Triboelectric nanogenerators (TENGs) can covert mechanical energy into electricity in a clean and sustainable manner. However, traditional TENGs are mainly limited by the low output current, and thus ...their practical applications are still limited. Herein, a new type of TENG is developed by using conductive materials as the triboelectric layers and electrodes simultaneously. Because of the matched density of states between the two triboelectric layers, this simply structured device reaches an open‐circuit voltage of 1400 V and an ultrahigh current density of 1333 mA m−2 when poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) film and copper (Cu) or aluminum (Al) foil are used as the triboelectric pair. The current density increases by nearly three orders of magnitude compared with traditional TENGs. More importantly, this device can work stably in high‐humidity environments, which is always a big challenge for traditional TENGs. Surprisingly, this TENG can even perform well in the presence of water droplets. This work provides a new and effective strategy for constructing high‐performance TENGs, which can be used in many practical applications in the near future.
A new type of triboelectric nanogenerator (TENG) is developed by using conductive materials as the triboelectric layers and electrodes simultaneously. Because of the matched density of states, the TENG reaches an ultrahigh current density of 1333 mA m−2. More importantly, this device can work stably in high‐humidity environments, which is always a big challenge for traditional TENGs.
•Proposal of multifarious methodology with diverse stages for stock market forecast modelling.•The significance of Hurst exponent and Entropy-based Indicators to attain optimal forecasting.•Hurst ...exponent as a determining and prominent indicator for stock market indices.•Guidance in volatile and uncertain financial markets characterized by unexpected developments.•Characterizing complexity and self-similarity based on Fractal and Entropy analyses.
Complex systems constitute components that interact with one another and involve phenomena which are not always easy to understand in terms of their components and interactions. Alternative mathematical models have been developed so that the users’ tasks can be facilitated and an actual assistance can be provided for decision-making processes in case of every encountered incident which requires critical decision-making. Within this framework, financial systems can be regarded as complex systems with their volatile and vulnerable nature along with various parameters and interactions involved. Forecasting shifts in stock indices is crucial to validate the potential strategies of monetary mechanisms. Therefore, forecasting is an essential step in financial decision-making to manage data selection and attain robust prediction. Our purpose is to optimize the stock indices’ forecasting model in the stock indices dataset, constructed from the daily values. The following steps were applied to demonstrate the critical significance of Hurst exponent (HE) computed by Rescaled Range (R/S) fractal analysis when used as indicator in conjunction with Shannon entropy (SE) and Renyi entropy (RE) for the future forecasting ability of the stock indices. With this aim, the following stages were performed with indicators obtained from the applications and added into the dataset in the respective order. The first stage consists of: i) HE indicator, ii) Entropy based SE and RE indicators, iii) HE, SE and RE indicators. As the second stage, stock indices day-to-day valuation was evaluated using Multi Layer Regression (MLR), Support Vector Regression (SVR) and Feed Forward Back Propagation (FFBP) algorithms, applied for each indicator for comparative analysis. When compared with earlier works, no relevant work exists in the literature in which the algorithms and above-mentioned indicators have been used in conjunction with one another. This paper, through the multistage methodology and proposed model, demonstrates that HE is obviously a significant and critical determining indicator compared to RE and SE indicators for forecasting purposes. Consequently, experimental results demonstrate the accuracy and applicability of the proposed method. Thus, this study attempts to illustrate a new frontier in domains concerning critical decision-making processes in non-linear, dynamic, and volatile environments.
•We proposed a novel (L, 2) transfer feature learning (L2TFL) approach.•L2TFL can elucidate the optimal layers to be removed prior to selection.•We developed a novel selection algorithm of pretrained ...network for fusion approach.•SAPNF can determine the best two pretrained models for fusion.•We introduced a deep CCT fusion discriminant correlation analysis fusion method.
: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images.
: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet.
: On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods.
: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.
Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a ...new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.
Covid-19 diagnosis by WE-SAJ Wang, Wei; Zhang, Xin; Wang, Shui-Hua ...
Systems science & control engineering,
12/2022, Letnik:
10, Številka:
1
Journal Article
Recenzirano
Odprti dostop
With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are ...one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and feature mutual information of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.
Alcoholism changes the structure of brain. Several somatic marker hypothesis network-related regions are known to be damaged in chronic alcoholism. Neuroimaging approach can help us better ...understanding the impairment discovered in alcohol-dependent subjects. In this research, we recruited subjects from participating hospitals. In total, 188 abstinent long-term chronic alcoholic participants (95 men and 93 women) and 191 non-alcoholic control participants (95 men and 96 women) were enrolled in our experiment via computerized diagnostic interview schedule version IV and medical history interview employed to determine whether the applicants can be enrolled or excluded. The Siemens Verio Tim 3.0 T MR scanner (Siemens Medical Solutions, Erlangen, Germany) was employed to scan the subjects. Then, we proposed a 10-layer convolutional neural network for the diagnosis based on imaging, including three advanced techniques: parametric rectified linear unit (PReLU); batch normalization; and dropout. The structure of network is fine-tuned. The results show that our method secured a sensitivity of 97.73 ± 1.04%, a specificity of 97.69 ± 0.87%, and an accuracy of 97.71 ± 0.68%. We observed the PReLU gives better performance than ordinary ReLU, clipped ReLU, and leaky ReLU. The batch normalization and dropout gained enhanced performance as batch normalization overcame the internal covariate shift and dropout got over the overfitting. The results of our proposed 10-layer CNN model show its performance better than seven state-of-the-art approaches.
Protein–protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study ...of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.
•An explainable rule-based machine learning model of protein-protein interaction was built.•Each protein was represented by a binary GO and KEGG annotation vector.•We used the sum and the absolute difference of two protein vectors to represent a protein-protein pair.•The key GO and KEGG function features were identified using feature selection method.•The prediction rules of protein-protein interactions were learned using decision tree.
The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma.
A total of 492 ...patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor. A radiomics signature was generated by use of the relief-based feature method and the support vector machine classification method. A ROC regression curve was drawn for the predictive performance of radiomics features. Multivariate logistic regression models based on clinicopathologic and radiomics features were compared for discriminating mediastinal LN metastasis.
Clinical variables (sex, tumor diameter, tumor location) and predominant subtype were risk factors for pathologic mediastinal LN metastasis. The accuracy of radiomics signature for predicting mediastinal LN metastasis was 91.1% in ROC analysis (AUC, 0.972; sensitivity, 94.8%; specificity, 92%). Radiomics signature (Akaike information criterion AIC value, 80.9%) showed model fit superior to that of the clinicohistopathologic model (AIC value, 61.1%) for predicting mediastinal LN metastasis.
The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.
With the rapid development of applications with different use cases and service demands for edge network, network slicing is an emerging solution for satisfying service-oriented requirements, while ...the low earth orbit (LEO) satellite caching-assisted communication has been considered as one of the key elements for effective services. With limited resources at the edge of the radio access network (RAN), it is challenging to take advantage of the LEO content cache to joint allocation of communication, computing and caching space (3C) resources. To this end, we investigate the problem of resource slicing and scheduling of joint 3C resources in RAN edge scenario assisted by LEO content caching. A hierarchical resource slicing framework is proposed for dynamic allocation of multidimensional resources. The optimization variables are relaxed and the constraints are adjusted. The sequential quadratic programming (SQP) iteration algorithm is proposed as theoretical offline baseline. Due to its complex solving process and limited real-time performance, we incorporate Long Short-Term Memory (LSTM) into the Soft Actor-Critic (SAC) algorithm to aware extract the distribution characteristics of historical information and propose the deep reinforcement learning algorithm of LSTM-SAC. Meanwhile, the proportional priority based scheduling algorithm is employed in the intra-slice. Compared to SAC, TD3 and DDPG algorithms, the proposed algorithm is the closest to the theoretical value, improves the objective function by 6.95%, 9.52% and 11.52% respectively, which can significantly improve the system rate while satisfying the service level agreements.
Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs ...are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.
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