Zombie of Biopower: Is the Concept Outdated?

The article is devoted to the discussion about what the concept of biopower, developed within the framework of the postmodern critical theory, means in the context of the modern world, both in general theoretical and empirical sense. According to the author’s conclusion, although this concept remains significant for Political Science, it is largely outdated and could turn into a scientific zombie idea. Giorgio Agamben, one of the classics of Political Philosophy, who denied the danger of the COVID-19 pandemic on the basis of the methodology of biopolitics, is case in point. Another evidence comes from the fact that researchers from different countries, including Russia, increasingly look for new approaches and tools of the biopolitical analysis, and try to saturate the concept with new ideas and data. This article proposes three ways how to make biopolitical research more relevant today. One way is to rethink the normative/moral foundations of biopower, to reject an implicitly negative assessment of the concept. Another way is to expand the historical framework, to pay greater attention to historical cases that allow us to trace different stages of the evolution of biopolitical patterns, to focus on the analysis of specific manifestations of biopower in concrete situations. The third way is to move towards the synthesis of critical biopolitics and evolutionary biopolitics, which draws data from the natural sciences. According to the author, these efforts together will make it possible to move from the unidirectional asymmetric link “the political influences the bio logical” to a more complex scheme of mutual reflective influence of the political and the biological.

Asymmetric impact of fiscal decentralization and environmental innovation on carbon emissions: Evidence from highly decentralized countries

This study examines the asymmetric link between fiscal decentralization, environmental innovation, and carbon emissions in highly decentralized countries. Our preliminary findings strictly reject the preposition of data normality and highlight that the observed relationship is quantile-dependent, which may disclose misleading results in previous studies using linear methodologies. Therefore, a novel empirical estimation technique popularized as Method of Moments Quantile Regression is employed that simultaneously deal with non-normality and structural changes. The results exhibit that fiscal decentralization significantly mitigates carbon emissions only at lower to medium emissions quantiles. On the other hand, environmental innovation reduces carbon emissions only at medium to higher emissions quantiles. Interestingly, the emissions-reducing effect of fiscal decentralization is highest for lower emissions quantiles and lowest for higher emissions quantiles. In contrast, the impact of environmental innovation is lowest for lower emissions quantiles and highest for higher emission quantiles. Economic growth and population increase carbon emissions, and their emissions-increasing effect are lowest for lower emissions quantiles and highest for higher emissions quantiles. Moreover, the heterogeneous panel causality test confirms a one-way causal association, implying that any policy intervention regarding fiscal decentralization and environmental innovation significantly affects carbon emissions.

Regression models for binary response applied to data on neonatal deaths in newborns

The present study presents binary data modeling regarding 1.6% of neonatal deaths in 3,448 newborns from an epidemiological and observational study with a cross-sectional design, involving the retrospective analysis of 4,293 medical records of high-risk pregnant women followed in a gestational outpatient clinic from September 2012 to September 2017. Different symmetric and asymmetric link functions were considered by means of Bayesian inference. The support of more accurate inferences regarding the parameters of the model will provide biological interpretations that are more reliable and consistent with the reality. The model that presented, significantly, the lowest value for the deviance information criterion (DIC = 398.8), was the binomial with power logit (PL) link function, whose median posterior value estimated and significant for the parameter asymmetry was l = 0.25 (0.14;1.17). This significance is observed in all other models of the power family, however with very different values ​​and significantly higher DIC values, indicating less parsimonious models. The Bayesian methodology proved to be flexible. Additionally, the results show that such model shows an accuracy = 97.4% and area under the ROC curve AUC = 89.4% in the prediction of neonatal deaths based on the weight of children at birth. Specifically, for 2.500g, a value predicted in the medical literature for low weight, the model predicts a probability of 1.43%.

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.