The purpose of academic writing is to convey scholarly knowledge and data to the intended audience in a concise and comprehensible way. Sometimes an academic text needs to be translated into a foreign language by a translator. In this case, the translator is expected to translate the source text into the target text in plain and understandable language. This study illustrates how the author can help the translator during the process of preparing the source text and discusses the strategies that could be used to achieve this. What makes whether an academic translation is of lower or higher quality is not only the level of knowledge and skills that the translator possesses but also the quality of the source text, which directly affects that of the output. In addition, academic writing has its own rules, and, albeit partially, this may differ from one language to another. In this sense, translators encounter some problems while translating academic texts. The purpose of this study is to illustrate such problems and to discuss the strategies that could be used to eliminate them. Common problems that affect the quality of translation are as follows: forming wordy sentences, using ambiguous expressions, organizing the text in a way that is not suitable for the discourse structure of English, using conjunctions imprecisely, using passive verbs unnecessarily, expressing thoughts indirectly instead of using direct language, using words with the same meaning sequentially and spelling mistakes, among others. This study discusses what the writer and the translator can do to improve an academic text in general. Although experienced translators may take effective actions, a good translation may not be possible out of a poorly written source text. High-quality academic translation requires a collaboration between the author and the translator. Therefore, it is important to raise awareness about this issue.

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.

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.