Set Of Simulations Research Articles

On the stochastic threshold of the COVID-19 epidemic model incorporating jump perturbations

This work delves into the intricate realm of epidemic modeling under the influence of unpredictable surroundings. By harnessing the power of white noise and Lévy noise, we construct a robust framework to capture the behavioral characteristics of the COVID-19 epidemic amidst erratic changes in the external environment. To enhance our comprehension of the intricate dynamics of the coronavirus, we conducted an investigation using a stochastic SIQS epidemic model that incorporates a dedicated compartment to represent populations under quarantine. Thanks to stochastic modeling techniques, we account for the inherent randomness in the transmission process and provide insights into the potential variations and uncertainties associated with the progression of the epidemic. Specifically, we show that the asymptotic behavior of our model is perfectly governed by two thresholds, Rσ,J and Rσ,J′. That is to say, if Rσ,J<1, the disease will be removed from the population, while it will persist if Rσ,J′>1. Our highlight lies in obtaining the necessary and sufficient conditions for extinction in the absence of jump noise, namely Rσ,0=Rσ,0′. This means that our sufficient conditions for extinction for the jump case are also almost necessary. Finally, we present a set of computational simulations to validate our theoretical findings, supporting the results developed throughout this article. Overall, this research contributes to our understanding of the COVID-19 pandemic and its impact on the global population.

Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures.

On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest

Abstract. We present an evaluation of the regional chemical transport model (CTM) WRF-CHIMERE (v2020r2) for the formation of biogenic secondary organic aerosol (BSOA) with a focus over the Finnish boreal forest. Formation processes of biogenic aerosols are still affected by different sources of uncertainties, and model predictions vary greatly depending on the levels of details of the adopted chemical and emissions schemes. In this study, air quality simulations were conducted for the summer of 2019 using different organic aerosol (OA) schemes (as currently available in the literature) to treat the formation of BSOA. First, we performed a set of simulations in the framework of the volatility basis set (VBS) scheme carrying different assumptions for the treatment of the aging processes of BSOA. The results of the model were compared against high-resolution (i.e., 1 h) organic aerosol mass and size distribution measurements performed at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR-II) site located in Hyytiälä, in addition to other gas-phase species such as ozone (O3), nitrogen oxides (NOx), and biogenic volatile organic compound (BVOC) measurements of isoprene (C5H10) and monoterpenes. We show that WRF-CHIMERE could reproduce well the diurnal variation of the measured OA concentrations for all the investigated scenarios (along with the standard meteorological parameters) as well as the increase in concentrations during specific heat wave episodes. However, the modeled OA concentrations varied greatly between the schemes used to describe the aging processes of BSOA, as also confirmed by an additional evaluation using organic carbon (OC) measurement data retrieved from the EBAS European databases. Comparisons with isoprene and monoterpene air concentrations revealed that the model captured the observed monoterpene concentrations, but isoprene was largely overestimated, a feature that was mainly attributed to the overstated biogenic emissions of isoprene. We investigated the potential consequences of such an overestimation by inhibiting isoprene emissions from the modeling system. Results indicated that the modeled BSOA concentrations increased in the northern regions of the domain (e.g., Finland) compared to southern European countries, possibly due to a shift in the reactions of monoterpene compounds against available radicals, as further suggested by the reduction in α-pinene modeled air concentrations. Finally, we briefly analyze the differences in the modeled cloud liquid water content (clwc) among the simulations carrying different chemical schemes for the treatment of the aging processes of BSOA. The results of the model indicated an increase in clwc values at the SMEAR-II site, for simulations with higher biogenic organic aerosol loads, most likely as a result of the increased number of biogenic aerosol particles capable of activating cloud droplets.

Impact of Injection Rate on Flow Mixing during the Refining Stage in an Electric Arc Furnace

During the refining stage of electric arc furnace (EAF) operation, molten steel is stirred to facilitate gas/steel/slag reactions and the removal of impurities, which determines the quality of the steel. The stirring process can be driven by the injection of oxygen, which is carried out by burners operating in lance mode. In this study, a computational fluid dynamics (CFD) platform is used to simulate the liquid steel flow dynamics in an industrial-scale scrap-based EAF. The CFD platform simulates the three-dimensional, transient, non-reacting flow of the liquid steel bath stirred by oxygen injection to analyze the mixing process. In particular, the CFD study simulates liquid steel flow in an industrial-scale EAF with three asymmetric coherent jets, which impacts the liquid steel mixing under different injection conditions. The liquid steel mixing is quantified by defining two variables: the mixing time and the standard deviation of the flow velocity. The results indicate that the mixing rate of the bath is determined by flow dynamics near the injection cavities and that the formation of very low-velocity regions or ‘dead zones’ at the center of the furnace and the balcony regions prevents flow mixing. This study includes a baseline case, where oxygen is injected at 1000 SCFM in all the burners. Two sets of cases are also included: The first set considers cases where oxygen is injected at a reduced and at an increased uniform flow rate, 750 and 1250 SCFM, respectively. The second set considers cases with non-uniform injection rates in each burner, which keep the same total flow rate of the baseline case, 3000 SCFM. Comparison between the two sets of simulations against the baseline case shows that by increasing the uniform flow rate from 1000 to 1250 SCFM, the mixing time is reduced by 10.9%. Moreover, all the non-uniform injection cases reduce the mixing time obtained in the baseline case. However, the reduction in mixing times in these cases is accompanied by an increase in the standard deviations of the flow field. Among the non-uniform injection cases, the largest reduction in mixing time compared to the baseline case is 10.2%, which is obtained when the largest flow rates are assigned to coherent jets located opposite each other across the furnace.

Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms.

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model's internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of "shared" neurons responding to all category instances-the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances ["proper names" (PN)] or symbols related to classes of instances sharing common features ["category terms" (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies.

Enhancing Smart Building Surveillance Systems in Thin Walls: An Efficient Barrier Design.

This paper introduces an efficient barrier model for enhancing smart building surveillance in harsh environment with thin walls and structures. After the main research problem of minimizing the total number of wall-recognition surveillance barriers, we propose two distinct algorithms, Centralized Node Deployment and Adaptation Node Deployment, which are designed to address the challenge by strategic placement of surveillance nodes within the smart building. The Centralized Node Deployment aligns nodes along the thin walls, ensuring consistent communication coverage and effectively countering potential disruptions. Conversely, the Adaptation Node Deployment begins with random node placement, which adapts over time to ensure efficient communication across the building. The novelty of this work is in designing a novel barrier system to achieve energy efficiency and reinforced surveillance in a thin-wall environment. Instead of a real environment, we use an ad hoc server for simulations with various scenarios and parameters. Then, two different algorithms are executed through those simulation environments and settings. Also, with detailed discussions, we provide the performance analysis, which shows that both algorithms deliver similar performance metrics over extended periods, indicating their suitability for long-term operation in smart infrastructure.

Testing scenarios for using telepresence robots in healthcare settings

The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.

Rapid cycle deliberate practice to improve airway skills and performance of trainees in a pediatric emergency department.

The study objective was to determine the effect of a rapid cycle deliberate practice (RCDP) program on simulated and actual airway skills by pediatric emergency medicine (PEM) fellows. We designed and implemented a 12-month RCDP airway skills curriculum for PEM fellows at an academic pediatric institution. The curriculum was designed using airway training literature, RCDP principals, and internal quality assurance airway video review program. Simulation training scenarios increased in complexity throughout the curriculum. PEM fellows participated in monthly sessions. Two PEM faculty facilitated the sessions, utilizing a step-by-step objective structured clinical evaluation (OSCE)-style tool for each scenario. Data were collected for all four levels of the Kirkpatrick Model of Training Evaluation-participant response (reaction, pre-post session survey), skills performance in the simulation setting (learning, pre-post OSCE), skills performance for actual patients (behavior, video review), and patient outcomes (results, video review). During the study period (August 2021 to June 2022), 13 PEM fellows participated in 112 sessions (mean nine sessions per fellow). PEM fellows reported improved comfort in all domains of airway management, including intubation performance. Participant OSCE scores improved posttraining (pretraining median score for trainees 57 [IQR 57-59], posttraining median 61 [IQR 61-62], p = 0.0005). Over the 12 months, PEM fellows performed 45 intubation attempts in the pediatric emergency department (median patient age 4 years [IQR 1-9 years]). Compared to a 5-year historical cohort, participants had higher first-pass success (87% vs. 71%, p = 0.028) and shorter attempt duration (22 s vs. 29 s, p = 0.018). There was no significant difference in the frequency of oxyhemoglobin desaturation in the training period versus the historical period (7% vs. 15%, p = 0.231). At multiple levels of educational outcomes, including participant behavior and patient outcomes, an RCDP program was associated with improved airway skills and performance of PEM fellows.

Dynamics aspects and bifurcations of a tumor-immune system interaction under stationary immunotherapy

We consider a three-dimensional mathematical model that describes the interaction between the effector cells, tumor cells, and the cytokine (IL-2) of a patient. This is called the Kirschner–Panetta model. Our objective is to explain the tumor oscillations in tumor sizes as well as long-term tumor relapse. We then explore the effects of adoptive cellular immunotherapy on the model and describe under what circumstances the tumor can be eliminated or can remain over time but in a controlled manner. Nonlinear dynamics of immunogenic tumors are given, for example: we prove that the trajectories of the associated system are bounded and defined for all positive time; there are some invariant subsets; there are open subsets of parameters, such that the system in the first octant has at most five equilibrium solutions, one of them is tumor-free and the others are of co-existence. We are able to prove the existence of transcritical and pitchfork bifurcations from the tumor-free equilibrium point. Fixing an equilibrium and introducing a small perturbation, we are able to show the existence of a Hopf periodic orbit, showing a cyclic behavior among the population, with a strong dominance of the parental anomalous growth cell population. The previous information reveals the effects of the parameters. In our study, we observe that our mathematical model exhibits a very rich dynamic behavior and the parameter μ̃ (death rate of the effector cells) and p̃1 (production rate of the effector cell stimulated by the cytokine IL-2) plays an important role. More precisely, in our approach the inequality μ̃2>p̃1 is very important, that is, the death rate of the effector cells is greater than the production rate of the effector cell stimulated by the cytokine IL-2. Finally, medical implications and a set of numerical simulations supporting the mathematical results are also presented.

Probing cosmology via the clustering of critical points

ABSTRACT Exclusion zones in the cross-correlations between critical points (peak-void, peak-wall, filament-wall, and filament-void) of the density field define quasi-standard rulers that can be used to constrain dark matter and dark energy cosmological parameters. The average size of the exclusion zone is found to scale linearly with the typical distance between extrema. The latter changes as a function of the matter content of the universe in a predictable manner, but its comoving size remains essentially constant in the linear regime of structure growth on large scales, unless the incorrect cosmology is assumed in the redshift–distance relation. This can be used to constrain the dark energy parameters when considering a survey that scans a range of redshifts. The precision of the parameter estimation is assessed using a set of cosmological simulations, and is found to be a 4σ detection of a change in matter content of 5 per cent, or about 3.8σ detection of 50 per cent shift in the dark energy parameter using a full sky survey up to redshift 0.5.

A Drop-By-Drop Self-Assembled All-Natural Hydrogel as a Desensitizer for Rapid and Enduring Management of Dentin Hypersensitivity.

Dentin hypersensitivity (DH) is a prevalent dental condition arising from the exposure of dentin tubules (DTs), leading to discomfort upon external stimuli. However, achieving swift and profound occlusion of these exposed DTs for immediate and enduring relief remains challenging due to the intricate dentin structure and oral environment. Herein, a pioneering and facile drop-by-drop strategy involving an in situ generated natural supramolecular hydrogel formed by self-assembling silk fibroin (SF) and tannic acid (TA) within the narrow DT space is proposed. When SF and TA aqueous solutions are applied successively to exposed dentin, they penetrate deeply within DTs and coassemble into compact gels, robustly adhering to DT walls. This yields a rapid and compact occlusion effect with an unprecedented depth exceeding 250µm, maintaining stable occlusion efficacy even under rigorous in vitro and in vivo erosion and friction conditions for no less than 21 days. Furthermore, the biocompatibility and effective occlusion properties are verified through cell studies in simulated oral settings and an in vivo rabbit model. This study, for the first time, demonstrates the translational potential of hydrogel-based desensitizers in treating DH with prompt action, superior occlusion depth and enduring treatment benefits, holding promise as clinical-friendly restorative solutions for delicate-structured biosystems.

A 3D time-dependent backward erosion piping model

Backward erosion piping (BEP) is a failure mechanism of hydraulic structures like dams and levees on cohesionless foundations subjected to seepage flows. This article models the time-dependent development of BEP using numerical simulation of the erosion process. A 3-dimensional finite element equilibrium BEP model is extended with a formulation for the sediment transport rate. The model is compared to and calibrated with small- and large-scale experiments. Finally, a large set of simulations is analyzed to study the effects of factors such as grain size, scale (seepage length) and overloading on the rate of pipe progression. The results show that the development of BEP in the small-scale experiments is predicted well. Challenges remain for the prediction at larger scales, as calibration and validation is hard due to limited large-scale experiments with sufficiently accurate measurements. The results show that the progression rate increases with grain size and degree of overloading and decreases with seepage length, which is consistent with experimental observations. The model results provide a better physical basis for incorporating time-dependent development in the risk assessment and design of levees.

Implementing the time-to-event continual reassessment method in the presence of partial orders in a phase I head and neck cancer trial

BackgroundIn this article we describe the methodology of the time-to-event continual reassessment method in the presence of partial orders (PO-TITE-CRM) and the process of implementing this trial design into a phase I trial in head and neck cancer called ADePT-DDR. The ADePT-DDR trial aims to find the maximum tolerated dose of an ATR inhibitor given in conjunction with radiotherapy in patients with head and neck squamous cell carcinoma.MethodsThe PO-TITE-CRM is a phase I trial design that builds upon the time-to-event continual reassessment method (TITE-CRM) to allow for the presence of partial ordering of doses. Partial orders occur in the case where the monotonicity assumption does not hold and the ordering of doses in terms of toxicity is not fully known.ResultsWe arrived at a parameterisation of the design which performed well over a range of scenarios. Results from simulations were used iteratively to determine the best parameterisation of the design and we present the final set of simulations. We provide details on the methodology as well as insight into how it is applied to the trial.ConclusionsWhilst being a very efficient design we highlight some of the difficulties and challenges that come with implementing such a design. As the issue of partial ordering may become more frequent due to the increasing investigations of combination therapies we believe this account will be beneficial to those wishing to implement a design with partial orders.Trial registrationADePT-DDR was added to the European Clinical Trials Database (EudraCT number: 2020-001034-35) on 2020-08-07.

Modelling the effect of roughness density on turbulent forced convection

By examining a systematic set of direct numerical simulations, we develop a model which captures the effect of roughness density on global and local heat transfer in forced convection. The surfaces considered are zero-skewed three-dimensional sinusoidal rough walls with solidities, $\varLambda$ (defined as the frontal area divided by the total plan area), ranging from low $\varLambda = 0.09$ , medium $\varLambda = 0.18$ to high $\varLambda = 0.36$ . For each solidity, we vary the roughness height characterised by the roughness Reynolds number, $k^+$ , from transitionally rough to fully rough conditions. The findings indicate that, as the fully rough regime is approached, there is a pronounced breakdown in the analogy between heat and momentum transfer, whereby the velocity roughness function $\Delta U^+$ continues to increase and the temperature roughness function $\Delta \varTheta ^+$ attains a peak with increasing $k^+$ . This breakdown occurs at higher sand-grain roughness Reynolds numbers ( $k_s^+$ ) with increasing solidity. Locally, we find that the heat transfer can be meaningfully partitioned into two categories: exposed, high-shear regions experiencing higher heat transfer obeying a local Reynolds analogy and sheltered, reversed-flow regions experiencing lower and spatially uniform heat transfer. The relative contribution of these distinct mechanisms to the global heat transfer depends on the fraction of the total surface area covered by these regions, which ultimately depends on $\varLambda$ . These insights enable us to develop a model for the rough-wall heat-transfer coefficient, ${C_{h,k}(k^+, \varLambda, Pr)}$ , where $Pr$ is the molecular Prandtl number, that assumes different heat-transfer laws in exposed and sheltered regions. We show that the exposed–sheltered surface-area fractions can be modelled through simple ray tracing that is solely dependent on the surface topography and a prescribed sheltering angle. Model predictions compare well when applied to heat-transfer data of traverse ribs from the literature.

Cluster halo shapes in CDM and SIDM models: unveiling the DM particle nature using a weak-lensing approach

ABSTRACT Self-interacting dark matter (SIDM) is an alternative to the standard collisionless cold dark matter model (CDM), allowing for interactions between the dark-matter particles through the introduction of a self-scattering cross-section. However, the observable effects between these two scenarios are hard to detect. In this work, we present a detailed analysis of an application of galaxy–galaxy lensing to measure with high precision the shapes of cluster haloes and how this approach can be used to obtain information regarding the nature of the dark-matter particle. Using two sets of simulated data, SIDM and CDM simulations, we compute stacked shear maps centred on several subsets of haloes with masses ≳1013.5 M⊙. From these maps, we obtain the quadrupole profiles related to the mean projected elongation of the particle distribution from which the shape parameters are derived. Accounting for a radial shape variation, this technique provides an enhancement of the observed differences between the simulated data sets. In particular, we obtain a higher slope of the power law for the shape-radial relation for the haloes identified in the SIDM simulation, which are rounder towards the centre. Also, as approaching to the mean virial radius, the projected semi-axis ratios converge to similar values than in the CDM simulation. Moreover, we account for the impact of the neighbouring mass, where more strongly elongated distributions are found for the haloes in the SIDM simulation, indicating that under dark matter self interaction, the large-scale structure imprints a more coherent accretion process.

Cadaveric study of ergonomics and performance using a robotic exoscope with a head-mounted display in spine surgery.

The conventional microscope has the disadvantage of a potentially unergonomic posture for the surgeon, which can affect performance. Monitor-based exoscopes could provide a more ergonomic posture, as already shown in pre-clinical studies. The aim of this study was to test the usability and comfort of a novel head-mounted display (HMD)-based exoscope on spinal surgical approaches in a simulated OR setting. A total of 21 neurosurgeons naïve to the device were participated in this prospective trial. After a standardized training session with the device, participants were asked to perform a single-level thoracolumbar decompression surgery on human cadavers using the exoscope. Subsequently, all participants completed a comfort and safety questionnaire. For the objective evaluation of the performance, all interventions were videotaped and analyzed. Twelve men and nine women with a mean age of 34 (range: 24-57) were participating in the study. Average time for decompression was 15min (IqR 9.6; 24.2); three participants (14%) terminated the procedure prematurely. In these dropouts, a significantly higher incidence of back/neck pain (p = 0.002 for back, p = 0.046 for neck pain) as well as an increased frequency of HMD readjustments (p = 0.045) and decreased depth perception (p = 0.03) were documented. Overall, the surgeons' satisfaction with the exoscope was 84% (IqR 75; 100). Using a standardized, pre-interventional training, it is possible for exoscope-naïve surgeons to perform sufficient spinal decompression using the HMD-based exoscope with a high satisfaction. However, inaccurate HMD setup prior to the start of the procedure may lead to discomfort and unsatisfactory results.

Impact of sea level rise on tidal energy budget in a macro-tidal coastal bay with archipelago

With sea level rise (SLR), tidal nuisance flooding has become a growing threat, especially around estuaries with large tidal amplitudes. This study investigated how sea level change affects tides in Hangzhou Bay, a macro-tidal estuary with high SLR rate. By downscaling climate projections to a regional hydrodynamic model, the amplitude of primary tidal constituent (M2) was predicted to increase by 0.25 m in the upper bay, where the amplitude of major diurnal tide (K1) was also predicted to increase by 15%. In addition, the sensitivity of tidal amplitude to mean sea level was examined by a set of numerical simulations with different SLR. It was found that the increase of tidal amplitude is nonlinear to SLR, and the tidal amplitudes almost cease to increase when SLR is over 1.5 m. Although predictions show less amplitude changes in the lower bay, Zhoushan Archipelago around the bay mouth strongly modulates the incoming tidal energy, thus affecting the tidal amplitude in the upper bay. Energy budget analysis revealed that the complex topography, such as narrow channels, in the archipelago area leads to strong horizontal shear, which dissipates approximately 25% of total tidal energy in the bay. On the other hand, around 60% of the energy is dissipated in the bottom boundary layer. However, the bottom dissipation decreases by 4% due to reduced friction, while horizontal dissipation increases by 10% due to enhanced horizontal shear with SLR. This suggests that the strong horizontal shear in the Zhoushan archipelago region can play a more important role in the tidal energy budget in the future.

Statistical distributions of free surface elevation and wave height for out-of-equilibrium sea-states provoked by strong depth variations

As unidirectional irregular wave trains propagate over a steep shoal, the sea-state becomes out-of-equilibrium and is continuously affected by the non-equilibrium dynamics (NED) over dozens of characteristic wavelengths. Using the set of accurate numerical simulations of Zhang et al. (2022), the NED effects on the probability distributions of free surface elevation and wave height, the statistical moments and maximum wave statistics are investigated, in both near-field and far-field regions after the water depth transition. The primary contribution of this work is to assess the applicability and limitations of several popular statistical distribution models in describing non-equilibrium statistics. In addition, a new distribution of the free surface elevation in a lognormal shape is proposed, which predicts the non-equilibrium free surface statistics with satisfactory performance and characterizes well the skewness–kurtosis relationship in the short scale. It is shown that the statistics in the far-field region are significantly influenced by the near-field wave-wave interaction, and beyond the capability of all statistical models considered here. Despite this complexity, the sea-state in the far-field region exhibits lower freak wave probability than a Gaussian sea-state. Implications of these findings for engineering practices are discussed.

High bandwidth control of slow sampling rate nonlinear systems using alternating predictive GPIOs

This paper investigates the problem of robust output feedback control tracking for a class of nonlinear dual-rate sample-data systems subject to time-varying disturbances. These dual-rate systems, known as slow measurement fast control (SMFC) systems, are characterised by a slow sampling rate of the sensor and a fast updating rate of the controller. To facilitate high-frequency signal tracking, this paper introduces an alternating predictive generalised proportional integral observer (APGPIO). The proposed control method not only effectively restores the system state and captures disturbance changes between samples but also enhances the control bandwidth and anti-disturbance ability, enabling effective tracking of high-frequency reference outputs. By combining it with a fast updating sample-data feedback controller, APGPIO utilises predictive methods to increase the sampling rate of the system's sensors, matching the update frequency of the controller. Stability analysis demonstrates that the tracking error of this hybrid nonlinear system converges to a bounded region centred at the origin. Finally, the designed method's high control bandwidth and anti-disturbance ability are demonstrated through multiple sets of simulations using a real system.

Laboratory and simulated semi-field larvicidal efficacy of Aframomum angustifolium (Sonn.) K. Schum and Tagetes patula essential oils against Anopheles gambiae

IntroductionMalaria is a vector-borne, important public health problem spread by mosquitoes in tropical regions. The use of synthetic insecticides for mosquito control has been linked to insecticide resistance and environmental consequences. Thus, there is an urgent need to search for new highly selective and biodegradable insecticides lacking long-term toxicity to humans and mammals which are botanical-based compounds from indigenous plant sources available in local communities. Therefore, this study focused on evaluating the larvicidal activity of Aframomum angustifolium and Tagetes patula essential oils against Anopheles gambiae larvae in laboratory and simulated fields. MethodsThe Eos was extracted by the hydro distillation method, and the chemical composition of the Eos was determined using gas chromatography-mass spectrometry (GC-MS). The WHO protocol was followed for the laboratory and the simulated bioassays. Both laboratory-reared and field-collected larvae, as well as non-targeted organisms (Gambusia affinis), were used in simulated field conditions. The collected data were analyzed using probit analysis, and their means were compared in one-way analysis of variance (ANOVA) using IBM SPSS Statistic version 26. ResultsThe main active ingredients in T. patula Eo were terpinolene (20.75 %) and (Z)-ocimene (17.10 %) and A. angustifolium were cis-pinen-3-ol (58.48 %) β-pinene (31.03 %). The EO of T. patula was the most efficacious in the bioassay. The larvicidal bioassay findings demonstrated that the mortality rate was dose- and exposure-dependent. In the laboratory, the EOs of A. angustifolium and T. patula have larvicidal activity against An. gambiae larvae, with LC50 values of 1.71 and 0.71 ppm after 48 h, respectively. Both binary combinations of the EOs showed synergistic interactions at 24 h but antagonistic interactions at 48 h. In the simulated field trial using laboratory-reared larvae, the mortality rate was higher in the simulated setting compared with the laboratory setting for all the tested concentrations for both EOs. Only A. angustifolium EO was toxic against Gambusia affins at 100 ppm. ConclusionThe findings of this study have shown that T. patula and A. angustifolium oils have good larvicidal activities for An. gambiae. The two plants are potential sources of larvicidal compounds that could be used to control the malaria vector.