Optimal Settings Research Articles

Plaque Reduction Neutralization Test (PRNT) Accuracy in Evaluating Humoral Immune Response to SARS-CoV-2.

Massive vaccination positively impacted the SARS-CoV-2 pandemic, being a strategy to increase the titers of neutralizing antibodies (NAbs) in the population. Assessing NAb levels and understanding the kinetics of NAb responses is critical for evaluating immune protection. In this study, we optimized and validated a PRNT50 assay to assess 50% virus neutralization and evaluated its accuracy to measure NAbs to the original strain or variant of SARS-CoV-2. The optimal settings were selected, such as the cell (2 × 105 cells/well) and CMC (1.5%) concentrations and the viral input (~60 PFU/well) for PRNT-SARS-CoV-2 with cut-off point = 1.64 log5 based on the ROC curve (AUC = 0.999). The validated PRNT-SARS-CoV-2 assay presented high accuracy with an intraassay precision of 100% for testing samples with different NAb levels (low, medium, and high titers). The method displays high selectivity without cross-reactivity with dengue (DENV), measles (MV), zika (ZIKV), and yellow fever (YFV) viruses. In addition, the standardized PRNT-SARS-CoV-2 assay presented robustness when submitted to controlled variations. The validated PRNT assay was employed to test over 1000 specimens from subjects with positive or negative diagnoses for SARS-CoV-2 infection. Patients with severe COVID-19 exhibited higher levels of NAbs than those presenting mild symptoms for both the Wuhan strain and Omicron. In conclusion, this study provides a detailed description of an optimized and validated PRNT50 assay to monitor immune protection and to subsidize surveillance policies applied to epidemiologic studies of COVID-19.

Optimizing 3D-printed workpieces for radiotherapy application: modelling the CT number and print time

This study aims to analyze the influence of specific printing parameters, including infilling, print speed, and layer height, on the CT numbers and printing time of 3D-printed workpieces fabricated from Polylactic Acid (PLA). The primary objective is to optimize these parameters to attain desired CT numbers and print time for radiotherapy applications. To achieve this objective, we employed the Taguchi experimental design and regression analysis methodologies. A series of experiments were conducted to systematically assess the effects of varying infilling, print speed, and layer height values on the CT numbers and printing time of the PLA workpieces. The resulting data were then used to create mathematical models for predicting optimal parameter settings. Our investigations revealed that specific adjustments to infilling and layer height significantly influence the CT numbers and printing time of 3D-printed workpieces. By leveraging the developed mathematical models, precise predictions can be made to optimize independent parameters for the desired CT numbers and printing times, enhancing the efficacy of 3D-printed workpieces for radiotherapy applications. This research contributes to the advancement of 3D-printed workpieces utilized in radiotherapy, offering a pathway to enhance the accuracy and efficiency of treatment delivery. The optimization of printing parameters outlined in this study provides a valuable tool for clinicians and researchers in the field, ultimately benefiting patients undergoing radiotherapy treatments.

Innovative Digital Model of the Infl uence of the Key Rate on the Profi tability of the Russian Banking System

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems. The scientific novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations and the formation of digital ecosystems. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm. The DL model showed high forecast accuracy.

IfNot: An approach towards mitigating interest flooding attacks in Named Data Networking of Things

Named Data Networking (NDN) has emerged as a model to accommodate content distribution, security, and mobility. Recently, NDN has been applied to the Internet of Things (IoT), referred to as Named Data Networking of Things (NDNoT). In the rapidly evolving landscape of the NDNoT securing data transmission is paramount. The main purpose and contribution of the research presented in this paper is to safeguard the security vulnerabilities of data transmission in NDNoT. This paper specifically addresses the critical issue of interest flooding attacks in NDNoT. These attacks can disrupt network operations posing far-reaching threats to data integrity and availability. To mitigate these attacks and threats the paper introduces the IfNoT mechanism and evaluates its performance through comprehensive simulations in a realistic and recognized simulator, called Cooja. IfNot identifies the potential interest flooding attacker nodes in the NDNoT environment. It reduces the impact of the attack and the undesirable interest traffic caused by such an attack, which optimizes the network resource utilization at a maximum level. The study also explores the influence of key parameters provided by the IfNoT mechanism. Moreover, the study also identifies optimum settings for these parameters to enhance network utilization. When evaluated using various metrics, including success rate, average latency, and total interest traffic, under different conditions and parameter settings, IfNoT was able to counter the interest flooding attacks effectively. IfNoT mechanism is able to increase success ratio up to 28%, decrease average latency up to 31%, and decrease total interest traffic up to 58%.

The optimal Holmium laser settings for disintegration of cystine and calcium oxalate monohydrate stones: In vitro study

ABSTRACT Objective Disintegrating cystine and calcium oxalate monohydrate stones present a formidable challenge owing to their hardness and distinct composition. This study aimed to establish optimal laser settings for these hard stones lithotripsy. Patients and Methods Cystine and calcium oxalate monohydrate stones were extracted from two patients. Two experiments were conducted in vitro by utilizing a 272 μm laser fiber with variable settings to disintegrate the cystine and calcium oxalate monohydrate stones. In the first experiment, energy was adjustable while frequency was constant, whereas the second experiment involved constant energy with adjustable frequency on each type of stone and each experiment was repeated three times to ensure robustness and reliability. Results Our findings indicated that for cystine stones, use of higher total power with high energy and low frequency proved to be effective. Conversely, for calcium oxalate monohydrate stones, settings involving higher total power with low energy and high frequency demonstrated superior efficacy and safety. Conclusion Holmium (Ho: YAG) laser settings with higher total power, high energy, and low frequency effectively disintegrate cystine stones despite increased heat, which was measured by a thermometer with a thermocouple. For calcium oxalate monohydrate stones, higher total power, high frequency, and low energy settings are recommended and safe.

Integrated degradation-based burn-in and maintenance model for heterogeneous and highly reliable items

In modern industries, manufacturers employ burn-in procedures to detect and remove faulty units before they are put into use. However, with the increasing prevalence of highly reliable items, traditional lifetime-based burn-in approaches may not be effective. So, the degradation-based approach screening units based on their degradation levels becomes more prevalent. Ensuring the safety and reliability of a system requires maintenance, making it crucial to find optimal burn-in settings and maintenance time simultaneously. This paper presents an integrated degradation-based burn-in and maintenance model, assuming a population comprising several heterogeneous subpopulations. The model has four decision parameters, three for the burn-in settings, and one for maintenance. To determine the parameters optimally, two optimization problems are presented. The first problem minimizes expected cost function by considering availability and safety as constraints. The second problem maximizes a joint function of cost and availability given a safety constraint. A comprehensive numerical analysis is conducted to offer valuable insights for practitioners. The proposed approach is compared to a stand-alone planned maintenance model, as well as models that neglect availability and safety constraints. The results of the comparative studies show the integrated model outperforms. The proposed model is illustrated using a case study on high-power semiconductor lasers.

Multi-response robust parameter design based on Bayesian mixed effects model

In robust parameter design for blocked experiments, the correlation of response observations within each block and model parameter uncertainty often impact acquiring ideal operating conditions. In this paper, a Bayesian mixed regression-based multi-response surface modeling and optimization method is suggested to address the above issues. Firstly, the mixed effects model is incorporated into the Bayesian framework, and posterior distributions of the model parameters are derived using Bayes' theorem. Secondly, the hybrid Monte Carlo algorithm is employed to calculate the model parameters. Thirdly, the expected quality loss function satisfying the specification is constructed to lessen the impact of outliers on the results of optimization, and the optimal factor settings are obtained by the hybrid genetic algorithm. In addition, the posterior probability is used to assess the conformance of the optimization results. Finally, a simulated study and real-world example of the additive manufacturing process are used to illustrate the viability of the proposed method. Compared with the current techniques, the proposed method can reduce the impact of model uncertainty on the modeling and optimization results, leading to more conformant and robust optimization results.

An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.

Honey Badger Algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

Significant Effect of Vacuum Bagging Processing on Inter-Laminar Shear Strength and Voids of Composite in Oven Cure

Autoclave had been constrained to longer time consumption, higher production costs and excessive residual stress. The challenges regarding with out-of-autoclave (OOA) cure involved the need to devise the techniques in preserving high quality of composite product during service life. The current work was proposed to quantify the impact and influence of pre-forming parameter variations in vacuum-bagging-only with oven curing (VBO-oven cure) of OOA manufacturing composite processing on the inter-laminar shear strength (ILSS) of low-cost conventional composite laminates. The relationship of ILSS and void quality characteristics was also investigated. Series of conventional lower-cost glass/epoxy aerospace-grade material was fabricated using 16 different processing routes in conventional oven. Burn-off and ILSS test was conducted based on ASTM standard to compute the ILSS and void content of cured laminates. Scanning electron microscopy (SEM) analysis was performed on the post-test ILSS coupons to evaluate the relationship between void characteristics and ILSS. Results indicated that mould release type and intensifier contributed highest effects towards ILSS of approximately 31.3% and 27.6%, respectively. The assessment of ILSS-void relationship was further carried out and it was found that void geometry has a greater influence towards ILSS than the void content of composite laminate. The combination of these factors in processing routes yielded the lowest ILSS and void content at 24.85 MPa and 5.74%, respectively. These concluded that an optimized manufacturing technique could be expanded from the optimum settings of these processing parameters.

Multiple cross displacement amplification combined with nanoparticle-based lateral flow biosensor for rapid and sensitive detection of Epstein-Barr virus.

Epstein-Barr virus (EBV) is a highly dangerous virus that is globally prevalent and closely linked to the development of nasopharyngeal cancer (NPC). Plasma EBV DNA analysis is an effective strategy for early detection, prognostication and monitoring of treatment response of NPC. Here, we present a novel molecular diagnostic technique termed EBV-MCDA-LFB, which integrates multiple cross displacement amplification (MCDA) with nanoparticle-based lateral flow (LFB) to enable simple, rapid and specific detection of EBV. In the EBV-MCDA-LFB system, a set of 10 primers was designed for rapidly amplifying the highly conserved tandem repeat BamHI-W region of the EBV genome. Subsequently, the LFB facilitate direct assay reading, eliminating the use of extra instruments and reagents. The outcomes showed that the 65°C within 40 minutes was the optimal reaction setting for the EBV-MCDA system. The sensitivity of EBV-MCDA-LFB assay reached 7 copies per reaction when using EBV recombinant plasmid, and it showed 100% specificity without any cross-reactivity with other pathogens. The feasibility of the EBV-MCDA-LFB method for EBV detection was successfully validated by 49 clinical plasma samples. The complete detection process, consisting of rapid template extraction (15 minutes), MCDA reaction (65°C for 40 minutes), and LFB result reading (2 minutes), can be finalized within a 60-minutes duration. EBV-MCDA-LFB assay designed here is a fast, extremely sensitive and specific technique for detecting EBV in field and at the point-of-care (PoC), which is especially beneficial for countries and regions with a high prevalence of the disease and limited economic resources.

Predicting low-occurrence wind speed at the pedestrian levels of simplified arrays: Artificial neural network models versus statistical methods

Low-occurrence wind speed in urban areas has a profound impact on the daily activities of people. Although several statistical methods were developed for estimating the low-occurrence wind speeds, their accuracy and flexibility still need improvements. Based on the inference from the favorable outcomes of statistical models in which low-occurrence wind speeds can be estimated by the statistics including high-order moments, in this study, the artificial neural network (ANN) models were designed to estimate the low-occurrence wind speeds based on the moments. After the sensitivity tests for the dimensions, hidden layers, epochs and training data ratios of the ANN models, the optimal setting was applied to predict the low-occurrence wind speeds of two building array cases including square and staggered layouts. The applicability of the ANN models was also evaluated for the extrapolation method between two building array cases and compared with the traditional statistical methods. It was found that the ANN models with the higher-order statistic input showed better performance than those with the second-order statistic input. The results obtained from the interpolation method are more accurate than those from the extrapolation method. Compared with traditional statistical methods, the ANN models show greater superiority in the prediction accuracy. According to the comprehensive evaluation of the ANN models, although they require ample training datasets connecting the statistics and low-occurrence wind speeds, it is proved to be a promising approach for estimating the low-occurrence wind speeds in urban wind environment.

Spray deposition in an artificial grapevine canopy as affected by spray angle regulation systems of radial airflow air-assisted sprayers

As the most critical factor affecting pesticide application efficiency and drift risk, the airflow characteristics of air-assisted sprayers have been of great concern. This study compared seven assisted airflow angle regulation methods regarding within-canopy deposition quality and off-target losses to the ground and behind the target canopies, including a newly proposed airflow swing device, three types of directional housings, and airflow deflector settings. An artificial grapevine canopy and a multiple-tracer methodology were used to evaluate deposition. Results showed that airflow angle regulation methods caused significant differences in canopy deposition and off-target losses (p < 0.001). The sprayer equipped with 0° housing had the highest deposition on the canopy (122.27 μg cm−2), followed by the sprayer equipped with the −30° housing (99.26 μg cm−2). The sprayer equipped with both airflow swing devices and optimal deflectors had a deposition of 87.76 μg cm−2. The radial airflow air-assisted sprayer without any airflow interventions caused the highest off-target losses, reaching 31.95% of the total spraying volume. The optimal deflector setting was conducive to reducing ineffective losses, but it also deteriorated the deposition uniformity on the canopy. Significantly, the airflow swing device improved the deposition uniformity on the abaxial side of the leaves to 23.89%, which can provide a reference for future optimization of new air-assisted sprayers.

Optimal burn-in for minimally repaired components from n subpopulations

Burn-in is a widely used technique to screen out defective components before they are put into field operation. In this paper, we propose a burn-in policy for the components from a heterogeneous population composed of [Formula: see text] ordered subpopulations. Assume that components can be minimally repaired when the failure occurs. We consider the total number of minimal repairs observed during burn-in process as a screening rule to remove defective components with poor reliability performance. First, we carry out the probabilistic analysis of the screening policy to show the effectiveness of burn-in. Second, we obtain the optimal burn-in settings which minimize the mean total cost or maximize the probability of passing the mission period. Finally, we give an example to illustrate the proposed model.

Numerical investigation on the impact of obstacles on phase transition in pedestrian counter-flow

In this paper, a modified real-coded microscopic model is utilized to investigate the influence of obstacles on the pedestrian counter-flow. The four typical phases, i.e., free-flow phase, lane-formation phase, intermediate phase and congestion phase are reproduced by this model. Through the numerical results, the influence of the geometric features of obstacles, such as the inclination angle and the major-to-minor axis ratio, on the pedestrian counter-flow are analyzed. With increasing the inclination angle of obstacle or reducing the ratio, it is found that the moving efficiency and the probability of asymmetric flow will be increased, the occurrence of congestion phase will be delayed. Furthermore, the occurrence density of congestion phase will be increased with the distance between sloped obstacles. Finally, from the view of engineering application, the optimal setting of obstacle is given by comparing the fundamental diagrams.

Sliding wear behaviour of micro-sized Kota stone dust reinforced epoxy composites using Taguchi method and Grey Wolf optimisation algorithm

Kota stone dust (KSD) is an unwanted material engendered throughout the process of manufacturing Kota stone. The present work comprises the appropriate consumption of KSD for evolving a composite system with epoxy as the base matrix material. The samples are developed by the hand lay-up technique. The micrographs clearly show that KSD is uniformly distributed within the epoxy matrix and establishes good adhesion with it. With the inclusion of filler, density unwillingly increases by 21.86%, but voids generated are limited to only 4.98% for a maximum filler content of 40 wt. %. A very low water absorption rate of 0.81% for maximum filler loading is observed. The compressive strength and micro-hardness increased by 31.8% and 26.25% respectively. Tensile strength, as well as flexural strength, improves for low filler loading of 20 wt. % and decreases thereafter. The sliding wear tests of the fabricated composites are studied in this research employing fairly advanced nature-inspired Grey wolf optimisation (GWO). The wear tests are based on a real-world issue that is framed in Taguchi L25 OA. A simple linear regression equation demonstrates adequate agreement between predicted and experimental values. The inclusion of KSD decreases the wear rate. Further, it is found that the KSD loading is the utmost significant factor, whereas normal load is the least significant factor that administrates the sliding wear rate of the composite system. Using the grey wolf optimiser, the optimal settings are 2500 m sliding distance, 40 wt.% KSD content, 52 cm/s sliding velocity, and 10 N normal load. The validation test results suggest that GWO is superior to the classical Taguchi approach. The wear loss mechanism is examined under the scanning electron microscope.

Modeling machine-side influences on the Z-Folding process of battery cells

AbstractThe modeling of stacking machines for battery cell production offers potentials for quantifying interdependencies and thus optimizing development and commissioning processes against the background of a targeted efficient production. This paper presents a methodology to develop a model for quantifying machine-side influences using the example of a Z-Folding machine. The components and aspects of the machine to be modeled and their level of detail are systematically derived. Subsequently, it is shown how to parameterize the derived aspects. The components and aspects of the machine are modeled and connected through a multi-physics simulation. This makes it possible to predict the effects on the separator material to be processed depending on the selected setting parameters on the machine. This opens up potentials, for example, to identify optimal setting parameters in a risk-free and model-based manner, depending on the materials to be processed. As a result, material waste can be reduced by eliminating previous "trial and error" approaches.

Welding investigation of Hastelloy C-276 and parametric optimization using integrated MRA-TLBO algorithm during A-TIG process

A-TIG welding is a modified form of GTAW process that has become popular due to its ability to create high-quality welded joints with minimal cracks and distortion. This technique is commonly used to join various metals, including stainless steel, aluminum, titanium, and nickel alloys like Hastelloy C-276, which is used in fabrication of complex components across marine, nuclear, aerospace, and chemical industries. In this investigation, A-TIG welding of Hastelloy C-276 was studied using three parameters - welding current, welding speed, and gas flow rate - at three distinct levels. The impact of these parameters were analyzed on two responses, namely penetration depth and width of weld. Macrostructural investigation, sensitivity analysis, and parametric studies revealed that welding current (I) had the most influence, followed by speed of welding (S) and flow rate of gas(G). Full quadratic multiple regression analysis-based models were developed for both responses, which were found to be suitable with a higher coefficient of determination and an average percentage error of 2.165% and 2.624%, respectively. Additionally, a teaching learning based optimization algorithm was integrated with these models to identify the most effective A-TIG parameter combination for higher penetration depth and lower weld width. The MRA-TLBO integrated approach resulted in an optimal parametric combination of I (170A), S (180 mm min−1), and G (11.37 l min−1) that resulted in a penetration depth and weld width of 4.503 mm and 4.684 mm, respectively. Validation at this optimal setting showed an improvement of 3.365% and 1.284% for weld width and penetration depth, respectively, suggesting the robustness of the developed methodology.

Fabrication of harsh environment‐tolerant and robust reinforced poly (tetrafluoroethylene) hollow fiber membranes with optimal heat setting technology

AbstractThe high‐temperature industrial waste oil treatment has put forward new requirements for membrane materials. Poly (tetrafluoroethylene) (PTFE) is regarded as the excellent membrane material for membrane process, due to its excellent properties. Herein, the reinforced PTFE hollow fiber membrane (PTFE‐HFM) was prepared by the combination of wrapping and heat setting technology. The objection of this study was focusing on the outstanding chemical and thermal resistance of PTEF‐HFM for overcoming harsh waste oil treatment. PTFE‐HFM exhibited better chemical resistance in the experiment of acid and alkali compared to PVDF‐HFM, which the surface of PVDF‐HFM corroded obviously and none of PTFE‐HFM. The pore size and porosity were precisely adjusted by heat setting technology. Firstly, the morphologies of PTFE‐HFM was investigated by scanning electron microscopy, which exhibited favorable interfacial bonding state between PTFE separation layer and supporting layer. Subsequently, the prepared membranes exhibited high‐temperature waste oil permeability of 195 L m−2 h−1 bar−1with only 3% attenuation in long‐term operation. Besides, the rejection of lubricants contained activated carbon also reached to 99%. Overall, the facile preparation and excellent performance of robust reinforced PTFE‐HFM highlights its great potential in treating high‐temperature waste oil.

The Optimal Operating Point for Linearizing an Integrated Optical Lithium Niobate Directional Coupler Modulator

The influence of an operating point on the linearity of an integrated optical lithium niobate directional coupler modulator was studied. It was found that the optimal setting for the position of an operating point for suppressing the third-order intermodulation distortion depended on the power of the high-frequency modulation signal. Thus, despite the simple design of the device, the directional coupler modulator requires a complex algorithm for setting an operating point to achieve a high linearity of operation. An active system for setting an operating point based on the low-frequency pilot signal and zeroing its third harmonic was used to demonstrate the possibility of linearization when the amplitude of the modulation signal changes. The use of an operating point control system became possible after limiting the drift of the operating point by etching the dielectric buffer layer in the interelectrode gap. The results obtained look promising for high-performance analog optical links.