Molybdenum Carbide/Ni Nanoparticles Embedded into Carbon Nanofibers as an Effective Non-Precious Catalyst for Green Hydrogen Production from Methanol Electrooxidation.

Molybdenum carbide co-catalyst and carbon nanofiber matrix are suggested to improve the nickel activity toward methanol electrooxidation process. The proposed electrocatalyst has been synthesized by calcination electrospun nanofiber mats composed of molybdenum chloride, nickel acetate, and poly (vinyl alcohol) under vacuum at elevated temperatures. The fabricated catalyst has been characterized using XRD, SEM, and TEM analysis. The electrochemical measurements demonstrated that the fabricated composite acquired specific activity for methanol electrooxidation when molybdenum content and calcination temperature were tuned. In terms of the current density, the highest performance is attributed to the nanofibers obtained from electrospun solution having 5% molybdenum precursor compared to nickel acetate as a current density of 107 mA/cm2 was generated. The process operating parameters have been optimized and expressed mathematically using the Taguchi robust design method. Experimental design has been employed in investigating the key operating parameters of methanol electrooxidation reaction to obtain the highest oxidation current density peak. The main effective operating parameters of the methanol oxidation reaction are Mo content in the electrocatalyst, methanol concentration, and reaction temperature. Employing Taguchi's robust design helped to capture the optimum conditions yielding the maximum current density. The calculations revealed that the optimum parameters are as follows: Mo content, 5 wt.%; methanol concentration, 2.65 M; and reaction temperature, 50 °C. A mathematical model has been statistically derived to describe the experimental data adequately with an R2 value of 0. 979. The optimization process indicated that the maximum current density can be identified statistically at 5% Mo, 2.0 M methanol concentration, and 45 °C operating temperature.

Population structure and dynamics of the Mediterranean Pond Turtle Mauremys leprosa (Schweigger, 1812) in contrasted polluted aquatic environments.

Pollution contributes to the degraded state of continental aquatic ecosystems and biodiversity. Some species appear to be tolerant to aquatic pollution, yet little is known about the effects of such pollution on population structure and dynamics. Here, we investigated how wastewater treatment plant (WWTP) effluents of the Cabestany City, in southern France, contribute to the pollution levels of the Fosseille River, and we tested how they could affect population structure and medium-term dynamics of the native freshwater turtle, the Mediterranean Pond Turtle Mauremys leprosa (Schweigger, 1812). Amongst the 68 pesticides surveyed from water samples collected along the river in 2018 and 2021, a total of 16 pesticides were detected, among which eight were found in the upstream section of the river, 15 in the river section located downstream of the WWTP, and 14 in the outfall of the WWTP, exhibiting the contribution of effluents to the river pollution. From 2013 to 2018 and in 2021, capture-mark-recapture protocols were carried out on the freshwater turtle population living in the river. Using robust design and multi-state models, we showed a stable population throughout the study period, with high year-dependent seniority, and a bidirectional transition occurring primarily from the upstream to the downstream river sections of the WWTP. The freshwater turtle population consisted mostly of adults, with a male biased sex ratio detected downstream of the WWTP neither related to sex-dependent survival, recruitment, nor transition, suggesting a male bias in the hatchlings or primary sex ratio. Also, the largest immatures and females were captured downstream of the WWTP, with females having the highest body condition, whereas no such differences were observed in males. This study highlights that population functioning of M. leprosa is driven primarily by effluents induced resources, at least over the medium-term.

Utilizing Robust Design to Optimize Composite Bioadhesive for Promoting Dermal Wound Repair.

Catechol-modified bioadhesives generate hydrogen peroxide (H2O2) during the process of curing. A robust design experiment was utilized to tune the H2O2 release profile and adhesive performance of a catechol-modified polyethylene glycol (PEG) containing silica particles (SiP). An L9 orthogonal array was used to determine the relative contributions of four factors (the PEG architecture, PEG concentration, phosphate-buffered saline (PBS) concentration, and SiP concentration) at three factor levels to the performance of the composite adhesive. The PEG architecture and SiP wt% contributed the most to the variation in the results associated with the H2O2 release profile, as both factors affected the crosslinking of the adhesive matrix and SiP actively degraded the H2O2. The predicted values from this robust design experiment were used to select the adhesive formulations that released 40-80 µM of H2O2 and evaluate their ability to promote wound healing in a full-thickness murine dermal wound model. The treatment with the composite adhesive drastically increased the rate of the wound healing when compared to the untreated controls, while minimizing the epidermal hyperplasia. The release of H2O2 from the catechol and soluble silica from the SiP contributed to the recruitment of keratinocytes to the wound site and effectively promoted the wound healing.

Functionality Versus Sustainability for PLA in MEX 3D Printing: The Impact of Generic Process Control Factors on Flexural Response and Energy Efficiency.

Process sustainability vs. mechanical strength is a strong market-driven claim in Material Extrusion (MEX) Additive Manufacturing (AM). Especially for the most popular polymer, Polylactic Acid (PLA), the concurrent achievement of these opposing goals may become a puzzle, especially since MEX 3D-printing offers a variety of process parameters. Herein, multi-objective optimization of material deployment, 3D printing flexural response, and energy consumption in MEX AM with PLA is introduced. To evaluate the impact of the most important generic and device-independent control parameters on these responses, the Robust Design theory was employed. Raster Deposition Angle (RDA), Layer Thickness (LT), Infill Density (ID), Nozzle Temperature (NT), Bed Temperature (BT), and Printing Speed (PS) were selected to compile a five-level orthogonal array. A total of 25 experimental runs with five specimen replicas each accumulated 135 experiments. Analysis of variances and reduced quadratic regression models (RQRM) were used to decompose the impact of each parameter on the responses. The ID, RDA, and LT were ranked first in impact on printing time, material weight, flexural strength, and energy consumption, respectively. The RQRM predictive models were experimentally validated and hold significant technological merit, for the proper adjustment of process control parameters per the MEX 3D-printing case.

A Study on Optimization of Noise Reduction of Powered Vehicle Seat Movement Using Brushless Direct-Current Motor.

In this paper, an optimal design model was developed to reduce noise and secure the torque performance of a brushless direct-current motor used in the seat of an autonomous vehicle. An acoustic model using finite elements was developed and verified through the noise test of the brushless direct-current motor. In order to reduce noise in the brushless direct-current motor and obtain a reliable optimization geometry of noiseless seat motion, parametric analysis was performed through the design of experiments and Monte Carlo statistical analysis. The slot depth, stator tooth width, slot opening, radial depth, and undercut angle of the brushless direct-current motor were selected as design parameters for design parameter analysis. Then, a non-linear prediction model was used to determine the optimal slot depth and stator tooth width to maintain the drive torque and minimize the sound pressure level at 23.26 dB or lower. The Monte Carlo statistical method was used to minimize the deviation of the sound pressure level caused by the production deviation of the design parameters. The result is that the SPL was 23.00-23.50 dB with a confidence level of approximately 99.76% when the level of production quality control was set at 3σ.

Energy Consumption vs. Tensile Strength of Poly[methyl methacrylate] in Material Extrusion 3D Printing: The Impact of Six Control Settings.

The energy efficiency of material extrusion additive manufacturing has a significant impact on the economics and environmental footprint of the process. Control parameters that ensure 3D-printed functional products of premium quality and mechanical strength are an established market-driven requirement. To accomplish multiple objectives is challenging, especially for multi-purpose industrial polymers, such as the Poly[methyl methacrylate]. The current paper explores the contribution of six generic control factors (infill density, raster deposition angle, nozzle temperature, print speed, layer thickness, and bed temperature) to the energy performance of Poly[methyl methacrylate] over its mechanical performance. A five-level L25 Taguchi orthogonal array was composed, with five replicas, involving 135 experiments. The 3D printing time and the electrical consumption were documented with the stopwatch approach. The tensile strength, modulus, and toughness were experimentally obtained. The raster deposition angle and the printing speed were the first and second most influential control parameters on tensile strength. Layer thickness and printing speed were the corresponding ones for the energy consumption. Quadratic regression model equations for each response metric over the six control parameters were compiled and validated. Thus, the best compromise between energy efficiency and mechanical strength is achievable, and a tool creates significant value for engineering applications.

Quantifying phenology and migratory behaviours of hummingbirds using single-site dynamics and mark-detection analyses.

Nuanced understanding of seasonal movements of partially migratory birds is paramount to species and habitat conservation. Using nascent statistical methods, we identified migratory strategies of birds outfitted with radio-frequency identification (RFID) tags detected at RFID feeders in two sites in California, USA. We quantified proportions of migrants and residents and the seasonal phenology for each movement strategy in Allen's and Anna's hummingbirds; we also validated our methodology by fitting our model to obligate migratory black-chinned hummingbirds. Allen's and Anna's hummingbirds exhibited characteristics of facultative migratory behaviour. We also quantified apparent annual survival for each migratory strategy and found that residents had significantly higher probabilities of apparent survival. Low survival estimates for migrants suggest that a high proportion of birds in the migrant group permanently emigrated from our study sites. Considered together, our analyses suggest that hummingbirds in both northern and southern California sites partake in diverse and highly plastic migratory behaviours. Our assessment elucidates the dynamics underlying idiosyncratic migratory behaviours of two species of hummingbirds, in addition to describing a framework for similar assessments of migratory behaviours using the multi-state open robust design with state uncertainty model and single-site dynamics.

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

Zero-inflated count distributions for capture-mark-reencounter data.

The estimation of demographic parameters is a key component of evolutionary demography and conservation biology. Capture-mark-recapture methods have served as a fundamental tool for estimating demographic parameters. The accurate estimation of demographic parameters in capture-mark-recapture studies depends on accurate modeling of the observation process. Classic capture-mark-recapture models typically model the observation process as a Bernoulli or categorical trial with detection probability conditional on a marked individual's availability for detection (e.g., alive, or alive and present in a study area). Alternatives to this approach are underused, but may have great utility in capture-recapture studies. In this paper, we explore a simple concept: in the same way that counts contain more information about abundance than simple detection/non-detection data, the number of encounters of individuals during observation occasions contains more information about the observation process than detection/non-detection data for individuals during the same occasion. Rather than using Bernoulli or categorical distributions to estimate detection probability, we demonstrate the application of zero-inflated Poisson and gamma-Poisson distributions. The use of count distributions allows for inference on availability for encounter, as well as a wide variety of parameterizations for heterogeneity in the observation process. We demonstrate that this approach can accurately recover demographic and observation parameters in the presence of individual heterogeneity in detection probability and discuss some potential future extensions of this method.

Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems.

Intelligent reconfigurable surfaces (IRSs) have gained much attention due to their passive behavior that can be a successor to relays in many applications. However, traditional relay systems might still be a perfect choice when reliability and throughput are the main concerns in a communication system. In this work, we use an IRS along with a decode-and-forward relay to provide a possible solution to address one of the main challenges of future wireless networks which is providing reliability. We investigate a robust transceiver design against the residual self-interference (RSI), which maximizes the throughput rate under self-interference channel uncertainty-bound constraints. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a novel mathematical method to find a lower bound on the performance of the IRS that can be used as a benchmark. Eventually, we show an important result in which, for the worst-case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory and singular value decomposition (SVD) is proposed to find the best response of the transmitters and relay against worst-case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. We show that our algorithm performs better that the state of the art in terms of time complexity as well as robustness. For instance, our numerical results show that the acheivable rate can be increased twofold and almost sixfold, respectively, for the case of small and large antenna array at transceivers.

Evidence of Závora Bay as a critical site for reef manta rays, Mobula alfredi, in southern Mozambique.

The largest known reef manta ray (Mobula alfredi) population in Africa has been monitored for more than 20 years at several locations on the coast of the Inhambane Province in southern Mozambique. Nonetheless, before this study, little had been reported on the population dynamics of M. alfredi from Závora, a remote bay in the region. Photographic mark-recapture was used to investigate the size and structure of M. alfredi that aggregate at "Red Sands," a reef cleaning station in Závora Bay. An 11 year photographic data set was used to identify 583 M. alfredi individuals between 2010 and 2021. More than half of M. alfredi individuals were resighted at least once, with most encounters (up to 18 for one individual) occurring during the peak sighting period in July-November each year. An even sex ratio was observed, 44% females and 50% males, with no significant difference in resightings between the sexes. Pollock's robust design population models were used to estimate annual abundance, emigration, annual apparent survival and capture probability at Red Sands from July to November over a 6 year period (2016-2021). Abundance estimates varied year to year, ranging from 35 (95% c.i. [30, 45]) up to 233 (95% c.i. [224, 249]) M. alfredi individuals. Given the seasonal affinity of M. alfredi observed at Red Sands, this study highlights the importance of understanding fine-scale site use within the larger home range of this population to develop local management strategies.

Physiological links with behavior and fitness: The acute adrenocortical response predicts trappability but not survival in male and female deermice.

The "Cort-Fitness" hypothesis predicts a negative relationship between baseline glucocorticoids (GCs) and fitness, although evidence for this hypothesis remains mixed. Such ambiguity could partially exist because blood GCs, typically used in field studies, can fluctuate too rapidly to measure accurately, while the relationship between GCs and trappability is often neglected. Here, by addressing these factors, we examined relationships between GC measures and survival of North American deermice (Peromyscus maniculatus; hereafter deermice) as a model system. To do this, we used more stable GC measures, including the integrated measures of baseline and stress response fecal corticosterone metabolites (FCMs), and downstream measures of neutrophil/lymphocyte ratio (N/L ratio), and body condition score (BCS), to characterize their relationships with survival and trappability. Over two years, deermice were live-trapped monthly, evaluated for BCS, and sampled for feces and blood. Stress response FCMs were evaluated only at first capture. Mark-recapture models, with GC measures as predictors of either survival or trappability, were compared to identify top models. We found that stress response FCMs negatively predicted trappability, and weaker evidence that BCS positively predicted survival. Although the latter provides some support for the "Cort-Fitness" hypothesis, there was no support when using integrated measures. Instead, our findings suggest that deermice with a lower adrenocortical response (i.e. stress response FCMs) were more likely to be captured. Therefore, GC-trappability relationships must be investigated in field studies to avoid linking the wrong GC profile to fitness, and physiological measures other than blood GCs may be useful for detecting GC-fitness patterns.

Robust Trajectory and Resource Optimization in UAV-Enabled IoT Networks under Probabilistic LoS Channel in Presence of Jammers.

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Subterranean movement inferred by temporary emigration in Barton Springs salamanders (Eurycea sosorum).

Movement behavior is an important aspect of animal ecology but is challenging to study in species that are unobservable for some portion of their lives, such as those inhabiting subterranean environments. Using four years of robust-design capture-recapture data, we examined the probability of movement into subterranean habitat by a population of endangered Barton Springs salamanders (Eurycea sosorum), a species that inhabits both surface and subterranean groundwater habitats. We tested the effects of environmental variables and body size on survival and temporary emigration, using the latter as a measure of subterranean habitat use. Based on 2,046 observations of 1,578 individuals, we found that temporary emigration was higher for larger salamanders, 79% of which temporarily emigrated into subterranean habitat between primary sampling intervals, on average. Body size was a better predictor of temporary emigration and survival compared to environmental covariates, although coefficients from lower ranked models suggested turbidity and dissolved oxygen may influence salamander movement between the surface and subsurface. Surface population dynamics are partly driven by movement below ground and therefore surface abundance estimates represent a fraction of the superpopulation. As such, while surface habitat management remains an important conservation strategy for this species, periodic declines in apparent surface abundance do not necessarily indicate declines of the superpopulation associated with the spring habitat.

A Robust Design for Aperture-Level Simultaneous Transmit and Receive with Digital Phased Array.

Aperture-level simultaneous transmit and receive (ALSTAR) attempts to utilize adaptive digital transmit and receive beamforming and digital self-interference cancellation methods to establish isolation between the transmit and receive apertures of the single-phase array. However, the existing methods only discuss the isolation of ALSTAR and ignore the radiation efficiency of the transmitter and the sensitivity of the receiver. The ALSTAR array design lacks perfect theoretical support and simplified engineering implementation. This paper proposes an adaptive random group quantum brainstorming optimization (ARGQBSO) algorithm to simplify the array design and improve the overall performance. ARGQBSO is derived from BSO and has been ameliorated in four aspects of the ALSTAR array, including random grouping, initial value presets, dynamic probability functions, and quantum computing. The transmit and receive beamforming carried out by ARGQBSO is robust to all elevation angles, which reduces complexity and is conducive to engineering applications. The simulated results indicate that the ARGQBSO algorithm has an excellent performance, and achieves 166.8 dB of peak EII, 47.1 dBW of peak EIRP, and -94.6 dBm of peak EIS with 1000 W of transmit power in the scenario of an 8-element array.

Comparing strategies to damp electromechanical oscillations through STATCOM with multi-band controller.

This paper aims to compare two strategies for damping low-frequency electromechanical oscillations in multi-machine power systems through Static Synchronous Compensators (STATCOM) with a multi-band controller. STATCOM is represented by a controllable voltage source behind an impedance and the multi-band controller acts as a power oscillation damper that modulates parameters of the voltage source in the transient period. In the first strategy, the multi-band controller acts on the voltage-control loop through the voltage modulation. In the second one, the controller acts on the real power-control loop to modulate the phase angle of the voltage source. The coordinated design of multi-band controllers and power systems stabilizers is performed through an optimization approach taking into account several operation conditions.

Estimating population parameters of broadnose sevengill sharks (Notorynchus cepedianus) using photo identification capture-recapture.

The broadnose sevengill shark (Notorynchus cepedianus) is a common high trophic-level predator around coastal New Zealand. Data on the ecology of the species in New Zealand are severely lacking, and anthropogenic impacts are unquantified. To partially address this, the authors undertook a study of the demographics of a population at Stewart Island. Sampling trips were carried out seasonally from winter 2016 to spring 2017. A baited underwater video system (BUV) was deployed on 133 occasions (mean = 22.2 deployments per season) in a shallow coastal embayment to capture underwater video of N. cepedianus for photo identification of individuals. N. cepedianus was detected on all but one deployment. Images extracted from video recorded the presence of 149 different individuals. Capture-recapture analysis of these data using robust design methods indicated a seasonal trend in abundance of the population using the study area, ranging from 34 (95% C.I. = 21-55) during winter 2016, to 94 (95% C.I. = 44-199) during spring 2017. This study presents the first data on demographic parameters of N. cepedianus in New Zealand.

Mate fidelity improves survival and breeding propensity of a long-lived bird.

Evolutionary and behavioural ecologists have long been interested in factors shaping the variation in mating behaviour observed in nature. Although much of the research on this topic has focused on the consequences of mate choice and mate change on annual reproductive success, studies of a potential positive link between mate fidelity and adult demographic rates have been comparatively rare. This is particularly true for long-lived birds with multi-year, socially monogamous pair bonds. We used a 26-year capture-mark-recapture dataset of 3,330 black brent Branta bernicla nigricans to test whether breeding with a familiar mate improved future breeding propensity and survival. We predicted that experienced breeders nesting with a new partner would have rates of survival similar to familiar pairs because long-lived species avoid jeopardizing survival since their lifetime fitness is sensitive to this vital rate. In contrast, we expected that any costs of breeding with a new partner would be paid through skipping the subsequent breeding attempt. We found that unfamiliar pairs had lower subsequent breeding propensity than faithful partners. However, contrary to our expectations, individuals breeding with a new mate also suffered reduced survival. These results add to a small number of studies indicating that a positive relationship between mate retention and adult demographic rates may exist in a diverse array of avian species. Given these results, researchers should consider costs of mate change that extend beyond within-season reproductive success to fully understand the potential adaptive basis for perennial social monogamy. We caution that if mate retention enhances survival prospects, improvements in annual reproductive success with pair-bond length could be a secondary factor favouring perennial social monogamy, particularly in species with slower life-history strategies. Furthermore, some cases where annual reproductive success does not improve with pair-bond duration, yet multi-year pair bonds are common, could be explained by benefits afforded by mate fidelity to adult vital rates.

Demography of the salt marsh harvest mouse (Reithrodontomys raviventris halicoetes) and associated rodents in tidal and managed wetlands.

Suisun Marsh (Solano County, California) is the largest contiguous marsh remaining on the West Coast of the United States, and makes up approximately 10% of the wetlands remaining in the San Francisco Estuary. Suisun Marsh has been safeguarded from development through the operation of over 100 privately owned waterfowl hunting clubs, which manage for diked waterfowl habitat. However, this management-and the subsequent loss of tidal influence-has been considered harmful for some species, including the endangered salt marsh harvest mouse (SMHM; Reithrodontomys raviventris). To determine the value of tidal wetlands relative to those managed for waterfowl, we performed periodic surveys for rodents in managed and tidal wetlands over 5 years, and used capture-mark-recapture analyses to estimate demographic parameters and abundance for the three most common rodents-the northern SMHM (R. r. halicoetes), the western harvest mouse (a sympatric native species; R. megalotis, WHM), and the house mouse (a sympatric invasive species; Mus musculus). Wetland type had no effect on detection, temporary emigration, or survival for any of these species. However, fecundity and population growth for all three species were affected by an interaction of season and wetland type, although none of these parameters was consistently superior in either habitat type. Estimated abundance of SMHM and Mus was similar in both wetland types, whereas WHM were more abundant in managed wetlands. Salt marsh harvest mice also showed no affinity for any microhabitat characteristics associated with tidal wetlands. Managed wetlands in Suisun Marsh support SMHM and Mus equally, and abundances of WHM were greater than in tidal wetlands, suggesting managed wetlands may be superior in terms of supporting native rodents. As climate change and sea level rise are predicted to threaten coastal marshes, these results suggest the recovery strategy for SMHM could incorporate managed wetlands.

Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data.

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.