Consumer satisfaction can be defined as the user’s response to a service or experience compared to the user’s expectations and perceived practical benefits. After reviewing consumer satisfaction models, it can be argued that there is no single model of consumer satisfaction assessment that is suitable for every service and every region of the world, as the causes and outcomes of satisfaction often vary. The research is original in its methodology: at the beginning, a theoretical research model is presented, then hypotheses are formulated, and correlation, factorial, regression analyses were made, which results confirmed hypotheses. The crop insurance system consists of relations between the state institution regulates insurance activities, farmers, insurers and insurance intermediaries. The aim of this article is to identify the factors that determine consumer satisfaction with crop insurance and to assess their impact. The empirical study found that consumer satisfaction is determined by the factors of recognizable value, functional (process) and technical (result) quality, consumer expectations, and image. The most important factors that determine consumer satisfaction of crop insurance are recognizable value, functional quality, and consumer expectations. Consumer satisfaction can be assessed by the cost paid and the quality received, the quality expected, and the consumers’ evaluation of the services. It was found that the socio-demographic elements of consumers do not have a decisive influence on the factors that determine service satisfaction and consumer satisfaction. It is also established that socio-demographic elements of consumers (farmer experience and insurance experience) have direct statistically significant but weak links with consumer satisfaction.

The purpose of the study was to examine the role of personalization in motivating senior citizens to use AI driven fitness apps. Vroom’s expectancy theory of motivation was applied to examine the motivation of senior citizens. The responses from participants were collected through structured interviews. The participants belonged to South Asian origin belonging to India, Bangladesh, Nepal and Bhutan. The authors adopted a content analysis approach where the gathered interview responses were coded in the context of elements of Vroom’s theory. The findings of the study indicated that a highly personalized approach in the context of motivation, expectancy, instrumentality and valence will motivate senior citizens to use AI based fitness apps. The study contributes to the personalization of AI fitness apps for senior citizens.

Real estate appraisal standards provide guidelines for the preparation of reliable valuations. These standards emphasize the central role of market data collection in market-oriented valuation methodologies such as the Market Comparison Approach (MCA), which is the most commonly used. The objective of this study is to highlight the difficulties in data finding, as well as the gap between the standards and the actual appraisal practices in Italy. Thus, a detailed comparison was made between the real estate data considered necessary by the standards and those ones reasonably detectable by appraisers, showing that some important market information is not reachable due to legal, technical and economic factors. Finally, a case study is presented in which the actual appraisal of a residential property is schematically described to support what is claimed with the research question and thus the degree of uncertainty around an estimate judgment.

In response to the challenges of climate change, this study explores the use of moringa pod powder as reinforcement in the manufacture of compressed earth bricks to promote sustainable building materials. The objective is to evaluate the impact of African locust bean pod powder on the mechanical properties of the bricks. Two types of soils from Togo were characterized according to geotechnical standards. Mixtures containing 8% African locust bean pod powder at various particle sizes (0.08 mm, 2 mm, and between 2 and 5 mm) were formulated and tested for compression and tensile strength. The results show that the addition of African locust bean pod reduces the mechanical strength of the bricks compared to the control sample without pods, with strengths ranging from 0.697 to 0.767 MPa, compared to 0.967 to 1.060 MPa for the control. However, the best performances for the mixtures were obtained with a fineness of less than 2 mm. This decrease in performance is attributed to several factors, including inadequate water content and suboptimal preparation and compaction methods. Optimizing formulation parameters is necessary to maximize the effectiveness of African locust bean pods. This work highlights the valorization of agro-industrial waste, paving the way for a better understanding of bio-based materials and future research for sustainable construction.

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.

Quartz sand was used as bed material in a small fluidized bed reactor with 1 kg/h feed. Corn straw powder with particle size of 20–40 mesh, 40–60 mesh, 60–80 mesh and 80–120 mesh was used as raw material for rapid pyrolysis at reaction temperatures of 400 °C, 450 °C, 500 °C and 550 °C. The bio-oil obtained after liquefaction of pyrolysis gas was analyzed. The variation trend of bio-oil composition in pyrolysis of corn straw powder with different reaction temperatures and raw material sizes was compared. The results show that: (1) the content of 3-hydroxyl-2-phenyl-2-acrylic acid in bio-oil increases with the decrease of raw material particle size, but it is less at 450 °C; (2) with the increase of reaction temperature, the content of hydroxyacetaldehyde in bio-oil increases at first and then decreases: the content of hydroxyacetaldehyde in bio-oil is the highest at 500 °C when the particle size is 20–40 mesh, and the highest at 450 °C with the other three particle sizes. Compared with other particle sizes, raw material with the particle size of 60–80 mesh is not conducive to the formation of aldehyde compounds; (3) the reaction temperature of 500 °C and the particle size of 60–80 mesh of raw materials are more conducive to the formation of phenolic compounds in bio-oil; (4) the ester compounds with particle size of 20–40 mesh in bio-oil is 20% higher than that of other particle sizes; (5) the reaction temperature and the particle size of raw materials had no significant effect on the formation of ketones, alcohols and alkane compounds in bio-oils.