Optimizing Lactic Acid Bacteria Proportions in Sourdough to Enhance Antifungal Activity and Quality of Partially and Fully Baked Bread.

The organic acids produced by lactic acid bacteria (LAB) during the fermentation of sourdoughs have the ability to reduce the growth of different molds. However, this ability depends on the LAB used. For this reason, in this study, the proportions of different LAB were optimized to obtain aqueous extracts (AEs) from sourdough to reduce fungal growth in vitro, control the acetic acid concentration, and obtain a specific lactic to acetic acid ratio. In addition, the optimized mixtures were used to formulate partially baked bread (PBB) and evaluate the mold growth and bread quality during refrigerated storage. Using a simplex-lattice mixture design, various combinations of Lactiplantibacillus plantarum, Lacticaseibacillus casei, and Lactobacillus acidophilus were evaluated for their ability to produce organic acids and inhibit mold growth. The mixture containing only Lpb. plantarum significantly reduced the growth rates and extended the lag time of Penicillium chrysogenum and P. corylophilum compared with the control. The AEs' pH values ranged from 3.50 to 3.04. Organic acid analysis revealed that using Lpb. plantarum yielded higher acetic acid concentrations than when using mixed LAB. This suggests that LAB-specific interactions significantly influence organic acid production during fermentation. The reduced radial growth rates and extended lag times for both molds compared to the control confirmed the antifungal properties of the AEs from the sourdoughs. Statistical analyses of the mixture design using polynomial models demonstrated a good fit for the analyzed responses. Two optimized LAB mixtures were identified that maximized mold lag time, targeted the desired acetic acid concentration, and balanced the lactic to acetic acid ratio. The addition of sourdough with optimized LAB mixtures to PBB resulted in a longer shelf life (21 days) and adequately maintained product quality characteristics during storage. PBB was subjected to complete baking and sensory evaluation. The overall acceptability was slightly higher in the control without sourdough (7.50), followed by bread formulated with the optimized sourdoughs (ranging from 6.78 to 7.10), but the difference was not statistically significant (p > 0.05). The sensory analysis results indicated that the optimization was used to successfully formulate a sourdough bread with a sensory profile closely resembling that of a nonsupplemented one. The designed LAB mixtures can effectively enhance sourdough bread's antifungal properties and quality, providing a promising approach for extending bread shelf life while maintaining desirable sensory attributes.

Anti-IgG Doped Melanin Nanoparticles Functionalized Quartz Tuning Fork Immunosensors for Immunoglobulin G Detection: In Vitro and In Silico Study.

The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork's small size and difficulty in modifying the prongs' surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.

Optimal design of fractional-order proportional integral derivative controllers for structural vibration suppression.

In designing control systems, it is known that fractional-order proportional integral derivative (FOPID) controllers often provide greater flexibility than conventional proportional integral derivative (PID) controllers. This higher level of flexibility has proven to be extremely valuable for various applications such as vibration suppression in structural engineering. In this paper, we study the optimization of FOPID controllers using twelve well-established algorithms to minimize structural responses under seismic excitations. The algorithms include crystal structure algorithm (CryStAl), stochastic paint optimizer, particle swarm optimization, krill herd, harmony search, ant colony optimization, genetic algorithm, grey wolf optimizer, Harris hawks optimization, sparrow search algorithm, hippopotamus optimization algorithm, and duck swarm algorithm. In addition to highlighting the benefits of fractional calculus in structural control, this study provides a detailed analysis of FOPID controllers as well as a brief description of the algorithms used to optimize them. To evaluate the efficiency of the proposed techniques, two building models with different numbers of stories are examined. FOPID controllers are designed based on oustaloup's approximation and the El Centro earthquake data. Using five well-known metrics, the performances of the developed methods are evaluated against five earthquake scenarios, including the recent earthquake in Turkey. A non-parametric (Friedman) test is also employed to compare the algorithms based on their corresponding vibration reduction. The findings of this analysis show that CryStAl consistently performs better than the other algorithms for both building models, thus resulting in superior vibration suppression.

Investigating the neural mechanisms of transcranial direct current stimulation effects on human cognition: current issues and potential solutions.

Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future.

Optimization and Evaluation of Tourism Mascot Design Based on Analytic Hierarchy Process-Entropy Weight Method.

With the tourism industry continuing to boom, the importance of tourism mascots in promoting and publicizing tourism destinations is becoming increasingly prominent. Three core dimensions, market trend, appearance design, and audience feedback, are numerically investigated for deeply iterating tourism mascot design. Further, a subjective and objective evaluation weighting model based on the hierarchical analysis method (AHP) and entropy weighting method is proposed, aiming to utilize the advantages of these methods and ensure the entireness and correctness of results. Taking the mascots of six famous tourist attractions in Xi'an as an example, the feasibility and effectiveness of the evaluation model are verified. Data analysis and modeling results confirm that the three core evaluation indexes of scalability, innovation, and recommendation should be focused on in the design of tourism mascots in the three dimensions of market trends, appearance design, and audience feedback. The evaluation index scores are 0.1235, 0.1170, and 0.1123, respectively, which further illustrates the priority of mascot design. The evaluation model constructed by the research provides decision-makers with a comprehensive evaluation tool from the perspective of tourist experience, and also effectively assists the optimization process of mascot design. In addition, the model has good versatility and adaptability in structural design and evaluation logic and can be widely used in the optimization and evaluation research of brand mascots.

Mechanobiological optimization of scaffolds for bone tissue engineering.

Synthetic bone graft scaffolds aim to generate new bone tissue and alleviate the limitations of autografts and allografts. To meet that aim, it is essential to have a design approach able to generate scaffold architectures that will promote bone formation. Here, we present a topology-varying design optimization method, the "mixed-topology" approach, that generates new designs from a set of starting structures. This approach was used with objective functions focusing on improving the scaffold's local mechanical microenvironments to mechanobiologically promote bone formation within the scaffold and constraints to ensure manufacturability and achieve desired macroscale properties. The results demonstrate that this approach can successfully generate scaffold designs with improved microenvironments, taking into account different combinations of relevant stimuli and constraints.

Performance analyses of weighted superposition attraction-repulsion algorithms in solving difficult optimization problems.

The purpose of this paper is to test the performance of the recently proposed weighted superposition attraction-repulsion algorithms (WSA and WSAR) on unconstrained continuous optimization test problems and constrained optimization problems. WSAR is a successor of weighted superposition attraction algorithm (WSA). WSAR is established upon the superposition principle from physics and mimics attractive and repulsive movements of solution agents (vectors). Differently from the WSA, WSAR also considers repulsive movements with updated solution move equations. WSAR requires very few algorithm-specific parameters to be set and has good convergence and searching capability. Through extensive computational tests on many benchmark problems including CEC'2015 and CEC'2020 performance of the WSAR is compared against WSA and other metaheuristic algorithms. It is statistically shown that the WSAR algorithm is able to produce good and competitive results in comparison to its predecessor WSA and other metaheuristic algorithms.

A Review of Pressure Drop and Mixing Characteristics in Passive Mixers Involving Miscible Liquids.

The present review focuses on the recent studies carried out in passive micromixers for understanding the hydrodynamics and transport phenomena of miscible liquid-liquid (LL) systems in terms of pressure drop and mixing indices. First, the passive micromixers have been categorized based on the type of complexity in shape, size, and configuration. It is observed that the use of different aspect ratios of the microchannel width, presence of obstructions, flow and operating conditions, and fluid properties majorly affect the mixing characteristics and pressure drop in passive micromixers. A regime map for the micromixer selection based on optimization of mixing index (MI) and pressure drop has been identified based on the literature data for the Reynolds number (Re) range (1 ≤ Re ≤ 100). The map comprehensively summarizes the favorable, moderately favorable, or non-operable regimes of a micromixer. Further, regions for special applications of complex micromixer shapes and micromixers operating at low Re have been identified. Similarly, the operable limits for a micromixer based on pressure drop for Re range 0.1 < Re < 100,000 have been identified. A comparison of measured pressure drop with fundamentally derived analytical expressions show that Category 3 and 4 micromixers mostly have higher pressure drops, except for a few efficient ones. An MI regime map comprising diffusion, chaotic advection, and mixed advection-dominated zones has also been devised. An empirical correlation for pressure drop as a function of Reynolds number has been developed and a corresponding friction factor has been obtained. Predictions on heat and mass transfer based on analogies in micromixers have also been proposed.

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems.

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

Design Optimization and Tradeoff Analysis of an Actuated Continuum Probe for Pulmonary Nodule Localization and Resection.

Pulmonary nodules are abnormal tissue masses in the lungs, typically less than 3.0 cm in diameter, commonly detected during imaging of the chest and lungs. While most pulmonary nodules are not cancerous, surgical resection may be required if growth is detected between scans. This resection is typically performed without the benefit of intraoperative imaging, making it difficult for surgeons to confidently provide appropriate margins. To enhance the efficacy of wedge resection, researchers have developed a modified ultrasound imaging approach that utilizes both multiple scattering (MS) and single scattering (SS) to enhance the accuracy of margin delineation. Clinical deployment of this novel ultrasound technology requires a highly maneuverable ultrasound probe, ideally one that could be deployed and actuated with minimal invasiveness. This study details the design optimization and tradeoff analysis of an actuated continuum probe for pulmonary nodule localization and resection. This device, deployed through intercostal ports, would enable the intraoperative imaging and precise mapping of nodules for improved margin delineation and patient outcomes. To achieve this objective, multiple objective genetic algorithms (MOGAs) and a design of experiments (DOE) study are used to explore the design space and quantify key dimensional relationships and their effects on probe actuation.

Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers.

Liquid crystal elastomers (LCEs) are responsive materials that can undergo large reversible deformations upon exposure to external stimuli, such as electrical and thermal fields. Controlling the alignment of their liquid crystals mesogens to achieve desired shape changes unlocks a new design paradigm that is unavailable when using traditional materials. While experimental measurements can provide valuable insights into their behavior, computational analysis is essential to exploit their full potential. Accurate simulation is not, however, the end goal; rather, it is the means to achieve their optimal design. Such design optimization problems are best solved with algorithms that require gradients, i.e., sensitivities, of the cost and constraint functions with respect to the design parameters, to efficiently traverse the design space. In this work, a nonlinear LCE model and adjoint sensitivity analysis are implemented in a scalable and flexible finite element-based open source framework and integrated into a gradient-based design optimization tool. To display the versatility of the computational framework, LCE design problems that optimize both the material, i.e., liquid crystal orientation, and structural shape to reach a target actuated shapes or maximize energy absorption are solved. Multiple parameterizations, customized to address fabrication limitations, are investigated in both 2D and 3D. The case studies are followed by a discussion on the simulation and design optimization hurdles, as well as potential avenues for improving the robustness of similar computational frameworks for applications of interest.

Effect of floating treatment wetland coverage ratio and operating parameters on nitrogen removal: toward design optimization.

Floating treatment wetlands (FTWs) have the potential to improve the quality of wastewater discharges, yet design basics are unavailable to size these systems. This study investigates the effect of FTWs' coverage ratio and hydraulic retention time on agri-food wastewater treatment. This was studied in a pilot-scale experiment comprising four lagoons (6.5 m3 each) fed with real effluent from an existing tertiary treatment lagoon. An evaluation of FTW of different sizes (L24, L48, and L72 representing 24, 48, and 72% of pilot lagoons surface areas) and a control, L0 (without FTW), was performed over 16 months. Overall, L72 and L48 moderately improved total nitrogen (TN) mass removal compared to L0 (p < 0.05), while L24 exhibited similar TN mass removal (p = 0.196). The highest improvement was observed for L72, exhibiting up to 55% (mean of 13%) greater N mass removal than the control. The net increase in TN removal by FTWs was mainly related to denitrification, promoted by decreasing dissolved oxygen for increasing FTW coverage ratio. Residence time, temperature, and dissolved oxygen were the main parameters driving TN removal by FTWs. Retrofitting existing lagoons with FTW can facilitate N retrieval through plant harvesting, thereby reducing N remobilization from sediment (common in conventional lagoons).

Markov decision process design: A framework for integrating strategic and operational decisions.

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates the design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.

Design optimization of buttress type premium casing connection by modifying lower corner radius of stab flank.

Full-scale drilling for shale gas has been deepening, and the horizontal definitions have been increasing for drilling efficiency. Pressures and bending stresses are generated in the joints of gas-cored pipes, which seriously affect their durability. Premium casing connections are primarily forced at the interfaces of two parts, so the design of the threads is crucial, and the choice of optimal parameters is precisely related to their resistance to stresses. This article proposes a novel premium connection design, and its performance is validated through simulations to demonstrate good noise, vibration, and harshness (NVH) and durability performance. A parametric study is especially performed with the change of lower corner radius to observe maximum stress changes of the system and it is advised that a parameter be optimized. This study demonstrates that it is possible to design a premium connection capable of reducing the stress concentrated on the stab flank contact portion when drilling deep gas wells in response to parameter changes.

Optimization of RF coil geometry for NMR/MRI applications using a genetic algorithm.

A simulation method that employs a genetic algorithm (GA) for optimizing radio frequency (RF) coil geometry is developed to maximize signal intensity in nuclear magnetic resonance (NMR)/magnetic resonance imaging (MRI) applications. NMR/MRI has a wide range of applications, including medical imaging, and chemical and biological analysis to investigate the structure, dynamics, and interactions of molecules. However, NMR suffers from inherently low signal intensity, which depends on factors related to RF coil geometry. The investigation of coil geometry is crucial for improving signal intensity, leading to a reduction in the number of scans and a shorter total scan time. We have explored a better optimization method by modifying RF coil geometry to maximize signal intensity. The RF coil geometry comprises wire elements, each of which is a small vector representing the current flow, and GA chooses some of the prepared wire elements for optimization. The optimization of a substrate coil with a surface perpendicular to a static field was demonstrated for single-sided NMR system applications while considering various cylindrical sample diameters. A non-optimized and a GA-optimized substrate coil were compared through simulation and experiment to confirm the performance of the GA simulation. The maximum error between simulation and experiment was below 5%, with an average of less than 3%, confirming simulation reliability. The results indicated that the GA improved signal intensity by approximately 10% and reduced the necessary total scan time by around 20%. Finally, we explain the limitations and explore other potential applications of this GA-based simulation method.

Designing Rigid DNA Origami Templates for Molecular Visualization Using Cryo-EM.

DNA origami, a method for constructing nanostructures from DNA, offers potential for diverse scientific and technological applications due to its ability to integrate various molecular functionalities in a programmable manner. In this study, we examined the impact of internal crossover distribution and the compositional uniformity of staple strands on the structure of multilayer DNA origami using cryogenic electron microscopy (cryo-EM) single-particle analysis. A refined DNA object was utilized as an alignment framework in a host-guest model, where we successfully resolved an 8 kDa thrombin binding aptamer (TBA) linked to the host object. Our results broaden the spectrum of DNA in structural applications.

Design Optimization of a Soft Robotic Rehabilitation Glove Based on Finger Workspace Analysis.

The finger workspace is crucial for performing various grasping tasks. Thus, various soft rehabilitation gloves have been developed to assist individuals with paralyzed hands in activities of daily living (ADLs) or rehabilitation training. However, most soft robotic glove designs are insufficient to assist with various hand postures because most of them use an underactuated mechanism for design simplicity. Therefore, this paper presents a methodology for optimizing the design of a high-degree-of-freedom soft robotic glove while not increasing the design complexity. We defined the required functional workspace of the index finger based on ten frequently used grasping postures in ADLs. The design optimization was achieved by simulating the proposed finger-robot model to obtain a comparable workspace to the functional workspace. In particular, the moment arm length for extension was optimized to facilitate the grasping of large objects (precision disk and power sphere), whereas a torque-amplifying routing design was implemented to aid the grasping of small objects (lateral pinch and thumb-two-finger pinch). The effectiveness of the optimized design was validated through testing with a stroke survivor and comparing the assistive workspace. The observed workspace demonstrated that the optimized glove design could assist with nine out of the ten targeted grasping posture functional workspaces. Furthermore, the assessment of the grasping speed and force highlighted the glove's usability for various rehabilitation activities. We also present and discuss a generalized methodology to optimize the design parameters of a soft robotic glove that uses an underactuated mechanism to assist the targeted workspace. Overall, the proposed design optimization methodology serves as a tool for developing advanced hand rehabilitation robots, as it offers insight regarding the importance of routing optimization in terms of the workspace.

Modelling bioremediation of contaminated effluents by Ulva ohnoi. - A predictive perspective.

Ulva spp. are tolerant to salinity variations and exhibit easy acclimation, playing an essential role in the depollution of aquatic ecosystems precisely due to their high efficiency in absorbing and accumulating nutrients. For this reason, Ulva spp. becomes an attractive solution for recovering areas that suffer the impacts of problems such as the eutrophication of anthropogenic origin. In addition to being a promising alternative for the blue bioeconomy, it can contribute to the sustainability of economic activities in coastal areas. Therefore, the present study aimed to develop and elucidate the behavior of Ulva ohnoi using predictive surface response models. The algae were grown under different concentrations of nutrient and salinity levels, as predicted by the experimental design, and it was evaluated according to the potential of the biomass to absorb the nutrients, as well as its photosynthetic performance and biochemical parameters. Our study confirmed the high efficiency and preference of Ulva ohnoi in the absorption of nitrogen dissolved in the medium in the form of NH4+ and that salinity is an essential factor in the dynamics and speed of ammonium absorption. The absorption of orthophosphate by U. ohnoi is reverted to the culture medium when subjected to long-term cultivation. This process was more intense because of low salinity, even at conditions of availability of the compound. The 3D-models of response surfaces elucidate the behavior of Ulva ohnoi, attributing a correlation between nutrient availability and salinity and the biological behavior of the species. In view of what is exposed by these models, as well as the effects of saline distribution along the Lagoon, the following regions of the lagoon are suggested: Center-North, Center and South - as potential areas for the implementation of bioremediation projects with Ulva ohnoi.

Machine Learning-Driven Design Optimization of Buckling-Induced Quasi-Zero Stiffness Metastructures for Low-Frequency Vibration Isolation.

Metastructures, artificial arrangements of micro/macrostructures, possess unique properties and are of significant interest in aerospace, stealth technology, and various other applications. Recent studies have focused on quasi-zero stiffness metastructures, providing an outstanding vibration isolation capability. However, existing methods are constrained to low preloads and lack the consideration of structural analysis, despite their intended use in practical structures. This study introduces metastructures with quasi-zero stiffness characteristics under high preloads by inducing local buckling. An optimization framework combining deep reinforcement learning and finite-element analysis is employed to derive an optimal model that considers both structural safety and quasi-zero stiffness characteristics. To validate the optimization results, quasi-zero stiffness metastructures are fabricated via 3D printing, and compression and vibration experiments are conducted. The fabricated metastructures exhibit quasi-zero stiffness characteristics under a high target preload along with outstanding vibration reduction performance, even in the low-frequency range.