Hybrid phase-change Lattice Boltzmann simulation of vapor condensation on vertical subcooled walls

Saturated vapor condensation on homogenous and heterogeneous subcooled walls is presented in this study by adopting a hybrid phase-change multiple-relaxation-time Lattice Boltzmann model. The effects of wall wettability on the condensation process, including droplets’ growth, coalescence and falling, and the influence of vapor flow to condensation are investigated. The results demonstrate that the heat fluxes around the triple-phase contact lines are higher than that in other cold areas in homogeneous subcooled walls, which actually indicates the fact that filmwise condensation is preventing the continuous condensation process. Furthermore, the dropwise condensation can be formed more easily on the heterogeneous surface with a mixed surface wettability. At last, the dynamic process of condensation of continuous vapor flow is also investigated by considering the homogenous and heterogeneous subcooled surfaces. The results show that the heterogeneous surface with mixed wettability doesn’t contribute to the formation, growth of droplets, when compared to the homogeneous surface. It is expected that this study can bring more attentions to simulate condensation using multiphase LBM for complex geometries in heat transfer community

Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method.Comment: This paper is accepted by The Web Conference 202

AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments

We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile to incorrect data associations, and are primarily limited to working only offline. AutoMerge utilizes multi-perspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, after assembling the segments, AutoMerge performs fine matching and pose-graph optimization to globally smooth the merged map. We demonstrate AutoMerge on both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8). The experiments show that AutoMerge (i) surpasses the second- and third- best methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to temporally-spaced revisits. To the best of our knowledge, AutoMerge is the first mapping approach that can merge hundreds of kilometers of individual segments without the aid of GPS.Comment: 18 pages, 18 figur

Real-time qPCR for the detection of puffer fish components from Lagocephalus in food: L. inermis, L. lagocephalus, L. gloveri, L. lunaris, and L. spadiceus

Puffer fish is a type of precious high-end aquatic product, is widely popular in Asia, especially in China and Japan, even though it naturally harbors a neurotoxin known as tetrodotoxin (TTX) that is poisonous to humans and causes food poisoning. With the increasing trade demand, which frequently exceeds existing supply capacities, fostering fraudulent practices, such as adulteration of processed products with non-certified farmed wild puffer fish species. To determine the authenticity of puffer fish processed food, we developed a real-time qPCR method to detect five common puffer fish species in aquatic products: Lagocephalus inermis, Lagocephalus lagocephalus, Lagocephalus gloveri, Lagocephalus lunaris, and Lagocephalus spadiceus. The specificity, cross-reactivity, detection limit, efficiency, and robustness of the primers and probes created for five species of puffer fish using TaqMan technology have been determined. No cross-reactivity was detected in the DNA of non-target sample materials, and no false-positive signal was detected; the aquatic products containing 0.1% of a small amount of wild puffer fish materials without certification can be reliably tracked; the statistical p-value for each method’s Ct value was greater than 0.05. The developed qPCR method was sensitive, highly specific, robust, and reproducibility, which could be used to validate the authenticity of wild puffer fish in aquatic products sold for commercial purposes

A novel intrusion detection method based on lightweight neural network for Internet of Things

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios

Thermal Recovery of the Electrochemically Degraded LiCoO₂/Li₇La₃Zr₂O₁₂:Al,Ta Interface in an All-Solid-State Lithium Battery

All-solid-state lithium batteries are promising candidates for next-generation energy storage systems. Their performance critically depends on the capacity and cycling stability of the cathodic layer. Cells with a garnet Li7La3Zr2O12 (LLZO) electrolyte can show high areal storage capacity. However, they commonly suffer from performance degradation during cycling. For fully inorganic cells based on LiCoO2 (LCO) as cathode active material and LLZO, the electrochemically induced interface amorphization has been identified as an origin of the performance degradation. This study shows that the amorphized interface can be recrystallized by thermal recovery (annealing) with nearly full restoration of the cell performance. The structural and chemical changes at the LCO/LLZO heterointerface associated with degradation and recovery were analyzed in detail and justified by thermodynamic modeling. Based on this comprehensive understanding, this work demonstrates a facile way to recover more than 80% of the initial storage capacity through a thermal recovery (annealing) step. The thermal recovery can be potentially used for cost-efficient recycling of ceramic all-solid-state batteries.</p

Multinational prospective cohort study of rates and risk factors for ventilator-associated pneumonia over 24 years in 42 countries of Asia, Africa, Eastern Europe, Latin America, and the Middle East: Findings of the International Nosocomial Infection Control Consortium (INICC)

Objective: Rates of ventilator-associated pneumonia (VAP) in low- and middle-income countries (LMIC) are several times above those of high-income countries. The objective of this study was to identify risk factors (RFs) for VAP cases in ICUs of LMICs. Design: Prospective cohort study. Setting: This study was conducted across 743 ICUs of 282 hospitals in 144 cities in 42 Asian, African, European, Latin American, and Middle Eastern countries. Participants: The study included patients admitted to ICUs across 24 years. Results: In total, 289,643 patients were followed during 1,951,405 patient days and acquired 8,236 VAPs. We analyzed 10 independent variables. Multiple logistic regression identified the following independent VAP RFs: male sex (adjusted odds ratio [aOR], 1.22; 95% confidence interval [CI], 1.16-1.28; P <.0001); longer length of stay (LOS), which increased the risk 7% per day (aOR, 1.07; 95% CI, 1.07-1.08; P <.0001); mechanical ventilation (MV) utilization ratio (aOR, 1.27; 95% CI, 1.23-1.31; P <.0001); continuous positive airway pressure (CPAP), which was associated with the highest risk (aOR, 13.38; 95% CI, 11.57-15.48; P <.0001)Revisión por pare

An international prospective study of INICC analyzing the incidence and risk factors for catheter-associated urinary tract infections in 235 ICUs across 8 Asian Countries

Background: Identify urinary catheter (UC)-associated urinary tract infections (CAUTI) incidence and risk factors (RF) in 235 ICUs in 8 Asian countries: India, Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Thailand, and Vietnam. Methods: From January 1, 2014, to February 12, 2022, we conducted a prospective cohort study. To estimate CAUTI incidence, the number of UC days was the denominator, and CAUTI was the numerator. To estimate CAUTI RFs, we analyzed 11 variables using multiple logistic regression. Results: 84,920 patients hospitalized for 499,272 patient days acquired 869 CAUTIs. The pooled CAUTI rate per 1,000 UC-days was 3.08; for those using suprapubic-catheters (4.11); indwelling-catheters (2.65); trauma-ICU (10.55), neurologic-ICU (7.17), neurosurgical-ICU (5.28); in lower- middle-income countries (3.05); in upper-middle-income countries (1.71); at public-hospitals (5.98), at private-hospitals (3.09), at teaching-hospitals (2.04). The following variables were identified as CAUTI RFs: Age (adjusted odds ratio [aOR] = 1.01; 95% CI = 1.01-1.02; P < .0001); female sex (aOR = 1.39; 95% CI = 1.21-1.59; P < .0001); using suprapubic-catheter (aOR = 4.72; 95% CI = 1.69-13.21; P < .0001); length of stay before CAUTI acquisition (aOR = 1.04; 95% CI = 1.04-1.05; P < .0001); UC and device utilization-ratio (aOR = 1.07; 95% CI = 1.01-1.13; P = .02); hospitalized at trauma-ICU (aOR = 14.12; 95% CI = 4.68-42.67; P < .0001), neurologic-ICU (aOR = 14.13; 95% CI = 6.63-30.11; P < .0001), neurosurgical-ICU (aOR = 13.79; 95% CI = 6.88-27.64; P < .0001); public-facilities (aOR = 3.23; 95% CI = 2.34-4.46; P < .0001). Discussion: CAUTI rate and risk are higher for older patients, women, hospitalized at trauma-ICU, neurologic-ICU, neurosurgical-ICU, and public facilities. All of them are unlikely to change. Conclusions: It is suggested to focus on reducing the length of stay and the Urinary catheter device utilization ratio, avoiding suprapubic catheters, and implementing evidence-based CAUTI prevention recommendations

Ensemble Methods of Rank-Based Trees

Advancements in genome-wide profiling techniques have revolutionized the generation of large-scale genomic data, enabling the successful identification and classification of cancer tissue samples based on their gene expression patterns. Despite these strides, the widespread adoption of such methodologies, particularly in constructing single sample predictors (SSPs) from gene expression profiles, has long faced challenges due to the lack of calibration across different gene expression measurement technologies. Nonetheless, research has highlighted the feasibility of phenotype classification through the expression order of multiple genes. Traditional TSP-based methods, while interpretable based on "relative expression reversals", struggle with complex pattern misclassification when more than two gene expression comparisons are involved in the decision boundary. To overcome this limitation, we propose a method that extends TSP rules by constructing rank-based trees, enabling the accommodation of gene-gene comparisons. To address overfitting, we incorporate two ensemble strategies, boosting, and random forest, to enhance the robustness of the rank-based trees. Our implementation of ensembled rank-based trees in boosting with LogitBoost cost and random forests on 12 binary and multi-class cancer gene expression datasets demonstrates superior classification accuracy compared to K-Top Scoring Pairs (k-TSP) and Nearest Template Prediction (NTP) methods. Furthermore, we elucidate the process of variable selection and the generation of concise yet precise decision rules from rank-based trees for interpretive purposes. Our research findings consistently underscore the substantial promise of ensemble rank-based trees for enhancing disease classification through gene expression data.&nbsp;Our software is available at https:// CRAN.R-project.org/package=ranktreeEnsemble.</p

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of “relative expression reversals”. Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble