Reviewing the links between climate change and resource conflict

Knowledge of the relationship between climate change and resource conflict is paramount in resolving resource conflict between farmers and herdsmen in Nigeria. However, there is yet no general agreement on how climate change causes or influences resource conflict. Thus, a review of existing literature that link climate change and resource conflict was conducted for identification of the missing link. These were achieved through the review of literature published in the era of the recent global climate change from late 90s to date. Selections of papers were based on the topic and date of publication. Result showed that there is general agreement that climate change influence resource conflicts. Some of the authors agreed that climate change cannot cause resource conflict in isolation but through influences on other factors that affect resource availability, accessibility and utility. These factors are also influenced by policies and socio-cultural system. Thus, resource conflict may be a secondary or tertiary effect of climate change. Climate change solution is scares in literature that linked climate change and resource conflict. Thus, future studies should be focused on climate change solution to resource conflict. Keywords: Climate change, Resource conflict, Literature review, Famers

Deterministic lightpath scheduling and routing in elastic optical networks

A huge torrent of information being generated from various heterogeneous applications and services is passing to and from the Internet backbone. Accommodating such heterogeneity and preserving the quality of service (QoS) of each traffic profile is a challenging task for operators. The heterogeneous traffic profile (HTP) considered in this work includes permanent lightpath demands (PLDs) and scheduled lightpath demands (SLDs). We propose various distance adaptive routing and spectrum assignment (DA-RSA) heuristics to resolve resource conflict among these two traffic profiles in elastic optical networks (EONs) under a full sharing environment. Conventionally, preemption was the only technique to resolve such conflict and ensure QoS for HTPs. Since excessive preemption leads to poor performance and lowers the degree of customer satisfaction, this work aims at reducing the preemption of demands. In order to do this, we propose to utilize bandwidth splitting as an alternative solution in such situations. Moreover, an integrated solution consisting of splitting and preemption is also proposed. We call this new integration as flow-based preemption. The simulation results demonstrate that utilizing splitting in place of preemption yields significant improvement in terms of all metrics of interest. Moreover, flow-based preemption is proved to be superior in performance than the only splitting-based solution. To the best of our knowledge, this is the first work addressing resource conflicts under a full-sharing environment by focusing on resource utilization at the link and node level. We believe that proposed heuristics support network operators to smoothly orchestrate network resources in the presence of such HTPs.

Preschoolers do not generalize dominance roles from one situation to another

Recognizing social rank as a third-party observer is an important social skill. With regard to dominance—the coercive form of social rank—infants expect the winner of a zero-sum conflict over one type of resource to win again when competing with the same agent over a different type of resource (Mascaro & Csibra, 2012). However, it is unclear whether preschoolers also expect dominant-subordinate roles to generalize across situations. The current study tested this question with preschoolers (3-5 years, n = 280, 140 female, USA, 80% White) and adults (n = 200, 99 female, USA, 75% White). Preschoolers and adults recognized the dominant puppet in two resource conflict situations (a toy to play with, a bench to sit on) in which one puppet won by force and the other lost (Exp. 1). Preschoolers did not expect the puppet who had been dominant in one situation (e.g. toy) to win again in a new situation (e.g. bench) (Exp. 2a and 2b). Adults, like infants, thought the dominant puppet would win again (Exp. 2b). When the concepts of dominance and fairness were primed (Exp. 3), preschoolers’ inferences about the winner diverged: Children who had been primed with dominance thought the subordinate would win, while those who had been primed with fairness responded at chance level. This finding, together with converging support from children’s justifications, suggests that preschoolers are particularly sensitive to the unfairness of dominance interactions, and indicates that this consideration affects their inferences about the stability of dominance.

Repress or Redistribute? The Chinese State's Response to Resource Conflicts

It is widely assumed that authoritarian states tend to use repression to suffocate social conflicts that threaten regime stability. Focusing on the Chinese state's responses to resource conflict, a particular type of social conflict triggered by mineral resource extraction, this research argues that authoritarian regimes may prefer to use redistributive policies to defuse social unrest under certain circumstances. Through mixed methods combining qualitative research and statistical analysis, I find that local governments in resource-rich regions do not spend heavily on coercive state apparatus. Instead, they generously hand out social security benefits to appease aggrieved citizens. Furthermore, the Chinese state actively involves mining companies in the redistribution process and requires them to share the financial costs of relief policies. Therefore, when conflicts arise between specific social groups with conflicting interests, redistribution may be a more effective strategy to preserve regime stability.

Prediction of multiproject resource conflict risk via an artificial neural network

PurposeInadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk.Design/methodology/approachA fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk.FindingsAccurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately.Originality/valueThe authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables.