The mesoscopic parameters of the discrete element method (DEM) are crucial for accurately simulating the strength characteristics of slope soil. Existing DEM parameter calibration methods suffer from insufficient multisource data mining and low levels of intelligence, severely limiting the accuracy and reliability of calibration results. Hence, this study proposes a multisource-intelligent calibration method for DEM parameters. First, the key DEM parameters are determined using multidimensional correlation analysis methods, and a multisource data set is constructed based on these key DEM parameters, test parameters, and inherent soil properties. Then, the Grid Search Cross Validation (GridSearchCV) and eXtreme Gradient Boosting (XGBoost) are used to mine information from multisource data, combined with multiple interpretability analysis algorithms to enhance the model's transparency, developing an interpretable soil strength prediction model. Finally, coupling the genetic algorithm (GA) with the prediction model enables dynamic optimization and intelligent calibration of key DEM parameters. The calibration results are then comprehensively validated and applied to analyze the macro-mesoscopic strength characteristics of slope soil. The results indicate that the GSCV-XGBoost model can effectively integrate multisource data. Its predictions not only meet physical consistency principles but also show excellent prediction accuracy. The proposed intelligent calibration method enhances the accuracy of DEM parameter calibration and has been validated as reliable, which also shows favorable application effects in a specific slope engineering.

Adversarial samples can easily compromise deep neural network models. The transferability of adversarial samples allows them to attack unknown neural network models, posing a significant threat to the applications of many neural network models. While existing ensemble-based attack methods have shown some improvements in enhancing the transferability of adversarial samples, they fail to maintain individual gradient information for each model during the optimization process, thereby limiting the transferability of adversarial samples. To address this issue, this paper proposes a method to enhance the transferability of adversarial samples through self-maintained momentum in an ensemble approach. Firstly, the gradient information from the ensemble of models is decomposed to focus on the individual gradient information of each model. Next, individual momentum is assigned to all models, allowing them to self-maintain momentum information during the iterative optimization process. Finally, the momentum information from all models is integrated into the ultimate gradient information. The experimental data demonstrates that the proposed approach in this study exhibits an improvement of approximately 2% on white-box models and a significant enhancement of 10% to 20% on black-box models, effectively augmenting the transferability of adversarial samples.

In the context of the Internet of Things (IoT), resource-constrained mobile edge computing (MEC) can no longer fully meet the needs of the rapidly growing number of mobile users; hence, cloud-edge collaborative computing has been developed. This paper focuses on the total energy consumption of the system and heterogeneity of scenarios, and a collaborative cloud-edge computation offloading approach with near real-time decision making is proposed. First, a general cloud-edge collaborative computation offloading model is abstracted from typical applications, and the energy consumption for edge and cloud offloading is calculated separately by considering both transmission and computational energy consumption. The problem is formulated as an integer linear program (ILP) with multidimensional resource constraints and is proven to be NP-hard. Then, a novel primal-dual computation offloading (PDCO) algorithm is designed to make near real-time offloading decisions one by one based on the sequential arrival of task requests. The approximation ratio of PDCO is derived through the weak duality property and the price-resource increment relationship. The experimental results show that under the guidance of total cost influenced by marginal prices, PDCO not only avoids blindly making offloading decisions but also effectively alleviates the shortage of resources on edge servers (ESs), approaching the optimal performance in terms of total energy consumption and resource utilization.

Chronic diseases, such as CKD, diabetes, and hypertension, are widely recognized as major challenges in healthcare, but timely diagnosis of these conditions remains challenging. In this regard, employing machine learning, particularly deep learning, to process EHR data for disease prediction presents a viable solution. Furthermore, leveraging the correlations among different chronic diseases for joint prediction might enhance effectiveness and reduce time and economic costs. In this work, we conduct research on the joint prediction of CKD, diabetes, and hypertension - three chronic diseases affecting the 18-65 age group - based on the MIMIC-IV dataset. We propose the MLNet and develop a comprehensive data preprocessing pipeline. This includes using a multi-label feature selection method based on mutual information to select optimal feature subsets and employing the Focal Loss to address data imbalance. For the task of jointly predicting the three chronic diseases, MLNet outperforms six other predictive algorithms, achieving a best Micro AUC of 90.1%. Among the three chronic diseases, MLNet demonstrates the best predictive performance for CKD, with an AUC of 95.9% and a recall rate of 95.8%.

This paper presents a 2MHz CMOS active rectifier for wireless power transfer (WPT) systems with novel PWM mode current compensation. By adding PWM mode feedback loops to generate compensation current adaptively, both on/off delay are eliminated. This rectifier can achieve high power conversion efficiency (PCE) and voltage conversion ratio (VCR) under various loading conditions and input power levels. Moreover, with PWM mode, compensation current range can be wide and flexible. This active rectifier was designed in 0.18$\mu$m CMOS process. Simulation results show that, with the input ac voltage ranging from 2-4V, peak PCE achieves 92.6% with 100$\Omega$ loading, peak VCR achieves 98.4% with 1K$\Omega$ loading, and maximum output power reaches 112mW.

Change detection (CD) is one of the most important topics in the field of remote sensing. In this letter, we propose an effective satellite images CD network named DifUnet++. As the presentation of explicit difference is more conducive to extract change features, we design a differential pyramid of two input images as the input of Unet++. Considering the scale diversity of changed regions in remote sensing images, a multiply side-outs fusion strategy is adopted to predict the detection results of different scales. Furthermore, a learning upsampling method is utilized to refine the details of CD. The proposed architecture is evaluated on two public satellite image CD data sets. The experimental results show that our method performs much better than state-of-the-art methods.

A novel quantum anonymous communication scheme is proposed on the basis of Dining Cryptographers (DC) Problem and nonmaximally entangled qubit pair. The scheme takes advantage of quantum-mechanical impossibility of local unitary transformation between certain nonmaximally entangled states to provide truly random number which can be brightly used in anonymous communication protocols based on DC-Nets. The analysis and discussions demonstrate that the proposed quantum anonymous communication scheme can be performed securely with high capacity and untraceability. The scheme can also be extended to a (2, 2) quantum secret sharing (QSS) scheme.