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As climate change intensifies, global publics will experience more unusual weather and extreme weather events. How will individual experiences with these weather trends shape climate change beliefs, attitudes, and behaviors? In this article, we review 73 papers that have studied the relationship between climate change experiences and public opinion. Overall, we find mixed evidence that weather shapes climate opinions. Although there is some support for a weak effect of local temperature and extreme weather events on climate opinion, the heterogeneity of independent variables, dependent variables, study populations, and research designs complicate systematic comparison. To advance research on this critical topic, we suggest that future studies pay careful attention to differences between self-reported and objective weather data, causal identification, and the presence of spatial autocorrelation in weather and climate data. Refining research designs and methods in future studies will help us understand the discrepancies in results, and allow better detection of effects, which have important practical implications for climate communication. As the global population increasingly experiences weather conditions outside the range of historical experience, researchers, communicators, and policymakers need to understand how these experiences shape-and are shaped by-public opinions and behaviors.

Reasonable and effective assessment of express service quality can help express company discover its own shortcomings and overcome them, which is crucial significant to enhance its service quality. When considering the decision assessment of express company, the key issue that emerge powerful ambiguity. Pythagorean fuzzy set as an efficient math tool can capture the indeterminacy successfully. The major focus of this manuscript is to explore various interactive Hamacher power aggregation operators for Pythagorean fuzzy numbers. Firstly, we defined novel interactive Hamacher operation, on this basis we presented some Pythagorean fuzzy interactive Hamacher power aggregation operators such as Pythagorean fuzzy interactive Hamacher power average, weighted average (PFIHPWA), ordered weighted average, Pythagorean fuzzy interactive Hamacher power geometric, weighted geometric (PFIHPWG) and ordered geometric operators,respectively. Meanwhile, we verified their general properties and specific cases as well. The salient feature of proposed operators is that they can not only reduce the impact of negative data and consider the interactions between membership and nonmembership degrees, but also provide more general results through a parameter. Secondly, we defined a Pythagorean fuzzy entropy measure, and then establish a method to determine the attribute weights. Further, based on the conceived PFIHPWA and PFIHPWG operators we explored a novel approach to manage multiple attribute decision making problems. At last, the proposed techniques are carried out in a real application concerning on the assessment of express service quality to display the applicability and effectiveness, as well as the influence of changed parameters on the results. In addition, its advantages are displayed by a systematic comparison with relevant approaches.

Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but almost completely disjoint communities (they have different journals, different conferences, different test functions, etc.) solving these problems: a broad community of practitioners using stochastic nature-inspired metaheuristics and people from academia studying deterministic mathematical programming methods. In order to bridge the gap between these communities we propose a visual technique for a systematic comparison of global optimization algorithms having different nature. Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations.

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent’s decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human–robot interaction community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses (1) from what underlying dimensions (e.g. homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, (2) what types of emotions have been derived from these dimensions, and (3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

A major limitation of existing international business (IB) research remains the rather thin view of institutional context. In this retrospective, we reflect upon and highlight different strategies for overcoming de-contextualized perspectives and developing thicker conceptions of institutions drawing on comparative research. Institutions shape firm behavior not only through their direct or additive effects, but have more complex influences by moderating relationships between firm-level variables or having interactive or configurational effects related to wider sets of institutions. These views can each be extended by adopting a dynamic perspective examining how multinational enterprise (MNE) agency contributes to processes of institutional change. Ultimately, a large gap remains in taking institutions seriously that IB scholars could fill by developing middle-range theories that link and compare how particular kinds of institutions or institutional configurations influence particular kinds of MNE activities.

Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R < 1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1971.

Given their historic emissions and economic capability, we analyze a leadership role for representative industrialized regions (EU, US, Japan, and Australia) in the global climate mitigation effort. Using the global integrated assessment model REMIND, we systematically compare region-specific mitigation strategies and challenges of reaching domestic net-zero carbon emissions in 2050. Embarking from different emission profiles and trends, we find that all of the regions have technological options and mitigation strategies to reach carbon neutrality by 2050. Regional characteristics are mostly related to different land availability, population density and population trends: While Japan is resource limited with respect to onshore wind and solar power and has constrained options for carbon dioxide removal (CDR), their declining population significantly decreases future energy demand. In contrast, Australia and the US benefit from abundant renewable resources, but face challenges to curb industry and transport emissions given increasing populations and high per-capita energy use. In the EU, lack of social acceptance or EU-wide cooperation might endanger the ongoing transition to a renewable-based power system. CDR technologies are necessary for all regions, as residual emissions cannot be fully avoided by 2050. For Australia and the US, in particular, CDR could reduce the required transition pace, depth and costs. At the same time, this creates the risk of a carbon lock-in, if decarbonization ambition is scaled down in anticipation of CDR technologies that fail to deliver. Our results suggest that industrialized economies can benefit from cooperation based on common themes and complementary strengths. This may include trade of electricity-based fuels and materials as well as the exchange of regional experience on technology scale-up and policy implementation.

We describe the development of solar water-splitting cells comprising earth-abundant elements that operate in near-neutral pH conditions, both with and without connecting wires. The cells consist of a triple junction, amorphous silicon photovoltaic interfaced to hydrogen-and oxygen-evolving catalysts made from an alloy of earth-abundant metals and a cobaltlborate catalyst, respectively. The devices described here carry out the solar-driven water-splitting reaction at efficiencies of 4.7% for a wired configuration and 2.5% for a wireless configuration when illuminated with 1 sun (100 milliwatts per square centimeter) of air mass 1.5 simulated sunlight. Fuel-forming catalysts interfaced with light-harvesting semiconductors afford a pathway to direct solar-to-fuels conversion that captures many of the basic functional elements of a leaf.