Reduction of thermal resistance of Ag-coated GFs/copper structure using nano-Ag paste as interconnection

Reduction of the thermal resistance between graphene films (GFs) and substrate is crucial to the application of GFs in thermal management. GFs/copper structures were prepared using nano-Ag paste as interconnection material. The effect of the thickness of nano-Ag paste on thermal resistance of GFs/copper structure was investigated. A thin layer of Ag was coated on GFs by physical vapor deposition (PVD) to further reduce thermal resistance. The thermal resistance of GFs/copper structure using Ag-coated GFs is 5.84% lower than that using raw GFs. The thermal resistance of GFs/copper structure decreases first and then increases with the increase of coating temperature and thickness of Ag layer. The minimum thermal resistance of 1.64 mm2\ub7K\ub7W-1 was gained for GFs/copper structure using GFs coated Ag at 300 ℃ for 60 min

Estimating Brain Age with Global and Local Dependencies

The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease. Achieving accurate brain age prediction is an essential prerequisite for optimizing the predicted brain-age difference as a biomarker. As a comprehensive biological characteristic, the brain age is hard to be exploited accurately with models using feature engineering and local processing such as local convolution and recurrent operations that process one local neighborhood at a time. Instead, Vision Transformers learn global attentive interaction of patch tokens, introducing less inductive bias and modeling long-range dependencies. In terms of this, we proposed a novel network for learning brain age interpreting with global and local dependencies, where the corresponding representations are captured by Successive Permuted Transformer (SPT) and convolution blocks. The SPT brings computation efficiency and locates the 3D spatial information indirectly via continuously encoding 2D slices from different views. Finally, we collect a large cohort of 22645 subjects with ages ranging from 14 to 97 and our network performed the best among a series of deep learning methods, yielding a mean absolute error (MAE) of 2.855 in validation set, and 2.911 in an independent test set

RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilities. In this work, we introduced RJUA-MedDQA, a comprehensive benchmark in the field of medical specialization, which poses several challenges: comprehensively interpreting imgage content across diverse challenging layouts, possessing numerical reasoning ability to identify abnormal indicators and demonstrating clinical reasoning ability to provide statements of disease diagnosis, status and advice based on medical contexts. We carefully design the data generation pipeline and proposed the Efficient Structural Restoration Annotation (ESRA) Method, aimed at restoring textual and tabular content in medical report images. This method substantially enhances annotation efficiency, doubling the productivity of each annotator, and yields a 26.8% improvement in accuracy. We conduct extensive evaluations, including few-shot assessments of 5 LMMs which are capable of solving Chinese medical QA tasks. To further investigate the limitations and potential of current LMMs, we conduct comparative experiments on a set of strong LLMs by using image-text generated by ESRA method. We report the performance of baselines and offer several observations: (1) The overall performance of existing LMMs is still limited; however LMMs more robust to low-quality and diverse-structured images compared to LLMs. (3) Reasoning across context and image content present significant challenges. We hope this benchmark helps the community make progress on these challenging tasks in multi-modal medical document understanding and facilitate its application in healthcare.Comment: 15 pages, 13 figure

Highly thermally conductive substrate based on graphene film

Heat dissipation has become one of the critical challenges of development for microelectronic products because of the increasing of heat accumulation in the devices. A novel laminated composite with high thermal conductivity was fabricated by hot-pressing using graphene films (GFs) and glass fiber reinforced epoxy resin (GFEP). The effect of GFs with different thicknesses and number of layers on the thermal properties of the composites was investigated. An in-plane thermal conductivity of 141 W \ub7 m-1 \ub7 K-1 for the laminated composites with GFs and GFEP were obtained. The heat dissipation capability of GFs/GFEP composites is evaluated by infrared thermal imaging technology. The maximum temperature difference between the heating elements on GFs/GFEP composites and GFEP increases with the rise of voltage applied to the heating elements. Moreover, the heat dissipation capability of the composite is enhanced with the increased of the number of layers of GFs. The temperature of the heating element assembled on GFs/GFEP composites is 144.3\ub0C lower than that on GFEP at the same voltage. The results indicate that the GFs/GFEP composites is a promising candidate of substrate material with high heat dissipation capability

A novel nano-Ag paste with Ag-rGO and its application in GF/Cu laminated structure

The high contact thermal resistance of the interface between graphene films (GF) and substrates has become one of the key obstacles for the application of GF in electronic devices. A novel nano-Ag paste with nano-Ag particles modified reduced graphene oxide (Ag-rGO) was introduced to enhance the heat transport via the interface of GF and substrate. The influence of the content of Ag-rGO on shear strength and electrical resistivity of sintered Ag structure was investigated. The maximum shear strength and the minimum electrical resistivity were got for sintered Ag structure with 0.5 wt.% Ag-rGO. Reduced contact thermal resistance of GF and Cu substrate (GF/Cu) laminated structure was gained by using sintered Ag structure with Ag-rGO as interconnect materials. The minimum value of the thermal resistance of 2.02 \ub1 0.26 mm2\ub7K/W was obtained for GF/Cu laminated structure connected by sintered Ag structure with 1 wt.% Ag-rGO

Edge computing task scheduling strategy based on load balancing

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency

Enhanced Mechanical and Thermal Properties of Ag Joints Sintered by Spark Plasma Sintering

Nano-silver paste has been considered to be one of the most promising materials for interconnects of semiconductor devices operating at high temperature. However, its application is limited by conventional sintering methods due to the long dwell time. In this paper, a spark plasma sintering (SPS) technique with a very short sintering time of no more than 5 min was explored for the sintering of nano-silver paste. The effects of sintering conditions including pretreatment time, sintering temperature, dwell time and applied pressure on shear strength and thermal conductivity of the sintered Ag joints were investigated. The shear strength of sintered Ag joints increased as the sintering temperature and applied pressure increased. Robust sintered Ag joints with an average shear strength of 40.18 MPa were obtained by sintering at 300 degrees C for 5 min under a pressure of 3 MPa. The thermal properties were improved by pretreating the nano-silver paste for a shorter time. Thermal conductivity of 41.50 W m(-1) K-1 is obtained for samples pretreated for 25 min and sintered at 250 degrees C for 5 min

Edge computing task scheduling strategy based on load balancing

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency

Alteration of brain structural connectivity in progression of Parkinson's disease: A connectome-wide network analysis

Pinpointing the brain dysconnectivity in idiopathic rapid eye movement sleep behaviour disorder (iRBD) can facilitate preventing the conversion of Parkinson's disease (PD) from prodromal phase. Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively. However, the intrinsic process of the human brain structural network evolving from iRBD to PD still remains largely unknown. To address this issue, 151 participants including iRBD, PD and age-matched normal controls were recruited to receive diffusion MRI scans and neuropsychological examinations. The connectome-wide association analysis was performed to detect reorganization of brain structural network along with PD progression. Eight brain seed regions in both cortical and subcortical areas demonstrated significant structural pattern changes along with the progression of PD. Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic. Our study shows that connectome-wide association analysis reveals the underlying structural connectivity patterns related to the progression of PD, and provide a promising distinct capability to predict prodromal PD patients