Asynchronous Distributed Coordinated Hybrid Precoding in Multi-Cell mmWave Wireless Networks by Meesam Jafri, Suraj Srivastava, Sunil Kumar, Aditya K. Jagannatham, Lajos Hanzo

“…To begin with, a semidefinite relaxation (SDR)-based fully-digital (FD) beamformer is designed for a centralized MCC system. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the FD beamformer into its analog and baseband components and construct a hybrid transmit precoder (TPC). …”
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Vancomycin population pharmacokinetic models: Uncovering pharmacodynamic divergence amid clinicobiological resemblance by Peggy Gandia, Sahira Chaiben, Nicolas Fabre, Didier Concordet

“…These differences raise an issue in choosing a model for performing therapeutic drug monitoring using a PopPK model with or without Bayesian approach. Thus, it is fundamental to evaluate the impact of these differences on both efficacy/toxicity.…”
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A review of participatory modelling techniques for energy transition scenarios by Jair K.E.K. Campfens, Mert Duygan, Claudia R. Binder

“…Findings reveal that techniques like Cross-Impact Balance analysis and Fuzzy Cognitive Mapping excel in incorporating normative aspects and capturing diverse actor perspectives, yet they face challenges in addressing non-linearity and uncertainty. Bayesian Networks and Agent-Based Models are strong in managing uncertainty and modelling emergent behaviours but show limitations in normative aspects. …”
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Using multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals by R.E. Caraka, R.C. Chen, Y. Lee, T. Toharudin, C. Rahmadi, M. Tahmid, A.S. Achmadi

“…<br /> <strong>RESULTS:</strong> The best theta parameter in the latent multivariate latent generalized linear latent variable model was found in the Akaike Information Criterion, Akaike Information Criterion Corrected and Bayesian Information Criterion values, andthe information obtained was used to create a spatial cluster. …”
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Association between exposure to urinary metal and all-cause and cardiovascular mortality in US adults. by Ting Cheng, Dongdong Yu, Geng Li, Xiankun Chen, Li Zhou, Zehuai Wen

“…We estimated the association between urine metal exposure and all-cause mortality using Cox regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. Additionally, we used a competing risk model to estimate the relationship between metal exposure and cardiovascular mortality.…”
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Preferences for public health insurance coverage of new anticancer drugs: a discrete choice experiment among non-small cell lung cancer patients in China by Jingyi Meng, Feifei Yan, Maochun Chen, Yuchen Ding, Zhe Feng, Wenzhang Lu, Jinsong Geng

“…Methods We identified six attributes of new anticancer drugs and adopted a Bayesian-efficient design to generate choice scenarios for a discrete choice experiment (DCE). …”
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Impact of non-pharmacological interventions on the first wave of COVID-19 in Portugal 2020 by Dinis B. Loyens, Constantino Caetano, Carlos Matias-Dias

“…Methods: A compartmental SEIR (Susceptible, Exposed, Infectious, Removed) model was employed to simulate the first COVID-19 wave in Portugal, using a Bayesian approach and symptom-onset incidence data. …”
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Effects of Metabolism-Related Indicators on Nonalcoholic Fatty Liver Disease in Nonobese Population Based on the National Health and Nutrition Examination Survey by XingWang Zhu, HaiPing Wang, HongLong Zhang, Guole Nie, Jun Yan, Xun Li

“…We used logistic regression models, Bayesian kernel machine regression (BKMR), and the weighted quantile sum (WQS) regression model to estimate the association between metabolism-related indicators and NAFLD in the nonobese population. …”
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Non-target effects of ten essential oils on the egg parasitoid Trichogramma evanescens by van Oudenhove, Louise, Cazier, Aurélie, Fillaud, Marine, Lavoir, Anne-Violette, Fatnassi, Hicham, Perez, Guy, Calcagno, Vincent

“…Specific experimental setups were developed, and data obtained from image analysis were interpreted with phenomenological models fitted with Bayesian inference. Results highlight the fumigant toxicity of EOs on parasitoid development. …”
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A generalised catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2. by Belinda Lombard, Cheryl Cohen, Anne von Gottberg, Jonathan Dushoff, Juliet R C Pulliam, Cari van Schalkwyk

“…<h4>Methods</h4>The catalytic model assumes the risk of reinfection is proportional to observed infections and uses a Bayesian approach to fit model parameters to the number of nth infections among individuals that occur at least 90 days after a previous infection. …”
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Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction by Yanzhan Chen, Qian Zhang, Fan Yu

“…While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, a clustering parameter stochastic optimization model and mixed-integer programming Bayesian optimization (MIPBO) algorithm are proposed to enhance the segmentation of large-scale 3D point clouds. …”
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Open Issues in Statistical Forecasting of Solar Proton Events: A Machine Learning Perspective by Mirko Stumpo, Simone Benella, Monica Laurenza, Tommaso Alberti, Giuseppe Consolini, Maria Federica Marcucci

“…Nevertheless, we demonstrate that a relevant FAR on the predictions is a natural consequence of the sample base rates. From a Bayesian point of view, we find that the FAR explicitly contains the prior knowledge about the class distributions. …”
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Nonlinear Waveform Sensing for Cognitive Radar Based on Reinforcement Learning by Peikun Zhu, Xu Si, Jiachen Han, Jing Liang

“…It embraces the Riccati equation and Riccati-like iterative calculations to obtain the prediction error covariance (PEC) and the prediction Bayesian Cram&#x00E9;r&#x2013;Rao lower bound (PBCRLB), respectively, which are used to guide the optimal waveform design. …”
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Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK by Peter Congdon

“…Here, we focus on a practical forecasting approach, applied to interim UK COVID data, using a bivariate Reynolds model (for cases and deaths), with implementation based on Bayesian inference. We show the utility of informative priors in developing and estimating the model and compare error densities (Poisson-gamma, Poisson-lognormal, and Poisson-log-Student) for overdispersed data on new cases and deaths. …”
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Spacetime modeling of mortality by infectious and parasitic diseases in Brazil: a 20-year ecological and population-based study by Lucas Almeida Andrade, Carlos Dornels Freire de Souza, Wandklebson Silva da Paz, Danilo de Gois Souza, José Augusto Passos Góes, Emerson Lucas Silva Camargo, Álvaro Francisco Lopes de Sousa, Liliane Moretti Carneiro, Isabel Amélia Costa Mendes, Karina Machado Araújo, Allan Dantas dos Santos, Márcio Bezerra-Santos

“…For spatial analysis, we used the local empirical Bayesian estimator and Moran indices. Retrospective spatiotemporal scan statistics were performed using the Poisson Probability Distribution Model. …”
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Unveiling unexpected adverse events: post-marketing safety surveillance of gilteritinib and midostaurin from the FDA Adverse Event Reporting database by Tingting Jiang, Yanping Li, Ni Zhang, Lanlan Gan, Hui Su, Guiyuan Xiang, Yuanlin Wu, Yao Liu

“…Methods: We conducted disproportionality analyses to identify drug-AE associations, including the reporting odds ratio and the Bayesian confidence propagation neural network. A signal was detected if both methods achieved statistical significance. …”
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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

“…In the third stage, coordinators are fine-tuned to real conditions using Bayesian random search. Conducted experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, showed greater effectiveness of Collaborative Federated Learning compared to independent training of coordinators or direct transfer of learning outcomes between coordinators.…”
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One and only SNARC? Spatial-Numerical Associations are not fully flexible and depend on both relative and absolute number magnitude by Lilly Roth, John Caffier, Ulf-Dietrich Reips, Hans-Christoph Nuerk, Annika Tave Overlander, Krzysztof Cipora

“…Within this Registered Report, we conducted two online experiments running Bayesian analyses with optional recruitment stopping at moderate evidence (BF10 above 3 or below 1/3). …”
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Glacial lakes outburst susceptibility and risk in the Eastern Himalayas using analytical hierarchy process and backpropagation neural network models by Sandeep Kumar Mondal, Jyotindra Narayan, Chitesh Sharma, Rishikesh Bharti, Santosha Kumar Dwivedy, Pinaki Roy Chowdhury, Ramesh P. Singh

“…Three backpropagation multilayer perceptron neural network (BPMLPNN) models with Bayesian Regularization (BR-), Levenberg-Marquardt (LM-), and Gradient Descent with Momentum and Adaptive Learning Rate (GDX-) optimizers are designed to have better prediction accuracies compared to the AHP target scores. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). …”
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