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This paper aims to assess the suitability of cement combination containing CEM I, fly ash, silica fume and metakaolin for durability design against carbonation-induced corrosion in concrete.

Cube compressive strengths at 28 days and accelerated carbonation depths at 28 days and at various exposure ages were determined at the water/cement ratios of 0.35, 0.50 and 0.65. To assess their suitability for carbonation-induced corrosion, the material costs and embodied carbon dioxide (eCO₂) contents of the concretes at equivalent performance were compared.

Cement combination concretes achieved equal carbonation resistance with CEM I at higher compressive strengths, lower water/cement ratios and higher cement contents. The comparison of the concretes, at equivalent performance, based on the carbonation-induced corrosion exposure classes XC3 and XC4 (Table A.4 of BS 8500-1), shows that ternary and more binary cement concretes have lower costs and eCO₂ contents than those recommended in Table A.6 of BS 8500-1.

This analysis is limited to a working life of 50 years. Further research is needed to verify the suitability of the cement combinations for a working life of 100 years and for the other aspects of durability design covered in BS 8500.

Cement combination concretes have lower eCO₂ content. Hence, when they are cheaper than CEM I concrete at equivalent performance, they would make concrete construction more economic and environmentally compatible.

This research suggests the inclusion of metakaolin and ternary cement combination concretes in BS 8500 for durability design against carbonation-induced corrosion.

This study aims to synthesize existing findings in the gig worker training literature and identify the training rationales adopted by these studies, using a synthesized framework of organizational training rationales. This study seeks to delineate the rationales behind gig worker training and highlight unaddressed training needs within digital platforms, ultimately proposing a research agenda for future studies in this area.

A systematic review methodology is adopted to synthesize and analyze empirical, peer-reviewed studies on gig worker training.

The systematic review reveals that competency and economic rationales are predominantly adopted in gig worker training studies, with the relationship rationale, common in traditional training, notably absent. This study also outlines seven future research directions to highlight identified challenges and unaddressed training needs.

To the best of the author’s knowledge, this study is the first work that systematically reviews existing findings on gig worker training.

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The purpose of this paper is to obtain the response time history curves of vertical and lateral acceleration in the span of the main beam under different loads through the finite element time-history analysis method, so as to realize the Serviceability Analysis of a Cable-Supported Footbridge Subjected to Human-Induced Loads, taking the long-span cable-supported footbridge over Dongtan River as an example.

The finite element method is used for analysis of the footbridge.

It is found that under the condition of low-density pedestrians walking freely, the response of human vertical vibration acceleration and the load conditions of pedestrian overpasses cannot meet the requirements of normal use. Therefore, the vertical acceleration of the footbridge should be designed to reduce vibration. Under these two loading conditions, the lateral acceleration response meets the requirements of normal use.

On the basis of summarizing the research at home and abroad, the analysis of human-induced vibration is mainly considered from two aspects: frequency regulation and dynamic response control. The walking load models mainly include Fourier series model, self-excitation model, impulse model, stochastic model and more; the crowd load models are divided into groups: low-density crowd walking freely, high-density crowd flowing and more. Therefore, it is very important to calculate the structural vibration response in the design of long-span cable-supported footbridges under pedestrian excitation to meet comfort requirements.

Pink ring is a ubiquitous problem arising during the manufacture of multilayer PCBs, being the manifestation of local delamination at the inner‐layer oxide interfaces around drilled holes and subsequent dissolution of the oxide during plating processes. Except in extreme cases, there is no evidence that the occurrence of pink ring identifies any in‐service reliability problem, but it is nevertheless a clear process indicator and is strictly monitored in statistical process control. The UK Printed Circuit Industry has carried out a collaborative research programme aimed at providing an understanding and a quantitative analysis of the pink ring condition. The research has advanced on two fronts: (i) an investigation into the micro‐mechanisms of the delamination and stress relief around drilled holes and subsequent rôles of the desmear and plating chemicals, and (ii) a statistical analysis of boards manufactured in a variety of ways, analysing the quantitative measurements of pink ring in terms of, for example, panel source, drill supplier, drill quality, drilling backing material, drilling chip rate, stack position, and panel entry/exit side.

This paper aims to elucidate the keys transformations of human resources (HR) tasks amid the age of artificial intelligence (AI).

This paper synthesizes recent theoretical and empirical research on the topic of AI and human resource management to establish a typology of AI-based HR tasks.

HR jobs will revolve around three types of tasks in the age of AI: mechanical, thinking and feeling.

AI radically changes HR function and it becomes essential for organizations to clearly define the purpose of using AI, its role and the context of its use in tasks. Strategic value of the HR function will lie in its future reorientation toward feeling tasks. HR managers need to possess the knowledge, skills and abilities to adapt to these tasks and ensure the responsible use of AI.

This paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).

This study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.

This study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.

The research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.