Breast tissue-mimicking phantoms are essential tools for validating microwave imaging systems designed for early breast cancer detection. In this work, we report the fabrication and comprehensive characterization of oil-in-gelatin phantoms emulating normal, benign, and malignant breast tissues. The phantoms were manufactured with controlled mixtures of kerosene, safflower oil, and gelatin, and their dielectric properties were experimentally evaluated using a free-space transmission method with a Vector Network Analyzer across the 100 MHz–10 GHz range. Results demonstrated significant contrast in permittivity and conductivity among the different tissue types, consistent with values reported in the literature. Long-term stability was confirmed for up to six months under controlled storage. Additional structural and thermal characterization was performed using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA), providing insight into molecular composition and thermal response. The proposed method enables reproducible, low-cost, and stable phantom fabrication, offering reliable tissue models to support experimental validation and optimization of microwave-based breast cancer detection systems.

The increasing accumulation of polyethylene terephthalate (PET) bottles represents a significant environmental challenge due to their high consumption and limited recovery. The purpose of this study was to develop a sustainable alternative for reusing this waste by manufacturing educational test specimens for laboratory exercises in electrical mechanical engineering and industrial engineering programs. The proposed method was based on mechanical recycling and incorporated, as an innovation, a stage for measuring bottle parameters before processing to improve the quality and uniformity of the recycled material. The results were calculated with the participation of 6 students per test type, which showed that 66.66% of the specimens intended for impact tests and 83.33% of those intended for torsion tests exhibited adequate functional performance. Likewise, it was estimated that a weekly production of 1 kg of crushed PET would cover approximately half a week (three days) of laboratory practices, considering the large number of students. Therefore, it could use 60 to 80% of the total consumption during a standard 15-week course, depending on the situation, demonstrating that integrating recycling into academic activities supports the circular economy, reduces the environmental impact of plastic waste, and strengthens students’ practical training and professional skills.

High-precision X–Y positioning systems driven by stepper motors are widely used in industrial automation, manufacturing, and scientific instrumentation. However, fixed feedforward–feedback controllers may degrade when operating conditions vary, particularly as step frequency changes and the risk of synchronism loss increases. This work proposes an adaptive fuzzy feedforward–feedback controller for stepper-motor-driven X–Y positioning systems. The controller uses a Takagi–Sugeno (T–S) fuzzy inference system to adjust the proportional, derivative, and feedforward actions according to the tracking error, step frequency, and an auxiliary error-based adaptation variable. The control law is integrated with the inverse kinematics of the platform to generate synchronized step-domain commands, and a practical synchronism-preservation condition is established. Experimental validation on a NEMA 17-based X–Y platform showed accurate trajectory tracking, with a steady-state error of approximately $1.6\left[\mathsf{\mu }\mathrm{m}\right]$ for a trapezoidal profile. For a multi-segment trajectory, the RMSE was $0.0749\left[\mathrm{mm}\right]$ without load and $0.0760\left[\mathrm{mm}\right]$ under a $7.5\left[\mathrm{kg}\right]$ external load. Compared with a conventional PID controller, the proposed method reduced the RMSE from $0.1741\left[\mathrm{mm}\right]$ to $0.0749\left[\mathrm{mm}\right]$, while preserving motor synchronism.

Background: Learning-based, bio-inspired, and hybrid methods report routing improvements, but differences in experimental design complicate comparison. This critical review examines the evidence supporting these claims. Methods: Structured screening and critical synthesis covered 240 journal studies from 2022 to 2026: 43 learning-based, 135 bio-inspired and evolutionary, and 62 hybrid. Full-text assessment using category-specific thresholds yielded 1243 primary decisions, complemented by resource-stage and bio-inspired evaluation analyses. Results: Learning generalization and controlled ablation were Supported in 14/43 and 17/43 studies; none met complete joint resource-accounting or live operational-validation thresholds. Bio-inspired studies reported repeated runs in 104/135 cases and public code with inputs in 9/135; complementary feasibility, fairness, and generalization thresholds were Supported in 10/135, 4/135, and 2/135. Among 56 hybrids with comparative routing outcomes, 39 met neither the matched-budget nor the integration-cost threshold. Conclusions: This review contributes a category-specific framework linking routing improvements to feasibility, experimental controls, computational resources, and validation scope. Under its operational thresholds, these reporting frequencies characterize evidence rather than overall study quality. The framework helps researchers and logistics practitioners distinguish Supported claims from those requiring additional evidence, without pooling incompatible performance results or ranking methodological families.