Efficient Approach Research Articles

Wrecking neutrophil extracellular traps and antagonizing cancer-associated neurotransmitters by interpenetrating network hydrogels prevent postsurgical cancer relapse and metastases

Tumor-promoting niche after incomplete surgery resection (SR) can lead to more aggressive local progression and distant metastasis with augmented angiogenesis-immunosuppressive tumor microenvironment (TME). Herein, elevated neutrophil extracellular traps (NETs) and cancer-associated neurotransmitters (CANTs, e.g., catecholamines) are firstly identified as two of the dominant inducements. Further, an injectable fibrin-alginate hydrogel with high tissue adhesion has been constructed to specifically co-deliver NETs inhibitor (DNase I)-encapsulated PLGA nanoparticles and an unselective β-adrenergic receptor blocker (propranolol). The two components (i.e., fibrin and alginate) can respond to two triggers (thrombin and Ca2+, respectively) in postoperative bleeding to gelate, shaping into an interpenetrating network (IPN) featuring high strength. The continuous release of DNase I and PR can wreck NETs and antagonize catecholamines to decrease microvessel density, blockade myeloid-derived suppressor cells, secrete various proinflammatory cytokines, potentiate natural killer cell function and hamper cytotoxic T cell exhaustion. The reprogrammed TME significantly suppress locally residual and distant tumors, induce strong immune memory effects and thus inhibit lung metastasis. Thus, targetedly degrading NETs and blocking CANTs enabled by this in-situ IPN-based hydrogel drug depot provides a simple and efficient approach against SR-induced cancer recurrence and metastasis.

One-pot two-step biocatalytic upgrading of 5-hydroxymethylfurfural to 2, 5-furandicarboxylic acid (FDCA) through a sequential oxidation process

2, 5-furandicarboxylic acid (FDCA) is a versatile bio-based building block that can be used for the synthesis of degradable polymers. Recently, developing green and sustainable catalytic processes for producing FDCA from renewable platform chemical molecules has attracted continuing attention. In this study, we proposed a green and sustainable one-pot, two-step biocatalytic process for the sequential oxidation of biomass-derived 5-hydroxymethylfurfural (HMF) to form FDCA with good yield and excellent selectivity. The initial step involves the oxidation of HMF to 2, 5-diformylfuran (DFF) and 5-formyl-2-furancarboxylic acid (FFCA) catalyzed by recombinant E. coli cells expressing a 5-hydroxymethylfurfural oxidase. The second step is to convert DFF and FFCA into FDCA final product via aldehyde oxidation reaction catalyzed by Deinococcus wulumuqiensis R12 whole cells. Combining the two different whole-cell biocatalysts, 180 mM of HMF could be easily converted into FDCA with a yield of 99% under optimized reaction conditions. This study offers an efficient and sustainable approach for the valorization of biomass-based HMF to form value-added FDCA.

Design of a handheld and portable fluorescence imaging system for quantitative detection of pregnancy-specific biomarkers in cattle.

Reproductive management significantly impacts dairy farm productivity, necessitating accurate timely pregnancy detection in cattle. This paper presents a novel handheld and portable fluorescence imaging system designed for quantitative assessment of pregnancy-specific biomarkers, addressing the limitations of current detection methods. The objective was to develop a cost-effective, at-farm solution for detecting pregnancy-specific protein B (PSPB) in bovine plasma samples. The system integrates an imaging module and a custom software application, enabling image capture, data processing, and PSPB concentration determination. Calibration utilizing known PSPB concentrations achieved a 0.6ng/mL limit of detection. Validation encompassed a comparison with a standard ELISA method using 100 bovine plasma samples; minimal bias and good agreement were observed within the linear range of the calibration curve for both methods. The system offers portability, user-friendliness, and potential for multiplex detection, promising real-time, at-farm reproductive management. This study demonstrates the successful development and validation of a portable fluorescence imaging system, offering an efficient and accurate approach to detecting pregnancy-specific biomarkers in cattle. Its implications extend to improving dairy farm productivity by enabling timely and reliable reproductive management practices.

Polyethylenimine Inclusion to Develop Aqueous Alginate-Based Core-Shell Capsules for Biomedical Applications.

Aqueous core-shell structures can serve as an efficient approach that allows cells to generate 3D spheroids with in vivo-like cell-to-cell contacts. Here, a novel strategy for fabricating liquid-core-shell capsules is proposed by inverse gelation of alginate (ALG) and layer-by-layer (LbL) coating. We hypothesized that the unique properties of polyethylenimine (PEI) could be utilized to overcome the low structural stability and the limited cell recognition motifs of ALG. In the next step, alginate dialdehyde (ADA) enabled the Schiff-base reaction with free amine groups of PEI to reduce its possible toxic effects. Scanning electron microscopy and light microscopy images proved the formation of spherical hollow capsules with outer diameters of 3.0 ± 0.1 mm for ALG, 3.2 ± 0.1 mm for ALG/PEI, and 4.0 ± 0.2 mm for ALG/PEI/ADA capsules. The effective modulus increased by 3-fold and 5-fold when comparing ALG/PEI/ADA and ALG/PEI to ALG capsules, respectively. Moreover, PEI-coated capsules showed potential antibacterial properties against both Staphylococcus aureus and Escherichia coli, with an apparent inhibition zone. The cell viability results showed that all capsules were cytocompatible (above 75.5%). Cells could proliferate and form spheroids when encapsulated within the ALG/PEI/ADA capsules. Monitoring the spheroid thickness over 5 days of incubation indicated an increasing trend from 39.50 μm after 1 day to 66.86 μm after 5 days. The proposed encapsulation protocol represents a new in vitro platform for developing 3D cell cultivation and can be adapted to fulfill the requirements of various biomedical applications.

Synthesis, modifications, and applications of iron-based nanoparticles.

Magnetic nanoparticles (MNPs) are appealing materials as assistant to resolve environmental pollution issues and as recyclable catalysts for the oxidative degradation of resistant contaminants. Moreover, they can significantly influence the advancement of medical applications for imaging, diagnostics, medication administration, and biosensing. On the other hand, due to unique features, excellent biocompatibility, high curie temperatures and low cytotoxicity of the Iron-based nanoparticles, they have received increasing attention in recent years. Using an external magnetic field, in which the ferrite magnetic nanoparticles (FMNPs) in the reaction mixtures can be easily removed, make them more efficient approach than the conventional method for separating the catalyst particles by centrifugation or filtration. Ferrite magnetic nanoparticles (FMNPs) provide various advantages in food processing, environmental issues, pharmaceutical industry, sample preparation, wastewater management, water purification, illness therapy, identification of disease, tissue engineering, and biosensor creation for healthcare monitoring. Modification of FMNPs with the proper functional groups and surface modification techniques play a significant role in boosting their capability. Due to flexibility of FMNPs in functionalization and synthesis, it is possible to make customized FMNPs that can be utilized in variety of applications. This review focuses on synthesis, modifications, and applications of Iron-based nanoparticles.

Mechanical stretch preconditioned adipose-derived stem cells elicit polarization of anti-inflammatory M2-like macrophages and improve chronic wound healing.

Transplantation of adipose-derived stem cells (ASCs) is a promising option in the field of chronic wounds treatment. However, the effectiveness of ASCs therapies has been hampered by highly inflammatory environment in chronic wound areas. These problems could be partially circumvented using efficient approaches that boost the survival and anti-inflammatory capacity of transplanted ASCs. Here, by application of mechanical stretch (MS), we show that ASCs exhibits increased survival and immunoregulatory properties invitro. MS triggers the secretion of macrophage colony stimulating factor (M-CSF) from ASCs, a chemokine that is linked to anti-inflammatory M2-like macrophages polarization. When the MS-ASCs were transplanted to chronic wounds, the wound area yields significantly faster closure rate and lower inflammatory mediators, largely due to macrophages polarization driven by transplanted MS-ASCs. Thus, our work shows that mechanical stretch can be harnessed to enhance ASCs transplantation efficiency in chronic wounds treatment.

A Microwave‐Assisted One‐Pot Three‐Step Metal‐Free [4+2] Annulation for the Sustainable Synthesis of Highly Substituted 1,2,4‐Triazines

AbstractHerein we report the first microwave‐assisted one‐pot three‐step metal‐free [4+2] procedure for the synthesis of 5,6‐substituted‐3‐hydrazinyl‐1,2,4‐triazines directly from commercially available aromatic and heteroaromatic ketones. The 3‐hydrazinyl‐1,2,4‐triazine can be isolated or further elaborated, after a simple work‐up, by direct condensation with different aldehydes or ketones to provide highly functionalized hydrazone‐derivatives. This protocol represents a rapid, efficient, and sustainable approach for the development of new highly functionalized 1,2,4‐triazine derivatives with potential biological interest.

An efficient methodological approach for synthesis of selenopyridines: generation, reactions, anticancer activity, EGFR inhibitory activity and molecular docking studies.

In the present work, we successfully synthesized Se-alkyl selenopyridines 1 and 3, selenopheno[2,3-b]pyridine 2, and bis-selenopyridine 4 derivatives using an eco-friendly method by utilizing NaHSe instead of toxic hydrogen selenide. The effect of the temperature on the reaction was screening at various temperatures. The regiospecific reaction of selenopyridine 1 with bromine afforded an unexpected product 4,6-diamino-5-bromo-2-[(cyanomethyl)selenyl]-pyridine-3-carbonitrile (5), which was cyclized to selenopheno[2,3-b]pyridine (7) by refluxing in the presence of TEA. While its treatment with thiophenol and/or p-chlorothiophenol gave 8a, b. On the other hand, its reaction with aminothiophenol afforded 2-(benzo[d]-thiazol-2-yl)-5-bromoselenopheno[2,3-b]pyridine-3,4,6-triamine (9). Also, N-(2-cyano-4-methyl-5H-1-seleno-3,5,8-triazaacenaphthylen-7-yl)acetamide (11) and a novel series of selenoazo dyes 12a-d were synthesized by treatment of selenopheno[2,3-b]pyridine 2 with acetic anhydride and/or diazonium chlorides of aromatic amines, respectively. Then, we ascertained the potential activity of synthesized compounds against highly metastatic prostate cancer cells (PC-3) and osteosarcoma cells (MG-63) and found that 12a, 12b, 12c, and 12d were more cytotoxic than doxorubicin in both tested cell lines, showing nearly the same anticancer activity with IC50 values ranging from 2.59 ± 0.02µM to 3.93 ± 0.23µM. Mechanistically, the most potent compounds 12a and 12b proved to be potent EGFR inhibitors with IC50 values of 0.301 and 0.123µM, respectively, compared to lapatinib as a positive reference (IC50 = 0.049µM). Moreover, the docking results are in good agreement with the anticancer activity as well as the EGFR inhibitory activity, suggesting these two compounds as promising EGFR anticancer candidates.

Inorganic salt starvation improves the polysaccharide production and CO2 fixation by Porphyridium purpureum.

The microalgae industry shows a promising future in the production of high-value products such as pigments, phycoerythrin, polyunsaturated fatty acids, and polysaccharides. It was found that polysaccharides have high biomedical value (such as antiviral, antibacterial, antitumor, antioxidative) and industrial application prospects (such as antioxidants). This study aimed to improve the polysaccharides accumulation of Porphyridium purpureum CoE1, which was effectuated by inorganic salt starvation strategy whilst supplying rich carbon dioxide. At a culturing temperature of 25°C, the highest polysaccharide content (2.89g/L) was achieved in 50% artificial seawater on the 12th day. This accounted for approximately 37.29% of the dry biomass, signifying a 25.3% increase in polysaccharide production compared to the culture in 100% artificial seawater. Subsequently, separation, purification and characterization of polysaccharides produced were conducted. Furthermore, the assessment of CO2 fixation capacity during the cultivation of P. purpureum CoE1 was conducted in a 10L photobioreactor. This indicated that the strain exhibited an excellent CO2 fixation capacity of 1.66g CO2/g biomass/d. This study proposed an efficient and feasible approach that not only increasing the yield of polysaccharides by P. purpureum CoE1, but also fixing CO2 with a high rate, which showed great potential in the microalgae industry and Bio-Energy with Carbon Capture and Storage.

Deep tomography for the three-dimensional atmospheric turbulence wavefront aberration

Multiconjugate adaptive optics (MCAO) can overcome atmospheric anisoplanatism to achieve high-resolution imaging with a large field of view (FOV). Atmospheric tomography is the key technology for MCAO. The commonly used modal tomography approach reconstructs the three-dimensional atmospheric turbulence wavefront aberration based on the wavefront sensor (WFS) detection information from multiple guide star (GS) directions. However, the atmospheric tomography problem is severely ill-posed. The incomplete GS coverage in the FOV coupled with the WFS detection error significantly affects the reconstruction accuracy of the three-dimensional atmospheric turbulence wavefront aberration, leading to a nonuniform aberration detection precision over the whole FOV. We propose an efficient approach for achieving accurate atmospheric tomography to overcome the limitations of the traditional modal tomography approach. We employed a deep-learning-based approach to the tomographic reconstruction of the three-dimensional atmospheric turbulence wavefront aberration. We propose an atmospheric tomography residual network (AT-ResNet) that is specifically designed for this task, which can directly generate wavefronts of multiple turbulence layers based on the Shack-Hartmann (SH) WFS detection images from multiple GS directions. The AT-ResNet was trained under different turbulence intensity conditions to improve its generalization ability. We verified the performance of the proposed approach under different conditions and compared it with the traditional modal tomography approach. The well-trained AT-ResNet demonstrates a superior performance compared to the traditional modal tomography approach under different atmospheric turbulence intensities, various turbulence layer distributions, higher-order turbulence aberrations, detection noise, and reduced GSs conditions. The proposed approach effectively addresses the limitations of the modal tomography approach, leading to a notable improvement in the accuracy of atmospheric tomography. It achieves a highly uniform and high-precision wavefront reconstruction over the whole FOV. This study holds great significance for the development and application of the MCAO technology.

FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

SURVEY ON FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and Mask R-CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

Recipes for institutional adoption of a teacher-driven learning analytics tool

The changing landscape of higher education is putting increasing strain on educators, leading to a diminishing ability to provide pedagogical and pastoral support to ballooning and diversifying cohorts. Learning analytics promises solutions to these challenges for educators, including by personalising learning support and experiences, streamlining data capture and analyses, and providing teachers with new, efficient teaching approaches. However, reports of these impacts, or widespread adoption of learning analytics, or even examples of cross-institutional collaboration are sparse. We argue that this may be because of a lack of educator-driven learning analytics tools that meet their felt needs, and present case studies from three Australian universities that have collaborated to implement such a tool. This tool, the Student Relationship Engagement System (SRES), empowers educators to collect, collate, analyse, and use student engagement and success data that they consider meaningful for their particular contexts. Developed by unfunded educators and widely adopted through collegiate recommendations, the SRES enables personalisation and targeting of student learning and support using relevant data, fostering positive student-teacher relationships and enhancing student engagement. Using the three case studies as a backdrop, we present a revised learning analytics adoption framework focussing on strategy, structure, support, and impact, and use this framework to systematically evaluate the implementation of the SRES at the three institutions to derive ‘recipes’ for adopting an educator-focussed learning analytics platform. We also discuss three core themes emerging from the case studies, around the needs of academics, the role of academic and educational developers, and flexible and agile information technology practices.

Phospholipid-Anchored Ligand Conjugation on Extracellular Vesicles for Enhanced Cancer Targeting.

Extracellular vesicles (EVs) are recognized as potential candidates for next-generation drug delivery systems. However, the inherent cancer-targeting efficiency is unsatisfactory, necessitating surface modification to attach cell-binding ligands. By utilizing phospholipase D from Streptomyces in combination with maleimide-containing primary alcohol, the authors successfully anchored ligands onto milk-derived EVs (mEVs), overcoming the issues of ligand leakage or functional alteration seen in traditional methods. Quantitative nano-flow cytometry demonstrated that over 90% of mEVs are effectively modified with hundreds to thousands of ligands. The resulting mEV formulations exhibited remarkable long-term stability in conjugation proportion, ligand number, size distribution, and particle concentration, even after months of storage. It is further shown that conjugating transferrin onto mEVs significantly enhanced cellular uptake and induced pronounced cytotoxic effects when loaded with paclitaxel. Overall, this study presents a highly efficient, stable, cost-effective, and scalable ligand conjugation approach, offering a promising strategy for targeted drug delivery of EVs.

A modified technique for simplifying full-arch screw-retained implant rehabilitation with rapid digital conversion: a case report.

In this case report, a novel technique is described for an efficient, simple, and effective digital approach to immediate provisionalization of the implant-supported full-arch prosthesis. Today's patient population is increasingly educated about treatment options and expects efficient, esthetic, and comfortable results. This novel technique delivers on these aims while eliminating the many challenges posed by previously described digital and analog techniques to the immediate provisionalization of the implant- supported full-arch prosthesis. This technique requires minimal chair time and cost to the provider and reduces patient discomfort and complication risk. Combining the best features of analog and digital methods to conversion of the implant-supported full-arch, this novel method presents a promising new protocol for this popular procedure.

Stereoscopic Polymer Network for Developing Mechanically Robust Flexible Perovskite Solar Cells with an Efficiency Approaching 25.

Flexible perovskite solar cells (pero-SCs) have the potential to overturn the application scenario of silicon photovoltaic technology. However, their mechanical instability severely impedes their practical applicability, and the corresponding intrinsic degradation mechanism remains unclear. In this study, we systematically analyzed the degradation behavior of flexible pero-SCs under mechanical stress and observed that the structural failure firstly occurs in the polycrystal perovskite film, then extend to interfaces. To suppress the structural failure, we employed pentaerythritol triacrylate, a crosslinked molecule with three stereoscopic crosslink sites, to establish a three-dimensional polymer network in both the interface and bulk perovskite. This network reduced the Young's modulus of the perovskite and simultaneously enhanced the interfacial toughness. As a result, the formation of cracks and delamination, which occur under a high mechanical stress, was significantly suppressed in the flexible pero-SC, which consequently retained 92% of its initial power conversion efficiency (PCE) after 20000 bending cycles. Notably, the flexible device also showed a record PCE of 24.9% (certified 24.48%). This article is protected by copyright. All rights reserved.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Enhanced image encryption using AES algorithm with CBC mode: a secure and efficient approach

Enhanced image encryption using AES algorithm with CBC mode: a secure and efficient approach

Cystic fibrosis management in pediatric population—from clinical features to personalized therapy

Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations of the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR). In 1949, it's been identified as a monogenic disease and was thought to primarily affect individuals of Northern European descent. It was the most prevalent autosomal recessive disease that shortens life. With the availability of multiple testing methodologies nowadays, there is a chance to create novel and enhanced treatment options. Even in the absence of a high sweat chloride test (SCT) result, the discovery of two causal mutations is diagnostic for cystic fibrosis (CF). For a CF diagnosis, however, at least two positive E sweat chloride tests are still required. In order to achieve early and active intervention to manage cystic fibrosis (CF) and its comorbidities, treatment regimens for pediatric patients should be evaluated, improved, and closely monitored. New developments in the treatment of cystic fibrosis (CF) have led to the development of medications derived from molecules that target the pathogenetic pathway of the illness. These options are very efficient and allow pediatric patients to receive individualized care. However, in order to better direct patient care and enhance patient outcomes, it is crucial to research uncommon CF mutations, which can provide crucial information about the prognosis of the disease and the relationships between genotype and phenotype. To ensure the success of creating novel, safer, and more efficient treatment approaches, a deeper understanding of the pathogeny of the illness is required. In the age of customized medicine, genetic research will be essential to improving patient care and quality of life for those with uncommon mutations.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.