In view of the large energy consumption of the regeneration process in the chemical absorption decarburization process, on the basis of the enrichment classification flow process, the nanoscale ceramic film is used as a new heat exchanger between the enriched liquid and the regeneration gas. The porous ceramic film is capable of coupling thermal-mass transfer to effectively recover part of the water vapor and the heat carried in the regeneration gas, so as to reduce the regenerative energy consumption of the system. The effects of parameters such as regeneration temperature, flow rate, molar fraction of water vapor, and MEA enrichment temperature, flow rate, and MEA concentration of shunt on the hydrothermal recovery effect of ceramic membranes of different pore sizes and lengths were studied by using the heat recovery flux and water recovery rate as the indicators. The results show that the hydrothermal recovery performance of the ceramic membrane increases with the increase of MEA enrichment flow, but decreases significantly with the increase of the enrichment temperature. At the same time, with the increase of regenerative gas velocity and the molar fraction of water vapor in the regenerative gas, the heat recovery flux will increase. The heat recovery performance of the 10 nm ceramic membrane is better than that of the 20 nm ceramic membrane.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The Method of Discretization in Time (MDT) is a hybrid numerical technique intended to alleviate upfront the computational procedure of timedependent partial differential equations of parabolic type upfront. The MDT engenders a sequence of adjoint second order ordinary differential equations, wherein the space coordinate is the independent variable and time becomes an embedded parameter. Essentially, the adjoint second order ordinary differential equations are considered of “quasistationary” nature. In this work, the MDT is used for the analysis of unsteady heat conduction in regular bodies (large wall, long cylinder and sphere) accounting for nearly constant thermophysical properties, uniform initial temperature and surface heat flux. In engineering applications, the surface heat flux is customarily provided by electrical heating, radiative heating and pool fire heating. It is demonstrated that the approximate, semianalytical temperature solutions of the first adjoint “quasistationary” heat conduction equations using the first time jump are easily obtainable for each regular body. For enhanced acccuracy, regression analysis is applied to the deviations of the dimensionless surface temperature as a function of the dimensionless time for each regular body.

Integrated risk value response is designed to reduce threats and increase opportunities, especially in terms of running the spun pile method innovation process in accordance with the ISO 56002:2019 standard. Implementing innovation can reduce risks and increase the competitiveness of the company. The method of making or producing spun piles is the research area examined in this study. Questionnaires were distributed to workers in precast concrete companies and most of them were involved in each spun pile production line in the company in order to identify the risk factors that existed in the production line for the spun pile manufacturing method. 30 respondents were workers from organizations in the positions of Director, Manager and Staff. The risk values and impacts are mapped for each dimension to the activity details and it is found that there are 5 high risks as dominant ones, mainly risks with codes R41, R10, R4, R37, and R36. Based on a survey, the highest risk of 30% was found in the stressing & spinning dimension, which is recommended for the innovation process. Innovation is conducted with 5 innovation processes, mainly identifying opportunities, creating concepts, validating concepts, developing solutions, and deploying solutions. Recommendations for improvements are made with preventive and corrective actions that must be taken from every aspect of the spun pile production method activities. Innovation recommendations are also proposed to monitor production activities in real-time utilizing existing information and communication technology. Handling of spun pile waste material must also be implemented with certain methods and produce products that add value for the company. Ultimately, to increase the company’s competitiveness by increasing assets, it is recommended to increase the company’s intangible assets. The company’s intangible assets encompass IPR ownership in the form of Patents and Copyrights.

In today’s manufacturing sector, high-quality materials that satisfy customers’ needs at a reduced cost are drawing attention in the global market. Also, as new applications are emerging, high-performance biocomposite products that complement them are required. The production of such high-performance materials requires suitable optimization techniques in the formulation/process design, not simply mixing natural fibre/filler, additives, and plastics, and characterization of the resulting biocomposites. However, a comprehensive review of the optimization strategies in biocomposite production intended for infrastructural applications is lacking. This study, therefore, presents a detailed discussion of the various optimization approaches, their strengths, and weaknesses in the formulation/process parameters of biocomposite manufacturing. The report explores the recent progress in optimization techniques in biocomposite material production to provide baseline information to researchers and industrialists in this field. Therefore, this review consolidates prior studies to explore new areas.

In the teaching of professional courses, the introduction of information technology teaching mode, currently the most widely used is blended teaching. This teaching mode highlights the student's learning subject status, and the overall teaching effect is significant. Linux course is a highly practical course, and the introduction of blended teaching mode in specific course teaching is of great significance for promoting curriculum reform and development. This article provides a brief introduction to Linux courses, analyzes the importance of blended teaching methods, and explores strategies for effectively applying online and offline mixed teaching modes in Linux courses.