Explore the vast range of titles available.

Known for centuries, the geographical pattern of increasing biodiversity from the poles to the equator is one of the most pervasive features of life on Earth. A long-standing goal of biogeographers has been to understand the primary factors that generate and maintain high diversity in the tropics. Many 'historical' and 'ecological' hypotheses have been proposed and debated, but there is still little consensus. Recent discussions have centred around two main phenomena: phylogenetic niche conservatism and ecological productivity. These two factors play important roles, but accumulating theoretical and empirical studies suggest that the single most important factor is kinetics: the temperature dependence of ecological and evolutionary rates. The relatively high temperatures in the tropics generate and maintain high diversity because 'the Red Queen runs faster when she is hot'.

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

During the COVID-19 pandemic, comprehensive, accurate, and timely digital contact tracing serves as a decisive measure in curbing viral transmission. Such a strategy integrates corporate innovation, government decision-making, citizen participation, and community coordination with big data analytics. This article explores how key stakeholders in an open innovation ecosystem interact within the digital context to overcome challenges to public health and socio-economic welfare imposed by the pandemic. To enhance the digital contact tracing effectiveness, communities are deployed to moderate the interactions between government, enterprises and citizens. As an example, we study the community-based digital contact tracing in Wuhan, a representative case of China's 'virus exceptionalism' in COVID-19 mitigation. We discuss the effectiveness of this strategy and raise critical ethical concerns regarding decision-making in R&D management. (This abstract was borrowed from another version of this item.)

Optimizing construction project scheduling has received a considerable amount of attention over the past 20 years. As a result, a plethora of methods and algorithms have been developed to address specific scenarios or problems. A review of the methods and algorithms that have been developed to examine the area of construction schedule optimization (CSO) is undertaken. The developed algorithms for solving the CSO problem can be classified into three methods: mathematical, heuristic and metaheuristic. The application of these methods to various scheduling problems is discussed and implications for future research are identified.

AIM: Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues remain. Here we address three critical ones: selecting appropriate evaluation data, detecting overfitting, and tuning program settings to approximate optimal model complexity. We integrate solutions to these issues for Maxent models, using the Caribbean spiny pocket mouse, Heteromys anomalus, as an example. LOCATION: North‐western South America. METHODS: We partitioned data into calibration and evaluation datasets via three variations of k‐fold cross‐validation: randomly partitioned, geographically structured and masked geographically structured (which restricts background data to regions corresponding to calibration localities). Then, we carried out tuning experiments by varying the level of regularization, which controls model complexity. Finally, we gauged performance by quantifying discriminatory ability and overfitting, as well as via visual inspections of maps of the predictions in geography. RESULTS: Performance varied among data‐partitioning approaches and among regularization multipliers. The randomly partitioned approach inflated estimates of model performance and the geographically structured approach showed high overfitting. In contrast, the masked geographically structured approach allowed selection of high‐performing models based on all criteria. Discriminatory ability showed a slight peak in performance around the default regularization multiplier. However, regularization levels two to four times higher than the default yielded substantially lower overfitting. Visual inspection of maps of model predictions coincided with the quantitative evaluations. MAIN CONCLUSIONS: Species‐specific tuning of model parameters can improve the performance of Maxent models. Further, accurate estimates of model performance and overfitting depend on using independent evaluation data. These strategies for model evaluation may be useful for other modelling methods as well.

Sustainable operations management (SOM) can be defined as the operations strategies, tactics and techniques, and operational policies to support both economic and environmental objectives and goals. The subject of sustainability has gained much attention from both researchers and practitioners in the past 6–8 years. Most of the articles deal with sustainability from environmental perspectives, but a limited number of them integrate both economic and environmental implications or focus on trading-off between profitability, competitiveness and environmental dimensions. Moreover, there is a limited focus on modelling and analysis (MA) of SOM integrating and balancing the interests of both economic and environmental interests. Therefore, an attempt has been made in this paper to review the extant literature on SOM. The objective is to understand the definition of SOM and present the current status of research in MA, as well as future research directions in the field. Considering the recent focus of the subject, we review the literature on MA of SOM beginning in 2000 in order to make our study current and more relevant for both researchers and practitioners. Finally, a summary of findings and conclusions is reported.