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Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This chapter aims to document the increase in cohabitation in the Southern Cone (Argentina, Chile, and Uruguay) between 2010 and 2021, to analyze the role played in it by certain associated factors such as changes in the educational level of the population, age structure, and income distribution, and to evaluate the importance of people’s preferences (in terms of ideas or values) for cohabitation versus marriage. The results suggest that the models of nuptiality identified in previous studies coexist in the region: the traditional and the modern one, while there is a convergence of the prevalence of cohabitation among social groups within countries. Furthermore, although the prevalence of cohabitation as a springboard to marriage cannot be rejected, there are indications that suggest the presence of perceptions and ideas favorable to cohabitation as an alternative form of family organization, closer to the predictions of the theory of the second demographic transition than to the postponement of the age of entry into marriage and the birth of children.

Construction is one of Australia’s largest employers of young people and the industry is facing a major labor shortage, with young people expected to account for much of the shortfall. Surprisingly however, there been little research into the pathways for young people into construction employment. The aim of this paper is to address this gap in research by exploring whether project-based intermediaries can support the development of disadvantaged young people’s trust in the often-problematic systems which are meant to help transition them into employment in construction.

Employing an in-depth case study approach, this research mobilizes theories of personalized and generalized trust to report the results of interviews with 15 sectoral leaders; focus groups with 12 young people working in construction; and interviews with 11 young people being transitioned into construction employment through a unique project-based intermediary developed by a major Australian construction company as part of its social procurement requirements.

Findings show that project-based intermediaries can play an important trust-building role in transitioning disadvantaged young people into work in construction. They do this by bridging a young person’s strong social ties (family and friendship) and weak social ties (with government and construction industry organizations), both of which can be problematic when used in isolation to seek employment in construction. By performing a crucial bridging role between a young person’s individual self-interest in acting alone to find work and their collective interest in being part of a collaborative group, the project-based intermediary creates a new form of linking social capital, enabling social procurement policies which target young people to work while also addressing wider systemic problems in Australia’s employment systems.

This research addresses the lack of employment research into young people in construction and the paucity of theory in social procurement research more broadly. It takes an original approach in aligning theories related to a duality of personalized trust and generalized trust seen against the duality of individual intentionality and agency (self-focused) and shared intentionality and agency (group-focused). By doing so it provides new conceptual and practical insights into the important role that construction project-based intermediaries like the one studied here can play in providing innovative cross-sector and collaborative solutions to the world’s growing youth unemployment crisis.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

This chapter focuses on the heterogeneous pathways (including marital and cohabiting union and parenting histories) through which people navigate their family life courses from adolescence through mid-life, and their implications for union dissolution in middle and later life. The analyses draw on data (retrospective, cross-sectional) from the 2011 and 2017 Canadian General Social Surveys. The study sample includes individuals aged 50 and over (n = 14,547) who were in a union at age 50. Sequence analyses are used to identify the most common family life course trajectories among these individuals from adolescence through midlife (ages 15–50). Logistic regression analyses then address the implications of these trajectories for union dissolution in middle and later life (ages 50+). The results reveal four main family trajectories that characterize the earlier years of the adult life course: married with children, cohabiting with children, single or cohabiting without children, and married without children. These family trajectories, together with their level of complexity, play an important role in relation to both marital and cohabiting union dissolution outcomes in later life.

The objective of this research is to explore integration and transition activities in large industrial projects. The purpose is to (a) obtain a better understanding of the integration and transition activities between the project front-end (FE) and project initiation phases (PIPs), (b) explore what, how and when these integrations and transitions occur, and (c) explore what the integration and transition activities mean to project practitioners.

A qualitative research design methodology is followed, based on interviews using open-ended questions. An expert panel is used to provide responses to questions pertaining to the integration and transition between the project FE and PIP. The research is focused on managing large projects in the South African electrical engineering industrial projects industry. A literature review combined with empirical analysis reflects the importance of integrating and transitioning in project business.

The findings provide guidance to researchers and practitioners on integration and transition mechanisms, how and when these occur. It highlights the benefits of integration and transition activities. Important lessons for researchers and practitioners are provided together with areas for future research.

This is an interpretative analysis of expert opinion. Expert panel members are experienced at senior decision-making level, and their expertise was accessed based on experience, education and knowledge. This extensive experience is shared in this paper providing insights into their opinions, experiences, success and failures. These inputs together with the literature review provide interesting implications for both a theoretical foundation as well as practical implications for practitioners.

The intensity of carbon emissions has led to the serious problem of global warming, and the consequences in terms of climatic disasters are gaining increasing attention worldwide. As the energy sector is responsible for most global emissions, developing clean energy is crucial to combat climate change. This study aims to examine the relationship between corporate governance and renewable energy (RE) consumption and explore the interaction between RE production and RE use.

The study adopts an econometric framework of a panel model, followed by the robustness check using alternative methods, including logit regressions. The bivariate probit model is used to analyze the interaction between the decision to use and the decision to produce RE. The analysis is based on a sample of 3,896 firms covering 45 countries worldwide.

The results reveal that appropriate governance mechanisms positively impact RE consumption. These include the existence of a sustainability committee; environmental, social and governance-based compensation policy; financial performance-based compensation; sustainability external audit; transparency; board gender diversity; and board independence. Firms with appropriate governance mechanisms are more likely to produce and use RE than others. Finally, while RE use positively impacts firm value and environmental performance, the authors find no significant effect on current profitability.

This study goes beyond previous research by exploring the impact of multiple governance mechanisms. To the best of the authors’ knowledge, this is also the first study examining the relationship between RE use and firm value. Overall, the findings suggest that RE transition requires, first of all, establishing appropriate governance mechanisms within companies.

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

This paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.

Research process involves creation of transitory and permanent volatilities via optimal component generalized autoregressive heteroscedasticity (CGARCH) model, while these volatilities are subsequently embedded in Markov switching model.

This study’s results indicate that bidirectional volatility transmission exists between the markets in the selected countries, whereas the effect from exchange rate to stocks is stronger than the other way around in both short-term and long-term. In particular, the authors find that long-term spillover effect from exchange rate to stocks is stronger than the short-term counterpart in all countries, which could suggest that flow-oriented model better explains the nexus between the markets than portfolio-balance approach. On the other hand, short-term volatility transfer from stock to exchange rate is stronger than its long-term equivalent.

This suggests that portfolio-balance theory also has a role in explaining the transmission effect from stock to exchange rate market, but a decisive fact is from which direction spillover effect is observed.

This paper is the first one that analyses the volatility nexus between stocks and exchange rate in short and long term in the four East European and two Eurasian countries.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.