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Background/Objectives: Extrauterine growth restriction (EUGR) is associated with high mortality and an increased incidence of poor neurodevelopmental outcomes in preterm infants. In this study, we aimed to compare the Intergrowth-21ST (IG-21ST) and Fenton charts in predicting long-term neurodevelopmental and anthropometric outcomes of very low birth weight (VLBW) infants. Methods: Data were collected from 2649 VLBW infants registered in the Korean Neonatal Network born between 240/7 and 316/7 weeks of gestational age from January 2013 to December 2017. Follow-up assessments were conducted at 18–24 months of age, corrected for prematurity. Multiple logistic regression analysis was performed to evaluate the association between EUGR and long-term outcomes. Results: Among the 2649 VLBW infants, 60.0% (1606/2649) and 36.9% (977/2649) were diagnosed as having EUGR defined by the Fenton chart (EUGRF) and by the IG-21ST chart (EUGRIG), respectively. The EUGRIG group exhibited a higher proportion of infants with cerebral palsy, neurodevelopmental impairment (NDI), and growth failure. In multiple logistic regression analysis, adjusted for risk factors for long-term outcome, the EUGRIG group showed higher risk of cerebral palsy (adjusted odds ratio [aOR], 1.66; 95% confidence interval [CI], 1.04–2.65), NDI (aOR, 2.09; 95% CI, 1.71–2.55), and growth failure (aOR, 1.57; 95% CI, 1.16–2.13). Infants with EUGRF tended to develop NDI (aOR, 1.29; 95%CI, 1.03–1.63) and experience growth failure (aOR, 2.44; 95% CI, 1.77–3.40). Conclusions: The IG-21ST chart demonstrated a more effective prediction of long-term neurodevelopmental outcomes, whereas the Fenton chart may be more suitable for predicting growth failure at 18–24 months. [ABSTRACT FROM AUTHOR]

Author(s): Seh Hyun Kim [sup.1] , Yo Han Ahn [sup.1] [sup.2] , Jeongmin Shin [sup.1] , Young Hwa Jung [sup.1] , Ji Young Kim [sup.4] , Chang Won Choi [sup.1] [...]

Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB images. Previous studies have shown that multi-modal feature fusion using convolution operation can be effective, but such methods rely solely on local feature correlations, which can degrade the performance capabilities. To address this issue, we propose an attention-based novel fusion network, referred to as INSANet (INtra-INter Spectral Attention Network), that captures global intra- and inter-information. It consists of intra- and inter-spectral attention blocks that allow the model to learn mutual spectral relationships. Additionally, we identified an imbalance in the multispectral dataset caused by several factors and designed an augmentation strategy that mitigates concentrated distributions and enables the model to learn the diverse locations of pedestrians. Extensive experiments demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art performance on the KAIST dataset and LLVIP dataset. Finally, we conduct a regional performance evaluation to demonstrate the effectiveness of our proposed network in various regions. [ABSTRACT FROM AUTHOR]

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries (LIBs) are the main driving force behind the proliferation of mobile devices and electric vehicles. The production technologies of LIBs have been developed with the aim of lowering the energy cost (US$ kWh−1) and environmental impact while increasing the production efficiency. Here, we report dry‐processed Ni‐rich oxide cathodes coated with carbon nanotubes (CNTs) for LIBs. Specifically, LiNi0.8Co0.15Al0.05O2 (NCA) particles coated with multi‐walled CNTs (MWCNTs) were used to fabricate the cathodes by employing dry‐type electrode processing. In addition, the amount of polytetrafluoroethylene binder in the electrode was minimized and the inclusion of conventional carbon black (CB) conductive additives was eliminated. This approach enabled the fabrication of CB‐free, dense cathodes with an extremely high NCA content (99.6 wt%) and high electrode density of ~4.0 g cm−3. The NCA cathode had a high volumetric capacity of ~821 mAh cm−3 at a current rate of 0.5 C (~4.6 mA cm−2) and delivered good full‐cell cycling performance over 300 cycles (~60% capacity retention). Our results offer a viable way to lower the energy cost and the environmental damage toward next‐generation LIBs. Highlights: Ni‐rich particles with a CNT‐coating were used for dry‐processed cathode preparation.The CNTs replace conventional carbon black conductive additives.The CNT‐coated Ni cathodes were fabricated with minimal binder content (0.4 wt%).The cathode has good rate and cycling performance with high volumetric capacities. [ABSTRACT FROM AUTHOR]

Increasing the light extraction efficiency has been widely studied for highly efficient organic light-emitting diodes (OLEDs). Among many light-extraction approaches proposed so far, adding a corrugation layer has been considered a promising solution for its simplicity and high effectiveness. While the working principle of periodically corrugated OLEDs can be qualitatively explained by the diffraction theory, dipolar emission inside the OLED structure makes its quantitative analysis challenging, making one rely on finite-element electromagnetic simulations that could require huge computing resources. Here, we demonstrate a new simulation method, named the diffraction matrix method (DMM), that can accurately predict the optical characteristics of periodically corrugated OLEDs while achieving calculation speed that is a few orders of magnitude faster. Our method decomposes the light emitted by a dipolar emitter into plane waves with different wavevectors and tracks the diffraction behavior of waves using diffraction matrices. Calculated optical parameters show a quantitative agreement with those predicted by finite-difference time-domain (FDTD) method. Furthermore, the developed method possesses a unique advantage over the conventional approaches that it naturally evaluates the wavevector-dependent power dissipation of a dipole and is thus capable of identifying the loss channels inside OLEDs in a quantitative manner.

Neonatal seizures are the most common neurological symptoms caused by various etiologies in the neonatal period, but their diagnosis and treatment are challenging because their pathophysiology and electroclinical manifestations differ from those of patients in older age groups. Many seizures present as electrographic-only events without clinical signs or as obscure clinical manifestations that are difficult to distinguish from other neonatal behaviors. Accordingly, a new definition and classification of neonatal seizures was recently proposed by the International League Against Epilepsy Task Force on neonatal seizures, highlighting the role of electroencephalography in diagnosing and treating neonatal seizures. Neonatal seizures are defined as electrographic events with sudden, paroxysmal, and abnormal alteration of activity and divided into electroclinical seizures and electrographic-only seizures according to their clinical signs, thus excluding clinical events without an electrographic correlation. Seizure types are described by their predominant clinical features and divided into motor (automatisms, clonic, epileptic spasms, myoclonic, tonic, and sequential), nonmotor (autonomic and behavioral arrest), and unclassified. Although many neonatal seizures are acute reactive events caused by hypoxic-ischemic encephalopathy or vascular insults, structural, genetic, or metabolic etiologies of neonatal-onset epilepsy should also be thoroughly evaluated to determine their appropriate management.