5,067 research outputs found
5-Butylamino-2-[2-(dimethylamino)ethyl]-1H-benz[de]isoquinoline-1,3(2H)-dione
The title compound, C20H25N3O2, is a new amonafide analogue, which exhibits antitumor activity. The asymmetric unit contains two molecules with similar conformations for the substituted aliphatic chains. The two independent molecules form dmers through N—H⋯N hydrogen bonds. The crystal structure is stabilized via π–π stacking interactions, the shortest centroid–centroid separation between six-membered rings being 3.673 (2) Å
Numerical Simulation of Parallel Cutting with Different Number of Empty Holes
The cutting blasting plays a key role in rock excavation construction, which determines the blasting effect and efficiency of the entire blasting project. In the cutting blasting, parallel holes are often used as the auxiliary free surface and the compensation space of blasting rock, and the empty holes have a great influence on the blasting effect. In this paper, Ansys/Ls-Dyna finite element analysis software is carried out to simulate four models with different number of empty holes. The simulation results show that the stronger the guiding effect of the empty holes on the crack propagation, the more obvious the inhibition effect on the crack in the remaining direction. The initial crack near the empty hole is generated by the continuous action of the stress wave, and the empty hole promotes the propagation of the explosion stress wave. The inconsistent guiding directions of adjacent empty holes are one of the reasons for the unsatisfactory blasting effect of multiple small diameter empty holes. The closer the empty hole is to the blasthole, the larger the maximum principal stress. By comparing the results of calculation with the numerical simulation, it is verified that the maximum principal stress near the empty hole is similar and the change rule is consistent. The above research has reference meaning to the location of the hollow hole in the actual blasting construction and the density of the empty hole
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
Multimodal medical data fusion has emerged as a transformative approach in
smart healthcare, enabling a comprehensive understanding of patient health and
personalized treatment plans. In this paper, a journey from data to information
to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart
healthcare. We present a comprehensive review of multimodal medical data fusion
focused on the integration of various data modalities. The review explores
different approaches such as feature selection, rule-based systems, machine
learning, deep learning, and natural language processing, for fusing and
analyzing multimodal data. This paper also highlights the challenges associated
with multimodal fusion in healthcare. By synthesizing the reviewed frameworks
and theories, it proposes a generic framework for multimodal medical data
fusion that aligns with the DIKW model. Moreover, it discusses future
directions related to the four pillars of healthcare: Predictive, Preventive,
Personalized, and Participatory approaches. The components of the comprehensive
survey presented in this paper form the foundation for more successful
implementation of multimodal fusion in smart healthcare. Our findings can guide
researchers and practitioners in leveraging the power of multimodal fusion with
the state-of-the-art approaches to revolutionize healthcare and improve patient
outcomes.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation
The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.This research was funded in part by the National Natural Science Foundation of China (grant nos. 41820104005, 42030112, 41904004) and in part by the and the Spanish Ministry of Science and Innovation (grant no. PID2020-117303GB-C22)
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Artificial Intelligence techniques can be used to classify a patient's
physical activities and predict vital signs for remote patient monitoring.
Regression analysis based on non-linear models like deep learning models has
limited explainability due to its black-box nature. This can require
decision-makers to make blind leaps of faith based on non-linear model results,
especially in healthcare applications. In non-invasive monitoring, patient data
from tracking sensors and their predisposing clinical attributes act as input
features for predicting future vital signs. Explaining the contributions of
various features to the overall output of the monitoring application is
critical for a clinician's decision-making. In this study, an Explainable AI
for Quantitative analysis (QXAI) framework is proposed with post-hoc model
explainability and intrinsic explainability for regression and classification
tasks in a supervised learning approach. This was achieved by utilizing the
Shapley values concept and incorporating attention mechanisms in deep learning
models. We adopted the artificial neural networks (ANN) and attention-based
Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and
classification of physical activities based on sensor data. The deep learning
models achieved state-of-the-art results in both prediction and classification
tasks. Global explanation and local explanation were conducted on input data to
understand the feature contribution of various patient data. The proposed QXAI
framework was evaluated using PPG-DaLiA data to predict heart rate and mobile
health (MHEALTH) data to classify physical activities based on sensor data.
Monte Carlo approximation was applied to the framework to overcome the time
complexity and high computation power requirements required for Shapley value
calculations.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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