions in. We used commercially available traps and lures to monitor adult flight phenology, specifically beetle trap-catch, along with weather data at four locations in Minnesota, to relate ambient field temperatures to population phenology. The concordance correlation coefficient (CCC), an index of both precision and accuracy, was used to develop a final degree-day model. Model development included evaluation of simple and sine-wave degree-day calculation methods, start dates between 1 Jan. and 1 April, and a range of lower (0-15 °C) and upper (20-37 °C) thresholds. The optimum model was found to be a simple degree-day calculation, using a biofix date of 1 Jan, and lower and upper thresholds of 15 and 21.7 °C, respectively, for predicting 10% beetle trap-catch. The model will aid in future integrated pest management (IPM) and regulatory strategies by providing a tool for prediction of P. japonica adult flight phenology.