Discrete dynamics of complex systems by Hermann Haken

“…This article extends the slaving principle of synergetics to processes with discrete time steps. Starting point is a set of nonlinear difference equations which contain multiplicative noise and which refer to multidimensional state vectors. …”
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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. …”
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Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals by Bangbei Tang, Bangbei Tang, Mingxin Zhu, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li

“…Additionally, compared with the dataset without baseline processing, the model’s accuracy increases by 3.50%, and the F1-score increases by 6.33% on the dataset after baseline processing.ConclusionsThe combination of physiological signals and machine learning models can effectively classify drivers' olfactory preferences. …”
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Self-synchronization Voltage Source LVRT Control Method for New Energy Inverter under Weak Grid by Dan LIU, Kezheng JIANG, Yiqun KANG, Xiaotong JI, Yunyu XU, Fang LIU

“…For this reason, the relationship between the grid voltage vector and the grid impedance and the grid-connected current in the case of voltage sag under the weak grid, as well as the influencing factors are derived in this paper. …”
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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. …”
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Starting driving style recognition of electric city bus based on deep learning and CAN data by Dengfeng Zhao, Zhijun Fu, Chaohui Liu, Junjian Hou, Shesen Dong, Yudong Zhong

“…The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. …”
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Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development by Lilia Lazli

“…Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for an accurate diagnosis of AD. …”
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Evaluating microstructural and machine learning predictive models for friction drilling of sustainable snail shell reinforced aluminium matrix composites by Rajesh Jesudoss Hynes Navasingh, R. Sankaranarayanan, Priyanka Mishra, Angela Jennifa Sujana J, Jebasingh Jeremiah Rajesh, Jana Petru

“…Random Forest (RF), Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Support Vector Machine (SVM) models were employed for the prediction of distinct output responses.…”
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GDNF overexpression in astrocytes enhances branching and partially preserves hippocampal function in an Alzheimer’s rat model by Ana Abril Vidal Escobedo, Facundo Peralta, Gustavo Ramón Morel, Martino Avallone, Tomas Björklund, Paula Cecilia Reggiani, Joaquín Pardo

“…In this study, we developed an adeno-associated viral vector (AAV9) designed to selectively overexpress glial cell line-derived neurotrophic factor (GDNF) in astrocytes, using the astrocyte-specific GFAP promoter and TdTomato for transduction tracking. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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Data-driven discovery of ultrahigh specific hardness alloys by Taeyeop Kim, Wook Ha Ryu, Geun Hee Yoo, Donghyun Park, Ji Young Kim, Eun Soo Park, Dongwoo Lee

“…Combinatorial experimental datasets and an ensemble of six ML algorithms (elastic net, support vector machine, Gaussian process regressor, random forest, artificial neural network, and convolutional neural network) were used to explore a compositional space blended by 28 metallic elements. …”
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Molecular Cloning and Characterization of Babesia orientalis Rhoptry Neck 2 BoRON2 Protein by Ngabu Malobi, Lan He, Long Yu, Pei He, Junwei He, Yali Sun, Yuan Huang, Junlong Zhao

“…A truncated fragment coding a 936 bps fragment was cloned in pMD-19T and subcloned into pET32 (a)+ expression vector, expressed in E. coli BL21. Purified recombinant BoRON2 was used to produce polyclonal antibody against BoRON2. …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. …”
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Combination of GEOBIA and data-driven approach for grassland habitat mapping in the Alta Murgia National Park by Cristina Tarantino, Marica De Lucia, Luciana Zollo, Mariagiovanna Dell’Aglio, Maria Adamo, Rocco Labadessa

“…These Mediterranean habitats are often characterized by small and fragmented patches, therefore, multi-season very high spatial resolution satellite images and data-driven Geographic Object-Based Image Analysis (GEOBIA) approach were considered to obtain grassland habitats mapping by an automatic classification process. Different classifiers such as Support Vector Machine (SVM) and Random Forest (RF) were evaluated, and their performance was compared by varying different input feature configurations such as the number of seasonal images used. …”
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A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization by Ruiqin Duan, Xiaosheng Peng, Cong Li, Zimin Yang, Yan Jiang, Xiufeng Li, Shuangquan Liu

“…In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). …”
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Functional Analysis of Pheophorbide a Oxygenase Gene and Its Promoter from Mango (Mangifera indica) Fruit by Beibei Bai, Kuanliang Liu, Zhichang Zhao, Ruixiong Luo, Aiping Gao, Huangtuan, Wang Zisong

“…Pheophorbide a oxygenase (PAO) is a key enzyme in the process of chlorophyll degradation. It can metabolize a produced in the process of chlorophyll degradation into red chlorophyll metabolite. …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Abstract Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. …”
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Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of c... by Sunderland Baker, Anand Tekriwal, Gidon Felsen, Elijah Christensen, Lisa Hirt, Steven G Ojemann, Daniel R Kramer, Drew S Kern, John A Thompson

“…A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. …”
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Party System Transformation in Central Asian Countries in the Context of Strengthening Political Regimes by Boris P. Guseletov, Nataliya M. Velikaya

“…The purpose of the study is to determine the vector of changes in the party and political systems of Kazakhstan, Kyrgyzstan and Uzbekistan and its impact on the transformation of the party and political system. …”
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Peculiarities of using geoinformation systems in training of future geography specialists in higher education institutions by Olena Korol, Olesia Kornus, Anatolii Kornus

“…The possibilities of open programs are also presented; they can be used in the educational process (PhotoFiltre (mapping), XnView (viewing), Picasa (adding geotagging with which you can specify the capture area of any photo, using Google Earth and Google maps), Quantum GIS (can be used as the main platform for teaching students how to implement GIS and how to create the highest quality maps), Google Earth network services (electronic globe), ArcGIS Online (used to manage and share maps and geographic information), Golden SoftWare (Surfer) (universal mapping software that runs on Microsoft Windows and is used to quickly and accurately erase map surfaces and convert the provided data into contour, relief or post maps, three-dimensional surfaces, 3D frames or vector graphics). …”
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