Aluminum Micropillar Surfaces with Hierarchical Micro- and Nanoscale Features for Enhancement of Boiling Heat Transfer Coefficient and Critical Heat Flux.

The rapid progress of electronic devices has necessitated efficient heat dissipation within boiling cooling systems, underscoring the need for improvements in boiling heat transfer coefficient (HTC) and critical heat flux (CHF). While different approaches for micropillar fabrication on copper or silicon substrates have been developed and have shown significant boiling performance improvements, such enhancement approaches on aluminum surfaces are not broadly investigated, despite their industrial applicability. This study introduces a scalable approach to engineering hierarchical micro-nano structures on aluminum surfaces, aiming to simultaneously increase HTC and CHF. One set of samples was produced using a combination of nanosecond laser texturing and chemical etching in hydrochloric acid, while another set underwent an additional laser texturing step. Three distinct micropillar patterns were tested under saturated pool boiling conditions using water at atmospheric pressure. Our findings reveal that microcavities created atop pillars successfully facilitate nucleation and micropillars representing nucleation site areas on a microscale, leading to an enhanced HTC up to 242 kW m-2 K-1. At the same time, the combination of the surrounding hydrophilic porous area enables increased wicking and pillar patterning, defining the vapor-liquid pathways on a macroscale, which leads to an increase in CHF of up to 2609 kW m-2.

Effect of Surface Wettability on Nanoparticle Deposition during Pool Boiling on Laser-Textured Copper Surfaces.

Prior studies have evidenced the potential for enhancing boiling heat transfer through modifications of surface or fluid properties. The deployment of nanofluids in pool boiling systems is challenging due to the deposition of nanoparticles on structured surfaces, which may result in performance deterioration. This study addresses the use of TiO2-water nanofluids (mass concentrations of 0.001 wt.% and 0.1 wt.%) in pool boiling heat transfer and concurrent mitigation of nanoparticle deposition on superhydrophobic laser-textured copper surfaces. Samples, modified through nanosecond laser texturing, were subjected to boiling in an as-prepared superhydrophilic (SHPI) state and in a superhydrophobic state (SHPO) following hydrophobization with a self-assembled monolayer of fluorinated silane. The boiling performance assessment involved five consecutive boiling curve runs under saturated conditions at atmospheric pressure. Results on superhydrophilic surfaces reveal that the use of nanofluids always led to a deterioration of the heat transfer coefficient (up to 90%) compared to pure water due to high nanoparticle deposition. The latter was largely mitigated on superhydrophobic surfaces, yet their performance was still inferior to that of the same surface in water. On the other hand, CHF values of 1209 kW m-2 and 1462 kW m-2 were recorded at 0.1 wt.% concentration on both superhydrophobic and superhydrophilic surfaces, respectively, representing a slight enhancement of 16% and 27% compared to the results obtained on their counterparts investigated in water.

Bayesian optimization of distributed neurodynamical controller models for spatial navigation.

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwarms controller, in which agent-based interactions were modeled by analogy to neuronal network interactions, including attractor dynamics and phase synchrony, that have been theorized to operate within hippocampal place-cell circuits in navigating rodents. This complexity precludes linear analyses of stability, controllability, and performance typically used to study conventional swarm models. Further, tuning dynamical controllers by manual or grid-based search is often inadequate due to the complexity of objectives, dimensionality of model parameters, and computational costs of simulation-based sampling. Here, we present a framework for tuning dynamical controller models of autonomous multi-agent systems with Bayesian optimization. Our approach utilizes a task-dependent objective function to train Gaussian process surrogate models to achieve adaptive and efficient exploration of a dynamical controller model's parameter space. We demonstrate this approach by studying an objective function selecting for NeuroSwarms behaviors that cooperatively localize and capture spatially distributed rewards under time pressure. We generalized task performance across environments by combining scores for simulations in multiple mazes with distinct geometries. To validate search performance, we compared high-dimensional clustering for high- vs. low-likelihood parameter points by visualizing sample trajectories in 2-dimensional embeddings. Our findings show that adaptive, sample-efficient evaluation of the self-organizing behavioral capacities of complex systems, including dynamical swarm controllers, can accelerate the translation of neuroscientific theory to applied domains.

Effect of Nanoparticle Size and Concentration on Pool Boiling Heat Transfer with TiO2 Nanofluids on Laser-Textured Copper Surfaces.

The enhancement of boiling heat transfer has been extensively shown to be achievable through surface texturing or fluid property modification, yet few studies have investigated the possibility of coupling both enhancement approaches. The present work focuses on exploring the possibility of concomitant enhancement of pool boiling heat transfer by using TiO2-water nanofluid in combination with laser-textured copper surfaces. Two mass concentrations of 0.001 wt.% and 0.1 wt.% are used, along with two nanoparticle sizes of 4-8 nm and 490 nm. Nanofluids are prepared using sonification and degassed distilled water, while the boiling experiments are performed at atmospheric pressure. The results demonstrate that the heat transfer coefficient (HTC) using nanofluids is deteriorated compared to using pure water on the reference and laser-textured surface. However, the critical heat flux (CHF) is significantly improved at 0.1 wt.% nanoparticle concentration. The buildup of a highly wettable TiO2 layer on the surface is identified as the main reason for the observed performance. Multiple subsequent boiling experiments using nanofluids on the same surface exhibited a notable shift in boiling curves and their instability at higher concentrations, which is attributable to growth of the nanoparticle layer on the surface. Overall, the combination of nanofluids boiling on a laser-textured surface proved to enhance the CHF after prolonged exposure to highly concentrated nanofluid, while the HTC was universally and significantly decreased in all cases.

TARA: Training and Representation Alteration for AI Fairness and Domain Generalization.

We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias. It includes the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors and training set alteration via intelligent augmentation to address bias-causing data imbalance by using generative models that allow the fine control of sensitive factors related to underrepresented populations via domain adaptation and latent space manipulation. When testing our methods on image analytics, experiments demonstrate that TARA significantly or fully debiases baseline models while outperforming competing debiasing methods that have the same amount of information-for example, with (% overall accuracy, % accuracy gap) = (78.8, 0.5) versus the baseline method's score of (71.8, 10.5) for Eye-PACS, and (73.7, 11.8) versus (69.1, 21.7) for CelebA. Furthermore, recognizing certain limitations in current metrics used for assessing debiasing performance, we propose novel conjunctive debiasing metrics. Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.

The Most Common Detected Bacteria in Sputum of Patients with Community Acquired Pneumonia (CAP) Treated In Hospital.

INTRODUCTION: Community acquired pneumonia (CAP) is the most common infective pulmonary disease. OBJECTIVE: To show the most common detected bacteria in bacterial culture of sputum in patients with CAP hospitalized in Clinic for Pulmonary Diseases and TB "Podhrastovi" in four-year period: from 2012 to 2015. MATERIAL AND METHODS: This is the retrospective analysis. Each patient gave sputum 3 days in a row when admitted to hospital. Sputum has been examined: bacterial culture with antibiotics sensitivity, Gram stain, Mycobacterium tuberculosis; in cases with high temperature blood cultures were done; when we were suspicious about bronchial carcinoma bronchoscopy with BAL (bronchoalveolar lavage) was done. We show analyzed patients according to age, sex, whether they had pneumonia or bronchopneumonia, bacteria isolated in sputum and in BAL. RESULTS: 360 patients with CAP were treated in four-year period (247 males and 113 females). 167 or 43, 39 % had pneumonia (119 males and 48 females). Number of males was significantly bigger (χ2 = 30,186; p<0,001). 193 or 53, 61 % had bronchopneumonia (128 males and 65 females). Number of males was significantly bigger (χ2 = 20,556; p<0,001). Number of patients with negative bacterial culture of sputum (131-78, 44%) was significantly bigger than number of patients with positive culture (36-21, 56%) (χ2 = 50,042; p<0,001) in pneumonia. Number of patients with negative bacterial culture of sputum (154- 79, 79%) was significantly bigger than number of patients with positive culture (39- 20, 21%) (χ2 = 68,523; p<0,001) in bronchopneumonia. Streptococcus pneumoniae was significantly most common detected bacterium compared with the number of other isolated bacteria; in pneumonia (χ2 =33,222; p<0,001) and in bronchopneumonia (χ2 =51,231; p<0,001). CONCLUSION: It is very important to detect the bacterial cause of CAP to administrate the targeted antibiotic therapy.