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1. Closing the Wearable Gap-Part VII: A Retrospective of Stretch Sensor Tool Kit Development for Benchmark Testing

   https://doi.org/10.3390/electronics9091457  

   Talegaonkar, Purva; Saucier, David; Carroll, Will; Peranich, Preston; Parker, Erin; Middleton, Carver; Davarzani, Samaneh; Turner, Alana; Persons, Karen; Casey, Landon; et al (September 2020, Electronics)

   null (Ed.)

   This paper presents a retrospective of the benchmark testing methodologies developed and accumulated into the stretch sensor tool kit (SSTK) by the research team during the Closing the Wearable Gap series of studies. The techniques developed to validate stretchable soft robotic sensors (SRS) as a means for collecting human kinetic and kinematic data at the foot-ankle complex and at the wrist are reviewed. Lessons learned from past experiments are addressed, as well as what comprises the current SSTK based on what the researchers learned over the course of multiple studies. Three core components of the SSTK are featured: (a) material testing tools, (b) data analysis software, and (c) data collection devices. Results collected indicate that the stretch sensors are a viable means for predicting kinematic data based on the most recent gait analysis study conducted by the researchers (average root mean squared error or RMSE = 3.63°). With the aid of SSTK defined in this study summary and shared with the academic community on GitHub, researchers will be able to undergo more rigorous validation methodologies of SRS validation. A summary of the current state of the SSTK is detailed and includes insight into upcoming experiments that will utilize more sophisticated techniques for fatigue testing and gait analysis, utilizing SRS as the data collection solution. 

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2. Cyber-physical energy systems modeling, test specification, and co-simulation based testing

   https://doi.org/10.1109/MSCPES.2017.8064528  

   van der Meer, A. A.; Palensky, P.; Heussen, K.; Bondy, D. E.; Gehrke, O.; Steinbrinki, C.; Blanki, M.; Lehnhoff, S.; Widl, E.; Moyo, C.; et al (April 2017, Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES))

   The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification. 

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3. New technologies and reagents in lateral flow assay (LFA) designs for enhancing accuracy and sensitivity

   https://doi.org/10.1039/D3AY00844D  

   Dey, Mohan K; Iftesum, Maria; Devireddy, Ram; Gartia, Manas R (September 2023, Analytical Methods)

   Lateral flow assays (LFAs) are a popular method for quick and affordable diagnostic testing because they are easy to use, portable, and user-friendly. However, LFA design has always faced challenges regarding sensitivity, accuracy, and complexity of the operation. By integrating new technologies and reagents, the sensitivity and accuracy of LFAs can be improved while minimizing the complexity and potential for false positives. Surface enhanced Raman spectroscopy (SERS), photoacoustic techniques, fluorescence resonance energy transfer (FRET), and the integration of smartphones and thermal readers can improve LFA accuracy and sensitivity. To ensure reliable and accurate results, careful assay design and validation, appropriate controls, and optimization of assay conditions are necessary. Continued innovation in LFA technology is crucial to improving the reliability and accuracy of rapid diagnostic testing and expanding its applications to various areas, such as food testing, water quality monitoring, and environmental testing. 

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4. An Additive Manufacturing Testbed to Evaluate Machine Learning-Based Autonomous Manufacturing

   https://doi.org/10.1115/1.4064321  

   Zhang, Zhi; George, Antony; Ferdous_Alam, Md; Eubel, Chris; Prasad_Vallabh, Chaitanya Krishna; Shtein, Max; Barton, Kira; Hoelzle, David J (March 2024, Journal of Manufacturing Science and Engineering)

   Abstract This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives. 

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5. A Review of Methods for the Geometric Post-Processing of Topology Optimized Models

   https://doi.org/10.1115/1.4047429  

   Subedi, Subodh C.; Verma, Chaman Singh; Suresh, Krishnan (December 2020, Journal of Computing and Information Science in Engineering)

   Abstract Topology optimization (TO) has rapidly evolved from an academic exercise into an exciting discipline with numerous industrial applications. Various TO algorithms have been established, and several commercial TO software packages are now available. However, a major challenge in TO is the post-processing of the optimized models for downstream applications. Typically, optimal topologies generated by TO are faceted (triangulated) models, extracted from an underlying finite element mesh. These triangulated models are dense, poor quality, and lack feature/parametric control. This poses serious challenges to downstream applications such as prototyping/testing, design validation, and design exploration. One strategy to address this issue is to directly impose downstream requirements as constraints in the TO algorithm. However, this not only restricts the design space, it may even lead to TO failure. Separation of post-processing from TO is more robust and flexible. The objective of this paper is to provide a critical review of various post-processing methods and categorize them based both on targeted applications and underlying strategies. The paper concludes with unresolved challenges and future work. 

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