Explore the vast range of titles available.

\"The book explores the central question facing humanity today: how can we best survive the ten great existential challenges that are now coming together to confront us? ... The author examines ten intersecting areas of activity (mass extinction, resource depletion, WMD, climate change, universal toxicity, food crises, population and urban expansion, pandemic disease, dangerous new technologies and self-delusion) which pose manifest risks to civilization and, potentially, to our species' long-term future. This isn't a book just about problems. It is also about solutions. Every chapter concludes with clear conclusions and consensus advice on what needs to be done at global level--but it also empowers individuals with what they can do for themselves to make a difference. Unlike other books, it offers integrated solutions across the areas of greatest risk. It explains why Homo sapiens is no longer an appropriate name for our species, and what should be done about it\"--Back cover.

The standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality , which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages. Understanding of complex contagions is crucial for explaining diffusion processes in networks. Guilbeault and Centola introduce topological mechanisms and measures to elucidate spreading dynamics and identify the most influential nodes in social, epidemic and economic networks.

\"This collection explores the productive potential of uncertainty for people living in Africa as well as for scholars of Africa. The relevance of the focus on uncertainty in Africa is not only that contemporary life is objectively risky and unpredictable (since it is so everywhere and in every period), but that uncertainty has become a dominant trope in the subjective experience of life in contemporary African societies. The contributors investigate how uncertainty animates people's ways of knowing and being across the continent. An introduction and eight ethnographic studies examine uncertainty as a social resource that can be used to negotiate insecurity, conduct and create relationships, and act as a source for imagining the future. These in-depth accounts demonstrate that uncertainty does not exist as an autonomous, external condition. Rather, uncertainty is entwined with social relations and shapes people's relationship between the present and the future. By foregrounding uncertainty, this volume advances our understandings of the contingency of practice, both socially and temporally\"-- Provided by publisher.

Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underexplored. This study presents a computational framework utilizing the Nonlinear Autoregressive network with Exogenous inputs (NARX) model to predict concentrations of key pollutants SO₂, PM₁₀, NO, NOX, and O₃ in Esenyurt, one of the most industrialized districts in Istanbul, Turkey. Through systematic feature selection techniques, the study determines the most influential seasonal factors for each pollutant, reducing model complexity while improving predictive accuracy. The developed framework exhibits substantial improvements in predictive performance, with the optimal models achieving high determination coefficients (up to R² = 0.965 for O₃) and low error metrics across training and validation datasets. Particularly, the inclusion of seasonal variables considerably improved prediction accuracy for NO, NO₂, and PM₁₀, while SO₂ predictions performed best when utilizing comprehensive seasonal indicators. These results demonstrate that seasonal dynamics play a crucial role in governing pollutant behavior and highlight the importance of incorporating such variables in forecasting models. This research contributes significantly to the field by advancing methodological approaches in air quality prediction while providing an adaptable model for policymakers and environmental agencies to implement in proactive pollution management strategies. Through examination of seasonal dependencies in air pollutant patterns, the study delivers a practical tool for urban planning and public health applications in rapidly expanding metropolitan regions.

In 1964, two researchers at RAND, Olaf Helmer and Theodore Gordon, presented what they argued was a general theory of prediction, a theory that, Helmer boasted, would \"enable people to deal with socio-economic and political problems as confidently as they do with problems in physics and chemistry.\" Work had begun at RAND in the early 1960s to find a systematic and scientific approach to the future. Computers had made it possible to \"amass all available information\" about ongoing developments and process it in a systematic way, providing \"the kind of massive data processing and interpreting capability that, in the physical sciences, created the breakthrough which led to the development of the atomic bomb.\" This meant a radical shift in notions of the future, a shift that was emphasized by many of the futurists of the period. The future, Helmer stated in another assertive piece, could now be liberated from the grip of utopian fantasy and superstition and be welcomed into the halls of science. Here, Andersson traces the global struggle for future representations in the multifaceted field of prediction (forecasting, futurology, futures studies) in the postwar period.

Practices used to address economic forecasting problems have undergone substantial changes over recent years. We review how such changes have influenced the ways in which a range of forecasting questions are being addressed. We also discuss the promises and challenges arising from access to big data. Finally, we review empirical evidence and experience accumulated from the use of forecasting methods to a range of economic and financial variables.

Traditional fully-deterministic algorithms, which rely on physical equations and mathematical models, are the backbone of many scientific disciplines for decades. These algorithms are based on well-established principles and laws of physics, enabling a systematic and predictable approach to problem-solving. On the other hand, AI-based strategies emerge as a powerful tool for handling vast amounts of data and extracting patterns and relationships that might be challenging to identify through traditional algorithms. Here, we bridge these two realms by using AI to find an optimal mapping of meteorological features predicted two days ahead by the state-of-the-art numerical weather prediction model by the European Centre for Medium-range Weather Forecasts (ECMWF) into lightning flash occurrence. The prediction capability of the resulting AI-enhanced algorithm turns out to be significantly higher than that of the fully-deterministic algorithm employed in the ECMWF model. A remarkable Recall peak of about 95% within the 0-24 h forecast interval is obtained. This performance surpasses the 85% achieved by the ECMWF model at the same Precision of the AI algorithm. In this work, authors propose a synergistic approach combining state-of-the-art deterministic forecasting model with artificial intelligence for predicting lightning occurrences. The strategy shows efficient predictive capabilities at medium-range forecast horizons.

Since the beginning of the modern age, studies of ongoing transformations of social life, human sociality, and social relations and institutions have been at the forefront of social theory, alongside changes in politics, culture, and economy - and links between all of the above. In the twenty-first century, the speed at which these transformations have been occurring has accelerated precipitously, and it is impossible to predict what human civilization will look and exist like in a few decades. The essays included in this volume illuminate mediations of the individual-society relationship from a variety of angles, both explicitly and implicitly. They highlight the need to consider the consequences of choices made by collective decision-makers, politicians and leaders of organizations; as well as from processes that sustain the functioning and stability of individual nation-states and global society, for better or worse, and to varying degrees. They represent diverse traditions of social theorizing, including sociological and critical theory, analytically as well as normatively oriented theory, and examine the impact of transformations on several dimensions of societal life today

Fine particulate matter (PM 2.5 ) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM 2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM 2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM 2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient ( R 2 ) of 0.928, and root mean square error of 30.325 µg/m 3 . The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM 2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

As digital transformation continues to penetrate various sectors of society, the issue of the digital divide has become increasingly prominent. Against the backdrop of accelerating population aging, the barriers that older adults face in accessing and utilizing digital information have exerted a profound impact on their quality of life. This study employs tree-based ensemble learning algorithms to predict and identify the key factors of the digital divide that influence life satisfaction among older adults. It also evaluates the predictive performance of these models, thereby providing interpretive insights into the impact of the digital divide on subjective well-being. Using original data from the ‘2023 Report on Digital Information Divide Survey’ conducted by the National Information Society Agency of South Korea, this study constructs an analytical framework that integrates both predictive capability and interpretability. First, the XGBoost model is employed to conduct feature importance analysis, identifying 15 key variables that are highly influential in predicting life satisfaction. These variables are further examined using the SHAP method to provide interpretive insights into their contributions. Subsequently, multiple tree-based ensemble learning algorithms—including Random Forest, XGBoost, LightGBM, and CatBoost—are applied to compare their predictive performance. The results indicate that variables related to technological self-efficacy, digital information literacy, social capital, experience and perception of AI services, and household monthly income are significant predictors of life satisfaction among older adults. Among the models tested, CatBoost demonstrates superior overall predictive accuracy, suggesting its effectiveness in forecasting life satisfaction in this demographic. This study expands the application of machine learning in areas such as aging research and the digital divide and proves the effectiveness of ensemble learning algorithms in predicting digital divide factors that affect the life satisfaction of older adults. This approach provides a novel and powerful methodological for addressing complex social problems. Moreover, the study uncovers the structural configuration of key digital information factors associated with life satisfaction, offering data-driven insights into the mechanisms through which the digital divide influences well-being. These results have practical implications for enhancing digital inclusion, improving adaptability among older adults, and fostering a stronger sense of participation and happiness in digital society.