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The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.

In this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

The proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.

The decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

This paper aims to define the socio-spatial considerations of apartment users in Cairo, during their decision-making process. It provides a set of socio-spatial guidelines for professional architecture designers that are based on regionalist sociological theories and the evaluation of participant responses. These guidelines can also help users choose better plan configurations or make socially conscious adjustments as formal residential interiors in Egypt are not arranged based on social interaction at home or the cultural specificity of the region. On the other hand, users have little clue about choosing better plan configurations for sustainable social relationships. Moreover, the private housing sector has mostly neglected the social boundaries that traditionally shaped home interiors. This is because the designers focus on physical attributes to satisfy market demand and economic aspects.

The research reviewed past literature on the impact of different home arrangements relative to inhabitant and social relationships. Simultaneously, preliminary open-ended sorting surveys were undertaken at the Cityscape 2020 exhibition. Based on the results, a comprehensive online survey was developed to map the socio-spatial preferences of users. Finally, a sample of 150 apartment plans was analyzed by using the justified plan graph (JPG) theory to reveal the most common arrangements in the speculative market, measuring unit depth.

The findings of this study will benefit the following: practitioners, including architects and real estate developers, will be able to learn about end-user preferences and offer better products (residential units). Designers can rely on a reference that visualizes recommended home arrangements in the form of justified graphs. This research will expose the academic theories that currently shape residential plans and those that are overlooked and need to be applied. Academics, on the other hand, will learn about the market, and the extent of the influence of architectural theory.

The value of this paper lies in the gathering of theoretical recommendations on traditional home arrangements and investigating the preferences of both professionals and laypeople when choosing between apartments. The open-ended study in this research will test its efficiency in the Egyptian context and serve as a reference for future social studies. It highlights the importance of cultural adequacy and how to design homes related to local residents’ natural lifestyle, by doing so, people will be able to overview the options available in the market and how to manipulate their own houses to control or encourage different social interactions.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

The sustainability of the construction industry is associated with the productivity, profitability and competitiveness of the firms, which are significantly affected by inefficient site supervision and labour management approaches. This study aims to use a case study with mixed methods to evaluate the site supervisory characteristics in labour management, labour performance assessment and labour productivity measurement towards developing meaningful guidelines in polishing construction supervision attributes.

Well-developed modern apprenticeship elements were applied to 62 construction supervisors who were selected using the snowball sampling method, and their relevant competency characteristics were assessed using a comprehensively developed grading mechanism connected with useful training manuals/tools. Academic reviews, experts’ consultations and other meticulous mixed approaches were applied at different stages of the research plan’s sequential layout.

The mean performance scores of supervisors indicate proficient-level grades in the competency characteristics related to applying efficient labour management procedures and developing-level grades in designing productivity measurement tools, performing assessments on efficiency and productivity and proposing enhancement practices on efficiency and productivity for site operations. The findings point to a modern generalised guideline that establishes the ranges of supervisory attributes within the scope of the study. The validity, reliability, adaptability and generalisability of the findings were assured by using pertinent statistical tests and professional assessments.

Though the study’s conclusions/findings are primarily applicable to the construction environment of a developing country comparable to the Sri Lankan context, they will considerably impact current/future industrial practices in various other countries and emerging industries.

The research has produced a conceptualised modern tool that guides determining the capacity levels of supervisory attributes for carrying out labour management, labour performance assessment and labour productivity measurement aspects in construction. The research has opened a pump that inflows new values of highly workable supervision features for strengthening the site management structures and filling the industry’s knowledge vacuum in the methodical execution of apprenticeships.

In the prior literature, the motivation to adopt wearable fitness technology (WFT) has been linked with either intrinsic or extrinsic. However, how the subcategories of extrinsic motivations (identified, introjected and external) affect the consumers’ WFT adoption decision remains sparse. Furthermore, do regulatory focus (prevention vs promotion) and gender differences the effects of different motivations on WFT adoption is almost unknown in the health-care marketing literature. This study aims to fill the above-mentioned gap and to unfold the WFT adoption beyond the traditional motivation by incorporating the organismic integration theory (part of self-determined theory) and regulatory focus theory.

This study used a questionnaire-based survey. Using the “AMOS” survey, questionnaire responses of 641 respondents were analyzed and validated by using structural equation modeling. All the variables were adopted from the literature.

The results show that intrinsic, identified and external motivations have the greatest impact on consumers’ decisions, while introjected motivation was not significant directly. The moderation effects of regulatory focus are significant in such a way that prevention focus influences the introjected motivation and promotion focus affects the external motivation and WFT adoption decision. Furthermore, the findings on gender moderation suggest that women are more intrinsically motivated, and men are more externally motivated for WFT adoption.

The new insights and contributions of this study provide a better understanding of WFT adoption and help sellers develop more effective marketing strategies.

This study incorporates subcategories of extrinsic motivations to provide a deeper understanding of consumers’ behavior. Furthermore, this study applies a unique framework of organismic integration theory to consumers’ WFT adoption. It is also among very few research that investigate regulatory focus and gender impact on consumers’ WFT adoption.

Examining synergies between Lean, Industry 4.0, and Industry 5.0 principles, the aim is to showcase how Lean's focus on people enhances Industry 5.0 implementations, leading to the development of the Lean 5.0 paradigm. In addition, insights from artisanal industries, like the fashion one, are specifically collected.

First, a literature review was conducted to define a comprehensive framework to understand how Lean fits into the Human-Centric (HC) paradigm of Industry 5.0. Second, a case study was employed to give empirical insights and identify practical initiatives that brands can pursue, involving two best-in-class leather goods brands located in Italy.

A conceptual framework to pave the way for new paradigm Lean 5.0 was defined and validated through a case study. To path the way for a case study in the fashion industry, the Lean HC paradigm is detailed into domains and related categories to group practices. The empirical insights demonstrate that Lean HC actions can be effectively supported by Industry 4.0 technologies in traditional sectors like the fashion industry, shifting towards Industry 5.0.

The proposed framework and related practices can be used by companies to facilitate their transition towards Industry 5.0, leveraging on Lean Manufacturing.

The innovative contribution of the present work mainly refers to the proposed conceptual framework, encompassing Lean, HC and Industry 4.0 and introducing Lean 5.0 paradigm. The case study enriches the empirical contributions in the fashion industry.

Forced labour is one of the most exploitative practices in supply chains, generating serious human right abuses. The authors seek to understand how relationships for reducing forced labour are influenced by institutional logics. The emerging supply chain efforts of social enterprises offer particularly intriguing approaches, as their social mission can spur creative new approaches and reshape widely adopted management practices.

The authors study supplier relationships in the smartphone industry and compare the evolving practices of two cases: the first, a growing novel social enterprise; and the second, a high-profile commercial firm that has adopted a progressive role in combating forced labour.

The underlying institutional logic influenced each firm's willingness to act beyond its direct suppliers and to collaborate in flexible ways that create systematic change. Moreover, while both focal firms had clear, well-documented procedures related to forced labour, the integration, rather than decoupling, of forced labour and general supply chain policies provided a more effective way to reduce the risks of forced labour in social enterprises.

As authors’ comparative case study approach may lack generalizability, future research is needed to broadly test their propositions.

The paper identifies preconditions in terms of institutional logics to successfully reduce the risk of forced labour in supply chains.

This paper discusses how social enterprises can provide a learning laboratory that enables commercial firms to identify options for supplier relationship improvement.

This study aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to improve low-frequency gross domestic product (GDP) forecasting. Their capabilities are assessed through direct forecasting comparisons.

This study compares quarterly GDP forecasts from unrestricted MIDAS (UMIDAS), standalone ANN and ANN-enhanced MIDAS models using five monthly predictors. Rigorous empirical analysis of recent US data is supplemented by Monte Carlo simulations to validate findings.

The empirical results and simulations demonstrate that the hybrid ANN-MIDAS performs best for short-term predictions, whereas UMIDAS is more robust for long-term forecasts. The integration of ANNs into MIDAS provides modeling flexibility and accuracy gains for near-term forecasts.

The model comparisons are limited to five selected monthly indicators. Expanding the variables and alternative data processing techniques may reveal further insights. Longer analysis horizons could identify structural breaks in relationships.

The findings guide researchers and policymakers in leveraging mixed frequencies amidst data complexity. Appropriate modeling choices based on context and forecast horizon can maximize accuracy.

Enhanced GDP forecasting supports improved policy and business decisions, benefiting economic performance and societal welfare. More accurate predictions build stakeholder confidence and trust in statistics underlying critical choices.

This direct forecasting comparison offers unique large-scale simulation evidence on harnessing mixed frequencies with leading statistical and machine learning techniques. The results elucidate their complementarity for short-term versus long-term modeling.