Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood Biocomposites

The low carbon footprint, biodegradability, interesting mechanical properties, and relatively low price are considered some of the reasons for the increased interest in polylactic acid-based (PLA-based) filaments supplied with natural fillers. However, it is essential to recognize that incorporating natural fillers into virgin PLA significantly impacts the printability of the resulting blends. The complex inter-relationship between process, structure, and properties in the context of fused deposition modeling (FDM)-manufactured biocomposites is still not fully understood, which thus often results in decreased reliability of this technology in the context of biocomposites, decreased accuracy, and the increased presence of defects in the manufactured biocomposite samples. In light of these considerations, this study aims to identify the optimal processing parameters for the FDM manufacturing process involving wood-filled PLA biocomposites. This study presents an optimization approach consisting of Grey Relational Analysis in conjunction with the Taguchi orthogonal array. The optimization process has identified the combination of a scanning speed of 70 mm/s, a layer height of 0.1 mm, and a printing temperature of 220 °C as the most optimal, resulting in the highly satisfactory combination of good dimensional accuracy (Dx = 20.115 mm, Dy = 20.556 mm, and Dz = 20.220 mm) and low presence of voids (1.673%). The experimentally determined Grey Relational Grade of the specimen manufactured with the optimized set of process parameters (0.782) was in good agreement with the predicted value (0. 754), substantiating the validity of the optimization process. Additionally, the research compared the efficacy of optimization between the integrated multiparametric method and the conventional monoparametric strategy. The multiparametric method, which combines Grey Relational Analysis with the Taguchi orthogonal array, exhibited superior performance. Although the monoparametric optimization strategy yielded specimens with favorable values for the targeted properties, the analysis of the remaining characteristics uncovered unsatisfactory results. This highlights the potential drawbacks of relying on a singular optimization approach.

Electrochemical Aptasensing Platform for the Detection of Retinol Binding Protein-4

Here, we present the results of our the electrochemical aptasensing strategy for retinol binding protein-4 (RBP-4) detection based on a thiolated aptamer against RBP-4 and 6-mercaptohexanol (MCH) directly immobilized on a gold electrode surface. The most important parameters affecting the magnitude of the analytical signal generated were optimized: (i) the presence of magnesium ions in the immobilization and measurement buffer, (ii) the concentration of aptamer in the immobilization solution and (iii) its folding procedure. In this work, a systematic assessment of the electrochemical parameters related to the optimization of the sensing layer of the aptasensor was carried out (electron transfer coefficients (α), electron transfer rate constants (k0) and surface coverage of the thiolated aptamer probe (ΓApt)). Then, under the optimized conditions, the analytical response towards RBP-4 protein, in the presence of an Fe(CN)63-/4- redox couple in the supporting solution was assessed. The proposed electrochemical strategy allowed for RBP-4 detection in the concentration range between 100 and 1000 ng/mL with a limit of detection equal to 44 ng/mL based on electrochemical impedance spectroscopy (EIS). The specificity studies against other diabetes biomarkers, including vaspin and adiponectin, proved the selectivity of the proposed platform. These preliminary results will be used in the next step to miniaturize and test the sensor in real samples.

Optimizing Patient Information Material for a New Psycho-Oncological Care Program Using a Participatory Health Research Approach in Germany

High-quality patient information material (PIM) is essential for patients´ informed decision-making, and its quality may influence a care program's acceptance. In the new psycho-oncological care program, isPO, the initial PIM was developed top-down and required optimization. In this paper, we report on the process and experiences of optimizing PIM's quality bottom-up by applying a Participatory Health Research (PHR) approach. Cancer-patient representatives of the national peer-support group contributed as co-researchers as part of the optimization team. A mixed-methods design was chosen. First, the quality of the initially utilized PIM was assessed with the newly designed user-friendly instrument UPIM-Check. Next, three Participatory Action Research loops were conducted, including cancers survivors and isPO service providers. The initial isPO PIM's were assed to be of low quality, limited usability and incomplete. Bottom-up generated optimization suggestions led to the improvement of two initially used PIMs (leaflet, patient information folder) and the design of two new PIMs (poster, study information overview). The optimized PIM facilitates isPO service providers' care provision and helps newly diagnosed cancer patients in understanding and accepting the new program. PIM optimization benefited from applying PHR. The patient representatives' contribution and active patient engagement were central for quality assessment and designing needs-driven, mature and complete PIM.

The Demand Supply Steady-State Process-Based Multi-Level Spare Parts Optimization

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand–supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy’s current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.

Single Mathematical Parameter for Evaluation of the Microorganisms' Growth as the Objective Function in the Optimization by the DOE Techniques

The cultivation of bacteria sets a ground for studying biological processes in many scientific disciplines. The development of the bacterial population is commonly described with three factors that can be used to evaluate culture conditions. However, selecting only one of them for the optimization protocol is rather problematic and may lead to unintended errors. Therefore, we proposed a novel mathematical approach to obtain a single factor that could be used as the objective function to evaluate the whole growth dynamic and support the optimization of the biomass production process. The sigmoidal-shape curve, which is the commonly used function to plot the amount of biomass versus time, was the base for the mathematical analysis. The key process parameters, such as maximal specific growth rate and lag-phase duration were established with the use of mathematical coefficients of the model curve and combined to create the single growth parameter. Moreover, this parameter was used for the exemplary optimization of the cultivation conditions of Klebsiella pneumoniae that was cultured to be further used in the production of lytic bacteriophages. The proposed growth parameter was successfully validated and used to calculate the optimal process temperature of the selected bacterial strain. The obtained results indicated that the proposed mathematical approach could be effortlessly adapted for a precise evaluation of growth curves.

Response surface methodology applied to tropical freshwater treatment

This paper presents the profits and disadvantages of the chemometrics approach instead of the one-factor-at-a-time (OFAT) coagulation diagram approach for tropical water physicochemical treatment. Central composite design associate to response surface methodology (CCD-RSM) is used to find the real best conditions for coagulant dosage and pH aiming at high-turbidity removal. The number of experiments needed to chemometrics model construction (12) is comparatively smaller than that used in the coagulation diagram (84), saving financial and environmental resources. Arguments for Water Treatment Plants (WTP) considering the replacement of coagulation diagram approach by the CCD-RSM approach in drinking water treatment are presented. Chemometrics models are all constructed on a free software platform, providing the best pH within the range 7.7-8.1, and the best coagulant dosage (polyaluminium chloride, PAC) between 2.0 and 3.1 mg Al₂O₃ L^-1 (equivalent in mass). CCD-RSM provides a faster, lower-cost and more reliable alternative tool for WTP decision-making instead of the OFAT model, mainly for waters more affected by seasonal effects as can be seen in tropical and subtropical countries.