Search

We propose an active weighting algorithm for composite adaptive control to reduce the state and estimate errors while maintaining the estimation quality. Unlike previous studies that construct the composite term by simply stacking, removing, and pausing observed data, the proposed method efficiently utilizes the data by providing a theoretical set of weights for observations that can actively manipulate the composite term to have desired characteristics. As an example, a convex optimization formulation is provided, which maximizes the minimum eigenvalue while keeping other constraints, and an illustrative numerical simulation is also presented.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. While knowledge transfer is one of the key components in determining Mergers and Acquisitions (M&A) success, the current M&A literature has produced inconsistent findings regarding its antecedents and consequences. To address this research gap, this study explores the roles of functional integration and shared goals in facilitating knowledge transfer, which will in turn determine M&A success. To provide a more nuanced understanding of knowledge transfer, this study examines bilateral knowledge flows (e.g., knowledge transfer to a target firm from the UK acquiring firm and knowledge transfer from a target firm to the UK acquiring firm). Our research framework is built upon two different theoretical perspectives, namely resource orchestration and social capital theories. Our propositions were tested empirically across a sample of 131 UK cross-border M&A firms. Our results reveal that the affirmative roles of functional integration and shared goals in increasing knowledge transfer both to and from a target firm are confirmed and that knowledge transfer to the target firm is deemed decisive for M&A success. Based on the findings, we discuss theoretical and practical implications, followed by limitations and future study consideration.

What: The aim of this workshop was to discuss the needs and challenges in using high-resolution climate model outputs for impacts-relevant modeling. Development of impacts-relevant climate projections in the polar regions requires effective collaboration between regional climate modelers and impacts-relevant modelers in the design stage of high-resolution climate projections for the polar regions. When: 8 November 2021 Where: Online.

PurposeThis study provides an integrative model that facilitates green product innovation (GPI) by adopting dynamic capabilities theory and the motivation-opportunity-ability (MOA) framework. Drawing on dynamic capabilities theory, this study emphasizes green supply chain integration (GSCI), consisting of internal and external integration, as a dynamic capability that drives GPI. Moreover, this study analyzes the environmental conditions that benefit the development of dynamic capabilities using the MOA framework and focuses specifically on government support (GS) and market greenness (MG) as precursors to GSCI.Design/methodology/approachSurvey data were collected from 300 Chinese manufacturing firms. The proposed hypotheses were tested using hierarchical multiple regression analysis.FindingsThe regression analysis reveals that (1) GS and MG positively affect both internal and external integration and (2) internal and external integration positively affect GPI.Originality/valueIn explicating a model of GPI, this study extends the theoretical lens of dynamic capabilities beyond the intraorganizational level to the supply chain level. Moreover, this study enhances the understanding of dynamic capability development by considering the environmental conditions that represent motivation- and opportunity-based drivers of GSCI.

Global amphibian population decline is a major concern. Therefore, conservation centers have become increasingly needed for population sustainability breeding programs. This need makes proper nutrition programs while in human care imperative. The specific nutritional focus of this current research was to analyze vitamin A, vitamin E, and carotenoid (apocarotenoid, β-carotene, β-carotene ester, β-cryptoxanthin, lutein, lutein ester, zeaxanthin, and zeaxanthin ester) concentrations in the liver of 66 free-range marine toads (Rhinella marina) over a two-month period after entering human management. Toads were fed supplemented crickets randomly assigned to one of two diets: Diet 1 consisted of brown house crickets (Acheta domestica) gut loaded with Mazuri® Cricket Diet 5M38 and small amounts of sweet potato and carrots; Diet 2 consisted of an identical diet with the gut loaded crickets additionally dusted with Repashy® Superfoods Vitamin A Plus. Ten toads were euthanized prior to human management on Day 0 to assess baseline liver nutrients. Seven toads consuming each of the two diets (14 toads total) were euthanized on Days 9, 15, 32, and 62. Regardless of diet treatment, there were decreases (p < 0.05) in all the analyzed nutrient concentrations over the 62-day human management period. The results from this study indicate that higher dietary vitamin A, vitamin E, and carotenoid content may be needed and/or the cricket gut loading and dusting techniques used to feed the amphibians food source insects may be inadequate.

Purpose: To analyze the difference in bony pelvimetry using magnetic resonance imaging (MRI) in women who delivered vaginally and those who underwent cesarean section (CS) due to cephalopelvic disproportion (CPD).Methods: This retrospective case-control study enrolled Korean women who gave birth to their first child between 37 and 42 weeks of pregnancy and underwent pelvic MRI between January 2014 and June 2020. These women were classified into 3 groups: vaginal delivery (VD), CS due to CPD (CPD-CS), and CS due to reasons other than CPD (Other-CS). In MRI images, true conjugate (TC), obstetric conjugate (OC), mid anterior-posterior (AP) distance (MAD), outlet AP distance (OAD), ischial spine distance (ISD), ischial tuberosity distance (ITD), and pubic angle (PA) were measured.Results: In total, 392 women were enrolled. There were no significant differences in maternal height and body mass index at birth among the 3 groups. The ISD was statistically different between the VD group (106.2±6.4 mm) and the Other-CS group (110.4±8.8 mm) (p=0.014). After multiple regression analysis, short ISD (adjusted odds ratio [aOR], 0.92; 95% confidence interval [CI], 0.87-0.97; p=0.002), short MAD (aOR, 0.95; 95% CI, 0.89-0.99; p=0.047), and short ITD (aOR, 0.95; 95% CI, 0.91-0.99; p=0.019) were significantly associated with CS due to CPD.Conclusion: Short ISD, MAD, and ITD, measured using MRI, are important parameters that can predict CS due to CPD. The accumulation of related research could promote the development of an excellent imaging method in terms of cost-effectiveness. Thus, it may be possible to establish a method to reduce obstetric and perinatal complications due to dystonia.

Limiting global warming to 1.5 °C by the end of the century is an ambitious target that requires immediate and unprecedented emission reductions. In the absence of sufficient near term mitigation, this target will only be achieved by carbon dioxide removal (CDR) from the atmosphere later during this century, which would entail a period of temperature overshoot. Next to the socio-economic feasibility of large-scale CDR, which remains unclear, the effect on biogeochemical cycles and climate are key to assessing CDR as a mitigation option. Changes in atmospheric CO2 concentration and climate alter the CO2 exchange between the atmosphere and the underlying carbon reservoirs of land and the ocean. Here, we investigate carbon cycle feedbacks under idealized and more realistic overshoot scenarios in an ensemble of Earth system models. The response of oceanic and terrestrial carbon stocks to changes in atmospheric CO2 concentration and changes in surface climate (the carbon-concentration and carbon-climate feedback, quantified by the feedback metrics 𝛽 and 𝛾, respectively) show a large hysteresis. This hysteresis leads to growing absolute values of 𝛽 and 𝛾 during phases of negative emissions. We find that this growth is spatially quite homogeneous, since the spatial patterns of feedbacks do not change significantly for individual models. We confirm that the 𝛽 and 𝛾 feedback metrics are a relatively robust tool to characterize inter-model differences in feedback strength since the relative feedback strength remains largely stable between phases of positive and negative emissions and between different simulations, although exceptions exist. When emissions become negative, we find that the model uncertainty (model disagreement) in 𝛽 and 𝛾 increases stronger than expected from the assumption that the uncertainties would accumulate linearly with time. This indicates that the model response to a change from increasing to decreasing forcing introduces an additional layer of uncertainty, at least in idealized simulations with a strong signal. We also briefly discuss the existing alternative definition of feedback metrics based on instantaneous carbon fluxes instead of carbon stocks and provide recommendations for the way forward and future model intercomparison projects.

Purpose Leveraging theory from the dynamic capability literature, this study aims to explore how information technology (IT) capability influences firm agility and subsequently translates into firm performance. Design/methodology/approach This study examines the proposed relationships by using survey data from a sample of 296 Chinese retail firms. Structural equation modeling is used to test this study’s hypotheses. Findings The following results are produced: the direct effect of IT capability on firm agility is confirmed; firm agility has a direct impact on firm performance; and the indirect effect of IT capability on firm performance via firm agility is demonstrated (i.e. partial mediation). Originality/value The catastrophic outbreak of the COVID-19 pandemic has heightened the importance of firm agility more than ever. Although the traumatic event is painful, however, there is nothing like a crisis to offer a tremendous business opportunity. In response to the pandemic circumstance, firms are required to operate their business by reacting to unpredictable and dynamic market changes quickly and efficiently. This study sheds light on why firms should develop their IT capability and how it affects firm performance via firm agility during the COVID-19 outbreak.

Chemically modified silk fibroin (SF) bioink has been used for three-dimensional (3D) bioprinting in tissue engineering because of its biocompatibility and printability. Also, fluorescent silk fibroin (FSF) from transgenic silkworms has been recently applied in biomedicine because of its fluorescence property. However, the fabrication of fluorescent hydrogel from FSF has not been elucidated. In this study, we showed the fabrication of a digital light processing (DLP) printable bioink from a chemically modified FSF. This bioink was fabricated by covalent conjugation of FSF and glycidyl methacrylate (GMA) and can be printed into various structures, such as the brain, ear, hand, lung, and internal organs. The physical properties of glycidyl methacrylated fluorescent silk fibroin (FSGMA) hydrogel was like the glycidyl methacrylated non-fluorescent silk fibroin (SGMA) hydrogel. The FSGMA hydrogel significantly retains its fluorescence property and has excellent biocompatibility. All these properties make FSGMA hydrogel a potent tool in encapsulated cell tracking and observing the scaffolds' degradation in vivo. This study suggested that our 3D DLP printable FSF bioink could play a promising role in the biomedical field.

Arctic permafrost is yet the largest non-seasonal component of Earth's cryosphere and has been proposed as a climate tipping element. Already today, permafrost thaw and ground ice loss have detrimental consequences for Arctic communities and are affecting the global climate via carbon-cycle–feedbacks. However, it is an open question whether climatic changes drive permafrost loss in a way that gives rise to a tipping point, crossing of which would imply abrupt acceleration of thaw and disproportional unfolding of its impacts.Here, we address this question by geospatial analyses and a comprehensive literature review of the mechanisms and feedbacks driving permafrost thaw across spatial scales. We find that neither observation-constrained nor model-based projections of permafrost loss provide evidence for the existence of a global-scale tipping point, and instead suggest a quasi-linear response to global warming. We identify a range of processes that drive rapid permafrost thaw and irreversible ground ice loss on a local scale, but these do not accumulate to a non-linear response beyond regional scales.We emphasize that it is precisely because of this overall linear response, that there is no „safe space“ for Arctic permafrost where its loss could be acceptable. Every additional amount of global warming will proportionally subject additional land areas underlain by permafrost to thaw, implying further local impacts and carbon emissions.

Within the framework of the project IMPOSE (Emit now, mitigate later? IMPlications of temperature OverShoots for the Earth system) six idealized emission-overshoot simulations have been performed with the Earth System Model NorESM2-LM2 and used as forcing for the 2nd generation dynamic global vegetation model LPJ-GUESS with its fire-model SIMFIRE-BLAZE to investigate the impact of different CO2 overshoots on global wildfire regimes.The simulations describe a set of scenarios with high, medium, and low accumulative CO2 emissions and each of which has a short (immediate) and a long (100 years) peak of accumulative CO2 emissions before declining towards a baseline simulation of 1500 PgC accumulatively emitted within the first 100 years.The results show that the height of the overshoot has an impact on global fire regimes while its duration does not seem to play a significant role 200 years after peak CO2. Overall, we can see that changes in vegetation composition following the temperature anomaly are the main driver for changes in global wildfire frequency. While in the low overshoot scenarios burnt area has almost converged towards the baseline simulation, the extremest scenarios show the lowest burnt area at the end of the simulation period, indicating that vegetation changes, especially in low latitudes, have been most significant and/or are still ongoing.

There is relevant research on temporal carbon accumulation changes, mostly in arctic permafrost peatlands in Norway, but little is known about the differences and comparisons with more oceanic and lower latitude peatlands in the region, where rainfall is one of the main climatic drivers. Climate projections in Norway for 2031-2060 and 2071-2100 show a rise in mean temperature and an increase of annual rainfall with more intense seasonal events in western, eastern, and northern parts. Under this rationale, this study hypothesizes that temporal variability of temperature and precipitation during the Holocene led to weaker and stronger evapotranspiration and moisture signals affecting local and regional vegetation in peatland ecosystems, water-table changes, and carbon accumulation capacity. This study will contribute to the generation of evidence of the roles and interactions of hydrology, temperature, vegetation, and land use changes on peatbogs carbon accumulation capacity during the Holocene. It will help to disentangle the responses of the carbon budget at different time scales. Methods involve the use of a set of proxies such as pollen, testate amoeba, LOI and bulk density to reconstruct the peat composition rate, organic matter, water table, and local (and regional) vegetation. A generation of an age-depth model and further multivariate analysis will allow to investigate the relationship between the proxies and carbon accumulation rate over the Holocene to further understand the temperature/precipitation correlation and the effects of a changing climate on the carbon budget.

With the current rate of climate change, exceeding the remaining global carbon budget for a warming target of 1.5 °C becomes increasingly likely. Hence, attention has been put towards carbon dioxide removal (CDR) strategies that, given the required socio-economic feasibility and sustainability, allow temporary emission overshoots. The balance between the potential viability of overshoot scenarios and the associated risks depends on their characteristics and how the global ecosystems respond during and after the overshoot. Here we investigate the global climate and regional ecosystem responses to emission-driven overshoot scenarios of different magnitude, duration and timing, imposing the potential for irreversible changes. Our analysis focuses on the behavior and reversibility of thermodynamic, hydrological, and biogeochemical processes and their impacts on a regional scale. Based on results from state-of-the-art Earth system models, physically driven mechanisms appear to be mostly reversible for moderate overshoot scenarios considering a response lag in the range of decades. However, feedbacks in high overshoot scenarios and biogeochemical processes show signs of irreversibility on a larger spatial scale – some of which have been brought into connection with the crossing of tipping points for certain elements of the Earth system. Our analysis informs about the reversibility of climate system processes, which allows refining thresholds for global climate change mitigation policies. Furthermore, the aspect of partial reversibility of temperature overshoot scenarios might not be the main concern, but rather the impacts and risks occurring during the periods of elevated temperatures during the overshoot.

Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post-thaw decomposition. We present three years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iskoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble-based Bayesian deep neural network framework. The three-year mean CO2-equivalent flux is estimated to be 106 gCO2 m-2 yr-1 for palsas, 1780 gCO2 m-2 yr-1 for ponds, and -31 gCO2 m-2 yr-1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

BackgroundImmune-related endocrinopathies are common after immune checkpoint inhibitor (ICI) therapy, among which destructive thyroiditis is the most prevalent. Improved survival outcomes have been associated with immune-related adverse events. We aimed to compare the clinical course and biochemical parameters of two subtypes of ICI-related destructive thyroiditis: a transient thyrotoxicosis that reverts to either euthyroidism (TT; transient thyroiditis) versus progression to permanent hypothyroidism (PH), and to identify prognostic markers in cancer patients receiving ICI therapy who developed DT.MethodsThis retrospective observational study included 124 patients who developed a transient thyrotoxicosis due to a destructive thyroiditis after ICI therapy from January 1, 2016 to April 30, 2021 at the Montefiore Medical Center. Patients were categorized as either TT or PH based on spontaneous renormalization of the TSH or the permanent need for thyroid hormone replacement, respectively. Thyroid hormone and antibody levels, serum inflammatory markers, eosinophils, and metabolic uptake of the thyroid on PET imaging, each corresponding closest to a suppressed TSH, were characterized. Survival from TT and PH were also analyzed.ResultsOf the 124 patients, 53 developed PH and 71 developed TT. The PH group developed thyrotoxicosis at a median of 42 days from the first ICI dose while the TT group took significantly longer at 56 days. Thyroidal PET uptake was increased in 18.9% of the PH group versus 6.0% of the TT group (P=0.04). Three different survival models consistently demonstrated a trend towards increased survival in the PH group, compared to the TT group.ConclusionOur results suggest that PH developing after ICI-induced destructive thyroiditis may be associated with a more robust inflammatory and antitumor response to ICI therapy. The results suggests that PH may be a potential clinical predictor of improved survival.

Intrauterine and early life exposure to folic acid has significantly increased in North America owing to folic acid fortification, widespread supplemental use, and periconceptional supplementation. We investigated the effects of maternal and postweaning folic acid supplementation on mammary tumor risk in the offspring. Female rats were placed on a control or folic acid–supplemented diet prior to mating and during pregnancy and lactation. At weaning, female pups from each maternal diet group were randomized to the control or supplemented diet and mammary tumors were induced with 7,12 dimethylbenz[a]anthracene at puberty. At necropsy, mammary tumor parameters, genomic DNA methylation, and DNA methyltransferase activity were determined in the offspring. Both maternal and postweaning folic acid supplementation significantly increased the risk of mammary adenocarcinomas in the offspring (OR = 2.1, 95% CI 1.2–3.8, P = 0.008 and OR = 1.9, 95% CI 1.1–3.3, P = 0.03, respectively). Maternal folic acid supplementation also significantly accelerated the rate of mammary adenocarcinoma appearance (P = 0.002) and increased the multiplicity of mammary adenocarcinomas (P = 0.008) in the offspring. Maternal, but not postweaning, folic acid supplementation significantly reduced global DNA methylation (P = 0.03), whereas postweaning, but not maternal, folic acid supplementation significantly decreased DNA methyltransferase activity (P = 0.05) in nonneoplastic mammary glands of the offspring. Our findings suggest that a high intrauterine and postweaning dietary exposure to folic acid may increase the risk of mammary tumors in the offspring. Further, they suggest that this tumor-promoting effect may be mediated in part by altered DNA methylation and DNMT activity. Cancer Res; 71(3); 988–97. ©2010 AACR.

Supplementary Table 1 from Effect of Maternal and Postweaning Folic Acid Supplementation on Mammary Tumor Risk in the Offspring

This study analyzed major issues related to diabetes during the coronavirus disease (COVID-19) pandemic by using topic modeling analysis of online news articles provided by BIGKind dating from 20 January 2020, the onset of the COVID-19 outbreak in Korea, to 17 April 2022, the lifting of the social distancing restrictions. We selected 226 articles and conducted topic modeling analysis to identify the main agenda of news related to patients with diabetes in the context of the COVID-19 pandemic; both latent Dirichlet allocation and visualization were conducted by generating keywords extracted from news text as a matrix using Python 3.0. Four main topics were extracted from the news articles related to “COVID-19” and “diabetes” during the COVID-19 pandemic, including “COVID-19 high-risk group,” “health management through digital healthcare,” “risk of metabolic disease related to quarantine policy,” and “child and adolescent obesity and diabetes.” This study is significant because it uses big data related to diabetes that was reported in the mass media during the new epidemic to identify problems in the health management of patients with diabetes during a new epidemic and discuss areas that should be considered for future interventions.

In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

Purpose: The purpose of this study was to identify the gaps in research related to developmentally supportive care in the neonatal intensive care unit (NICU). The ultimate goal was to explore directions of further research on developmentally supportive care for premature patients.Methods: The Arksey and O'Malley scoping review method was used. Articles on developmentally supportive care for preterm infants in the NICU, written in English or Korean, were identified through electronic search engines. A total of 279 papers were identified in the initial search, of which 22 full-text papers were included in this review.Results: Several nursing studies have been published in the past 5 years. The important elements of developmentally supportive care were family-centered care and management of the NICU environment. The primary developmentally supportive care interventions were training programs to promote the care competency of NICU nurses.Conclusion: It is necessary to actively develop comprehensive developmental support interventions that consider the various elements of developmentally supportive care for preterm infants. Additional studies should be done to develop programs that provide direct intervention for premature infant and their families.

Sustainability has become a critical concern for the textile and apparel industry. Over years, a variety of initiatives and practices have been implemented in the industry to reduce the impact to the environment. Offering sustainable products has been an endeavor for many companies in the apparel industry, including the outdoor wear segment. While Consumers are increasingly aware of and interested in buying sustainable products, much research is still needed in terms of important attributes influencing consumers’ purchase decision as well as tradeoffs consumers make when important attributes conflicting against each other. Employing a conjoint analysis approach, this study aims to investigate how different product attributes (i.e., type of fabric, type of down, eco-label certification, and price) jointly affect consumers’ sustainable apparel purchase decision in more realistic settings. As a result of a full profile fractional factorial design, a parsimonious orthogonal array of eight profiles were generated, representing eight different realistic combinations of product attributes. Four hundred and four participants were recruited to participate in this study to evaluate and indicate their purchase intention toward each product profile. Results suggest that price, as expected, is still the most important attribute to consumers, followed by type of fabric(recycled), availability of eco-labels, and type of down (traceable). Moreover, the results reveal that the tradeoffs consumers made varied due to their demographics in terms of gender, education, income, and past purchase experience. This research provides insightful managerial implications to fashion practitioners in their efforts in formulating strategies for product development and marketing by identifying preferred product attributes from the consumer-centric perspective.

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines. publishedVersion

In the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. Slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here, we compare taxonomic and functional community compositional responses to a long-term (17 years) warming experiment in Svalbard, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by twelve size- and leaf economics-related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and leaf mass increased, and leaf δ15N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting functional resilience of those communities, dominated by long-lived plants, due to either phenotypic plasticity or genotypic variation that most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO2 fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long-term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.

Diabetes mellitus renders patients susceptible to chronic wounds and various infections. Regarding the latter, fungal infections are of particular concern since, although they are the source of significant morbidity and mortality in immunocompromised patients, they are generally resistant to conventional treatment and a definite treatment strategy has not yet been established. Herein, we report the treatment of skin wounds in a diabetic rat model, infected by Candida albicans, with low temperature helium plasma generated in a hand-held atmospheric jet device. A fungal infection was induced on two dorsal skin wounds of the diabetic rats, and one wound was treated with the plasma jet whereas the other served as a control. Histological analysis revealed accelerated skin wound healing and decreased evidence of fungal infection in the plasma-treated group, as compared to the control group. Regeneration of the epidermis and dermis, collagen deposition, and neovascularization were all observed as a result of plasma treatment, but without wound contraction, scar formation or any evidence of thermal damage to the tissue. These findings demonstrate that the He plasma jet is remarkably effective in diabetic skin wounds infected by Candida albicans, thereby providing a promising medical treatment option for diabetes mellitus patients with skin wound and fungal infections.

Background Since COVID-19 limits safe clinical practice settings, virtual reality (VR) emerged as an alternative to practical education. Using VR simulation to learn mechanical ventilation is rare in nursing education. Methods This study developed a VR simulation program for mechanical ventilation care and evaluated its effects. We adopted a quasi-experiment design. Participants were fourth-year students across nursing colleges in South Korea. Results The interaction effect of the intervention time point and control group, to which the VR simulation program was applied, showed a significant difference in self-efficacy (F = 19.54, p p p = .026). The level of learning immersion (t = − 3.13, p = .003) and learning satisfaction (t = − 3.49, p = .001) were statistically significantly higher in the experimental group than in the control group. Conclusion These findings confirmed that the VR stimulation program for nursing students had a positive effect on their self-efficacy, clinical reasoning capacity, and learning satisfaction. Furthermore, it was effective in improving their nursing competence.

SEM RESUMO

Background Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education. Objectives This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic. Design This quasi-experimental study used a nonequivalent control group non-synchronized pretest–posttest design. Methods The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests. Results The experimental group—in which the artificial intelligence chatbot program was applied—did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants’ interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group. Conclusion The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students’ interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students’ skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.

Terrestrial ecosystems respond to changes in environmental conditions, mainly via key climatic controls of precipitation and temperature on vegetation activities and decomposition processes (Taylor et al., 2017). Yet, the relationship between climate and the overall spatiotemporal dynamics and uncertainties of the global carbon cycle, i.e., gross primary productivity (GPP), effective ecosystem carbon turnover times (τ), and consequently the total ecosystem carbon stock (Ctotal), are unclear (Anav et al., 2013; Friedlingstein et al., 2014; Friend et al., 2014; Jones et al., 2013). Using a global observation-based synthesis, we first show that the apparent partial spatial climate sensitivities of GPP and τ are associated with relative availability of precipitation and temperature, and are therefore modulated by aridity. The apparent sensitivity of GPP to temperature increases from arid to humid climatic regions. In contrast, its sensitivity to precipitation is invariant throughout different climatic regions. Simultaneously, the τ-precipitation response is strongly non-linear resulting in ~2 times longer τ in arid regions compared to humid regions for a given temperature. Compared with these observed patterns, the offline carbon cycle simulations of seven European Earth System Models (ESMs), that participated in CMIP6, perform relatively better for climate sensitivities of GPP than those of τ. This leads to a large spread and bias in Ctotal in both warm and cold semi-arid and arid regions where only a few models capture the observed τ-precipitation relationship. The emergence of the hydrological controls, modulated by aridity, on global carbon cycle implies that the changes in precipitation may moderate the temperature-driven climate feedback of the global carbon cycle under climate change.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9days advance and 5.5days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56days) or among years (mean range 32days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

In recent years, robots have been playing the role of counselor or conversational partner in everyday dialogues and interactions with humans. For successful human–robot communication, it is very important to identify the best conversational strategies that can influence the responses of the human client in human–robot interactions. The purpose of the present study is to examine linguistic behaviors in human–human conversation using chatting data to provide the best model for effective conversation in human–robot interaction. We analyzed conversational data by categorizing them into question types, namely Wh-questions and “yes” or “no” (YN) questions, and their correspondent linguistic behaviors (self-disclosure elicitation, self-disclosure, simple “yes” or “no” answers, and acknowledgment). We also compared the utterance length of clients depending on the question type. In terms of linguistic behaviors, the results reveal that the Wh-question type elicited significantly higher rates of self-disclosure elicitation and acknowledgment than YN-questions. Among the Wh-subtype, how was found to promote more linguistic behaviors such as self-disclosure elicitation, self-disclosure, and acknowledgment than other Wh-subtypes. On the other hand, YN-questions generated significantly higher rates of simple “yes” or “no” answers compared to the Wh-question. In addition, Wh-question type elicited longer utterance than the YN-question type. We suggested that the type of questions of the robot counselor must be considered to elicit various linguistic behaviors and utterances of humans. Our research is meaningful in providing efficient conversation strategies for robot utterances that conform to humans’ linguistic behaviors.