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Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

This research investigates whether low-literate consumers process written advertisements differently than high-literate consumers do. Consistent with resource-matching theory (RMT), the first experiment reveals that, unlike high-literate processors, when low-literate processors read ads of moderate complexity, involvement with the ad does not affect processing. The second experiment extends RMT's applicability to both low- and high-literate consumers by demonstrating that low-literate processors' reading outcomes mirror those of high-literate processors when ads are written to reflect their reading capability. ► We test whether low-literate consumers are different from high-literate consumers. ► Involvement does not affect low-literate consumers’ moderately complex ad processing. ► Low-literate consumers’ reading mirrors high-literate consumers in a simple ad.

Mast seeding is a widespread and widely studied phenomenon. However, the physiological mechanisms that mediate masting events and link them to weather and plant resources are still debated. Here, we explore how masting is affected by plant resource budgets, fruit maturation success, and hormonal coordination of cues including weather and resources. There is little empirical support for the commonly stated hypothesis that plants store carbohydrates over several years to expend in a high-seed year. Plants can switch carbohydrates away from growth in high-seed years, and seed crops are more probably limited by nitrogen or phosphorus. Resources are clearly involved in the proximate mechanisms driving masting, but resource budget (RB) models cannot create masting in the absence of selection because some underlying selective benefit is required to set the level of a ‘full’ seed crop at greater than the annual resource increment. Economies of scale (EOSs) provide the ultimate factor selecting for masting, but EOSs probably always interact with resources, which modify the relationship between weather cues and reproduction. Thus, RB and EOS models are not alternative explanations for masting – both are required. Experiments manipulating processes that affect mast seeding will help clarify the physiological mechanisms that underlie mast seeding.

How organisms gather and utilize information about their landscapes is central to understanding land-use patterns and population distributions. When such information originates beyond an individual’s immediate vicinity, movement decisions require integrating information out to some perceptual range. Such nonlocal information, whether obtained visually, acoustically, or via chemosensation, provides a field of stimuli that guides movement. Classically, however, models have assumed movement based on purely local information (e.g., chemotaxis, step-selection functions). Here we explore how foragers can exploit nonlocal information to improve their success in dynamic landscapes. Using a continuous time/continuous space model in which we vary both random (diffusive) movement and resource-following (advective) movement, we characterize the optimal perceptual ranges for foragers in dynamic landscapes. Nonlocal information can be highly beneficial, increasing the spatiotemporal concentration of foragers on their resources up to twofold compared with movement based on purely local information. However, nonlocal information is most useful when foragers possess both high advective movement (allowing them to react to transient resources) and low diffusive movement (preventing them from drifting away from resource peaks). Nonlocal information is particularly beneficial in landscapes with sharp (rather than gradual) patch edges and in landscapes with highly transient resources.

A priori, it is not clear if masting should be expected in savannas and few studies have attempted to detect masting in savannas. We tracked the flower and fruiting phenology of 18 savanna woody species on a monthly basis in Kruger National Park for 8 years. We used multiple metrics to detect masting including phenological intensity and its CV, phenological volatility, synchrony and the proportion of failure years. Additionally, we used a process-based model of plant growth to test whether resource matching could explain the observed phenological behaviour. Overall, the measured masting metrics provided no unequivocal evidence for masting. For 4 of the 18 study species, the fruiting CV, synchrony and volatility were consistent with masting. The process-based model of plant growth could reproduce observed flowering and fruiting behaviour, suggesting that resource matching could explain the observed phenological behaviour of the species. We propose that future research should explore the possibility that masting may not be selected for in savannas due to the prevalence of generalist pollinators, dispersal agents and seed predators. Although masting does not appear to be a prevalent phenological strategy in savannas, we detected large between species variation in reproductive phenology, which is likely to have consequences for the trophic dynamics of the study system.

PurposeIn recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.Design/methodology/approachThe main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.FindingsNumerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.Research limitations/implicationsWhile the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.Practical implicationsCompared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.Social implicationsLarge-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.Originality/valueThe current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Well-matched pattern of water and soil resources can provide strong support for agricultural development. Most previous studies have considered the total amount of water resources, the available water resources, or the amount of irrigation water; the characteristics of water resources in different districts have been ignored. This study proposes a method to evaluate the matching of agricultural water and soil resources by combining administrative units and subbasins at different spatial scales while accounting for the variability of precipitation “green water” resources within each spatial unit. Taking the Yellow River Basin in China as an example, the generalized water and soil resource matching coefficient was applied to evaluate the spatial match between water and soil resources in nine provinces among the secondary water subregions within the Yellow River Basin. The results show the following: the degree of matching between agricultural water and soil resources in Sichuan Province (above Longyangxia) was relatively good, while that in the Inner Mongolia Autonomous Region (endorheic region) was relatively poor. The temporal variation trends of water and soil resources in Qinghai Province (Longyangxia to Lanzhou), Gansu Province (Longmen to Sanmenxia), Shanxi Province (Longmen to Sanmenxia), and Ningxia Hui Autonomous Region (Lanzhou to Hekou town) were significantly reduced, and the remaining provinces exhibited no significant changes. According to the relationship between the generalized agricultural water and soil resource matching coefficient and the ratio of “blue water” to “green water,” the study area was divided into four zones, and specific policy measures were proposed for each zone, especially those with unsatisfactory or unstable matching characteristics over time. For zones I and II with a relatively high degree of water and soil resource matching, the government should actively build irrigation facilities to ensure that the water conservancy conditions therein can be fully utilized. For zone III, the government should support the construction of water conservancy facilities and improve the utilization rate of water resources. The water shortage problem in zone IV can be alleviated by establishing an interconnected water system project with zones I and II or a cross-basin water transfer project.

Many nutrition labeling studies only consider how consumers process health information about a single food product (i.e., in a noncomparative processing context). However, consumers also often comparatively evaluate many different food products at once in more complex shopping environments (i.e., in comparative processing contexts). Directly addressing these important differences, the results of two online studies and two retail laboratory studies demonstrate that the effects of different types of front-of-package nutrition cues (objective vs. evaluative) vary across consumers’ processing contexts (comparative vs. noncomparative). When consumers evaluate a single food item in a noncomparative context, objective nutrition cues that offer specific quantitative information lead to higher evaluations and intentions to purchase healthier products than do evaluative nutrition cues (which provide interpretive information about a product’s overall healthfulness and/or nutrients). However, these effects are reversed when consumers evaluate multiple food items simultaneously in a comparative context, such that evaluative cues have a more positive impact on evaluations and purchase intentions of healthier products. The authors integrate processing fluency and resource matching theoretical frameworks to explain why evaluative (objective) front-of-package cues are more influential in comparative (noncomparative) processing contexts. Implications for consumer health, the food and retail grocery industries, and public policy are offered.

Understanding how staple-crop geography aligns with climate resources is important for food-security planning under climate change. Focusing on rice, winter wheat and maize in China from 2001 to 2020, this study constructed 1 km crop-abundance grids from annual 30 m crop-distribution data and integrated weighted centres of gravity (COGs), Standard Deviational Ellipses (SDEs), effective accumulated temperature and a Climate Resource Matching Index (CRMI) to evaluate crop migration, spatial-form change and matching with thermal, pluvial and radiant resource centres. Results show that rice exhibited the strongest northeastward migration, with a cumulative COG path of 448.9 km, but its CRMI declined markedly, indicating that thermal relaxation did not translate into coordinated multi-resource improvement. Winter wheat remained anchored in the Huang-Huai-Hai Plain, with adjustment mainly occurring through internal concentration and persistent moisture constraints. Maize showed expansion before 2015 followed by partial correction, and its CRMI trough in 2015 was robust under alternative weighting schemes. Overall, China’s staple-crop change represents a differentiated spatial reconfiguration rather than a uniform northward shift. Because these metrics are national-scale, the findings should inform crop zoning as broad spatial signals rather than direct local yield responses.