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Multiplicities of Time in Management and Organizational Research
Get PDFThis paper explores the multiplicity of time in organizational life through the lens of life-history interviews with business elites, conducted as part of a longitudinal Bourdieusian study of power dynamics within French and British business systems. Drawing on Braudel’s concept of the dialectic between the longue durée and l’histoire événementielle, we examine how the professional trajectories of three senior executives - a global asset management CEO, a multinational media CEO, and an energy sector managing director - reveal the interplay between sustained patience and transformative critical incidents. The findings highlight how temporal multiplicity shapes managerial agency and decision-making, demonstrating that organizational lives unfold in rhythms punctuated by pivotal moments requiring reflexive action. In emphasizing the value of patience amid turbulent conditions, we contribute to temporal theorizing in organizational studies by illuminating how historical reflexivity and temporal plurality inform leadership practices and organizational trajectories. This reflection enriches understanding of temporal dynamics that shape contemporary managerial realities
The Quality of Government and Educational Performance Across Countries
Get PDFUsing a new indicator of government quality, two different indicators of educational performance and two different datasets covering up to 120 countries, this study finds robust evidence that a higher quality of government improves educational performance. This is probably because a competent bureaucracy, a good legal system, and an able government that is responsive to its people all combine to support and impel education providers to achieve high standards. By contrast, poor governance, as exemplified by widespread corruption, military involvement in politics and a weak, incompetent and unpopular government, hampers the working of the educational system, thus reducing learning outcomes.</p
Novel adaptive sliding-mode control of digital hydraulic systems with nonlinear flow prediction and friction identification
Get PDFDigital hydraulics has emerged as a novel technology widely utilized in engineering equipment, heavy-duty manipulators, and new energy vehicles. However, the high-frequency discrete fluid generated by high-speed on/off valves (HSVs) exacerbates the nonlinear characteristics of digital hydraulic systems (DHSs), thereby limiting control accuracy during fluid transmission. To address this issue, a model-based adaptive sliding-mode control method (ASMC) is proposed, which incorporates two soft measurement methods that integrate friction identification for the DHS with nonlinear flow prediction for the HSV to accurately describe the kinetic model. Subsequently, the coupling parameters in the Stribeck friction model are precisely identified using the particle swarm optimization-least squares algorithm, replacing previous empirical values. Additionally, a high-precision output flow prediction model for the HSV is constructed utilizing a back propagation neural network to address the drawbacks associated with mechanical inertia in the flowmeter. A second-order integral sliding-mode surface is designed to eliminate steady-state error. By incorporating a boundary layer saturation function, the error jitter can be effectively suppressed, allowing the DHS to converge rapidly to a quasi-sliding mode. Furthermore, the stability of the controlled system is validated by the Lyapunov theory. Results indicate that ASMC significantly enhances the dynamic-static performance of the DHS compared to the traditional integral sliding-mode control method, which overlooks the nonlinear behaviors of output flow and friction force. The response characteristic’s setting time is dramatically reduced from 0.86 s to 0.36 s, while the maximum average steady-state error under various loads greatly decreases from 112.4 μm to 23.4 μm. Therefore, the proposed ASMC with the two soft measurement methods presents an innovative solution for the high-precision motion control of the DHS and holds significant engineering application value
ITSEF:Inception-based two-stage ensemble framework for P300 detection
Get PDFTo address the problems of low signal-to-noise ratio, significant individual differences between subjects, and class imbalance in P300-based brain-computer interface (BCI), this paper proposes a novel Inception-based two-stage ensemble framework (ITSEF) to improve detection accuracy. Firstly, an Inception-based convolutional neural network (ICNN) is designed to extract multi-scale features and conduct cross-channel learning. In addition, a two-stage ensemble framework (TSEF) combined with a pre-training and fine-tuning strategy is developed, aiming to enhance the classification performance of the minority class and improve the generalization ability of the model. The framework comprises a conventional learning branch and a re-balancing branch, each based on an ICNN pre-trained with a different loss function. The prediction results of both branches are dynamically weighted by a cumulative learning strategy, so that the model gradually shifts its learning focus from the majority class to the minority class, comprehensively improving the identification ability for both classes. Experimental results on two datasets, Dataset II of BCI Competition III and BCIAUT-P300, demonstrate that the proposed ITSEF achieves state-of-the-art performance in the P300 classification task, with average classification accuracies of 86.16 % and 92.13 %, respectively. Compared with the existing state-of-the-art methods, the ITSEF achieves improvements of 4.61 % and 1.01 % on the two datasets, respectively. Furthermore, it exhibits significant improvements compared to baseline models and widely used class re-balancing strategies. The proposed ITSEF method provides an innovative deep learning framework for P300 signal analysis and has application potential in the field of P300-BCI.</p
Coherent forecasts for tourism demand with automated immutability constraints
Get PDFThis study tackles key challenges in tourism demand forecasting within a hierarchical time series framework. To ensure coherence across aggregation levels and improve forecasting performance, we incorporate immutability constraints that preserve forecasts for strategically important nodes. Two automated selection methods are proposed to identify such nodes: (i) a clustering-based approach that ensures dispersion across levels, and (ii) a penalized optimization approach that selects immutable nodes based on data-driven criteria. Through Monte Carlo simulations, and two empirical applications, we demonstrate that the proposed methods improve forecast accuracy, robustness and flexibility while preserving interpretability. The framework is model-agnostic with respect to base forecasts and provides tourism managers with a scalable, data-driven tool to focus on critical segments, improve resource allocation, and support strategic planning in tourism management
Involving community members in designing behavioural weight management programmes:A scoping review
Get PDFBackgroundInvolving community members when developing health programmes can improve intervention outcomes. We undertook a scoping review to describe how community members contributed to the development of Behavioural Weight Management Programmes (BWMPs). Different terms have been used to describe this process, including co-design, co-production, Community-Based Participatory Research, or Patient and Public Involvement and Engagement. Our aim was to describe: (1) at what stage(s) communities were involved (e.g. planning, delivering and/or evaluating); (2) what level of involvement they had (e.g. leading, collaborating, consulted, informed or not involved); and (3) examples of how they were involved.MethodsWe searched MEDLINE, EMBASE and CINAHL databases from 2010 to 2023. Two authors independently screened papers and extracted information using predefined criteria. We extracted data on study characteristics, and stages, levels and methods of community involvement.ResultsWe identified 58 BWMPs reported in 91 papers. Most were conducted in the US (n = 48, 83%). Their focus included race and ethnicity (n = 43, 73%), gender (n = 17, 29%) or low-income/underserved communities. Community members initiated the development of BWMPs in 36% of programmes (n = 21). Most programmes used community involvement to adapt an existing intervention (n = 33, 57%). Community involvement was highest at the planning stage where 55% (n = 32) of studies included community members as collaborators and 9% (n = 5) had community members leading the process. At the delivery stage, nine studies (16%) were led by community members and 19 (33%) included them as collaborators. In the evaluation stage, no studies were led by community members but a quarter (n = 14, 24%) included them as collaborators. Few programmes reported either the cost (n = 3, 5%) or the duration (n = 13, 22%) of community involvement. Programme adaptations ranged from relatively easy-to-implement changes such as changing language or menus, to more substantive adaptations like format, activity and personnel.ConclusionsOur review identified substantial levels of community involvement (leadership or collaboration) in planning BWMPs, but less so in their delivery, and rarely in evaluation. Greater involvement of communities in evaluation would ensure programmes focus on what matters most to them. Reporting of community involvement, especially costs and time involved, should be improved to allow for shared learning
A review on energy harvesting for sustainable IoT monitoring systems
Get PDFAutonomous condition monitoring is essential for advancing intelligent systems in both industrial and domestic Internet of Things (IoT) applications. However, continuous long-term condition monitoring is challenged by the limited energy availability for wireless sensor nodes (WSNs). Therefore, energy harvesting offers a promising approach by converting ambient or host energy into electrical power to sustain WSN operation. To bridge the gap between energy harvesting and condition monitoring, this review provides an overview and synthesis of recent advances in energy harvesting technologies tailored for condition monitoring applications. State-of-the-art developments in energy harvesting are categorized into six domains: healthcare, ocean, machinery, grid, railway, and infrastructure. The characteristics of these energy sources and their domain-specific monitoring requirements are analyzed. Furthermore, this review examines harvesting transducers, structural designs, and optimization methods employed in energy harvesters. Finally, the review discusses current challenges and future prospects for energy-autonomous condition monitoring systems, aiming to support the deployment of sustainable IoT sensing solutions.</p
Mafia Expansion: The 'Ndrangheta in Established Democracies by Z. L. Hauser Oxford, Oxford University Press, 2025, 288 pp., £100 (hardback), ISBN: 9780198895541
Get PDFAnchor entrepreneurs as microfoundational agents of regional industry emergence:the genesis of Scotland’s space industry
Get PDFTen questions on indoor greening and environmental quality
Get PDFWhile outdoor urban greening is recognised for its benefits, indoor green infrastructure (iGI) in shaping indoor environmental quality (IEQ) - including air quality, thermal comfort, and bioaerosols - remains underexplored. This ten-question paper identifies key challenges, opportunities, and research gaps in the iGI-IEQ nexus, organised under 10 questions across five thematic clusters: (1) biophysical and technical performance; (2) ecological and microbiological dynamics; (3) human health and wellbeing; (4) equity, access, and socio-economic factors; and (5) implementation and systems integration. Findings indicate that iGI can improve air quality, regulate humidity, and enhance thermal comfort. However, its performance depends strongly on plant density, species selection, and ventilation. Most evidence comes from controlled settings. iGI may offer positive psychological and cognitive benefits, and can reduce health inequalities through affordable indoor interventions. However, significant data scarcity exists for long-term field studies, indoor microbial ecosystem effects, and socio-economic accessibility. Widespread adoption of iGI requires quantification of proven benefit conditions, followed by overcoming technical, operational, and regulatory barriers via adaptive design, digital monitoring, and interdisciplinary collaboration. As a culminating synthesis, this study introduces a newly developed comprehensive matrix that classifies twenty-six indoor greening types across twenty IEQ parameters, incorporating an assessment of current data confidence. This matrix lays a foundational framework for informed decision-making and design guidance. This review offers evidence-based insights for researchers, policymakers, and practitioners to effectively leverage iGI where suitable, in creating healthier, climate-resilient residential and commercial buildings, addressing both immediate IEQ challenges and supporting long-term sustainability objectives.</p
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