Dynamic topology optimization of 3D-Printed transtibial orthopedic implant using tunable isotropic porous metamaterials.

In this paper, we introduce the design and manufacturing process of a transtibial orthopedic implant. We used medical-grade polyurethane polymer resin to fabricate a 3D porous architected implant with tunable isotropy, employing a high-speed printing method known as Continuous Liquid Interface Production (CLIP). Our objective is to enhance the weight-bearing capabilities of the bone structures in the residual limb, thereby circumventing the traditional reliance on a natural bridge. To achieve a custom-made design, we acquire the topology and morphology of the residual limb as well as the bone structure of the tibia and fibula, utilizing computed tomography (CT) and high-resolution 3D scanning. We employed a dynamic topological optimization method, informed by gait cycle data, to effectively reduce the mass of the implant. This approach, which differs from conventional static methods, enables the quantification of variations in applied forces over time. Using the Euler-Lagrange energy approach, we propose the equations of motion for a homologous multibody model with three degrees of freedom. The versatility of the Solid Isotropic Material with Penalization (SIMP) method facilitates the integration of homogenization methods for microscale porous architectures into the optimized domain. The design of these porous architectures is based on a bias-driven tuning symmetry isotropy of a Triply Periodic Minimal Surface (Schwarz Primitive surface). The internal porosity of the structure significantly reduces weight without compromising the isotropic behavior of the implant.

Self-Diagnosis of SARS-CoV-2 from Saliva Samples at Home: Isothermal Amplification Enabled by Do-It-Yourself Portable Incubators and Laminated Poly-ethyl Sulfonate Membranes.

COVID-19 made explicit the need for rethinking the way in which we conduct testing for epidemic emergencies. During the COVID-19 pandemic, the dependence on centralized lab facilities and resource-intensive methodologies (e.g., RT-qPCR methods) greatly limited the deployment of widespread testing efforts in many developed and underdeveloped countries. Here, we illustrate the development of a simple and portable diagnostic kit that enables self-diagnosis of COVID-19 at home from saliva samples. We describe the development of a do-it-yourself (DIY) incubator for Eppendorf tubes that can be used to conduct SARS-CoV-2 detection with competitive sensitivity and selectivity from saliva at home. In a proof-of-concept experiment, we assembled Eppendorf-tube incubators at our home shop, prepared a single-tube mix of reagents and LAMP primers in our lab, and deployed these COVID-19 detection kits using urban delivery systems (i.e., Rappifavor or Uber) to more than 15 different locations in Monterrey, México. This straightforward strategy enabled rapid and cost-effective at-home molecular diagnostics of SARS-CoV-2 from real saliva samples with a high sensitivity (100%) and high selectivity (87%).

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

EEG-Based Tool for Prediction of University Students' Cognitive Performance in the Classroom.

This study presents a neuroengineering-based machine learning tool developed to predict students' performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students' performance, and to design the machine learning tool. This analysis showed a negative correlation between students' performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.

E-Skin Development and Prototyping via Soft Tooling and Composites with Silicone Rubber and Carbon Nanotubes.

The strategy of embedding conductive materials on polymeric matrices has produced functional and wearable artificial electronic skin prototypes capable of transduction signals, such as pressure, force, humidity, or temperature. However, these prototypes are expensive and cover small areas. This study proposes a more affordable manufacturing strategy for manufacturing conductive layers with 6 × 6 matrix micropatterns of RTV-2 silicone rubber and Single-Walled Carbon Nanotubes (SWCNT). A novel mold with two cavities and two different micropatterns was designed and tested as a proof-of-concept using Low-Force Stereolithography-based additive manufacturing (AM). The effect SWCNT concentrations (3 wt.%, 4 wt.%, and 5 wt.%) on the mechanical properties were characterized by quasi-static axial deformation tests, which allowed them to stretch up to ~160%. The elastomeric soft material's hysteresis energy (Mullin's effect) was fitted using the Ogden-Roxburgh model and the Nelder-Mead algorithm. The assessment showed that the resulting multilayer material exhibits high flexibility and high conductivity (surface resistivity ~7.97 × 104 Ω/sq) and that robust soft tooling can be used for other devices.