Applied Sciences

Research

18 pages, 19752 KiB

Open AccessArticle

Design and Application of New Aeration Device Based on Recirculating Aquaculture System

by Chengbiao Tong, Kang He and Haoyu Hu

Appl. Sci. 2024, 14(8), 3401; https://doi.org/10.3390/app14083401 - 17 Apr 2024

Cited by 1 | Viewed by 1501

This study optimized the design of an aeration device for pond engineered recirculating aquaculture systems (RASs) whose application is aimed at increasing dissolved oxygen (DO) levels in RAS aquaculture practice. DO is a key factor in aquaculture productivity, and oxygenators are the power [...] Read more.

This study optimized the design of an aeration device for pond engineered recirculating aquaculture systems (RASs) whose application is aimed at increasing dissolved oxygen (DO) levels in RAS aquaculture practice. DO is a key factor in aquaculture productivity, and oxygenators are the power devices used for regulating its levels in aquaculture ponds. In this study, grass carp (Ctenopharyngodon idellus) aquaculture trials were conducted in a self-built RAS by using the new aeration device (NAD); the microporous and impeller aeration components were individually tested in terms of performance, and then combined for the orthogonal testing of their operating parameters in order to assess the NAD’s oxygenation capacity. The test results show that the device effectively increased the dissolved oxygen levels in the RAS tank, enhanced the upper–lower water layer exchange and directional flow, and met the design and parameter selection requirements. Compared with the existing RAS oxygenation equipment, the NAD operated with the optimal parameters and increased the oxygen transfer rate in the pond water tank by 122%. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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16 pages, 5457 KiB

Open AccessArticle

Temperature Prediction of a Temperature-Controlled Container with Cold Energy Storage System Based on Long Short-Term Memory Neural Network

by Jiaming Guo, Dongfeng Liu, Shitao Lin, Jicheng Lin and Wenbin Zhen

Appl. Sci. 2024, 14(2), 854; https://doi.org/10.3390/app14020854 - 19 Jan 2024

Cited by 2 | Viewed by 1271

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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13 pages, 4540 KiB

Open AccessArticle

A Simulation Method for Layered Filling of Grain Piles Based on the Discrete Element Method

by Kaimin Yang, Xinming Du, Yudong Mao, Xin Li, Jiying Liu and Yuancheng Wang

Appl. Sci. 2023, 13(20), 11347; https://doi.org/10.3390/app132011347 - 16 Oct 2023

Cited by 1 | Viewed by 1294

Abstract

The Discrete Element Method (DEM) has been widely employed to investigate the behavior of particle systems at a macroscopic scale. However, effectively simulating the gradual filling of bulk cereal grains within silos using the DEM remains a formidable challenge due to time constraints. [...] Read more.

The Discrete Element Method (DEM) has been widely employed to investigate the behavior of particle systems at a macroscopic scale. However, effectively simulating the gradual filling of bulk cereal grains within silos using the DEM remains a formidable challenge due to time constraints. Thus, there is a critical need to develop a simplified model capable of substantially reducing the computational time required for simulating cereal grain accumulation. This study introduces a Layered Filling Method (LFM) designed to expedite the computational process for cereal grain piles within silos. By utilizing particle kinetic energy as a specific criterion, this model identifies particles as stable situations when their kinetic energy drops below a designated threshold. Throughout the filling process, lower particles that were judged to satisfy the condition of stability are isolated, forming sub-heaps that are exempt from persistent detection. The whole particle heap is subsequently segregated into multiple sub-piles and a main pile till the process’s culmination, and these divisions are merged back together. In order to validate the model’s feasibility and accuracy, a comparative analysis was performed on the characteristics of the porosity and airflow patterns of grain piles generated using the LFM and the progressive filling method (PFM), respectively. The research results indicate that there is a marginally higher porosity value in the grain pile simulated by the LFM in comparison to the PFM. However, the average relative error remains below 5.00%. Both the LFM and PFM exhibit a similar spiral upward trend in the simulated airflow paths. Notably, the LFM demonstrates a substantial reduction in the time required to construct grain piles. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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22 pages, 14927 KiB

Open AccessArticle

A Non-Destructive Measurement of Trunk Moisture Content in Living Trees Based on Multi-Sensory Data Fusion

by Yin Wu, Zenan Yang and Yanyi Liu

Appl. Sci. 2023, 13(12), 6990; https://doi.org/10.3390/app13126990 - 9 Jun 2023

Cited by 1 | Viewed by 1595

Abstract

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to [...] Read more.

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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14 pages, 5965 KiB

Open AccessArticle

A Screening Model of Antibacterial Agents Based on Escherichia coli Cell-Division Protein

by Qiuyu Fan, Jianwen Wu, Bolin Xi, Chunxiao Li, Xiumin Wang and Huanrong Li

Appl. Sci. 2023, 13(7), 4493; https://doi.org/10.3390/app13074493 - 1 Apr 2023

Viewed by 1987

Abstract

Pathogenic Escherichia coli cannot be killed by most antibiotics (including colistin, a last-resort drug) due to the rapid development of antibiotic resistance. A highly conserved prokaryotic mitotic protein, filamenting temperature-sensitive protein Z (FtsZ) with GTPase activity, plays a key role in cell division [...] Read more.

Pathogenic Escherichia coli cannot be killed by most antibiotics (including colistin, a last-resort drug) due to the rapid development of antibiotic resistance. A highly conserved prokaryotic mitotic protein, filamenting temperature-sensitive protein Z (FtsZ) with GTPase activity, plays a key role in cell division and has become a promising target for screening novel antibacterial agents. In this study, the amplified ftsZ gene was inserted into cloning/expression vectors and recombinantly produced in E. coli; the recombinant FtsZ protein was purified by the Ni²⁺-NTA affinity column and then was used to screen for natural antibacterial agents. The results showed that the ftsZ gene with a size of 1170 bp was successfully amplified from E. coli and inserted into the pET-28a expression vector. After induction with 0.2 mM isopropyl β-D-1-thiogalactopyranoside (IPTG), FtsZ was expressed in E. coli BL21 as inclusion bodies. After purification, the recombinant FtsZ protein showed GTPase activity. The highest GTPase activity (0.998 nmol/mL/min) of FtsZ was observed at a GTP concentration of 1.25 mM. Several alkaloids were screened by a constructed model of FtsZ inhibitors. Sanguinarine chloride exhibited higher antibacterial activity against E. coli and Salmonella enteritidis (with minimum inhibitory concentrations (MICs) of 0.04–0.16 mg/mL and minimum bactericidal concentrations (MBCs) of 0.16–0.32 mg/mL) than tetrandrine (0.16–0.32 mg/mL) and berberine hydrochloride (0.32–0.64 mg/mL). Berberine hydrochloride prevented FtsZ polymerization in a concentration-dependent manner and bound to FtsZ protein by hydrogen bonding interaction. This study suggested that the FtsZ-based E. coli screening model could be exploited for the development of novel antibacterial agents for clinical applications. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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21 pages, 5197 KiB

Open AccessArticle

Study on the Influence of Consumers’ Purchase Intention of Selenium-Rich Agricultural Products

by Ling Zhang, Risheng Gao, Haitao Zhang, Xin Luo and Zhenjiang Song

Appl. Sci. 2023, 13(3), 1859; https://doi.org/10.3390/app13031859 - 31 Jan 2023

Cited by 1 | Viewed by 1868

Abstract

As people’s awareness of the special functions of selenium continues to deepen, the development of selenium-rich food continues to develop, and selenium-rich places have been vigorously developing this resource-based industry. The development of selenium-rich agriculture is of great significance to improve people’s quality [...] Read more.

As people’s awareness of the special functions of selenium continues to deepen, the development of selenium-rich food continues to develop, and selenium-rich places have been vigorously developing this resource-based industry. The development of selenium-rich agriculture is of great significance to improve people’s quality of life and promote agricultural, rural and regional economic development. This paper analyzes the factors affecting consumers’ willingness to purchase selenium-rich agricultural products in detail through questionnaire survey data of some consumers in Jiangxi, Ensh, Hubei and Ankang, Shaanxi, using three analytical methods of descriptive statistical analysis, factor analysis and logistic regression analysis with SPSS software. The research results show that consumers’ attitude toward selenium-rich agricultural products, price concerns, consumers’ family characteristics, health and environmental protection motives, gender and other factors have certain influence on consumers’ willingness to purchase selenium-rich agricultural products, among which the attitude factor and family characteristics have the greatest influence. Finally, the market prospect of selenium-rich agricultural products in China is analyzed and prospected, and the measures and suggestions for developing selenium-rich food are proposed in response to the problems of developing selenium-rich food in China. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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17 pages, 1062 KiB

Open AccessArticle

Improved Evaluation of Cultivation Performance for Maize Based on Group Decision Method of Data Envelopment Analysis Model

by Wei Huang, Han Li, Kaifeng Chen, Xiaohua Teng, Yumeng Cui, Helong Yu, Chunguang Bi, Meng Huang and You Tang

Appl. Sci. 2023, 13(1), 521; https://doi.org/10.3390/app13010521 - 30 Dec 2022

Cited by 1 | Viewed by 1717

Abstract

Maize cultivation performance, including the efficiency of the input and output of maize, which reflect the allocation and utilization of resources in the process of maize cultivation, is crucial for evaluating and improving maize cultivation. This paper adopts the method of quadratic regression [...] Read more.

Maize cultivation performance, including the efficiency of the input and output of maize, which reflect the allocation and utilization of resources in the process of maize cultivation, is crucial for evaluating and improving maize cultivation. This paper adopts the method of quadratic regression orthogonal rotation combination experimental design to explore the effects of four main cultivation measures (planting density, nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer) on maize yield at five levels (−2, −1, 0, 1; 2). The CCR (A. Charnes, W. Cooper and E. Rhodes) model, which is the basic model of data envelopment analysis (DEA), was used to evaluate the 36 groups of cultivation measures. The results show that 9 groups are CCR-effective cultivation measures, but the performance of these cultivation measures cannot be further evaluated. To improve the evaluation of cultivation performance, a novel method termed as the group decision method of DEA (GDM-DEA) is proposed to detect the improvement of evaluation performance and is tested using the measurements of maize cultivation. The results suggest that the GDM-DEA method can classify and sort the performance of all the cultivation measures, which is more sensitive and accurate than the CCR method. For the effective cultivation measures that meet the requirements of GDM-DEA, the optimal cultivation measures could be determined according to the ranking of yield. This method determined the most effective cultivation measure. Further independent validation showed that the final optimal cultivation measures fall in the range of the expected cultivation measures. The GDM-DEA model is capable of more effectively evaluating cultivation performance. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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16 pages, 6181 KiB

Open AccessArticle

Detection of Camellia oleifera Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation

by Delin Wu, Shan Jiang, Enlong Zhao, Yilin Liu, Hongchun Zhu, Weiwei Wang and Rongyan Wang

Appl. Sci. 2022, 12(22), 11318; https://doi.org/10.3390/app122211318 - 8 Nov 2022

Cited by 101 | Viewed by 9151

Abstract

Rapid and accurate detection of Camellia oleifera fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A Camellia oleifera fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to [...] Read more.

Rapid and accurate detection of Camellia oleifera fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A Camellia oleifera fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect Camellia oleifera fruit in complex field scenes. Firstly, the images of Camellia oleifera fruit were collected in the field to establish training and test sets. Detection performance was then compared among YOLOv7, YOLOv5s, YOLOv3-spp and Faster R-CNN networks. The YOLOv7 network with the best performance was selected. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96.03%, 94.76%, 95.54%, 95.15% and 0.025 s per image, respectively. Therefore, YOLOv7 combined with data augmentation can be used to detect Camellia oleifera fruit in complex scenes. This study provides a theoretical reference for the detection and harvesting of crops under complex conditions. Full article

(This article belongs to the Special Issue State-of-the-Art Agricultural Science and Technology in China)

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