This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation.

Background: Image-based dietary assessment provides a more intuitive approach for nutritional monitoring and health management. However, in multi-category bowl-based meals, food boundary adhesion, spatial stacking, and staple-food occlusion by upper-layer dishes still affect the accuracy of volume, weight, and nutritional composition prediction. Methods: This study proposes a nutrition prediction method for bowl-based foods by integrating semantic segmentation, multi-view three-dimensional reconstruction, and occlusion compensation. The improved DBP-FDSNet was used to extract food-category masks from top-view RGB images, while detail enhancement, boundary-assisted supervision, and spatial position encoding were incorporated to improve the segmentation quality of food boundaries and adhesion regions. The visible food surface inside the bowl was reconstructed using a bowl instance model and RGB-TSDF-based multi-view fusion, and the two-dimensional semantic results were mapped into the height-field parameter domain for category-level volume integration. For partially occluded, severely occluded, or completely invisible staple foods, a layered compensation strategy was introduced to reduce staple-food volume prediction errors and the erroneous assignment of upper-layer food volume. Food weight and whole-bowl Calories, Fat, Carbohydrate, and Protein were finally predicted using food density and a nutritional composition database. Results: DBP-FDSNet achieved a meanIntersectionoverUnion ($mIoU$) of 80.51% and a BoundaryF1 Score ($bF1$) of 85.73%. At the whole-bowl level, the MeanAbsolutePercentageError ($MAPE$) values for Calories, Fat, Carbohydrate, Protein, and total food mass were 13.23%, 18.51%, 14.18%, 13.35%, and 10.85%, respectively. Conclusions: The method improves the stability of category-level volume and nutritional composition prediction in complex bowl-based meal scenarios, providing a feasible solution for image-based dietary assessment and intelligent nutrition management.

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions.

This paper proposes a high-precision automated testing system based on a LabVIEW-Python collaborative architecture for evaluating the dynamic response of solenoid valves. The system integrates LabVIEW’s high-precision data acquisition capabilities with Python’s automated feature extraction tools, thereby addressing the limitations of traditional single-platform solutions. During testing, a step voltage signal is applied to the solenoid valve while the drive current and output pressure responses are synchronously acquired at a 5 kHz sampling rate. Using the current signal as a time reference, the analysis algorithm automatically extracts seven dynamic parameters, including the opening time (T ₂ ), closing time (t _E1 ), pressure build-up gradient (G _u ), and pressure release gradient (G _d ). Experimental results demonstrate a 40% improvement in testing efficiency and a 60% improvement in parameter extraction completeness compared to conventional methods. The measured values (T ₂ = 154.4 ms, G _u = 46.4 bar/s, t _E1 = 138.8 ms, G _d = 46.83 bar/s) show high consistency with the acquired waveforms. The testing process strictly adheres to the ISO 12238:2023 standard. This system provides an efficient, reliable, and standardized solution for validating solenoid valve performance and establishes a foundation for integrating machine learning technologies to enable intelligent diagnostics and predictive maintenance.