Design and Experimental Validation Of FPGA Based Digital Control Scheme Of The Nearest Level Modulation Method For Modular Multilevel Converter Topology by Ahmet Mete Vural, Fatih Eroğlu, Mehmet Kurtoğlu

“…In this context, very high-speed integrated circuit hardware description language (VHDL) modules were designed to achieve the proposed architectural design of the NLM method by presenting each VHDL module and algorithm used in the implementation. …”
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Enhancing Positional Accuracy of Mechanically Modified Industrial Robots Using Laser Trackers by Mojtaba A. Khanesar, Aslihan Karaca, Minrui Yan, Mohammed Isa, Samanta Piano, David Branson

“…Different trajectories are used to test the accuracy of the proposed positioning algorithm. From the implementation results obtained using the proposed control structure, it is observed that the accuracy of the industrial robot improves significantly. …”
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Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China by Xueling Wu, Jiaxin Lu, Chaojie Lv, Liuting Qin, Rongrui Liu, Yanjuan Zheng

“…It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. …”
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Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…In this study, a comprehensive approach that integrates descriptive statistics, correlation analysis, and machine learning algorithms is employed to develop models and predict punchouts in CRCP. …”
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A VHDL Code for Offset Pulse Position Modulation Working with Reed Solomon System by Using ModelSim by Ahmed H. Albatoosh, Mohamed Ibrahim Shuja'a, Basman M. Al-Nedawe

“…The simulation was carried out in very high-speed integrated circuit hardware description language (VHDL), and a field-programmable gate array was used (FPGA) It made use of a Boolean function to function to program code for an algorithm that is working. …”
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Explainable AI and optimized solar power generation forecasting model based on environmental conditions. by Rizk M Rizk-Allah, Lobna M Abouelmagd, Ashraf Darwish, Vaclav Snasel, Aboul Ella Hassanien

“…This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. …”
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An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization by Farid Yuli Martin Adiyatma, Panarat Cherntanomwong

“…The model integrates two components: 1) a clustering algorithm that partitions the radio map based on fingerprint signal similarity, and 2) an adaptive ensemble regression framework that combines multiple base regressors using weighted averaging, where weights are inversely proportional to each regressor’s mean absolute error (MAE). …”
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INTELLECTUAL POTENTIAL OF A REGION: APPRAISAL AND CONTROL TOOL IN INNOVATIVE ACTIVITY by O. Loseva

“…It is suggested that the entropy of particular indicators should be taken into account when calculating summary rating in order to increase ranking reliability.The tool of control over the region’s intellectual potential quality in innovation is shown, the former comprising the following tools of diagnostics and monitoring: 1) system of indicators of intellectual potential characterizing its state in the areas that are crucial for effective innovation, i. e. innovation and entrepreneurship, research and cultural-educational ones; 2) procedure of the intellectual potential quality estimation, based on determination of integral rankings, carried out according to the level of development and stability of the indicator values of a particular region within the system of other regions, and taking into account indicators entropy; 3) factor analysis algorithm of the intellectual potential dynamics on the basis of the index method.The paper sets the guidelines for of use of the obtained results in the interests of the region’s innovation development and anticipated effects from their practical implementation: financial, budgetary, administrative, general economic effects.The conclusion is made that methodology for appraisal of intellectual potential and the tool of control over its quality can be effectively applied in order to stir up innovation in the region.…”
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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection by Yizhe Li, Yidong Xie, Hu He

“…The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection.…”
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Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing by Ahmet Hakan İnce, Serkan Özbay

“…Empirical validation was conducted using data from 150 students solving 30 mathematics items on the “TestYourself” platform, integrating response accuracy and timing metrics. Student abilities (θ), item difficulties (b), and time–effect parameters (λ) were estimated using the L-BFGS-B algorithm to ensure numerical stability. …”
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Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma by Beichen Cai, Beichen Cai, Beichen Cai, Qian Lin, Qian Lin, Qian Lin, Ruonan Ke, Ruonan Ke, Ruonan Ke, Jiaqi Yu, Jiaqi Yu, Jiaqi Yu, Lu Chen, Lu Chen, Xuejun Ni, Xuejun Ni, Hekun Liu, Xiuying Shan, Xiuying Shan, Biao Wang, Biao Wang, Biao Wang

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. A KRAS-Macrophage Prognostic Associated Gene (KMPAG) signature was developed by integrating these findings, followed by validation using a least absolute shrinkage and selection operator (LASSO) regression model. …”
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Mathematical model of a stacker crane with a slewing boom by Korytov M.S., Bezrodina A.E.

“…The current values of drive powers were determined as products of moments and forces of drives on the speeds of the corresponding links of the boom and carriage. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. …”
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OPT-IQA: Automated camera parameters tuning framework with IQA-guided optimization by Jan-Henner Roberg, Vladyslav Mosiichuk, Ricardo Silva, Luís Rosado

“…It also facilitates the seamless integration of supplementary IQA metrics and optimization algorithms to support additional use cases. …”
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Control over biopower in cognitive and surveillance capitalism by Stanković-Pejnović Vesna

“…Digital networks do not only collect data on users, but they "cluster" these users with the help of algorithms and encourage specific desired behaviors. …”
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Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

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Automated Calibration of SWMM for Improved Stormwater Model Development and Application by Hossein Ahmadi, Durelle Scott, David J. Sample, Mina Shahed Behrouz

“…During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. …”
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Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors by Fan Yang, Fan Yang, Fan Yang, Fan Yang, Chun-wei Liu, Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei, Mo Yang, Mo Yang, Mo Yang

“…Leveraging the XGBoost (XGB) algorithm within the Scikit-learn (Sklearn) machine-learning library, three distinct predictive models were developed: a clinical ultrasound imaging model (US model), an ultrasonographic radiomics model (US radiomics model), and a combined model integrating both clinical, ultrasound, and radiomics features. …”
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Identifying time patterns in Huntington’s disease trajectories using dynamic time warping-based clustering on multi-modal data by Alexia Giannoula, Audrey E. De Paepe, Ferran Sanz, Laura I. Furlong, Estela Camara

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Application of Categorical Boosting to Modelling the Friction Behaviour of DC05 Steel Sheets in Strip Drawing Test by Marek Szewczyk, Krzysztof Szwajka, Sherwan Mohammed Najm, Salwa O Mohammed

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