Technology and the labour process : insights from Indian e-commerce warehouses

In the context of innovation in and the application of information and communication technology (ICT), this article seeks to understand how ICT-enabled tools, including algorithmic processing interfaces, cloud computing software, QR codes and barcodes, have become a new managerial equipment for organising, controlling, and disciplining the labour force in the warehouses of e-commerce enterprises in India. This article engages with labour process theory which accords analytical importance to technology in organising work, for managerial control and disciplinary regimes in furtherance of capital accumulation. The evidence here derives from four month’s field work in 2022–23 from Bangalore in south India. Data were generated from 74 semi-structured interviews with employees of, principally, Amazon and Flipkart. The major findings are that an integrated, digitised control system operating in tandem with direct human supervision, ensures the simultaneous processing of products orders and the monitoring of workers’ performance. Further, it investigates how they contribute to work intensification and exacerbated job-related insecurities and vulnerabilities. The outcome is extreme work intensity and the creation of new forms of worker insecurity and vulnerability

The impact of the COVID-19 pandemic on maternal healthcare costs : a time series analysis of pregnancies of multi-ethnic mothers in South London, United Kingdom

Background: Due to the COVID-19 pandemic, maternity care reconfigurations disrupted in-person care, which shifted towards virtual care and self-monitoring. We assessed the impact of these changes on maternity service provision costs. Methods: Data from October 2018 to April 2023 were used from the population-based early-LIfe data cross-LInkage in Research, Born in South London (eLIXIR-BiSL) platform linking maternity, neonatal, and mental healthcare data from three National Health Service (NHS) hospitals in South London, United Kingdom. Maternity costs were generated from the NHS perspective, using national unit costs and individual-level use of maternity, mental health, and primary care services. Interrupted time series analysis estimated the pandemic impact on monthly mother-newborn costs over time. Cross-sectional pre-pregnancy cost models isolated the impact of virtual care and gestational diabetes (GDM) self-monitoring using the GDm-Health app. Ethnic inequalities in the impact of the pandemic on maternity costs were assessed via interaction terms. Results: Among 36,895 pregnancies, the monthly cost time series level dropped by 4% (£ − 38, 95% confidence interval: [£ − 65 to − 10]), during the first pandemic lockdown, and by 7.6% (£ − 72 [£ − 108 to − 36]), when lockdowns were lifted compared with the pre-pandemic period. However, the pre-pandemic slightly upward timeseries slope of costs (£4 per month, [£0.30 to £6.83]) was unchanged during the pandemic (£0.46 [£ − 2.93 to 3.84]). Monthly costs increased with first lockdown for Black (£103 [£26 to 181]) and Asian women (£128 [£38 to 218]) and increased more slowly during post-lockdown (£ − 12 [£ − 23 to − 2]), for Asian women, remaining higher throughout the pandemic for Black and Asian women compared with White women. A 1% increase in virtual care was associated with a £7 (£3 to 10) increase in maternity costs. GDM self-monitoring via GDm-Health was cost-neutral (£140 [£ − 68 to 348]). Conclusions: The pandemic was associated with temporary reductions in maternity costs due to lower healthcare utilisation. Ongoing, rising maternity costs were unchanged. The pandemic had differential effects on Black and Asian women compared with White women. Further research is needed into clinical outcomes of virtual care (associated with higher costs) and use of GDm-Health (cost-neutral)

The link between cultural heritage protection and children in armed conflict : the absence of a dedicated resolution and the potential role of the United Nations Security Council

This article explores the intersection between the protection of children in armed conflict and the safeguarding of cultural heritage, highlighting the absence of a dedicated United Nations Security Council (UNSC) resolution addressing this crucial connection. While existing frameworks provide protection for both children and cultural heritage separately, they fail to acknowledge the profound impact that the destruction of cultural heritage has on children's identity, education, and long-term psychological well-being. The article argues for the necessity of an integrated approach that formally recognizes cultural heritage as an essential component of child protection in conflict zones. By examining international legal instruments, UNSC resolutions, and case studies, this study underscores the urgent need for a resolution that explicitly links these two areas, strengthening global efforts to ensure both the physical and cultural survival of children affected by war

Introduction : Heritage (still) in war and peace

The fourth edition of the international ‘Heritage in War and Peace' Conference took place at the School of Law, University of Strathclyde in December 2025, bringing almost one hundred participants from all over the world. This introduction to the volume of selected working papers from the conference puts forward the conference's co-chair perspectives on its organisation, as well as provides an overview of each of the papers

Developing a (research) community of practice : diversifying assessment in masters projects for authentic learning

A common feature across many postgraduate programmes is the output of research from a "dissertation project". We present a case study on the redesign of the 60-credit Individual Project, a core component of the MSc Advanced Chemical Engineering and Sustainable Engineering programmes at the University of Strathclyde, Glasgow, UK. The study addresses critical challenges in traditional dissertation assessment: the overemphasis on written final reports, which limits student motivation for broader skill development; the vulnerability of such reports to AI-assisted production, raising concerns about academic integrity; and the lack of authenticity in experience; which restricts students membership into research communities of practice. The redesign shifts the focus from output-based assessment to a more holistic approach, emphasising skill development and authentic learning. Key innovations included diversifying assessment types to prioritize continuous performance and skills (40%), alongside a 7,000-word journal article (40%) and a presentation with Q&A (20%). Reflective logs, supported by formative feedback meetings, were introduced to give students the opportunity to engage more deeply with their own research skills development. Supervisory pairings ensured continuity, scalability, and quality assurance, while detailed rubrics offered quality control in assessment criteria and feedback. Informing these changes included extensive consultation with academic supervisory, complemented by workshops on reflective writing, research ethics, AI use, and presentation skills. Feedback from students and supervisors highlighted high satisfaction with staff support, communication, and the journal article format. Reflective logs were valued for tracking progress, though some students and supervisors noted challenges with repetition and application. Supervisors observed increased student ownership, improved writing, and enhanced engagement, while appreciating the flexibility of supervisory pairings. This study demonstrates the value of authentic and diverse approaches to assessment in catalysing deeper and reflective learning; fostering authentic communities of practice around research for Masters students; mitigating AI-related risks; and enhancing student and supervisor satisfaction. The approach offers a scalable model for rethinking dissertation projects across disciplines, particularly in STEM fields, to better align assessment with learning outcomes and contemporary challenges

Thermodynamic potential of ferroelectric nematic liquid crystals and consequences for polarization switching

The ferroelectric nematic (N_{f}) liquid crystal phase is a highly polar fluid, with spontaneous polarization (P_{S}) values of the order of µCcm^{-2} and viscosities of around 10 Pas. The combination of high polarity and fluidity makes these materials unique polar dielectrics. We consider the free energy of the ferroelectric nematic phase and derive its thermodynamic potentials. This allows us to predict that the spontaneous polarization will saturate as a function of applied voltage, rather than field. Further, we determine that the inclusion of an alignment layer, which is usual in liquid crystal devices, could provide a significantly enhanced energy barrier to switching. Indeed, an insulating alignment layer introduces a polar anchoring energy in addition to the orientational anchoring energy usually considered in liquid crystal devices. We confirm experimentally that measurements of the spontaneous polarization depend very slightly on the thickness of the N_{f} layer and more dramatically on the polar interactions of the phase with the confining surfaces. In relatively thin devices (∼10µm) with an alignment layer present, we demonstrate that this effect can be so pronounced that polarization switching is completely suppressed. We also explore the influence of the preparation conditions of a thin film of ferroelectric nematic material on the stability and lifetime of the sample

A versatile molecular dynamics force field for modelling polyhydroxyalkanoate structure and barrier properties

Polyhydroxybutyrate (PHB) is a sustainable and compostable polyester, which has great potential for use as food packaging film, having similar barrier properties to conventional plastics. PHB is semi-crystalline and is often copolymerised with polyhydroxyvalerate (PHV) to form poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). Molecular dynamics (MD) simulations provide valuable insight into the polymer structure and gas diffusion, but the accuracy of MD simulations depends on the force field. This work presents a modified all-atom General Amber Force Field that enables PHB, PHV and PHVB copolymers to be modelled. The structural properties of crystal and amorphous phases of PHB and PHV were in good agreement with experiment. The diffusion coefficients of water and oxygen in amorphous PHB were also in good agreement with experimental values. The diffusion coefficient of oxygen in PHV was larger than in PHB, mainly due to the lower density of PHV. The diffusion coefficient of water in PHV was similar to PHB as its diffusion is hindered by the interaction of water with the polar ester groups on the polymer chains. This force field can be used to investigate the diffusion of water and oxygen in PHB, PHV and PHBV copolymers, and to optimise the barrier properties of PHBV-based plastic film

Modeling and exact solution approaches for the distance-based critical node and edge detection problems

The performance of many networked systems including energy, telecommunication and transport networks is dependent on the functionality of a few components of these systems whose malfunction compromises optimal performance of the system. With respect to network connectivity as a performance metric, such components are termed critical nodes and edges. The optimisation problem associated with identifying critical nodes of a network is termed the critical node detection problem (CNDP). The CNDP has gained significant amount of research owing to its applicability in diverse real life problems including disaster management, social network analysis, and disease epidemiology, as well as its computational complexity. However, traditional models, whose underlying objective is to maximize network fragmentation fail to capture cohesiveness and extent of connectivity within the resultant network. Therefore, a new variant of the problem termed the distance-based critical node detection problem (DCNDP) was proposed to address this gap. The DCNDP takes into account pairwise distances between nodes as part of its network connectivity objective, which are modelled by pre-defined distance-based connectivity measure. Distance-based connectivity plays an important part in everyday life. For instance, our choice of route of travel and the cost of a flight ticket are influenced by the duration of travel and number of stopovers involved. Therefore, while a source-destination route might exist, if the duration of a trip via the route precludes attainment of a time-bound activity, then such is a practical disconnection. Similarly, in communication and telecommunication networks, speed and coverage are key operational issues for assessing connectivity which are both related to pairwise distances in the network. In this chapter, we study a generalization of the DCNDP on weighted networks, where distance between any source-destination (s-t) pair is not limited to hop distance (number of edges along an s-t path). We present a new model with fewer entities than the models in previous studies. Moreover, we show that the proposed model admits different distance-based connectivity measures, hence is valid for all existing classes of the distance-based critical node detection problem. We introduce a new version of the problem, in which edges rather than nodes are to be deleted. This version is useful for application contexts where it is impractical or too expensive to delete nodes. Furthermore, we study social and transportation networks, where we also demonstrate practical aspects of the problem. Some computational experiments on instances of different real-world networks are presented for the different application context studied using the proposed models and algorithm. The Chapter concludes with directions for future research

Local language, global perspective : Mandarin Chinese language pedagogies in Scottish schools

This paper contributes to the panel discussion on Chinese language education by examining how Mandarin can be meaningfully integrated into Scottish primary schools through innovative and interdisciplinary pedagogies. While Mandarin is often viewed as a global or "foreign" language, this contribution explores how it might be repositioned as part of Scotland’s evolving local language landscape—supporting both global citizenship and local diversity aims within the Curriculum for Excellence. This contribution will be of interest to researchers, teacher educators, and practitioners concerned with language policy, curriculum design, and the future of multilingual education in Scotland and beyond. Focusing on recent research and practitioner insights, the paper discusses how Mandarin teaching in primary schools can move beyond stand-alone language lessons to become embedded across the curriculum. It considers interdisciplinary approaches that link language learning with social studies, expressive arts, literacy, and technologies, enabling children to encounter Chinese language and culture in varied and contextually relevant ways. The paper also reflects on the implications for primary educators, many of whom are engaging with Mandarin without prior linguistic or cultural expertise. It explores models of collaboration with Chinese language assistants, community partnerships, and university-led initiatives, and considers how teacher education and professional learning can support sustainable and confident delivery. By framing Mandarin as a language that can be locally embedded as well as globally oriented, the paper invites a rethinking of pedagogical assumptions and practical strategies. It contributes to ongoing debates around language diversification in Scottish education and the role of Asian languages in shaping inclusive, outward-facing school environments

Multi model machine learning approach for automated data analysis of carbon fiber reinforced polymer composites

The aerospace industry is increasingly shifting towards using automated solutions for sensor delivery and data acquisition for Ultrasonic Testing (UT) of Carbon Fibre Reinforced Polymers (CFRPs). While this transition has enabled faster and more reliable inspections, it generates large volumes of data in a short time, which are still analysed and interpreted manually, making the process lengthy and prone to human error [1]. This creates a bottleneck in the manufacturing and Non-Destructive Evaluation (NDE) workflows, particularly given the increasing use of CFRPs in flagship aircraft models by Airbus and Boeing, currently accounting for up to 50% of the total material weight [2, 3]. The manual NDE data analysis is sometimes paired with simple traditional rule-based tools such as signal thresholding. However, these tools often struggle to effectively manage complex data patterns or high noise levels, leading to unreliable defect detection. Additionally, they require frequent manual adjustments to set appropriate parameters for varying inspection conditions, which can be inefficient and error-prone in dynamic or fast-paced environments. In contrast, Artificial Intelligence (AI) analysis tools have demonstrated improvements over traditional methods, offering greater accuracy in defect detection and adaptability to higher variability within captured signals. However, their adoption in industrial settings remains limited due to challenges associated with model trust and their “black box” nature. Additionally, practical guidelines for implementing AI tools into NDE workflow are rarely discussed, motivating this work to explore various integration strategies across different automation levels. Three levels of automation were explored, ranging from basic AI-assisted workflows, where tools provide suggestions, to advanced applications where multiple AI models simultaneously process data in a comprehensive analysis, shifting human operators to a supervisory role. Proposed strategies of AI integration into the NDE automation workflow were evaluated on inspection of two defective complex-geometry CFRP components, commonly used in aerospace and energy sectors for safety-critical structures such as aircraft fuselages and wind turbine blades. The experimental scans were conducted using a phased array UT roller probe mounted on an industrial manipulator, closely replicating industrial practices, and successfully identifying 36 manufactured defects through a combination of supervised object detection on ultrasonic amplitude C-scans, unsupervised anomaly detection on ultrasonic B-scans, and a self-supervised AI model for processing full volumetric ultrasonic data. Specifically, a Faster Region-based Convolutional Neural Network was used for object detection, trained exclusively on simulated data to mitigate data scarcity issues. Meanwhile, the anomaly detection model, implemented as a convolutional autoencoder, and the self-supervised AI model, designed as a forecasting model for time-series data, were both trained on pristine CFRP samples. This inclusion of multiple AI models led to an improvement of up to 17.2% in the F1 score compared to single-model approaches. Additionally, this framework enables integration into existing NDE workflows by incorporating a human-in-the-loop mechanism, improving trust in automation and allowing process customisation depending on specific application requirements. Unlike manual data analysis, which take hours for larger components, the proposed approach completes the analysis in 94.03 and 57.01 seconds for the two inspected samples, respectively