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This study was conducted to examine the associations among uncertainty, depression, social support, and self-care compliance in patients undergoing hemodialysis, and to identify the factors influencing self-care compliance. A convenience sample of 152 patients receiving hemodialysis was selected. Data were analyzed using descriptive statistics, independent -test, ANOVA, Pearson correlations, and hierarchical regression analysis with the SPSS 23.0 program. Participants performed a moderate level of self-care consisting of factors such as knowledge of hemodialysis, dietary knowledge of hemodialysis, dietary compliance with hemodialysis, and compliance with hemodialysis order. The self-care compliance of participants undergoing hemodialysis showed a significant relationship with depression, uncertainty, and social support. The factors significantly influencing self-care compliance were social support and occupation. These variables explained 24.9% of the variance in self-care compliance. Findings from this study confirmed that uncertainty, depression, and social support are major factors affecting self-care compliance, and that the higher the patients' uncertainty, the lower their self-care compliance. Thus, interventions should be performed to reduce uncertainty and to improve self-care through accurate information and education on disease progression and self-care.

We report a large-area quantum dot light-emitting diode (QLED) with sputtered Zn 0.85 Mg 0.15 O (ZMO) as an electron transport layer (ETL). Uniform ZMO is applied as ETL of the inverted structured QLED and the adjustment of Ar/O 2 ratio on device characteristics is studied in detail. Compared to pristine ZMO, ZMOs with O 2 gas are found to be beneficial to the charge balance in the emitting layer of QLEDs mainly by their upshifted conduction band minimum, which in turn limits an electron injection. Additionally, it is found that oxygen vacancies in the ZMO, acting as the exciton quenching sites, are responsible for the device stability. QLEDs with 6:1 ZMO produce a maximum luminance of 136,257 cd/m 2 and external quantum efficiency of 5.15%, which are the best device performances to date among QLEDs with sputtered ETLs. These results indicate that the sputtered ZMO shows great promise for use as an inorganic ETL for future large-area QLEDs. Graphical Abstract

ObjectiveDespite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.Materials and methodsWe conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.ResultsThis study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.ConclusionThis study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.Critical relevance statementAdoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.Key points1. Practical and actionable insights into successful AI implementation in radiology are lacking.2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation3. Holistic approach aids organisations to create sustainable value through AI implementation.

This study investigates the combined effects of impurities in CO2 stream, geochemistry, water salinity, and wettability alteration on oil recovery and CO2 storage in carbonate reservoirs and optimizes injection strategy to maximize oil recovery and CO2 storage ratio. Specifically, it compares the performance of pure CO2 water-alternating gas (WAG), impure CO2-WAG, pure CO2 low-salinity water-alternating gas (LSWAG), and impure CO2-LSWAG injection methods from perspectives of enhanced oil recovery (EOR) and CO2 sequestration. CO2-enhanced oil recovery (CO2-EOR) is an effective way to extract residual oil. CO2 injection and WAG methods can improve displacement efficiency and sweep efficiency. However, CO2-EOR has less impact on the carbonate reservoir because of the complex pore structure and oil-wet surface. Low-salinity water injection (LSWI) and CO2 injection can affect the complex pore structure by geochemical reaction and wettability by a relative permeability curve shift from oil-wet to water-wet. The results from extensive compositional simulations show that CO2 injection into carbonate reservoirs increases the recovery factor compared with waterflooding, with pure CO2-WAG injection yielding higher recovery factor than impure CO2-WAG injection. Impurities in CO2 gas decrease the efficiency of CO2-EOR, reducing oil viscosity less and increasing interfacial tension (IFT) compared to pure CO2 injection, leading to gas channeling and reduced sweep efficiency. This results in lower oil recovery and lower storage efficiency compared to pure CO2. CO2-LSWAG results in the highest oil-recovery factor as surface changes. Geochemical reactions during CO2 injection also increase CO2 storage capacity and alter trapping mechanisms. This study demonstrates that the use of impure CO2-LSWAG injection leads to improved oil recovery and CO2 storage compared to pure CO2-WAG injection. It reveals that wettability alteration plays a more significant role for oil recovery and geochemical reaction plays crucial role in CO2 storage than CO2 purity. According to optimization, the greater the injection of gas and water, the higher the oil recovery, while the less gas and water injected, the higher the storage ratio, leading to improved storage efficiency. This research provides valuable insights into parameters and injection scenarios affecting enhanced oil recovery and CO2 storage in carbonate reservoirs.

CO2 is the main solvent used in enhanced oil recovery (EOR). However, its low density and viscosity compared to oil cause a decrease in sweep efficiency. Recently, dimethyl ether (DME), which is more efficient than CO2, has been introduced into the process. DME improves oil recovery by reducing minimum miscible pressure (MMP), interfacial tension (IFT), and oil viscosity. Since DME is an expensive solvent, price reduction and appropriate injection scenarios are needed for economic feasibility. In this study, a compositional model was developed to inject DME with impure CO2 streams, where the CO2 was derived from one of these three purification methods: dehydration, double flash, and distillation. It was assumed that such a mixed solvent was injected into a heterogeneous reservoir where gravity override was maximized. As a result, lower oil recovery is achieved for the higher impurity content of the CO2 stream, lower DME content, and more heterogeneous reservoir. When a high-purity CO2 stream is used, the change in oil recovery according to DME content and heterogeneity of the reservoir is increased. When the lowest-purity CO2 stream is used, the net present value (NPV) is the highest. For a homogeneous reservoir, the NPV is highest for all impure CO2 streams. This optimization indicates a greater impact on revenue from reduced CO2 purchase cost than on profit loss due to reduced oil recovery by impurities. Additional benefits can be expected when considering solvent reuse and carbon capture and storage (CCS) credits.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy; however, their use is frequently associated with immune-related adverse events (irAEs). In this study, anti-PD-L1 therapy exacerbates muscle wasting in tumor-bearing male mice despite its anti-tumor efficacy, accompanied by an accumulation of CD8 + T cells in muscle. Single-cell RNA sequencing identifies these cells as tissue-resident memory-like CD49a + CD8 + T cells. While CD8 + T cell depletion prevents muscle wasting, it compromises the anti-tumor efficacy of anti-PD-L1. To resolve this paradox, we identify cathepsin L (CTSL) as a dual-target capable of suppressing both tumor progression and CD8 + T cell-mediated muscle wasting, through integrative transcriptomic analysis. Pharmacological inhibition of CTSL not only mitigates anti-PD-L1-induced muscle wasting but also further suppresses tumor growth, potentially via downregulation of BNIP3. Here, we show that CTSL is a dual-action target to uncouple anti-tumor efficacy from muscle-specific irAEs, offering a strategy to improve clinical outcomes of ICIs. Immune checkpoint inhibitors (ICIs) are associated with side effects such as muscle wasting. Here, the authors discover that targeting cathepsin L not only suppresses ICI-induced CD8 + T cell-mediated muscle wasting but also enhances ICI anti-tumor efficacy.

Background: The prevalence of diabetes is a significant concern and is particularly impactful on the older adult population. Auricular acupressure is recognized as an effective complementary treatment for Type 2 diabetes. Purpose: The purpose of this study is to examine the effect of auricular acupressure on glycemic markers, stress, and sleep quality in older adults with Type 2 diabetes in South Korea. Methods: The study involved weekly acupressure therapy sessions for 8 weeks, with 25 participants in the intervention group and 26 in the placebo group. Specific acu-points associated with diabetes, sleep, and stress were targeted in the intervention group, while unrelated acu-points were used in the control group. Subjective indicators such as stress and sleep scales, along with objective measures such as blood tests, heart rate variability, and sleep activity recorders, were employed in the analysis. Results: Significant differences were observed in blood sugar (F = 5.20, p ＜ .001) and glycated hemoglobin (Z = -2.345, p = .019) between the two groups after administration of the acupressure therapy. However, no significant between-group differences were found in either glycated albumin or fructosamine. Also, activity in the sympathetic and parasympathetic nerves showed significant between-group variation. Although no significant between-group differences were found for subjective sleep indicators, notable changes in the number of awakenings, duration of awakening, REM sleep, and deep sleep conditions were identified. Conclusions: Although the effects are not strong, the findings suggests auricular acupressure influences glycemic index, stress, and sleep quality in older individuals with Type 2 diabetes positively. The results of this study support the potential of using auricular therapy as a nursing intervention in diabetes management.

This study evaluates a time-lagged ensemble averaging strategy to improve the accuracy and robustness of short-range streamflow point forecasts when hydrological simulations are driven by deterministic numerical weather prediction (NWP) forcing. We implemented WRF-Hydro in standalone mode for the Geumho River basin, South Korea, using Local Data Assimilation and Prediction System (LDAPS) forecasts initialized every 6 h with lead times up to 48 h. Time-lagged ensembles were constructed by averaging overlapping WRF-Hydro predictions from successive LDAPS initializations. Across two contrasting flood-producing storms, ensemble-mean forecasts consistently reduced lead-time-dependent skill degradation relative to single-initialization forecasts; the event-wise median Nash–Sutcliffe efficiency at the downstream gauge improved from 0.39 to 0.81 at 48 h (Event 2020) and from 0.48 to 0.85 at 24 h (Event 2022), while RMSE decreased by up to 48%. The most effective ensemble window varied with storm evolution and forecast horizon, indicating additional gains from adaptive time-lag selection. Overall, time-lagged ensemble averaging provides a practical, low-cost post-processing approach to enhance operational short-range streamflow prediction with NWP forcings.

The integrated injection of low-salinity water (LSW) and carbon dioxide (CO2) into the water-alternating-gas (WAG) process offers advantages, primarily increasing oil recovery and reducing operating costs. However, CO2 has challenges in sweep efficiency due to significant differences in density and viscosity compared with oil. While LSW and dimethyl ether (DME) have shown promise in improving recovery through wettability alteration and reducing minimum miscible pressure, interfacial tension (IFT), and CO2 mobility, their synergistic integration with CO2-WAG remains poorly understood. Existing DME-based enhanced oil recovery (EOR) studies have not explored low-salinity water injection as a cost-effective alternative to mitigate high DME operating costs. This study introduces the CO2/DME-LSWAG method, systematically evaluating the effect of DME concentrations (0%, 10%, 25%) and LSWs (seawater, twice-diluted seawater, ten-times-diluted seawater) on sweep and displacement efficiencies, oil recovery, and CO2 storage in a 2D cross-sectional carbonate reservoir model. Results showed that DME dramatically reduces IFT (67% and 95% at 10% and 25% DME solvent, respectively) while salinity effects are relatively small. Compared with CO2-LSWAG, the oil recovery factor improved by 5.2–13.1% depending on DME concentration and water salinity, with DME performance maximized at higher salinity water. CO2 storage efficiency showed opposing trends. Structural trapping decreased, while solubility trapping increased with lower salinity. The sensitivity analysis identified DME concentration as the dominant factor for CO2 storage. The composition modeling and simulation of the CO2/DME-LSWAG process provide critical engineering guidance for the design of future EOR and CO2 storage projects that utilize DME in carbonate reservoirs.

To explore the most desirable pathway for a successful local energy transition, a fuzzy-set qualitative comparative analysis was conducted on 16 regional cases in South Korea. We developed four propositions based on previous studies and theories as a causal set. Based on the South Korean context, we selected the solar photovoltaic (PV) generation and solar PV expansion rate as barometers for measuring the success of a local energy transition. Our analysis highlights the importance of the International Council for Local Environmental Initiatives (ICLEI) membership (network), local legislation, and the environmental surveillance of locally-based non-governmental organizations (NGOs). The implications of this study will provide insights for developing or newly industrialized countries where an energy transition is underway.