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The value principle in organizational economics states that the net market value of the goods that a firm sells is a key determinant of its organizational design. We survey and extend some recent developments in the theoretical literature at the nexus of organizational and industrial economics, focusing on this precept as the unifying theme. Under perfect competition, we study how market price influences the use of scarce professional management and the degree of organizational heterogeneity in an industry. In a more general setting, we show how changes in demand influence not only the use of professional management, but also the size and the market power of firms. And we show how prices can affect the internal control structure of firms, sometimes in highly distorted ways. We discuss applications to comparative industrial organization and to technological diffusion.

This paper details a method for investigating the stability of a layered slope that combines random field theory with horizontal integration. Both the cohesion and friction coefficient are treated as a Gaussian random field. The closed-form solution of the factor of safety and the probability of failure of the layered slope are derived, in which the effects of spatial correlation lengths and spatial correlation parameters on the stability of the layered slope are considered. The derived probability of failure is greatly affected by the input spatial correlation length of the soil parameters. The effectiveness of this proposed method is validated with the results of the Monte Carlo simulation.

Resource decision making is complex and variable when considering the vertical integration problem (VIP) and the horizontal integration problem (HIP) in an enterprise. The VIP involves sharing the enterprise's resource or information with the suppliers and customers as directly as possible to increase all participants' profits. On the other hand, the HIP considers allying with other competitors when the enterprise is short of the needed resources. The challenges associated with the VIP and HIP are usually dependent and inseparable. In this paper, the integral method called dummy goal programming is proposed to deal with the HIP and VIP simultaneously. On the basis of our numerical example, we can conclude that this proposed method can cope with these problems completely and that it provides the overall aspiration level for the enterprise.

This paper examines one of the NHS England Pioneers programmes of Integrated Care, which was implemented in three localities in East London, covering the area served by one of the largest hospital groups in the UK and bringing together commissioners, providers and local authorities. The partners agreed to build a model of integrated care that focused on the whole person. This qualitative and participatory evaluation looked at how an ambitious vision translated into the delivery of integrated care on the ground. The study explored the micro-mechanisms of integrated care relationships based on the experience of health and social care professionals working in acute and community care settings. We employed a participatory approach, the Researcher in Residence model, whereby the researcher was embedded in the organisations she evaluated and worked alongside managers and clinicians to build collaboration across the full range of stakeholders, develop shared learning, and find common ground through competing interests, while trying to address power imbalances. A number of complementary qualitative methods of data generation were used, including documentary analysis, participant observations, semi-structured interviews, and coproduction workshops with frontline health and social care professionals to interpret the data and develop recommendations. Our fieldwork exposed persistent organisational fragmentation, despite the dominant rhetoric of integration and efforts to build a shared vision at senior governance levels. The evaluation identified several important themes, including: a growing barrier between acute and community services; a persisting difficulty experienced by health and social care staff in working together because of professional and cultural differences, as well as conflicting organisational priorities and guidelines; and a lack of capacity and support to deliver a genuine multidisciplinary approach in practice, despite the ethos of multiagency being embraced widely. By focusing on professionals' working routines, we detailed how and why action taken by organisational leaders failed to have tangible impact. The inability to align organisational priorities and guidelines on the ground, as well as a failure to acknowledge the impact of structural incentives for organisations to compete at the expense of cooperation, in a context of limited financial and human resources, acted as barriers to more coordinated working. Within an environment of continuous reconfigurations, staff were often confused about the functions of new services and did not feel they had influence on change processes. Investing in a genuine bottom-up approach could ensure that the range of activities needed to generate system-wide cultural transformation reflect the capacity of the organisations and systems and address genuine local needs. The authors acknowledge several limitations of this study, including the focus on one geographical area, East London, and the timing of the evaluation, with several new interventions and programmes introduced more or less simultaneously. Some of the intermediate care services under evaluation were still at pilot stage and some teams were undergoing new reconfigurations, reflecting the fast-pace of change of the past decade. This created confusion at times, for instance when discussing specific roles and activities with participants. We tried to address some of these challenges by organising several workshops with different teams to co-interpret and discuss the findings.

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term ‘data integration’ has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods. As the number of single-cell experiments with multiple data modalities increases, Argelaguet and colleagues review the concepts and challenges of data integration.

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue. Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data.

Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing.

We need to combine generalist and specialist skills

Although the manifestation of trauma in the body is a phenomenon well-endorsed by clinicians and traumatized individuals, the neurobiological underpinnings of this manifestation remain unclear. The notion of somatic sensory processing, which encompasses vestibular and somatosensory processing and relates to the sensory systems concerned with how the physical body exists in and relates to physical space, is introduced as a major contributor to overall regulatory, social-emotional, and self-referential functioning. From a phylogenetically and ontogenetically informed perspective, trauma-related symptomology is conceptualized to be grounded in brainstem-level somatic sensory processing dysfunction and its cascading influences on physiological arousal modulation, affect regulation, and higher-order capacities. Lastly, we introduce a novel hierarchical model bridging somatic sensory processes with limbic and neocortical mechanisms regulating an individual’s emotional experience and sense of a relational, agentive self. This model provides a working framework for the neurobiologically informed assessment and treatment of trauma-related conditions from a somatic sensory processing perspective.

Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations. Many methods for single cell data integration have been developed, though mosaic integration remains challenging. Here the authors present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches, that jointly learns cell representations and marker features across modalities for different cell clusters, to interpret the cell clusters from different modalities.