Inexpensive Model Research Articles

Line-search based optimization using function approximations with tunable accuracy

This paper develops a line-search algorithm that uses objective function models with tunable accuracy to solve smooth optimization problems with convex constraints. The evaluation of objective function and its gradient is potentially computationally expensive, but it is assumed that one can construct effective, computationally inexpensive models. This paper specifies how these models can be used to generate new iterates. At each iteration, the model has to satisfy function error and relative gradient error tolerances determined by the algorithm based on its progress. Moreover, a bound for the model error is used to explore regions where the model is sufficiently accurate. The algorithm has the same first-order global convergence properties as standard line-search methods, but only uses the models and the model error bounds. The algorithm is applied to problems where the evaluation of the objective requires the solution of a large-scale system of nonlinear equations. The models are constructed from reduced order models of this system. Numerical results for partial differential equation constrained optimization problems show the benefits of the proposed algorithm.

Sacrospinous Hysteropexy-Video Tutorial of Construction and Application of a Feasible and Inexpensive Teaching Model for Simulation.

Sacrospinous hysteropexy is one of the preeminent uterus-preserving surgical techniques for treating pelvic organ prolapse supported by level one evidence. As training on models greatly improves surgical skills and outcomes, we developed a simple and inexpensive model to simulate sacrospinous hysteropexy. A step-by-step instruction for the production of the model is available to be viewed online. To keep production costs low, readily available materials were used, with a total cost per model of about 2 EUR (Austria, August 2023). All important anatomical landmarks (prolapsing uterus, vagina, ischial spine and sacrospinous ligament) were all represented. We present a detailed instructional video on how to construct the model and the practical training, detailing the individual steps of a successful sacrospinous hysteropexy, available online. Thus, trainees are able to practice the individual movements of the entire surgical procedure on this simulator model guided by the tutorial video. In this way, trainees will be able to practice the entire surgical procedure. An introduction to the model with explanation of all anatomical landmarks and a standardised explanation of the surgery with its individual steps (handout distributed). The presented video showcases the feasibility of the easy construction and application of a model for the surgical skill training of sacrospinous hysteropexy. Easily accessible, inexpensive material and its simple build make this a reproducible model regardless of geographic or socioeconomic resources.

Metallic nanoparticles: a promising novel therapeutic tool against antimicrobial resistance and spread of superbugs.

In recent years, antimicrobial resistance (AMR) has become an alarming threat to global health as notable increase in morbidity and mortality has been ascribed to the emergence of superbugs. The increase in microbial resistance because of harboured or inherited resistomes has been complicated by the lack of new and effective antimicrobial agents, as well as misuse and failure of existing ones. These problems have generated severe and growing public health concern, due to high burden of bacterial infections resulting from scarce financial resources and poor functioning health systems, among others. It is therefore, highly pressing to search for novel and more efficacious alternatives for combating the action of these super bacteria and their infection. The application of metallic nanoparticles (MNPs) with their distinctive physical and chemical attributes appears as promising tools in fighting off these deadly superbugs. The simple, inexpensive and eco-friendly model for enhanced biologically inspired MNPs with exceptional antimicrobial effect and diverse mechanisms of action againsts multiple cell components seems to offer the most promising option and said to have enticed many researchers who now show tremendous interest. This synopsis offers critical discussion on application of MNPs as the foremost intervening strategy to curb the menace posed by the spread of superbugs. As such, this review explores how antimicrobial properties of the metallic nanoparticles which demonstrated considerable efficacy against several multi-drugs resistant bacteria, could be adopted as promising approach in subduing the threat of AMR and harvoc resulting from the spread of superbugs.

Trial of an Inexpensive Training Simulation Model for Laparoscopic Appendicectomy.

Introduction Laparoscopic surgery is now the gold standard for many common procedures; however, there has been a lack of emphasis on laparoscopic training at the junior level. Simulation is an effective form of training in surgery, but surgical simulation models and courses can be expensive and inaccessible to medical students and foundation doctors. With procedures becoming more minimally invasive, it is key that we train laparoscopic skills at an earlier stage; and to do so, we need to remove the barriers of cost and accessibility. Our study aims to develop and assess the effectiveness of a simulated laparoscopic appendicectomy training model in developing laparoscopic knowledge, skills, and confidence in a cohort of foundation doctors. Methods A simulated mesoappendix model was fashioned from supermarket chicken wings with the bone removed, and it was put inside a laparoscopic box trainer with laparoscopic tools and equipment. This was trialed in a surgical-themed hub day for foundation doctors in the East of England Deanery, UK, and the simulated model was set up as a workshop following a teaching session detailing the steps of a laparoscopic appendicectomy. Participants completed questionnaires pre- and post-session to assess perceived skills, experience, and confidence in laparoscopic surgery, laparoscopic skills, and the usefulness of this training model. Results A total of 29 foundation doctors with limited formal surgical training completed the survey. The model is quick to prepare and assemble, costing around £0.30 ($0.40). Trainees found the model acceptable and helpful in developing their laparoscopic skills and knowledge of laparoscopic surgery. There was also an increase in self-perceived confidence in performing laparoscopic procedures under supervision. Discussion This modification to the model allows the dissection and division of a simulated mesoappendix using only laparoscopic scissors and forceps in a box trainer. This simulation model is a promising and inexpensive tool which can be used for early-stage laparoscopic training for medical students and junior doctors.

A hybrid continuum-beam optimisation model: the virtual extensometer method for efficient optimisation of lattice materials

AbstractAdditive manufacturing (AM) enables the fabrication of complex lattice structures that are infeasible with traditional manufacturing processes. These structures are typically implemented with constant cross-sectional strut elements; this strategy is expedient but leads to sub-optimal structural efficiency. Numerical continuum models allow robust mechanical modelling of complex lattice geometry but provide equally complex data fields that are ill-suited for traditional optimisation techniques. By contrast, numerical beam models represent the fundamental tensile and bending stress states but are incapable of capturing nuanced geometrical effects that lead to local stress concentrations. In this research, a hybrid continuum-beam method is proposed for the systematic optimisation of strut cross section: a representative unit cell is simulated by continuum elements, and the local strut stress tensor is acquired by embedded beam elements that act as virtual extensometers. By integrating a computationally inexpensive beam model into a more robust continuum model, the volume and the quality of data returned are significantly increased whilst being provided in a readily usable format for optimisation techniques, for a trivial increase in computational cost. The presented method is generalised and can be applied to any strut-based lattice structure. Body-centred cubic (BCC) and BCC with z-strut (BCCZ) lattice structures optimised by the proposed method are fabricated using laser-based powder bed fusion (LB-PBF) in AlSi10Mg and are mechanically evaluated. On average, performance for BCC lattice structures improves by 33% and 26% for relative yield strength and relative Young’s modulus respectively. BCCZ lattice structures saw a performance improvement of 25% and 19% for relative yield strength and relative Young’s modulus respectively, thus confirming superior performance at lower relative densities when compared to incumbent designs.

An In Vitro Orbital Flow Model to Study Mechanical Loading Effects on Osteoblasts.

Flow induced by an orbital shaker is known to produce shear stress and oscillatory flow, but the utility of this model for studying mechanical loading effects in osteoblasts is not well defined. To test this, osteoblasts derived from the long bones of adult male C57BL/6J mice were plated on 6-well plates and subjected to orbital shaking at various frequencies (0.7, 1.4, and 3.3 Hz) for 30 and 60 min in serum-free differentiation media. The shear stress on cells produced by 0.7, 1.4, and 3.3 Hz shaking frequencies were 1.6, 4.5, and 11.8 dynes/cm2, respectively. ALP activity measured 72 h after shaking (orbital flow) showed a significant increase at 0.7 and 1.4 Hz, but not at 3.3 Hz, compared to static controls. Orbital flow-induced mechanical stress also significantly increased (25%) osteoblast proliferation at a 0.7 Hz flow compared to static controls. Additionally, expression levels of bone formation markers Osf2, Hif1a, Vegf, and Cox2 were significantly increased (1.5- to 3-fold, p < 0.05) in cells subjected to a 0.7 Hz flow compared to non-loaded control cells. We also evaluated the effect of orbital flow on key signaling pathways (mTOR, JNK, and WNT) known to mediate mechanical strain effects on osteoblasts. We found that blocking mTOR and WNT signaling with inhibitors significantly reduced (20-30%) orbital flow-induced ALP activity compared to cells treated using a vehicle. In contrast, inhibition of JNK signaling did not affect flow-induced osteoblast differentiation. In conclusion, our findings show that the flow produced by an orbital shaker at a lower frequency is an appropriate inexpensive model for studying the molecular pathways mediating mechanical strain effects on primary cultures of osteoblasts in vitro.

A Novel Training Model for Superficial Temporal Artery- Middle Cerebral Artery Anastomosis Using Microsurgical Techniques

To create a reusable and inexpensive training model with technological tools that simulates cerebral bypass surgery and a sensor system that provides tactile feedback to the surgeon. Furthermore, we aimed to evaluate the anastomotic stability and contribution to the surgeon's learning curve. We created a superficial temporal artery-middle cerebral artery bypass simulation model using chicken and turkey brachial arteries. A cranium model was printed with a three-dimensional printer for craniotomy and cerebral parenchyma was created by pouring silicone into the cranial mold. A blood flow simulation system was also prepared. Pressure-sensitive sensors were placed on parenchyma and tactile conditioning was performed via audible warning from the sensors. Twenty-four anastomosis were performed with different sutures and hand tools. Anastomosis completion times and durability and the number of touches and pressures applied to the parenchyma were recorded. The stability of the anastomoses was evaluated by increasing the pressure in the blood flow simulation system, so usefulness of the training model was evaluated. The time required for anastomosis completion decreased as the number of practices increased (P<0.05). As the number of practices increased, the number of parenchymal touches decreased (P<0.05). With practice, the time required for anastomosis completion and number of parenchymal touches decreased. Thus, the model is useful, inexpensive, reusable, easily accessible, and contributes to the surgeon's learning curve. Our model with pressure-sensitive sensors can be used for microsurgery practice, enabling the surgeons to gain tactile conditioning and evaluate anastomotic stability and leakage.

Inexpensive high fidelity melt pool models in additive manufacturing using generative deep diffusion

Defects in Laser Powder Bed Fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects. Therefore, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by upscaling lightweight coarse mesh simulations. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. We predict the melt pool depth within 3 μm based on low-fidelity input data 4× coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.

Establishment of a humanized mouse model of HIV-1 infection and quantification of integrated HIV proviral DNA in vivo

In recent years, virological, pathological, and immunological studies need to be carried out for the emerging anti-human immunodeficiency virus (HIV) therapies such as gene therapy, broadly neutralizing antibodies, and the derived chimeric antigen receptor (CAR)-T immunotherapy, which necessitates suitable, simple, and inexpensive small-animal models and methods for accurate quantification of the viral genome in the HIV-1 infected. In our research, the HIV-∆ENV-Jurkat-EGFP-mCherry cell line was engineered through the infection with a dual-labelled HIV pseudovirus. A nested quantitative PCR (nested-qPCR) method with the cellular genome as the integrated standard was established for the quantification of HIV proviral copies. We administered intravenous injections of healthy human peripheral blood mononuclear cell (PBMC) into NOD/Prkdcscid/IL2rgnull (NPG) mice. To verify engraftment kinetics, we analyzed the percentages of hCD45+, hCD3+, hCD4+, and hCD8+ cells in the peripheral blood of hu-PBMC-NPG mice. To evaluate HIV-1 infection in hu-PBMC-NPG mice, we inoculated these mice with HIV NL4-3-NanoLuc by intraperitoneal (IP) injection. We then monitored the luciferase expression by the small animal imaging system and measured the viral load in the spleen by qPCR. The infiltration of human PBMCs in mice was detected 3-5 weeks after intravenous injection, and the percentage of hCD45 in humanized mouse PBMCs were more than 25% five weeks after IP inoculation. The expression of the virus-associated luciferase protein was detected by luciferase imaging 27 days post infection. Moreover, the viral total DNA, RNA, and proviral DNA copies reached 18 000 copies/106 cells, 15 000 copies/μg RNA, and 15 000 copies/106 cells, respectively, in the mouse spleen. Taken together, we reported a convenient method for building a simple humanized mouse model of HuPBMC-NPG/severe combined immunodeficiency (SCID) by intravenous injection with hu-PBMCs without advanced surgical skills and irradiation. Furthermore, we established a convenient method for the efficient determination of proviral DNA to assess HIV replication in vivo, viral reservoir sizes, and efficacy of novel anti-HIV therapies including CAR-T immunotherapy and gene therapy.

Development and calibration of a fast flow model for solid oxide cell stack internal manifolds

Due to the commercialization of solid oxide cells (SOC) progressing at an accelerated pace, computationally inexpensive SOC models adapted to the iterative nature of the engineering process are in increasing demand. Flow simulation in the stack is especially challenging in this regard because detailed computational fluid mechanic models are computationally demanding, while simplified models rely on pressure loss coefficients and friction factors not readily available in the literature. In this study, a computationally inexpensive algebraic model of an SOC stack internal manifold is developed and calibrated for laminar flow conditions. Thereby, pressure loss coefficients and Darcy friction factors are determined for a broad range of operating conditions through symbolic regression of Navier–Stokes flow simulation results of stacks of 20 to 40 cells. The derived Darcy friction factors for the inlet and outlet manifolds prove to be of particular importance, as they deviate strongly from the expressions assumed in similar modeling studies. The predictive power of the developed model is demonstrated by providing accurate predictions of the flow distribution in the stack, even outside of the calibration window.

Impact of Surface Functionality on Biodistribution of Gold Nanoparticles in Silkworms

To date, animal models are still indispensable for studying biodistribution and elimination of nanomaterials. However, the use of mammals for in vivo experiments faces various challenges including increasing regulatory hurdles and costs. This study aims to validate larvae of the domestic silkworm Bombyx mori as an alternative invertebrate model for preliminary in vivo research. Organ distribution and elimination of gold nanoparticles (AuNPs) are compared with four different surface functionalities in silkworms after systemic administration: AuNPs coated with poly(ethylene glycol) (PEG), with polyglycidols (PGs) that are slightly hydrophobic (PG(alkyl)), positively charged (PG(+)), or negatively charged (PG(−)). Subsequent inductive coupled plasma mass spectrometry 6 or 24 h after AuNPs administration reveals the biodistribution in silkworm hemolymph, midgut, epidermis, and excrements. Even after 24 h incubation, hemolymph contains the highest AuNPs concentrations, independent of surface functionalization indicating a prolonged circulation time and slow distribution into different silkworm organs and tissues. Positively charged PG(+)AuNPs show three times higher concentrations in the midgut and are excreted at the fastest rate when compared to other AuNPs. In the findings, a surface‐dependent biodistribution and elimination of AuNPs are indicated in silkworms, and the feasibility of using this inexpensive animal model for time‐ and cost‐effective, preliminary in vivo studies of NPs is confirmed.

Experimental validation of a design model for latent thermal energy storage extended with sensible heat

Latent thermal energy storage can be a key technology for a green energy transition by matching fluctuating heat demand and supply. In order to implement a storage system, it needs to be designed which requires estimating the outlet temperature of a system for a given geometry and time history of the heat transfer fluid’s mass flow rate and inlet temperature. Currently, design methods are either overly simplistic, focusing solely on e.g. the phase change time or requiring the solution of partial differential equations which can be computationally expensive. The present paper proposes a novel approach where a latent thermal energy storage system is decomposed into a heat transfer fluid vessel, a sensible storage system and a storage system with only latent heat. Computationally inexpensive models are available for all three of these sub heat exchangers. A heat exchanger model is obtained by connecting the sub heat exchangers in parallel. This novel approach is used to model an industrial scale shell and tube latent thermal energy storage heat exchanger. The predicted outlet temperature is compared to the measured outlet temperature and the design model obtains good agreement.

Bubble dynamics in high-pressure flow boiling conditions

High resolution measurements of key parameters relevant to bubble departure processes in high-pressure water systems has been a major challenge over the past several decades due to the small size and fast motion of vapor bubbles, making conventional optical measurements unreliable. Addressing this difficulty is critical, as accurate bubble departure data and computationally inexpensive models are needed for future two-phase heat transfer models. To close this gap, we use high-speed imaging to track bubble size and velocity in high-pressure flow boiling conditions (10 – 40 bar). It is observed that bubbles are almost always perfectly spherical in shape and slide along the boiling surface immediately after nucleating. To predict sliding, we conduct a force balance on the bubble and derive an equation of motion in the direction of the bulk flow. Interestingly, we find that at high pressure conditions, the equation of motion can be significantly simplified, resulting in an analytical expression for the bubble motion, growth time and departure diameter, allowing us to develop simple and computationally efficient equations. The applicability of the model and departure criterion across a broader range of high-pressure conditions is discussed, and recommendations for further characterization of bubble departure processes are made.

Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations

Abstract. Maximising the power production of large wind farms is key to the transition towards net zero. The overarching goal of this paper is to propose a computational method to maximise the power production of wind farms with two practical design strategies. First, we propose a gradient-free method to optimise the wind farm power production with high-fidelity surrogate models based on large-eddy simulations and a Bayesian framework. Second, we apply the proposed method to maximise wind farm power production by both micro-siting (layout optimisation) and wake steering (yaw angle optimisation). Third, we compare the optimisation results with the optimisation achieved with low-fidelity wake models. Finally, we propose a simple multi-fidelity strategy by combining the inexpensive wake models with the high-fidelity framework. The proposed gradient-free method can effectively maximise wind farm power production. Performance improvements relative to wake-model optimisation strategies can be attained, particularly in scenarios of increased flow complexity, such as in the wake steering problem, in which some of the assumptions in the simplified flow models become less accurate. The optimisation with high-fidelity methods takes into account nonlinear and unsteady fluid mechanical phenomena, which are leveraged by the proposed framework to increase the farm output. This paper opens up opportunities for wind farm optimisation with high-fidelity methods and without adjoint solvers.

Plasma surrogate modelling using Fourier neural operators

Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and accurately allow us to quickly iterate over design and control strategies on current Tokamak devices and future reactors. Modelling plasma evolution using numerical solvers is often expensive, consuming many hours on supercomputers, and hence, we need alternative inexpensive surrogate models. We demonstrate accurate predictions of plasma evolution both in simulation and experimental domains using deep learning-based surrogate modelling tools, viz., Fourier neural operators (FNO). We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models, while maintaining a high accuracy (Mean Squared Error in the normalised domain ≈10−5 ). Our modified version of the FNO is capable of solving multi-variable Partial Differential Equations, and can capture the dependence among the different variables in a single model. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak, i.e. cameras looking across the central solenoid and the divertor in the Tokamak. We show that FNOs are able to accurately forecast the evolution of plasma and have the potential to be deployed for real-time monitoring. We also illustrate their capability in forecasting the plasma shape, the locations of interactions of the plasma with the central solenoid and the divertor for the full (available) duration of the plasma shot within MAST. The FNO offers a viable alternative for surrogate modelling as it is quick to train and infer, and requires fewer data points, while being able to do zero-shot super-resolution and getting high-fidelity solutions.

A deep-learning model for rapid spatiotemporal prediction of coastal water levels

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

ILOSCAR: interactive Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir model v1.0

Computationally inexpensive carbon cycle models serve as critical and efficient tools for illuminating the complex dynamics of the carbon cycle and its interplay with the climate system, offering insights into how our planet has responded to climate perturbations throughout its history. During geologic hyperthermal events, carbon cycle models are employed to trace the trajectory of carbon emissions and establish a connection between the emission trajectory and changes in the Earth's surface environment. To date, the prevalent method to estimate the carbon emission rate relies on coupling the carbon cycle modeling and proxy reconstructions. Most previous studies employ a forward methodology, i.e., they force the model with an array of predefined carbon injection scenarios and select the one that produces the best fit to one or more specific proxy-derived records (e.g., atmospheric pCO2, sea surface pH, and calcite compensation depth) as the optimal solution. However, this forward method has two potential disadvantages. First, it can be computationally expensive, particularly when tens of thousands of scenarios need to be conducted to find the best solution. Second, it might not yield the best injection trajectory if none of the predefined carbon emission curves represents the realistic emission curve. Hence, an inverse model that can reconstruct the carbon emission trajectory directly from the record/records is urgently needed. In this study, building upon the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir (LOSCAR) Model (Zeebe, 2012), we develop an interactive carbon cycle model (named iLOSCAR) using the open-source Python language and include two options, a forward model and an inverse model. The forward model replicates the original LOSCAR model, while the inverse model calculates the emission trajectory constrained by the proxy records in a single run. Both models are accessible via a web-based interface, which allows users to interactively tune model parameters and conduct experiments. In this paper, we present the details of iLOSCAR, including model structure and derivation of key equations. We then validate iLOSCAR's performance through an identical twin test and model intercomparison. We also apply it to a climatic perturbation event to diagnose features of the emission pattern that were overlooked in previous studies. Finally, we discuss the possible directions for model's future development.

An XGBoost-assisted evolutionary algorithm for expensive multiobjective optimization problems

Many expensive optimization problems exist in various real-world applications. However traditional evolutionary algorithms are inadequate for solving these problems directly. Surrogate-assisted evolutionary algorithm (SAEA) can effectively solve expensive optimization problems using computationally inexpensive surrogate models. However, both the Kriging and ensemble models most SAEAs adopted have limited uncertainty of prediction, especially for expensive multiobjective optimization problems (EMOPs). To enhance the optimization performance of SAEA for EMOPs, this paper proposes a new XGBoost-assisted evolutionary algorithm, calling XGBEA. Specifically, XGBoost is used as the surrogate model, and a neighborhood density selection strategy based on a mixed population and archive space (NDS-MPA) is proposed to measure the uncertainties of individuals. XGBoost helps to best fit objective functions with different fitness landscapes. NDS-MPA selects non-dominated individuals with minimal density for re-evaluation, incorporating considerations of convergence, diversity and uncertainty. Experimental results on two well-studied benchmarks demonstrated the superiority of XGBEA over seven state-of-the-art SAEAs.

A method for teaching depth of corneal lesions using a portable slit lamp (biomicroscope).

A novel, simple, and inexpensive model for teaching depth of lesions within the cornea using slit lamp biomicroscopy to veterinary students, ophthalmology residents, and general clinicians with access to a slit lamp biomicroscope is described. Using common laboratory items, this method can be created in any clinic and can be used to teach and quiz students through independent study with objective self-testing achieved.

In-vitro gadolinium retro-microdialysis in agarose gel-a human brain phantom study.

Cerebral microdialysis is a technique that enables monitoring of the neurochemistry of patients with significant acquired brain injury, such as traumatic brain injury (TBI) and subarachnoid haemorrhage (SAH). Cerebral microdialysis can also be used to characterise the neuro-pharmacokinetics of small-molecule study substrates using retrodialysis/retromicrodialysis. However, challenges remain: (i) lack of a simple, stable, and inexpensive brain tissue model for the study of drug neuropharmacology; and (ii) it is unclear how far small study-molecules administered via retrodialysis diffuse within the human brain. Here, we studied the radial diffusion distance of small-molecule gadolinium-DTPA from microdialysis catheters in a newly developed, simple, stable, inexpensive brain tissue model as a precursor for in-vivo studies. Brain tissue models consisting of 0.65% weight/volume agarose gel in two kinds of buffers were created. The distribution of a paramagnetic contrast agent gadolinium-DTPA (Gd-DTPA) perfusion from microdialysis catheters using magnetic resonance imaging (MRI) was characterized as a surrogate for other small-molecule study substrates. We found the mean radial diffusion distance of Gd-DTPA to be 18.5 mm after 24 h (p < 0.0001). Our brain tissue model provides avenues for further tests and research into infusion studies using cerebral microdialysis, and consequently effective focal drug delivery for patients with TBI and other brain disorders.