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The purpose of this study is to explore the intersection of mindfulness practices and ethical decision-making within organisational leadership. Drawing from ancient Buddhist principles and contemporary neuroscience, this study aims to illuminate how mindfulness can enhance cognitive and emotional regulation, thereby fostering ethical behaviour and improved decision-making among leaders and employees. By examining the theoretical and practical implications of mindfulness in the context of organisational behaviour, this research seeks to contribute to the development of more compassionate, ethical and effective leadership practices, ultimately promoting a more mindful and sustainable business environment.

This concept paper explores the integration of mindfulness meditation practices with decision-making, particularly its influence on ethical choices, through a comparative study of modern techniques and the ancient teachings of the Tripitaka. Using a methodology that spans literature review in organisational behaviour and leadership, alongside in-depth analysis of the Tripitaka and contributions from scholars like Bhikkhu Bodhi, the paper examines the potential of mindfulness in enhancing ethical decision-making. It incorporates a range of sources, including peer-reviewed journals and seminal books across various disciplines, to underscore the transformative potential of mindfulness in addressing contemporary challenges and guiding leadership practices.

This discussion explores how mindfulness, rooted in ancient Buddhist philosophy and aligned with modern neuroscience, can significantly enhance managerial decision-making by fostering a balance between cognitive and emotional factors. It delves into the transformative potential of mindfulness in refining thought processes, promoting ethical decision-making and mitigating cognitive biases. By bridging traditional wisdom with contemporary scientific insights, the analysis underscores mindfulness as an active, dynamic process crucial for personal growth and effective leadership in complex environments.

One limitation of this research is its reliance on theoretical frameworks and literature reviews, which may not capture the full range of practical challenges in implementing mindfulness practices within organisations. Additionally, the diversity in mindfulness methodologies and the subjective nature of mindfulness experiences may affect the generalisability of the findings. Future research should include empirical studies to validate the proposed benefits of mindfulness in organisational settings and explore the most effective strategies for integrating mindfulness practices into leadership and decision-making processes. This would help in understanding how mindfulness can be tailored to suit different organisational cultures and individual preferences.

The practical implications of applying mindfulness in organisational settings include enhanced decision-making abilities, improved leadership effectiveness and increased employee well-being. Mindfulness training can equip leaders and employees with the skills to manage stress, navigate complex ethical decisions and maintain focus amidst distractions, leading to more thoughtful and responsible business practices. Organisations might see a reduction in conflict, enhanced creativity and better teamwork, contributing to a more harmonious and productive workplace. Implementing mindfulness programmes could also support talent retention and attraction by promoting a workplace culture that values mental health and ethical behaviour.

The social implications of integrating mindfulness into organisational decision-making and leadership, as suggested by the document, include promoting ethical behaviour, enhancing emotional regulation and improving team dynamics. Mindfulness practices can lead to more informed and conscious decision-making, reducing cognitive biases and fostering a culture of ethical awareness within organisations. This shift towards mindful leadership could potentially transform organisational cultures, encouraging greater compassion, ethical responsibility and collective well-being, thereby contributing positively to broader societal values and norms.

The originality and value of this research lie in its novel integration of mindfulness concepts derived from ancient Buddhist teachings with contemporary neuroscience and organisational behaviour studies. By exploring the deep-rooted philosophical underpinnings of mindfulness and their applicability to modern ethical decision-making and leadership practices, this work offers a unique perspective that bridges historical wisdom with current scientific understanding. It provides a comprehensive framework for understanding the transformative potential of mindfulness in organisational settings, highlighting its capacity to foster ethical leadership, enhance decision-making processes and contribute to a more mindful, compassionate and sustainable business environment.

This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.

The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.

The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.

This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

The forest products processing industry is a key component of the forestry economy, and the level of companies’ operating efficiency directly affects its profitability and market competitiveness.

In order to deeply study the operation status of forest product processing industry, this paper takes the panel data of 70 listed forest product processing companies from 2015 to 2022 as the basis, and adopts BBC, CCR and DEA-Malmquist models to measure the operating efficiency of these companies. Meanwhile, the Tobit model is applied to deeply explore the impact of innovation input on operating efficiency.

The results of the paper show that: (1) the overall operating efficiency of listed forest product processing companies performs well, and the improvement of technology level promotes the growth of total factor productivity; (2) innovation input plays a significant positive role in listed forest product processing companies, which positively affects the operating efficiency.

A scientific and reasonable evaluation of the operating efficiency of listed forest product companies is of great practical significance to the development of the forestry industry The study of forest product processing industry is of key significance to the social economy.

This paper explores the improvement of production and operation efficiency of forest products processing enterprises for the purpose of in-depth analysis of the current situation of China's forest products processing enterprises, which is conducive to improving the innovation and operation efficiency of China's forest products processing enterprises, and realizing the high-quality development of China's forest products processing industry.

While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.

To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.

The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.

In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

Through the scientific and reasonable evaluation of the site selection of the emergency material reserve, the optimal site selection scheme is found, which provides reference for the future emergency decision-making research.

In this paper, we have chosen three primary indicators and twelve secondary indicators to construct an assessment framework for the determination of suitable locations for storing emergency material reserves. By mean of the improved entropy weight-order relationship weight determination method, the evaluation model of kullback leibler-technique for order preference by similarity to an ideal solution (KL-TOPSIS) emergency material reserve location based on relative entropy is established. On this basis, 10 regional storage sites in Beijing are selected for evaluation.

The results show that the evaluation model of the location of emergency material reserve not only respects the objective knowledge, but also considers the subjective information of the experts, which makes the ranking result of the location of the emergency material reserve more accurate and reliable.

Firstly, the modification factor is added to the calculation formula of traditional entropy weight method to complete the improvement of entropy weight method. Secondly, the order relation analysis method is used to assign subjective weights to the indicators. The principle of minimum information entropy is introduced to determine the comprehensive weight of the index. Finally, KL distance and TOPSIS method are combined to determine the relative entropy and proximity degree of alternative solutions and positive and negative ideal solutions, and the scientific and effective of the method is proved by case study.

This study aims to determine how the attitudes toward artificial intelligence (AI) of religious tourists affect their AI self-efficacy and their engagement in AI. This study specifically intends to investigate the mediating role of AI self-efficacy in the relationship between attitudes toward AI and the engagement in AI of religious tourists. This study also seeks to identify the role of AI assistant use as a moderator in the relationship between attitudes toward AI and AI self-efficacy.

The data used in this study was gathered from a sample of 282 religious tourists who had just visited Karbala, central Iraq. Purposive sampling, which comprises a focused and systematic approach to data collection, was used after carefully assessing the distinctive characteristics and properties of the research population.

The results showed that attitudes to AI had a noticeable impact on AI self-efficacy, which, in turn, exerted a positive impact on engagement with AI. In addition, the use of AI assistants acted to positively moderate AI self-efficacy in terms of mediating the link between attitudes to AI and AI engagement.

The distinctive focus on religious tourists adds an original perspective to the existing literature, shedding light on how their attitudes towards AI impact not only their self-efficacy but also their engagement in dealing with AI. In addition, this study delves into the moderating role of AI assistant use, introducing a unique factor in understanding the complex interplay between attitudes, self-efficacy, and engagement in the context of religious tourism. The selection of Karbala, central Iraq, as this study site further adds originality, providing insights into a specific religious and cultural context.