Smoothing effect of rough differential equations driven by fractional Brownian motions

In this work we study the smoothing effect of rough differential equations driven by a fractional Brownian motion with parameter H > 1/4. The regularization estimates we obtain generalize to the fractional Brownian motion previous results by Kusuoka and Stroock

A group decision making procedure for selecting data warehouse systems

Evaluating and selecting the most suitable data warehouse system for development is complex and challenging. To effectively solve this problem, this paper presents a group decision making procedure for evaluating and selecting data warehouse systems. The subjectiveness and imprecision of the decision making process are adequately modeled by the use of interval-valued based intuitionistic fuzzy numbers. The concept of ideal solutions is adopted for determining the overall performance of each alternative data warehouse system across all the selection criteria on which the decision is made. An example is presented for demonstrating the applicability of the proposed procedure for solving real world data warehouse system selection problems

Evaluating the performance of e-waste recycling programs using fuzzy multiattribute group decision making model

This paper presents a fuzzy multiattribute group decision making model for evaluating the performance of e-waste recycling programs to meet the best sustainability interests of an e-waste recycling company. Intuitionistic fuzzy numbers are used for representing the subjective and imprecise assessments of the decision maker in evaluating the relative importance of the attributes and the performance of individual e-waste recycling programs. An effective algorithmis developed based on the concept of ideal solutions for calculating the overall performance index for each e-waste recycling program across all attributes. An example is presented for demonstrating the applicability of the fuzzy multiattribute group decision making model for dealing with real world e-waste recycling program performance evaluation problems

An intuitionistic fuzzy multicriteria group decision model for solid waste disposal site selection

This paper formulates the selection of solid waste disposal sites as a multicriteria decision making problem and presents an intuitionistic fuzzy multicriteria group decision model for effectively solving the problem. The subjectiveness and imprecision of the decision making process is adequately handled using intuitionistic fuzzy numbers. The concept of ideal solutions is adopted for determining the overall performance of each solid waste disposal site alternative across all the selection criteria. As a result, effective decisions can be made on the selection of the most suitable solid waste disposal site. An example is presented that shows the proposed decision model is simple and effective for solving the selection problem in real world settings

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

BackgroundCOVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day.MethodThis study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap.ResultsThe proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%.ConclusionOur MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.</div

Public reporting as a prescriptions quality improvement measure in primary care settings in China: variations in effects associated with diagnoses

The overprovision and irrational use of antibiotics and injections are a major public health concern. Public reporting has been adopted as a strategy to encourage good prescribing practices. This study evaluated the effects of public reporting on antibiotic and injection prescriptions in urban and rural primary care settings in Hubei province, China. A randomized control trial was conducted, with 10 primary care institutions being subject to public reporting and another 10 serving as controls. Prescription indicators were publicly reported monthly over a one-year period. Prescriptions for bronchitis, gastritis and hypertension before and after the intervention were collected. Difference-in-difference tests were performed to estimate the effect size of the intervention on five prescription indicators: percentage of prescriptions containing antibiotics; percentage of prescriptions containing two or more antibiotics; percentage of prescriptions containing injections; percentage of prescriptions containing antibiotic injections; and average prescription cost. Public reporting had varied effects on prescriptions for different diagnoses. It reduced antibiotic prescribing for gastritis. Prescriptions containing injections, especially antibiotic injections, also declined, but only for gastritis. A reduction of prescription costs was noted for bronchitis and gastritis. Public reporting has the potential to encourage good prescribing practices. Its effects vary with different disease conditions

Fuzzy multicriteria decision support for solid waste disposal method and site selection

This paper presents a fuzzy multicriteria decision making approach for evaluating and selecting the most suitable solid waste disposal method and disposal site respectively. Case-based reasoning and fuzzy IF-THEN rules are adopted forevaluating and determining the most suitable solid waste disposal method based on past experiences. An efficient algorithm is developed for producing a performance index for every disposal site alternative across all selection criteria. As a result, thedecision on the most suitable site can be made. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An example is presented for demonstrating the applicability of the approach

In Situ Diagnosis of Multi-site Wire Bonding Failures for Multichip IGBT Power Modules Based on Crosstalk Voltage

The online detection of aging bond wires is key to the health status awareness of smart power converters. In this paper, a new health precursor of the gate voltage undershoot VGE(pk) of the complementary switch in the half-bridge structure is proposed. It can be used to identify and distinguish multi-site bonding wire failures for both insulated gate bipolar transistors (IGBTs) and freewheeling diodes (FWDs) in multichip IGBT modules. A theoretical analysis is conducted to derive this novel precursor, which is then verified by experimental results. Then, a dedicated read-out circuit is designed for the data acquisition front end that can be integrated into gate drivers for in-situ monitoring. Finally, the effectiveness of this method is evaluated under changing operating conditions including the DC-bus voltage, the load current, and the junction temperature. The effects of their fluctuations are studied and quantified, with corresponding calibration relationships provided to improve precursor accuracy

TBNet: a context-aware graph network for tuberculosis diagnosis

Background and objectiveTuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT imagesMethodsTraditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normalResultsThe proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experimentsConclusionsOur TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.</div

Description generation for remote sensing images using attributes

Image captioning generates a semantic description of an image. It deals with image understanding and text mining, which has made great progress in recent years. However, it is still a great challenge to bridge the “semantic gap” between low-level features and high-level semantics in remote sensing images, in spite of the improvement of image resolutions. In this paper, we present a new model with an attribute attention mechanism for the description generation of remote sensing images. Therefore, we have explored the impact of the attributes extracted from remote sensing images on the attention mechanism. The results of our experiments demonstrate the validity of our proposed model. The proposed method obtains six higher scores and one slightly lower, compared against several state of the art techniques, on the Sydney Dataset and Remote Sensing Image Caption Dataset (RSICD), and receives all seven higher scores on the UCM Dataset for remote sensing image captioning, indicating that the proposed framework achieves robust performance for semantic description in high-resolution remote sensing images