Background and objective: Reliable detection and monitoring of circulatory shock is critical to provide timely and effective treatments. Specifically, continuous cerebral hemodynamic monitoring is essential as cerebral tissues are vulnerable to hypoxic-ischemic conditions. Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique that measures cerebral blood flow (CBF) in microvasculature by calculating a cerebral blood flow index (BFI). Current CBF monitoring by DCS commonly relies on BFI magnitude changes or changes relative to a baseline (rBFI) of optical signals. However, BFI and rBFI are often confounded by robust compensatory mechanisms meant to stabilize CBF, thereby masking potential early indicators of shock. To address this limitation, we applied a cumulative sum approach to develop BFI-derived Hemodynamic Trend Indices (HTIs), to selectively monitor dynamic trends in CBF leading to shock. Methods: Newborn piglet models of controlled blood loss (BL) and hypoxia were selected to investigate BFI, rBFI, and HTIs response to shock. Ascending and descending HTIs (aHTI and dHTI) were calculated from two-sided cumulative sum of BFI slopes. Machine learning and statistical models evaluated these biomarkers' shock detection performance and sensitivity to insult progression. Results: In hemorrhage and hypoxia groups, HTIs diverged from baseline at earlier insult epochs; HTIs at 10% BL (Hedges' g =-0.85, 1.13) versus BFI at 50% BL, and after only similar to 10-30 min of hypoxia, while BFI and rBFI declined after 40 min. In No-Shock vs. Shock classification, dHTI outperformed absolute BFI, achieving similar to 0.90 accuracy and >0.8 F1-scores. Conclusion: The proposed hemodynamic trend indices can monitor directional changes in CBF during insult progression towards shock, and may be further developed as potential clinical biomarkers for early indication of cerebral hemodynamic decompensation prior to circulatory collapse.
The normalized distance Laplacian matrix $\mathcal{D}^{\mathcal{L}}(G)$ of a graph $G$ is a natural generalization of the normalized Laplacian matrix, arising from the matrix of pairwise distances between vertices rather than the adjacency matrix. Following the motif that this matrix behaves quite differently to the normalized Laplacian matrix, we show that both the spectral gap and Cheeger constant of $\mathcal{D}^{\mathcal{L}}(G)$ are bounded away from $0$ independently of the graph $G$. The spectral result holds more generally for finite metric spaces.
This study evaluates the thermal resilience of a district composed of seventeen buildings located in Colorado (USA) during a 36-hour winter power outage. The thermal performance of each building was simulated using EnergyPlus combined with MATLAB (R). Three winter scenarios were investigated. The first corresponds to a baseline case where power comes from the grid. The second considers a decentralized photovoltaic (PV) system operating with and without storage. Finally, the third scenario evaluates a centralized district-level configuration based on shared PV generation, with and without storage. The results show that if PV generation alone delays the decrease in indoor temperature following the power outage, the integration of storage extends the metric as much as 16 h, depending on the building. In addition, the recovery time indicates reductions of up to 69 % when PV generation is combined with storage. Furthermore, the centralized strategy is capable of sustaining the prioritized buildings for nearly the entire 36-hour outage period. Overall, the results suggest that coordinated energy-sharing strategies can substantially improve district thermal resilience while simultaneously reducing storage requirements.
Innovation research has long shown that firms depend on their R&D personnel to strengthen their innovation capabilities, sparking scholarly interest in how to best staff R&D departments for competitive advantage, with a focus on gender diversity. However, left unexamined are the mechanisms linking gender diversity in R&D departments and firm-level innovation. Further, despite growing interest in the conditions under which diversity may enhance innovation, researchers have not adequately addressed how department-level gender rank inequality-the disproportionate concentration of men and women at various hierarchical ranks-interacts with numerical diversity to affect innovation outcomes. We address both limitations by integrating insights from the information/decision-making perspective on diversity and research on inequality with research on innovation capabilities. Specifically, we develop and test a model explaining how R&D department gender diversity and gender rank equality jointly affect three firm-level innovation capabilities: breadth of external knowledge search, use of external knowledge, and quality of the innovation implementation process. We also examine the indirect effects of gender diversity on firm-level innovation, conditional on gender rank equality, via these capabilities. Our analyses of 552 corporate R&D departments over 12 years show how gender diversity and gender rank equality in R&D departments jointly relate to higher levels of all three firm-level innovation capabilities and, indirectly, to greater firm-level innovation. Our findings suggest that improving a firm's innovation capabilities and performance requires both increased representation of women in R&D departments and proportional representation of men and women at all ranks of R&D departments.
