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Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force ( V o_null ) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor's performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to V o_null readings; nonetheless, a relationship between V o_null and hysteresis was not found. However, a classification rule base on k -means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on V o_null readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques.

Developments in the field of materials science are contributing to providing solutions for the recycling of industrial residues to develop new materials. Such approaches generate new products and provide optimal alternatives to the final disposal of different types of industrial wastes. This research focused on identifying and characterizing slag, fly ash, and glass cullet from the Boyacá region in Colombia as raw materials for producing glass-ceramics, with the innovative aspect of the use of these three residues without the addition of nucleating agents to produce the glass-ceramics. To characterize the starting materials, X-ray diffraction (XRD), X-ray fluorescence (XRF), and Scanning Electron Microscopy (SEM) techniques were used. The results were used to evaluate the best conditions to produce mixtures of the three waste components and to determine the specific compositions of glass-ceramics to achieve products with attractive technical properties for potential industrial applications. The proposed mixtures were based on three compositions: Mixture 1, 2, and 3. The materials were obtained through thermal treatment at 1200 °C in a tubular furnace in accordance with the results of a comprehensive characterization using thermal analysis. The microstructure, thermal stability, and structural characteristics of the samples were examined through SEM, differential thermal analysis (DTA), and XRD analyses, which showed that the main crystalline phases were diopside and anorthite, with a small amount of enstatite and gehlenite. The obtained glass-ceramics showed properties of technical significance for structural applications.

The research problem was: what are the effects of preparation for mastectomy/hysterectomy on women's self-care behaviors during the immediate post-operative period, and 1 month after surgery? Study subjects were 60 women, 30 mastectomy and 30 hysterectomy, equally divided into an experimental and control group. The independent variable, structured teaching, was administered by a nurse clinician before and after surgery. The dependent variable, self-care, during the immediate post-operative period, was defined as the performance of three ambulation tasks on day 1. Five additional exercises were rated for the mastectomy group on day 1 and four on day 2. Task performance was rated on three criteria: time interval, amount of prompting and amount of assistance required for the first complete performance (as demonstrated and practiced pre-operatively) of each exercise. During the clinic phase, 1 month post-surgery, self-care was the score obtained on a Self-Care Rating Scale, obtained by patient report and based on the discharge planning content. Findings showed that for the in-hospital phase, patients in the group given pre-operative instructions performed at a significantly higher level. They required neither prompting nor assistance in the initiation and completion of ambulation tasks compared to the uninstructed group. Most patients in the uninstructed group did not initiate nor complete the tasks despite prompting and physical assistance from the nurse. For the clinic phase, both the instructed post-mastectomy and post-hysterectomy groups performed self-care activities at home significantly better than and more frequently than the uninstructed groups.