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This article examines Mark’s use of the (Deuteronomy 6:4–5), arguing that the Gospel writer utilized the within his narrative to express an early high Christology. Building on the work of Joel Marcus and John Lee, the study argues that Mark develops six recurring themes (God’s oneness, scribal opposition, accusation of blasphemy, God’s kingdom, love, and Son of Man) across four key passages (Mark 2:1–12; 10:17–30; 12:28–37; 14:61–62) to subtly identify Jesus with the one God of Israel. The analysis argues against Marcus’s ‘quasi-divine agent’ interpretation, contending that Mark’s narrative ambiguity is better understood as a rhetorical strategy for advancing a controversial theological claim rather than deflecting accusations of blasphemy. This view is then tested by following the narrative trajectory in the Gospel of Mark. In Mark 2, the unanswered scribal question about forgiveness raises the possibility of Jesus’ divinity; in Mark 10, Jesus raises the question of his goodness, then places himself in the role of God as the one to whom total covenantal devotion is owed; in Mark 12, the Shema citation in combination with Jesus’ question about the Messiah in Psalm 110 reinforces that such devotion must be directed toward Jesus; and in Mark 14, Jesus’ climactic self-revelation as the Son of Man confirms his divine identity. The study further strengthens its case through comparison with John’s Gospel in interaction with Lori Baron. It is suggested that John read Mark’s implicit Christology and made it explicit throughout his gospel, but particularly in John 10:22–39.

Background Dementia-related stigma is a prominent barrier for people living with dementia, leading to poor well-being and social isolation. Adolescents are an under-researched group in society that may already have experience of dementia and are more susceptible to attitudinal change which makes them ideal targets for anti-stigma initiatives outlined by public health policy. For the development of evidence-based anti-stigma initiatives in adolescents, it is important to understand which socio-demographic groups are most likely to develop stigmatising attitudes and why. This study aims to identify factors of dementia-related stigma in adolescents. Methods A total of 1,044 adolescents (aged 11–18 years) from across six regions of England were included in the analysis of this cross-sectional, survey-based study. Descriptive statistics and multiple regressions were employed to explore the association between demographic variables, modifiable factors of dementia-related stigma and the outcome of dementia-related stigma. A path analysis via a structural equation model was employed to test for direct and mediatory effects. Results Multiple regression models revealed that younger adolescents, those with higher levels of contact with dementia, higher levels of empathy, higher levels of dementia knowledge, and higher affinity to older adults, are associated with more positive dementia attitudes in adolescents ( p  < 0.05). Within the accepted structural equation model, empathy, level of contact and dementia knowledge were key mediators of dementia-related stigma ( p  < 0.05). Conclusion This study highlights that modifiable factors such as level of contact, ageism, and empathy have a potentially important role in how dementia-related stigma may start to form in the adolescent years. Developing contact-based strategies that stimulate empathetic responses may be useful targets for stigma reduction initiatives for adolescents.

This paper investigates using simulation to predict the benefits and costs of digitalising cold distribution chains. The study focuses on the distribution of refrigerated beef in the UK, where digitalisation was implemented to re-route cargo carriers. By comparing simulations of both digitalised and non-digitalised supply chains, the study found that digitalisation can reduce beef waste and decrease the number of miles driven per successful delivery, leading to potential cost savings. Note that this work is not attempting to prove that digitalisation is appropriate for the chosen scenario, only to justify a simulation approach as a decision making tool. The proposed modelling approach provides decision-makers with more accurate predictions of the cost–benefit of increased sensorisation in supply chains. By accounting for stochastic and variable parameters, such as weather and demand fluctuations, simulation can be used to identify potential challenges and estimate the economic benefits of digitalisation. Moreover, qualitative assessments of the impact on customer satisfaction and product quality can help decision-makers consider the broader impacts of digitalisation. Overall, the study suggests that simulation can play a crucial role in facilitating informed decisions about the implementation of digital technologies in the food supply chain. By providing a better understanding of the potential costs and benefits of digitalisation, simulation can help organisations make more strategic and effective decisions.

Background The evidence base concerning the socio‐demographic factors associated with diagnostic timing is limited and studies often use traditional regression methods, focussing solely on direct associations and ignoring those indirect pathways; thereby simplifying the links and missing potentially informative associations. No studies to date have sought to map out the direct and indirect factors associated with an early and timely diagnosis of dementia. Methods DETERMIND consists of 940 newly diagnosed people with dementia, and their 698 carers, across three diverse areas of England. Drawing on baseline data we created multiple study‐designed indices of early diagnosis using severity of dementia at diagnosis (cognitive) or time between self‐reported first signs of symptoms and clinically reported diagnosis date (temporal). ‘Timely’ diagnosis indices used self‐report items from people with dementia and their carers comparing those who reported the diagnosis was ‘made at the right time’ to those who believed it was ‘too early’ or ‘too late.’ Complex pathway analysis enabled us to investigate the direct and indirect associations between background variables and our indices of early and timely diagnosis. Result Our findings suggested numerous and sometimes contradictory effects of socio‐demographic determinants on the direct and indirect pathways to diagnosis and these varied depending on the definition of diagnostic timing being examined. Cognitively early diagnosis was more frequent in care dyads who were white, whereas temporally early diagnosis was more frequent in people with dementia who were not white. Our findings also highlighted different determinants associated with an early vs timely diagnosis of dementia. Conclusion Using complex pathway analysis we have generated unique insights into the socio‐demographic determinants associated with an early vs late and timely vs untimely diagnosis. Our findings highlight the importance for clearly operationalising ‘early’ diagnosis to compare findings across studies and generate a comprehensive understanding of factors associated with diagnostic timing. Furthermore, we suggest early diagnosis does not necessarily equate to ‘timely’ diagnosis and so these concepts cannot be used interchangeably in research or in policy.

There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and deep learning with the activities. A mixed-methods design was used to explore students' interactions with the online activities. This method integrated learning analytics data with responses from 63 survey, nine interview, and 16 focus group participants. We found that students used a combination of mobile devices to access the online learning activities across a variety of locations during opportunistic study sessions in order to fit study into their daily routines. The online activities were perceived positively, facilitating affective, cognitive, and behavioural engagement as well as stimulating deep learning. Activities that were authentic, promoted problem-solving, applied theory to real-life scenarios, and increased students' feelings of being supported were perceived as most beneficial to learning. These findings have implications for the future design of online activities, where activities need to accommodate students' need for flexibility as students' study habits become more mobile. [Author abstract]

Hybrid prototyping (HP) – the combination of mixed prototyping media within a single product design – has shown potential to substantially reduce process cost and fabrication time. However, previous work has not considered how HP processes and fabrication should be aligned with designer intent or activity needs, how these may change the realised savings or good practice guidance for successful implementation. This work proposes three approaches for HP with Lego and additive manufacturing, targeted towards enabling mixed fidelity for prototype flexibility, parallelisation for rapid fabrication and component reuse to minimise material waste. It then establishes good practice guidance and proposes a HP method that accounts for practical and process constraints, then implemented through an automated hybridisation tool. Approaches are compared through a simulation study and a case study to establish relative benefit. Results show potential time and material savings of 56% and 76%, respectively, depending on the approach chosen, demonstrating the substantial and practical scope for savings that HP provides.

Complete exploration of design spaces is often computationally prohibitive. Classical search methods offer a solution but are limited by challenges like local optima and an inability to traverse dislocated design spaces. Quantum computing (QC) offers a potential solution by leveraging quantum phenomena to achieve computational speed-ups. However, the practical capability of current QC platforms to deliver these advantages remains unclear. To investigate this, we apply and compare two quantum approaches – the Gate-Based Grover’s algorithm and quantum annealing (QA) – to a generic tile placement problem. We benchmark their performance on real quantum hardware (IBM and D-Wave, respectively) against a classical brute-force search. QA on D-Wave’s hardware successfully produced usable results, significantly outperforming a classical brute-force approach (0.137 s vs 14.8 s) at the largest scale tested. Conversely, Grover’s algorithm on IBM’s gate-based hardware was dominated by noise and failed to yield solutions. While successful, the QA results exhibited a hardware-induced bias, where equally optimal solutions were not returned with the same probability (coefficient of variation: 0.248–0.463). These findings suggest that for near-term engineering applications, QA shows more immediate promise than current gate-based systems. This study’s contribution is a direct comparison of two physically implemented quantum approaches, offering practical insights, reformulation examples and clear recommendations on the utilisation of QC in engineering design.

Minimally Intelligent Agent-Based Manufacturing concerns the provision of agents with the minimal intelligence required to autonomously negotiate and broker work across machines and jobs. The minimal intelligence of agents can be updated in real-time and when coupled with technologies, such as Additive Manufacturing (AM), robotics, automated inventory, and Computer Numerical Control (CNC) affords highly flexible, re-configurable, resilient and responsive systems. The concept has become topical as changes to global supply chains are necessitating a shift to responsive and resilient on-demand manufacturing. However, understanding and characterising how these systems operate under various demand profiles is required to support operators operating these future systems. This paper reports a numerical study into the operating behaviour of minimally intelligent agent-based manufacturing systems operating across the Average Demand Interval and Coefficient of Variation (ADI-CV) demand state space. An established state space used in spare part supply chain research. The results show minimally intelligent manufacturing systems are stable across much of the ADI-CV demand space 85%, 63% and 76-84% for constant, triangular and lognormal manufacturing time distribution profiles respectively. The stability is largely independent of the combination of intelligence. Rather, the combination of intelligence impacts job Time-in-System and system response.

Mixed Reality enables individuals to visualise and interact with artefacts and environments through a combination of physical and virtual assets. It has received increased interest from the design community as a means to accelerate, enrich and enhance prototyping activities. This article concerns MR’s ability to deceive an individual through the combination of virtual and physical assets and their underlying traits (e.g., mass, size), and a user’s cognitive ability to ‘join the dots’. If properly implemented, MR could save time and resources by reducing the required prototype fidelity and the need to fully realise variants. However, there is a gap in understanding how the traits of physical and virtual assets and cognition combine to form reality. This article presents a study investigated the role mass, virtual and physical model size played on users perception of an MR prototype. The relative impact of these factors was determined by varying these parameters and assessing the user’s perceived change. The key finding from this study was that the virtual model size had a far greater influence on prototype perceived by the user. This suggests that the required physical fidelity of an MR prototype can be lower than the virtual. Furthermore, exploring size design variants can be achieved exclusively through changes to the virtual model.