ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method.

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

Data-Driven Safe Policy Optimization for Black-Box Dynamical Systems With Temporal Logic Specifications.

Learning-based policy optimization methods have shown great potential for building general-purpose control systems. However, existing methods still struggle to achieve complex task objectives while ensuring policy safety during learning and execution phases for black-box systems. To address these challenges, we develop data-driven safe policy optimization (D 2 SPO), a novel reinforcement learning (RL)-based policy improvement method that jointly learns a control barrier function (CBF) for system safety and a linear temporal logic (LTL) guided RL algorithm for complex task objectives. Unlike many existing works that assume known system dynamics, by carefully constructing the data sets and redesigning the loss functions of D 2 SPO, a provably safe CBF is learned for black-box dynamical systems, which continuously evolves for improved system safety as RL interacts with the environment. To deal with complex task objectives, we take advantage of the capability of LTL in representing the task progress and develop LTL-guided RL policy for efficient completion of various tasks with LTL objectives. Extensive numerical and experimental studies demonstrate that D 2 SPO outperforms most state-of-the-art (SOTA) baselines and can achieve over 95% safety rate and nearly 100% task completion rates. The experiment video is available at https://youtu.be/2RgaH-zcmkY.

D3K: The Dissimilarity-Density-Dynamic Radius K-means Clustering Algorithm for scRNA-Seq Data.

A single-cell sequencing data set has always been a challenge for clustering because of its high dimension and multi-noise points. The traditional K-means algorithm is not suitable for this type of data. Therefore, this study proposes a Dissimilarity-Density-Dynamic Radius-K-means clustering algorithm. The algorithm adds the dynamic radius parameter to the calculation. It flexibly adjusts the active radius according to the data characteristics, which can eliminate the influence of noise points and optimize the clustering results. At the same time, the algorithm calculates the weight through the dissimilarity density of the data set, the average contrast of candidate clusters, and the dissimilarity of candidate clusters. It obtains a set of high-quality initial center points, which solves the randomness of the K-means algorithm in selecting the center points. Finally, compared with similar algorithms, this algorithm shows a better clustering effect on single-cell data. Each clustering index is higher than other single-cell clustering algorithms, which overcomes the shortcomings of the traditional K-means algorithm.

Irisin: A bridge between exercise and neurological diseases.

Exercise plays a beneficial regulating role on each organ of the body through different mechanisms and is a powerful weapon to prevent disease. Irisin is released from muscle and widely distributed in the human body, participating in the physiological processes of multiple human systems and playing a protective role in multiple human organs. The protective effect of irisin on the nervous system is particularly remarkable, which can improve cognitive function, reduce the risk of ischemic stroke and improve its prognosis. Irisin also plays a guiding role in the prevention and treatment of neurodegenerative diseases and ischemic cerebrovascular diseases. Exercise is the driving factor promoting irisin secretion, and different exercise modes, intensity, frequency, and time all affect the level of serum irisin. As a result of analyzing the effects of various exercise modes on irisin secretion, we proposed an exercise program with a higher level of irisin secretion.

An Automatic Instrument Integration Scheme for Interoperable Ocean Observatories.

Due to the heterogeneity, high cost, and harsh environment, ocean observatories demand a flexible, robust, and capable scheme to integrate science instruments. To deal with the challenges of automatic instrument integration and machine-to-machine interaction in ocean observatories, a systematic scheme is proposed based on Zero Configuration Networking (Zeroconf), Programmable Underwater Connector with Knowledge (PUCK), Constrained Application Protocol (CoAP), and Message Queuing Telemetry Transport (MQTT) protocols, as well as a smart interface module to achieve instrument plug-and-play and standard communication among heterogeneous ocean instruments. The scheme specifically considers the resource-constrained ocean observatories and machine-to-machine interoperability, which is of great significance to interoperable ocean observatories. The laboratory tests have verified the feasibility of the proposed scheme.

Fluorescent probe based on N-doped carbon dots for the detection of intracellular pH and glutathione.

Carbon dots (CDs) as fluorescent probes have been widely exploited to detect biomarkers, however, tedious surface modification of CDs is generally required to achieve a relatively good detection ability. Here, we synthesized N-doped carbon dots (N-CDs) from triethylenetetramine (TETA) and m-phenylenediamine (m-PD) using a one-step hydrothermal method. When the pH increases from 3 to 11, the fluorescence intensity of the N-CDs gradually decreases. Furthermore, it displays a linear response to the physiological pH range of 5-8. Au3+ is reduced by amino groups on the surface of N-CDs to generate gold nanoparticles (AuNPs), causing fluorescence quenching of the N-CDs. If glutathione (GSH) is then added, the fluorescence of the N-CDs is recovered. The fluorescence intensity of the N-CDs is linearly correlated with the GSH concentration in the range of 50-400 µM with a limit of detection (LOD) of 7.83 µM. The fluorescence probe was used to distinguish cancer cells from normal cells using pH and to evaluate intracellular GSH. This work expands the application of CDs in multicomponent detection and provides a facile fluorescent probe for the detection of intracellular pH and GSH.

Carbon dots as an "on-off-on" fluorescent probe for detection of Cu(II) ion, ascorbic acid, and acid phosphatase.

Carbon dot (CD)-based fluorescent probes have been widely exploited; however, multi-component detection using CDs without tedious surface modification is always a challenging task. Here, we develop a convenient and simple CD-based "on-off-on" fluorescent probe for detection of copper(II) ion (Cu2+), ascorbic acid (AA), and acid phosphatase (ACP). Cu2+ leads to the fluorescence quenching of CDs. The limit of detection (LOD) for Cu2+ is 2.4 µM. When AA is added into the CDs + Cu2+ solution, Cu2+ is reduced by AA to Cu+, causing the fluorescence recovery of CDs. The fluorescent intensity linearly correlates with the concentration of AA in the range of 100-2800 µM with LOD of 60 µM. Besides, the probe has potential application for detection of AA in real samples such as VC tablets, orange juice, and fresh orange. The probe can also indirectly detect ACP that enzymatically hydrolyzes ascorbic acid-phosphate (AAP) to produce AA. This work expands the application of CDs in the multi-component detection and provides a facile fluorescent probe for detection of AA in real samples. Graphical abstract.

Recent Advances in Cyclodextrin-Based Light-Responsive Supramolecular Systems.

Cyclodextrins (CDs), one of the host molecules in supramolecular chemistry, can host guest molecules to form inclusion complexes via non-covalent and reversible host-guest interactions. CD-based light-responsive supramolecular systems are typically constructed using CDs and guest molecules with light-responsive moieties, including azobenzene, arylazopyrazole, o-nitrobenzyl ester, pyrenylmethyl ester, coumarin, and anthracene. To date, numerous efforts have been reported on the topic of CD-based light-responsive supramolecular systems, but these have not yet been highlighted in a separated review. This review summarizes the efforts reported over the past ten years. The main text of this review is divided into five sections (vesicles, micelles, gels, capturers, and nanovalves) according to the formation of self-assemblies. This feature article aims to afford a comprehensive understanding of the light-responsive moieties used in the construction of CD-based light-responsive supramolecular systems and to provide a helpful guide for the further design of CD-based light-responsive supramolecular systems.

Copper(I)-mediated cascade reactions: an efficient approach to the synthesis of functionalized benzofuro[3,2-d]pyrimidines.

A novel cascade reaction was developed for the synthesis of diverse members of a series of benzofuro[3,2-d]pyrimidine derivatives. The process utilizes readily prepared 3-chlorochromenones and various commercially available amidines and their analogues as starting materials. This tandem reaction is promoted by using a simple copper(I) reagent and involves a chemoselective Michael addition-heterocyclization-intramolecular cyclization sequence.

A base-promoted desalicyloylative dimerization of 3-(1-alkynyl)chromones: an unusual approach to 2-alkynyl xanthones.

A novel base-promoted cascade desalicyloylative dimerization of 3-(1-alkynyl)chromones to produce 2-alkynyl xanthones has been developed. This tandem process involves multiple reactions, such as Michael additions/cyclizations/desalicyloylation without a transition metal catalyst and inert atmosphere.