Effects of narrow gap wave resonance on a dual-floater WEC-breakwater hybrid system

The effects of gap wave resonance on the performance of a dual-floater hybrid system consisting of an oscillating-buoy type wave energy converter (WEC) and a floating breakwater are important for the design of such a hybrid system. This paper investigates the gap wave resonance by employing a two-dimensional numerical wave flume developed using the Star-CCM + software. The maximum wave elevation in the WEC-breakwater gap and the effects of the gap wave resonance on the performance of the dual-floater hybrid system were studied. The influence of the WEC motion and the geometrical parameters of the hybrid system on the maximum wave elevation were analyzed. The maximum gap wave elevation is essentially controlled by the vertical velocity of the free surface in the WEC-breakwater gap. The gap wave resonance was found to significantly improve the wave energy extraction performance of the hybrid system. This allowed the maximum conversion efficiency to exceed the well-known limit of 0.50 for a symmetric body in single degree-of-freedom motion. The wave resonance frequencies in the WEC-breakwater gap decreased with the increase of the gap width and the WEC draft. Due to the energy extraction of the WEC, the horizontal and vertical forces on the breakwater were reduced by up to 0.79 and 0.59, respectively

AdaCare:Explainable Clinical Health Status Representation Learning via Scale Adaptive Feature Extraction and Recalibration

Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays an important role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using the prediction model as a black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative in- interpretability. We conduct health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability which is verifiable by clinical experts

Iron Contamination Mechanism and Reaction Performance Research on FCC Catalyst

FCC (Fluid Catalytic Cracking) catalyst iron poisoning would not only influence units’ product slate; when the poisoning is serious, it could also jeopardize FCC catalysts’ fluidization in reaction-regeneration system and further cause bad influences on units’ stable operation. Under catalytic cracking reaction conditions, large amount of iron nanonodules is formed on the seriously iron contaminated catalyst due to exothermic reaction. These nodules intensify the attrition between catalyst particles and generate plenty of fines which severely influence units’ smooth running. A dense layer could be formed on the catalysts’ surface after iron contamination and the dense layer stops reactants to diffuse to inner structures of catalyst. This causes extremely negative effects on catalyst’s heavy oil conversion ability and could greatly cut down gasoline yield while increasing yields of dry gas, coke, and slurry largely. Research shows that catalyst’s reaction performance would be severely deteriorated when iron content in E-cat (equilibrium catalyst) exceeds 8000 μg/g

ConCarE:Personalized Clinical Feature Embedding via Capturing the Healthcare Context

Predicting the patient’s clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between vis- its. Although those works have shown superior performances in healthcare prediction, they fail to thoroughly explore the personal characteristics during the clinical visits. Moreover, existing work usually assumes that a more recent record has a larger weight in the prediction, but this assumption is not true for certain clinical features. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be diversely captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. More importantly, ConCare is able to extract medical findings which can be confirmed by human experts and medical literature

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients

The study aims to develop AICare, an interpretable mortality prediction model, using Electronic Medical Records (EMR) from follow-up visits for End-Stage Renal Disease (ESRD) patients. AICare includes a multi-channel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform a personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AI Care outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships, variations in feature importance, and provides reference values. An AI-Doctor interaction system is developed for visualizing patients’ health trajectories and risk indicators

Regulating the coordination mode of Ti atoms in the beta zeolite framework to enhance the 1-Hexene Epoxidation

Regulating the Ti active sites in titanosilicates with different coordination modes is of prime scientific and industrial significance to the rational design of efficient catalysts for olefin epoxidation. In this study, the Ti species in Ti-beta zeolite catalysts (open/closed tetra-coordinated Ti sites, hexa-coordinated Ti species, and TiO2) were keenly controlled via the dealumination-metallization approach. By multiple characterizations, kinetics study, and multivariate model analysis, it is found that the open tetra-coordinated framework Ti(OH)(OSi)3 species contribute more to the catalytic performance for 1-hexene epoxidation with H2O2. Moreover, the Ti-beta with rich open tetra-coordinated Ti(OH)(OSi)3 species showed significantly improved reaction performance (TON: 401, conversion: 64%, selectivity: 98%, H2O2 efficiency: 97%) with lower apparent activation energy. This study not only opens up new prospects for the design of efficient titanosilicates by modifying Ti microenvironments but also proposes the strategy to improve the content of open tetra-coordinated Ti sites

Synthesis of a ZSM-5(core)/SAPO-5(shell) composite and its application in FCC

A core/shell structure composite was synthesized via a new method of pre-coating one raw material. The composite was characterized by X-ray diffraction, SEM, TEM and N2 isothermal adsorption–desorption and Py-FTIR. In addition, the catalytic performance of the composite in cracking of heavy oil for producing olefin was also investigated. The characterization results show that the composite with a core/shell structure had smaller particle size, uniform SAPO-5 shell, and fewer acid sites than ZSM-5, accelerating the transport of reactant and product molecules between different zeolites. Consequently, the light olefins on the composites had high specific selectivity