This work performed the first numerical investigation on the forced convection of flow past two tandem rectangular cylinders in a channel at Re = 100. The aspect ratio (AR) and gap ratio (GR) of the two cylinders are chosen at AR = 1(1)4 and GR = 1(1)8, respectively. The objective of the present work is to explore the effects of AR and GR on the characteristic flow and heat transfer quantities for the rectangular geometry that has not been studied before. The effects of the two parameters are presented by the instantaneous flow pattern, characteristic aerodynamic and heat transfer quantities, local heat transfer rate, flow patterns in the gap and near wake, and temperature distribution on the channel walls. Both time-averaged and fluctuating quantities are analyzed and presented. Numerical results reveal that for cylinders of all ARs, there are two flow regimes categorized based on the GR: the steady flow regime at GR ≤ 3, where the gap flow is steady, and the unsteady flow regime at GR ≥ 4. The characteristic aerodynamic and heat transfer quantities abruptly change as the flow transits from steady to unsteady regime especially for the downstream cylinder. The time-averaged and maximum fluctuating local heat transfer rate for the upstream cylinder almost does not vary with the GR, whereas they substantially vary for the downstream cylinder. The AR affects the magnitude of the quantities but not their variation trends. For flows in the unsteady regime, the recovery of the wake flow after the downstream cylinder is much more rapidly than those of steady flows due to the acceleration arising from the instability brought by the incoming shedding vortices. The violent shedding also effectively enhances heat transfer and increases the temperature of the channel walls.

Light scattering is a pervasive problem in many areas. Recently, deep learning was implemented in speckle reconstruction. To better investigate the key feature extraction and generalization abilities of the networks for sparse pattern reconstruction, we develop the “one-to-all” self-attention armed convolutional neural network (SACNN). It can extract the local and global speckle properties of different types of sparse patterns, unseen glass diffusers, and untrained detection positions. We quantitatively analyzed the performance and generalization ability of the SACNN using scientific indicators and found that, compared with convolutional neural networks, the Pearson correlation coefficient, structural similarity measure, and Jaccard index for the validation datasets increased by more than 10% when SACNN was used. Moreover, SACNN is capable of reconstructing features 75 times beyond the memory effect range for a 120 grits diffuser. Our work paves the way to boost the field of view and depth of field for various sparse patterns with complex scatters, especially in deep tissue imaging.

Background: This study examines the microcirculation of patients with sepsis and septic shock using Laser Speckle Contrast Imaging (LSCI) technology, to enhance monitoring and predict outcomes of sepsis and septic shock.Methods: From 01 July 2021, to 31 January 2022, 44 patients diagnosed with septic shock and sepsis were included in the study, their clinical data were collected, and LSCI was used to monitor the mean peripheral blood flow perfusion index (PI).Results: The average peripheral blood flow PI of septic shock patients was significantly lower than that of septic patients, with a cutoff value of 26.25. The average peripheral blood flow PI negatively correlated with acute physiology and chronic health evaluation (APACHE) Ⅱ score (p = .01 < .05), sequential organ failure assessment (SOFA) score (p < .01), and lactic acid levels (p = .01 < .05). We report average peripheral blood flow no correlation with age, mean arterial pressure, body temperature, oxygen saturation, heart rate, and body mass index. There was no correlation with procalcitonin, C-reactive protein (CRP), red blood cell distribution width, or platelet distribution width (p > .05). PI significantly correlated with the group sepsis and septic shock (p < .001, r = −.865). And PI significantly correlated with the outcome or mortality (p = .007 < .05, r = −.398). The ROC curve was calculated for PI and the sensitivity was 81.3%, and the specificity was 75% when PI cutoff value chooses 20.88.Conclusion: LSCI technology successfully detected the fingertip microcirculation of patients with septic shock. LSCI can reliably differentiate patients with sepsis vs patients with septic shock. Additionally, the average peripheral blood PI negatively correlated with APACHE Ⅱ, SOFA score, and lactate acid levels, providing useful and supplementary information for the diagnosis and monitoring of septic shock. Trial registration: Chictr2100046761. Registered on May 28, 2021.Clinical Trial Registration:clinicaltrials.gov, identifier Chictr2100046761

Heterogeneous catalysis at the metal surface generally involves the transport of molecules through the interfacial water layer to access the surface, which is a rate-determining step at the nanoscale. In this study, taking the oxygen reduction reaction on a metal electrode in aqueous solution as an example, using accurate molecular dynamic simulations, we propose a novel long-range regulation strategy in which midinfrared stimulation (MIRS) with a frequency of approximately 1,000 cm⁻¹ is applied to nonthermally induce the structural transition of interfacial water from an ordered to disordered state, facilitating the access of oxygen molecules to metal surfaces at room temperature and increasing the oxygen reduction activity 50-fold. Impressively, the theoretical prediction is confirmed by the experimental observation of a significant discharge voltage increase in zinc-air batteries under MIRS. This MIRS approach can be seamlessly integrated into existing strategies, offering a new approach for accelerating heterogeneous reactions and gas sensing within the interfacial water system.