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