Romanian consumers have started to buy and consume more organic products. Their decision-making process is influenced by multiple variables. The theory of planned behaviour is widely accepted and used to predict behaviours in certain contexts, including the buying of organic food products. Other researchers have identified values that the consumer of organic products hold and that influence their buying behaviour. This study analyses the factors that have an impact on buying intention and behaviour of Romanian organic products from these two perspectives. A proposed model was designed by combining the two frameworks. It was evaluated by using structural equation modelling with the SmartPLS 3 software package (v. 3.2.7, SmartPLS GmbH, Bönningstedt, Germany, 2017). Results confirm the model proposed in the theory of planned behaviour while integrating the relationships of consumer values. Health consciousness was found to have a significant effect both on buying intention as well as on personal attitude. Food safety has a significant effect on buying intention regardless of personal attitude. Environmental concerns, social consciousness, perception of quality and lifestyle although important in personal attitude, do not affect buying intention directly.

This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts.

Impression materials are used to record and reproduce the exact morphology of the patient’s oral cavity. The dimensional stability of a material is its ability to maintain the accuracy of recording the details of the oral cavity for a longer period of time, including the time during imprinting and immediately after. The aim of this study was to evaluate the accuracy of three different impression materials commonly used in the dental practice with the aid of an extra-oral three-dimensional (3D) scanner using an in vitro analysis. A typodont tooth model of the maxillary dental arch and mandibular dental arch, containing 16 permanent teeth, was used for evaluation. With the aid of three different impression materials, this model was imprinted fifteen times, resulting in fifteen different plaster models. A capsule extra-oral scanner device was used to digitalize the models and the same device was later used to align, compare, and measure scanned model surfaces. After performing the Kruskal–Wallis test for each measurement category (model), only two out of the fifteen measurements showed statistically significant differences between the material groups: vestibular-oral and mesial-distal width. Post hoc analysis showed that the alginate model (mean range = 29.13) had significantly higher bias scores than the addition silicone model (mean range = 16.75) (z = 2.501, p = 0.037). The difference between the average values of the model bias made from condensation-based silicone and addition-based silicone was initially significant, but after applying the Bonferroni correction for further comparisons, this relationship did not remain significant (z = 2.197, p = 0.084). Addition-based silicone models had the highest accuracy in terms of fidelity, followed by condensation-based silicones, and then by alginate models. Silicone-based impression materials improved over time, being highly used in all areas of dentistry. Alginate impressions had the lowest pattern of accuracy among those studied.

Additive manufacturing is a technology that has many uses across a variety of fields. Its usage spans many fields, including the fields of art, design, architecture, engineering and medicine, including dentistry. The study aims to evaluate and compare the accuracy of three-dimensional printed dental models based on ethylene di-methacrylate using the SLA and DLP techniques. For evaluation, a reference model containing 16 maxillary permanent molars was chosen. An ATOS Capsule 3D scanner was used to scan the reference model. Using a photo-cured liquid resin, eight three-dimensional printed models were obtained using the reference model as benchmark. Four of the models (A1–A4) were obtained using SLA printing technology and four models (B1–B4) were manufactured using DLP printing technology. A standard best fit method was used to pre-align the reference and the printed model surfaces. The height of the teeth, and the mesial–distal and buccal–lingual distances were analyzed. The assessment of the two manufacturing methods was achieved by using non-parametric tests to compare the mean ranks for the assessed features. The results show that models obtained through DLP had a higher precision but also a higher bias. Both methods still are within the required accuracy range for dental models.

Digital impression devices are used alternatively to conventional impression techniques and materials. The aim of this study was to evaluate the precision of extraoral digitalization of three types of photosensitive resin polymers used for 3D printing with the aid of a digital extraoral optical scanner. The alignment of the scans was performed by a standard best-fit alignment. Trueness and precision were used to evaluate the models. The trueness was evaluated by using bias as a measure and the standard deviation was used to evaluate the precision. After assessing the normality of the distributions, an independent Kruskal–Wallis test was used to compare the trueness and precision across the material groups. The Mann–Whitney test was used as a post-hoc test for significant differences. The result of the analysis showed significant differences (U = 66, z = −2.337, p = 0.019) in trueness of mesiodistal distances. Upon visual inspection of the models, defects were noticed on two out of nine of the models printed with a photosensitive polymer. The defects were presented as cavities caused by air bubbles and were also reflected in the scans. Mean precision did not vary too much between these three photosensitive polymer resins, therefore, the selection of 3D printing materials should be based on the trueness and the required precision of the clinical purpose of the model.

This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.