Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods by Carmina Liana Musat, Claudiu Mereuta, Aurel Nechita, Dana Tutunaru, Andreea Elena Voipan, Daniel Voipan, Elena Mereuta, Tudor Vladimir Gurau, Gabriela Gurău, Luiza Camelia Nechita

“…By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.…”
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Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…Residual frequency analysis and APRE results further confirmed these ensemble models’ low bias and high reliability, with error distributions centered near zero. DT performed slightly lower, with R2 values of 0.93, while LR struggled to model the non-linear behavior of IFT. …”
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Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin by Junqiang Xu, Fan Wang, Chao Ren, Jianmin Bian, Tao Li, Zikai Ping

“…The artificial neural network-based prediction framework achieved high performance with Pearson correlation coefficients exceeding 0.90 across all datasets. The average relative error was 1.31 % (training), 1.39 % (validation), and 1.24 % (test), with MAPE values below 2 %. …”
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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors by Celeste McFarlane, Paulson Kasereka Isevulambire, Raymond Sinsi Lumbuenamo, Arnold Murphy Elouma Ndinga, Ranil Dhammapala, Xiaomeng Jin, V. Faye McNeill, Carl Malings, R. Subramanian, Daniel M. Westervelt

“…The raw PurpleAir data from September 2019 through February 2020 strongly correlated with the BAM-1020 measurements (R2 = 0.88) but also exhibited a mean absolute error (MAE) of approximately 14 µg m−3. Employing two calibration models, namely, multiple linear regression and random forests, decreased the MAE to 3.4 µg m−3 and increased R2 to 0.96. …”
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Practical guidelines for reproducible N₂O flux chamber measurements in nutrient-poor ecosystems by N. Y. Triches, N. Y. Triches, J. Engel, A. Bolek, T. Vesala, M. E. Marushchak, A.-M. Virkkala, M. Heimann, M. Göckede

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Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe by C. Poppe Terán, C. Poppe Terán, C. Poppe Terán, B. S. Naz, B. S. Naz, H. Vereecken, H. Vereecken, R. Baatz, R. A. Fisher, H.-J. Hendricks Franssen, H.-J. Hendricks Franssen

“…CLM5<span class="inline-formula">_PFT</span> exhibited a low systematic error in simulating the ET at the ICOS sites (average bias of <span class="inline-formula">−4.68</span> %), indicating that PFT-specific ET closely matches the observations' magnitude. …”
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Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture by Mo Zhang, Yong Ge, Jianghao Wang

“…The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

“…In general, the MEMLM approaches, even when trained on only dimensionally reduced assemblage data, performed substantially better than the WA approaches in the larger training sets, as judged by cross-validatory prediction error. When applied to fossil data, MEMLM variants sometimes generated qualitatively different palaeoenvironmental reconstructions from each other and from reconstructions based on WA approaches. …”
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The Use of Explainable Machine Learning for the Prediction of the Quality of Bulk-Tank Milk in Sheep and Goat Farms by Daphne T. Lianou, Yiannis Kiouvrekis, Charalambia K. Michael, Natalia G. C. Vasileiou, Ioannis Psomadakis, Antonis P. Politis, Angeliki I. Katsafadou, Eleni I. Katsarou, Maria V. Bourganou, Dimitra V. Liagka, Dimitrios C. Chatzopoulos, Nikolaos M. Solomakos, George C. Fthenakis

“…Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. …”
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Status of the Sapo National Park elephant population and implications for conservation of elephants in Liberia by Yaw Boafo, Massalatchi Sani

“… Dung counts are used to estimate abundance and distribution of elephants in tropical forests and give precise population estimates (Barnes, 2002). …”
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Habitat Characteristics and the Species Response of Astragalus curvirostris Boiss. to the Environmental Factors in Lorestan Rangelands by Reza Siahmansour, Nadia Kamali, Hamid Reza Mirdavoodi, Javad Motamedi

“…Applying the GAM with Poisson error distribution showed that the variables, height above sea level, percentage of organic matter and soil nitrogen, as well as the percentage of stone are effective on the yield of the species. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…In addition, the use of various fine-tuning models can realize the tasks of generating error control and drawing style migration.ConclusionThe approach proposed in the research has some limitations. …”
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Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink by B. Bukosa, S. Mikaloff-Fletcher, G. Brailsford, D. Smale, E. D. Keller, E. D. Keller, W. T. Baisden, M. U. F. Kirschbaum, D. L. Giltrap, L. Liáng, S. Moore, R. Moss, S. Nichol, J. Turnbull, A. Geddes, D. Kennett, D. Hidy, Z. Barcza, L. A. Schipper, A. M. Wall, S.-I. Nakaoka, H. Mukai, A. Brandon

“…The overall findings of this study are robust with respect to extensive tests to assess the potential biases in the inverse model due to transport error, prior biosphere, ocean and fossil fuel estimates, background CO<span class="inline-formula">₂</span>, and diurnal cycles. …”
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