Reinforcing the polymer with nanoparticles and fibers improves the mechanical, thermal and electrical properties. Owing to this, the functional parts produced by the FDM process of such materials can be used in industrial applications. However, optimal parameters’ selection is crucial to produce parts with optimal properties, such as mechanical strength. This paper focuses on the analysis of influential process parameters on the tensile strength of FDM printed parts. Two statistical methods, RSM and ANN, were applied to investigate the effect the layer thickness, printing speed, raster angle and wall thickness on the tensile strength of test specimens printed with a short carbon fiber reinforced polyamide composite. The reduced cubic model was developed by the RSM method, and the correlation between the input parameters and the output response was analyzed by ANOVA. The results show that the layer thickness and raster angle have the most significant influence on tensile strength. As for machine learning, among the nine different tested ANN topologies, the best configuration was found based on the lowest MAE and MSE test sample result. The results show that the proposed model could be a useful tool for predicting tensile strength. Its main advantage is the reduction in time needed for experiments with the LOSO (leave one subject out) k-fold cross validation scheme, offering better generalization ability, given the small set of learning examples.

Abstract Despite increasing number of studies on incidental vocabulary learning through viewing in a foreign language, glosses, which have been proven to have facilitative effects in incidental vocabulary learning via other modes of input, have remained underexplored in audiovisual input. The current study investigates the effects of (1) traditional gloss (TG), (2) multiple-choice gloss (MCG) and (3) no gloss (NG) conditions on incidental L2 vocabulary learning through viewing animated short videos. Turkish-L1 upper-intermediate to advanced EFL learners (N = 127), who were randomly assigned to three conditions, watched three short, animated videos twice. The results revealed that while the MCG group and the TG group significantly outperformed the NG group in vocabulary meaning recall test, there was not a significant difference between the glossed conditions. The results also suggested that the participants learnt new words through interacting with MCGs. Furthermore, the participants’ prior vocabulary knowledge positively correlated with incidental vocabulary learning from the videos. The results are discussed within the framework of previous research and the Involvement Load Hypothesis.

This convergent mixed methods study aims to compare effectiveness of using three online consultation resources entailing deductive vs. data-driven learning on learning and retention of 18 verb-noun collocations by EFL learners. The participants ( N =45) randomly assigned to treatment conditions consulted three different online resources in different orders to complete the same online error correction tasks and gap-fill exercises in three sessions. The participants were given the Vocabulary Size Test (VST), and a productive collocation translation test (CTT) as the pretest, the posttest and the retention test. A sub-set of the participants also performed think aloud protocols during the treatment. After the treatment, the participants were given the CTT and responded a rating scale and an open-ended question. The results indicated that all of the resources led to significantly higher learning and retention rates with no significant differences among the resources. It was also found that the participants rate the resources differently and go through different cognitive processes when consulting resources. The VST scores positively correlated with the posttest scores, suggesting that participants’ vocabulary sizes can be a moderating variable. The findings are discussed based on previous research and within the framework of data-driven learning

Microneedles (MNs) represent the concept of attractive, minimally invasive puncture devices of micron-sized dimensions that penetrate the skin painlessly and thus facilitate the transdermal administration of a wide range of active substances. MNs have been manufactured by a variety of production technologies, from a range of materials, but most of these manufacturing methods are time-consuming and expensive for screening new designs and making any modifications. Additive manufacturing (AM) has become one of the most revolutionary tools in the pharmaceutical field, with its unique ability to manufacture personalized dosage forms and patient-specific medical devices such as MNs. This review aims to summarize various 3D printing technologies that can produce MNs from digital models in a single step, including a survey on their benefits and drawbacks. In addition, this paper highlights current research in the field of 3D printed MN-assisted transdermal drug delivery systems and analyzes parameters affecting the mechanical properties of 3D printed MNs. The current regulatory framework associated with 3D printed MNs as well as different methods for the analysis and evaluation of 3D printed MN properties are outlined.

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

Microneedles (MNs) have been manufactured using a variety of methods from a range of materials, but most of them are expensive and time-consuming for screening new designs and making any modifications. Therefore, stereolithography (SLA) has emerged as a promising approach for MN fabrication due to its numerous advantages, including simplicity, low cost, and the ability to manufacture complex geometrical products at any time, including modifications to the original designs. This work aimed to print MNs using SLA technology and investigate the effects of post-printing curing conditions on the mechanical properties of 3D-printed MNs. Solid MNs were designed using CAD software and printed with grey resin (Formlabs, UK) using Form 3 printer (Formlabs, UK). MNs dimensions were 1.2 × 0.4 × 0.05 mm, arranged in 6 rows and 6 columns on a 10 × 10 mm baseplate. MNs were then immersed in an isopropyl alcohol bath to remove unpolymerized resin residues and cured in a UV-A heated chamber (Formlabs, UK). In total, nine samples were taken for each combination of curing temperature (35°C, 50°C, and 70°C) and curing time (5 min, 20 min, and 60 min). Fracture tests were conducted using a hardness apparatus TB24 (Erweka, Germany). MNs were placed on the moving probe of the machine and compressed until fracture. The optimization of the SLA process parameters for improving the strength of MNs was performed using the Taguchi method. The design of experiments was carried out based on the Taguchi L9 orthogonal array. Experimental results showed that the curing temperature has a significant influence on MN strength improvements. Improvement of the MN strength can be achieved by increasing the curing temperature and curing time.

The Internet and the software stores for mobile devices come with a huge number of digital tools for any task, and those intended for digital formative assessment (DFA) have burgeoned exponentially in the last decade. These tools vary in terms of their functionality, pedagogical quality, cost, operating systems and so forth. Teachers and learners need guidance on how to choose the most effective digital formative software to make the most out of it. This study provides an in-depth critical review of the features of most popular formative assessment tools available on the Internet. It aims to unearth what current DFA tools can do and what further developments are needed for more effective use. The tools for analysis were sampled using frequency of mentions in educational technology websites and blogs and two scholarly databases (Web of Science and Scopus). After identifying the most frequently recommended, reviewed and researched formative assessment tools, the researchers inspected 14 tools in terms of various issues, ranging from platforms and devices used, item-types offered by the software, features related with monitoring student performance and providing feedback (through student/instructor dashboards), grading, scoring of open-response items and collaborative responses. The results indicated that all closed-ended items were common to all the tools examined and they were automatically scored, while only a few of them offered underdeveloped methods of grading open-ended items. All the tools provided learner analytics with diverse forms of data and different mechanisms for feedback, yet the most common form of data were immediate answers and numerical scores. It was also clear that popularity did not necessarily mean offering more functionalities and better tools. Based on the status of the tools, avenues for further research are discussed.

Multimodality of input in incidental L2 vocabulary learning has recently been a topic of interest among language acquisition researchers, yet the results have been somewhat contradictory. This study seeks to compare the impacts of two different multimodalities on incidental L2 vocabulary learning, namely, reading-plus-watching (experimental group I) vs. reading-plus-listening (experimental group II), as compared to the reading only condition, which is included as a control measure. Experimental group I watched and read the transcriptions of four news texts with electronic glosses for the target words, while experimental group II read and listened to the same news texts again with electronic glosses for the same 20 target words. Next, the two experimental groups swapped roles with a new set of four news texts glossed for another group of 20 target words. The control group only read the same eight news texts without glosses. The results suggest that reading-plus-listening can be a more conducive multimodal presentation for incidental vocabulary learning as compared to reading-plus-watching. The results also challenge the validity of some principles of the Cognitive Theory of Multimedia Learning in incidental L2 vocabulary learning, while providing supporting evidence for some other principles.

Although transdermal drug delivery systems (DDS) offer numerous benefits for patients, including the avoidance of both gastric irritation and first-pass metabolism effect, as well as improved patient compliance, only a limited number of active pharmaceutical ingredients (APIs) can be delivered accordingly. Microneedles (MNs) represent one of the most promising concepts for effective transdermal drug delivery that penetrate the protective skin barrier in a minimally invasive and painless manner. The first MNs were produced in the 90s, and since then, this field has been continually evolving. Therefore, different manufacturing methods, not only for MNs but also MN molds, are introduced, which allows for the cost-effective production of MNs for drug and vaccine delivery and even diagnostic/monitoring purposes. The focus of this review is to give a brief overview of MN characteristics, material composition, as well as the production and commercial development of MN-based systems.

Experimental Investigation and Modelling of FDM Process Parameters for Tensile Strength Improvement Using RSM Abstract Fused Deposition Modeling (FDM) is one of the most popular additive manufacturing technologies for various engineering applications. The FDM built part is especially anisotropic in nature due to layer-by-layer building mechanism. Therefore, the mechanical properties, especially the tensile strength severaly depend on the process parameters. Hence, the present work focuses on extensive study to understand the effect of four important parameters such as layer thickness, infill density, printing temperature and wall thickness on the tensile strength of test specimens. A total of 30 test specimens were printed using varying processing parameters according Central Composite Design of experiments (CCD) in order to reduce the experimental runs. The RSM method was used to generate a mathematical model, ie an equation (second order polynomial) which describes the process. Experimental results indicate that the wall thickness and infill density have the significant influence on tensile strength, and tensile strength increases with increasing wall thickeness and infill density. Printing temperature and layer thickness have less of an effect on tensile strength. Tensile strength increases with increasing printing temperature and decreases with increasing in layer thickness, especially at lower printing temperature. This paper examines the influence of selected FDM process parameters (layer thickness, infill density, printing temperature and wall thickness) on the tensile strength of the built parts. Design of experiment for doing the experiments makes use of Circumscribed Central Composite Design (CCCD). Empirical relationship between response and different process parameters is established using RSM, and its validity is checked using ANOVA. The developed relationship between tensile strength (output) and process parameters (input) is able to explain the 91.84% of variability in the response. Effect of various factors and their interactions are explained using response surface plots. It shown that the tensile strength is influenced significantly infill density and wall thickness; and less significantly printing temperature and layer thickness. In order to improve the tensile strength of FDM parts made of PLA materials, it is necessary to increase the infill density and wall thickness, decrease the layer thickness, and set the printing temperature in range 200 – 230 °C. The future research is to investigate the effect of all analyzed parameters on tensile strength for different building directions.

Fused Deposition Modeling (FDM) is one of the most popular additive manufacturing technologies for various engineering applications. Due to the mechanism of building of products on the principle of adding layer by layer, the mechanical characteristics and quality of the product directly depend on the values of process parameters. Therefore, in this paper, test tubes for mechanical properties testing and model samples for dimensional testing were made using FDM technology. Two different polymeric materials, PLA and PC, were used to make the tubes and models. During the production of test tubes and models, three more influential process parameters varied on three levels: printing temperature, infill density and layer height. The test tubes were printed and tensile tested according to the ISO 527-2 standard. Dimensional measurements were performed on the made samples of models for dimensional tests, and dimensional deviations of the finished product in relation to the designed dimensions of the 3D model were analyzed, deviations from the shape, as well as the presence of surface defects and irregularities. At the end of the paper, recommendations are given which combination of process parameters gives for both analyzed materials better mechanical characteristics and smaller dimensional deviations of the product in the manufacture of FDM technology. PLA material, where it is concluded that the model has the smallest deviation in the direction of dimensions a1 a2 and a3 if printed at a temperature of 200-230 °C, which also belongs to the recommended printing temperature by the material manufacturer. Figure 2b. shows the influence of the printing temperature on the average value of the deviation of external dimensions for PC material, where it is concluded that the model has the smallest deviation in the direction of dimensions a1 , a2 and a3 if printed at a temperature from 255-280 °C, which also belongs to the recommended printing temperature by the material manufacturer, except for the largest dimension a3 where the smallest dimensional deviations in the temperature range from 230 to 255 °C.

This paper investigates the influence of the fuel injector nozzle geometry on the liquid fuel contraction coefficient and Reynolds number. The main three fuel injector nozzle geometry parameters: nozzle diameter (d), nozzle length (l) and nozzle inlet radius (r) have a strong influence on the liquid fuel contraction coefficient and Reynolds number. The variation of the nozzle geometry variables at different liquid fuel pressures, temperatures and injection rates was analyzed. The liquid fuel contraction coefficient and Reynolds number increase with an increase in the nozzle diameter, regardless of the fuel injection rate. An increase in the r/d ratio causes an increase in the fuel contraction coefficient, but the increase is not significant after r/d = 0.1. A nozzle length increase causes a decrease in the fuel contraction coefficient. Increase in the nozzle length of 0.5 mm causes an approximately similar decrease in the contraction coefficient at any fuel pressure and any nozzle length. Fuel injectors should operate with minimal possible nozzle lengths in order to obtain higher fuel contraction coefficients.