Systematic, computational discovery of multicomponent and one-pot reactions by Rafał Roszak, Louis Gadina, Agnieszka Wołos, Ahmad Makkawi, Barbara Mikulak-Klucznik, Yasemin Bilgi, Karol Molga, Patrycja Gołębiowska, Oskar Popik, Tomasz Klucznik, Sara Szymkuć, Martyna Moskal, Sebastian Baś, Rafał Frydrych, Jacek Mlynarski, Olena Vakuliuk, Daniel T. Gryko, Bartosz A. Grzybowski

“…Moreover, when supplemented by models to approximate kinetic rates, the algorithm can predict reaction yields and identify reactions that have potential for organocatalysis. …”
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An artificial market model for the forex market by Kimihiko Sasaki, Daisuke Yokouchi

“…Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. …”
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Application of Composite Method for Determining Fault Location on Electrical Power Distribution Lines by Chinweike Okoli, Boniface Anyaka, Chidiogo Nwokedi, Victor Anya

“…The study, therefore, is on how a composite fault location technique can be applied to predict the location of faults on the distribution lines. …”
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Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles Using Time Series Analysis by Ahmad Mohammadi, Reza Ahmari, Vahid Hemmati, Frederick Owusu-Ambrose, Mahmoud Nabil Mahmoud, Parham Kebria, Abdollah Homaifar

“…This detection is facilitated through time series analysis at 25 and 50 s intervals to build a profile of data errors and distribution to predict the probability of such attacks. To evaluate the algorithm's effectiveness, five different test datasets depicting four types of spoofing scenarios—turn-by-turn, overshoot, stop, and multiple small biased attacks—were created using data from the publicly accessible Honda Research Institute Driving Dataset (HDD). …”
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Unsplit Youngs Method for Tracking the Moving Interface of Expansible Grout by Xiaolong Li, Tiantian Zhang, Meimei Hao, Bei Zhang, Wenyi Yao, Zhenzhou Shen

“…The numerical method based on the unsplit algorithm was validated by examples of predicting the propagation of expansible grout in a fracture. …”
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DESIGN AND FIELD TEST OF FUZZY PID CONTROL SYSTEM OF ACTIVE SUSPENSION FOR BLUEBERRY HARVESTER by Cun Chu, Haibin Wang, Ruiqing Zhang, Zhiyong Chu

“…In field tests, these reductions were 36.51%, 33.84%, and 30.21%, respectively. …”
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Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter by Ze Cheng, Jikao Lv, Yanli Liu, Zhihao Yan

“…The results show that the model can essentially predict the dynamic voltage behavior of the lithium-ion battery, and the FDEKF algorithm can maintain good accuracy in the estimation process and has strong robustness against modeling error.…”
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Used economy market insight: Sailboat industry pricing mechanism and regional effects. by Zhanni Huang, Hansheng Hu, Di Wu

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”
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A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring by Can WANG, Jianxin PENG, Xiaowen ZHANG

“…A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. …”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Artificial intelligence in drug development: reshaping the therapeutic landscape by Sarfaraz K. Niazi, Zamara Mariam

“…Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. …”
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A mode of action protein based approach that characterizes the relationships among most major diseases by Hongyi Zhou, Brice Edelman, Jeffrey Skolnick

“…Abstract Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). …”
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Classification of periodontitis stage and grade using natural language processing techniques. by Nazila Ameli, Tahereh Firoozi, Monica Gibson, Hollis Lai

“…Our proposed BERT model predicted the patients' stage and grade with 77% and 75% accuracy, respectively. …”
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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

“…Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. …”
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Hull form optimization of fully parameterized small ships using characteristic curves and deep neural networks by Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo

“…To address this issue, this paper proposes an approach that involves defining a range of hull forms with characteristic curves, predicting their performance using Deep Neural Networks (DNNs), and subsequently determining the optimal hull form based on these predictions. …”
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SPINE: SParse eIgengene NEtwork linking gene expression clusters in Dehalococcoides mccartyi to perturbations in experimental conditions. by Cresten B Mansfeldt, Benjamin A Logsdon, Garrett E Debs, Ruth E Richardson

“…Based on the model predictions, we discovered new response mechanisms for DMC, notably when the bacterium is exposed to solvent toxicity. …”
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Performance of quantum approximate optimization with quantum error detection by Zichang He, David Amaro, Ruslan Shaydulin, Marco Pistoia

“…Abstract The quantum approximate optimization algorithm (QAOA) is a promising candidate for scaling up to tackle real-world applications. …”
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THE MODEL OF FORECASTING THE EXCHANGE RATE OF THE RUBLE IN OPERATIONAL PLANNING AND MANAGEMENT FINANCIAL AND ECONOMIC ACTIVITY by Alexander Gladilin, Olga Gridina

“…In the article the questions of creation of adequate models that predict the dependence of the exchange rate of the national currency on the price of oil for use in operational planning and forecasting offinancial and economic activity of enterprises and organizations. …”
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Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke by Francesco Di Gregorio, Giada Lullini, Silvia Orlandi, Valeria Petrone, Enrico Ferrucci, Emanuela Casanova, Vincenzo Romei, Fabio La Porta

“…Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). …”
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Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models by Xiuxian Chen, Guzhong Chen, Kunchi Xie, Jie Cheng, Jiahui Chen, Zhen Song, Zhiwen Qi

“…Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. …”
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