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

Explainable History Distillation by Marked Temporal Point Process

Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this paper, we build a machine learning system to automatically generate explanations of happened events from history by \gls{ca} based on the \acrfull{tpp}. Specifically, we propose a new task called \acrfull{ehd}. This task requires a model to distill as few events as possible from observed history. The target is that the event distribution conditioned on left events predicts the observed future noticeably worse. We then regard distilled events as the explanation for the future. To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}. This work fills the gap between our task and existing works, which only spot the difference between factual and counterfactual worlds after applying a predefined modification to the environment. Experiment results on Retweet and StackOverflow datasets prove that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines and can reveal the rationale underpinning real-world processes

Therapeutic effect of a combination of montelukast and vitamins A and D drops in children with bronchial asthma, and its influence on quality of life

Purpose: To investigate the efficacy of a combination of montelukast and vitamins A and D drops in bronchial asthma children, and its effect on quality of life.Methods: Sixty bronchial asthma children from June 2018 to June 2020 were collected and randomized into study group and control group (30 cases in each group). Control group received montelukast sodium (chewable tablets), while the study group received vitamins A and D drops (capsules) plus. Clinical efficacy, lung function, serum inflammatory factors, and quality of life were evaluated and compared.Results: Compared with control group, total treatment effectiveness was higher and the symptom remission period was shorter in the study group (p < 0.05). Post-treatment, the parameters of FEV1 and FVC increased in both groups, but higher in the study group (p < 0.05). Serum levels of CRP and IL-4 in both groups decreased after treatment, while serum IL-10 levels were significantly up-regulated. Compared with control group, the levels of these indicators were improved in the study group (p < 0.05). Post-treatment Chinese Version of Pediatric Quality of Life Asthma Specific Scale (PedSQL) score was higher than before treatment, with higher values (for all indicators) in the study group (p < 0.05).Conclusion: The combination therapy of montelukast and vitamins A and D drops produces good clinical efficacy in children with bronchial asthma. It significantly shortens the time taken for relief of clinical symptoms, improves lung function, reduces inflammatory response, controls asthma, and improves the quality of life of the patients

Trustworthy Recommender Systems

Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs

RF-Transformer: A Unified Backscatter Radio Hardware Abstraction

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6-74.2 less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92-678. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RFTransformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer