Algorithmic aspects for multiple-choice hardware/software partitioning

Hardware–software partitioning (HW/SW) divides an application into software and hardware. It is one of the crucial steps in embedded system design. For a given task, hardware with different areas may provide different execution speeds due to the potential of parallel execution in hardware implementation. Thus, one task may have multiple-choice in hardware implementation according to the available hardware areas. Existing HW/SW partitioning approaches typically consider only a single implementation manner in hardware, overlooking the multiple-choice of hardware implementations. This paper presents a computing model to cater for the HW/SW partitioning problems with the multiple-choice implementation in hardware. An efficient heuristic algorithm is proposed to rapidly generate approximate solution, that is further refined by a tabu search algorithm also customized in this paper. Moreover, a dynamic programming algorithm is proposed for the exact solution of the relatively small problems. Extensive simulation results show that the approximate solutions are very close to the exact ones, and they can be refined by tabu search to the solutions with the error no more than 1.5% for all cases considered in this paper

CRACK INDIRECT IDENTIFICATION METHOD FOR DYNAMIC VIBRATION FREQUENCY OF WIND BLADES

In view of the problem of crack location in wind turbine blade, based on the relation between the difference ratio parameter of natural frequency of cracked blade and damage location, the crack location parameter of natural frequency difference ratio was put forward.The database of crack location parameters was established by numerical simulation calculation, the crack location parameters and crack location criteria were put forward to realize the crack location in the blade. Meanwhile, the variation law of crack location parameters in the crack zone was studied. The mapping relationship between the crack location parameters and the relative position of the crack region was established, and the accurate location of the blade crack interval was realized. The effectiveness of the method is verified by numerical simulation, and the positioning accuracy is high, which provides a powerful basis for practical application

Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning

Background Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning.Methods This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models.Results A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0 vs. 9.6 d, p = 0.002, OR = 0.94), higher serum cystatin C levels (1.2 vs. 3.2 mg/L, p < 0.001, OR = 0.46), and the presence of septic shock (28.1% vs. 41.5%, p < 0.001, OR = 0.63) were associated with reduced likelihood of RRT weaning. Conversely, a positive furosemide stress test (FST) (60.2% vs. 40.7%, p < 0.001, OR = 2.75) and higher total urine volume 3 d before RRT withdrawal (755 vs. 125 mL/d, p < 0.001, OR = 2.12) were associated with an increased likelihood of successful weaning from RRT. Next, we demonstrated that machine learning models, especially Random Forest and XGBoost, achieving an AUROC of 0.95. The XGBoost model exhibited superior accuracy, yielding an AUROC of 0.849.Conclusion High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy

Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning

Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning. This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models. A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0 vs. 9.6 d, p = 0.002, OR = 0.94), higher serum cystatin C levels (1.2 vs. 3.2 mg/L, p vs. 41.5%, p vs. 40.7%, p vs. 125 mL/d, p  High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.</p

Formation Mechanism and Control Technology of an Excavation Damage Zone in Tunnel-Surrounding Rock

Loosened rock circle is formed around the tunnel when the tunnel is constructed by the drilling and blasting method. The size of the loosened rock circle around the tunnel and the degree of internal rock fragmentation has an important influence on the support parameters, durability, and safety of the tunnel. Firstly, referencing an existing tunnel project, blasting tests using nonelectronic and electronic detonators were carried out to determine the influence of blasting construction on the scope of the rock loose circle and the degree of rock fragmentation. Then, a numerical simulation was used to study the contribution of the blasting impact and surrounding rock stress redistribution on the loosened rock circle around the tunnel. The results showed that the range of the loosened rock circle around the tunnel generated by the normal blasting of nonelectronic detonators was 1.5~2.3 m, and the wave velocity of the rock mass in the loosened rock circle around the tunnel decreased to 23~36%. The size of the loosened rock circle around the tunnel generated by the blasting impact was 0.66 m, accounting for 33% of the range of the loosened rock circle around the tunnel. The range of the loosened rock circle around the tunnel produced by electronic detonator blasting was 0~1.4 m. The wave velocity of the rock mass in the loosened rock circle around the tunnel decreased to 12~17%. The range of the loosened rock circle around the tunnel was approximately 60~76% of that of detonator blasting, and the broken degree of the surrounding rock in the loosened rock circle around the tunnel was small. The research results can provide a reference for the optimization design of preliminary support parameters of tunnels, such as anchors and steel arches in blasting construction

The Domain Decomposition Method With Adaptive Time Step for the Transient Thermal Analysis of 3-D ICs

With the continuous emergence of various advanced packaging technologies such as copper interconnection and 3-D packaging technology, it is essential to efficiently and accurately investigate the thermal analysis of high-performance, high-power and complicated electronic devices to better design heat dissipation structures. However, multiscale transient thermal analysis of complex electronic devices by existing numerical methods is still a challenge. In this work, the 3-D domain decomposition method (DDM) with the adaptive time step for the transient thermal analysis of integrated circuits (ICs) is proposed to tackle this problem. By flexible multiscale mesh generation and automatically time step changes based on posteriori errors, the new method significantly improves computational efficiency. Some illustrative numerical examples are presented to verify the accuracy and efficiency of the proposed method by considering 3-D transient heat transfer with thermal conduction, natural convection and radiation boundaries

Dual Delivery of bFGF- and NGF-Binding Coacervate Confers Neuroprotection by Promoting Neuronal Proliferation

Background/Aims: Basic fibroblast growth factor (bFGF) and nerve growth factor (NGF) are essential for proper development, survival, growth, and maintenance of neurons in the central and peripheral nervous systems. However, because bFGF and NGF have short half-life and rapid diffusion rate, they have limited clinical efficacy. Thus, there is an urgent need to develop an effective delivery system to protect bFGF and NGF from proteolysis while maintaining their normal bioactivities. Methods: To more efficiently deliver bFGF and NGF, we used a coacervate (synthesized with heparin and a biodegradable polycation at mass ratio of 500: 100). The maximal package loads of GFs in coacervate were determined by Western Blotting; release efficiency of bFGF and NGF was measured by ELISA. Additionally, we evaluated the effect of bFGF and NGF on the viability, survival, and proliferation of neurons by MTT assay, BrdU cell proliferation, and calcein staining. Results: Our coacervate incorporated bFGF and NGF and continuously released them for at least three weeks. This enhanced the growth and proliferation of PC12 cells and SH-SY5Y cells. Moreover, co-delivery of bFGF and NGF using coacervate was more neuroprotective than free application of both factors or coacervate delivery of each GF separately. Conclusions: Dual delivery of bFGF and NGF binding coacervate was neuroprotective via stimulating the growth and proliferation of neurons