Association of Moderate Hypothermia at Admission with Short-Term and Long-Term Outcomes in Extremely Low Birth Weight Infants

Purpose Extremely low birth weight (ELBW) infants exhibit immature thermoregulation and are easily exposed to hypothermia. We investigated the association between hypothermia on admission with short- and long-term outcomes. Methods Medical records of ELBW infants admitted to the neonatal intensive care unit of a tertiary medical center between June 2012 and February 2017 were retrospectively analyzed. Upon admission, the axillary body temperature was measured. Moderate hypothermia was defined as admission temperature below 36 ℃. Results A total of 208 infants with gestational age of 26.4±2.3 weeks and birth weight of 746.7±154.9 g were included. Admission temperature ranged from 33.5 to 36.8 ℃ (median 36.1 ℃). Univariate analyses of maternal and infant characteristics were performed for moderately hypothermic and control (normothermic to mildly hypothermic) infants. Lower gestational age, lower birth weight, and vaginal delivery correlated with moderate hypothermia. Logistic regression analyses adjusted for confounders revealed that the incidence of hemodynamically significant patent ductus arteriosus (hsPDA) was associated with moderate hypothermia in ELBW infants. Moreover, abnormal mental developmental index scores on the Bayley Scales of Infant Development II at a corrected age of 18 to 24 months were associated with moderate hypothermia, but not with the psychomotor developmental index, incidence of blindness, deafness, or cerebral palsy. Conclusion Moderate hypothermia at admission is not only correlated with short-term neonatal morbidities such as hsPDA, but may also be associated with long-term neurodevelopmental impairment in ELBW infants. Future large-scale studies are required to clarify the long-term consequences of hypothermia upon admission

Being a "good daddy": exploring the stability of paternal identity

Based on the theoretical frame of identity theory, this research purposes to find the shared meanings between one's paternal identity and other role identities such as professional identity and religious identity. The research employed face-to-face interviews with semi-structured questionnaires. Five Korean, four Korean-American or Korean permanent residents, and five Anglo-American interviewees participated in the research. Findings show that paternal role players share specific meanings with other role identities. Although shared meanings vary from person to person, these meanings are commonly emphasized by interviewees. The existence of shared meanings among multiple identities leads the researcher to draw the conclusion about the dynamics among multiple identities in accordance with a hierarchical structure of meanings.</p

Physical layer aided authentication and key agreement for the Internet of things

In this paper, we propose a physical layer aided authentication and key agreement (PL-AKA) protocol for massive Internet of things (IoT) scenarios. Conventional AKA protocols such as evolved packet system AKA used in long-term evolution (LTE) systems may suffer from congestions in core networks by the large signaling overhead as the number of IoT devices increases. In order to reduce the signaling overhead, the physical layer challenge response authentication is employed in the proposed PL-AKA so that the IoT devices can be locally authenticated in a base station. Through theoretical analysis and numerical simulation, we demonstrate that the proposed protocol significantly reduces the signaling overhead while maintaining competitive authentication performance by taking advantage of both physical layer authentication and cryptography-based authentication

Frequency Selective Auto-Encoder for Smart Meter Data Compression

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process

A unified approach for compression and authentication of smart meter reading in AMI

In this paper, we propose a unified approach for compression and authentication of smart meter reading in advanced metering infrastructure (AMI). In general, smart meters are urged to send sampled reading signals at a high rate for high-quality services. Meanwhile, power reading signals have to be authenticated to prevent impersonation attacks, which can cause serious economic loss. However, the security in smart grids faces more challenges than conventional human-type communications because of limited hardware resources of a smart meter (e.g., small memory). Motivated by these problems, we study simultaneous compression and authentication for power reading signals in multicarrier systems based on the notion of compressive sensing (CS). The CS-based compression and authentication method are applied to empirically modeled signals with a shared secret key, a measurement matrix in CS between a data concentrator unit (DCU) and a legitimate smart meter. In particular, for authentication, the residual error of a received signal at the DCU is used as a test statistic for hypothesis testing, which determines whether the signal is a legitimate signal or an intrusion signal in the proposed approach. Through the analysis and simulation results, we demonstrate that the CS-based compression approach can be applied to smart meter reading with good energy efficiency. In addition, it is shown that the proposed scheme can obtain a low authentication error probability under reasonable conditions. For example, when the number of subcarriers is 64, the DCU can distinguish legitimate and intrusion smart meters with a probability of 1 - P E , where P E &le; 10 -4