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Background Software Defect Prediction (SDP) enables developers to investigate unscrambled faults in the inaugural parts of the software progression mechanism. However, SDP faces the threat of high dimensionality. Feature selection (FS) selects the finest features while carefully discarding others. Several meta-heuristic algorithms, like Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization, have been used to develop defect prediction models. However, these models have drawbacks like high cost, local optima trap, lower convergence rate, and higher parameter tuning. This study applies an innovative FS technique (FSCOA) rooted in Chernobyl Disaster Optimizer (CDO) technique. The proposed procedure intends to unwrap the best features for a prediction model while minimizing errors. Methods The proposed FSCOA investigated twelve public NASA software datasets from the PROMISE archive on Decision Tree, K-nearest neighbor, Naive Bayes, and Quantitative Discriminant Analysis classifiers. Furthermore, the accuracy of the recommended FSCOA method was correlated with existing FS techniques, like FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experiment indicated that the proposed FSCOA approach bettered the accuracy in majority of the instances and achieved an average rank of 1.75 among other studied FS approaches while applying the Friedman test. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The results illustrated the supremacy of the prospective FSCOA procedure over extant FS techniques with higher accuracy in almost all cases due to its advantages like enhanced accuracy, the ability to deal with convoluted, high-magnitude datasets not grounded in local optima, and a faster convergence rate. These advantages empower the suggested FSCOA method to overcome the challenges of the other studied FS techniques.

On April 26, 1986, Unit Four of the Chernobyl nuclear reactor exploded in then Soviet Ukraine. More than 3.5 million people in Ukraine alone, not to mention many citizens of surrounding countries, are still suffering the effects.Life Exposedis the first book to comprehensively examine the vexed political, scientific, and social circumstances that followed the disaster. Tracing the story from an initial lack of disclosure to post-Soviet democratizing attempts to compensate sufferers, Adriana Petryna uses anthropological tools to take us into a world whose social realities are far more immediate and stark than those described by policymakers and scientists. She asks: What happens to politics when state officials fail to inform their fellow citizens of real threats to life? What are the moral and political consequences of remedies available in the wake of technological disasters? Through extensive research in state institutions, clinics, laboratories, and with affected families and workers of the so-called Zone, Petryna illustrates how the event and its aftermath have not only shaped the course of an independent nation but have made health a negotiated realm of entitlement. She tracks the emergence of a \"biological citizenship\" in which assaults on health become the coinage through which sufferers stake claims for biomedical resources, social equity, and human rights.Life Exposedprovides an anthropological framework for understanding the politics of emergent democracies, the nature of citizenship claims, and everyday forms of survival as they are interwoven with the profound changes that accompanied the collapse of the Soviet Union.

The 1983shootdown of KAL 007 and the 1986 Chernobyl nuclear accident dramatically changedthe Soviet Union in unpredictable ways. The Communist Party, which struggled tomaintain control of political messaging after the KAL crisis, lost control inthe aftermath of Chernobyl.

Many individuals who were affected by the Great East Japan earthquake and tsunami and the subsequent Fukushima Daiichi Nuclear Power Plant accident continue to face a challenging recovery. We reviewed the long-term mental health consequences of three major nuclear power plant accidents: the Three Mile Island (TMI, 1979), Chernobyl (1986), and Fukushima (2011) nuclear disasters. We examined the relevant prospective cohort studies and before-and-after studies that covered more than two timepoints, searching four databases (PubMed, Ichushi, PsyArticles, and PTSDPub). We identified a total of 35 studies: TMI, n = 11; Chernobyl, n = 6; and Fukushima, n = 18. The smaller numbers of early-phase studies (within 6 months) of the Chernobyl and Fukushima disasters may also indicate the chaotic situation at those timepoints, as large-scale interviews were conducted in the early phase after the TMI disaster. Although the patterns of effects on mental health outcomes were diverse, more than half of the participants in the studies we evaluated were categorized into low or under-threshold symptom groups in all three disasters. Across the three disasters, the radiation exposure level estimated by the proximity and stigma were the common risk factors for mental health outcomes. Our findings will contribute to a comprehensive understanding of the impact of the worst nuclear accidents in history on the affected individuals’ mental health, and our results illustrate the longitudinal consequences of such disasters.

Havingexploded on the margins of Europe, Chornobyl marked the end of the Soviet Unionand tied the era of postmodernism in Western Europe with nuclear consciousness.The Post-Chornobyl Library becomes a metaphor of a new Ukrainian literature of the 1990s,which emerges out of the Chornobyl nuclear trauma.

Background Software defects can have catastrophic consequences. Therefore, fixing these defects is crucial for the evolution of software. Software Defect Prediction (SDP) enables developers to investigate unscramble faults in the inaugural parts of the software progression mechanism. However, SDP faces many challenges, including the high magnitude of attributes in the datasets, which can degrade the prognostic performance of a defect forecasting model. Feature selection (FS), a compelling instrument for overcoming high dimensionality, selects only the relevant and best features while carefully discarding others. Over the years, several meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have been used to develop defect prediction models. However, these models suffer from several drawbacks, such as high cost, local optima trap, lower convergence rate, and higher parameter tuning. To overcome the above shortcomings, this study aims to develop an innovative FS technique, namely, the Chernobyl Optimization Algorithm (FSCOA), to unwrap the most informative features that can produce a precise prediction model while minimizing errors. Methods The proposed FSCOA approach mimicked the process of nuclear radiation while attacking humans after an explosion. The proposed FSCOA approach was combined with four widely used classifiers, namely Decision Tree (DT), K-nearest neighbor (KNN), Naive Bayes (NB), and Quantitative Discriminant Analysis (QDA), to determine the finest attributes from the SDP datasets. Furthermore, the accuracy of the recommended FSCOA method is correlated with existing FS techniques, such as FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experimental findings showed that the proposed FSCOA approach yielded the best accuracy in most cases and achieved an average rank of 1.75, followed by the other studied FS approaches. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The experimental findings showed that the prospective FSCOA procedure eclipsed alternative FS techniques with higher accuracy in almost all cases while selecting optimal features.

The significance of accurately forecasting natural gas prices is far-reaching and significant, not only for the stable operation of the energy market, but also as a key element in promoting sustainable development and addressing environmental challenges. However, natural gas prices are affected by multiple source factors, presenting complex, unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models. To address this issue, this study proposes an innovative multivariate combined forecasting model for natural gas prices. Initially, the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions: the production, marketing, commodities, political and economic indicators of the United States and temperature. Subsequently, this study employs the least absolute shrinkage and selection operator, grey relation analysis, and random forest for dimensionality reduction, effectively screening out the most influential key variables to serve as input features for the subsequent learning model. Building upon this foundation, a suite of machine learning models is constructed to ensure precise natural gas price prediction. To further elevate the predictive performance, an intelligent algorithm for parameter optimization is incorporated, addressing potential limitations of individual models. To thoroughly assess the prediction accuracy of the proposed model, this study conducts three experiments using monthly natural gas trading prices. These experiments incorporate 19 benchmark models for comparative analysis, utilizing five evaluation metrics to quantify forecasting effectiveness. Furthermore, this study conducts in-depth validation of the proposed modelʼs effectiveness through hypothesis testing, discussions on the improvement ratio of forecasting performance, and case studies on other energy prices. The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy. It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities.

Honorable Mention - American Association for Ukrainian Studies (AAUS) 2018-2019 Book Prize Having exploded on the margins of Europe, Chornobyl marked the end of the Soviet Union and tied the era of postmodernism in Western Europe with nuclear consciousness. The Post-Chornobyl Library in Tamara Hundorova's book becomes a metaphor of a new Ukrainian literature of the 1990s, which emerges out of the Chornobyl nuclear trauma of the 26th of April, 1986. Ukrainian postmodernism turns into a writing of trauma and reflects the collisions of the post-Soviet time as well as the processes of decolonization of the national culture. A carnivalization of the apocalypse is the main paradigm of the post-Chornobyl text, which appeals to \"homelessness\" and the repetition of \"the end of histories.\" Ironic language game, polymorphism of characters, taboo breaking, and filling in the gaps of national culture testify to the fact that the Ukrainians were liberating themselves from the totalitarian past and entering the society of the spectacle. Along this way, the post-Chornobyl character turns into an ironist, meets with the Other, experiences a split of his or her self, and witnesses a shift of geo-cultural landscapes.