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Amid the growing global emphasis on sustainable agriculture, organizations and governments face a pressing need to equip farmers with the knowledge and tools necessary for the adoption of sustainable farming practices, aligning with the Sustainable Development Goals (SDGs). However, understanding the complex relationship between training programs and the adoption of sustainable practices among small-scale farmers remains a critical challenge. Taking a human resource approach, this paper attempts to understand the interrelationships between training effectiveness, farmers’ psychological and demographic characteristics in explaining the adoption of sustainable farming practices.

We employed a multi-stage random sampling method and administered a structured questionnaire to collect data from 331 small farmers who were part of a government-led, large-scale intervention aimed at training them in sustainable farming practices.

Our research findings not only emphasize the critical role of HR approach through training but also underscore its importance in the broader mission of aligning with the SDGs. Specifically, we demonstrate that sustained exposure to training, intrinsic motivation to acquire knowledge, and the innovative capacity of farmers collectively enhance the effectiveness of training programs, thereby contributing significantly to the widespread adoption of sustainable farming practices in line with SDGs.

Drawing from self-determination theory, training effectiveness literature, and the call for improved alignment with the SDGs, this study presents a model that explains how psychological characteristics, combined with the quality and quantity of training influence the adoption of sustainable farming practices among small-scale farmers.

This study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.

This study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.

Two regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.

This study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.

India is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

The purpose of this paper is to develop some weight determining methods for hesitant fuzzy multi-criterion decision making (MCDM) in which the preference information on attributes is collected over different periods.

Based on the proposed weight determining methods and dynamic hesitant fuzzy aggregation operators, an approach is developed to solve the hesitant fuzzy multi-stage multi-attribute decision-making problem where all the preference information of attributes over different periods is represented in hesitant fuzzy values.

In order to determine the weights associated with dynamic hesitant fuzzy operators, the authors propose the improved maximum entropy method and the minimum average deviation method.

This paper does not consider the multi-stage multi-criteria group decision-making problem.

An example concerning the evaluation of rangelands is given to illustrate the validation and efficiency of the proposed approach. It should be stated that the proposed approach can also be implemented into other multi-stage MCDM problems.

The concept of hesitant fuzzy variable (HFV) is defined. Some operational laws and properties of the HFVs are given. Moreover, to fuse the multi-stage hesitant fuzzy information, the aggregation operators of hesitant fuzzy sets are extended to that of the HFVs.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

With respect to multi-stage group risk decision-making problems in which all the attribute values take the form of grey number, and the weights of stages and decision makers are unknown, the purpose of this paper is to propose a new decision-making method based on grey target and prospect theory.

First, the sequencing and distance between two grey numbers are introduced. Then, a linear operator with the features of the “rewarding good and punishing bad” is presented based on the grey target given by decision maker, and the prospect value function of each attribute based on the zero reference point is defined. Next, weight models of stages and decision makers are suggested, which are based on restriction of stage fluctuation, the maximum differences of alternatives and the maximum entropy theory. Furthermore, the information of alternatives is aggregated by WA operator, the alternatives are selected by their prospect values.

The comprehensive cumulative prospect values are finally aggregated by WA operator, alternatives are selected or not are judged by the sign of the comprehensive prospect theory, if the prospect value of alternative is negative, the corresponding alternative misses the group decision makers’ grey target, on the contrary, if the prospect value of alternative is positive, the corresponding alternative is dropped into the group decision makers’ grey target, the alternative with positive prospect value whose value is the maximum is selected.

Compared with the traditional decision-making methods using expected utility theory which suppose the decision makers are all completely rational, the proposed method is based on irrational which is more in line with the decision maker’s psychology. And this method considers the decision maker’s psychological expectation values about every attribute, different satisfactory grey target about attributes will directly affect decision-making result.

For the multi-stage and multi-attribute risk group decision-making problem, the attribute weight, decision-maker weight and time weight are unknown. The attribute value is grey information. The purpose of this paper is to discuss a decision-making method.

Analysis techniques and the theory about distance degree are used to determine the decision-maker weight within single stage. Grey relational analysis method is applied to determine the attribute weight. Moreover, the uncertainty of time weight and the proximity between the attribute value and positive/negative value are taken into account. A multi-objective optimization model is established based on maximum entropy to obtain time weights, so the comprehensive value is determined.

An example shows the effectiveness and practicability.

For a decision-making process, the results are different in different periods. This method is computationally very simple, easily comprehensible.

The purpose of this paper is to present a multi-staged methodology for the assessment of technology maturity profiles. In particular, this research is being developed to simplify the maturity evaluation procedure in order to combine a large number of inputs from R&D projects and thus to obtain a broad picture of technology maturity profiles that is not specific to a particular organisation, industry sector or particular process.

A multi-stage method was employed. The first stage of which was a workshop involving a range of eminent academics and senior professionals from institutes or industry in order to outline the maturity scale and its defining characteristics. The second stage was to develop a questionnaire to investigate the maturity of particular technologies in the wider research portfolio. Finally, a case study was conducted to validate the practicability of the method by assessment of industry capability and advancement.

Based on the responses received from the questionnaire, a maturity profile was constructed for each project, displaying percentages of R&D efforts along the adopted maturity scale. The findings demonstrate that the real value of the generic matrix is in tailoring the framework according to the particular context of a firm in order to identify risks that would compromise the exploitation of the emerging technologies under development.

There are some limitations in this study which provide ground for future research. For instance, the proposed methodology could be applied to industrially sponsored R&D projects in addition to the publicly funded projects, which have been targeted in this study. This study uses a case study to demonstrate the applicability of the method, but this could be applicable to other industry domain. Further testing of the method is necessary in order to increase its robustness and to better understand its applicability and feasibility.

It could be considered that the success of this study could be emulated in a wider context of new manufacturing technologies which are being taken up by industry, utilising a comparable but amended scale of technology vs level of take-up and/or funding. It is potentially a useful way for funding bodies to monitor impact of sponsored R&D projects. For industry, it is also a vital link to the academic institutions developing emerging technologies, by guiding both industry sectors and individual customers to the relative maturity of particular technologies.

The design method of high-resolution capacitor arrays was proposed to improve the precision of successive approximation register (SAR) analog-to-digital converters (ADCs) without calibration and optimize the circuit area.

According to calculation of equivalent series capacitors and change of voltage at the comparator input node, two three-stage structures of capacitor arrays and a general design flow of the multi-stage capacitor arrays were presented. Non-ideal factors on the capacitor arrays were analyzed, and the applications of the two structures were explained based on the capacitor mismatch.

A multi-stage capacitor array for 16-bit SAR ADCs was implemented. The simulation result shows that its nonlinear error was less than 0.3LSB with no gain error and the sampling capacitance accounted for 92.42% of the total capacitance. Effects of capacitive parasitic and mismatch on capacitor arrays were confirmed.

The proposed method focused on capacitor arrays design of high-resolution SAR ADCs. It effectively reduced nonlinear errors, improved SNR and optimized the area of SAR ADCs. The design method was suitable for SAR ADCs with different resolutions to improve their precision.