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

DrAgent: empowering Large Language Models as medical agents for multi-hop medical reasoning

Although large language models (LLMs) have demonstrated outperforming human experts in medical examinations, it remains challenging to adopt LLMs in real-world clinical decisionmaking that typically involves multi-hop medical reasoning. Common practices include prompting commercial LLMs and fine-tuning LLMs on medical data. However, in the clinical domain, using commercial LLMs raises privacy concerns regarding sensitive patient data. Finetuning competitive medical LLMs for different tasks usually requires extensive data and computing resources, which are difficult to acquire, especially in medical institutions with limited infrastructure. We propose DrAgent, which can build LLMs as agents to deliver accurate medical decision-making and reasoning. In implementation, we take a lightweight LLM as the backbone to collaborate with diverse clinical tools. To make efficient use of data, DrAgent introduces recursive curriculum learning to optimize the LLM in an easy-to-hard progression. The results show that our approach achieves competitive performance on diverse datasets

Effectiveness of Multifaceted Strategies to Increase Influenza Vaccination Uptake: A Cluster Randomized Trial

Abstract Importance: Influenza vaccination rates remain low among primary school students and vary by school in Beijing, China. Theory-informed, multifaceted strategies are needed to improve influenza vaccination uptake. Objective: To evaluate the effectiveness of multifaceted strategies in improving influenza vaccination uptake among primary school students. Design, Setting, and Participants: This cluster randomized trial was conducted from September 2022 to May 2023 across primary schools in Beijing, China. Schools were allocated randomly in a 1:1 ratio to multifaceted strategies or usual practice. Schools were deemed eligible if the vaccination rates in the 2019 to 2020 season fell at or below the district-wide average for primary schools. Eligible participants included students in grades 2 and 3 with no medical contraindications for influenza vaccination. Intervention: The multifaceted strategies intervention involved system-level planning and coordination (eg, developing an implementation blueprint, building social norms, and enhancing supervision), school-level training and educating school implementers (eg, conducting a 1-hour training and developing educational materials), and individual-level educating and reminding students and parents (eg, conducting educational activities and sending 4 reminders about vaccination). Main Outcomes and Measures: The primary outcomes were influenza vaccination uptake at school reported by school clinicians as well as overall vaccine uptake either at school or outside of school as reported by parents at 3 months. Generalized linear mixed models were used for analysis. Results A total of 20 schools were randomized. One intervention school and 2 control schools did not administer vaccination on school grounds due to COVID-19, resulting in a total of 17 schools (9 intervention and 8 control). There was a total of 1691 students aged 7 to 8 years (890 male [52.6%]; 801 female [47.4%]) including 915 in the intervention group and 776 in the control group. Of all participants, 848 (50.1%) were in grade 2, and 1209 (71.5%) were vaccinated in the 2021 to 2022 season. Participants in the intervention and control groups shared similar characteristics. At follow-up, of the 915 students in the intervention group, 679 (74.5%) received a vaccination at school, and of the 776 students in the control group, 556 (71.7%) received a vaccination at school. The overall vaccination rates were 76.0% (695 of 915 students) for the intervention group and 71.3% (553 of 776 students) for the control group. Compared with the control group, there was significant improvement of vaccination uptake at school (odds ratio, 1.40; 95% CI, 1.06–1.85; P = .02) and overall uptake (odds ratio, 1.49; 95% CI, 1.12–1.99; P = .01) for the intervention group. Conclusions and Relevance: In this study, multifaceted strategies showed modest effectiveness in improving influenza vaccination uptake among primary school students, which provides a basis for the implementation of school-located vaccination programs of other vaccines in China, and in other countries with comparable programs. Trial registration: Chinese Clinical Trial Registry: ChiCTR2200062449Abstract Importance: Influenza vaccination rates remain low among primary school students and vary by school in Beijing, China. Theory-informed, multifaceted strategies are needed to improve influenza vaccination uptake. Objective: To evaluate the effectiveness of multifaceted strategies in improving influenza vaccination uptake among primary school students. Design, Setting, and Participants: This cluster randomized trial was conducted from September 2022 to May 2023 across primary schools in Beijing, China. Schools were allocated randomly in a 1:1 ratio to multifaceted strategies or usual practice. Schools were deemed eligible if the vaccination rates in the 2019 to 2020 season fell at or below the district-wide average for primary schools. Eligible participants included students in grades 2 and 3 with no medical contraindications for influenza vaccination. Intervention: The multifaceted strategies intervention involved system-level planning and coordination (eg, developing an implementation blueprint, building social norms, and enhancing supervision), school-level training and educating school implementers (eg, conducting a 1-hour training and developing educational materials), and individual-level educating and reminding students and parents (eg, conducting educational activities and sending 4 reminders about vaccination). Main Outcomes and Measures: The primary outcomes were influenza vaccination uptake at school reported by school clinicians as well as overall vaccine uptake either at school or outside of school as reported by parents at 3 months. Generalized linear mixed models were used for analysis. Results A total of 20 schools were randomized. One intervention school and 2 control schools did not administer vaccination on school grounds due to COVID-19, resulting in a total of 17 schools (9 intervention and 8 control). There was a total of 1691 students aged 7 to 8 years (890 male [52.6%]; 801 female [47.4%]) including 915 in the intervention group and 776 in the control group. Of all participants, 848 (50.1%) were in grade 2, and 1209 (71.5%) were vaccinated in the 2021 to 2022 season. Participants in the intervention and control groups shared similar characteristics. At follow-up, of the 915 students in the intervention group, 679 (74.5%) received a vaccination at school, and of the 776 students in the control group, 556 (71.7%) received a vaccination at school. The overall vaccination rates were 76.0% (695 of 915 students) for the intervention group and 71.3% (553 of 776 students) for the control group. Compared with the control group, there was significant improvement of vaccination uptake at school (odds ratio, 1.40; 95% CI, 1.06–1.85; P = .02) and overall uptake (odds ratio, 1.49; 95% CI, 1.12–1.99; P = .01) for the intervention group. Conclusions and Relevance: In this study, multifaceted strategies showed modest effectiveness in improving influenza vaccination uptake among primary school students, which provides a basis for the implementation of school-located vaccination programs of other vaccines in China, and in other countries with comparable programs. Trial registration: Chinese Clinical Trial Registry: ChiCTR220006244

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

Co-culture of STRO1 + human gingival mesenchymal stem cells and human umbilical vein endothelial cells in 3D spheroids: enhanced in vitro osteogenic and angiogenic capacities

Stem cell spheroid is a promising graft substitute for bone tissue engineering. Spheroids obtained by 3D culture of STRO1+ Gingival Mesenchymal Stem Cells (sGMSCs) (sGMSC spheroids, GS) seldom express angiogenic factors, limiting their angiogenic differentiation in vivo. This study introduced a novel stem cell spheroid with osteogenic and angiogenic potential through 3D co-culture of sGMSCs and Human Umbilical Vein Endothelial Cells (HUVECs) (sGMSC/HUVEC spheroids, GHS). GHS with varying seeding ratios of sGMSCs to HUVECs (GHR) were developed. Cell fusion within the GHS system was observed via immunofluorescence. Calcein-AM/PI staining and chemiluminescence assay indicated cellular viability within the GHS. Furthermore, osteogenic and angiogenic markers, including ALP, OCN, RUNX2, CD31, and VEGFA, were quantified and compared with the control group comprising solely of sGMSCs (GS). Incorporating HUVECs into GHS extended cell viability and stability, initiated the expression of angiogenic factors CD31 and VEGFA, and upregulated the expression of osteogenic factors ALP, OCN, and RUNX2, especially when GHS with a GHR of 1:1. Taken together, GHS, derived from the 3D co-culture of sGMSCs and HUVECs, enhanced osteogenic and angiogenic capacities in vitro, extending the application of cell therapy in bone tissue engineering

Assessing the impact of a multidimensional approach and an 8-component bundle in reducing incidences of ventilator-associated pneumonia across 35 countries in Latin America, Asia, the Middle East, and Eastern Europe

Background: Ventilator associated pneumonia (VAP) occurring in the intensive care unit (ICU) are common, costly, and potentially lethal. Methods: We implemented a multidimensional approach and an 8-component bundle in 374 ICUs across 35 low and middle-income countries (LMICs) from Latin-America, Asia, Eastern-Europe, and the Middle-East, to reduce VAP rates in ICUs. The VAP rate per 1000 mechanical ventilator (MV)-days was measured at baseline and during intervention at the 2nd month, 3rd month, 4–15 month, 16–27 month, and 28–39 month periods. Results: 174,987 patients, during 1,201,592 patient-days, used 463,592 MV-days. VAP per 1000 MV-days rates decreased from 28.46 at baseline to 17.58 at the 2nd month (RR = 0.61; 95% CI = 0.58–0.65; P < 0.001); 13.97 at the 3rd month (RR = 0.49; 95% CI = 0.46–0.52; P < 0.001); 14.44 at the 4–15 month (RR = 0.51; 95% CI = 0.48–0.53; P < 0.001); 11.40 at the 16–27 month (RR = 0.41; 95% CI = 0.38–0.42; P < 0.001), and to 9.68 at the 28–39 month (RR = 0.34; 95% CI = 0.32–0.36; P < 0.001). The multilevel Poisson regression model showed a continuous significant decrease in incidence rate ratios, reaching 0.39 (p < 0.0001) during the 28th to 39th months after implementation of the intervention. Conclusions: This intervention resulted in a significant VAP rate reduction by 66% that was maintained throughout the 39-month period