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Understanding and managing uncertainty is critical for robust governance. In groundwater management, where collaborative, community-based governance is increasingly common, scientific uncertainty about hydrological conditions could pose challenges to effective and equitable resource management. This study bridges two literatures – collaborative governance and collective action – to examine whether scientific uncertainty about hydrologic conditions undermines the performance of groups that engage in collaborative governance of shared groundwater resources. We conducted a modified groundwater game experiment, based on Meinzen-Dick et al. (2016), where participants engage as resource users in a crop choice game over multiple rounds. But unlike the original game, where participants had full information about recharge rate, two treatments introduced scientific uncertainty in water recharge: uncertainty framed as a range of estimates about groundwater recharge, and uncertainty framed as competing hydrological models predicting different groundwater recharge rates. We also expand on the original game by exploring a wider range of outcomes that include not only sustainable resource use but also group earning and equitable distribution of earnings across players. Analyzing data from 30 group games, our findings suggest that scientific uncertainty can help safeguard shared groundwater resources by prompting users to exercise caution in the face of uncertain recharge rates. This effect was more consistent for the range of estimates treatment than for the competing hydrological models treatment. To unpack the mechanisms behind the experimental result, we also analyzed participants’ communications during the game to understand the strategies that collaborative groups use to cope with uncertainty. In the presence of scientific uncertainty, collaborative processes foster cautious behavior and protect shared resources.

Successful collaborative governance (CG) of social-ecological systems (SES) involves multiple stakeholders convening iteratively over the long term to reach a commonly held vision. This often involves building knowledge for social learning processes induced to come to collective decisions about managing complex systems in flux. Because of the complexity of any SES in the Anthropocene, this coproduced knowledge is frequently transdisciplinary, using a convergence of applied and scientific knowledge from a variety of disciplines and stakeholders outside academia. We find evidence that these cases of effective SES CG involve both knowledge coproduction and convergence research. We evaluated seven case studies of CG across four continents using criteria (principles and methods) developed to facilitate and describe convergence research on SES and found them to be largely present. We also assess these CG cases using indicators of knowledge coproduction, and show that they all involved transdisciplinary knowledge coproduction, which can provide an informative lens for deepening our shared understanding of convergence and its application to complex adaptive systems. All the cases selected for this paper are examples of CG of SES in which research was conducted as part of a collaborative effort to improve the social-ecological conditions in a particular place, and several incorporate various forms of knowledge and ways of knowing. We suggest that these cases demonstrate both convergence research and knowledge coproduction because of the overlap and similarity of these concepts, providing a brief comparison and contrasting of these approaches to addressing sustainability problems collaboratively.

The tribological performance and friction-induced vibration of Gd0.2-Ce0.8O1.9 (GDC) reinforced nickel oxide (NiO) metal matrix composites prepared via sintering on the tribological performance, as well as friction induced vibration were investigated. Compared to pure NiO, the composites exhibit improved mechanical properties, such as a relatively high dislocation density, hardness and small grain size. The results show that GDC-reinforced NiO nanocomposites feature improved tribological performance and can suppress the occurrence of friction-induced vibration under variable loading conditions. Furthermore, the generated acceleration can be suppressed by wear particles generated during the friction process, acting as the third body at the contact interface. As a result, the addition of GDC reduces the grain size of the composite, increases hardness, and improves tribological properties through the synergetic effect of the solid lubricating action of NiO and the role of the third body of the wear particle. Graphical Abstract

Graphene oxide (GO), which is the oxidized form of graphene, has holes and functional groups on the surface and thus has high potential to be used as an electrochemical transport channel material. In this study, differently modified GO membranes are applied as electrolytes of proton exchange membrane fuel cells (PEMFCs) with controlled carbon/oxygen ratios. The critical and desired properties of the electrolyte, such as electron conductivity, proton conductivity, interfacial reactivity, and cell performance are evaluated in identical platinum-sputtered model electrodes. Among them, with the help of an increased concentration of oxygen-containing groups, a GO membrane with a low carbon/oxygen ratio shows a 2.9-fold improved maximum power density and advanced electrochemical properties compared with the pristine GO membrane. The characterization of GO suggests that the redox state of the membrane is an important factor for controlling the proton conductivity, interfacial reactivity, and maximum power density of PEMFCs.

When people use shared resources, overextraction can occur. While deliberation tends to mitigate shared resource exploitation problems, the question remains: under what conditions does group chat improve cooperation in shared resource dilemmas? This study analyzes chat and game data from about 1500 rounds of gameplay involving 143 groups across 4 resource types using Sentiment Analysis and Structural Topic Model. We find that, despite their fundamental differences, the 4 games tend to have similar discussions, including strategizing actions, coordinating choices, and socialization, but they differ in which topics explain cooperation within each game. Discussion topics promoting cooperation include coordination in the foraging game (FOR) and long-term goals in the groundwater game (GG). However, discussion topics negatively associated with cooperation include off-topic/socialization in FOR and the irrigation game (IRR) and crop choice affirmation in GG. We suggest that the context in which communication occurs matters, and biophysical characteristics, rules of the game, and levels of uncertainty explain some variations of our findings.

Scholars have recognized the importance of uncertainty as institutional context in collaborative water management, but the relationship between uncertainty and collaborative performance is mixed. On the one hand, increased uncertainty will positively enhance the performance of collaborative governance through new ties and innovations. On the other hand, certain types of uncertainties are negatively associated with collaborative performance. To understand the puzzle of uncertainty and collaborative performance, I take the problem of groundwater management for theory development and empirical testing. Aquifers are being depleted faster than they can recharge, leaving municipalities, irrigators, and ranchers vulnerable to ever-reducing water availability over time, but the management of groundwater problems is wicked because it involves complex social, ecological, scientific, administrative, and political issues. The effectiveness of collaborative governance depends in large part on the way in which stakeholders perceive, interpret, and use uncertain information.This dissertation fills the theoretical and empirical gap by using multi method research design. The first research question is: What are the nature and characteristics of uncertainty in collaborative governance? This question is addressed based on the in-depth case study of Upper San Pedro Watershed Partnership in Arizona, U.S. Based on the various sources of empirical data, including 22 in-depth interviews, policy reports, and local news articles, conceptual typology and theoretical propositions are proposed to develop theories of collaborative governance under uncertainties. Results suggest that scientific and managerial uncertainty are significant and tend to have negative effects on the performance of groundwater management, but the relationship between uncertainty and collaborative performance can be positively or negatively moderated by the quality of relation management including integrative leadership and cohesion building between participants. Results also suggest that levels and sources of uncertainty tend to change as collaboration evolves and thus the relationship between uncertainty and performance may shift over time.Having recognized that understanding scientific uncertainty is important in groundwater management based on the case study, this dissertation asks two questions: How and to what extent does scientific uncertainty affect collaborative performance? Do collaborative management tools have an impact on different types of collaborative outcomes, particularly under the condition of scientific uncertainty? This dissertation modified a groundwater game experiment where groups of 4-5 participants play a crop choice game for multiple rounds as resource users (Meinzen-Dick et al. 2016). The goal of this game for each participant is to grow as many profitable crops as possible under conditions where all users share groundwater resources with limited ability to recharge. But unlike the original game, where participants had full information about recharge rate, two treatments are introduced about scientific uncertainty in water recharge: uncertainty operationalized as a range of values (Treatment 1) and uncertainty operationalized as competing hydrological models (Treatment 2). Using quantitative and qualitative game experimental data from 30 groups, results suggest that more uncertain information tends to reduce individual earnings and thus increase shared resources. A range of uncertain information has a more significant impact on resource behavior than competing information. Finally, post-experimental analysis shows that diverse collaboration strategies tend to reduce distributional inequity among game participants. This dissertation contributes to the literature of collaborative governance and collective action by explicitly theorizing and modelling the relationship between uncertainty, collaboration process, and performance.

HighlightsThis article systematically reviews the thermal management wearables with a specific emphasis on materials and strategies to regulate the human body temperature.Thermal management wearables are subdivided into the active and passive thermal managing methods.The strength and weakness of each thermal regulatory wearables are discussed in details from the view point of practical usage in real-life.Thermal management is essential in our body as it affects various bodily functions, ranging from thermal discomfort to serious organ failures, as an example of the worst-case scenario. There have been extensive studies about wearable materials and devices that augment thermoregulatory functionalities in our body, employing diverse materials and systematic approaches to attaining thermal homeostasis. This paper reviews the recent progress of functional materials and devices that contribute to thermoregulatory wearables, particularly emphasizing the strategic methodology to regulate body temperature. There exist several methods to promote personal thermal management in a wearable form. For instance, we can impede heat transfer using a thermally insulating material with extremely low thermal conductivity or directly cool and heat the skin surface. Thus, we classify many studies into two branches, passive and active thermal management modes, which are further subdivided into specific strategies. Apart from discussing the strategies and their mechanisms, we also identify the weaknesses of each strategy and scrutinize its potential direction that studies should follow to make substantial contributions to future thermal regulatory wearable industries.

UPF1, a novel posttranscriptional regulator, regulates the abundance of transcripts, including long noncoding RNAs (lncRNAs), and thus plays an important role in cell homeostasis. In this study, we revealed that UPF1 regulates the abundance of hepatocellular carcinoma upregulated EZH2-associated lncRNA (lncRNA-HEIH ) by binding the CG-rich motif, thereby regulating hepatocellular carcinoma (HCC) tumorigenesis. UPF1-bound lncRNA-HEIH was susceptible to degradation mediated by UPF1 phosphorylation via SMG1 and SMG5. According to analysis of RNA-seq and public data on patients with liver cancer, the expression of lncRNA-HEIH increased the levels of miR-194-5p targets and was inversely correlated with miR-194-5p expression in HCC patients. Furthermore, UPF1 depletion upregulated lncRNA-HEIH , which acts as a decoy of miR-194-5p that targets GNA13, thereby promoting GNA13 expression and HCC proliferation. The UPF1/lncRNA-HEIH/miR-194-5p/GNA13 regulatory axis is suggested to play a crucial role in cell progression and may be a suitable target for HCC therapy. UPF1’s key role in regulating hepatocellular carcinoma tumorigenesis unveiled Hepatocellular carcinoma (HCC, a type of liver cancer), has a high death rate due to limited effective treatments. Current medications often result in resistance and only prolong life by a few months. This research investigates the role of UPF1, a crucial component in a process called nonsense-mediated mRNA decay (the elimination of faulty genetic messages), in controlling the expression of a long noncoding RNA (lncRNA, a type of genetic material) known as lncRNA-HEIH. The scientists discovered that reducing UPF1 increased the level of lncRNA-HEIH, which subsequently encouraged the growth of HCC cells. Moreover, lncRNA-HEIH was found to distract a molecule named miR-194-5p, thus increasing the expression of a cancer-promoting gene called GNA13. This UPF1/lncRNA-HEIH/miR-194-5p/GNA13 regulatory pathway could potentially be targeted for therapeutic interventions in HCC. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Background and objectives Post-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke. Methods This retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3–6 months, defined as an adjusted z -score of less than − 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables. Results A total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts. Conclusion Our findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.

An aging population and human longevity is a global trend. Many developed countries are struggling with the yearly increasing healthcare cost that dominantly affects their economy. At the same time, people living with old adults suffering from a progressive brain disorder such as Alzheimer’s disease are enduring even more stress and depression than those patients while caring for them. Accordingly, seniors’ ability to live independently and comfortably in their current home for as long as possible has been crucial to reduce the societal cost for caregiving and thus give family members peace of mind, called ‘aging in place’ (AIP). In this paper we present a way of building AIP services using standard-based IoT platforms and heterogeneous IoT products. An AIP service platform is designed and created by combining previous standard-based IoT platforms in a collaborative way. A service composition tool is also created that allows people to create AIP services in an efficient way. To show practical usability of our proposed system, we choose a service scenario for medication compliance and implement a prototype service which could give old adults medication reminder appropriately at the right time (i.e., when it is time to need to take pills) through light and speaker at home but also wrist band and smartphone even outside the home.