Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.

Antibiotics and organic residues from Tetracycline (TC) and other pharmaceuticals administered to aquatic living organism have negative impacts on aquatic environment by killing-off non-target living organisms and developing antibiotic-resistant bacteria. In this study, Micro-Nano Bubble (MNB) system was used to remove TC residues. MNB system demonstrated good level of degradation efficiency, as resulted in experiment where in time of 100 min, the TC degraded at rate of 82.66% from its initial concentration of TC when the initial concentration was 1 mg/L. When the initial concentration was increased to 10 mg/L, MNB system degraded TC at 64.35% of their initial, this means MNB system demonstrated good level of efficiency for TC removal and indicated that it is more efficient in TC degradation under the conditions of low initial TC concentration and high availability of dissolved oxygen (DO). In the system as the temperature increased there was a significant decrease in DO saturation which was related to the TC complex structure that contain multiple function groups such as amino groups, hydroxyl and carboxyl which possess high strong affinity with oxygen that leads to their adsorption onto bubble surface. This study provides significant insights into the application of MNB system for the removal of organic residues within aquatic ecosystem and underscores the need for further exploration of MNB technology for environmental remediation.

The galvanic dissolved oxygen sensor finds widespread applications in multiple critical fields due to its high precision and excellent stability. As its core sensing components, the oxygen-permeable membrane, electrode, and electrolyte significantly impact the sensor’s performance. To systematically investigate the comprehensive effects of these core sensing components on the performance of galvanic dissolved oxygen sensors, this study selected six types of oxygen-permeable membranes made from two materials (Perfluoroalkoxy Polymer (PFA) and Fluorinated Ethylene Propylene Copolymer (FEP)) with three thicknesses (0.015 mm, 0.03 mm, and 0.05 mm). Additionally, five concentrations of KCl electrolyte were configured, and four different proportions of lead–tin alloy electrodes were chosen. Single-factor and crossover experiments were conducted using the OxyGuard dissolved oxygen sensor as the experimental platform. The experimental results indicate that under the same membrane thickness conditions, PFA membranes provide a higher output voltage compared to FEP membranes. Moreover, the oxygen permeability of FEP membranes is more significantly affected by temperature. Furthermore, the oxygen permeability of the membrane is inversely proportional to its thickness; the thinner the membrane, the better the oxygen permeability, resulting in a corresponding increase in sensor output voltage. When the membrane thickness is reduced from 0.05 mm to 0.015 mm, the sensor output voltage for PFA and FEP membranes increases by 86% and 74.91%, respectively. However, this study also observed that excessively thin membranes might compromise measurement accuracy. In a saturated, dissolved oxygen environment, the sensor output voltage corresponding to the six oxygen-permeable membranes used in the experiment exhibits a highly linear inverse relationship with temperature (correlation coefficient ≥ 98%). Meanwhile, the lead–tin ratio of the electrode and electrolyte concentration have a relatively minor impact on the sensor output voltage, demonstrating good stability at different temperatures (coefficient of variation ≤ 0.78%). In terms of response time, it is directly proportional to the thickness of the oxygen-permeable membrane, especially for PFA membranes. When the thickness increases from 0.015 mm to 0.05 mm, the response time extends by up to 2033.33%. In contrast, the electrode material and electrolyte concentration have a less significant effect on response time. To further validate the practical value of the experimental results, the best-performing combination of core sensing components from the experiments was selected to construct a new dissolved oxygen sensor. A performance comparison test was conducted between this new sensor and the OxyGuard dissolved oxygen sensor. The results showed that both sensors had the same response time (49 s). However, in an anaerobic environment, the OxyGuard sensor demonstrated slightly higher accuracy by 2.44%. This study not only provides a deep analysis of the combined effects of oxygen-permeable membranes, electrodes, and electrolytes on the performance of galvanic dissolved oxygen sensors but also offers scientific evidence and practical guidance for optimizing sensor design.

This study addresses the challenges of large sample size dependency and sample imbalance in traditional pork color scoring models. We propose a rapid method for constructing an accurate color scoring model using six standard color board images and compare its performance with traditional models based on 525 real pork samples from seven pig herds. The results show that the classification accuracy of the CS_1 models, after intercept calibration with mixed herd images, is comparable to traditional models. Specifically, accuracies for CS_1_L, CS_1_La*, and CS_1_Lab models within a ± 0.50 scale are 91.43%, 95.62%, and 94.10%, respectively. Calibration using individual herd images significantly improves accuracy, with CS_1_L, CS_1_La*, and CS_1_Lab* models achieving accuracies of 93.75%, 95.90%, and 96.10%, respectively. This method offers advantages such as small sample sizes and rapid intercept calibration, providing a new approach for objective pork color assessment.