MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

“…Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. …”
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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

“…The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. …”
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A multimodal approach for ADHD with coexisting ASD detection for children by Jungpil Shin, Sota Konnai, Md. Maniruzzaman, Yoichi Tomioka, Yong Seok Hwang, Akiko Megumi, Akira Yasumura

“…Each task had two conditions: trace and predict. Various statistical features were derived from pen tablet and fNIRs data for each task. …”
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Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments. by Janice Yang, Neil J Daily, Taylor K Pullinger, Tetsuro Wakatsuki, Eric A Sobie

“…We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. …”
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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
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Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation by Bowei Jiang, Tongyuan Bai, Peng Zheng, Tieru Wu, Rui Ma

“…Then, we pre-compute the PPS of 3D scenes from existing datasets and train a ScoreNet to efficiently predict the PPS of the generated scenes. Finally, the predicted PPS is used to guide the fine-tuning of existing scene generators and produce indoor scenes with higher PPS, indicating improved suitability for robot navigation. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…Abstract Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty. …”
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Research on the Molding Design and Optimization of the Molding Process Parameters of the Automobile Trunk Trim Panel by Youmin Wang, Zhaozhe Zhu, Lingfeng Tang, Qinshuai Jiang

“…In order to put forward the theoretical calculation formula for the compression force of the compression mold of the trunk trim panel, obtain the influence trend of the process parameters on the molding quality of the trunk trim panel, and obtain the optimal process parameters combination for the compression molding of the trunk trim panel, four process parameters, the heating temperature, time, compression pressure, and holding time, which affected the compression molding, were selected as the level factors; the maximum thinning rate, maximum thickening rate, and shrinkage rate of the trunk trim panel were selected as evaluation indicators and orthogonal experiments were designed and completed; the comprehensive weighted scoring method was used to obtain the comprehensive score results and obtain the comprehensive evaluation indicators of the best combination of process parameters of trunk trim panel; BP neural network and genetic algorithm were used to study the change trend of the evaluation indicators of trunk trim panel with the changes of process parameters; based on the optimal process parameter combination and the established neural network’s prediction function, the maximum thinning rate, maximum thickening rate, and shrinkage rate under a single process parameter change could be predicted, and the influence of a single process parameter on the maximum thinning rate, maximum thickening rate, and shrinkage rate could be obtained; the process parameters were optimized, and a maximum thinning rate of 28%, a maximum thickening rate of 4.3%, and a shrinkage rate of 0.8% were obtained; the optimal molding process parameters of the trunk trim panel were heating temperature of 209°C, heating time of 62 s, molding pressure of 14 kPa, and holding pressure time of 49 s; after optimization, the maximum shrinkage rate was 28.0880%, the maximum thickening rate was 44.3264%, and the shrinkage rate was 0.8901%; according to the optimal process parameters, the quality of the trunk trim panel was very good, which met the production quality requirements.…”
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Semi-analytical modeling and multi-objective optimization of horizontal-well deep borehole heat exchangers by Zhiwei Wang, Xuemei Zhang, Hui Zhang, Hangyu Liu, Shangkuan Yang, Zheng Qian, Jianguo Feng

“…This study develops a high-efficiency semi-analytical model to predict the long-term thermal behavior of horizontal-well deep borehole heat exchangers (DBHEs). …”
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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

“…The study aimed at evaluating model performance and sensitivity of parameters in predicting streamflow for tropical watersheds. Calibration and uncertainty analysis (UA) for SWAT was performed using four UA techniques available in the SWAT (SWAT-CUP) and the genetic algorithm was used for parameter estimation for conceptual models. …”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives that rely on discrepancies between predicted and observed concentrations cannot be directly applied to determine the permissible loadings, limiting the application of the adjoint equation method to this issue. …”
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5-Fluorouracil Toxicity: Revisiting the Relevance of Pharmacokinetic Parameters by Hans Mielke, Engi Abd Elhady Algharably, Ursula Gundert-Remy

“…<b>Results</b>: The model predictions matched well with experimental data, confirming the suitability of the model. …”
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Transcriptional landscape of pleural mesothelioma patients in relation to NF2 gene mutational status by Carlos Orozco-Castaño, Alejandro Mejía-Garcia, Hsuan Megan Tsao, Diego A. Bonilla, Carlos Carvajal-Fierro, Ricardo Bruges-Maya, Alba Combita, Rafael Parra-Medina

“…Immune and stromal infiltration were inferred via the xCell algorithm, cytokine signaling analyzed with Cytosig, and chemotherapeutic sensitivity predicted using the pRRophetic R package. …”
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Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search by Yuan Yang, Yiyang Wang, Bowen Xue, Changxu Wang, Bo Yang

“…Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. …”
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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. …”
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The spatial extent and the dispersal strategy of species shape the occupancy frequency distribution of stream insect assemblages by I. Szivák, Z. Csabai, D. Schmera, A. Móra

“…For instance, the metapopulation dynamic model predicts bimodal OFD pattern indicating the dominance of dispersal processes in structuring the assemblages, while the niche‐based model predicts unimodal right‐skewed OFD pattern, and thus assemblages are driven mostly by niche processes. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…This study demonstrates that the machine learning prediction model based on BP artificial neural network technology has well-applicability, which can quickly determine the model parameters under the current inclination angle and axial static load of the coal rock structural plane, provide an efficient data-driven correction method for the parameters of the Barton-Bandis intrinsic model of the coal rock structural plane and also predict the parameters of numerical simulation of the coal rock structural plane under the larger inclination angle and axial static load ranges other than the given training samples.…”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…The adjustment value for lateral effluents is calculated by solving the adjoint equations and employing the BFGS optimization algorithm, iteratively determining the permitted pollutant loadings from lateral discharges. …”
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ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO₂ Emissions by Sahar Zargarzadeh, Aditya Ramnarayan, Felipe de Castro, Michael Ohadi

“…In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. …”
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Reconstructing the dynamics of HIV evolution within hosts from serial deep sequence data. by Art F Y Poon, Luke C Swenson, Evelien M Bunnik, Diana Edo-Matas, Hanneke Schuitemaker, Angélique B van 't Wout, P Richard Harrigan

“…HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. …”
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