Estimating forest aboveground carbon sink based on landsat time series and its response to climate change by Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

“…We found that (1) GA can effectively improve the estimation accuracy of RF, the R 2 can be improved by up to 34.8%, and the optimal GA-RF model R 2 is 0.83. (2) The CSI of Pinus densata in Shangri-La was 0.45–0.72 t C·hm− 2 from 1987 to 2017. (3) Precipitation has the most significant effect on CSI. …”
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Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…Moreover, forecast values of sweep force at the current moment help formulate the dispatch plan for the cleaning mechanism in the next moment, thus ensuring precise control of the physical model. The Improved Whale Optimization Algorithm (IWOA) is employed to optimize parameters in this blueprint, facilitating real-time scheduling by rapidly converging to the global optimum. …”
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Improving TerraClimate hydroclimatic data accuracy with XGBoost for regions with sparse gauge networks: A case study of the Meknes plateau and the Middle Atlas Causse, Morocco by Hammoud Yassine, Allali Youssef, Saadane Abderrahim

“…Applying the XGBoost algorithm significantly improves the raw TerraClimate data, reducing the average Mean Absolute Error (MAE) across all parameters from 3.08 to 0.29, and the average Root Mean Square Error (RMSE) from 4.84 to 0.46, and increasing the average Nash-Sutcliffe Efficiency (NSE) from 0.82 to 0.99. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Signal quality can be improved by traditional noise removal algorithms, but deep learning models outperform non-learning methods. …”
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Reconstruction of Highway Vehicle Paths Using a Two-Stage Model by Weifeng Yin, Junyong Zhai, Yongbo Yu

“…In the first stage, a Gaussian Mixture Model (GMM) is integrated into a path choice model to estimate the mean and standard deviation of travel times for each road segment, utilizing an improved Expectation Maximization (EM) algorithm. In the second stage, based on the estimated time parameters, path choice prior probabilities and observed data are combined using maximum likelihood estimation to infer the most probable paths among candidate routes. …”
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Simulating Methylamine Using a Symmetry-Adapted, Qubit Excitation-Based Variational Quantum Eigensolver by Konstantin M. Makushin, Aleksey K. Fedorov

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Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks by Nader AL Theeb, Hazem Smadi, Naser Al-qaydeh

“…Furthermore, using BO algorithm for hyperparameters tuning improved the forecasting accuracy. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Finally, during fracturing design, the optimal productivity prediction model was matched to each interval based on its characteristics to predict post-fracturing productivity. …”
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Classification of Paddy Rice Planting Area Through Feature Selection Method Using Sentinel-1/2 Time Series Images by Shiyu Zhang, Pengao Li, Yong Xie, Wen Shao, Xueru Tian

“…Therefore, this study took Liyang City as the study area, reconstructed Sentinel-2 cloud-free time series optical images, and extracted spectral features, vegetation indexes, and other features, in combination with the polarization features of the Sentinel-1 time series radar images. The optimal feature subset was selected through the feature selection method, and machine learning algorithms were optimized for paddy rice planting area classification. …”
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Accelerated Reconstruction of Scenes Using CUDA-Based Parallel Computing by Gui Zou, Jin Jiang, Qi Chen

“…Since the drone needs to use an embedded onboard computer and stereo matching algorithms require massive computing resources, this paper studies the optimization of CUDA-based stereo matching algorithms on embedded devices to improve the reconstruction effect of scenes and achieve rapid reconstruction, thereby reducing time costs, and enhancing drone efficiency.…”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…ObjectiveThis study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector machine (SVM) algorithm, offering precise early decision support.MethodsThis study retrospectively included 462 NPC patients, without or with oligometastasis defined by ESTRO/EORTC criteria. …”
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Improving health-promoting workplaces through interdisciplinary approaches. The example of WISEWORK-C, a cluster of five work and health projects within Horizon-Europe by Deborah De Moortel, Michelle C Turner, Ella Arensman, Alex Binh Vinh Duc Nguyen, Víctor Gonzalez

“…These shifts are giving rise to new forms of work (eg, hybrid work, gig economy jobs) and reshaping management and work organization practices (eg, through algorithmic decision-making or digital monitoring of worker performance). …”
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Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020 by Luis Gabriel Cuervo, Carmen Juliana Villamizar, Daniel Cuervo, Pablo Zapata, Maria B. Ospina, Sara Marcela Valencia, Alfredo Polo, Ángela Suárez, Maria O. Bula, J. Jaime Miranda, Gynna Millan, Diana Elizabeth Cuervo, Nancy J. Owens, Felipe Piquero, Janet Hatcher-Roberts, Gabriel Dario Paredes, María Fernanda Navarro, Ingrid Liliana Minotta, Carmen Palta, Eliana Martínez-Herrera, Ciro Jaramillo, on behalf of the AMORE Project Collaboration

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Energy Management of Plug-In Hybrid Electric Vehicles for Autonomous Driving in a Following Environment Based on Fuzzy Adaptive PID Control by Jixin Wang, Yujin Zhou

“…Therefore, this study is based on a fuzzy adaptive proportional integral differential controller, combined with an improved Cuckoo search algorithm, to perform group optimization on various parameters of the control system. …”
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On differential privacy for federated learning in wireless systems with multiple base stations by Nima Tavangaran, Mingzhe Chen, Zhaohui Yang, José Mairton B. Da Silva Jr., H. Vincent Poor

“…To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. …”
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Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment by Oluwatobi Adeleke, Obafemi O. Olatunji, Tien-Chien Jen, Iretioluwa Olawuyi

“…Moreover, understanding the relative importance and contribution of different waste properties to HHV prediction is critical for improving the model's predictive capability and optimizing the waste-to-energy (WTE) process. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Objectives] To enhance water quality prediction accuracy, this study aims to address the following challenges: (1) traditional prediction methods often rely on simple, elementary decomposition techniques, limiting their ability to extract meaningful data features. (2) Single models and basic optimization algorithms result in low prediction accuracy. (3) Most approaches fail to leverage the advantages of different networks to analyze components of varying complexity, leading to inefficient model utilization. (4) Few studies incorporate error correction after prediction. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…Conclusion: VMAT-4P is identified as the most effective method for radiotherapy in left-sided breast cancer, providing an excellent balance between optimal target coverage and improved protection of surrounding OAR. …”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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Strategic Integration of Battery Energy Storage Systems for Effective EV Charging Demand Management in Transactive Energy Markets by T. H. M. Sumon Rashid, Md. Rabiul Islam, Md Muminur Rahman Sonic

“…Using the DIgSILENT PowerFactory power system software for modeling and simulation, the active and reactive power dispatch of BESS is optimized via a differential evolution (DE) algorithm. …”
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