Construction of a molecularly imprinted fluorescent sensor based on an amphiphilic block copolymer-metal-organic framework for the detection of oxytetracycline in milk.

A metal-organic framework (MOF) is a good carrier for molecular imprinting due to its high surface area and strong adsorption capacity, but its poor dispersibility in aqueous solution is one of the significant drawbacks, which can severely impede its effectiveness. Amphiphilic block copolymers are good hydrophilic materials and have the potential to overcome the shortcomings of MOFs. In order to improve the hydrophilicity of molecularly imprinted fluorescent materials, we have applied a combination of molecularly imprinted technology and amphiphilic block copolymers on MOFs for the first time. Amphiphilic PAVE copolymer is selected as the molecular imprinted functional monomer to improve the hydrophilicity of UiO-66-NH2. The synthesized PAVE-MOF-MIP has adequate water dispersion ability and fluorescence activity. When encountering oxytetracycline, PAVE-MOF-MIP will produce fluorescence quenching, it is used to construct a fluorescence detection platform for oxytetracycline detection. Compared with traditional MIP@MOF, PAVE-MOF-MIP has better water dispersion ability and detection accuracy. Under optimal conditions, the linear range of oxytetracycline detection is 10-100 µmol L-1, and the minimum limit of detection (LOD) is 86 nmol L-1. This paper proposes a novel approach to use amphiphilic block copolymers as molecularly imprinted monomers on MOFs, providing an innovative idea that has not been previously explored.

What's the Situation With Intelligent Mesh Generation: A Survey and Perspectives.

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at https://github.com/xzb030/IMG_Survey.

Noah: Reinforcement-Learning-Based Rate Limiter for Microservices in Large-Scale E-Commerce Services.

Modern large-scale online service providers typically deploy microservices into containers to achieve flexible service management. One critical problem in such container-based microservice architectures is to control the arrival rate of requests in the containers to avoid containers from being overloaded. In this article, we present our experience of rate limit for the containers in Alibaba, one of the largest e-commerce services in the world. Given the highly diverse characteristics of containers in Alibaba, we point out that the existing rate limit mechanisms cannot meet our demand. Thus, we design Noah, a dynamic rate limiter that can automatically adapt to the specific characteristic of each container without human efforts. The key idea of Noah is to use deep reinforcement learning (DRL) that automatically infers the most suitable configuration for each container. To fully embrace the advantages of DRL in our context, Noah addresses two technical challenges. First, Noah uses a lightweight system monitoring mechanism to collect container status. In this way, it minimizes the monitoring overhead while ensuring a timely reaction to system load changes. Second, Noah injects synthetic extreme data when training its models. Thus, its model gains knowledge on unseen special events and hence remains highly available in extreme scenarios. To guarantee model convergence with the injected training data, Noah adopts task-specific curriculum learning to train the model from normal data to extreme data gradually. Noah has been deployed in the production of Alibaba for two years, serving more than 50000 containers and around 300 types of microservice applications. Experimental results show that Noah can well adapt to three common scenarios in the production environment. It effectively achieves better system availability and shorter request response time compared with four state-of-the-art rate limiters.

An SOI-Structured Piezoresistive Differential Pressure Sensor with High Performance.

This paper presents a piezoresistive differential pressure sensor based on a silicon-on-insulator (SOI) structure for low pressure detection from 0 to 30 kPa. In the design phase, the stress distribution on the sensing membrane surface is simulated, and the doping concentration and geometry of the piezoresistor are evaluated. By optimizing the process, the realization of the pressure sensing diaphragm with a controllable thickness is achieved, and good ohmic contact is ensured. To obtain higher sensitivity and high temperature stability, an SOI structure with a 1.5 µm ultra-thin monocrystalline silicon layer is used in device manufacturing. The device diaphragm size is 700 µm × 700 µm × 2.1 µm. The experimental results show that the fabricated piezoresistive pressure sensor has a high sensitivity of 2.255 mV/V/kPa and a sensing resolution of less than 100 Pa at room temperature. The sensor has a temperature coefficient of sensitivity (TCS) of -0.221 %FS/°C and a temperature coefficient of offset (TCO) of -0.209 %FS/°C at operating temperatures ranging from 20 °C to 160 °C. The reported piezoresistive microelectromechanical systems (MEMS) pressure sensors are fabricated on 8-inch wafers using standard CMOS-compatible processes, which provides a volume solution for embedded integrated precision detection applications of air pressure, offering better insights for high-temperature and miniaturized low-pressure sensor research.