A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review,meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.     

Iron diffusivity into superconducting YBa2Cu3O7−δ at oxygen-assisted sintering: structural,magnetic, and transport properties

Optimization of materials exhibiting high-temperature superconductivity for producing controllable nano-devices is crucial for industrial applications. Herein we report a comprehensive study of the diffusion process between YBa₂Cu₃O_7−δ (YBCO) and iron particles. Fe diffusion into the YBCO matrix could be fundamental for multilayer systems with YBCO/Fe-alloy interfaces. We have found that the orthorhombic YBCO structure adopts to 3 wt% Fe, while for higher Fe content, a formation of BaFeO_3−δ and iron oxides was observed. Complementary measurements confirmed the strong superconductivity suppression in YBCO-Fe materials containing more than 7 wt% Fe. The YBCO with diffused Fe material retain the unit cell orthorhombicity (max. 3 wt% Fe), and their superconducting properties follow the principle of critical scaling with different exponents (γ). The critical current density (J_c), pinning fields (H_P) exhibit γ = 1, the first critical field (H_c1) shows γ = 1/2, and critical temperature (T_c) demonstrates γ = 7/4.     

A Light Weight Secure Communication Scheme for Wireless Sensor Networks

Wireless Personal Communications - The majority of security systems for wireless sensor networks are based on symmetric encryption. The main open issue for these approaches concerns the...     

Design and Analysis of Single-Bit Ternary Matched Filter

Wireless Personal Communications - Matched filtering has found its way in many diverse applications such as communication, signal processing and more. The emphasis of this paper is on the design...     

Utility-scale energy storage in an imperfectly competitive power sector

Interest in sustainability has increased the share of variable renewable energy sources (VRES) in power generation. Energy storage systems' potential to mitigate intermittencies from non-dispatchable VRES has enhanced their appeal. However, the impacts of storage vary based on the owner and market conditions. We examine the policy implications of investments in utility-scale battery storage via a bi-level optimization model. The lower level depicts power system operations, modeled as either perfect competition or Cournot oligopoly to allow for the assessment of producer market power. The upper-level investor is either a welfare-maximizer or a profit-maximizing standalone merchant to reflect either welfare enhancement or arbitrage, respectively. We implement a realistic case study for Western Europe based on all possible size-location storage investment combinations. We find that market competition affects investment sizes, locations, and their profitability more than the investor's objectives. A welfare-maximizer under perfect competition invests the most in storage capacity. Consumers typically gain most from storage investments in all cases, exceeding the gains for the investors. Specifically, our results show that storage investments may either not occur or be located differently than at social optimum, if market power is exerted. Thus, policy makers need to anticipate producer market power when setting regulation.