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Local microclimates can both amplify and mitigate extreme temperatures associated with climate change
Get PDFClimate change is a threat to global biodiversity, with changes to mean temperatures and increasing frequency and intensity of extreme weather events. Heatwaves in particular pose a threat to species’ persistence, as temperatures may rise above physiological tolerance. However, individuals rarely experience temperatures measured at the macroclimatic scale: topographic or vegetation differences result in microclimates that provide cool refugia (local temperatures below ambient) or even result in heat traps (local temperatures above ambient) during heatwaves. However, little is known about the stability of microclimates through a period of regional warming. In this study, we recorded microclimate temperatures across different microhabitats within a calcareous grassland nature reserve in Bedfordshire, UK, in 2018, 2019 and 2022. During this time, six heatwave events occurred, including the highest air temperatures ever recorded in the UK. We found that the ability of microhabitats to offset air temperatures varied with topographic aspect, slope, amount of bare ground, shelter, vegetation height, and vegetation type, with encroaching scrub and north-facing slopes showing the strongest abilities to maintain relatively stable microclimate temperatures with increasing air temperatures, in contrast to short vegetation on south-facing slopes which became heat traps. However, no combinations of environmental structures consistently maintained cool refugia during heatwaves. Microclimate temperatures were amplified close to the ground, whereas at 50 cm height temperatures were more stable and similar to the macroclimate temperature, therefore surface-dwelling species, such as many insects, may be particularly vulnerable to extreme heat. We identified a breakdown in the ability of microhabitats to maintain cool refugia above 7 °C, implying cool refugia become increasing rare and unpredictable with increasing temperatures. Our results indicate that many microhabitats will amplify the effects of climate change rather than mitigate them
Agreement and reflexives in non-native sentence processing
Get PDFHow native (L1) and non-native (L2) readers utilise syntactic constraints on linguistic dependency resolution during language comprehension is debated, with previous research yielding mixed findings. To address this discrepancy, we report two large-scale studies, using self-paced reading and grammaticality judgements, investigating subject-verb agreement and reflexives in L1 English speakers and Arabic leaners of L2 English. We manipulated sentence grammaticality and the properties of ‘distractor’ constituents (The key(s) to the cabinet(s) were rusty) in two studies testing number in agreement and gender/number in reflexives. Study 1 showed L2ers’ performance largely patterned with L1ers’. Though grammaticality effects were smaller for agreement in L2ers than L1ers, proficiency modulated L2 performance. Study 2 revealed no significant between-group differences. Contrasting some L1 studies, significant distractor effects were only detected for reflexives in Study 1. Together, these results imply that L2ers compute syntactic dependencies similarly to L1ers, and potential differences might be driven by L2 proficiency
Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set
Get PDFThis study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure
Heritage education as a tool of social, ethnic, and religious cohesion in Iraq: empirical insights
Get PDFWhile the politicization of cultural heritage in Iraq is widely covered by research, little attention is dedicated to how exactly heritage education has been implemented, the knowledge gap this article aims to address. Focusing on the methods employed to educate an average citizen in cultural heritage (how) rather than on the motivation behind them (why), the article unveils novel empirical data drawn from 25 oral history interviews with Iraqi heritage-sector stakeholders demonstrating several important findings. First, a profound gap emerges between cultural awareness levels in pre-1990 and contemporary Iraqi society. Second, the data allows us to identify 12 categories of pre-1990 heritage education strategies, nearly all of which are currently missing or slowly being revived. Third, the respondents disclose a strong reliance on heritage education potential for raising cultural awareness in the sectarianism-torn society. Finally, the article suggests a holistic and inclusive heritage education methodology as a tool of social, ethnic, and religious cohesion in Iraq
Anomaly detection using invariant rules in Industrial Control Systems
Get PDFIndustrial Control Systems (ICS) are intelligent control systems that integrate computing, physical processes, and communication to manage critical infrastructures such as power grids, oil and gas processing facilities, and water treatment plants. In recent years, ICS have been increasingly targeted by malicious attacks, causing severe consequences. Anomaly detection systems utilized in ICS are crucial in safeguarding ICS from potential threats by sending out an alert upon detecting any network attacks. However, existing methods for ICS anomaly detection often suffer from limitations. Supervised machine learning methods encounter the issue of imbalanced positive and negative samples, while residual-based anomaly detection methods face challenges in detecting stealthy attacks. This paper presents an unsupervised anomaly detection method for ICS using association rule mining techniques. Utilizing the proposed variation-driven predicate generation strategy, the method incorporates temporal features of sensor readings
into the generated predicates, achieving the mining of invariant rules that take into account the temporal dependencies among physical variables. This approach allows for a more comprehensive exploration of the invariant patterns maintained in the dynamic processes of systems. Through experiments conducted on two public datasets, the method demonstrates high detection efficiency,
meeting the real-time demands of online detection. Experimental results showcase its notable efficacy in anomaly detection, with a substantial enhancement in the recall rate. Furthermore, the method’s capability to promptly issue warnings enables it to detect multiple attacks with low latency
Epistemic deprivation
Get PDFIt is often claimed that gender data gaps (GDGs) are unjust, but the nature of the
injustice has not been interrogated. We argue that injustices arising from such data
gaps are not merely socio-political but also epistemic: they arbitrarily skew the
epistemic landscape in favour of one group over another. GDGs place a greater
epistemic burden on women and gender minorities; they have to do more to avoid error
and the pay-off is worse: they have a smaller pool of true beliefs on which to act. We
suggest that there are both pragmatic and conceptual reasons to differentiate the
injustice arising from GDGs from other more familiar varieties (such as testimonial and
hermeneutical injustice), and so we introduce the new concept of epistemic deprivation
to capture this injustice
Constraining the uncertainty associated with sea salt aerosol parameterizations in global models using nudged UKESM1‐AMIP simulations
Get PDFSea salt is the largest source of natural aerosol in the atmosphere by mass. Formed when ocean waves break and bubbles burst, sea salt aerosols (SSA) influence Earth's climate via direct and indirect processes. Models participating in the sixth Coupled Model Intercomparison project (CMIP6) demonstrate a negative effective radiative forcing (ERF) when SSA emissions are doubled. However, the magnitude of the ERF ranges widely from −0.35 0.04 W to −2.28 0.07 W , with the largest difference over the Southern Ocean. Differences in the response to doubled SSA emissions arise from model uncertainty (e.g., individual model physics, aerosol size distribution) and parameterization uncertainty (e.g., how SSA is produced in the model). Here, we perform single‐model experiments with UKESM1‐AMIP incorporating all of the SSA parameterizations used by the current generation of CMIP6 Earth system models (ESMs). Using a fixed SSA size distribution, our experiments show that the parameterization uncertainty causes large inter‐model diversity in SSA emissions in the models, particularly over the tropics and the Southern Ocean. The choice of parameterization influences the ambient aerosol size distribution, cloud condensation nuclei and cloud droplet number concentrations, and therefore direct and indirect radiative forcing. We recommend that modeling groups evaluate their SSA parameterizations and update them where necessary in preparation for future model intercomparison activities
A game theory analysis of regional innovation ecosystems
No full textDespite the popularity of innovation ecosystems in the regional studies literature, the organization of knowledge cooperation and transfer within these ecosystems remains largely unexplored, particularly in countries and regions where the government plays a significant role. Regional studies and innovation literature have not addressed how and under which conditions actors start cooperation in the ecosystem, often assuming that knowledge cooperation between agents is a truism. This paper employs a Game Theory approach to examine the knowledge cooperation networks within the Nishapur Innovation Ecosystem. The model results demonstrated that seven key actors are pivotal in explaining knowledge cooperation within the ecosystem. Eighteen feasible states were identified based on the actors and their strategies. Results indicated that with the current actors and their current preferences, the current state is most likely to continue, therefore, the probability of operationalizing one model of behavior in the Nishapur innovation ecosystem is low. The results obtained from the reverse game analysis revealed that appropriate regional policies are required to ensure that the ecosystem is socially, economically, and environmentally sustainable
Building resilience in a crisis through boards – exploring the mediating effect of board behavior
No full textThe present study examines two board behaviors, their antecedents, and their consequences in the COVID-19 crisis. The two behaviors are 1) board involvement in crisis management planning and 2) board creative effort in finding solutions. The antecedents are board expertise and cognitive diversity. The consequence is firm resilience. The study builds its theoretical argument using the classical and refined upper echelons theory, stating that the two board behaviors mediate the effect of board expertise and cognitive diversity on firm resilience. Survey data from the U.S. during early 2020 was used. We found strong support for our overall argument that board involvement in crisis management planning and board creative effort in finding solutions are critical mediators. Our study also shows that the context of a crisis matters. During COVID-19, board cognitive diversity can negatively affect board behavior.
We conclude the paper with discussions and future research proposals
Self‐assembly of a conjugate of lipoic acid with a collagen‐stimulating pentapeptide showing cytocompatibility and wound healing properties, and chemical and photolytic disassembly
Get PDFLipoic acid is a biocompatible compound with antioxidant activity that is of considerable interest in cosmetic formulations, and the disulfide group in the N-terminal ring confers redox activity. Here, we study the self-assembly and aspects of the bioactivity of a lipopeptide (peptide amphiphile) comprising the KTTKS collagen-stimulating pentapeptide sequence conjugated to an N-terminal lipoic acid chain, lipoyl-KTTKS. Using SAXS, SANS and cryo-TEM, lipoyl-KTTKS is found to form a population of curly fibrils (wormlike micelles) above a critical aggregation concentration. Upon chemical reduction, the fibrils (and β-sheet structure) are disrupted because of the breaking of the disulfide bond, which produces dihydrolipoic acid. Lipoyl-KTTKS also undergoes photo-degradation in the presence of UV radiation. Through cell assays using fibroblasts, we found that lipoyl-KTTKS has excellent cytocompatibility across a wide concentration range, stimulates collagen production, and enhances the rate of cell coverage in a simple in vitro scratch assay of ‘wound healing’. Lipoyl-KTTKS thus has several notable properties that may be useful for the development of cosmetics, cell scaffolds or tissue engineering materials