Low-temperature storage is essential for postharvest litchi preservation; however, its effectiveness largely depends on how rapidly field heat is removed and how chemical preservatives are applied during pre-cooling. In practice, preservative application is commonly conducted by immersion, yet the interaction between cooling kinetics and the mode of application remains poorly understood. To address this gap, 'Guiwei' litchi fruit were subjected to either rapid pre-cooling (spray or immersion) to 5 degrees C or conventional non-pre-cooled treatments (initial temperature 25 degrees C), combined with prochloraz application or no preservative, and subsequently stored at 4-8 degrees C. Two-way ANOVA revealed significant interaction effects between rapid cooling and preservative application, primarily in suppressing enzymatic browning (polyphenol oxidase (PPO) and peroxidase (POD) activities) and microbial proliferation. Results showed that the combined spray pre-cooling and prochloraz treatment most effectively delayed quality deterioration. After 21 days of storage, the decline in pericarp lightness was limited to 4.27%, significantly lower than the 8.89% decline observed in non-pre-cooled fruit. Relative electrical conductivity remained below 35.5%, and fungal colony counts were restricted to 5.05 lg(CFU g(-1)). While spray and immersion treatments exhibited comparable effects during early storage (0-14 days), spray pre-cooling maintained superior redness and firmness during late storage. Overall, rapid pre-cooling was identified as the primary factor in preserving physicochemical quality, whereas prochloraz mainly enhanced fungal suppression. Their combined application provided the best overall preservation performance, with interaction effects suggesting synergistic benefits particularly in mitigating enzymatic browning and microbial decay. This combined approach presents a safer and more efficient alternative to conventional dipping-based practices for postharvest handling.

To enhance the precision of Discrete Element Method (DEM) simulation parameters for the grading of mechanically harvested fresh tea leaves, this study systematically measured the intrinsic physical and basic contact parameters of the Yinghong No. 9 cultivar. Addressing the distinction between primary and secondary contact interfaces during roller screening, the extreme boundary validation method was first employed to determine simplified fixed values for the contact parameters of the secondary component. Based on the measured physical angle of repose of 36.3°, Plackett–Burman screening, steepest ascent, and Box–Behnken tests were conducted sequentially to construct and optimize a second-order regression model relating significant parameters to the angle of repose. The results indicated that the static friction coefficient between tea leaves (0.723), the rolling friction coefficient between tea leaves (0.031), and the static friction coefficient between tea leaves and the PVC roller (0.547) were the key parameters affecting the angle of repose. Verification tests demonstrated that the simulated static angle of repose was 36.9° against the measured 36.3°, yielding a relative error of 1.65%. The simulated dynamic angle of repose in the rotating drum was 39.8° compared to the physical 38.3°, representing a relative error of 3.92%, and the errors in screening efficiency on the grading bench were all less than 5%. These results indicate that the calibrated parameters accurately characterize the material properties of mechanically harvested tea leaves, providing a reliable theoretical foundation for the structural optimization of grading equipment.

Curved-wall origami (CWO) structures exhibit intrinsic elastic behavior arising from coupled crease folding and panel bending and offer a lightweight route for energy absorption. However, conventional CWO designs rely on predefined crease patterns and often suffer premature global instability, limiting stable progressive collapse and energy-absorption efficiency. Inspired by the irregular walls of cuttlebone, this study introduces controlled geometric irregularity and proposes an irregular curved-wall origami (ICWO) that reprograms curvature distributions to regulate instability evolution. An analytical model is first developed to describe the entire compression process of ICWO, linking the load response to curvature features and instability evolution. Quasi-static experiments and finite element analyses show that ICWO suppresses premature collapse and activates local buckling modes, enabling a stable progressive deformation pathway in which energy dissipation is governed by buckling evolution. A buckling-load response surface quantifies curvature-driven transitions among dominant buckling modes. Machine learning is further leveraged to explore the ICWO design space and maximize energy-absorption efficiency, with experiments confirming a maximum increase of 108% in specific energy absorption (SEA) over regular counterparts. Moreover, this work provides a new paradigm for metamaterial design based on curved-wall origami. The geometric regularity-breaking greatly expands the design space of origami, creating new opportunities for origami-inspired metamaterials.

Expansion microscopy (ExM) enables sub-diffraction imaging by physically expanding labeled tissue samples. This increases the tissue volume relative to the instrument point spread function (PSF), thereby improving the effective resolution by reported factors of 4 - 20X. However, this volume increase dilutes the fluorescence signal, reducing both signal-to-noise ratio (SNR) and acquisition speed. This paper proposes and validates a method for mitigating these challenges. We overcame the limitations of ExM by developing a fast photo-stable protocol to enable scalable widefield three-dimensional imaging with ExM. We combined widefield imaging with quantum dots (QDots). Widefield imaging provides a significantly faster acquisition of a single field-of-view (FOV). However, the uncontrolled incoherent illumination induces photobleaching. We mitigated this challenge using QDots, which exhibit a long fluorescence lifetime and improved photostability. First, we developed a protocol for QDot labeling. Next, we utilized widefield imaging to obtain 3D image stacks and applied deconvolution, which is feasible due to reduced scattering in ExM samples. We show that increased transparency, which is a side-effect of ExM, enables widefield deconvolution, dramatically reducing the acquisition time for three-dimensional images compared to laser scanning microscopy. The proposed QDot labeling protocol is compatible with ExM and provides enhanced photostability compared to traditional fluorescent dyes. Widefield imaging significantly improves SNR and acquisition speed compared to conventional confocal microscopy. Combining widefield imaging with QDot labeling and deconvolution has the potential to be applied to ExM for faster imaging of large three-dimensional samples with improved SNR.

This paper presents a valve-controlled pipeline humidification system aimed at achieving precise and uniform humidity regulation in the multi-layer cultivation environments of plant factories. Since grafted seedlings require stable humidity conditions for effective healing, the system was designed to enable fine-grained adjustments across different cultivation layers. A quadratic regression orthogonal rotational combination design was employed to investigate how valve opening angles affect mean relative humidity (MRH), and a regression prediction model was developed accordingly. The model exhibited strong predictive performance, achieving an R2 of 0.9907 and an average relative error of only 0.67%. The optimal valve-opening angles were 60°, 50°, and 50°, respectively, which ensured that the MRH remained above 90% throughout operation. Experimental verification confirmed that the model accurately predicted humidity responses, while the proposed system improved uniformity by reducing the humidity variation from 6.1% to 0.3% and increasing the compliance rate from 58.3% to 100%. To enhance short-term humidity forecasting, three machine learning algorithms—Random Forest, XGBoost, and Transformer—were trained to predict humidity trends within a 6-h window. Among them, the RF model achieved the highest accuracy with an R2 of 0.9543, outperforming the other models in both stability and precision. The main contribution of this study is the identification of the optimal valve-opening combination through a quadratic orthogonal rotation regression combination experiment. Additionally, the RF obtained the optimal machine learning model for predicting humidity within 6 h.

This paper establishes a comprehensive theoretical model for the nonlinear equilibrium path of inflated beams, and for the first time, presents an explicit solution to the complex mechanical response arising from the combined effects of internal pressurization and external loading. Building upon a rigorous derivation of the governing equations, the model captures the highly intricate features of equilibrium paths-such as extremum, snap-through, and bifurcations-which are often convoluted and difficult to identify. The validity of the theoretical model and solution is demonstrated through close agreement with previously published experimental results, further confirming its capability to accurately predict complex nonlinear behaviors. However, the identification of critical instability points remains challenging due to the entangled nature of the equilibrium path. To overcome this challenge, an incremental path-unfolding method is proposed, enabling systematic clarification of stiffness evolution, load transitions, and critical points throughout the deformation process. The results show that variations in stiffness distribution and internal pressure fundamentally alter the instability landscape-centrally reinforced beams promote higher global stability and delay the onset of snap-back, while reduced stiffness at the beam ends intensifies local snap-through and bistable responses. These findings provide a robust theoretical basis for the design optimization of inflated beams, with broad implications for aerospace, lightweight structural systems, and adaptive engineering applications.

Thermal insulation is a crucial performance indicator for cold-chain transportation equipment. Improving the thermal insulation performance can effectively reduce transportation energy consumption and, thus, lower costs. To enhance the thermal insulation performance of refrigerated trucks, this study conducted tests, analyzed the insulation performance using high-reflectivity insulation materials, and analyzed energy consumption. The study obtained data on the heat flux through the box, air cooling rate inside the box, and temperature uniformity. The experiments demonstrated that applying high-reflectivity insulation materials reduced the peak temperature of the external wall surface of the box by 23.6 ℃, leading to less heat transfer into the compartment through the roof. In the absence of refrigeration, the proportion of heat flux reduction was 46.3%, while at the set refrigeration temperature of 5 ℃, the reduction ranged from 16.7% to 26%. The insulation material improved the temperature uniformity of the external wall of the compartment to 1.12 and the internal uniformity to 1.68. This simultaneously allows the refrigeration system to reach the set temperature more quickly and maintain a lower temperature more easily. Compartments with insulation materials reduced the operating frequency of the compressor by 9.1%, leading to energy savings and good energy efficiency. The research results provide new insights into the energy-efficient use of cold chain transportation equipment and are relevant for facilities such as granaries and cold storage facilities with insulation requirements.

To rigorously assess black tea quality in large-scale production, this study introduces a multi-modal fusion approach integrating computer vision (CV) with Near-Infrared Spectroscopy (NIRS). CV technology is first applied to evaluate the tea's appearance quality, while NIRS quantifies key chemical components, including tea polyphenols (TP), free amino acids (FAA), and caffeine (CAF). Additionally, different methods are employed to extract potential quality features from NIR spectra. The information are then fused, and a classifier is utilized to accurately identify tea quality. Results show that the Temporal Convolutional Network (TCN) fused model achieves a 98.2 % accuracy rate, surpassing both the Convolutional Neural Network (CNN) fused model and traditional methods. This study demonstrates that TCNs effectively extract spectral features and that data fusion significantly enhances tea quality testing, offering valuable insights for production optimization.

Lychee quality deteriorates rapidly post-harvest. Appropriately ventilated packaging can help maintain quality during the supply chain. However, interruptions in the cold chain can lead to temperature differences between the inside and outside of packages, increasing condensation and liquid water formation, which negatively affects lychee quality. This study used numerical simulations to analyse forced ventilation in different packaging structures, focusing on how the number of top openings affects internal temperature, humidity, condensation, and water loss. Additionally, the stacking of packaged lychee was modelled to predict water loss and condensation under actual supply conditions. The results showed that increasing the number of package openings reduces humidity differences and condensation, with eight openings resulting in 9.18 % less water loss rate compared to six openings. Six openings reduced condensation by 23.67 % compared to four openings. Furthermore, during storage and transportation, the amount of water loss and condensation varied by location, with lychees near the air outlet losing less water but experiencing more condensation. The findings of this study provide insights into reducing in-package condensation and water loss in the lychee supply chain, offering a reference for optimising storage and transportation strategies.

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

“Foam container + ice pack” is a common packaging form for e-commerce logistics of litchis. However, there are numerous factors affecting the temperature variation under this logistics mode, making it difficult to control the packaging temperature and litchi quality during the e-commerce logistics process. In order to explore the impact of the packaging scheme on the packaging environment temperature and the quality variation in litchis during the “foam container + ice pack” logistics process, this paper takes the number of ice packs, the terminal pre-cooling temperature of litchis, the weight of litchis, and whether to use aluminum foil insulating film as variable factors to study the impact rules of these factors on the EPS (Expanded Polystyrene) foam container environment temperature, the total number of fruit pericarp, and the marketable fruit rate. The experimental results show the following trends: the terminal pre-cooling temperature has a significant impact on the daily average temperature of the fruit layer; the packaging environment temperature of the 15 °C pre-cooling group on the first day and the second day is 5.00 °C and 2.78 °C higher than that of the 5 °C pre-cooling experimental group, respectively. Moreover, under this treatment, the growth rate of fruit pericarp fungi is relatively fast, which could reach 3.87 Lg (CFU/g) on the second day. Increasing the amount of litchis could maintain a lower temperature environment, but it will cause the relative conductivity increasing 4.12% compared with the groups with no weight increasing. Increasing the number of ice packs could significantly reduce the decline rate of fruit soluble solids in the first two days. The research results of this paper are expected to provide a certain reference for the quality assurance logistics and the formulation of long-distance transportation strategies for perishable agricultural products.

Benefiting from advanced features like high stiffness-to-weight ratios, sandwich structures are widely used in aerospace for primary and secondary structures. As tasks grow more complex and structures increase in scale, high-dimensional design spaces inevitably arise. Optimizing large-scale sandwich structures efficiently and intelligently presents certain challenges. Additionally, functional requirements and constraints, such as thermal deformation, should be fully considered in the design of practical structures like solar arrays, which involve a large expensive analysis and make the problem more complicated. This paper proposes to use efficient Bayesian optimization with the active subspace (AC) method to address this type of problem. The active subspace method, combined with the adaptive kriging and global sensitivity analysis (GSA), is employed for dimension reduction and reconstructing the design space. Then the structure is optimized within the reconstructed design space using the efficient Bayesian method. The proposed optimization strategy is applied to a case study of solar array with sandwich panels, demonstrating that the developed framework is feasible and effective for structural optimization of large-scale sandwich structures.

Accurate recognition of agricultural pests is crucial for effective pest management and reducing pesticide usage. In recent research, deep learning models based on residual networks have achieved outstanding performance in pest recognition. However, challenges arise from complex backgrounds and appearance changes throughout the pests’ life stages. To address these issues, we develop a multi-task learning framework utilizing the discriminative attention multi-network (DAM-Net) for the main task of recognizing intricate fine-grained features. Additionally, our framework employs the residual network-50 (ResNet-50) for the subsidiary task that enriches texture details and global contextual information. This approach enhances the main task with comprehensive features, improving robustness and precision in diverse agricultural scenarios. An adaptive weighted loss mechanism dynamically adjusts task loss weights, further boosting overall accuracy. Our framework achieves accuracies of 99.7% on the D0 dataset and 74.1% on the IP102 dataset, demonstrating its efficacy in training high-performance pest-recognition models.

Temperature prediction is important for controlling the environment in the preservation of fresh products. The phase change materials for cold storage make the heat transfer process complex, and the use of physical models for characterization and temperature prediction can be challenging. In order to predict the variation of the thermal environment in a temperature-controlled container with a cold energy storage system, we propose an LSTM model based on historical temperature data in which the trends of temperature variations of the fresh-keeping area, the phase change material (PCM), and the fresh products can be predicted immediately without considering the complex heat transfer process. An experimental platform of a temperature-controlled container with a cold energy storage system is built to obtain the experimental data for the prediction model’s construction and validation. The prediction results based on the LSTM model are compared to the results of a physical model. In order to optimize the input data for better prediction performance, the proportion of input samples from the dataset is set to 80%, 50%, 20%, and 10%. The prediction results from different input groups are compared and analyzed. The results show that the LSTM model is able to accurately predict temperature variations of the fresh-keeping area and products, and the predicted values are in agreement with the actual values. The LSTM-based prediction model has a higher accuracy compared to the physical-based prediction model; the RMSE, MAE, and MAPE are 0.105, 0.103, and 0.010, respectively, and the relative error for the prediction of effective control hours of environmental temperature is 0.92%. It is suggested to use the initial 20% of the historical temperature data as the input to predict the future temperature variation for better prediction performance. The results of this paper offer valuable insights for accurate temperature prediction in the fresh-keeping environment with a cold energy storage system.

South China has a climate characteristic of high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. This paper studied the effect of ventilation conditions on the spatial and temporal distribution of temperature and relative humidity in a Venlo greenhouse. Two ventilation conditions, with and without a fan-pad system, were studied. A GA + BP neural network was applied to predict the temperature and relative humidity in fan-pad ventilation in the greenhouse. The results show that the temperature in the Venlo greenhouse ranged from 15.8 °C to 48.5 °C, and the relative humidity ranged from 24.9% to 100% during the tomato-planting cycle. The percentage of days when the temperature exceeded 35 °C was 67.3%, and the percentage of days when the average relative humidity exceeded 70% was 83.7%. The maximum temperature differences between the three heights under NV (Natural Ventilation) and FPV (Fan-pad Ventilation) conditions were 3.4 °C and 4.5 °C, respectively. The maximum relative humidity differences between the three heights under NV and FPV conditions were 8.4% and 21.7%, respectively. The maximum temperature difference in the longitudinal section under the FPV conditions was 3.2 °C, while the relative humidity was 11.4%. The cooling efficiency of the fan-pad system ranged from 16.6% to 70.2%. The non-uniform coefficients of the temperature under the FPV conditions were higher than those under the NV conditions, while the nonuniform coefficients of the relative humidity were the highest during the day. The R2, MAE, MAPE and RMSE of the temperature-testing model were 0.91, 0.94, 0.11, and 1.33, respectively, while those of relative humidity model were 0.93, 2.83, 0.10, and 3.86, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

To conveniently and precisely evaluate orthodox black tea appearance quality, we here present a novel method based on computer vision integrated image processing and deep learning. Tea images were collected using the custom-built image acquisition device and processed with Adaptive Local Tone Mapping (ALTM), and Unsharp Masking to optimize illumination and sharpness. These images were then used to train six convolutional neural networks (CNNs) to find suitable network structure by transfer learning. It was found that the CNNs constructed with the MBconv modules had better performance in this task. Consequently, a CNN classification model (Improved Inception Network) based on the MBconv modules was constructed and trained, which yielded a test accuracy of 95%, performed better than the other CNNs; the test accuracy of original Inception V3 was 89%. The proposed method obtained 97.22% accuracy for independent set in validation, which demonstrated the viability of applying image processing and deep learning approaches to solve practical problems in the field of tea assessment.

[目的]我国是世界猪肉生产和消费大国,目前市面上大部分鲜肉的销售方式较为粗放,只有少部分采用冷藏.为提高鲜肉销售时的品质、延长货架期,提出一种基于超声波雾化加湿技术的冷藏保鲜方法.[方法]以敞开式冷藏陈列柜销售猪肉为对象,研究超声波雾化加湿技术对猪肉冷藏销售品质的影响,探究猪肉冷藏 24 h内剪切力、色差、蒸煮损失率和pH等品质参数的变化.[结果]单控温(Temperature Control,TC)工况冷藏12 h后,猪肉的剪切力与初始值差异较小;冷藏24 h后,剪切力迅速下降至65.2 N.温湿双控(Temperature and Humidity Control,THC)工况冷藏 12 h后的猪肉剪切力下降较快;冷藏 12～24 h,剪切力维持在 86 N左右.TC和THC工况下,冷藏猪肉的亮度(L*)值、红度(a*)值和黄度(b*)值随时间的变化趋势相似,但TC工况的L*值始终较THC工况高,而a*值和b*值较THC工况低.冷藏 24 h后,两种工况下猪肉微生物的数量分别为 28 000、149 713 CFU/mL,其变化与色差变化有一定关联.两种工况冷藏猪肉蒸煮损失率随时间的变化趋势相似,且冷藏 12 h后两者差异较小,TC和THC工况下蒸煮损失率分别 26.2%、24.6%.冷藏 24 h后,TC和THC工况的猪肉pH值分别下降 0.4、0.2,且THC工况的猪肉仍属于一级肉.在单个制冷周期内,两种工况下柜内温度和相对湿度均呈现先下降再上升的趋势,但TC工况的柜内最低温度较THC工况高1.2℃,两种工况下的相对湿度变化趋势和幅度相近.[结论]THC工况可减缓猪肉的剪切力和pH下降速率,降低猪肉的蒸煮损失率,但其微生物数量是TC工况下的 5.3 倍.研究结果对提高猪肉销售品质和优化冷藏陈列柜参数具有一定的参考意义.