The emergence of superhydrophobic antibacterial materials represents a promising approach to maintaining surface cleanliness and hygiene by effectively preventing bacterial adhesion. This research outlines the synthesis of a superhydrophobic coating with anti-adhesion and bacteriostatic properties, utilizing silica nanoparticles (SiO₂ NPs) modified with 1H,1H,2H,2H-Perfluorodecyltriethoxysilane (PFDTES). Transmission electron microscopy (TEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and Fourier-transform infrared (FTIR) spectroscopy were conducted to analyze the coating’s morphology and surface characteristics. The coating was applied to glass substrates using the spray coating method, and the number of layers was varied to evaluate its antibacterial and bacteriostatic properties. These properties were measured using turbidimetry and inhibition halo techniques. Additionally, the durability of the coatings was assessed by exposing them to outdoor conditions for 35 days. This study aimed to evaluate the antibacterial and bacteriostatic capacities of the superhydrophobic coating, along with its resistance to outdoor weathering. The results indicate that a superhydrophobic coating with a contact angle ≥ 150° and a sliding angle ≤ 10° was successfully synthesized using SiO₂ NPs smaller than 10 nm, modified with PFDTES. The coating demonstrated an ability to inhibit bacterial growth by preventing the adhesion of bacteria such as Escherichia coli and Staphylococcus aureus. Furthermore, the number of coating layers significantly influenced its bacteriostatic efficacy. The coating also exhibited strong durability under outdoor conditions. These findings highlight the potential application of superhydrophobic coatings for the prevention of bacterial adhesion and growth in environments where such contamination poses risks.

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R²<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

This article reports the optimization of film cooling on a leading edge of a gas turbine blade model, with showerhead configuration, it is based on five input parameters, which are hole diameter, hole pitch, column holes pitch, injection angle, and velocity at plenum inlet. This optimization increased the Area-Averaged Film Cooling Effectiveness $({\eta }_{\mathit{Aav}})$ and reduced the consumption of coolant flow. Differential Evolution assisted by artificial neural networks was used as optimization algorithm. Reynolds Averaged Navier–Stokes computations were carried out to getting the net database and to evaluate the optimized models predicted by artificial neural network. The results show an effective increment of ${\eta }_{\mathit{Aav}}$ by 36% and a mass flow reduction by 66%. These results were reached by means of a better distribution of cooling flow at blade surface as function of the input parameters. To assure the reliability of the numerical model, particle image velocimetry technique was used for its validation.

The new product development process (NPDP) is crucial for maintaining business up and running in manufacturing corporations worldwide. NPDP helps firms improve their profits, where stakeholder attention is mostly focused. NPDP Risk Assessment (NPDP-RA) is a vital activity to achieve a successful new product launch. However, managing different kinds of risks over the process presents enormous challenges; thus, risk assessment over NPDP still shows gaps, even when different tools are employed to identify, mitigate, and eliminate risks throughout the process. The identified gaps are mainly produced because the uncertainty added by the individuals assessing the risks, and the currently used tools are generally focused on a single phase of the NPDP, disregarding the stakeholders’ project objectives. This paper presents the main NPDP risks for stakeholders through the NPDP phases; likewise, the integration of Pythagorean fuzzy dimensional analysis – failure mode and effect analysis and value stream mapping (PFDA-FMEA-VSM). This novel integrated method is intended to improve the manner to perform the NPDP-RA for stakeholders. A practical example is presented to demonstrate the proposed integration of PFDA-FMEA-VSM for stakeholders at NPDP of electronic devices project.

Mandibular fractures are common facial lesions typically treated with titanium plate and screw systems; nevertheless, this material is associated with secondary effects. Absorbable material for implants is an alternative to titanium, but there are also problems such as incomplete screw insertion and screw breakage due to high pretension in the screw caused by the insertion torque. The purpose of this paper is to find the optimal screw pretension (SP) in absorbable plate and screw systems by means of artificial neural network (ANN) and its inverse (ANNi). This optimal SP must satisfy a desired maximum von Mises strain (MVMS). For training the ANN, a database was generated by means of a design of experiments (DOE). Each DOE configuration was solved by means of finite element method (FEM) calculations. To obtain the optimal value for (SP) in the mini absorbable screw for fracture fixation, a strategy to invert the ANN is developed. Using the ANN coefficients, a sensitive study was performed to identify the influence of the design parameters in the MVMS. The optimal SP obtained was 14.9742 N. The MVMS condition was satisfied with an error less than 1.1% in comparison with FEM and ANN results. The screw shaft length is the most influencing MVMS parameter.