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ABSTRACT We present here a novel methodology to analyze data from two‐level factorial experimental designs, with or without missing runs, with just one method: partial least squares regression with one response variable (PLS1, hereinafter PLS). This property is very attractive for practitioners because, to the best of our knowledge, no other statistical tool has comparable versatility. In the case of a full and fractional factorial design, the one‐PLS component model yields the same analytical solution as multiple linear regression (MLR), not only in the estimation of the effects but also in their statistical significance. When having missing runs in the factorial design, PLS is of particular interest as it is a powerful tool when dealing with complex correlation structures, as opposed to MLR. Thus, we challenge the widely held view that PLS is useful only when dealing with nonexperimental design (i.e., correlated observational data). The methodology is illustrated by two illustrative examples and synthesized by an easy‐to‐follow route map useful for practitioners. [ABSTRACT FROM AUTHOR]

The flowers of the ylang ylang tree contain an essential oil which is utilized in high-quality perfumes. The traditional mode of extraction is by steam distillation but it has been shown that the more modern supercritical fluid extraction (SFE) using carbon dioxide has potential for replacing steam distillation. This technology, however, generally operates under high pressures, up to 500 bar. The work described in this paper examines the possibility of using carbon dioxide at much lower pressures, close to the critical point, i.e., 75 bar and 30 °C. Two series of experiments were therefore carried out under such conditions, the first using carbon dioxide alone and the second utilizing ethanol as a co-solvent, the conditions being chosen by applying the Design of Experiments (DOE) technique over ranges of pressure from 80 to 120 bar and temperatures from 35 to 50 °C. Extraction curves are presented which show the rates of extraction to be significantly increased by the use of the co-solvent, with the measured values being 0.74% to 0.97% with no co-solvent addition, increasing to 0.92% to 1.16% with co-solvent addition. These rates are, however, lower than the rates previously reported at higher pressures, i.e., 0.9 to 1.8%. Better quality oils are, however, produced compared to those at higher pressures, with the major components being benzene benzoate, benzene salicylate, cubebene, and benzyl acetate. It is recommended that an economic study be carried out to evaluate whether it is feasible to utilize this process commercially. [ABSTRACT FROM AUTHOR]

Nectarines, a popular pit fruit closely related to peaches, are renowned for their nutritional value and associated health benefits. However, challenges arise in maintaining optimal organoleptic properties during harvest and handling, eventually leading to production waste and heterogeneous quality in the fruit that arrives to the consumer. This study investigates the impact of nectarine position on trees during the whole ripening process using non‐destructive near‐infrared (NIR) spectroscopy. Nectarines exposed to more sunlight mature faster and this influences sugar content and acidity, emphasising the significance of considering height, prominence and orientation in ripening dynamics of the fruit. Different data unfolding strategies were compared, using ANOVA‐Simultaneous Component Analysis (ASCA) to reveal the significance of in‐tree position factors at different ripening stages, and observing high significance at harvest. This underscores the necessity for growers and handlers to consider these factors for reducing waste. NIR spectroscopy, with adequate data analysis, is a valuable tool for the holistic analysis of fruit ripening, providing crucial insights for maintaining optimal fruit organoleptic properties from harvest to consumer. This study explores how the position of nectarines on trees affects ripening, using near‐infrared (NIR) spectroscopy. Nectarines exposed to more sunlight mature faster, impacting sugar content and acidity. ANOVA‐Simultaneous Component Analysis (ASCA) revealed significant in‐tree position effects at harvest, highlighting the importance of height, prominence, and orientation on different stages of the ripening. The findings stress the need for growers to consider these factors to reduce waste, with NIR spectroscopy proving useful for ensuring optimal fruit quality. [ABSTRACT FROM AUTHOR]

The fabrication of dissimilar metal joints, particularly between AA 6061 aluminum alloy (Al) and AZ31B magnesium alloy (Mg), poses significant technical challenges due to their distinct metallurgical characteristics and the inherent difficulties associated with welding such materials. These challenges include the propensity for intermetallic compound formation, thermal cracking, and differences in thermal and mechanical properties between the two alloys. Cold Metal Transfer (CMT) welding, known for its low heat input and controlled metal transfer, offers a potential solution to these issues. However, optimizing the process parameters to ensure strong, defect-free joints requires a systematic approach. This study aims to optimize CMT welding parameters using parametric mathematical modeling (PMM) to produce high-strength Al and Mg dissimilar joints and to study the effects of CMT parameters on tensile strength (TS) and weld metal hardness (WMH), as well as the microstructural features of AA 6061 aluminum alloy/AZ31B magnesium alloy (Al/Mg) dissimilar joints. Al/Mg dissimilar butt joints were produced by the CMT process using ER4043 as filler wire. CMT, a low-heat input welding technique, was used to mitigate issues such as intermetallic compounds (IMCs), wider heat-affected zone (HAZ), and distortion. The CMT parameters, particularly wire feed speed (WFS), welding speed (WS), and arc length correction (ALC), were optimized using response surface methodology (RSM) to maximize the TS and WMH of the Al/Mg dissimilar joints. Polynomial regression was employed to create PMMs that integrated these CMT parameters to forecast the TS and WMH of the joints. An analysis of variance (ANOVA) was applied to assess the feasibility of the PMMs. The results indicated that the Al/Mg dissimilar joints, produced using a WFS of 4700 mm/min, a WS of 280 mm/min, and an ALC of 10%, exhibited higher TS and WMH values of 33 MPa and 95.8 HV, respectively. The PMMs provided precise forecasts for the TS and WMH of the Al/Mg joints with an error rate of less than 1% and a confidence level of 97%. [ABSTRACT FROM AUTHOR]

The fabrication of dissimilar metal joints, particularly between AA 6061 aluminum alloy (Al) and AZ31B magnesium alloy (Mg), poses significant technical challenges due to their distinct metallurgical characteristics and the inherent difficulties associated with welding such materials. These challenges include the propensity for intermetallic compound formation, thermal cracking, and differences in thermal and mechanical properties between the two alloys. Cold Metal Transfer (CMT) welding, known for its low heat input and controlled metal transfer, offers a potential solution to these issues. However, optimizing the process parameters to ensure strong, defect-free joints requires a systematic approach. This study aims to optimize CMT welding parameters using parametric mathematical modeling (PMM) to produce high-strength Al and Mg dissimilar joints and to study the effects of CMT parameters on tensile strength (TS) and weld metal hardness (WMH), as well as the microstructural features of AA 6061 aluminum alloy/AZ31B magnesium alloy (Al/Mg) dissimilar joints. Al/Mg dissimilar butt joints were produced by the CMT process using ER4043 as filler wire. CMT, a low-heat input welding technique, was used to mitigate issues such as intermetallic compounds (IMCs), wider heat-affected zone (HAZ), and distortion. The CMT parameters, particularly wire feed speed (WFS), welding speed (WS), and arc length correction (ALC), were optimized using response surface methodology (RSM) to maximize the TS and WMH of the Al/Mg dissimilar joints. Polynomial regression was employed to create PMMs that integrated these CMT parameters to forecast the TS and WMH of the joints. An analysis of variance (ANOVA) was applied to assess the feasibility of the PMMs. The results indicated that the Al/Mg dissimilar joints, produced using a WFS of 4700 mm/min, a WS of 280 mm/min, and an ALC of 10%, exhibited higher TS and WMH values of 33 MPa and 95.8 HV, respectively. The PMMs provided precise forecasts for the TS and WMH of the Al/Mg joints with an error rate of less than 1% and a confidence level of 97%.

The ankle joint is pivotal in prosthetic feet, especially in Energy-Storing-and-Releasing feet, favoured by individuals with moderate to high mobility (K3/K4) due to their energy efficiency and simple construction. ESR feet, mainly designed for sagittal-plane motion, often exhibit high stiffness in other planes, leading to difficulties in adapting to varied ground conditions, potentially causing discomfort or pain. This study aims to present a systematic methodology for modifying the ankle joint’s stiffness properties across its three motion planes, tailored to individual user preferences, and to decouple the sagittal-plane behaviour from the frontal and transverse ones. To integrate the multi-axial ankle inside the MyFlex-η, the designing of experiments using finite element analysis was conducted to explore the impact of geometric parameters on the joint’s properties with respect to design constraints and to reach the defined stiffness targets on the three ankle’s motion planes. A prototype of the multi-axial ankle joint was then manufactured and tested under FEA-derived load conditions to validate the final configuration chosen. Composite elastic elements and complementary parts of the MyFlex-η, incorporating the multi-axial ankle joint, were developed, and the prosthesis was biomechanically tested according to lower limb prosthesis ISO standards and guidelines from literature and the American Orthotic and Prosthetic Association (AOPA). Experimental tests showed strong alignment with numerical predictions. Moreover, implementing the multi-axial ankle significantly increased frontal-plane compliance by 414% with respect to the same prosthesis with only one degree of freedom on the sagittal plane without affecting the main plane of locomotion performance.

This article focuses on the influence of generated electromagnetic noise (energy) during the micro-perforation process. This study aims to investigate the critical parameters and effects of using laser technology in the processing of textile materials for airbags. Different levels of electromagnetic noise and material thicknesses were investigated to ensure the quality of manufactured parts and the best component performance. A factorial analysis (DOE) was developed to evaluate the influence of electromagnetic noise levels over pull test results and its effect on the micro-perforation process. The overall inferential analysis concludes a significant influence of the noise levels on micro-perforation processing. The detailed analysis suggests that 1.2 V is an optimal level of electromagnetic noise where the material maintains its mechanical properties in a more predictable and consistent manner. Additionally, the factorial design provides significant evidence for an interaction and main effects' influences of analyzed factors. The obtained results in this study have demonstrated that monitoring and controlling the noise level have beneficial effects over the laser processing. This ensures that the safety aspect of the produced parts is entirely upheld and protected. Also, this research contributes to improving the manufacturing process and ensures that high-quality products are obtained, being suitable for use in sensitive applications such as automotive airbags. [ABSTRACT FROM AUTHOR]

A pediatric dosage form for crizotinib (Xalkori) was commercialized using quality-by-design principles in a material-sparing fashion. The dosage form consists of spherical multiparticulates (microspheres or pellets) that are coated and encapsulated in capsules for opening. The crizotinib (Xalkori)-coated pellet product is approved in the US for pediatric patients 1 year of age and older and young adults with relapsed or refractory, systemic anaplastic large cell lymphoma (ALCL) and unresectable, recurrent, or refractory inflammatory myofibroblastic tumor (IMT) that is ALK-positive. The product is also approved in the US for adult patients with non-small cell lung cancer (NSCLC) who are unable to swallow intact capsules. The lipid multiparticulate is composed of a lipid matrix, a dissolution enhancer, and an active pharmaceutical ingredient (API). The API, which remains crystalline, is embedded within the microsphere at a 60% drug loading in the uncoated lipid multiparticulate to enable dose flexibility. The melt spray congealing technique using a rotary atomizer is used to manufacture the lipid multiparticulate. Following melt spray congealing, a barrier coating is applied via fluid bed coating. Due to their particle size and content uniformity, this dosage form provides the dosing flexibility and swallowability needed for the pediatric population. The required pediatric dose is achieved by opening the capsules and combining doses of different encapsulated dose strengths, followed by administration of the multiparticulates directly to the mouth. The encapsulation process was optimized through equipment modifications and by using a design of experiments approach to understand the operating space. A limited number of development batches produced using commercial-scale equipment were leveraged to design, understand, and verify the manufacturing process space. The quality by design and material-sparing approach taken to design the melt spray congeal and encapsulation manufacturing processes resulted in a pediatric product with exceptional content uniformity (a 95% confidence and 99% probability of passing USP <905> content uniformity testing for future batches). [ABSTRACT FROM AUTHOR]

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method (DEM) simulation, design of experiments (DOE), and metaheuristic optimization algorithms. Specifically, the study employs the firefly algorithm (FA), a metaheuristic optimization technique, to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions. The experimental methodology involves several key steps: screening experiments to identify significant factors affecting bucket elevator operation, central composite design (CCD) experiments to further explore these factors, and response surface methodology (RSM) to create predictive models for transport mass and mass flow rate discharge. The FA algorithm is then applied to optimize these models, and the results are validated through simulation and empirical experiments. The study validates the optimized parameters through simulation and empirical experiments, comparing results with DEM simulation. The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters, showcasing less than 10% and 15% deviation for transport mass and mass flow rate discharge, respectively, between predicted and actual values. Overall, this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters, contributing to more efficient material handling in various industrial applications. [ABSTRACT FROM AUTHOR]

The ankle joint is pivotal in prosthetic feet, especially in Energy-Storing-and-Releasing feet, favoured by individuals with moderate to high mobility (K3/K4) due to their energy efficiency and simple construction. ESR feet, mainly designed for sagittal-plane motion, often exhibit high stiffness in other planes, leading to difficulties in adapting to varied ground conditions, potentially causing discomfort or pain. This study aims to present a systematic methodology for modifying the ankle joint's stiffness properties across its three motion planes, tailored to individual user preferences, and to decouple the sagittal-plane behaviour from the frontal and transverse ones. To integrate the multi-axial ankle inside the MyFlex-η, the designing of experiments using finite element analysis was conducted to explore the impact of geometric parameters on the joint's properties with respect to design constraints and to reach the defined stiffness targets on the three ankle's motion planes. A prototype of the multi-axial ankle joint was then manufactured and tested under FEA-derived load conditions to validate the final configuration chosen. Composite elastic elements and complementary parts of the MyFlex-η, incorporating the multi-axial ankle joint, were developed, and the prosthesis was biomechanically tested according to lower limb prosthesis ISO standards and guidelines from literature and the American Orthotic and Prosthetic Association (AOPA). Experimental tests showed strong alignment with numerical predictions. Moreover, implementing the multi-axial ankle significantly increased frontal-plane compliance by 414% with respect to the same prosthesis with only one degree of freedom on the sagittal plane without affecting the main plane of locomotion performance. [ABSTRACT FROM AUTHOR]

The bearing capacity of shallow foundations on geocell-reinforced sand (GRS) depends not only on the soil characteristics but also on the geometries and properties of geocell. The geometry characteristics of GRS comprise height, length and cell aperture size of geocell as well as the sand layer thickness engaged between footing and GRS layer. Using the design-of-experiments (DOE) method the trends of bearing capacity ratio (BCR) as a function of selected geometry parameters of geocell are systematically visualized. Furthermore, trends of BCR as a function of geocell-selected geometry parameters are analysed. To this end, the study deploys the response surface methodology (RSM) to identify several configurations of geocell by conducting only twenty-seven laboratory tests. The results show that the DOE-RSM method gives an efficient interpretation of the variable's interaction while it lessens the number of tests. In addition, it has been found that the geocell height is the most influential parameter on BCR of footings on GRS. Moreover, an interaction between geocell aperture size and geocell height is recognised. The results are formulated and compared with the literature, resulting in good agreement. [ABSTRACT FROM AUTHOR]

This research explores the development of thermoplastic vulcanizate (TPV) blends derived from natural rubber (NR) and ethylene–butene copolymer (EBC) using a specific blend ratio and melt mixing technique. A comprehensive full factorial design of experiments (DOE) methodology is employed to optimize the processing parameters. TPVs are produced through dynamic vulcanization, combining rubber crosslinking and melt blending within a thermoplastic matrix under high temperatures and shear. The physico-mechanical properties of these TPVs are then analyzed. The objective is to enhance their mechanical performance by assessing the influence of blend ratio, mixing temperature, rotor speed, and mixing time on crucial properties, including tensile strength, elongation at break, compression set, tear strength, and hardness. Analysis of variance (ANOVA) identifies the optimal processing conditions that significantly improve material performance. Validation is achieved through atomic force microscopy (AFM), confirming the phase-separated structure and, thus, the success of dynamic vulcanization. Rubber process analyzer (RPA) and dynamic mechanical analyzer (DMA) assessments provide insights into the viscoelastic behavior and dynamic mechanical responses. Deconvolution analysis of temperature-dependent tan δ peaks reveals intricate microstructural interactions influencing the glass transition temperature (Tg). The optimized TPVs exhibit enhanced stiffness and effective energy dissipation capabilities across a wide temperature range, making them suitable for applications demanding thermal and mechanical load resistance. This study underscores the pivotal role of precise processing control in tailoring the properties of NR/EBC TPVs for specialized industrial uses. It highlights the indispensable contribution of the DOE methodology to TPV optimization, advancing material science and engineering, particularly for industries requiring robust and flexible materials. [ABSTRACT FROM AUTHOR]

This study focuses on optimizing the synthesis of ZnO/Kaolin nanoparticles and their application in adsorptive bleaching of palm oil. The reaction mechanism involves the dissolution of metal oxides (Fe2O3, Al2O3, CaO, MgO) from clay lattices using mineral acids, increasing the surface area and triggering ion exchange processes. The kaolin is beneficiated through crushing, washing, acid treatment, and drying, and then activated with ZnCl2. Zinc oxide nanoparticles are synthesized using Moringa leaf extract and zinc acetate, followed by the synthesis of ZnO/Kaolin composites using Central Composite Design (CCD) to optimize parameters like kaolin ratio, calcination temperature, and time. The composites are characterized by XRD, XRF, and BET surface area analysis. The bleaching efficiency is evaluated through absorbance and iodine value, with ANOVA analysis confirming the significance of dosage, temperature, and time. The optimized conditions identified are essential for maximizing the composite’s adsorptive efficiency in palm oil bleaching. [ABSTRACT FROM AUTHOR]

The study addresses the requirement for better machining of titanium alloy (Ti–6Al–4V) in Electrical Discharge Machining (EDM) with a Cu–TiB 2 ceramic composite powder metallurgy electrode. The electrode underwent analysis for relative density, hardness, and microstructure using field emission scanning electron microscopy and X-ray diffraction. Results indicated a relative density of approximately 90.08%, a microhardness of around 142 H V, and an electrical conductivity of 46% IACS. Response Surface Methodology (RSM) was used as Design of Experiment (DoE) technique for this study to investigate the effects of pulse current (Ip), pulse on time (Ton), and discharge voltage (V) on Material Removal Rate (MRR) and Surface Roughness (SR). Analysis of variance (ANNOVA) at a 5% significance level highlights the significant impact of Ip, Ton & V, and the interaction between selected parameter on MRR and SR. Results show a significant influence of pulse current (Ip) and pulse duration (Ton) on both MRR and SR. Rising pulse current correlates with improved MRR, with optimal parameters identified as a pulse current of 15 A, a pulse on time of 150 μs, and a voltage of 50 V. The study also looks into the usage of reverse polarity to improve surface finish compared to typical EDM methods. The use of Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS) analyses adds depth to the evaluation of surface formations on the workpiece. EDS analysis indicates the presence of 63.13 wt (%) of boron on the machined work surface, affirming the effectiveness of the developed Cu–TiB 2 electrode in machining titanium alloy. [ABSTRACT FROM AUTHOR]

This study investigates the effect of extrusion screw speed and carbon nanotube (CNT) concentration on the thermal, mechanical, and electromagnetic interference shielding effectiveness (EMI SE) properties of Polycarbonate (PC)/acrylonitrile-butadiene-styrene (ABS) and its polymer nanocomposites (PNCs) by means of design of experiments (DoE) approach. A masterbatch method was employed to obtain the best dispersion of the CNTs throughout the polymer matrix. This study evaluates the thermo-mechanical characterisation of the polymers and PNCs at varying screw speeds to assess filler matrix bonding. The results highlight that CNT concentration has a significant effect on all mechanical properties, while screw speed only affects the Charpy impact strength and flexural properties of the samples. Compounding at 200 rpm has the best flexural and tensile strength, which is attributed to the best filler matrix bonding (highest storage modulus) of the PNCs. The best EMI SE results were obtained at 10 wt.% CNTs. This research contributes valuable insights into the effect of CNT concentration and extrusion screw speed on the mechanical, thermal and EMI SE properties of PC/ABS and its PNCs. [ABSTRACT FROM AUTHOR]

Background: Pazopanib hydrochloride (PZB) is a protein kinase inhibitor approved by the United States Food and Drug Administration and European agencies for the treatment of renal cell carcinoma and other renal malignancies. However, it exhibits poor aqueous solubility and inconsistent oral drug absorption. In this regard, the current research work entails the development and evaluation of the extrudates of pazopanib hydrochloride by the hot-melt extrusion (HME) technique for solubility enhancement and augmenting oral bioavailability. Results: Solid dispersion of the drug was prepared using polymers such as Kollidon VA64, hydroxypropylmethylcellulose (HPMC), Eudragit EPO, and Affinisol 15LV in a 1:2 ratio by the HME process through a lab-scale 18 mm extruder. Systematic optimization of the formulation variables was carried out with the help of custom screening design (JMP Software by SAS, Version 14.0) to study the impact of polymer type and plasticizer level on the quality of extrudate processability by measuring the torque value, appearance, and disintegration time as the responses. The polymer blends containing Kollidon VA64 and Affinisol 15LV resulted in respective clear transparent extrudates, while Eudragit EPO and HPMC extrudates were found to be opaque white and brownish, respectively. Furthermore, evaluation of the impact of process parameters such as screw rpm and barrel temperature was measured using a definitive screening design on the extrude appearance, torque, disintegration time, and dissolution profile. Based on the statistical outcomes, it can be concluded that barrel temperature has a significant impact on torque, disintegration time, and dissolution at 30 min, while screw speed has an insignificant impact on the response variables. Affinisol extrudates showed less moisture uptake and faster dissolution in comparison to Kollidon VA64 extrudates. Affinisol extrudates were evaluated for polymorphic stability up to a 3-month accelerated condition and found no recrystallization. PZB–Extrudates using the Affinisol polymer (Test formulation A) revealed significantly higher bioavailability (AUC) in comparison to the free Pazopanib drug and marketed formulation. [ABSTRACT FROM AUTHOR]

Using High‐Throughput Experimentation (HTE), a visible light‐mediated etherification method was developed, employing the organic photocatalyst thioxanthen‐9‐one (TXO) and a commercially available, air‐stable nickel source. Design of Experiments (DoE) techniques were utilized in conjunction with HTE to identify optimal reaction conditions which are mild, cost‐effective, robust and therefore suitable for use in an industrial setting. A diverse substrate scope was prepared via parallel synthesis and selected examples were demonstrated on a 4.2 mmol scale in batch. Furthermore, the reaction was successfully performed on an 83 mmol scale, utilizing a standard jacketed reactor and Kessil PR160 L lamps. [ABSTRACT FROM AUTHOR]

Lignin, a complex and heterogeneous biopolymer derived from wood, holds immense potential as a sustainable feedstock for various industrial applications. Kraft pulping, a widely employed process in the paper industry, generates black liquor containing lignin along with other organic and inorganic compounds. The precipitation of lignin from black liquor offers an opportunity for valorization, contributing to the circular economy and reducing the environmental impact. Although the precipitation process of softwood lignin is extensively documented and outlined in the existing literature, the identical process originating from hardwood-derived black liquor poses challenges attributed to the distinct composition of hardwood. This study systematically investigates the individual and combined influences of temperature and pH on the precipitation of hardwood lignin from kraft black liquor, utilizing a factorial design to evaluate lignin functional characteristics. The characterization of the precipitated lignin was performed using various analytical techniques, mainly NIR, elemental composition, UV-VIS, and calorimetry. The results reveal remarkable interactions between temperature and pH, indicating their synergistic effects on lignin precipitation. The optimal conditions for hardwood lignin precipitation were identified and successfully upscaled during piloting experiments conducted under industrial conditions. This research provides valuable insights into the fundamental factors governing hardwood lignin precipitation from kraft black liquor and offers a basis for the development of efficient and sustainable lignin recovery processes in the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using the additive manufacturing selective laser melting technique and were subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells' parameters setup. The findings described highlight the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for the aerospace market. This study also emphasizes the role of additive manufacturing in producing complex structures with pointwise-tailored mechanical properties. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.

Abstract Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass. Graphical Abstract

Biological soil improvement techniques, such as enzyme-induced calcium precipitation (EICP), have attracted increasing attention as a sustainable and durable solution for ground improvement. However, recent life-cycle environmental assessment studies of EICP ground improvement have shown that enzyme stabilizers such as nonfat milk powder have a significant environmental impact. Hence, this study explores the potential of using sustainable biopolymer [sodium alginate (SA)] as a modifier in EICP cementing solution. The interaction effect between different EICP cementing solution constituents (calcium chloride, urease enzyme, urea, and SA) on the performance of sand-treated specimens is investigated. Response surface methodology (RSM), a design of experiments (DOE) approach, is utilized to design the testing program. In addition, RSM is adopted to model the effect of different constituents on the performance of EICP-treated soils. An optimal mix is suggested for the EICP-SA modified cementing solution with a maximum unconfined compressive strength of 1,762 kPa, compared with 460 kPa of the EICP-milk treated specimens. Moreover, the practical considerations of the SA-modified EICP solution are investigated through scanning electron microscopy, energy dispersive spectrometry, and water retention. Notably, the study demonstrated that SA-modified EICP specimens have superior water retention characteristics compared with regular EICP-treated soils due to the inclusion of SA in the EICP solution. Additionally, a set of durability tests revealed the promising performance of the SA-modified EICP-treated specimens under wetting–drying cycles, with results complying with ASTM standards. These findings suggest that the SA-modified EICP approach offers a potent and environmentally friendly alternative for soil stabilization and improvement. [ABSTRACT FROM AUTHOR]

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass. [ABSTRACT FROM AUTHOR]

The tannery wastewater from the tanning stage (TWT) comprises organic and Cr pollutants, which can adversely affect aquatic life and have carcinogenic effects. In this study, we investigated the performance of a Fenton-like process using commercial Nano-scale zero-valent iron (nZVI) for the simultaneous removal of Cr and organic matter from real TWT. We used an experimental design to select the principal operating parameters. A Plackett–Burman design identified variables for Cr-total and COD removal, followed by a central composite design (CC-D) to determine optimal variable levels. Finally, the response surface methodology (RSM) was used to find the optimum concentration of individual variables influencing Cr-total removal. Additionally, the effect of the leather-related, co-existing substances that influenced the efficiency of the process and the possibility of recycling nZVI were explored. The inclusion of nZVI was significantly more effective at removing both Cr-total and COD (97.3% ± 5.7% and 73.9% ± 9.1%, respectively), whereas the traditional Fenton process achieved lower removal rates (55.6% ± 10.0% for Cr-total and 34.8% ± 10.9% for COD). The optimal conditions for the Fenton-like process were nZVI/H2O2 = 1.05 w/w, and pH = 2.93. We obtained the best results during the first 5 min of the reaction, which increased after 48 h of agitation and subsequent neutralization. According to the results of four consecutive cycles, nZVI exhibited high reusability (97%) without compromising its adsorption potency. XPS analysis confirmed Cr removal through the adsorption mechanism on the nZVI surface. Hence, a Fenton-like process based on nZVI can be used as a promising alternative for treating organic and Cr wastewater. [ABSTRACT FROM AUTHOR]

Virus filtration is one of the most important steps in ensuring viral safety during the purification of monoclonal antibodies (mAbs) and other biotherapeutics derived from mammalian cell cultures. Regarding the various virus retentive filters, including Planova filters, a great deal of data has been reported on the virus retention capability and its mechanism. Along with the virus retention capability, filterability is a key performance indicator for designing a robust and high‐throughput virus filtration step. In order to obtain higher filterability, optimization of the feed solution conditions, and filter selection is essential; however, limited data are available regarding the filtration characteristics of Planova filters. Furthermore, for Planova 20N and Planova BioEX, the virus retention characteristics were reported to differ due to their respective membrane materials and layer structures. Whether these filters differ in their filtration characteristics is an interesting question, but no comparative evaluations have been reported. In this study, the filterability of the two filters was investigated and compared using 15 feed mAb solutions of a single mAb selected by design of experiments with different combinations of pH, NaCl concentration, and mAb concentration. The filterability of Planova 20N was affected not only by the feed solution viscosity, but also by the mAb aggregate content of the feed mAb solution and mAb‐membrane electrostatic interactions. In contrast, the filterability of Planova BioEX decreased under some buffer conditions. These findings and the established design spaces of these filters provide valuable insights into the process optimization of virus filtration. [ABSTRACT FROM AUTHOR]

Background: The multimodal chromatography resins, such as Capto adhere, are considered good candidates to be utilized in downstream processing due to their high capacity and selectivity; however, their multimodal interactions lead to an intricacy in the adsorption-desorption patterns and systematic characterization of conditions for process steps is necessary. Methods: Capto adhere, a strong ion exchanger with multimodal functionality, was used in this study for the final aim of recombinant hepatitis B surface antigen (rHBsAg) purification from Pichia pastoris (P. pastoris) industrial feedstock. Optimization of various parameters was done using the design of experiments (DOE) approach to determine the best binding and non-binding conditions. Results: Maximum rHBsAg binding on Capto adhere occurred in 20 mM sodium acetate, pH 4.5, and a binding capacity of about 0.75 mg/ml was achieved, which was much higher than rHBsAg binding capacity of other resins reported so far. In elution optimization investigations, it was revealed that 1 M arginine (buffered in 50 mM sodium phosphate, pH 6.5) was the most efficient eluting agent. The binding and elution optimal conditions were utilized for further purification of rHBsAg from P. pastoris industrial feedstock in bind-elute mode, and the recovery and purity of the obtained rHBsAg were about 60% and 100%, respectively. Following optimization in the flow-through purification mode, the target protein recovery was significantly increased (up to 97%) and the target protein purity of more than 95% was achievable. SEC-HPLC analysis showed that the obtained retention times for the purified rHBsAg were similar to those reported previously. Conclusions: These results suggest that Capto adhere under such optimized conditions can be considered as a good candidate for efficient purification of rHBsAg from P. pastoris industrial feedstock in downstream processing.

The flowers of the ylang ylang tree contain an essential oil which is utilized in high-quality perfumes. The traditional mode of extraction is by steam distillation but it has been shown that the more modern supercritical fluid extraction (SFE) using carbon dioxide has potential for replacing steam distillation. This technology, however, generally operates under high pressures, up to 500 bar. The work described in this paper examines the possibility of using carbon dioxide at much lower pressures, close to the critical point, i.e., 75 bar and 30 °C. Two series of experiments were therefore carried out under such conditions, the first using carbon dioxide alone and the second utilizing ethanol as a co-solvent, the conditions being chosen by applying the Design of Experiments (DOE) technique over ranges of pressure from 80 to 120 bar and temperatures from 35 to 50 °C. Extraction curves are presented which show the rates of extraction to be significantly increased by the use of the co-solvent, with the measured values being 0.74% to 0.97% with no co-solvent addition, increasing to 0.92% to 1.16% with co-solvent addition. These rates are, however, lower than the rates previously reported at higher pressures, i.e., 0.9 to 1.8%. Better quality oils are, however, produced compared to those at higher pressures, with the major components being benzene benzoate, benzene salicylate, cubebene, and benzyl acetate. It is recommended that an economic study be carried out to evaluate whether it is feasible to utilize this process commercially.

In this work, a comparison was made between the synthesis of niobium-based materials (Nb2O5), both in terms of material characterization and catalytic performance. The methods used were chemical mixtures: modified sol-gel and Pechini. The materials were calcined at different temperatures (753, 873 and 993K) and characterized by the following techniques: photoacousticspectroscopy (PAS), zero charge point (pHPZC), scanning electron microscopy (SEM/EDS), thermogravimetric analysis (TGA/DTG) and X-ray diffraction (XRD). The photocatalytic process was carried out to evaluate the degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) under UV radiation (250 W mercury vapor lamp) and different experimental conditions. In addition, to better understand the influence of parameters such as pH, catalyst concentration (0.2, 0.5 and 0.8 g L−1) and calcination temperature, a Design of Experiments (DoE) was used. The results indicated that despite having similar structures and phases in the XRD analysis, the morphology presents two distinct surfaces, due to the preparation method. Differences in the synthesis method affected the catalytic activity in the parameters studied. Although the zero charge point values are close (6.18-6.36), we observed differences in the band gap depending on the calcination temperature. In the optimal condition studied, the catalyst prepared by the sol-gel method obtained the best results. [ABSTRACT FROM AUTHOR]

Degradation caused by corrosion is a major problem for S235JR steel and is responsible for many failures. Ball burnishing is a superficial mechanical finishing treatment that improves surface quality as well as its mechanical properties. The aim of this experimental work is to study the influence of this process on the surface quality and corrosion behavior of S235JR steel. The response surface method (RSM), based on Box-Behnken experimental design, was used to minimize the surface roughness, which has a significant influence on corrosion resistance. "Ra" and "Rz" roughness measurements, as well as 3D surface topography observations, were carried out. Furthermore, the subsurface microhardness distribution was examined, and an X-ray diffraction analysis was performed. The polarization technique (Tafel) and electrochemical impedance spectroscopy (EIS) were used to evaluate corrosion resistance. Finally, analysis by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) was carried out before and after the corrosion tests. The results showed that burnishing force and ball diameter are the main parameters influencing roughness. The optimal regime reduced roughness "Ra" and "Rz" by more than 96% and 97% respectively, producing a smooth and uniform topography. XRD analysis revealed surface grain refinement without phase change. The subsurface microhardness increased to a depth of 40 μm, suggesting a burnished layer of equivalent thickness. Electrochemical corrosion tests and SEM observation showed that the corrosion resistance of the burnished surface was significantly improved, mainly attributed to grain refinement. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is a crucial branch of additive manufacturing (AM), performing repairs, cladding, and processing of multi-material components. 316L austenitic stainless steel is widely used in applications such as the food, aerospace, automotive, marine, energy, biomedical, and nuclear reactor industries. Nevertheless, there is need for process parameter optimization and a comprehensive understanding of the individual and complex synergistic effects of process parameters on the geometry, microstructure, and properties of the deposited material or component. This is essential for ensuring repeatable manufacturing of parts across a single or series of platforms over time, or for minimizing defects such as porosity. In this study, the response surface methodology (RSM) and central composite design (CCD) were employed to investigate the effects of laser power, laser scan speed, and powder mass flow rate on layer thickness, density, microstructure, and microhardness of 316L steel processed by Laser Engineered Net Shaping (LENS®) DED. Polynomial empirical prediction models correlating the applied processing parameters and the studied responses were developed. [ABSTRACT FROM AUTHOR]

Airbags are important safety devices in modern vehicles. However, their effectiveness is linked to the occupants being seated in standard positions. Although autonomous vehicles are less accident-prone, they are also equipped with airbags, similar to any other vehicle. Additionally, autonomous vehicles allow for occupants seated in non-standard positions, so in the case of a collision, the airbags' effectiveness decreases. In this work, an airbag design suitable for both assisted and autonomous driving conditions is proposed, the driver's airbag being the object of interest. Airbag geometry, threads, seam strength, and seam geometries were selected following Design of Experiments (DoE) methodologies and a series of experimental tests. Moreover, an adaptive system based on sewn tethers allows the airbag to adapt to the driving mode, which is proposed and validated. Finally, all the findings were experimentally tested on two different geometries. The results were satisfactory as the deployed airbag shape and dimensions were as expected, indicating that this airbag design is capable of protecting the driver of a vehicle capable of autonomous driving. [ABSTRACT FROM AUTHOR]

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model’s performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. [ABSTRACT FROM AUTHOR]

Airbags are important safety devices in modern vehicles. However, their effectiveness is linked to the occupants being seated in standard positions. Although autonomous vehicles are less accident-prone, they are also equipped with airbags, similar to any other vehicle. Additionally, autonomous vehicles allow for occupants seated in non-standard positions, so in the case of a collision, the airbags’ effectiveness decreases. In this work, an airbag design suitable for both assisted and autonomous driving conditions is proposed, the driver’s airbag being the object of interest. Airbag geometry, threads, seam strength, and seam geometries were selected following Design of Experiments (DoE) methodologies and a series of experimental tests. Moreover, an adaptive system based on sewn tethers allows the airbag to adapt to the driving mode, which is proposed and validated. Finally, all the findings were experimentally tested on two different geometries. The results were satisfactory as the deployed airbag shape and dimensions were as expected, indicating that this airbag design is capable of protecting the driver of a vehicle capable of autonomous driving.

This article focuses on the influence of generated electromagnetic noise (energy) during the micro-perforation process. This study aims to investigate the critical parameters and effects of using laser technology in the processing of textile materials for airbags. Different levels of electromagnetic noise and material thicknesses were investigated to ensure the quality of manufactured parts and the best component performance. A factorial analysis (DOE) was developed to evaluate the influence of electromagnetic noise levels over pull test results and its effect on the micro-perforation process. The overall inferential analysis concludes a significant influence of the noise levels on micro-perforation processing. The detailed analysis suggests that 1.2 V is an optimal level of electromagnetic noise where the material maintains its mechanical properties in a more predictable and consistent manner. Additionally, the factorial design provides significant evidence for an interaction and main effects’ influences of analyzed factors. The obtained results in this study have demonstrated that monitoring and controlling the noise level have beneficial effects over the laser processing. This ensures that the safety aspect of the produced parts is entirely upheld and protected. Also, this research contributes to improving the manufacturing process and ensures that high-quality products are obtained, being suitable for use in sensitive applications such as automotive airbags.

This research explores the development of thermoplastic vulcanizate (TPV) blends derived from natural rubber (NR) and ethylene–butene copolymer (EBC) using a specific blend ratio and melt mixing technique. A comprehensive full factorial design of experiments (DOE) methodology is employed to optimize the processing parameters. TPVs are produced through dynamic vulcanization, combining rubber crosslinking and melt blending within a thermoplastic matrix under high temperatures and shear. The physico-mechanical properties of these TPVs are then analyzed. The objective is to enhance their mechanical performance by assessing the influence of blend ratio, mixing temperature, rotor speed, and mixing time on crucial properties, including tensile strength, elongation at break, compression set, tear strength, and hardness. Analysis of variance (ANOVA) identifies the optimal processing conditions that significantly improve material performance. Validation is achieved through atomic force microscopy (AFM), confirming the phase-separated structure and, thus, the success of dynamic vulcanization. Rubber process analyzer (RPA) and dynamic mechanical analyzer (DMA) assessments provide insights into the viscoelastic behavior and dynamic mechanical responses. Deconvolution analysis of temperature-dependent tan δ peaks reveals intricate microstructural interactions influencing the glass transition temperature (Tg). The optimized TPVs exhibit enhanced stiffness and effective energy dissipation capabilities across a wide temperature range, making them suitable for applications demanding thermal and mechanical load resistance. This study underscores the pivotal role of precise processing control in tailoring the properties of NR/EBC TPVs for specialized industrial uses. It highlights the indispensable contribution of the DOE methodology to TPV optimization, advancing material science and engineering, particularly for industries requiring robust and flexible materials.

Lignin, a complex and heterogeneous biopolymer derived from wood, holds immense potential as a sustainable feedstock for various industrial applications. Kraft pulping, a widely employed process in the paper industry, generates black liquor containing lignin along with other organic and inorganic compounds. The precipitation of lignin from black liquor offers an opportunity for valorization, contributing to the circular economy and reducing the environmental impact. Although the precipitation process of softwood lignin is extensively documented and outlined in the existing literature, the identical process originating from hardwood-derived black liquor poses challenges attributed to the distinct composition of hardwood. This study systematically investigates the individual and combined influences of temperature and pH on the precipitation of hardwood lignin from kraft black liquor, utilizing a factorial design to evaluate lignin functional characteristics. The characterization of the precipitated lignin was performed using various analytical techniques, mainly NIR, elemental composition, UV-VIS, and calorimetry. The results reveal remarkable interactions between temperature and pH, indicating their synergistic effects on lignin precipitation. The optimal conditions for hardwood lignin precipitation were identified and successfully upscaled during piloting experiments conducted under industrial conditions. This research provides valuable insights into the fundamental factors governing hardwood lignin precipitation from kraft black liquor and offers a basis for the development of efficient and sustainable lignin recovery processes in the pulp and paper industry.

Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using the additive manufacturing selective laser melting technique and were subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells’ parameters setup. The findings described highlight the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for the aerospace market. This study also emphasizes the role of additive manufacturing in producing complex structures with pointwise-tailored mechanical properties.

Background: Pazopanib hydrochloride (PZB) is a protein kinase inhibitor approved by the United States Food and Drug Administration and European agencies for the treatment of renal cell carcinoma and other renal malignancies. However, it exhibits poor aqueous solubility and inconsistent oral drug absorption. In this regard, the current research work entails the development and evaluation of the extrudates of pazopanib hydrochloride by the hot-melt extrusion (HME) technique for solubility enhancement and augmenting oral bioavailability. Results: Solid dispersion of the drug was prepared using polymers such as Kollidon VA64, hydroxypropylmethylcellulose (HPMC), Eudragit EPO, and Affinisol 15LV in a 1:2 ratio by the HME process through a lab-scale 18 mm extruder. Systematic optimization of the formulation variables was carried out with the help of custom screening design (JMP Software by SAS, Version 14.0) to study the impact of polymer type and plasticizer level on the quality of extrudate processability by measuring the torque value, appearance, and disintegration time as the responses. The polymer blends containing Kollidon VA64 and Affinisol 15LV resulted in respective clear transparent extrudates, while Eudragit EPO and HPMC extrudates were found to be opaque white and brownish, respectively. Furthermore, evaluation of the impact of process parameters such as screw rpm and barrel temperature was measured using a definitive screening design on the extrude appearance, torque, disintegration time, and dissolution profile. Based on the statistical outcomes, it can be concluded that barrel temperature has a significant impact on torque, disintegration time, and dissolution at 30 min, while screw speed has an insignificant impact on the response variables. Affinisol extrudates showed less moisture uptake and faster dissolution in comparison to Kollidon VA64 extrudates. Affinisol extrudates were evaluated for polymorphic stability up to a 3-month accelerated condition and found no recrystallization. PZB–Extrudates using the Affinisol polymer (Test formulation A) revealed significantly higher bioavailability (AUC) in comparison to the free Pazopanib drug and marketed formulation.