Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting.

Method for multi-task learning fusion network traffic classification to address small sample labels.

In the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples.

Molecular basis for the catalytic mechanism of human neutral sphingomyelinases 1 (hSMPD2).

Enzymatic breakdown of sphingomyelin by sphingomyelinase (SMase) is the main source of the membrane lipids, ceramides, which are involved in many cellular physiological processes. However, the full-length structure of human neutral SMase has not been resolved; therefore, its catalytic mechanism remains unknown. Here, we resolve the structure of human full-length neutral SMase, sphingomyelinase 1 (SMPD2), which reveals that C-terminal transmembrane helices contribute to dimeric architecture of hSMPD2 and that D111 - K116 loop domain is essential for substrate hydrolysis.

Geometry-Gradient Magnetocontrollable Lubricant-Infused Microwall Array for Passive/Active Hybrid Bidirectional Droplet Transport.

Manipulation of droplets has increasingly garnered global attention, owing to its multifarious potential applications, including microfluidics and medical diagnostic tests. To control the droplet motion, geometry-gradient-based passive transport has emerged as a well-established strategy, which induces a Laplace pressure difference based on the droplet radius differences in confined state and transport droplets with no consumption of external energy, whereas this transportation method has inevitably shown some critical limitations: unidirectionality, uncontrollability, short moving distance, and low velocity. Herein, a magnetocontrollable lubricant-infused microwall array (MLIMA) is designed as a key solution to this issue.