Classification of small renal masses based on CT images and machine learning algorithms

Kidney tumor is among the leading causes of tumors and deaths worldwide. In all kidney tumor cases, an increasing number of small renal masses (SRMs) with a size smaller than 4 cm have been detected and they are becoming a typical problem for radiologists and surgeons. Most SRMs are either of renal angiomyolipoma (AML) or renal cell carcinoma (RCC), the former being benign and the latter being malignant. The malignant ones can be further classified into three types, clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (chRCC). Different kind of renal tumor requires varied treatment and management. In recent years, four-phase computer tomography (CT) has become the standard approach for kidney tumor examination. In most circumstances, classic AMLs and RCCs can be classified by a radiologist reading the CT images. While fat poor angiomyolipomas (fp-AML) set barriers to this classification method due to the loss of typical diagnosis characteristics. Radiologists are also incapable of differentiating malignant tumors. For now, SRM classification is mainly performed by pathological examination, which is time and resource consuming. Machine learning and one of its branch, deep learning, has been extended to medical image processing field. In this paper, support vector machine (SVM) and convolutional neural network (CNN) were respectively used to build models with the input of one of the last three phases of CT images and the combination of them. For the establishment of each model, at least 20% of overall patient cases were picked out randomly as independent testing subset and the rest undertook 10-fold cross validation for an objective and reliable evaluation of the models. It turned out that SVM algorithm using a linear kernel with phase 2 (corticomedullary) images as input acquired an accuracy of 0.93 and a sensitivity of 0.97 on patient’s tumor type prediction of fp-AML/RCC classification. CNN algorithm, consisting of 12 layers including 4 convolutional layers each followed by a max-pooling layer, one flatten layer, and three densely connected layers, with the help of activation functions, dropout strategy, and stochastic gradient descent (SGD) optimization method, achieved an accuracy of 0.85 on pRCC/chRCC/ccRCC categorization with phase 2 images as input. Images of corticomedullary stage were proved to be eligible for classifiers. This can be seen as a breakthrough since it is the first successful application of deep learning networks in renal tumor classification. Meanwhile, these two models were both balanced over different classes and they together provide a comprehensive solution to SRM classification. Given these findings, the two models can be a preliminary step for machine learning and especially deep learning algorithms to assist, improve, and finally revolutionize the conventional clinical decision making process to guide appropriate management and treatment

Equal Incremental Cost-Based Optimization Method to Enhance Efficiency for IPOP-Type Converters

Systematic optimization over a wide power range is often achieved through the combination of modules of different power levels. This paper addresses the issue of enhancing the efficiency of a multiple module system connected in parallel during operation and proposes an algorithm based on equal incremental cost for dynamic load allocation. Initially, a polynomial fitting technique is employed to fit efficiency test points for individual modules. Subsequently, the equal incremental cost-based optimization is utilized to formulate an efficiency optimization and allocation scheme for the multi-module system. A simulated annealing algorithm is applied to determine the optimal power output strategy for each module at given total power flow requirement. Finally, a dual active bridge (DAB) experimental prototype with two input-parallel-output-parallel (IPOP) configurations is constructed to validate the effectiveness of the proposed strategy. Experimental results demonstrate that under the 800W operating condition, the approach in this paper achieves an efficiency improvement of over 0.74\% by comparison with equal power sharing between both modules

VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantic compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks

Three-dimensional graphene nanosheets as cathode catalysts in standard and supercapacitive microbial fuel cell

© 2017 The Authors Three-dimensional graphene nanosheets (3D-GNS) were used as cathode catalysts for microbial fuel cells (MFCs) operating in neutral conditions. 3D-GNS catalysts showed high performance towards oxygen electroreduction in neutral media with high current densities and low hydrogen peroxide generation compared to activated carbon (AC). 3D-GNS was incorporated into air-breathing cathodes based on AC with three different loadings (2, 6 and 10mgcm−2). Performances in MFCs showed that 3D-GNS had the highest performances with power densities of 2.059±0.003Wm-2, 1.855±0.007Wm-2 and 1.503±0.005Wm-2 for loading of 10, 6 and 2mgcm−2 respectively. Plain AC had the lowest performances (1.017±0.009Wm-2). The different cathodes were also investigated in supercapacitive MFCs (SC-MFCs). The addition of 3D-GNS decreased the ohmic losses by 14–25%. The decrease in ohmic losses allowed the SC-MFC with 3D-GNS (loading 10mgcm−2) to have the maximum power (Pmax) of 5.746±0.186Wm-2. At 5mA, the SC-MFC featured an “apparent” capacitive response that increased from 0.027±0.007F with AC to 0.213±0.026F with 3D-GNS (loading 2mgcm−2) and further to 1.817±0.040F with 3D-GNS (loading 10mgcm−2)

Microbial fuel cells: From fundamentals to applications. A review

© 2017 The Author(s) In the past 10–15 years, the microbial fuel cell (MFC) technology has captured the attention of the scientific community for the possibility of transforming organic waste directly into electricity through microbially catalyzed anodic, and microbial/enzymatic/abiotic cathodic electrochemical reactions. In this review, several aspects of the technology are considered. Firstly, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Secondly, the development of the concept of microbial fuel cell into a wider range of derivative technologies, called bioelectrochemical systems, is described introducing briefly microbial electrolysis cells, microbial desalination cells and microbial electrosynthesis cells. The focus is then shifted to electroactive biofilms and electron transfer mechanisms involved with solid electrodes. Carbonaceous and metallic anode materials are then introduced, followed by an explanation of the electro catalysis of the oxygen reduction reaction and its behavior in neutral media, from recent studies. Cathode catalysts based on carbonaceous, platinum-group metal and platinum-group-metal-free materials are presented, along with membrane materials with a view to future directions. Finally, microbial fuel cell practical implementation, through the utilization of energy output for practical applications, is described

Application of Improved Q-Learning Algorithm in Dynamic Path Planning for Aircraft at Airports

Guiding and controlling aircraft within an airport is a decision-making process based on safety and efficiency in a highly dynamic and stochastic environment. Currently, many airports rely on manual monitoring and command to provide appropriate taxiing paths for aircraft. With the increasing complexity of airport structures and flight volumes, there is a need for an algorithm that can autonomously search for the shortest taxiing paths while satisfying the specific taxiing regulations and maintaining safe separations between aircraft in a dynamic scenario. We propose an improved approach based on the Q-Learning algorithm, a reinforcement learning method, to provide taxiing path guidance for aircraft. The Q-Learning algorithm exhibits adaptability in dynamic and stochastic environments. However, the traditional Q-Learning algorithm lacks the iteration stability and computational efficiency required in high-dynamic scenarios, and the shortest paths found often fail to meet the requirements due to the specific regulations of airport control. We first make three improvements to the Q-Learning algorithm to address these challenges. These improvements include optimizing Q-table exploration, resetting initial Q-table values, and introducing a dynamic exploration factor to enhance the algorithm’s computational efficiency and accuracy. We also incorporate conflict avoidance strategies related to civil aviation regulations to ensure that the final path adheres to airport control regulations. Finally, we validate the fused and improved algorithm in a gridded airport environment model. Compared to traditional methods, the results demonstrate that the improved algorithm provides more efficient taxiing guidance for aircraft while ensuring operational safety. Furthermore, the algorithm strategically avoids conflicts with other moving aircraft, thereby increasing the utilization of airport taxiing resources

Effect of forming angle on microstructure and properties of 4Cr5MoSiV1 steel formed by selective laser melting

Selective laser melting(SLM) 4Cr5MoSiV1 steel has good strength/hardness and wear resistance, which is the important guarantee to improve its service life. In order to optimize the structure and properties of 4Cr5MoSiV1 steel formed by selective laser melting, the microstructure, microhardness, tensile properties and wear resistance of 4Cr5MoSiV1 steel samples were studied under the different forming angles. The results show that with the increase of forming angle, the heat accumulation between the melt channels of sample is decreased, the grain size is decreased, and the fine grain strengthening is enhanced, so the microhardness of the sample is increased. With the increase of forming angle, the number of slip lap surfaces and the degree of slip of tensile sample are increased, and the normal stress at the melt boundary is decreased, so the tensile strength and elongation after fracture of the sample are increased. The wear mechanisms of the wear sample are mainly adhesive wear and oxidation wear, and the wear resistance of the sample is increased with the increase of forming angle. At the same forming angle, after repeated heat accumulation on the surface of the sample bottom layer, the fine grain strengthening and solid solution strengthening are weakened, and the microhardness and wear resistance are reduced, so the microhardness and wear resistance of the sample are decreased. The microhardness, wear resistance and tensile properties of 4Cr5MoSiV1 steel sample formed by SLM are positively correlated. The mechanical properties of sample are the highest at 45° forming angle, the highest tensile strength is 1576.5 MPa, the highest elongation is 17%, the highest micro-hardness of upper surface is 608.4HV, and the lowest wear resistance of the upper surface is 4.95×10-9 kg·N-1·m-1

In situ observation of dynamic galvanic replacement reactions in twinned metallic nanowires by liquid cell transmission electron microscopy

Galvanic replacement is a versatile approach to prepare hollow nanostructures with controllable morphology and elemental composition. The primary issue is to identify its fundamental mechanism. In this study, in situ liquid cell transmission electron microscopy was employed to monitor the dynamic reaction process and to explore the mechanism of galvanic replacement. The detailed reaction process was revealed based on in situ experiments in which small Au particles first appeared around Ag nanowires; they coalesced, grew, and adhered to Ag nanowires. After that, small pits grew from the edge of Ag nanowires to form tubular structures, and then extended along the Ag nanowires to obtain hollowed structures. All of our experimental observations from the viewpoint of electron microscopy, combined with DFT calculations, contribute towards an in-depth understanding of the galvanic replacement reaction process and the design of new materials with hollow structures