Self-Assembly for Creating Vertically-Aligned Graphene Micro Helices with Monolayer Graphene as Chiral Metamaterials.

Graphene's emergence enables creating chiral metamaterials in helical shapes for terahertz (THz) applications, overcoming material limitations. However, practical implementation remains theoretical due to fabrication challenges. This paper introduces a dual-component self-assembly technique that enables creating vertically-aligned continuous monolayer graphene helices at microscale with great flexibility and high controllability. This assembly process not only facilitates the creation of 3D microstructures, but also positions the 3D structures from a horizontal to a vertical orientation, achieving an aspect ratio (height/width) of ≈2700. As a result, an array of vertically-aligned graphene helices is formed, reaching up to 4 mm in height, which is equivalent to 4 million times the height of monolayer graphene. The benefit of these 3D chiral structures made from graphene is their capability to infinitely extend in height, interacting with light in ways that are not possible with traditional 2D layering methods. Such an impressive height elevates a level of interaction with light that far surpasses what is achievable with traditional 2D layering methods, resulting in a notable enhancement of optical chirality properties. This approach is applicable to various 2D materials, promising advancements in innovative research and diverse applications across fields.

Uncertainty in surrogate decision-making about end-of-life care for people with dementia: An integrative review.

AIM: To describe uncertainty in surrogate decision-making regarding end-of-life care for people with dementia using Mishel's reconceptualized uncertainty in illness theory. DESIGN: Integrative literature review using Whittemore and Knafl's approach. DATA SOURCES: PubMed, CINAHL, EMBASE, Scopus and Web of Science were searched using terms such as uncertainty/unpredictability, decision-making/advance care planning/end-of-life care planning, surrogate/family/caregiver/proxy and dementia. The search was initially conducted on 28 September 2021 and updated on 31 July 2023. REVIEW METHODS: Through systematic screening, 20 research articles were included in the analysis. Content related to uncertainty in surrogate decision-making regarding end-of-life care was extracted and analysed, focusing on the reconceptualized uncertainty in illness theory. RESULTS: First, surrogate uncertainty exists in various areas of surrogate decision-making regarding end-of-life care. Second, antecedents of surrogate uncertainty include numerous intrinsic and extrinsic factors. Third, surrogates exhibited some negative psychological responses to uncertainty but continually processed and structured their uncertainty through certain approaches, leading them to grow as decision-makers. Finally, research-based evidence on surrogates' processing of uncertainty and shifts to new life perspectives remains limited. CONCLUSION: Surrogates' uncertainty in decision-making regarding end-of-life care for people with dementia is well characterized using the reconceptualized uncertainty in illness theory. Healthcare providers should help surrogates manage their uncertainty in surrogate decision-making more constructively throughout the dementia trajectory. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The findings highlight the importance of assessing how surrogates process uncertainty and gauging how to help them process uncertainty and transition to new life perspectives. IMPACT: This review contributes to healthcare professionals' understanding of surrogates' uncertainty in end-of-life care planning for people with dementia, especially what they are uncertain about, what influences their uncertainty and how they process it. REPORTING METHOD: This study adheres to the PRISMA reporting guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

What Are the Challenges of School Nurses in South Korea in Managing Obese Children From Low-Income Households?

Given that the obesity rate among school-age children is increasing, school nurses can play a vital role in managing obesity and encouraging healthy living in school settings. Obese children from low-income backgrounds are more vulnerable than other students and require more careful attention and intervention. This qualitative study aimed to explore and understand the barriers recognized by school nurses in managing obesity in low-income household children. A focus group interview was conducted with 17 school nurses working at an elementary school. Children, home, school, political and structural, and social areas were revealed as intricate factors in obesity management. This study can help understand school nurses' obstacles in managing obese children from low-income families and can help them prepare practical measures to overcome these obstacles.

2020 International brain-computer interface competition: A review.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

End-of-life care practices in Korean nursing homes: A national survey.

BACKGROUND: Both globally and nationally, determinants of end-of-life care practices in nursing homes, such as laws and workforce, vary widely, resulting in a huge disparity in the quality of such practices. Despite the rapid growth in the number of nursing homes and increasing social attention being paid to end-of-life care in South Korea, little is known about these practices and how they differ because of nursing home-related regulations and nurse staffing. OBJECTIVES: To examine end-of-life care practices in nursing homes and compare them between nursing homes with 30 beds or more and those with fewer than 30 beds (the cut-point where nursing home-related regulations differ) and between nursing homes with and without registered nurses. DESIGN: A cross-sectional exploratory study using data from a national survey conducted by the National Health Insurance Service. SETTINGS: Nursing homes certified by the national long-term care insurance in South Korea. PARTICIPANTS: Nursing representatives from 836 nursing homes. METHODS: Measures of end-of-life care practices included the identification of residents' (or families') preferences for the place of death and a do-not-resuscitate order, the provision of end-of-life care on-site, and the presence of end-of-life care protocols. Descriptive statistics and logistic regression were used to compare end-of-life care practices by bed size and registered-nurse staffing. RESULTS: Of the 836 nursing homes, 85% and 84.1% identified the preference for the place of death and a do-not-resuscitate order mostly from surrogates, respectively. The most preferred places of death were nursing homes (53.3%) and hospitals (46.0%). Approximately, 72% responded that on-site end-of-life care protocols were present, and 50.8% reported providing end-of-life care on-site. Compared to nursing homes with 10-29 beds, those with 30 beds or more were more likely to identify the preference for a do-not-resuscitate order (OR 2.392, 95% CI 1.643-3.482); have in place end-of-life care protocols (OR 1.829, 95% CI 1.341-2.496); and provide end-of-life care on-site (OR 1.556, 95% CI 1.169-2.072). Compared to nursing homes without registered nurses, those with registered nurses were also more likely to identify the preference for a do-not-resuscitate order (OR 1.717, 95% CI 1.142-2.583) and provide end-of-life care on-site (OR 1.663, 95% CI 1.254-2.206). CONCLUSIONS: The findings indicate a huge disparity in end-of-life care practices in nursing homes across South Korea by bed size-based nursing-home regulations and registered-nurse staffing. Law/regulation- and policy-level changes are needed to promote robust end-of-life care in nursing homes. STUDY REGISTRATION: Not registered.

Realization of Curved Circular Nanotubes Using In Situ Monitored Self-Assembly.

Curved fluidic channels with a circular cross-section play an important role in biology, chemistry, and medicine. However, in nanofluidics, a problem that is largely unsolved is the lack of an effective fabrication method for curved circular nanotubes (10-1000 nm). In this work, an electron-beam-induced self-assembly process was applied to achieve fine curved nanostructures for the realization of nanofluidic devices. The diameter of the tube could be precisely controlled by an atomic layer deposition process. Fluid transported through the nanochannels was verified and characterized using a dark-field microscope under an optical diffraction limit size. The fluid flow demonstrates that the liquid's evaporation (vapor diffusion) in the nanochannel generates compressed vapor, which pumps the liquid and pushes it forward, resulting in a directional flow behavior in the ∼100 nm radius of tubes. This phenomenon could provide a useful platform for the development of diverse nanofluidic devices.

Perceived barriers to implementing case management for medical-aid beneficiaries in South Korea.

Medical aid (MA) in South Korea, a national public assistance scheme, provides minimum medical security for low-income families. The Korean government has adopted a case management (CM) programme to control MA beneficiaries' inappropriate medical use and enhance beneficiaries' quality of life. This study aimed to explore case managers' experiences of implementing CM for MA beneficiaries' potential barriers. This study employed a qualitative phenomenological tradition using focus-group interviews. Three focus groups with 19 MA case managers were conducted (July to August 2018) using semistructured interview questions. Data were analysed through qualitative content analysis using the NVivo software programme, followed by consensus meetings between researchers. Participants perceived the structural barriers to CM implementation as a contradictory MA system, lack of communication among government departments and CM guidelines not reflecting reality. Five themes were derived as the personal barriers: burnout, the conflict between private values and CM goals, role confusion, beneficiaries' blame, and lack of acknowledgement from coworkers. To improve case managers' experiences concerning MA beneficiaries, policy solutions and a collaborative environment with beneficiaries, public nurses and social service providers are needed to ensure the workforce's sustainability. Also, it is required to develop practical guidelines that reflect the reality of implementing CM.

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.

Hybridized Radial and Edge Coupled 3D Plasmon Modes in Self-Assembled Graphene Nanocylinders.

Current graphene-based plasmonic devices are restricted to 2D patterns defined on planar substrates; thus, they suffer from spatially limited 2D plasmon fields. Here, 3D graphene forming freestanding nanocylinders realized by a plasma-triggered self-assembly process are introduced. The graphene-based nanocylinders induce hybridized edge (in-plane) and radial (out-of-plane) coupled 3D plasmon modes stemming from their curvature, resulting in a four orders of magnitude stronger field at the openings of the cylinders than in rectangular 2D graphene ribbons. For the characterization of the 3D plasmon modes, synchrotron nanospectroscopy measurements are performed, which provides the evidence of preservation of the hybridized 3D graphene plasmons in the high precision curved nanocylinders. The distinct 3D modes introduced in this paper, provide an insight into geometry-dependent 3D coupled plasmon modes and their ability to achieve non-surface-limited (volumetric) field enhancements.

Electron Beam Maneuvering of a Single Polymer Layer for Reversible 3D Self-Assembly.

Reversible self-assembly that allows materials to switch between structural configurations has triggered innovation in various applications, especially for reconfigurable devices and robotics. However, reversible motion with nanoscale controllability remains challenging. This paper introduces a reversible self-assembly using stress generated by electron irradiation triggered degradation (shrinkage) of a single polymer layer. The peak position of the absorbed energy along the depth of a polymer layer can be modified by tuning the electron energy; the peak absorption location controls the position of the shrinkage generating stress along the depth of the polymer layer. The stress gradient can shift between the top and bottom surface of the polymer by repeatedly tuning the irradiation location at the nanoscale and the electron beam voltage, resulting in reversible motion. This reversible self-assembly process paves the path for the innovation of small-scale machines and reconfigurable functional devices.

Development of a Human-Display Interface with Vibrotactile Feedback for Real-World Assistive Applications.

It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30-0.45 gpeak with similar sensitivity in the 80-270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment.

Characteristics of Advance Care Planning Interventions Across Dementia Stages: A Systematic Review.

PURPOSE: Little is known regarding how advance care planning (ACP) interventions change with the progression of dementia. Thus, the primary purpose of this systematic review is to compare characteristics of ACP interventions across dementia stages. We also identify the role of nurses in implementing ACP interventions for persons with dementia and their surrogates. DESIGN: A systematic review of ACP intervention studies. METHODS: After searching PubMed, Web of Science, EMBASE, PsycArticles, the Cumulative Index to Nursing and Allied Health Literture (CINAHL), and Scopus, the final sample included 11 studies representing 10 interventions. We conducted a quality assessment and extracted data on dementia stage, intervention characteristics, and the role of nurses in the intervention. The extracted data were categorized according to stages of dementia, and analyzed to identify commonalities and differences between intervention characteristics. FINDINGS: Three ACP interventions focused on mild dementia and seven on advanced dementia. We observed four primary findings. First, we found a major difference in intervention recipients between the two dementia stages. Second, most ACP interventions included structured discussions regarding the person's life goals and values, goals of care, and preferences concerning future care via individual, face-to-face interactions. Third, ACP interventions designed to promote ongoing discussions and documentation were lacking. Finally, nurses played important roles in implementing ACP interventions. CONCLUSIONS: The findings suggest more nurse-led, dementia-related ACP interventions. In addition, ACP interventions should promote ongoing discussions and documentation and target persons with dementia and their surrogates in various countries. CLINICAL RELEVANCE: Many persons with dementia and their surrogates have limited knowledge about ACP; thus, more nurse-led ACP programs that reflect dementia stages may help them prepare for the situations in which persons with dementia lack decision-making capacity.

Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy.

Electroencephalogram (EEG) based braincomputer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49(±8.74)% for motor execution (ME) and 40.36(±3.39)% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions.

BACKGROUND: Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. FINDINGS: We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. CONCLUSIONS: The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

Self-Assembled 3D Nanosplit Rings for Plasmon-Enhanced Optofluidic Sensing.

Plasmonic sensors are commonly defined on two-dimensional (2D) surfaces with an enhanced electromagnetic field only near the surface, which requires precise positioning of the targeted molecules within hotspots. To address this challenge, we realize segmented nanocylinders that incorporate plasmonic (1-50 nm) gaps within three-dimensional (3D) nanostructures (nanocylinders) using electron irradiation triggered self-assembly. The 3D structures allow desired plasmonic patterns on their inner cylindrical walls forming the nanofluidic channels. The nanocylinders bridge nanoplasmonics and nanofluidics by achieving electromagnetic field enhancement and fluid confinement simultaneously. This hybrid system enables rapid diffusion of targeted species to the larger spatial hotspots in the 3D plasmonic structures, leading to enhanced interactions that contribute to a higher sensitivity. This concept has been demonstrated by characterizing an optical response of the 3D plasmonic nanostructures using surface-enhanced Raman spectroscopy (SERS), which shows enhancement over a 22 times higher intensity for hemoglobin fingerprints with nanocylinders compared to 2D nanostructures.

Electron Irradiation Driven Nanohands for Sequential Origami.

Sequence plays an important role in self-assembly of 3D complex structures, particularly for those with overlap, intersection, and asymmetry. However, it remains challenging to program the sequence of self-assembly, resulting in geometric and topological constrains. In this work, a nanoscale, programmable, self-assembly technique is reported, which uses electron irradiation as "hands" to manipulate the motion of nanostructures with the desired order. By assigning each single assembly step in a particular order, localized motion can be selectively triggered with perfect timing, making a component accurately integrate into the complex 3D structure without disturbing other parts of the assembly process. The features of localized motion, real-time monitoring, and surface patterning open the possibility for the further innovation of nanomachines, nanoscale test platforms, and advanced optical devices.

Service Providers' Perspectives on Barriers of Healthy Eating to Prevent Obesity among Low-income Children Attending Community Childcare Centers in South Korea: A Qualitative Study.

This study aimed to identify individual and socio-ecological barriers in managing healthy eating among low-income children from the perspective of community childcare (CCC) center workers (n = 18) through focus group interviews. They perceived the increase in obesity among low-income children. The interviews revealed that the child's eating habits are affected by not only individual determinants including self-regulation and risk awareness but also environmental factors such as family, CCC centers, policy and social structure. To enhance children's health and behavior, it is necessary to consider the interactions among systems at a variety of levels, for example, local community and health care policies.

Factors Affecting Healthcare Utilization among Patients with Single and Multiple Chronic Diseases.

BACKGROUND: We aimed to investigate the factors affecting healthcare utilization among patients with single and multiple chronic diseases using the Andersen healthcare utilization model. METHODS: We used a combination of the data from the sixth and seventh Korea National Health and Nutrition Survey (2014-2016). The study population was 3,901 patients with single chronic disease and 1,829 patients with multiple chronic diseases as defined by the WHO. Participants were aged 19 yr or older. Multiple regression analysis was employed using the Andersen model to identify factors affecting healthcare utilization (inpatient and outpatient). RESULTS: According to the Andersen model (comprising predisposing, enabling, and need factors and health behaviors), the factors that increased outpatients with single chronic disease were female, being aged 65 yr or older, having basic livelihood security benefit, immobility, and poor subjective health status. Factors that increased inpatients with single chronic disease were being aged under 65, having private insurance, immobility, poor subjective health status, and nondrinking. Moreover, factors that increased outpatients and inpatients with multiple chronic diseases were female, being aged under 65, immobility, and poor subjective health status and immobility, poor subjective health status, nondrinking, and not engaging in physical activity, respectively. CONCLUSION: We identified factors affecting outpatient and inpatient care utilization among patients with single and multiple chronic diseases, using the Andersen healthcare utilization model. The findings can be used as foundational data to develop preventive and management strategies in healthcare utilization among patients with single and multiple chronic diseases.