RESUMEN

Post-ripening fruits need to be ripened to reach edible conditions, as they are not yet mature enough when picked. Ripening technology is based mainly on temperature control and gas regulation, with the proportion of ethylene being one of the key gas regulation parameters. A sensor's time domain response characteristic curve was obtained through the ethylene monitoring system. The first experiment showed that the sensor has good response speed (maximum of first derivative: 2.01714; minimum of first derivative: -2.01714), stability (xg: 2.42%; trec: 2.05%; Dres: 3.28%), and repeatability (xg: 20.6; trec: 52.4; Dres: 2.31). The second experiment showed that optimal ripening parameters include color, hardness (Change Ⅰ: 88.53%, Change Ⅱ: 75.28%), adhesiveness (Change Ⅰ: 95.29%, Change Ⅱ: 74.72%), and chewiness (Change Ⅰ: 95.18%, Change Ⅱ: 74.25%), verifying the response characteristics of the sensor. This paper proves that the sensor was able to accurately monitor changes in concentration which reflect changes in fruit ripeness, and that the optimal parameters were the ethylene response parameter (Change Ⅰ: 27.78%, Change Ⅱ: 32.53%) and the first derivative parameter (Change Ⅰ: 202.38%, Change Ⅱ: -293.28%). Developing a gas-sensing technology suitable for fruit ripening is of great significance.

Asunto(s)

Etilenos , Frutas , Frutas/fisiología , Temperatura , Dureza , Proteínas de Plantas

RESUMEN

Due to affordability, and the ability to parametrically control the vital processing parameters, material extrusion is a widely accepted technology in tissue engineering. Material extrusion offers sufficient control over pore size, geometry, and spatial distribution, and can also yield different levels of in-process crystallinity in the resulting matrix. In this study, an empirical model based on four process parameters-extruder temperature, extrusion speed, layer thickness, and build plate temperature-was used to control the level of in-process crystallinity of polylactic acid (PLA) scaffolds. Two sets of scaffolds were fabricated, with low- and high-crystallinity content, and subsequently seeded with human mesenchymal stromal cells (hMSC). The biochemical activity of hMSC cells was tested by examining the DNA content, lactate dehydrogenase (LDH) activity, and alkaline phosphatase (ALP) tests. The results of this 21-day in vitro experiment showed that high level crystallinity scaffolds performed significantly better in terms of cell response. Follow-up tests revealed that the two types of scaffolds were equivalent in terms of hydrophobicity, and module of elasticity. However, detailed examination of their micro- and nanosurface topographic features revealed that the higher crystallinity scaffolds featured pronounced nonuniformity and a larger number of summits per sampling area, which was the main contributor to a significantly better cell response.

RESUMEN

With the enhancement of consumers' food safety awareness, consumers have become more stringent on meat quality. This study constructs an intelligent dynamic prediction model based on knowledge rules and integrates flexible humidity sensors into the non-destructive monitoring of the Internet of Things to provide real-time feedback and dynamic adjustments for the chilled chicken cold chain. The optimized sensing equipment can be attached to the inside of the packaging to deal with various abnormal situations during the cold chain, effectively improving the packaging effect. Through correlation analysis of collected data and knowledge rule extraction of critical factors in the cold chain, the established quality evaluation and prediction model achieved detailed chilled chicken quality level classification and intelligent quality prediction. The obtained results show that the accuracy of the prediction model is higher than 90.5%, and all the regression coefficients are close to 1.00. The relevant personnel (workers and cold chain managers) were invited to participate in the performance analysis and optimization suggestion to improve the applicability of the established prediction model. The optimized model can provide a more efficient theoretical reference for timely decision-making and further e-commerce management.

RESUMEN

Wireless sensors networks (WSNs) are characterized by flexibility and scalability in any environment. These networks are increasingly used in agricultural and industrial environments and have a dual role in data collection from sensors and transmission to a monitoring system, as well as enabling the management of the monitored environment. Environment management depends on trust in the data collected from the surrounding environment, including the time of data creation. This paper proposes a trust model for monitoring humidity and moisture in agricultural and industrial environments. The proposed model uses a digital signature and public key infrastructure (PKI) to establish trust in the data source, i.e., the trust in the sensor. Trust in data generation is essential for real-time environmental monitoring and subsequent analyzes, thus timestamp technology is implemented here to further ensure that gathered data are not created or changed after the assigned time. Model validation is performed using the Castalia network simulator by testing energy consumption at the receiver and sender nodes and the delay incurred by creating or validating a trust token. In addition, validation is also performed using the Ascertia TSA Crusher application for the time consumed to obtain a timestamp from the free TSA. The results show that by applying different digital signs and timestamps, the trust entity of the WSN improved significantly with an increase in power consumption of the sender node by up to 9.3% and receiver node by up to 126.3% for a higher number of nodes, along with a packet delay of up to 15.6% and an average total time consumed up to 1.186 s to obtain the timestamp from the best chosen TSA, which was as expected.

RESUMEN

Nowadays, wireless sensor networks (WSN) are widely used in agriculture monitoring to improve the quality and productivity of farming. In this application, sensors gather different types of data (i.e., humidity, carbon dioxide level, and temperature) in real-time scenarios. Thus, data gathering, transmission, and rapid response to new circumstances require a secured data mechanism to avoid malicious adversaries. Therefore, this paper focuses on data security from the data origin source to the end-user, and proposes a general data security model that is independent of the network topology and structure, and can be widely used in the agriculture monitoring application. The developed model considers practical aspects, the architecture of the sensor node, as well as the necessity to save energy while ensuring data security, and optimize the model through the application of organizational and technical measures. The model evaluation is conducted through simulation in terms of energy consumption. The result shows that the proposed model ensures good data security at the cost of a slight increase in energy consumption at receiver and sender nodes, and energy consumption per bit, up to 2%, 7%, and 1.3%, respectively, due to overhead added for authentication in the network.