Experimental Design Optimization Research Articles

Synchronized magnet-induced displacement detection in non-magnetic plates via smart materials near-field sensing

Accurate monitoring of small displacements in non-magnetic thin-walled plates is essential for assessing their manufacturing precision and vibrational characteristics. Optical techniques are limited by environmental disturbances and spatial constraints, while contact methods are compromised by mechanical forces and wear. A synchronized magnet-induced displacement detection method (SMDDM) using heteropolar permanent magnets (HPMs) and smart materials for non-magnetic plates is proposed. Supported by a magnetic-mechanical–electrical coupling model and a joint algorithm with dynamic linear extension and analytic hierarchy process (DLE-AHP), the prototype’s parameters are refined through experimental design, neural networks, and multi-objective optimization. Comprehensive tests show that SMDDM achieves an accuracy of 2 μm and a resolution of 1 μm, with smaller measurement errors observed in low-frequency, small-displacement tests. This validates the method’s effectiveness in multi-scale displacement measurement and vibration testing, highlighting its potential for in-situ monitoring in thin-plate applications.

Experimental design and multiple response optimization of batch leaching tests of volcanic ashes.

Volcanic eruptions can release large amounts of tephra, lava, and gases, drawing attention due to their magnitude, energy, and impact on life and the environment. Among the most documented and sometimes dramatic effects of volcanic ashes are those linked to the input of diverse elements in the environment, which are released as a consequence of ash weathering. Laboratory studies have been conducted to investigate and predict the environmental input of chemical elements from volcanic ashes. This research paper describes the optimization of batch leaching tests used to investigate the release of ions from ashes collected in the Andes Cordillera after the eruption of the Puyehue volcano in 2011. Chemometric multivariate strategies were employed to evaluate the influence of variables affecting the leaching of volcanic ash. The effects of the main variables, namely contact time, the acidity of the leaching agent, the solid/liquid ratio, the particle size, and the stirring speed, were studied in leaching tests. To determine the optimal conditions for selected metal determinations, we employ Darringer's desirability function, which allows for the simultaneous optimization of the selected responses (element concentrations during the leaching process). Multielemental analysis (Na, Mg, Al, Si, P, Cl, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Cd, Hg, Tl, and Pb) was quantified by ICP-MS (inductively coupled plasma-mass spectrometry) following adequate dilution of test leaching. These results established the optimal experimental conditions for leaching volcanic ash. The most significant variables were the solid/liquid ratio and the stirring speed, resulting in two groups of elements with an adequate global desirability function (D) value.

Aerodynamic design and optimization analysis of a 200 kW radial-inflow turbine

Abstract In low-temperature waste heat recovery for organic Rankine cycle (ORC) power generation, the radial-inflow turbine (RIT) plays a crucial role. This study focuses on the preliminary design, three-dimensional simulations, and optimization of a 200 kW RIT for R245fa working fluid. The turbine’s preliminary design relies on selected empirical coefficients, leading to performance uncertainty. The performance of the turbine obtained from the initial design was evaluated using computer simulations, indicating that achieving outstanding isentropic efficiency and power targets solely relying on the design of empirical coefficients can sometimes be challenging. Design of Experiments (DOE) on five variables was performed using the Isight platform, highlighting rotor inlet blade height as crucial for turbine performance. The optimized RIT showed a 10.5% efficiency increase and 45.6 kW power gain. Entropy production analysis confirmed substantial improvement. Systematic theoretical calculations followed by DOE optimization effectively enhance turbine efficiency, providing valuable and innovative insights for satisfactory design solutions.

Design of experiment for extraction optimization and antioxidant assay studies of biologically active compounds of propolis

Bees gather propolis from tree sap and combine it with their saliva to seal and sanitize the hive. There are various countries where propolis extracts are used in food and beverage as additives mainly due to the presence of phenolic compounds. Therefore, extraction is the initial and the most vital step in the purification and recovery of phenolic compounds from propolis. The purpose of this study is to enhance propolis extract yield by thoroughly optimizing the extraction process and to evaluate and compare the antioxidant assay of the propolis extracts. Several factors such as the chemical nature of phenolic compounds, composition of solvents, temperature and solvent pH may influence the extraction efficiency and quantity of phenolic compounds. These factors have been extensively studied by univariant optimization; however, the univariant optimization fails to consider the effect of interaction between variables on extraction efficiency. Therefore, the Design of Experiment (DoE) approach is adopted in this study in order to evaluate the correlation between various parameters and their impacts on the extraction of propolis in order to determine TPC, TFC, and DPPH scavenging activity. The DoE was carried out to correctly optimize different extraction parameters including the percentage of ethanol in extraction solvent, temperature and time. The ethanol percentage showed the highest F ratio and minimum p-value for the extraction of 28 total phenolic compounds and flavonoids with a negative correlation showing the presence of more polar compounds in propolis. While in the case of DPPH scavenging assay, the temperature was found to have a significantly positive effect on the extraction efficiency. Optimized extraction parameters obtained by the design were found to be different for TFC, TPC and DPPH, e.g., optimum solvent composition for TFC, TPC and DPPH were 51%, 50% and 90%, respectively. The ultrasonic extracts of propolis showed a higher TPC (574.017 µg GAE/g); in 51% ethanol/water at 25 °C while sonicated for only 2 min. The total flavonoid content (TFC) was found higher (353.16 µg Quercetin/g) in 50% ethanol/water when sonicated for 60 min at 56 °C. DPPH scavenging assay showed higher capacity (1617.27 µg Ascorbic acid/g) in 90% ethanol/water at 62 °C for 60 min sonication. Fourteen propolis samples from Pakistan and Turkey were extracted in order to compare TPC, TFC, and DPPH assay results. In Pakistani propolis, the TPC, TFC, and DPPH scavenging assay varies from 418–1026 µg/g, 264–866 µg/g, and 302–1162 µg/g, respectively. Turkish propolis was demonstrated to have higher TPC, TFC, and DPPH scavenging assay values than Pakistani propolis, with values of 1312–1535 µg/g, 1182–1446 µg/g, and 885–2756 µg/g, respectively. It was found that propolis produced naturally possesses more potential phenolic compounds than propolis purchased from farmer’s markets.

Automated, rapid, and convergent hyperparameter optimizer (ARCH) for neural network classifiers

There are many “hyperparameters” that dictate the performance of Artificial Neural Network (ANN) models in tasks such as classification and object detection. This study proposes an Automated, Rapid, Convergent Hyperparameter Optimizer (ARCH) implementing Design of Experiments (DOE). Before building ARCH, there are two controversial issues in using DOE for Hyperparameter Optimization (HO) problems: design choice and model selection. To tackle these issues, a “meta-experiment” with three meta-factors is devised to analyze the effects of four types of experimental designs, two types of model selections (linear regression and picking the best samples), and 11 classification datasets. The datasets are included to generalize the findings. The meta-experiment’s results support the practical value of using smaller experimental designs to achieve high classification accuracies comparable to larger designs in less time. The findings also show that the HO problem is nonlinear; therefore, picking the best run from the experiments is more effective than optimizing a linear regression model. Additionally, the meta-experiment showed that including the dataset as a random effect is significant in HO problems. Furthermore, to benchmark ARCH's performance, it is compared to Bayesian optimization and genetic algorithms on CIFAR-10. The comparison demonstrates that ARCH has an advantage when resources are limited.

A Novel Apparatus for the Fully Automated Extraction and Online Liquid Chromatographic Analysis of Solid Environmental Samples: Application to the Pressurized Hot Water Extraction of Pharmaceuticals in Soil.

A new self-assembled apparatus for the extraction of solid samples was designed and implemented to perform a recirculated pressurized hot water extraction (R-PHWE) directly coupled to liquid chromatography-tandem mass spectrometry. To investigate the potential of this new extraction apparatus, 34 target pharmaceutical compounds were analyzed in loam, silt-loam, and silty-clay-loam soils. The target analytes were characterized by heterogeneous physicochemical properties (e.g., -1.60 ≤ log D ≤ 5.91 at pH = 7.2, i.e., at the mean pH values of the three soils). Design of experiments (DoE) was used to identify the best extraction conditions for the target analytes by studying temperature, pressure, and number of extraction cycles. The results of DoE optimization pointed out the significant influence of the number of cycles on recovery. The application of DoE set point to the three reference soils provided recoveries ≥60% for 21-25 out the 34 target analytes, depending on soil. Good recovery precision (<25%) and moderate suppressive matrix effect (≤40%) were found for most target analytes, regardless of the soil considered. The optimized R-PHWE procedure evidenced statistically higher recoveries for 16 out of 34 target analytes when compared to conventional off-line dynamic PHWE.

Experimental designoptimisation and validation by accuracy profile of a novel method for Hg speciation analysis by HPLC-ICP-MS and application to Total Diet Studies.

This study addresses the development and validation of an analytical method for speciation analysis of mercury (inorganic/Hg2+ and methylmercury/CH3Hg+) in fishery products. The Hg species are separated by reversed-phase (RP) high-performance liquid chromatography (HPLC) coupled to inductively coupled plasma mass spectrometry (ICP-MS). The effective separation of Hg2+ and CH3Hg+ was achieved in <8min using a peptide mapping RP column and a mobile phase containing 2-mercaptoethanol at 0.25% (v/v) and methanol at 1%(v/v). The optimization was carried out using an experimental design through response surface methodology (RSM) with central composite design (CCD), addressing both the HPLC separation and the sample extraction. The method validation was carried out based on the accuracy profile approach. For this purpose, six series of measurements were carried out in duplicate over a time span of 2 months. The limits of quantification (LOQ) were 2.5µg/kg (wet weight, ww) for CH3Hg+ and 1.2µg/kg (ww) for Hg2+. The intermediate reproducibility in terms of coefficient of variation (CVR) was <6%. The bias (%) obtained for the analysis of four certified reference materials (CRMs), namely TORT-3 (lobster hepatopancreas), SRM 1566-b (oyster tissue), SQID-1 (cuttlefish) and NMIJCRM7402-a (cod fish tissue) was <7%. This demonstrates the method robustness and suitability for routine speciation analysis of CH3Hg+ and Hg2+ in fishery products. The method is intended to be applied for the analysis of the panel of fishery products and fish-based foods in the framework of the (ongoing) third French Total Diet Study.

Optimization of the use of sorghum flour, peanuts, and moringa leaves in the making of high-protein gluten-free biscuits [Optimasi penggunaan tepung sorgum, kacang tanah dan daun kelor pada pembuatan biskuit non-gluten tinggi protein

Protein is one of the macronutrients that the body needs in large quantities in addition to carbohydrates and fats. Eating high-protein biscuits can be one way to meet daily protein needs. Biscuits made from sorghum flour, peanuts, and Moringa leaves can produce non-gluten biscuits high in protein. This study aimed to determine the optimal balance of using sorghum flour, peanuts, and moringa leaves to produce high-protein biscuits with acceptable sensory values. The research method used is an experimental method with descriptive data analysis. The optimization method uses the Response Surface Methodology (RSM) experimental design with a central composite design (CCD). The best biscuit formula, as a result of the RSM experimental design optimization, has a protein nutrient content of 8.71%, 2.60% moisture content, 2.60% ash content, 32.99% fat content, and 53.85% carbohydrate content. The combination of using flour significantly affects the texture of the biscuits but does not significantly affect the color. Using composite flour consisting of 60% sorghum flour, 37% peanuts, and 4% moringa leaves to manufacture non-gluten biscuits can produce biscuits that are high in protein and have physicochemical properties and good sensory acceptability.

Enhancing aircraft crack repair efficiency through novel optimization of piezoelectric actuator parameters: A design of experiments and adaptive neuro-fuzzy inference system approach

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips—a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Application of design of experiments for single-attribute optimization using response surface methodology for flow over non-linear curved stretching sheet

Optimizing the heat transmission rate of the flow through the statistically designed chain of experiments has major applications in product designing and improvising. Here, statistic design is framed by implementing response surface optimization methodology to contemplate the flow of dual non-Newtonian fluids allowed to flow over non-linear stretched geometry. The fluid flow model is designed under Cattaneo-Christov diffusion theory over a magnetized-radiative surface. The boundary condition is enriched with the slip regime and Newtonian heating. Numerical computations are acquired by employing the shooting technique approach in association with Runge-Kutta Fehlberg numerical scheme. Statistical and numerical consequences disclose that non-linear stretching index causes a depletion in velocity and temperature. When the Eckert number and the heat generating parameter are both large, the heat transfer rate is also large. Mass transport rate is lowest for higher ratios of diffusion coefficients. The 0.265365 is the maximum possible heat transport rate attained for the 8th experimental run with the key elements: Eckert number, heat source and radiation parameter. The critical point claimed by the Pareto chart shows that the slightest manipulation of the radiation parameter is critical for the heat transport rate, whereas the heat source parameter and Eckert number have almost similar influences on the response.

Optimization of automotive aerodynamic performance based on DOE

Based on a new-energy sedan as the research object, the Design of Experiments (DOE) optimization integrated grid deformation method was used to optimize and improve the aerodynamic performance of a certain development model. A three-dimensional flow field comparison analysis was conducted on the improved areas of the whole vehicle flow field, and wind tunnel tests were conducted for verification. Through CFD simulation and wind tunnel test verification, the accuracy of the CFD simulation method was verified. The engineering feasible DOE optimization recommended area for the whole vehicle was summarized. The optimal prediction results based on the DOE optimization method and the corresponding CFD simulation results were analyzed for reliability. The correlation between the optimization variables and the target function was analyzed, and 6 counts reduced the whole vehicle drag coefficient by adjusting the optimization variables within a small range. Finally, wind tunnel tests were used to verify the effectiveness of the optimized variable scheme. The results provide an important reference for the optimization design and development of vehicle aerodynamic performance.

Factorial experimental design optimisation of adsorption process for removing chrome ion using crude biomass

ABSTRACT The pollution of water and the environment by heavy metals has recently increased dramatically, creating a severe environmental problem due to their non-biodegradability and high toxicity. Among these metals, we are interested in chromium (Cr), essential for human life but can be toxic in large quantities. It is vital to develop a simple, effective, and inexpensive method for its removal from aqueous media. This work aims to combine two aspects: environmental protection and waste recovery. We studied the adsorption capacity of Cr(VI) by biomass consisting of a mixture of walnut shells, pecan nuts, and peanuts. The ion was quantified using high-resolution magnetic sector inductively coupled plasma mass spectrometry, and several techniques characterised the raw adsorbent. To optimise the removal of Cr(VI), four factors were optimised using a 24-full factorial design, enabling us to determine the optimum adsorption conditions: a contact time of 360 min, an adsorbent mass of 30 mg, an initial ion concentration of 50 mg/L, and a pH of 6. The response efficiency Y (Qe) achieved its optimum at 58.47976 ± 4.168804 (mg/g), resulting in a maximum desirability of 0.957419. The adsorption phenomenon was studied kinetically, and it was found to follow the pseudo-first-order reaction (R 2 = 0.9894); concerning adsorption isotherms, the Freundlich isotherm proved to be the best (R 2 = 1). Overall, the present biosorbent demonstrates excellent capacity to remove Cr (VI) from water and can be used on a large scale.

Photocatalytic Degradation of Tartrazine and Naphthol Blue Black Binary Mixture with the TiO2 Nanosphere under Visible Light: Box-Behnken Experimental Design Optimization and Salt Effect

In this study, TiO2 nanospheres (TiO2-NS) were synthesized by the solvothermal method. Firstly, the synthesized nanomaterial was characterized by X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR), scanning electron microscopy (SEM) and UV-Vis Diffuse Reflectance Spectroscopy (DRS). To study the photocatalytic degradation of Tartrazine (TTZ) and Naphthol Blue Black (NBB) in a binary mixture, the influence of some key parameters such as pH, pollutant concentration and catalyst dose was taken into account under visible and UV light. The results show a 100% degradation efficiency for TTZ after 150 min of UV irradiation and 57% under visible irradiation at 180 min. The kinetic study showed a good pseudo-first-order fit to the Langmuir–Hinshelwood model. Furthermore, in order to get closer to the real conditions of textile wastewater, the influence of the presence of salt on TiO2-NS’s photocatalytic performance was explored by employing NaCl as an inorganic ion. The optimum conditions provided by the Response Surface Methodology (RSM) were low concentrations of TTZ (2 ppm) and NBB (2.33 ppm) and negligible salt (NaCl) interference. The percentage of photodegradation was high at low pollutant and NaCl concentrations. However, this yield became very low as NaCl concentrations increased. The photocatalytic treatment leads to 31% and 53% of mineralization yield after 1 and 3 h of visible light irradiation. The synthesis of TiO2-NS provides new insights that will help to develop an efficient photocatalysts for the remediation of contaminated water.

Phase separation methods for protein purification: A meta-analysis of purification performance and cost-effectiveness.

Protein purifications based on phase separations (e.g., precipitation and liquid-liquid extraction) have seen little adoption in commercial protein drug production. To identify barriers, we analyzed the purification performance and economics of 290 phase separation purifications from 168 publications. First, we found that studies using Design of Experiments for optimization achieved significantly greater mean yield and host cell protein log10 removal values than those optimizing one factor at a time (11.5% and 53% increases, respectively). Second, by modeling each reported purification at scales from 10 to 10,000kg product/year and comparing its cost-effectiveness versus chromatography, we found that cost-effectiveness depends strongly on scale: the fraction of phase separations predicted to be cost-effective at the 10, 100, and 1000kg/year scales was 8%, 15%, and 43%, respectively. Total cost per unit product depends inversely on input purity, with phase separation being cheaper than chromatography at the 100kg/year scale in 100% of cases where input purity was ≤ 1%, compared to about 25% of cases in the dataset as a whole. Finally, we identified a simple factor that strongly predicts phase separation process costs: the mass ratio of reagents versus purified product (the "direct materials usage rate"), which explains up to 58% of variation in cost per unit of purified product among all 290 reports, and up to 98% of variation within particular types of phase separation.

CRRfast: an emulator for the cosmological recombination radiation with effects from inhomogeneous recombination

ABSTRACT The cosmological recombination radiation (CRR) is one of the guaranteed ΛCDM spectral distortion (SD) signals. Even if very small in amplitude, it provides a direct probe of the three recombination eras, opening the path for testing one of the key pillars in our cosmological interpretation of the measured CMB anisotropies. Here, we develop a new emulator, CRRfast, to quickly and accurately represent the CRR for a wide range of cosmologies, using the state-of-the-art CosmoSpec code as a reference. CRRfast has been made publicly available both as stand-alone code and as part of class, thereby completing the set of average ΛCDM sources of SDs that can be modelled with class. With this newly developed pipeline we investigate the full constraining power of SDs within ΛCDM and highlight possible future applications to experimental design optimization. Furthermore, we show that the inhomogeneous evolution of the recombination process imprints second-order contributions to the CRR spectrum, leading to a broadening and shifting of the CRR features. These second-order terms are naturally captured by the emulator and allow us to evaluate the contribution of the ΛCDM primordial perturbations to the average CRR as well as to illustrate the effect of perturbed recombination due to Primordial Magnetic Fields (PMFs). As it turns out, while the second-order ΛCDM signal can be neglected, it could be significantly enhanced in the beyond-ΛCDM models. In particular in the case of PMFs, we demonstrate that through these non-linear terms the parameter space relevant to the Hubble tension could be tested with future CMB spectrometers.

Optimizing encapsulation of black carrot extract using complex coacervation technique: Maximizing the bioaccessibility and release kinetics in different food matrixes

The encapsulation of bioactive compounds at the micro-scale presents an advanced approach for the food and nutraceutical industries. However, the challenge lies in determining optimal encapsulation parameters for each specific bioactive compound and encapsulation method. In addressing this, our study demonstrates the application of statistical programs to streamline the optimization of wet-lab experimental designs. Focusing on the encapsulation technique of complex coacervates for black carrot phenolic extract (BCPE), the response surface methodology was employed to optimize encapsulation parameters in terms of coating material, the core-to-coating material ratio, and the pH of the encapsulation environment. The optimum conditions predicted by RSM to produce BCPE-loaded complex coacervates were found to be a pH of 3.02, a core-to-coating ratio of 10g/100g, and a coating material composition of 59.10g/100 mL maltodextrin, and 0.90g/100 mL whey protein isolate. According to the RSM pattern with 84% desirability, encapsulation efficiency was found 86.08%. In addition, the effect of different food matrixes was examined on the in vitro bioaccessibility of spray-dried BCPE loaded complex coacervated powder (BCPE-CCp). To explore the impact of protein and carbohydrate richness, along with food temperature on capsule stability, the BCPE-CCp was incorporated into skim milk, apple juice, and chocolate beverages (1g/100 mL). Notably, heat treatment had no significant effect on the in vitro bioaccessibility of BCPE-CCp in terms of total phenolic compound and antioxidant activity (p < 0.05), indicating its suitability for hot formulations. Furthermore, the release of BCPE in a protein-rich environment was observed to be higher than in a carbohydrate-rich food matrix under both gastric and intestinal conditions. These findings provide valuable insights into the stability and release dynamics of BCPE-CCp in different food settings, supporting its adaptability for diverse formulations, including those involving elevated temperatures.

Experimental optimization design synthesis for up-conversion luminescence performance in SrBi4Ti4O15: Er3+/Yb3+ red-green phosphors

Er3+/Yb3+ co-doped SrBi4Ti4O15 crystalline powders were synthesized using the high-temperature solid-phase method. The crystal structure of the obtained phosphors was analyzed through x-ray diffraction (XRD), confirming the purity of all products such as SrBi4Ti4O15. Employing experimental design optimization theory, regression equations were established to correlate the Er3+/Yb3+ doping concentrations with the luminescent intensities. The genetic algorithm was utilized to compute the optimal solutions of the equations, resulting in Er3+ and Yb3+ doping concentrations of 3 mol% (molar fraction) and 20 mol% under 980 nm laser excitation and 3 mol% and 29.79 mol% under 1550 nm laser excitation. The up-conversion fluorescence emission spectra of the samples were measured under 980 nm excitation, revealing intense green emissions at 525 nm, 550 nm, and 662 nm, corresponding to the 2H11/2→4I15/2, 4S3/2→4I15/2, and 4F9/2→4I15/2 energy levels, respectively. Under 1550 nm excitation, peaks corresponding to the same energy levels were observed at 523 nm, 548 nm, and 661 nm. The relationship between the up-conversion fluorescence and the laser operating current for the optimal samples under 980 nm and 1550 nm excitations was explored, indicating two-photon and three-photon processes, respectively. Detailed analysis and discussion of the up-conversion fluorescence mechanisms were conducted. Additionally, the relationship between the up-conversion fluorescence and the temperature for the optimal samples was investigated, revealing excellent temperature sensing characteristics under 980 and 1550 nm laser excitations. The CIE coordinates for the optimal samples were calculated as (0.3111, 0.6747) and (0.5254, 0.4671) under 980 nm and 1550 nm excitations, respectively.

Investigation on carbon fiber-reinforced polymer combined with graphene nanoparticles subjected to drilling operation using response surface methodology and non-dominated sorting genetic algorithm-II

In this article, drilling of carbon fiber-reinforced plastics combined with graphene nanoparticles is investigated. These nanocomposites have the potential to be used widely in different industrial applications such as electrical, biomedical and aerospace engineering due to their superior mechanical and thermal specifications. Nonetheless, to be assembled on different sets, drilling nanocomposites is required especially for rivets and joints. In drilling carbon fiber-reinforced polymers (CFRPs), delamination is one of the main challenges that should be minimized in order to enhance the produced hole quality. Besides, machining force is a chief factor to control the drilling-induced damages, especially delamination. This article evaluates the effects of machining parameters on machining force and delamination using response surface methodology experimental design and non-dominated sorting genetic algorithm-II (NSGA-II) multiobjective optimization methodology. Examining delamination is conducted in two parts including peel-up and push-down delamination which are the principal drawbacks in drilling of fiber-reinforced composites. Analysis of variance is also carried out in order to examine the effects of the machining parameters. In addition, predictive quadratic correlations are established by regression analysis. Eventually, multiobjective optimization was implemented by NSGA-II and the optimal values were obtained from pareto-optimal solution set aimed at enhancing the drilling process.

PXPermute reveals staining importance in multichannel imaging flow cytometry

Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.

Deep eutectic solvent-based ferrofluid for highly efficient preconcentration and determination of metronidazole by vortex-assisted liquid-phase microextraction under experimental design optimization

To determine metronidazole in water samples, we developed an environmentally friendly, efficient, and straightforward ferrofluid-based liquid-liquid microextraction sample pretreatment technique. It is coupled with a high-performance liquid chromatography-ultraviolet analytical technique known for its sensitivity, speed, and precision. The magnetic separation of metronidazole-containing ferrofluid from the matrix was effortlessly achieved through the application of an external magnetic field, eliminating the need for centrifugation. Response surface optimization was employed to systematically determine the key experimental parameters influencing extraction efficiency, including pH, NaCl concentration, ferrofluid volume, and vortex duration. With a low detection limit (0.116 ng mL−1), a broad linear range between 0.5 and 700 ng mL−1 was achieved at optimal conditions. Additionally, acceptable spiking recoveries (94.3–97.3 %) and RSD values (≤3.7 %) for intra- and inter-day precision were attained in water samples. In conclusion, the effectiveness of the vortex and ferrofluid combination, along with the convenience of collection and elimination of the need for centrifugation, bestows a highly valuable technique for determining metronidazole in water samples.