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Here are summaries of more papers given at the International Conference on Hot Dip Galvanising which met at Oxford in July under the auspices of the Zinc Development Association and which was attended by about 200 experts from Britain, the Continent, and America. The first report on the Conference appeared in our August issue.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

This chapter explores the insider–outsider dynamics within Web3 communities through three complementary lenses: community studies, technology diffusion, and inclusion. It examines how digital communities form and operate, challenging traditional concepts of community boundaries and social cohesion. The chapter looks deeply into explanatory frameworks and ideas of technological innovation and diffusion. Through the inside–outside binary, I surface the tension between techno-solutionist mindsets and the social shaping of technology. The chapter then unpacks the insider–outsider concept as it relates to financial and digital inclusion, exploring how Web3 technologies both promise and potentially hinder equitable access to financial services and digital participation. The chapter concludes by considering more-than-human futures, proposing a shift away from human-centric thinking in shaping our digital futures. Throughout, it emphasises the importance of understanding Web3 as a reflection of broader societal needs and desires, while critically examining its potential to address or exacerbate existing inequalities.

A new analogue approach is presented for the solution to the multi‐celled, multi‐stringer tube subjected to flexural and torsional loads. This approach is based upon the condition of current continuity and an equivalent to Castigliano's theorem which holds for certain types of electrical networks. The associated equations lend themselves to creating an analogue with a high degree of pictorial similarity, so that the network can be constructed without formulating the structural equations. The analogue of a simple two‐celled structure is devised and the results obtained by electrical circuit analysis are checked against the structure.

The purpose of this study is to assess how managerial capability affects artificial intelligence (AI) adoption and employee well-being now in a dynamic context of organizational change. This study investigated the role that managerial capability and organizational support play in facilitating successful AI technology implementation within organizations. The study seeks to provide an integrated perspective on how organizations can help mitigate the effects of AI anxiety and improve the well-being of employees.

A survey questionnaire was administered to collect data from 324 employees and managers working in small- and medium-sized enterprises (SMEs) located in Pakistan. Partial least squares-structural equation modeling (PLS-SEM) was employed using Smart PLS version 4.1.0.3 to analyze the relationships between the study variables.

The findings of the study show that AI anxiety can significantly impact employee well-being. However, the relationship was moderated by organizational support. When organizational support was high, the effects of AI anxiety decline on employee well-being.

This study offers three important implications; it adds to our understanding regarding AI adoption and its effect on employee well-being by addressing how managerial interventions may facilitate the smooth integration of AI technology and examining the moderating effect that organizational support might have over the association between anxiety and employee well-being. Additionally, we have offered a nuanced view of the potential impact of AI adoption on employees and offered practical recommendations for organizations to undertake to address AI anxiety and promote employee well-being during AI implementation.

This paper examines the role of some basic variables that may be critical in children with difficulties in expressive writing. Preliminary data demonstrating the role of a series of variables are presented. In particular, based on these data, a model was derived using structural equations showing how orthography, neuropsychological functions (idea generation and planning), and revision affect the performance of tasks requiring children to describe the content of pictures. These variables appeared to significantly discriminate between children with good and poor expressive writing skills.

The purpose of this paper is to describe the Italian state of art of Health Lean Management (HLM) and to analyze the Italian projects that connect this approach with clinical risk management (CRM).

After introducing Italian healthcare system and its main challenges, relevant Italian experiences have been searched investigating regional health plans (RHPs), managerial reports, books, workshops, conference proceedings and hospital web sites. The degree of experience of each Italian region has been first studied. Further, field of applicability, objectives, tools, practices and results of the projects with first signs of HLM and CRM integration have been analyzed.

Although interest in new managerial approaches is spreading in almost all the territory and new managerial solutions are fostered in many RHPs, HLM projects are implemented patchy in Italy. For what regards HLM projects with CRM connections, the Italian context seems aligned with the international one, apart from few features. First indications for the implementation of HLM projects with CRM connections emerged.

Healthcare systems are facing multiple challenges in a context where public funds decrease but quality of care must be guaranteed. Combining different managerial approaches could solve these issues. In this research, for the first time, a map about Italian HLM adoption has been drawn, and Italian HLM projects with CRM connections have been analyzed. The results constitute one of the first contributions useful to develop guidelines for the implementation of projects pursuing efficiency, quality and safety objectives.

The aim of this article is to produce alternative solution suggestions at a conceptual level, by utilizing technologies in the field of Augmented Reality (AR) and Artificial Intelligence (AI), to address the increasing personnel shortages encountered in the tourism and hospitality industry. The discussion is based on a review of the literature.

This article presents a qualitative study investigating the impact of Artificial Intelligence (AI) and Augmented Reality (AR) technologies on the workforce turnover rate in the tourism and hospitality industry in general.

Although Artificial Intelligence (AI) and Augmented Reality (AR) technologies have both positive and negative aspects for the hospitality and tourism industry and its employees, these technologies can be used to reduce the factors that cause employee turnover. In particular, it leads to improvements in job satisfaction, job commitment and career opportunities of sector employees, reduction of job stress, and selection and retention of the right employees.

This study examines the factors that tourism sector employees encounter in the sector and that cause the workforce turnover rate to increase, and emphasizes the importance of the possible benefits of the use of Artificial Intelligence (AI) and Augmented Reality (AR) technologies in reducing these factors that cause the workforce turnover rate.

The paper is aimed at modelling time varying betas via a state space representation in order to decompose the marginal contribution to risk of downside and upside deviations of asset returns in portfolio optimisation.

The approach enables to take into account the relationship between risk and excess returns in up‐side and down‐side markets and to arrange a flexible asset allocation model which directly incorporates the investor risk tolerance to positive or negative expected market moves. The model volatility through state space models and the Kalman filter, widely used to recursively and optimally estimate time varying betas.

The study shows that the application of an asset allocation model which splits beta in two parts, one related to Bear and the other to Bull markets, and reconciles them with a non negative risk aversion parameter may produce interesting financial results if compared with typical passive portfolios. The proposed model was tested by conducting extensive empirical evaluations on a set securities belonging to eight different markets. The outcomes show that active strategies can be developed and can lead to better performances.

The research affects optimisation models in particular considering the volatility indicators usually estimated not only by researchers but also by practitioners.

In financial literature we find empirical evidence that the constant beta model may be inaccurate and hazardous to use in asset allocation decisions and many statistical techniques have been developed to estimate time dependent betas. Rolling regression procedures allow to capture beta dynamics but require the definition of the estimation period. The paper provides an empirical analysis referred both to European and American market data which let us to allocate assets avoiding the usual limits of standard volatility indicators.

The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.

In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.

The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.

The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.