The influence of artificial aging time on the microstructures and mechanical properties of the 6063 aluminum alloy profile extruded by porthole die was investigated through hardness testing, expansion testing, scanning electron microscope (SEM), and transmission electron microscope (TEM). The results showed that the artificial aging time had a significant impact on the size, morphology, distribution of precipitated phases, and mechanical properties of the porthole die extruded 6063 aluminum alloy profiles. As the artificial aging time increased, the second phase particles gradually precipitated, and the precipitation strengthening gradually enhanced, resulting in an increase in the hardness of the profile. The hardness of the welding zone was lower than that of the matrix zone. Compared with the precipitation in the matrix zone, the size and distribution of the precipitates were uneven, and the time for the precipitation was long in the welding zone due to the influence of grain size. The width of the precipitate free zone (PFZ) in the welding zone was greater than that in the matrix zone. The expansion ratio decreased with the increase of aging time, which indicated that the artificial aging treatment was adverse to the plastic deformation ability of the profiles.

The objective of this study was to study the effect of microwave vacuum puffing (MVP) conditions on dielectric properties of berries. A central composite response surface experiment, with four factors and five levels, was carried out to assess the effect of puffing conditions on the dielectric properties. The microwave volumetric heat, dielectric constant and dielectric loss factor were, accordingly, evaluated based on the dynamic dielectric property index model of berries. As a result, the initial moisture content had significant effects on the dielectric constant and dielectric loss factor of the berry crisps. Specifically, in the early stage of puffing, the dielectric constant decreases with increasing initial moisture content. However, in the later stage of puffing, the dielectric constant first decreases and then increases as the initial moisture content rises. Meanwhile, the dielectric loss factor increases consistently with increasing initial moisture content throughout the puffing process. The MVP conditions exert a complex effect on dielectric properties of berries. Based on these findings, a theoretical for the dynamic processing mode and equipment design for MVP of berries can be provided.

In order to analyze the effect of the solenoid valve parameters on electromagnetic force and retention force at different thickness of coil frame and different temperatures conditions. The electromagnetic valve in the refrigeration system is used as an example to simulate the electromagnetic force and retention force based on ANSYS in this study. The electromagnetic force is greater than 11.1 N under typical working condition of 80 ℃. It’s greater than 9.4 N under extreme working condition of 130 ℃. The thickness of the magnet is more than 0.8 mm (weight 8 g) and the electromagnetic force is more than 16 N. Compared with the conventional thickness of 1.5mm magnet (17g), the weight can be reduced by 46%. The results suggest that the electromagnetic force and retention force of solenoid valve can be simulated through finite element analysis. The thickness of 0.8 mm can meet the air gap conditions of 0.8mm and the power less than 10W. References can be provided for the design.

The objective of this study was to study the effect of microwave vacuum puffing (MVP) conditions on dielectric properties of berries. A central composite response surface experiment, with four factors and five levels, was carried out to assess the effect of puffing conditions on the dielectric properties. The microwave volumetric heat, dielectric constant and dielectric loss factor were, accordingly, evaluated based on the dynamic dielectric property index model of berries. As a result, the initial moisture content had significant effects on the dielectric constant and dielectric loss factor of the berry crisps. Specifically, in the early stage of puffing, the dielectric constant decreases with increasing initial moisture content. However, in the later stage of puffing, the dielectric constant first decreases and then increases as the initial moisture content rises. Meanwhile, the dielectric loss factor increases consistently with increasing initial moisture content throughout the puffing process. The MVP conditions exert a complex effect on dielectric properties of berries. Based on these findings, a theoretical for the dynamic processing mode and equipment design for MVP of berries can be provided.

Growing food demand and climate stresses drive smart agriculture implementation, but existing Cyber-Physical Systems (CPS) lack dependable cross-layer integration and real-time flexibility, limiting performance in dynamic environments. This protocol aims to provide a cross-layer cyber-physical modeling and optimization strategy for intelligent greenhouse agriculture. It demonstrates potential applicability for enhancing the reliability and adaptability of agricultural Cyber-Physical Systems. The approach integrates a physical layer with the Soil-Plant-Atmosphere Continuum model and Ensemble Kalman Filter (EnKF) calibration for accurate soil moisture prediction. It includes a network layer employing multi-protocol fusion with Stochastic Petri Net modeling to evaluate communication reliability. A control layer builds on a stochastic hybrid system to coordinate joint decision-making. Reliability is further assessed through a functional-temporal-ecological indicator framework, while optimization combines multi-objective reinforcement learning with safety constraints and Bayesian meta-learning to enable rapid adaptation during crop switching. An edge-intelligent deployment ensures robust control during communication interruptions. Results from greenhouse tomato cultivation in Shouguang, China, show reproducible and stable performance in yield prediction, water use efficiency, and control latency under challenging conditions. This methodology provides a practical and replicable workflow for implementing adaptive and reliable agricultural Cyber-Physical Systems.

Growing food demand and climate stresses drive smart agriculture implementation, but existing Cyber-Physical Systems (CPS) lack dependable cross-layer integration and real-time flexibility, limiting performance in dynamic environments. This protocol aims to provide a cross-layer cyber-physical modeling and optimization strategy for intelligent greenhouse agriculture. It demonstrates potential applicability for enhancing the reliability and adaptability of agricultural Cyber-Physical Systems. The approach integrates a physical layer with the Soil-Plant-Atmosphere Continuum model and Ensemble Kalman Filter (EnKF) calibration for accurate soil moisture prediction. It includes a network layer employing multi-protocol fusion with Stochastic Petri Net modeling to evaluate communication reliability. A control layer builds on a stochastic hybrid system to coordinate joint decision-making. Reliability is further assessed through a functional-temporal-ecological indicator framework, while optimization combines multi-objective reinforcement learning with safety constraints and Bayesian meta-learning to enable rapid adaptation during crop switching. An edge-intelligent deployment ensures robust control during communication interruptions. Results from greenhouse tomato cultivation in Shouguang, China, show reproducible and stable performance in yield prediction, water use efficiency, and control latency under challenging conditions. This methodology provides a practical and replicable workflow for implementing adaptive and reliable agricultural Cyber-Physical Systems.