Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.

This study provides a systematic mechanical characterization and manufacturing analysis of stereolithography-printed microneedle arrays across six geometric designs (300–400 μm diameter and three aspect ratios: 2:1, 3:1, and 4:1) and three array configurations (1 × 1, 5 × 5, 10 × 10). Compression testing to 50 N revealed geometry-dependent optimization: low-aspect-ratio designs (Designs 1, 4, 5) exhibited superior performance in high-density arrays (10 × 10), while high-aspect-ratio designs (Designs 2, 3) performed better as single needles. Manufacturing success rates increased significantly with array density: from 44.2% (95% CI: 41.1–47.3%) for single needles to 67.3% (95% CI: 63.2–71.4%) for 10 × 10 arrays, with 400 μm diameter designs showing higher reliability. Two-way ANOVA confirmed significant effects of both geometric design [F(5, 72) = 145.3, p < 0.001, η² = 0.91] and array configuration [F(2, 72) = 78.2, p < 0.001, η² = 0.68] on compressive displacement. Design 5 (400 μm diameter, 3:1 aspect ratio) in a 10 × 10 format exhibited optimal mechanical characteristics, including controlled displacement (0.578 ± 0.036 mm), a high safety factor (SF = 13.32), and a superior manufacturing yield. These findings provide quantitative design guidelines for optimizing 3D-printed microneedle arrays.

This exploratory study investigates preliminary trends in the optimization of mechanical properties in 3D-printed components produced via Fused Deposition Modeling (FDM) using polycarbonate (PC) and polylactic acid (PLA). Through a systematic full factorial experimental design, three critical parameters were examined: material types (PC and PLA), layer thickness (0.2 mm and 0.4 mm), and build orientation (horizontal and vertical). Preliminary trends suggest that vertically oriented specimens showed up to 64.7% higher tensile strength compared to horizontal builds, though with significantly reduced ductility. Contributing to growing evidence regarding layer thickness effects, thicker layers (0.4 mm) showed improved ultimate strength by up to 36.2% while simultaneously reducing production time by 50%. However, statistical power analysis revealed insufficient sample size (n = 1 per condition) to establish significance for orientation effects, despite large practical differences observed. PC specimens demonstrated superior strength (maximum 67.5 MPa) and fracture energy, while PLA offered better ductility (up to 22.4% strain). These exploratory findings provide promising directions for future adequately powered investigations for tailored parameter selection according to specific application requirements.

Microneedles are a new technology in transdermal drug delivery that allows for the pain-free administration of drugs. Recently, these microneedles have gained popularity compared to traditional injections. Nevertheless, the selection of the most suitable materials is a significant issue, requiring a systematic analysis of various performance parameters. This paper developed a multi-criteria decision-making model based on the analytic hierarchy process (AHP) to systematically evaluate four primary material types for therapeutic microneedle applications: polymers, metals, ceramics, and silicon. The researchers defined five performance criteria, ranked by importance: biocompatibility (48.8%), mechanical properties (25.3%), manufacturability (15.8%), cost-effectiveness (6.6%), and compatibility with different types of microneedles (3.5%). The validity of the framework was established using the TOPSIS and ELECTRE methods, which showed strong agreement in the rankings, and a sensitivity analysis revealed that the rankings did not change with a ±20% variation in the parameters in 95% of the cases. The outcomes indicated that polymers are the most suitable, with the highest global priority score (38.3%), and they are good in biocompatibility (53.0% local priority), manufacturability (53.3%), and relative cost advantages (62.2%), though medical-grade polymer costs remain substantial. Metals were placed second (31.8%) due to their better mechanical properties (50.3%), followed by ceramics (17.6%) and silicon-based materials (12.3%). The framework offers clear decision guidelines: polymers for dissolving microneedle systems and controlled drug release applications; metals for precise liquid delivery devices; ceramics for specialized pharmaceutical uses that require extreme chemical compatibility; and silicon for research applications requiring precise geometries.