Planning Model for Integrated Energy Supply System in Park Level Regions Under the Energy Internet

With the reduction of traditional fossil fuels and the increasing severity of environmental issues, it is of great significance to study energy system planning and optimization models that complement and integrate multiple energy utilization methods in the context of the energy internet for building an integrated energy supply system. Firstly, this article divides the planning indicators of the regional integrated energy supply system into four categories based on the goal of “two highs and three lows”; Secondly, analyze the three key issues of exergy efficiency, economy, and multi energy coupling in regional integrated energy planning; Finally, a multi-objective planning model for regional integrated energy systems that takes into account equipment capacity planning and operation scheduling optimization is proposed, with the optimization objectives of minimizing the annual value of full life cycle cost and maximizing efficiency, and a double-layer optimization structure is designed for efficient solution

Equivalence of the mean square stability between the partially truncated Euler–Maruyama method and stochastic differential equations with super-linear growing coefficients

Abstract For stochastic differential equations (SDEs) whose drift and diffusion coefficients can grow super-linearly, the equivalence of the asymptotic mean square stability between the underlying SDEs and the partially truncated Euler–Maruyama method is studied. Using the finite time convergence as a bridge, a twofold result is proved. More precisely, the mean square stability of the SDEs implies that of the partially truncated Euler–Maruyama method, and the mean square stability of the partially truncated Euler–Maruyama method indicates that of the SDEs given the step size is carefully chosen

Planning Model for Integrated Energy Supply System in Park Level Regions Under the Energy Internet

With the reduction of traditional fossil fuels and the increasing severity of environmental issues, it is of great significance to study energy system planning and optimization models that complement and integrate multiple energy utilization methods in the context of the energy internet for building an integrated energy supply system. Firstly, this article divides the planning indicators of the regional integrated energy supply system into four categories based on the goal of “two highs and three lows”; Secondly, analyze the three key issues of exergy efficiency, economy, and multi energy coupling in regional integrated energy planning; Finally, a multi-objective planning model for regional integrated energy systems that takes into account equipment capacity planning and operation scheduling optimization is proposed, with the optimization objectives of minimizing the annual value of full life cycle cost and maximizing efficiency, and a double-layer optimization structure is designed for efficient solution

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively captures long-dependent information in time series. To evaluate the validity of the hybrid model, inertial measurement units (IMUs) and pressure sensors were used to obtain motion data from 15 subjects. Five locomotion modes were tested for the hybrid model, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. Furthermore, the data features of the ramp were inconspicuous, leading to large recognition errors. To address this challenge, the SENet module was incorporated, which improved recognition rates to some extent. The proposed model automatically extracted the features and achieved an average recognition rate of 97.93%. Compared with known algorithms, the proposed model has substantial recognition results and robustness. This work holds promising potential for applications such as limb support and weight bearing

Parametric Study on the Ground Control Effects of Rock Bolt Parameters under Dynamic and Static Coupling Loads

Dynamic and static coupling loads (DSLs) are one of the most common stress environments in underground engineering. As the depth of a roadway increases over the life of a mine, the static load of the ground stress field increase multiplies, and the cyclic operation at the working face releases a large amount of dynamic energy. Therefore, deep roadways easily induce dynamic disasters during production. In this paper, a deep roadway numerical model was built with FLAC3D to test the deep roadway under DSLs and was simulated with 16 different support designs. The ground stability in each support condition was examined and compared in terms of the ground deformation and scope of failure. The underlying support mechanism was further analyzed with numerical modeling in view of the deformation in the surrounding rock mass induced by variations in the support parameters. The results show that shortening the bolt spacing is an effective measure to control the deformation of surrounding rock whatever DSLs or static load. Under static load, the larger the anchoring length is, the more stable the surrounding rock is. Under DSLs, end grouting length (S = 600 mm) and full grouting length (S = 1800 mm) can effectively control the deformation of surrounding rocks and enhance the stability of surrounding rocks. The results contribute to the design of supports in the field of underground coal mines and provide a basis for determining the reasonable support scheme for roadways

Self-Assembled Fe₃O₄/Polymer Hybrid Microbubble with MRI/Ultrasound Dual-Imaging Enhancement

An Fe₃O₄ nanoparticle/polymer hybrid microbubble was developed using a facile self-assembly approach. This approach involves two steps, including the initial fabrication of the iron oxide nanoparticle (IONP)/polymer hybrid microcapsules via self-assembly and a subsequent gas-filling process to yield the final microbubbles. Both in vitro and in vivo experiments demonstrated that the composite gas-filled microbubbles exhibit excellent <i>T</i>₂-weighted magnetic resonance imaging (MRI) enhancement as well as ultrasound (US) imaging enhancement capabilities. Besides, this flexible approach allows the facile control of the microbubbles’ size and thus the imaging capabilities of the microbubbles through the tuning of the molar ratio between the precursors

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Background: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms