Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement.

To solve the research-practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R2: 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ ("Pre-CMJ") was the most impactful predictor, followed by age ("Age"), the total number of training sessions received ("Total number of training_session"), controlled or non-controlled conditions ("Control (no training)"), whether the training program included squat, lunge, deadlift, or hip thrust exercises ("Squat_Lunge_Deadlift_Hipthrust_True", "Squat_Lunge_Deadlift_Hipthrust_False"), or "Plyometric (mixed fast/slow SSC)", and whether the athlete was from an Asian pacific region including Australia ("Race_Asian or Australian"). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed.

Recent advances in printable thermoelectric devices: materials, printing techniques, and applications.

Thermoelectric devices have great potential as a sustainable energy conversion technology to harvest waste heat and perform spot cooling with high reliability. However, most of the thermoelectric devices use toxic and expensive materials, which limits their application. These materials also require high-temperature fabrication processes, limiting their compatibility with flexible, bio-compatible substrate. Printing electronics is an exciting new technique for fabrication that has enabled a wide array of biocompatible and conformable systems. Being able to print thermoelectric devices allows them to be custom made with much lower cost for their specific application. Significant effort has been directed toward utilizing polymers and other bio-friendly materials for low-cost, lightweight, and flexible thermoelectric devices. Fortunately, many of these materials can be printed using low-temperature printing processes, enabling their fabrication on biocompatible substrates. This review aims to report the recent progress in developing high performance thermoelectric inks for various printing techniques. In addition to the usual thermoelectric performance measures, we also consider the attributes of flexibility and the processing temperatures. Finally, recent advancement of printed device structures is discussed which aims to maximize the temperature difference across the junctions.

Direct Electrohydrodynamic Patterning of High-Performance All Metal Oxide Thin-Film Electronics.

In this paper, we propose a scalable approach toward all-printed high-performance metal oxide thin-film transistors (TFTs), using a high-resolution electrohydrodynamic (EHD) printing process. Direct EHD micropatterning of metal oxide TFTs is based on diverse precursor solutions to form semiconducting materials (In2O3, In-Ga-ZnO (IGZO)), conductive metal oxide (Sn-doped In2O3 (ITO)), as well as aluminum oxide (Al2O3) gate dielectric at low temperatures. The fully printed TFT devices exhibit excellent electron transport characteristics (average electron mobilities of up to 117 cm2 V-1 s-1), negligible hysteresis, excellent uniformity, and stable operation at low-operating voltage. Furthermore, integrated logic gates such as NOT and NAND have been printed and demonstrated. All-printed logic with individual gating and symmetric input/output behavior, which is crucial for large-scale integration, is also demonstrated. The devices and fabrication process described in this paper enable high-performance and high-reliability transparent electronics.

Fully Solution-Processed Transparent Artificial Neural Network Using Drop-On-Demand Electrohydrodynamic Printing.

Artificial neural networks (ANN), deep learning, and neuromorphic systems are exciting new processing architectures being used to implement a wide variety of intelligent and adaptive systems. To date, these architectures have been primarily realized using traditional complementary metal-oxide-semiconductor (CMOS) processes or otherwise conventional semiconductor fabrication processes. Thus, the high cost associated with the design and fabrication of these circuits has limited the broader scientific community from applying new ideas, and arguably, has slowed research progress in this exciting new area. Solution-processed electronics offer an attractive option for providing low-cost rapid prototyping of neuromorphic devices. This article proposes a novel, wholly solution-based process used to produce low-cost transparent synaptic transistors capable of emulating biological synaptic functioning and thus used to construct ANN. We have demonstrated the fabrication process by constructing an ANN that encodes and decodes a 100 × 100 pixel image. Here, the synaptic weights were configured to achieve the desired image processing functions.

Self-Organized Nanostructure Modified Microelectrode for Sensitive Electrochemical Glutamate Detection in Stem Cells-Derived Brain Organoids.

Neurons release neurotransmitters such as glutamate to communicate with each other and to coordinate brain functioning. As increased glutamate release is indicative of neuronal maturation and activity, a system that can measure glutamate levels over time within the same tissue and/or culture system is highly advantageous for neurodevelopmental investigation. To address such challenges, we develop for the first time a convenient method to realize functionalized borosilicate glass capillaries with nanostructured texture as an electrochemical biosensor to detect glutamate release from cerebral organoids generated from human embryonic stem cells (hESC) that mimic various brain regions. The biosensor shows a clear catalytic activity toward the oxidation of glutamate with a sensitivity of 93 ± 9.5 nA·µM-1·cm-2. It was found that the enzyme-modified microelectrodes can detect glutamate in a wide linear range from 5 µM to 0.5 mM with a limit of detection (LOD) down to 5.6 ± 0.2 µM. Measurements were performed within the organoids at different time points and consistent results were obtained. This data demonstrates the reliability of the biosensor as well as its usefulness in measuring glutamate levels across time within the same culture system.

A Silk Fibroin Bio-Transient Solution Processable Memristor.

Today's electronic devices are fabricated using highly toxic materials and processes which limits their applications in environmental sensing applications and mandates complex encapsulation methods in biological and medical applications. This paper proposes a fully resorbable high density bio-compatible and environmentally friendly solution processable memristive crossbar arrays using silk fibroin protein which demonstrated bipolar resistive switching ratio of 104 and possesses programmable device lifetime characteristics before the device gracefully bio-degrades, minimizing impact to environment or to the implanted host. Lactate dehydrogenase assays revealed no cytotoxicity on direct exposure to the fabricated device and support their environmentally friendly and biocompatible claims. Moreover, the correlation between the oxidation state of the cations and their tendency in forming conductive filaments with respect to different active electrode materials has been investigated. The experimental results and the numerical model based on electro-thermal effect shows a tight correspondence in predicting the memristive switching process with various combinations of electrodes which provides insight into the morphological changes of conductive filaments in the silk fibroin films.