Energy and exergy analysis of a newly designed photovoltaic thermal system featuring ribs, petal array, and coiled twisted tapes: Experimental analysis by Banw Omer Ahmed, Adnan Ibrahim, Hariam Luqman Azeez, Sharul Sham Dol, Ali H.A. Al-Waeli, Mahmoud Jaber

“…Photovoltaic systems suffer from electrical efficiency loss due to increases in surface temperature. To tackle this problem, Photovoltaic Thermal, consisting of photovoltaic and tube configurations attached to its back, is suggested. …”
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Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack by Zhengquan Luo, Yunlong Wang, Nianfeng Liu, Zilei Wang

“…Abstract Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. …”
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ENGLISH VERSION: AGE FEATURES OF RADIOACTIVE IODINE (131I) ABSORPTION BY RAT THYROID GLANDS IN CORRECTION OF THE DIETARY IODINE DEFICIENCY WITH ORGANIC IODINE by O.I. Ryabukha

“…The insufficient efficacy of inorganic iodine drugs poses the problem of search for new means for iodine deficiency treatment and prevention. …”
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Special Features of a 24-hour Arterial Blood Pressure Profile in Healthy Children During the Period of Adaptation to Studying at Different School Types by I. N. Zakharova, T. M. Tvorogova, I. I. Pshenichnikova, M. A. Shkolnikova

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Epidemic situation and features of accompanying therapy in the treatment of socially significant infectious diseases in penitentiary populations before, during, and after the COVID... by V. M. Kolomiets, N. A. Polshikova, A. Yu. Petrov, A. L. Kovalenko, E. V. Talikova

“…The second stage of the study was the analysis of data on tuberculosis (TB): features of the clinical course, etiotropic treatment, and the possibility of increasing efficiency through accompanying therapy.Results. …”
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Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network by Bo Huang, Yiwei Lu, Hao Ma, Changsheng Yin, Ruopeng Yang, Yongqi Shi, Yu Tao, Yongqi Wen, Yihao Zhong

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Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification by Aysel Topşir, Ferdi Güler, Ecesu Çetin, Mehmet Furkan Burak, Melih Ağraz

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Microscopic research of shoots of the Salix cinerea L. of Ukrainian flora by N. V. Borodina, V. M. Kovalov, O. M. Koshovyi, O. V. Hamulia

“…The purpose of this work is the determination of microscopic diagnostic features of the willow goat (Salix cinerea L.) shoots of Ukrainian flora for the plant material identification. …”
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Hearing problems in humans and mouse models with rare copy number variants associated with schizophrenia: a scoping review protocol [version 2; peer review: 2 approved, 1 approved... by Noushin Saadullah Khani, Maria Richards-Brown, Jennifer F Linden, Lauren Varney, Elvira Bramon, Stephen Murtough, Chen Lu, Daniele Panconesi, Marius Cotic, Rosemary Abidoph, Alvin Richards-Belle, Daisy Mills

“…Looking ahead, if hearing problems are a clinical feature in these groups (including humans and related mouse models), they may serve as useful genetic models for future mechanistic studies.…”
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CO₂ Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework by Kezhi Tu, Yanfeng Wang, Xian Li, Xiangxi Wang, Zhenzhong Hu, Bo Luo, Liu Shi, Minghan Li, Guangqian Luo, Hong Yao

“…Traditional CO₂ emission accounting methods of power plants are deficient in computational efficiency and accuracy. To solve these problems, this study proposes a novel RF-RFE-DF-Optuna (random forest–recursive feature elimination–deep forest–Optuna) framework, enabling accurate CO₂ emission prediction for coal-fired power plants. …”
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Cryptographic hardness assumptions identification based on discrete wavelet transform by Ke Yuan, Yu Du, Yizheng Liu, Rongjin Feng, Bowen Xu, Gaojuan Fan, Chunfu Jia

“…Based on these wavelets, a feature extraction method is designed to extract features from both ciphertexts and digital signatures. …”
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Prediction of Remaining Life of Cutting Tool Based on DNN by LIU Sheng-hui, ZHANG Ren-jing, ZHANG Shu-li, MA Chao, ZHANG Hong-guo

“…In order to better solve the problem that the remaining life of cutting tool is difficult to predict accurately, this paper studies three aspects of the selection of monitoring indexes, the extraction of data features and the establishing of prediction models Firstly, Cutting force and vibration frequency were selected as the indirect monitoring indexes of cutting tool These two indexes can accurately reflect the state of cutting tool, and also can solve the problem that the selecting the direct monitoring indexes causes, the wear analysis results of cutting tool being too subjective in the traditional state monitoring method Secondly, feature extraction is carried out by using wavelet packet analysis, and then the entropy values of the monitoring data are obtained They are taken as the input data Thirdly, the input data are used as the training data and testing data of the prediction model based on Deep Neural Network (DNN) Finally, the simulation experiments of the prediction method are carried out by using the real data of the workshop The results show that the model can effectively predict the useful life…”
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Novel View Synthesis of Defocused Blur Scenes Based on Neural Radiance Fields by Zhaoji Lin, Yuxin Zheng, Li Yao

“…For the problem of similar two-dimensional coordinates restricting the model to distinguish scene details in the non-focal plane background, a fine sampling weight using multiscale depth feature fusion is further proposed, and a staged optimization strategy is designed. …”
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Research on hybrid intrusion detection based on improved Harris Hawk optimization algorithm by Pengzhen Zhou, Huifu Zhang, Wei Liang

“…The algorithm introduces the singer map to initialise the population, uses multi-information fusion to obtain the best prey position, and applies the sine function-based escape energy to execute a prey search strategy to obtain the optimal subset of features. In addition, the original data is preprocessed by the k-nearest neighbour and deep denoising autoencoder (KNN-DDAE) to relieve the imbalance problem of the network traffic data. …”
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A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance by Rui Zou, Zhihui Xin, Guisheng Liao, Penghui Huang, Rui Wang, Yuhu Qiao

“…CBAM enhances flame recognition by weighting important channel features and focusing spatially on small flame areas, helping address the class imbalance problem. …”
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The Imbalanced Target Classification Method Based on Mixed Learning of Virtual and Real Data by Fengyu Yang, Peng Wang, Wutao Qin, Zhangze Liao

“…We proposes a category imbalance classification model based on mixed feature enhancement between virtual and real domains to address the class imbalance problem in maritime target classification applications. …”
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Robust tampering detection and localization of composite image by Xiao WANG, Rong ZHANG, Lijun GUO

“…Aiming at the problem of tamper detection of composite image of natural images and highly simulated computer-generated images,a method of extracting image block color and texture feature based on differential histogram and local binary texture descriptor in YCbCr color space was proposed.By training posterior probability support vector machine,the image block to be measured was identified.In the case of non-overlapping block,the approximate tampering area was general judged,then the block was discriminated by pixel in the region,ultimately the accurate location of tampering area was achieved.The experimental results show that the recognition rate of 128 dpi×128 dpi image blocks is 94.75%,which is higher than other methods.The tapering region of the synthesized image can be precisely positioned,and the rotation and scaling operation show good coercivity.…”
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Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

“…Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.…”
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1D-Concatenate based channel estimation DNN model optimization method by Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE

“…In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed.In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient disappearance problem was suppressed, and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation.In order to verify the effectiveness of the optimization method, a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments.Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method, and the channel gain can be increased by up to 3 dB under high signal-to-noise ratio.This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication, especially the improvement effect is significant under high signal-to-noise ratio.…”
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Few-shot Named Entity Ｒecognition for Medical Text by QIN Jian, HOU Jian-xin, XIE Yi-ning, HE Yong-jun

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