Recent papers on Generalizability of Findings

Sorted by publication year (newest first) via OpenAlex. List regenerates every 24h.

  1. Generalizability of findings from neurobiological studies of individuals with first-episode psychosis: a cohort study

    2026 · Schizophrenia · Cullen, Alexis E., Lee, Maria, Josefsson, Pontus et al.

    2026
  2. Brain Age Prediction in Generalized Anxiety Disorder: Findings From the ENIGMA-Anxiety Working Group

    2026 · Biological Psychiatry · Richier, Corey, Zugman, André, Thompson, Paul M. et al.

    2026
  3. Prolonged ambulatory EEG findings and risk of seizure recurrence in adults with generalized tonic-clonic seizures alone: A multicentre study

    2026 · Epilepsy & Behavior · Hoxhaj, Domeniko, Irelli, Emanuele Cerulli, Pizzanelli, Chiara et al.

    2026
  4. Finding the number and the entropy of spanning trees of a generalized class of self–similar fractal graphs

    2026 · International Journal of Geometric Methods in Modern Physics · Elsaid, Ayman, Aboutahoun, Abdallah W., El-Safty, Fatma

    2026
  5. Delayed motherhood and maternal health risks in India: findings from a generalized linear model analysis

    2026 · Frontiers in Public Health · Singh, Manisha, Shekhar, Chander, Gupta, Jagriti

    2026
  6. Finding zeros of generalized monotone operators via first-order dynamical systems

    2026 · Computational and Applied Mathematics · Hai, Pham Viet, Hai, Trinh Ngoc

    2026
  7. Complications and Laboratory Test Findings Among Patients With Generalized Pustular Psoriasis: A Retrospective Chart Review Study

    2026 · Experimental Dermatology · Okuyama, Ryuhei, Okubo, Yukari, Imafuku, Shinichi et al.

    2026
  8. Crop yield distribution modeling faces three key challenges: complex distributional structure, limited historical data at the county level, and the need to incorporate evolving climate conditions into distributional dynamics. We propose a Fixed-Effect Panel Neural Mixture (FEPNM) framework to address these challenges. FEPNM extends finite mixture models to a panel data setting, allowing information sharing across counties through fixed effects to mitigate short time-series limitations. We further generalize the mixture model into a Mixture-of-Experts (MoE) type specification by introducing a neural-network gating mechanism that flexibly maps climate variables and conservation practices to time-varying regime probabilities. This structure enables direct modeling of the probability of yield loss as a nonlinear function of climate exposure and management adoption. Simulations demonstrate that FEPNM substantially improves the precision of structural parameter estimates and average partial effects, particularly in short-T settings. In an empirical application to U.S. county-level corn yields, FEPNM outperforms conventional mixture and single-distribution specifications in both in-sample and out-of-sample likelihood. Our results provide structural evidence on how climate exposure and conservation practices jointly shape corn yield distributions. Heating Degree Days (HDD) significantly increase the probability of yield loss, while adoption of cover crops and no-tillage practices significantly reduces downside yield risk. These findings highlight the importance of incorporating nonlinear climate effects and management practices into distributional modeling for agricultural risk management and crop insurance design.

    2026 · AgEcon Search (University of Minnesota, USA) · Li, Yixuan, Ker, Alan, Aglasan, Serkan

    2026
  9. FISN: FInding Spatial Neighborhoods for Generalizable Novel View Synthesis

    2026 · IEEE Transactions on Visualization and Computer Graphics · Bao, Yanqi, Ding, Tianyu, Huo, Jing et al.

    2026
  10. Bridging the Gap: Enhancing the Generalizability of Clinical Trial Findings

    2026 · SSRN Electronic Journal · Williams, Andre, Horne, Cassandre, Freeman, Katherine

    2026
  11. 2025
  12. How does the inclusion of women in clinical trials impact the generalizability of research findings?

    2025 · Zenodo (CERN European Organization for Nuclear Research) · Tripdatabase

    2025
  13. Generalized Jeffreys’s approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and detecting the type I errors in 71,126 clinical trial findings

    2025 · OSF Preprints (OSF Preprints) · Review, Anonymous Peer

    2025
  14. [1]A. Moffet, "Minimum-redundancy linear arrays", IEEE Transactions on Antennas and Propagation, Vol. 16, Iss. 2, pp. 172-175, 1968. DOI: 10.1109/TAP.1968.1139138 [2]G. S. Bloom and S. W. Golomb, "Applications of numbered undirected graphs", in Proceedings of the IEEE, Vol. 65, Iss. 4, pp. 562-570, 1977. DOI: 10.1109/PROC.1977.10517 [3]P. Pal and P. P. Vaidyanathan, "Nested arrays in two dimensions, Part I: Geometrical considerations", IEEE Transactions on Signal Processing, Vol. 60, No. 9, pp. 4694-4705, 2012. DOI: 10.1109/TSP.2012.2203814 [4]P. Pal and P. P. Vaidyanathan, "Nested arrays: A novel approach to array processing with enhanced degrees of freedom", IEEE Transactions on Signal Processing, Vol. 58, No. 8, pp. 4167-4181, 2010. DOI: 10.1109/TSP.2010.2049264 [5]C. Wen, G. Shi, and X. Xie, "Estimation of directions of arrival of multiple distributed sources for nested array", Signal Processing, Vol. 130, pp. 315-322, 2017. DOI: 10.1016/j.sigpro.2016.07.011 [6]J. Li, Y. He, P. Ma, X. Zhang, and Q. Wu, "Direction of arrival estimation using sparse nested arrays with coprime displacement", IEEE Sensors Journal, Vol. 21, Iss. 4, pp. 5282-5291, 2020. DOI: 10.1109/JSEN.2020.3034761 [7]P. Zhao et al., "Generalized nested array configuration family for direction-of-arrival estimation", IEEE Transactions on Vehicular Technology, Vol. 72, Iss. 8, pp. 10380-10392, 2023. DOI: 10.1109/TVT.2023.3260196 [8]Y. Lou, X. Qu, D. Wang, and J. Cheng, "Direction-of-arrival estimation for nested acoustic vector-sensor arrays using quaternions", IEEE Transactions on Geoscience and Remote Sensing, Vol. 61, pp. 1-14, 2023. DOI: 10.1109/TGRS.2023.3274182 [9]G. Jiang, J. Huang, and Y. Yang, "Dual-sparse parallel nested array for two-dimensional direction of arrival estimation", Circuits, Systems, and Signal Processing, Vol. 43, No. 12, pp. 8060-8073, 2024. DOI: 10.1007/s00034-024-02816-w [10]L. Zhou, Z. Feng, K. Ye, J. Qi, and S. Hong, "Design of relocating sparse nested arrays for DOA estimation of non-circular signals", AEU - International Journal of Electronics and Communications, Vol. 173, 2024. DOI: 10.1016/j.aeue.2023.154976 [11]G. Jiang, J. Huang, and Y. Yang, "High-accuracy 2D DOA estimation with three parallel sparse nested array", AEU - International Journal of Electronics and Communications, Vol. 179, 2024. DOI: 10.1016/j.aeue.2024.155319 [12]C. Qin, L. Yang, J. Dang, B. Dang, and Y. Zhou, "An enhanced expanding and shift sparse array based on the coprime array and nested array", IET Radar, Sonar & Navigation, Vol. 18, Iss. 3, pp. 493-499, 2024. DOI: 10.1049/rsn2.12495 [13]S. He, N. Sun, and Z. Yang, "Designing sparse extended nested arrays with high degrees of freedom and low coupling", Signal Processing, Vol. 227, 2025. DOI: 10.1016/j.sigpro.2024.109702 [14]N. V. Son, N. T. Chinh, and N. N. Dong, "A novel directional finding algorithm applying to underwater passive sonar system based on sparse representation combined adaptive comb filter", The International Conference on Intelligent Systems & Networks, 2024, Springer, pp. 382-391. EXTENDING THE CBSL ALGORITHM FOR DIRECTION OF ARRIVAL ESTIMATION WITH A NESTED ARRAY IN PASSIVE SONAR

    2025 · Journal of Science and Technique · Nguyen, Van, Navy, Vietnam People's

    2025
  15. Regional prevalence of generalized anxiety disorder in Germany: findings from the NAKO study

    2025 · European Journal of Public Health · Yessimova, Dinara, Ito, Chihiro, Berger, Kenneth I. et al.

    2025
  16. Are Findings of Key Insect Metrics Generalizable Across Different Taxa in Malaise Trap Samples?

    2025 · Ecology and Evolution · Remmel, Nicole, Enß, Julian, Haase, Peter et al.

    2025

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