Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling by Naomi Stricker, Reto Da Forno, Lothar Thiele

“…Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. …”
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Phylogenetically informed predictions outperform predictive equations in real and simulated data by Jacob D. Gardner, Joanna Baker, Chris Venditti, Chris L. Organ

“…Here, we use a comprehensive set of simulations to demonstrate two- to three-fold improvement in the performance of phylogenetically informed predictions compared to both ordinary least squares and phylogenetic generalised least squares predictive equations. …”
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Predictive Assessment of Organic/mineral Dust Explosion by José Serrano, Andr Laurent, Fabrice Putier, Laurent Perrin, Olivier Dufaud

“…Additionally, explosion severity parameters were reliably predicted within a confidence interval that extends 50 % beyond standard experimental variability. …”
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Audiometry as a predictive proxy for balance dysfunction by Cécile Nicolas-Puel, Jérôme Bourien, Régis Nouvian, Jean-Luc Puel, Jean-Charles Ceccato

“…Caloric irrigation results demonstrated a correlation with hearing loss in the more affected ear. …”
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Predictive model for necrotising fasciitis (NF) outcome by Sushmitha Kalakeri, Utsav Balar, Gehad Abdelkhalek

“…Further research with larger sample sizes is recommended to refine the model and improve predictive accuracy.…”
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Risk-Stratified Predictive Models of Pregnancy Loss by O. V. Shirai, B. I. Aslanov, S. V. Rishchuk, S. E. Melnikova

“…The developed models demonstrated excellent predictive performance, with high sensitivity and specificity (AUC 0.86 and 0.79).Conclusion. …”
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Relating sparse and predictive coding to divisive normalization. by Yanbo Lian, Anthony N Burkitt

“…This two-layer model is constructed in a way that implements sparse coding with a network structure that is constructed by implementing predictive coding. We demonstrate how a homeostatic function that regulates neural responses in the model can shape the nonlinearity of neural responses in a way that replicates different forms of divisive normalization. …”
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Modeling and Discovering Direct Causes for Predictive Models by Yizuo Chen, Amit Bhatia

“… We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. …”
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Exceedance probabilities using Nonparametric Predictive Inference by Ali M.Y. Mahnashi, Frank P.A. Coolen, Tahani Coolen-Maturi

“…This study employs Nonparametric Predictive Inference (NPI), a method that provides probability statements for a range of events of interest. …”
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CEG-0598, a novel dual inhibitor of EGFR and C5aR demonstrates in vitro anticancer and antimetastatic activity in prostate cancer cells by Ayed A. Dera, Majed Al Fayi

“…Kinome-wide off-target virtual screening predicted EGFR to have above-average docking scores. …”
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Statin effects on the lipidome: Predicting statin usage and implications for cardiovascular risk prediction by Changyu Yi, Kevin Huynh, Yvette Schooneveldt, Gavriel Olshansky, Amy Liang, Tingting Wang, Habtamu B. Beyene, Aleksandar Dakic, Jingqin Wu, Michelle Cinel, Natalie A. Mellett, Gerald F. Watts, Joseph Hung, Jennie Hui, John Beilby, Joanne E. Curran, John Blangero, Eric K. Moses, John Simes, Andrew M. Tonkin, Leonard Kritharides, David Sullivan, Jonathan E. Shaw, Dianna J. Magliano, Agus Salim, Corey Giles, Peter J. Meikle, A. Tonkin, P. Aylward, D. Colquhoun, P. Glasziou, P. Harris, D. Hunt, A. Keech, S. MacMahon, P. Magnus, D. Newel, P. Nestel, N. Sharpe, J. Shaw, R.J. Simes, P. Thompson, A. Thomson, M. West, H. White, A. Thomson, S. Simes, D. Colquhoun, W. Hague, S. MacMahon, R.J. Simes, R.J. Simes, P. Glasziou, S. Caleo, J. Hall, A. Martin, S. Mulray, P. Barter, L. Beilin, R. Collins, J. McNeil, P. Meier, H. Willimott, P. Harris, W. Hague, D. Smithers, A. Tonkin, P. Wallace, H. Willimott, D. Hunt, J. Baker, P. Aylward, P. Harris, M. Hobbs, P. Thompson, N. Sharpe, D. Hunt, M. West, P. Thompson, H. White, P. Aylward, D. Colquhoun, D. Sullivan, A. Keech, P. Thompson, S. MacMahon, A. Tonkin, M. West, H. White, N. Anderson, G. Hankey, R.J. Simes, S. Simes, J. Watson, R.J. Simes, N. Sharpe, A. Thomson, A. Tonkin, H. White, W. Hague, J. Baker, M. Arulchelvam, S. Chup, J. Daly, J. Hanna, A. Leach, M. Lee, J. Loughhead, H. Lundie-Jenkin, J. Morrison, A. Martin, S. Mulray, S. Netting, A. Nguyen, H. Pater, R. Philip, G. Pinna, D. Rattos, S. Ryerson, V. Sazhin, S. Simes, R. Walsh, A. Keech, R.J. Simes, A. Clague, M. Mackie, J. Yallop, K. Boss, S. MacMahon, M. Whiting, M. Shepard, J. Leach, M. Gandy, J. Joughin, J. Seabrook, R. Abraham, J. Allen, F. Bates, I. Beinart, E. Breed, D. Brown, N. Bunyan, D. Calvert, T. Campbell, D. Condon-Paoloni, B. Conway, L. Coupland, J. Crowe, N. Cunio, B. Cuthbert, N. Cuthbert, S. D’Arcy, P. Davidson, B. Dwyer, J. England, C. Friend, G. Fulcher, S. Grant, K. Hellestrand, M. Kava, L. Kritharides, D. McGill, H. McKee, A. McLean, M. Neaverson, G. Nelson, M. O’Neill, C. Onuma, F. O’Reilly, A. Owensby, D. Owensby, J. Padley, G. Parnell, S. Paterson, C. Pawsey, R. Portley, K. Quinn, D. Ramsay, M. Russell, J. Ryan, B. Sambrook, L. Shields, J. Silberberg, S. Sinclair, D. Sullivan, P. Taverner, D. Taylor, M. Taylor, M. Threlfall, J. Turner, A. Viles, J. Waites, R. Walker, W. Walsh, K. Wee, P. West, R. Wikramanayake, D. Wilcken, J. Woods, R.K. Barnett, Z. Bogetic, H. Briggs, A. Broughton, L. Brown, A. Buncle, P. Calafiore, L. Carrick, Y. Cavenett, L. Champness, R. Clark, H. Connor, J. Counsell, J. Deague, G. Derwent-Smith, A. Driscoll, B. Feldtmann, L. Fisher, B. Forge, A. Hamer, H. Harrap, S. Hodgens, M. Hooten, J. Hurley, B. Jackson, J. Johns, J. Krafchek, H. Larwill, I. Lyall, S. Marks, M. Martin, B. Mason, J. McCabe, C. Medley, L. Morgan, L. Mullan, D. Ogilvy, G. Phelps, P. Phillips, H. Prendergast, D. Rose, G. Rudge, W. Ryan, M. Sallaberger, G. Savige, B. Sia, A. Soward, C. Steinfort, K. Tankard, J. Tippett, B. Tyack, J. Voukelatis, M. Wahlqvist, N. Walker, S. Whitten, R. Yee, M. Zanoni, R. Ziffer, K. Anderson, G. Aroney, C. Atkinson, K. Boyd, R. Bradfield, G. Cameron, D. Careless, A. Carle, P. Carroll, T. Carruthers, D. Chaseling, B. Cooke, S. Coverdale, B. Currie, M. d’Emden, F. Ekin, R. Elder, T. Elsley, L. Ferry, C. Gnanaharan, K. Graham, K. Gunawardane, C. Hadfield, C. Halliday, R. Halliday, A. Heyworth, B. Hicks, P. Hicks, T. Htut, L. Hughes, J. Humphries, H. LeGood, J. Nye, D. O’Brien, G. Real, K. Roberts, L. RossLee, J. Sampson, I. Scott, H. Smith, V. Smith-Orr, Y. Tan, B. Wicks, J. Wicks, S. Woodhouse, J. Bradley, L. Callaway, A. Calvert, J. Crettenden, A. Dufek, B. Dunn, C. Dunphy, D. Gow, I. Hamilton-Craig, K. Herewane, S. Keynes, L. McLeay, R. McLeay, L. Ng, C. Thomas, P. Tideman, L. Wilson, R. Yeend, C. Zhang, Y. Zhang, P. Bradshaw, M. Brooks, R. Burton, J. Garrett, K. Gotch-Martin, J. Hargan, B. Hockings, G. Lane, S. Ross, R. Cutforth, D. D’Silva, W. Hitchener, V. Kimber, G. Kirkland, P. Neid, R. Parkes, B. Singh, C. Singh, M. Smith, S. Smith, M. Templer, N. Whitehouse, R. Allen-Narker, R. Anandaraja, S. Anandaraja, P. Barclay, S. Baskaranathan, P. Bridgman, J. Brown, J. Bruning, J. Calton, A. Clague, M. Clark, D. Clarke, T. Cook, R. Coxon, M. Denton, A. Doone, R. Easthope, J. Elliott, C. Ellis, P. FosterPratt, C. Frenneux, M. Frenneux, D. Friedlander, D. Fry, L. Gibson, M. Gluyas, A. Hall, K. Hall, A. Hamer, H. Hart, P. Healy, J. Hedley, P. Heuser, H. Ikram, D. Jardine, J. Kenyon, H. King, T. Kirk, T. Lawson, P. Leslie, G. Lewis, E. Low, R. Luke, S. Mann, D. McClean, D. McHaffie, L. Nairn, H. Patel, L. Pearce, K. Ramanathan, R. Rankin, J. Reddy, S. Reuben, R. Ronaldson, D. Roy, H. Roy, P. Scobie, D. Scott, J. Scott, K. Skjellerup, R. Stewart, D. Walters, T. Wilkins, A. Vitanachy, P. Wright, A. Zambanini

“…To address this limitation, we demonstrate that statin usage can be accurately predicted using lipidomic data. …”
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Comparative analysis and enhancing rainfall prediction models for monthly rainfall prediction in the Eastern Thailand by Preeyanuch Chuasuk, Tachanat Bhatrasataponkul, Aniruj Akkarapongtrakul

“…A novel hybrid deep learning model was developed with respect to different conditions of the El Niño and Southern Oscillation (ENSO). - Our research compared the performance of five deep learning models in predicting monthly rainfall over five selected stations in the Eastern Thailand. - Different lag times were initially verified to optimize the time-interdependency between ONI and local meteorological parameters. - Our novel hybrid model demonstrated an improved accuracy across three distinct climate phases: El Niño, La Niña, and neutral events.…”
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Ground data analysis for PM2.5 Prediction using predictive modeling techniques by Elham Nourmohammad, Yousef Rashidi

“…Results: Results indicate that XGBoost excelled in daily predictions when using solely meteorological data, achieving an R² score of 0.998674, while ARIMA demonstrated strong predictive capacity but struggled with added complexity. …”
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Experimental Demonstration of 73 Gb/s QPSK and 150 Gb/s QAM-32 Wireless Data Links in the Sub-THz Band Through Frequency Selective Surface Filters by Walter Fuscaldo, Daniele Pirrone, Dimitrios C. Zografopoulos, Antonio Ferraro, Pascal Szriftgiser, Guillaume Ducournau, Romeo Beccherelli

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InvSim algorithm for pre-computing airplane flight controls in limited-range autonomous missions, and demonstration via double-roll maneuver of Mirage III fighters by Osama A. Marzouk

“…We finally demonstrate the proposed numerical procedure of flight mechanics inverse simulation (InvSim) through an example case that is representative of the Mirage III family of French fighter airplanes, in which a straight subsonic flight with a double-roll maneuver over a duration of 30 s at an altitude of 5 km (3.107 mi or 16,404 ft) is inversely simulated.…”
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Predictive Factors for Fetal Growth Restriction in Patients with Preeclampsia: A Clinical Prediction Study by Yan M, Li F, Jun S, Li L, You W, Hu L

“…The model’s discrimination, clinical usefulness, and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve, and calibration analysis.Results: The study identified 256 women with FGR and 458 without FGR.The research identified nine significant predictors for FGR in PE patients, including family history of hypertension, aspartate aminotransferase (AST), uric acid (URIC), mode of delivery, mean platelet volume (MPV), prothrombin time (PT), severity of preeclampsia, post-pregnancy weight, and gestational age. The nomogram demonstrated excellent predictive performance, with an area under the ROC curve (AUC) of 0.93 (95% CI 0.91– 0.96) in the training cohort and 0.90 (95% CI 0.85– 0.95) in the validation cohort. …”
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Predictive coding in musical anhedonia: A study of groove. by Peter Benson, Nicholas Kathios, Psyche Loui

“…Groove, or the pleasurable urge to move to music, offers unique insight into the relationship between emotion and action. The predictive coding of music model posits that groove is linked to predictions of music formed over time, with stimuli of moderate complexity rated as most pleasurable and likely to engender movement. …”
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Robust Predictive Functional Control for Quadrotor Flight Systems by Kai Masuda, Kenji Uchiyama

“…Predictive Functional Control (PFC) has a control structure similar to Model Predictive Control (MPC), determining control inputs by predicting future states using a motion model. …”
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EchoPT: A Pretrained Transformer Architecture That Predicts 2D In-Air Sonar Images for Mobile Robotics by Jan Steckel, Wouter Jansen, Nico Huebel

“…In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.…”
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Predictive analytics in customer behavior: Anticipating trends and preferences by Hamed GhorbanTanhaei, Payam Boozary, Sogand Sheykhan, Maryam Rabiee, Farzam Rahmani, Iman Hosseini

“…The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values.…”
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