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Starch content in wheat is an important characteristic for various purposes. Starch is composed of two components known as amylose and amylopectin, and the proportion between these two components plays a crucial role in determining the properties and structure of starch. One of the key genes involved in starch biosynthesis in wheat is granule bound starch synthase I (GBSSI), also known as the waxy protein, which synthesizes amylose in the endosperm. A mutant cultivar, named Gunji-3, was created by crossing the waxy type wheat (Shinmichal 1) with the wild type bread wheat (Keumkang). When comparing the Wx-B1 allele of the waxy protein gene in Gunji-3 with that of the waxy type wheat, there were 12 SNPs and one deletion difference. Additionally, when comparing amino acid sequences with Wx-B1 alleles, differences at two positions are found, indicating that the mutant carries new Wx-B1 allele which is named as Wx-B1o. In terms of the physicochemical properties of Gunji-3 starch, it had a lower amylose content than the parental wheat varieties and showed higher protein content and greater swelling power. These newly introduced waxy type wheat could provide important basis not only for understanding the starch characteristics of wheat but also for various breeding programs.

Post-synthetic surface redesign significantly enhances the stability of perovskite nanocrystals.

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Objectives Early detection of developmental issues in infants and necessary intervention are important. To identify the comorbid conditions, a comprehensive evaluation is required. The study’s objectives were to 1) generate scale items by identifying and eliciting concepts relevant to young children (12–71 months) with developmental delays, 2) develop a comprehensive screening tool for developmental delay and comorbid conditions, and 3) assess the tool’s validity and cut-off. Methods Multidisciplinary experts devised the “Infant Comprehensive Evaluation for Neurodevelopmental Delay (ICEND),” an assessment method that comes in two versions depending on the age of the child: 12–36 months and 37–71 months, through monthly seminars and focused group interviews. The ICEND is composed of three parts: risk factors, resilience factors, and clinical scales. In parts 1 and 2, there were 41 caretakers responded to the questionnaires. Part 3 involved clinicians evaluating ten subscales using 98 and 114 questionnaires for younger and older versions, respectively. The Child Behavior Checklist, Strengths and Difficulties Questionnaire, Infant- Toddler Social Emotional Assessment, and Korean Developmental Screening Test for Infants and Children were employed to analyze concurrent validity with the ICEND. The analyses were performed on both typical and high-risk infants to identify concurrent validity, reliability, and cut-off scores. Results A total of 296 people participated in the study, with 57 of them being high-risk (19.2%). The Cronbach’s alpha was positive (0.533–0.928). In the majority of domains, the ICEND demonstrated a fair discriminatory ability, with a sensitivity of 0.5–0.7 and specificity 0.7–0.9. Conclusion The ICEND is reliable and valid, indicating its potential as an auxiliary tool for assessing neurodevelopmental delay and comorbid conditions in children aged 12–36 months and 37–71 months.

Anatomically terminal parts of the urinary, reproductive, and digestive systems of birds all connect to the cloaca. As the feces drain through the cloaca in chickens, the cloacal bacteria were previously believed to represent those of the digestive system. To investigate similarities between the cloacal microbiota and the microbiota of the digestive and reproductive systems, microbiota inhabiting the colon, cloaca, and magnum, which is a portion of the chicken oviduct of 34-week-old, specific-pathogen-free hens were analyzed using a 16S rRNA metagenomic approach using the Ion torrent sequencer and the Qiime2 bioinformatics platform. Beta diversity via unweighted and weighted unifrac analyses revealed that the cloacal microbiota was significantly different from those in the colon and the magnum. Unweighted unifrac revealed that the cloacal microbiota was distal from the microbiota in the colon than from the microbiota in the magnum, whereas weighted unifrac revealed that the cloacal microbiota was located further away from the microbiota in the magnum than from the microbiota inhabiting the colon. Pseudomonas spp. were the most abundant in the cloaca, whereas Lactobacillus spp. and Flavobacterium spp. were the most abundant species in the colon and the magnum. The present results indicate that the cloaca contains a mixed population of bacteria, derived from the reproductive, urinary, and digestive systems, particularly in egg-laying hens. Therefore, sampling cloaca to study bacterial populations that inhabit the digestive system of chickens requires caution especially when applied to egg-laying hens. To further understand the physiological role of the microbiota in chicken cloaca, exploratory studies of the chicken's cloacal microbiota should be performed using chickens of different ages and types.

A highly scalable synapse device based on a junctionless (JL) ferroelectric (FE) FinFET is presented for neuromorphic applications. The synaptic behaviors of the JL metal-ferroelectric-insulator-silicon FinFET were experimentally demonstrated after verifying the ferroelectric characteristics of the HfZrO X (HZO) film using a metal-ferroelectric-metal capacitor. The fabricated synapse showed distinguishable polarization switching behaviors with gradually controllable channel conductance. From neural network simulations using the proposed JL FE FinFET as synapses, the pattern recognition accuracy for hand-written digits was validated to be approximately 80% for neuromorphic applications.

We report a non-contact type polyvinylidene fluoride (PVDF)-based liquid volume sensor. When a liquid container vibrates due to an applied impact, our sensor that is attached to the wall of the container detects the resonance frequency of vibration, which shifts as a result of change in liquid volume. The sensitivity of our sensor was enhanced by stacking multiple sensors in series. A PVDF bimorph actuator was also fabricated to demonstrate an integrated actuator-sensor system. We believe that the results presented in this work will pave the way for novel applications in volume sensing.

Machine intelligence-based optimizing synthesis parameters offers exciting opportunities in various domain of materials chemistry where large and comprehensive dataset are available. Aiming to construct a “design-to-device” pipeline for battery application with small discrepancy, we adapt machine learning model based on actual experimental data from curated literatures to predict the discharge capacity of Nickel-Manganese-Cobalt (NCM) cathode material. However, missing data is an inherent problem from the results based on literature. Herein we introduce imputation techniques to fill the missing data based. To this end, we show that our model have remarkable predictive capability with the average coefficient of determinant, R2 = 0.84 for training sets. In addition, we also validate our model prediction by the experiments, which yields in less than 8% error. The present work highlights a promising data driven materials synthesis approach, which underpins nonlinear structure-properties relationship for broad range of materials development.

Fabric-based wearable electronics are becoming more important in the fourth industrial revolution (4IR) era due to their connectivity, wearability, comfort, and durability. Conventional fabric-based wearable electronics have been demonstrated by several researchers, but still need complex methods or additional supports to be fabricated and sewed in clothing. Herein, a cost-effective, high throughput, and strongly integrated fabric-based wearable piezoelectric energy harvester (fabric-WPEH) is demonstrated. The fabric-WPEH has a heterostructure of a ferroelectric polymer, poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] and two conductive fabrics via simple fabrication of tape casting and hot pressing. Our fabrication process would enable the direct application of the unit device to general garments using hot pressing as graphic patches can be attached to the garments by heat press. Simulation and experimental analysis demonstrate fully bendable, compact and concave interfaces and a high piezoelectric d 33 coefficient (−32.0 pC N−1) of the P(VDF-TrFE) layer. The fabric-WPEH generates piezoelectric output signals from human motions (pressing, bending) and from quantitative force test machine pressing. Furthermore, a record high interfacial adhesion strength (22 N cm−1) between the P(VDF-TrFE) layer and fabric layers has been measured by surface and interfacial cutting analysis system (SAICAS) for the first time in the field of fabric-based wearable piezoelectric electronics.

Recently, there have been efforts in many countries to reduce social expenses and casualties resulting due to car accidents. As such, ASS (active safety systems) are being researched and developed to prevent accidents and target mitigation. Among many useful active safety systems, it has been reported that commercial AEBS (Advanced Emergency Braking System) effectively prevents accidents and reduces casualties simultaneously. Accordingly, it is necessary to set Korean AEBS standards, and this research analyzes international AEBS standards for this necessity. In addition to the evaluation methodology of AEBS ECE Regulation, this study suggests traffic scenarios that occur frequently in domestic road environments, and provides an evaluation system for AEBS of the scenarios. Finally, the performance and safety of AEBS are evaluated through field test.