A Meta-Analysis of the Effects of Comprehensive Sexuality Education Programs on Children and Adolescents.

Childhood and adolescence are crucial periods for developing one's awareness of sexuality. Comprehensive Sexuality Education (CSE) during these stages is essential for overall growth, fostering healthy self-concepts, and addressing diverse sexual issues among children and adolescents globally. A meta-analysis was conducted to analyze the effectiveness of CSE programs. A literature search was performed on EMBASE, PubMed, CINAHL, Cochrane Library, and PsycInfo for studies published before 14 June 2023, and based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We used the Comprehensive Meta-Analysis (CMA, V4) software version 4.0 for the analysis and interpreted the effect sizes according to Cohen's definition. Between 2011 and 2020, 21 studies on CSE were published, with the United States having the most publications (17). Of the 34 studies reviewed, 20 were randomized controlled trials. The primary population for CSE was middle/high school students (15), with the most frequent age range being 10-19 years (26). The overall effect size of CSE was significant (effect size = 1.31, p < 0.001), with cognition (effect size = 5.76, p < 0.001) being the most significant. CSE is an effective educational tool for children and adolescents with a significant impact on variables such as cognition and abstinence. It should be incremental from childhood and adolescence to adulthood.

Development of a Nurse Turnover Prediction Model in Korea Using Machine Learning.

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units.

Factors influencing the learning transfer of nursing students in a non-face-to-face educational environment during the COVID-19 pandemic in Korea: a cross-sectional study using structural equation modeling.

PURPOSE: The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning. METHODS: In this cross-sectional study, data were collected via online surveys from February 9 to March 1, 2022, from 218 nursing students in Korea. Learning transfer, learning immersion, learning satisfaction, learning efficacy, self-directed learning ability and information technology utilization ability were analyzed using IBM SPSS for Windows ver. 22.0 and AMOS ver. 22.0. RESULTS: The assessment of structural equation modeling showed adequate model fit, with normed χ2=1.74 (P<0.024), goodness-of-fit index=0.97, adjusted goodness-of-fit index=0.93, comparative fit index=0.98, root mean square residual=0.02, Tucker-Lewis index=0.97, normed fit index=0.96, and root mean square error of approximation=0.06. In a hypothetical model analysis, 9 out of 11 pathways of the hypothetical structural model for learning transfer in nursing students were statistically significant. Learning self-efficacy and learning immersion of nursing students directly affected learning transfer, and subjective information technology utilization ability, self-directed learning ability, and learning satisfaction were variables with indirect effects. The explanatory power of immersion, satisfaction, and self-efficacy for learning transfer was 44.4%. CONCLUSION: The assessment of structural equation modeling indicated an acceptable fit. It is necessary to improve the transfer of learning through the development of a self-directed program for learning ability improvement, including the use of information technology in nursing students' learning environment in non-face-to-face conditions.

Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference.

The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.

Oxygen scavenging of HfZrO2-based capacitors for improving ferroelectric properties.

HfO2-based ferroelectric (FE) materials have emerged as a promising material for non-volatile memory applications because of remanent polarization, scalability of thickness below 10 nm, and compatibility with complementary metal-oxide-semiconductor technology. However, in the metal/FE/insulator/semiconductor, it is difficult to improve switching voltage (V sw), endurance, and retention properties due to the interfacial layer (IL), which inevitably grows during the fabrication. Here, we proposed and demonstrated oxygen scavenging to reduce the IL thickness in an HfZrO x -based capacitor and the thinner IL was confirmed by cross-sectional transmission electron microscopy. V sw of a capacitor with scavenging decreased by 18% and the same P r could be obtained at a lower voltage than a capacitor without scavenging. In addition, excellent endurance properties up to 106 cycles were achieved. We believe oxygen scavenging has great potential for future HfZrO x -based memory device applications.

Heterogeneous and Monolithic 3D Integration of III-V-Based Radio Frequency Devices on Si CMOS Circuits.

Next-generation wireless communication such as sixth-generation (6G) and beyond is expected to require high-frequency, multifunctionality, and power-efficiency systems. A III-V compound semiconductor is a promising technology for high-frequency applications, and a Si complementary metal-oxide-semiconductor (CMOS) is the never-beaten technology for highly integrated digital circuits. To harness the advantages of these two technologies, monolithic integration of III-V and Si electronics is beneficial, so that there have been everlasting efforts to accomplish the monolithic integration. Considering that the on horizon 6G wireless communication requires faster and more energy-efficient system-on-chip technologies, it is imperative to realize a radio frequency (RF) system in which III-V technology and Si CMOS technology are integrated at a device level. Here we report heterogeneous and monolithic three-dimensional (3D) analog/RF-digital mixed-signal integrated circuits that contain two types of InGaAs high-electron-mobility transistors (HEMTs) designed for high fT and fMAX in the top and Si CMOS mixed-signal circuits consisting of an analog-to-digital converter and digital-to-analog converter in the bottom. A high unity current gain cutoff frequency of 448 GHz and unity power gain cutoff frequency of 742 GHz have been achieved by the fT oriented and fMAX oriented InGaAs HEMTs, respectively, without being affected by mixed-signal interference. At the same time, the bottom Si CMOS circuits provide valid signals without any performance degradation by the integration process.

Nursing Students' Subjective Happiness: A Social Network Analysis.

Improving nursing students' subjective happiness is germane for efficiency in the nursing profession. This study examined the subjective happiness of nursing students by applying social network analysis (SNA) and developing a strategy to improve the subjective happiness of nursing. The study adopted a cross sectional survey to measure subjective happiness and social network of 222 nursing students. The results revealed that the centralization index, which is a measure of intragroup interactions from the perspective of an entire network, was higher in the senior year compared with the junior year. Additionally, the indegree, outdegree, and centrality of the social network of students with a high level of subjective happiness were all found to be high. This result suggests that subjective happiness is not just an individual's psychological perception, but can also be expressed more deeply depending on the subject's social relationships. Based on the study's results, to strengthen self-efficacy and resilience, it is necessary to utilize strategies that activate group dynamics, such as team activities, to improve subjective happiness. The findings can serve as basic data for future research focused on improving nursing students' subjective happiness by consolidating team-learning social networks through a standardized program approach within a curriculum or extracurricular programs.

Bioinspired Photoresponsive Single Transistor Neuron for a Neuromorphic Visual System.

Realizing a neuromorphic-based artificial visual system with low-cost hardware requires a neuromorphic device that can react to light stimuli. This study introduces a photoresponsive neuron device composed of a single transistor, developed by engineering an artificial neuron that responds to light, just like retinal neurons. Neuron firing is activated primarily by electrical stimuli such as current via a well-known single transistor latch phenomenon. Its firing characteristics, represented by spiking frequency and amplitude, are additionally modulated by optical stimuli such as photons. When light is illuminated onto the neuron transistor, electron-hole pairs are generated, and they allow the neuron transistor to fire at lower firing threshold voltage. Different photoresponsive properties can be modulated by the intensity and wavelength of the light, analogous to the behavior of retinal neurons. The artificial visual system can be miniaturized because a photoresponsive neuronal function is realized without bulky components such as image sensors and extra circuits.

Characterization of Subgap Density-of-States by Sub-Bandgap Optical Charge Pumping in In0.53Ga0.47As Metal-Oxide-Semiconductor Field-Effect Transistors.

We report an experimental characterization of the interface states (Dit(E)) by using the subthreshold drain current with optical charge pumping effect in In0.53Ga0.47As metal-oxide-semiconductor fieldeffect transistors (MOSFETs). The interface states are derived from the difference between the dark and photo states of the current-voltage characteristics. We used a sub-bandgap photon (i.e., with the photon energy lower than the bandgap energy, Eph < Eg) to optically excite trapped carriers over the bandgap in In0.53Ga0.47As MOSFETs. We combined a gate bias-dependent capacitance model to determine the channel length-independent oxide capacitance. Then, we estimated the channel length-independent interface states in In0.53Ga0.47As MOSFETs having different channel lengths (Lch = 5, 10, and 25 [µm]) for a fixed overlap length (Lov = 5 [µm]).

3D Stackable Synaptic Transistor for 3D Integrated Artificial Neural Networks.

Although they have attracted enormous attention in recent years, software-based and two-dimensional hardware-based artificial neural networks (ANNs) may consume a great deal of power. Because there will be numerous data transmissions through a long interconnection for learning, power consumption in the interconnect will be an inevitable problem for low-power computing. Therefore, we suggest and report 3D stackable synaptic transistors for 3D ANNs, which would be the strongest candidate in future computing systems by minimizing power consumption in the interconnection. To overcome the problems of enormous power consumption, it might be necessary to introduce a 3D stackable ANN platform. With this structure, short vertical interconnection can be realized between the top and bottom devices, and the integration density can be significantly increased for integrating numerous neuromorphic devices. In this paper, we suggest and show the feasibility of monolithic 3D integration of synaptic devices using the channel layer transfer method through a wafer bonding technique. Using a low-temperature processible III-V and composite oxide (Al2O3/HfO2/Al2O3)-based weight storage layer, we successfully demonstrated synaptic transistors showing good linearity (αp/αd = 1.8/0.5), a high transconductance ratio (6300), and very good stability. High learning accuracy of 97% was obtained in the training of 1 million MNIST images based on the device characteristics.

Monolithic integration of visible GaAs and near-infrared InGaAs for multicolor photodetectors by using high-throughput epitaxial lift-off toward high-resolution imaging systems.

In this study, multicolor photodetectors (PDs) fabricated by using bulk p-i-n-based visible GaAs and near-infrared InGaAs structures were monolithically integrated through a high-throughput epitaxial lift-off (ELO) process. To perform multicolor detection in integrated structures, GaAs PDs were transferred onto InGaAs PDs by using a Y2O3 bonding layer to simultaneously detect visible and near-infrared photons and minimize the optical loss. As a result, it was found that the GaAs top PD and InGaAs bottom PD were vertically aligned without tilting in x-ray diffraction (XRD) measurement. A negligible change in the dark currents for each PD was observed in comparison with reference PDs through electrical characterization. Furthermore, through optical measurements and simulation, photoresponses were clearly revealed in the visible and near-infrared band for the material's absorption region, respectively. Finally, we demonstrated the simultaneous multicolor detection of the visible and near-infrared region,which implies individual access to each PD without mutual interference. These results are a significant improvement for the fabrication of multicolor PDs that enables the formation of bulk-based multicolor PDs on a single substrate with a high pixel density and nearly perfect vertical alignment for high-resolution multicolor imaging.

Strategy toward the fabrication of ultrahigh-resolution micro-LED displays by bonding-interface-engineered vertical stacking and surface passivation.

In this study, we proposed a strategy to fabricate vertically stacked subpixel (VSS) micro-light-emitting diodes (µ-LEDs) for future ultrahigh-resolution microdisplays. At first, to vertically stack the LED with different colors, we successfully adopted a bonding-interface-engineered monolithic integration method using SiO2/SiNx distributed Bragg reflectors (DBRs). It was found that an intermediate DBR structure could be used as the bonding layer and color filter, which could reflect and transmit desired wavelengths through the bonding interface. Furthermore, the optically pumped µ-LED array with a pitch of 0.4 µm corresponding to the ultrahigh-resolution of 63 500 PPI could be successfully fabricated using a typical semiconductor process, including electron-beam lithography. Compared with the pick-and-place strategy (limited by machine alignment accuracy), the proposed strategy leads to the fabrication of significantly improved high-density µ-LEDs. Finally, we systematically investigated the effects of surface traps using time-resolved photoluminescence (TRPL) and two-dimensional simulations. The obtained results clearly demonstrated that performance improvements could be possible by employing optimal passivation techniques by diminishing the pixel size for fabricating low-power and highly efficient microdisplays.