Validation of the Korean criteria for trauma team activation

Objective We conducted a study to validate the effectiveness of the Korean criteria for trauma team activation (TTA) and compared its results with a two-tiered system. Methods This observational study was based on data from the Korean Trauma Data Bank. Within the study period, 1,628 trauma patients visited our emergency department, and 739 satisfied the criteria for TTA. The rates of overtriage and undertriage in the Korean one-tiered system were compared with the two-tiered system recommended by the American College of Surgery-Committee on Trauma. Results Most of the patient’s physiologic factors reflected trauma severity levels, but anatomical factors and mechanism of injury did not show consistent results. In addition, while the rate of overtriage (64.4%) was above the recommended range according to the Korean criteria, the rate of undertriage (4.0%) was within the recommended range. In the simulated two-tiered system, the rate of overtriage was reduced by 5.5%, while undertriage was increased by 1.8% compared to the Korean activation system. Conclusion The Korean criteria for TTA showed higher rates of overtriage and similar undertriage rates compared to the simulated two-tier system. Modification of the current criteria to a two-tier system with special considerations would be more effective for providing optimum patient care and medical resource utilization

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

GAN-Dummy Fill: Timing-aware Dummy Fill Method using GAN

The chemical mechanical polishing (CMP) dummy fill method is commonly used for the planarization of the CMP process, resulting in the development of many automated methods. We propose a dummy fill method using a generative adversarial network (GAN) that improves the existing dummy fill methods in terms of the uniformity of metal density and timing of critical nets. The dummy patterns created were similar to those of existing methods. However, the GAN dummy fill method applies additional optimizations to make the CMP dummy fill pattern efficient. The method learns by adding density and parasitic capacitance to the loss function of the GAN. Compared to dummy patterns generated from commercial tools, dummy patterns generated from GAN-dummy fill reduced the negative timing slack due to parasitic capacitance by up to 45%.1

Advances and Trends in miRNA Analysis Using DNAzyme-Based Biosensors

miRNAs are endogenous small, non-coding RNA molecules that function in post-transcriptional regulation of gene expression. Because miRNA plays a pivotal role in maintaining the intracellular environment, and abnormal expression has been found in many cancer diseases, detection of miRNA as a biomarker is important for early diagnosis of disease and study of miRNA function. However, because miRNA is present in extremely low concentrations in cells and many types of miRNAs with similar sequences are mixed, traditional gene detection methods are not suitable for miRNA detection. Therefore, in order to overcome this limitation, a signal amplification process is essential for high sensitivity. In particular, enzyme-free signal amplification systems such as DNAzyme systems have been developed for miRNA analysis with high specificity. DNAzymes have the advantage of being more stable in the physiological environment than enzymes, easy to chemically synthesize, and biocompatible. In this review, we summarize and introduce the methods using DNAzyme-based biosensors, especially with regard to various signal amplification methods for high sensitivity and strategies for improving detection specificity. We also discuss the current challenges and trends of these DNAzyme-based biosensors

A fast and scalable qubit-mapping method for noisy intermediate-scale quantum computers

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Sulfurization-induced growth of single-crystalline high-mobility β-In2S3 films on InP

Metalorganic chemical vapor deposition was used to grow single-crystalline tetragonal β-In2S3 films on InP to afford covalently bonded In2S3/InP heterostructures, with the crystal structure of these films identified by high-resolution scanning transmission electron microscopy, X-ray diffraction, and Raman spectroscopy analyses, and the corresponding bandgap energies determined by photoluminescence measurements at room (300 K) and low temperatures (40 K). RT-PL measurements reveal the three peaks spectral emission at 464.3, 574.7, and 648.5 nm associated with luminescence from band-edge and two above conduction band-edge, respectively, although the LT-PL (40K) measurements of β-In2S3 film found two dominant peaks. Moreover, the above films exhibited n-type conductivity, with background electron concentration = 4.9 × 1015 cm–3, electron mobility = 1810.9 cm2 V–1 s–1, and resistivity = 0.704 Ω cm. Thus, single-crystalline β-In2S3 films deposited on InP are promising constituents of high-performance next-generation electronic, optoelectronic, and photovoltaic devices

Stochastic Photon Emission from Nonblinking Upconversion Nanoparticles

Because of their well-known optical properties, upconversion nanoparticles (UCNPs) are regarded as some of the most promising nanomaterials for bioimaging, biosensors, and solar cells. The nonblinking nature of their upconversion emissions has been a particularly beneficial advantage for live-cell imaging. However, the origin of this unique property has never been seriously investigated. We report, for the first time, the observation of stochastic photon emission (SPEM) in core/shell UCNPs (NaYF₄:Yb³⁺,Er³⁺/NaYF₄) on the microsecond and nanosecond time scales, even under continuous irradiation at 980 nm. This SPEM was attributed to slow “upconversion cycles”. We consider that the conventionally reported, nonblinking nature of UCNP emissions can be attributed to the averaging of SPEMs from multiple Er³⁺ ions and the low temporal resolution of previous observation. The off-time distribution, which possesses kinetics information for the upconversion pathways, was well fitted to a single exponential indicating involvement of a single rate-determining step. The distinct behaviors of the green and red emissions confirm their different photophysical pathways