Robust time series analysis for forecasting photovoltaic energy yield by Sapundzhi Fatima, Chikalov Aleksandar, Georgiev Slavi, Georgiev Ivan, Todorov Venelin

“…The analysis proceeds with the generation of monthly predictions for the dataset, complete with their own confidence bounds, thereby showcasing the forecasting strength of the models. …”
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Mortality impact, risks, and benefits of general population screening for ovarian cancer: the UKCTOCS randomised controlled trial by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Anne Dawnay, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Mahesh Parmar, Ian J Jacobs

“…There was no significant reduction in ovarian and tubal cancer deaths in either MMS (p = 0.580) or USS (p = 0.360) groups compared to the C group. …”
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A New Finite-Difference Method for Nonlinear Absolute Value Equations by Peng Wang, Yujing Zhang, Detong Zhu

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Using AI in Optimizing Oral and Dental Diagnoses—A Narrative Review by Amelia Surdu, Dana Gabriela Budala, Ionut Luchian, Liliana Georgeta Foia, Gina Eosefina Botnariu, Monica Mihaela Scutariu

“…AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes.…”
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Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids by Alejandro Hernandez-Matheus, Kjersti Berg, Vinicius Gadelha, Mònica Aragüés-Peñalba, Eduard Bullich-Massagué, Samuel Galceran-Arellano

“…This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. …”
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Optimization of Quantitative Financial Data Analysis System Based on Deep Learning by Meiyi Liang

“…In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. …”
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Nove lincidence matrix differential power analysis for resisting ghost peak by Zijing JIANG, Qun DING

“…At present, differential power analysis (DPA) is one of the most important threats to the security of block ciphers in chips.When the collected power trace is insufficient, DPA is vulnerable to ghost peak caused by the difference mean value generated by the wrong key.Based on DPA, a incidence matrix differential power analysis (IMDPA) was proposed which could effectively resist ghost peak.The prediction difference mean matrix was constructed to avoid the influence of the non leaking interval on the key guessing of the leaking interval by using the weak correlation of the guessing key in the non leaking interval.The proposed IMDPA was tested in different leak intervals of AES-128 algorithm.The results show that compared with traditional DPA, IMDPA requires less (up to 85%) power trace to guess the correct key.At the same time, the key guessing efficiency of AES-128 under the implementation of protective measures by IMDPA still has obvious advantages.In order to further verify the universality of IMDPA in block ciphers, experimental verification is conducted on SM4 algorithm.Compared with traditional DPA, IMDPA requires less (up to 87.5%) power traces to guess the correct key.…”
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Low Complexity Mode Decision for 3D-HEVC by Qiuwen Zhang, Nana Li, Yong Gan

“…The basic idea of the method is to utilize the correlations between depth map and motion activity in prediction mode where variable size CU and DE are needed, and only in these regions variable size CU and DE are enabled. …”
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Performance of digital filtering and synchronization method for APD communication receiver by Hui-Jun Zhang, Peng Lin, Tong Wang, Zi-Qi Zhang, Bai-Qiu Zhao, Wen-Fang Jiao, Xiao-Nan Yu

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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. …”
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Time-varying channel estimation in reconfigurable intelligent surface assisted communication system by Kai SHAO, Ben LU, Guangyu WANG

“…Aiming at the key problems need to be solved, such as cascade channel sparse representation, time-varying channel parameter tracking and signal reconstruction, for time-varying cascade channels estimation of reconfigurable intelligent surface (RIS) assisted communication system, a Khatri-Rao and hierarchical Bayesian Kalman filter (KR-HBKF) algorithm was proposed.Firstly, the Khatri-Rao product and Kronecker product transformations were used to obtain the sparse representation of RIS cascaded channels based on the sparse characteristics of channels, thus the RIS cascaded channel estimation problem was transformed into a low-dimensional sparse signal recovery problem.Then, according to the state evolution model of RIS cascaded channel, the time correlation parameter was introduced into the prediction model of HBKF algorithm, and the improved HBKF was applied to solve the problem of time-varying channel parameter tracking and signal reconstruction for completing the time-varying cascaded channels estimation.The sparsity and time correlation of the channel were comprehensively considered in the KR-HBKF algorithm, thus better estimation accuracy could be obtained with small pilot overhead.Compared with the traditional compressed sensing algorithm, the simulation results show that the proposed algorithm has about 5 dB estimated performance improvement, and better robustness performance under different time-varying channel conditions.…”
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Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement by Hongqiang Liu, Zhongliang Zhou, Haiyan Yang

“…Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. Then the probability distribution and probability distribution of measurement prediction residual are combined into a new likelihood function to improve the efficiency of updating the model probability. …”
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Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury by Jiamu Ma, Peng Wei, Xiao Xu, Ruijuan Dong, Xixi Deng, Feng Zhang, Mengyu Sun, Mingxia Li, Wei Liu, Jianling Yao, Yu Cao, Letian Ying, Yuqing Yang, Yongqi Yang, Xiaopeng Wu, Gaimei She

“…It was postulated that the effective compounds would bind with key targets from the PI3K-AKT signaling pathway, as indicated by the prediction model of compound-target interaction (R2 > 0.95). …”
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Radiomics in the differential diagnosis of glioblastoma under the primary neurooncoimaging conditions by Nikita E. Maslov, Daria A. Valenkova, Alexander M. Sinitсa, Gennadiy E. Trufanov, Vladimir M. Moiseenko, Alexander Yu. Efimtsev, Vera V. Chernobrivtseva

“…The aim of our study is to develop a radiomics model for IDH mutation status prediction, which can be applied to primary diagnostic imaging in patients with suspected adult-type diffuse gliomas. …”
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Classifying schizophrenia using functional MRI and investigating underlying functional phenomena by Yangyang Liu, Bi Wan, Zixuan Liu, Shuaiqi Zhang, Pei Liu, Ningning Ding, Yuxin Wang, Jun Dong, Moiz Kabeer Ahmad, Haisan Zhang

“…Results: The average prediction accuracy of various ML classifiers reached 0.9241 by fALFF, ReHo, and DC values. …”
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Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America by Débora de Melo Almeida, Sara Sebastiana Nogueira, Emanuel Araújo Silva, João Matheus Ferreira de Souza, Antonio Leandro Chaves Gurgel, Alex Nascimento de Sousa

“…Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. …”
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Remote Embedded System for Agricultural Field Monitoring: Enhancing Resource Allocation in Agriculture by Ronald J. Contijo, Lucas C. de Camargo, Renan O. A. Takeuchi, André L. S. Moscato, Lafaiete H. R. Leme, Wenderson N. Lopes

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Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer by Zhihao Dai, Jin Jiang, Qianping Chen, Minghua Bai, Quanquan Sun, Yanru Feng, Dong Liu, Dong Wang, Tong Zhang, Liang Han, Litheng Ng, Jun Zheng, Hao Zou, Wei Mao, Ji Zhu

“…Area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were determined. …”
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On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates by Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz

“…For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/°C on average from five simulations). …”
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Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement by Murilo Eduardo Casteroba Bento

“…The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.…”
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