Optimizing Logical Execution Time Model for Both Determinism and Low Latency

The Logical Execution Time (LET) programming model has recently received considerable attention, particularly because of its timing and dataflow determinism. In LET, task computation appears always to take the same amount of time (called the task's LET interval), and the task reads (resp. writes) at the beginning (resp. end) of the interval. Compared to other communication mechanisms, such as implicit communication and Dynamic Buffer Protocol (DBP), LET performs worse on many metrics, such as end-to-end latency (including reaction time and data age) and time disparity jitter. Compared with the default LET setting, the flexible LET (fLET) model shrinks the LET interval while still guaranteeing schedulability by introducing the virtual offset to defer the read operation and using the virtual deadline to move up the write operation. Therefore, fLET has the potential to significantly improve the end-to-end timing performance while keeping the benefits of deterministic behavior on timing and dataflow. To fully realize the potential of fLET, we consider the problem of optimizing the assignments of its virtual offsets and deadlines. We propose new abstractions to describe the task communication pattern and new optimization algorithms to explore the solution space efficiently. The algorithms leverage the linearizability of communication patterns and utilize symbolic operations to achieve efficient optimization while providing a theoretical guarantee. The framework supports optimizing multiple performance metrics and guarantees bounded suboptimality when optimizing end-to-end latency. Experimental results show that our optimization algorithms improve upon the default LET and its existing extensions and significantly outperform implicit communication and DBP in terms of various metrics, such as end-to-end latency, time disparity, and its jitter

Oceanic and ecological response to native Typhoons Cempaka and Lupit (2021) along the northern South China Sea continental shelf: comparison and evaluation of global and regional Operational Oceanography Forecasting Systems

The Global Operational Oceanography Forecasting System from the Mercator Ocean (MO) and the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2) were compared and evaluated using in situ and satellite observations, with a focus on the oceanic and ecological response to two consecutive native typhoons, Cempaka and Lupit, that occurred in July–August 2021. Results revealed a better simulation of the chlorophyll a (Chla) structure by SCSOFSv2 and a better simulation of the temperature profile by MO in the Pearl River Estuary. In addition, SCSOFSv2 sea surface temperature (SST) and MO Chla variations corresponded well with observations along the northern SCS shelf. Simulated maximum SST cooling was larger and 2–3 days earlier than those observations. Maximum Chla was stronger and led the climatological average by 2 days after the typhoon passage. Typhoon-induced vertical variations of Chla and NO3 indicated that different Chla bloom processes from coastal waters to the continental shelf. Discharge brought extra nutrients to stimulate Chla bloom in coastal waters, and model results revealed that its impact could extend to the continental shelf 50–150 km from the coastline. However, bottom nutrients were uplifted to contribute to Chla enhancement in the upper and middle layers of the shelf. Nutrients transported from the open sea along the continental slope with the bottom cold water could trigger Chla enhancement in the Taiwan Bank. This study suggests considering strong tides and waves as well as regional dynamics to improve model skills in the future

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk

Correlation study on firing temperature and color of plain pottery excavated from the Tang Dynasty tomb of Liu Jing in Shaanxi, China

Abstract Plain pottery excavated from the Tang Dynasty tomb of Liu Jing was taken as the research object. The color, chemical composition, microstructure, and phase were tested to investigate the influencing factors of color for plain pottery fragments. The results indicated that the contents of Fe2O3 and TiO2 in all fragments varied little, and the influence of humic acids in clay as well as the firing atmosphere on the appearance color of plain pottery was excluded. Therefore, the main factor affecting color saturation (C*) was identified as the firing temperature (T). More importantly, the correlation between C* and firing temperature was established by replicas fired at different temperatures. Before the appearance of the glass phase, iron-containing minerals played a major role in coloring, and after that, iron ions in the glass phase and iron crystallization rose the important function of coloring. Consequently, with the increase of firing temperature, C* value increased firstly and then decreased. The inflection point of the fitted C* − T curve corresponded to the glass phase formation temperature. By comparing the estimated firing temperatures obtained by the fitted C* − T correlation curve with the known firing temperature of replicas, it was demonstrated that the color measurement is an ideal method for deducing the firing temperatures of ancient plain pottery

Additional file 1 of Correlation study on firing temperature and color of plain pottery excavated from the Tang Dynasty tomb of Liu Jing in Shaanxi, China

Additional file 1: Table S1. Chroma values on the surfaces of thirty-two plain pottery fragments. Table S2. Chemical compositions obtained by XRF on the surfaces of thirty-two plain pottery fragments (wt%). Table S3. Color saturation values on the surfaces of replicas. Table S4. The standard deviation and error of the estimated firing temperatures of the samples fired by the loess from Yaozhou kiln site. Table S5. The standard deviation and error of the estimated firing temperatures of the samples fired by the eastern mausoleum of Qin Dynasty in Shaanxi Province. Fig S1. Porosity and average pore size of MS-07, TMS-08 and, TMS-09. Fig S2. Thermal expansion curves and the first order derivative curves of replicas fired at 600 °C with 3, 5, and 10 °C/min, respectively. Fig S3. The C*−T correlation curve of the samples fired by the loess near the eastern mausoleum of Qin Dynasty in Shaanxi Province