D-Tunes: Configuration Engine for Geo-Replicated Cloud Storage

When developing a web-based application, developers are facing stringent requirements to balance the latency, scalability and availability for their cloud database. Application developers need a specific replication configuration strategy based on the requirement of their application. To deal with this problem, some geo-replicated cloud strategy systems have emerged recently, like Cassandra. This project serves to design a web tool that can help configure the best replication strategies for geo-distributed data stores, which uses quorum-based protocols. Currently, our web tool D-Tunes, require a minimum input from users and generate specific scripts based on the inputs user provided. The program running these scripts can output a text result and also map a figure showing the recommended replication strategy. The results of D-Tunes recommend the best replication strategies including the number of replicas, the location of replicas and read/write quorum size. Our web-tool also generates the applicable strategy, which is a simulation of the real experiment on EC2 and Probe test-bed with Cassandra system. In conclusion, this project has successfully provides cloud application developers a strategy of data-store configuration and has contributes to the ongoing research on cloud computing for Cassandra based solution

“Open space” integrated mangrove intelligent monitoring platform based on the Internet of Things

In order to comprehensively promote mangrove restoration, this paper developed a set of “open space” integrated mangrove intelligent monitoring platform based on the Internet of Things, in order to solve the problems of traditional mangrove monitoring technology, such as simple means and small scale. The platform built LoRa Mesh AD hoc network and NB-IoT Internet of things to collect and transmit mangrove wetland environmental data, developed the upper computer website and the lower computer control system for real_x005ftime monitoring of environmental sensor data, and carried out imaging hyperspectral load with UAV to collect mangrove plant species and distribution and perform spectral analysis. It aims to use information technology to help the construction of ecological civilization, and contribute to fully supporting Zhanjiang to build a “mangrove city”. The test results show that the data transmission is normal, the hyperspectral image picture is clear, the PC analysis is complete, the abnormal environmental data alarm is accurate and rapid, and the control system responds in time

Liver organoids: a promising three-dimensional model for insights and innovations in tumor progression and precision medicine of liver cancer

Primary liver cancer (PLC) is one type of cancer with high incidence rate and high mortality rate in the worldwide. Systemic therapy is the major treatment for PLC, including surgical resection, immunotherapy and targeted therapy. However, mainly due to the heterogeneity of tumors, responses to the above drug therapy differ from person to person, indicating the urgent needs for personalized treatment for PLC. Organoids are 3D models derived from adult liver tissues or pluripotent stem cells. Based on the ability to recapitulate the genetic and functional features of in vivo tissues, organoids have assisted biomedical research to make tremendous progress in understanding disease origin, progression and treatment strategies since their invention and application. In liver cancer research, liver organoids contribute greatly to reflecting the heterogeneity of liver cancer and restoring tumor microenvironment (TME) by co-organizing tumor vasculature and stromal components in vitro. Therefore, they provide a promising platform for further investigation into the biology of liver cancer, drug screening and precision medicine for PLC. In this review, we discuss the recent advances of liver organoids in liver cancer, in terms of generation methods, application in precision medicine and TME modeling

A method and system for unified authentication management and control of power secondary equipment based on blockchain

Blockchain technology is an advanced database mechanism that allows transparent sharing of information across corporate networks. By analyzing the insufficiency and improvement plan of the operation and maintenance safety management of power secondary equipment, it is proposed to use the operation and maintenance management and control system to select EOS as the underlying scheme of the blockchain, and discuss it based on a trusted blockchain network system

Ultra-low threshold continuous-wave quantum dot mini-BIC lasers

Highly compact lasers with ultra-low threshold and single-mode continuous wave (CW) operation have been a long sought-after component for photonic integrated circuits (PICs). Photonic bound states in the continuum (BICs), due to their excellent ability of trapping light and enhancing light-matter interaction, have been investigated in lasing configurations combining various BIC cavities and optical gain materials. However, the realization of BIC laser with a highly compact size and an ultra-low CW threshold has remained elusive. We demonstrate room temperature CW BIC lasers in the 1310 nm O-band wavelength range, by fabricating a miniaturized BIC cavity in an InAs/GaAs epitaxial quantum dot (QD) gain membrane. By enabling effective trapping of both light and carriers in all three dimensions, ultra-low threshold of 12 {\mu}W (0.052 kW/cm^2) is achieved. Single-mode lasing is also realized in cavities as small as only 5*5 unit-cells (~2.5*2.5 {\mu}m^2 cavity size) with a mode volume of 1.16({\lambda}/n)^3. With its advantages in terms of a small footprint, ultralow power consumption, robustness of fabrication and adaptability for integration, the mini-BIC lasers offer a perspective light source for future PICs aimed at high-capacity optical communications, sensing and quantum information

Long-term exposure to road traffic noise and incident heart failure: evidence from UK Biobank

Background Evidence on road traffic noise and heart failure (HF) is limited, and little is known on the potential mediation roles of acute myocardial infarction (AMI), hypertension, or diabetes. Objectives The purpose of this study was to evaluate the impacts of long-term road traffic noise exposure on the risk of incident HF considering air pollution, and explore the mediations of the previously mentioned diseases. Methods This prospective study included 424,767 participants without HF at baseline in UK Biobank. The residential-level noise and air pollution exposure was estimated, and the incident HF was identified through linkages with medical records. Cox proportional hazard models were used to estimate HRs. Furthermore, time-dependent mediation was performed. Results During a median 12.5 years of follow-up, 12,817 incident HF were ascertained. The HRs were 1.08 (95% CI: 1.00-1.16) per 10 dB[A] increase in weighted average 24-hour road traffic noise level (Lden), and 1.15 (95% CI: 1.02-1.31) for exposure to Lden >65dB[A] compared with the reference category (Lden ≤55dB[A]), respectively. Furthermore, the strongest combined effects were found in those with both high exposures to road traffic noise and air pollution including fine particles and nitrogen dioxide. Prior AMI before HF within 2 years’ time interval mediated 12.5% of the association of road traffic noise with HF. Conclusions More attention should be paid and a preventive strategy should be considered to alleviate the disease burden of HF related to road traffic noise exposure, especially in participants who survived AMI and developed HF within 2 years

Tunable quantum dots in monolithic Fabry-Perot microcavities for high-performance single-photon sources

Cavity-enhanced single quantum dots (QDs) are the main approach towards ultra-high-performance solid-state quantum light sources for scalable photonic quantum technologies. Nevertheless, harnessing the Purcell effect requires precise spectral and spatial alignment of the QDs' emission with the cavity mode, which is challenging for most cavities. Here we have successfully integrated miniaturized Fabry-Perot microcavities with a piezoelectric actuator, and demonstrated a bright single photon source derived from a deterministically coupled QD within this microcavity. Leveraging the cavity-membrane structures, we have achieved large spectral-tunability via strain tuning. On resonance, we have obtained a high Purcell factor of approximately 9. The source delivers single photons with simultaneous high extraction efficiency of 0.58, high purity of 0.956(2) and high indistinguishability of 0.922(4). Together with a small footprint, our scheme facilitates the scalable integration of indistinguishable quantum light sources on-chip, and therefore removes a major barrier to the solid-state quantum information platforms based on QDs.Comment: 12 pages, 4 figure

Metasurface-based Spectral Convolutional Neural Network for Matter Meta-imaging

Convolutional neural networks (CNNs) are representative models of artificial neural networks (ANNs), that form the backbone of modern computer vision. However, the considerable power consumption and limited computing speed of electrical computing platforms restrict further development of CNNs. Optical neural networks are considered the next-generation physical implementations of ANNs to break the bottleneck. This study proposes a spectral convolutional neural network (SCNN) with the function of matter meta-imaging, namely identifying the composition of matter and mapping its distribution in space. This SCNN includes an optical convolutional layer (OCL) and a reconfigurable electrical backend. The OCL is implemented by integrating very large-scale, pixel-aligned metasurfaces on a CMOS image sensor, which accepts 3D raw datacubes of natural images, containing two-spatial and one-spectral dimensions, at megapixels directly as input to realize the matter meta-imaging. This unique optoelectronic framework empowers in-sensor optical analog computing at extremely high energy efficiency eliminating the need for coherent light sources and greatly reducing the computing load of the electrical backend. We employed the SCNN framework on several real-world complex tasks. It achieved accuracies of 96.4% and 100% for pathological diagnosis and real-time face anti-spoofing at video rate, respectively. The SCNN framework, with an unprecedented new function of substance identification, provides a feasible optoelectronic and integrated optical CNN implementation for edge devices or cellphones with limited computing capabilities, facilitating diverse applications, such as intelligent robotics, industrial automation, medical diagnosis, and astronomy