Volume 2, 2017

Everba Archive: Volume 2

Featuring comprehensive peer-reviewed contributions across neurosciences, data ethics, sustainable energy systems, empirical humanities, and global economic structures. Published quarterly in 2017.

2 Issues Total

15 Peer-Reviewed Articles

eISSN: 1668-1002

Volume 2, Issue 1 (2017)

Published: March 2017 | pp. 1–112

Original Research Article Pages: 1–16

Neuroplasticity in Bilingual Cognitive Control: Functional MRI Evidence from Simultaneous Interpreters

Dr. Julianna Varga | Department of Cognitive Neuroscience, Semmelweis University, Budapest, Hungary
Dr. Matteo Rossi | Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain

Keywords: Neuroplasticity, Bilingualism, Cognitive Control, fMRI, Executive Function

Abstract

The lifelong management of multiple linguistic systems requires exceptional executive functioning and cognitive control, making professional simultaneous interpreters an ideal population for studying structural and functional brain neuroplasticity. This study utilizes high-resolution functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) to investigate neural activation patterns and grey matter density among professional interpreters versus balanced bilingual controls during high-load linguistic switching tasks. Our findings reveal significant gray matter volume enhancements within the left dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and bilateral basal ganglia structures among professional interpreters. Furthermore, task-based fMRI contrasts demonstrate optimized neural efficiency, characterized by reduced metabolic activation in routine lexical retrieval networks alongside enhanced functional connectivity between frontoparietal control networks and language processing hubs. These neuroimaging results provide robust empirical evidence supporting the hypothesis that extreme cognitive demands induce targeted structural plasticity in human neural architectures dedicated to inhibitory control and task-switching. We discuss the implications of these findings for cognitive reserve models and neurorehabilitation paradigms targeting executive dysfunction in aging populations.

DOI: 10.1668/ev.2017.2.1.01

Review Article Pages: 17–33

Data Sovereignty and Algorithmic Governance in the Age of Transnational Cloud Computing

Dr. Thabo Mthembu | School of Law and Information Governance, University of Cape Town, South Africa
Dr. Astrid Lindgren-Holm | Department of Law and Society, Uppsala University, Sweden

Keywords: Data Sovereignty, Cloud Computing, Algorithmic Governance, Privacy Law, Transnational Data Flows

Abstract

The rapid migration of institutional and personal data repositories into transnational cloud computing infrastructures has profoundly complicated traditional paradigms of national jurisdiction, territorial sovereignty, and data protection law. As multinational technology conglomerates centralize digital storage across geographically dispersed data centers, nation-states face unprecedented difficulties in enforcing domestic privacy standards and regulatory compliance. This systematic review examines the evolving legal and technical dimensions of data sovereignty against the backdrop of algorithmic governance. We analyze legislative frameworks such as the European Union's General Data Protection Regulation (GDPR) alongside emerging data localization mandates across African, Asian, and Latin American jurisdictions. Our analysis reveals a growing tension between the seamless global interoperability demanded by cloud service providers and the defensive regulatory nationalism adopted by states seeking to protect citizen privacy and economic intelligence. Furthermore, we evaluate cryptographic and decentralized architectural solutions—including homomorphic encryption and federated data governance models—that attempt to reconcile cross-border data utility with rigorous local sovereignty enforcement. The paper concludes by proposing a cosmopolitan legal framework for transnational data governance that balances multi-stakeholder innovation with fundamental human rights protections in the digital era.

DOI: 10.1668/ev.2017.2.1.02

Empirical Study Pages: 34–49

Microalgal Bioremediation of Heavy Metal Contaminated Effluents in Industrial Wastewater

Dr. Mei-Ling Zhou | Department of Environmental Engineering, National Taiwan University, Taipei
Dr. Gabriel Santos | Institute of Aquatic Biotechnology, Federal University of Rio de Janeiro, Brazil

Keywords: Bioremediation, Microalgae, Heavy Metals, Wastewater Treatment, Biosorption

Abstract

Industrial discharge containing toxic heavy metals such as cadmium, lead, and hexavalent chromium represents a severe ecological hazard to aquatic ecosystems and human health globally. Conventional physicochemical remediation methods are frequently cost-prohibitive and generate secondary chemical sludge. This empirical study investigates the biosorption and bioaccumulation efficacy of native microalgal strains (Chlorella vulgaris and Scenedesmus obliquus) isolated from industrial wastewater stabilization ponds. Using atomic absorption spectroscopy (AAS) and Fourier-transform infrared spectroscopy (FTIR), we evaluate metal removal efficiency across varying pH levels, initial contaminant concentrations, and biomass dosages under controlled photobioreactor conditions. Our experimental results demonstrate maximum biosorption capacities exceeding 92.4% for cadmium and 88.7% for chromium within 48 hours of contact time. FTIR analysis confirms that functional groups including hydroxyl, carboxyl, and amino moieties located on the microalgal cell walls serve as the primary active binding sites for heavy metal sequestration. Furthermore, continuous-flow column tests indicate robust reusability of immobilized microalgal beads across multiple operational cycles. This research underscores the viability of microalgal biotechnology as an eco-efficient, scalable, and economically sustainable alternative for treating heavy metal-polluted industrial effluents.

DOI: 10.1668/ev.2017.2.1.03

Original Research Article Pages: 50–65

Rhetoric, Ritual, and Resistance: Commemorative Monuments in Post-Conflict Landscapes

Dr. Fiona O'Connor | School of Art History and Cultural Heritage, Trinity College Dublin, Ireland
Dr. Ibrahim Al-Hassan | Department of Architectural History, University of Jordan, Amman

Keywords: Commemorative Monuments, Post-Conflict Landscapes, Collective Memory, Spatial Rhetoric, Cultural Heritage

Methodological Paper Pages: 66–84

Bayesian Hierarchical Modeling of Macroeconomic Shocks in Emerging Market Economies

Dr. Alejandro Morales | Department of Quantitative Economics, EGADE Business School, Monterrey, Mexico
Dr. Svetlana Petrova | Higher School of Economics, Department of Macroeconomic Analysis, Moscow, Russia

Keywords: Bayesian Econometrics, Hierarchical Modeling, Macroeconomic Shocks, Emerging Markets, Financial Volatility

Abstract

Emerging market economies are routinely subjected to acute external macroeconomic shocks, including sudden capital outflows, commodity price volatility, and abrupt shifts in global monetary tightening cycles. Modeling these complex transmission mechanisms on traditional linear time-series frameworks frequently fails to account for parameter heterogeneity and structural instability across diverse developing nations. In this methodological contribution, we develop a flexible Bayesian hierarchical vector autoregression (B-HVAR) framework equipped with stochastic volatility and cross-country shrinkage priors to analyze the propagation of international financial shocks across twenty emerging economies from 2000 to 2016. Utilizing Markov Chain Monte Carlo (MCMC) simulation algorithms, our hierarchical structure pools information across countries while accommodating unique domestic structural characteristics. The empirical results demonstrate that our Bayesian hierarchical model achieves superior out-of-sample forecasting precision regarding inflation volatility and exchange rate depreciation compared to standard benchmark VAR specifications. Furthermore, impulse response functions reveal significant non-linearities in how domestic central bank reserve buffers mitigate imported inflationary pressures during global liquidity crunches. This methodological advancement provides central bankers and policy analysts with a robust statistical tool for real-time macroeconomic risk surveillance.

DOI: 10.1668/ev.2017.2.1.05

Original Research Article Pages: 85–98

Epistemic Authority and Public Trust in Science Communication via Social Media Platforms

Dr. Hannah Bergström | Department of Media and Communication Studies, Uppsala University, Sweden
Dr. Kenji Sato | Institute for Science and Society, University of Tokyo, Japan

Keywords: Science Communication, Epistemic Authority, Public Trust, Social Media, Misinformation

Abstract

The democratization of information dissemination facilitated by social media platforms has profoundly altered the traditional pathways through which scientific knowledge is communicated to the general public. While digital platforms offer unprecedented opportunities for direct scientist-to-citizen dialogue, they simultaneously expose scientific consensus claims to algorithmically amplified misinformation, partisan polarization, and eroded epistemic authority. This empirical study investigates how public trust in scientific institutions is mediated by online platform architectures, source presentation heuristics, and peer-endorsement networks. Conducting a large-scale survey experiment (n = 2,400) across Sweden and Japan, we evaluate participant credibility ratings regarding climate science, vaccine safety, and genetic engineering announcements delivered via conventional news outlets versus peer-to-peer social networks. Our findings indicate that while institutional credentials remain strong trust anchors for educated demographics, lay audiences increasingly rely on peer-network signaling and affective resonance when evaluating scientific claims online. Furthermore, exposure to hostile comment sections significantly diminishes perceived scientific consensus and degrades institutional trust. We advocate for proactive digital communication strategies that train researchers in transparent, dialogic public engagement to safeguard epistemic authority in hyper-connected information ecosystems.

DOI: 10.1668/ev.2017.2.1.06

Original Research Article Pages: 99–112

Thermodynamic Optimization of Organic Rankine Cycles for Low-Temperature Waste Heat Recovery

Dr. Lars H. Jørgensen | Department of Thermal Energy Engineering, Technical University of Denmark, Kongens Lyngby
Dr. Fatma Yilmaz | Faculty of Mechanical Engineering, Istanbul Technical University, Turkey

Keywords: Organic Rankine Cycle, Waste Heat Recovery, Thermodynamic Optimization, Energy Efficiency, Sustainability

Abstract

Industrial manufacturing processes discharge vast quantities of low-grade waste heat (below 150°C) into the atmosphere, representing a significant thermodynamic inefficiency and contributing to global energy waste. The Organic Rankine Cycle (ORC) has emerged as a premier technology for converting low-grade thermal energy into clean electrical power. In this research article, we present a comprehensive thermodynamic model and multi-objective optimization framework for evaluating working fluid performance in subcritical and transcritical ORC systems powered by industrial flue gas waste heat. Using advanced equation-of-state fluid property libraries, we analyze fourteen environmentally benign working fluids (including hydrofluoroolefins and hydrocarbons) across varying evaporation temperatures, expansion pressures, and pinch-point temperature differentials. Our multi-objective optimization—balancing exergy efficiency against total specific investment cost—reveals that zeotropic working fluid mixtures significantly outperform pure working fluids by reducing thermal irreversibilities in the evaporator heat exchanger. Specifically, optimized R245fa/R134a mixtures achieved a 14.8% enhancement in net power output under identical waste heat input conditions. This study provides engineers with practical design guidelines for maximizing exergic performance in sustainable industrial cogeneration plants.

DOI: 10.1668/ev.2017.2.1.07

Volume 2, Issue 2 (2017)

Published: September 2017 | pp. 113–225

Original Research Article Pages: 113–128

Epigenetic Inheritance of Environmental Stress Responses in Plant Model Systems

Dr. Siobhan O'Malley | Department of Plant Molecular Biology, Australian National University, Canberra
Dr. Jean-Paul Laurent | Institute of Plant Sciences Paris-Saclay, CNRS, France

Keywords: Plant Epigenetics, Transgenerational Memory, Stress Adaptation, DNA Methylation, Histone Modifications

Abstract

Sessile organisms such as plants cannot relocate in response to sudden environmental stressors including drought, salinity, and extreme thermal fluctuations, necessitating highly adaptive physiological and molecular response mechanisms. Beyond immediate phenotypic acclimation, accumulating evidence indicates that plants can transmit stress-induced phenotypic memories to subsequent unstressed progeny via transgenerational epigenetic inheritance. In this study, we investigate the stability and heritability of epigenetic modifications—specifically DNA methylation patterns (CG, CHG, and CHH contexts) and histone H3 lysine 4 trimethylation (H3K4me3)—in Arabidopsis thaliana lineages subjected to multi-generational drought stress conditions. Utilizing whole-genome bisulfite sequencing (WGBS) and chromatin immunoprecipitation followed by sequencing (ChIP-seq), we identify differential epimutations stably inherited across three successive generations of progeny without direct exposure to drought stress. Progeny exhibiting these conserved epigenetic marks demonstrated enhanced stomatal regulation, accelerated osmotic adjustment, and improved survival rates under subsequent water-deficit challenge experiments. However, our longitudinal tracking also revealed reversion dynamics in specific epialleles, highlighting a complex regulatory balance between environmental stress memory retention and genomic stability. These insights open innovative avenues for breeding climate-resilient agricultural crops through epigenetic priming rather than genetic modification alone.

DOI: 10.1668/ev.2017.2.2.01

Review Article Pages: 129–147

Urban Resilience and Climate Adaptation Infrastructures in Coastal Megacities

Dr. Hiroshi Tanaka | Institute of Urban Disaster Mitigation, Kyoto University, Japan
Dr. Beatriz Gomez | Center for Urban Environmental Research, Universidad de Buenos Aires, Argentina

Keywords: Urban Resilience, Climate Adaptation, Coastal Megacities, Flood Mitigation, Infrastructure Engineering

Abstract

Coastal megacities house a substantial fraction of the global population and economic productivity, yet they remain acutely vulnerable to accelerating sea-level rise, severe storm surges, and extreme precipitation events driven by global climate change. This systematic review evaluates contemporary urban adaptation paradigms, contrasting traditional gray engineering defenses (seawalls, tidal barrier gates, and subterranean drainage tunnels) with nature-based solutions (mangrove restoration, constructed wetlands, living shorelines, and porous urban pavements). Synthesizing empirical case studies from Tokyo, New York, Rotterdam, and Buenos Aires published between 2010 and 2017, we analyze the governance frameworks, financing mechanisms, and socio-ecological trade-offs associated with mega-infrastructure adaptation projects. Our findings indicate that while gray infrastructure provides immediate structural protection against catastrophic flooding, it often creates path dependency, exacerbates ecological degradation, and disproportionately displaces vulnerable coastal communities. Conversely, hybrid adaptation models—integrating decentralized green infrastructure with robust coastal barriers—exhibit superior systemic resilience and long-term cost-effectiveness. The review concludes by proposing an integrated municipal governance framework that embeds environmental justice and participatory community planning into climate adaptation engineering.

DOI: 10.1668/ev.2017.2.2.02

Empirical Study Pages: 148–164

Algorithmic Transparency and Consumer Trust in Automated Financial Advisory Systems

Dr. Marcus Vance | Department of Finance and Behavioral Economics, London School of Economics, UK
Dr. Anika Sundaram | Indian Institute of Management Bangalore, Department of Financial Technology, India

Keywords: Robo-Advisory, Algorithmic Transparency, Consumer Trust, FinTech, Behavioral Finance

Abstract

The rapid proliferation of automated financial advisory platforms ("robo-advisors") has revolutionized retail investment management by lowering portfolio management fees and democratizing access to algorithmic wealth advisory services. However, the "black-box" nature of underlying machine learning portfolio allocation algorithms frequently engenders consumer skepticism, perceived loss of control, and adoption friction, especially during market downturns. This empirical study investigates the psychological mechanisms governing consumer trust, perceived competence, and adoption intent regarding robo-advisory systems across varying degrees of algorithmic explanation and transparency. Conducting a controlled behavioral experiment involving 1,500 retail investors in the UK and India, we evaluate how varying levels of explainable artificial intelligence (XAI)—ranging from simple performance summaries to detailed feature-attribution explanations of risk-return weightings—influence investor trust and capital allocation decisions. Our empirical findings demonstrate that moderate levels of algorithmic transparency significantly enhance perceived trustworthiness and willingness to invest, whereas excessive technical complexity induces cognitive overload and diminishes user confidence. Furthermore, financial literacy moderates this relationship significantly. We provide actionable design recommendations for FinTech developers seeking to balance proprietary algorithmic secrecy with user-centric explainability.

DOI: 10.1668/ev.2017.2.2.03

Original Research Article Pages: 165–181

Multilingualism and Cultural Translation in Post-Modern Diaspora Literature

Dr. Daria Kowalski | Department of Comparative Literature, Jagiellonian University, Kraków, Poland
Dr. Kofi Mensah | Department of English and Cultural Studies, University of Ghana, Legon

Keywords: Multilingualism, Cultural Translation, Diaspora Literature, Post-Colonial Studies, Transnational Identity

Abstract

Post-modern diaspora literature frequently transcends monolingual literary norms, employing strategic multilingualism, code-switching, and untranslated vernacular idioms to articulate the complex phenomenological realities of transnational displacement and hybrid identity formation. This paper examines the poetics and politics of cultural translation in contemporary works by writers spanning Central European and West African diasporic traditions. Utilizing a close-reading methodology informed by post-colonial translation theory (particularly Walter Benjamin and Homi Bhabha), we analyze how authors deliberately resist seamless assimilation into dominant metropolitan languages, instead transforming the literary text into a contact zone where distinct cultural semiotics collide and negotiate. Our analysis demonstrates that strategic untranslatability functions as a powerful aesthetic device for preserving ancestral memory, asserting linguistic sovereignty, and unsettling hegemonic monolingual reading expectations. Furthermore, we investigate how digital publishing and global literary circulation platforms impact the reception of multilingual diaspora texts. The study concludes by proposing an expanded hermeneutic model for comparative literature that treats multilingualism not as a barrier to comprehension, but as an indispensable epistemological asset for navigating a pluralistic global culture.

DOI: 10.1668/ev.2017.2.2.04

Methodological Paper Pages: 182–199

Quantum Machine Learning Architectures for Phase Classification in Condensed Matter Physics

Dr. Erik Lindqvist | Department of Quantum Physics, Chalmers University of Technology, Gothenburg, Sweden
Dr. Xiaodan Wang | Institute for Quantum Information, Tsinghua University, Beijing, China

Keywords: Quantum Machine Learning, Condensed Matter Physics, Phase Classification, Variational Circuits, Quantum Computing

Abstract

Classifying complex phases of matter—such as topological insulators, spin liquids, and quantum critical points—represents a central computational challenge in modern condensed matter physics, as the dimensionality of quantum many-body Hilbert spaces scales exponentially with system size. While classical machine learning classifiers have shown notable success in identifying order parameters from simulated lattice configurations, they remain bottlenecked by classical data encoding overheads. In this methodological paper, we propose a hybrid quantum-classical machine learning framework utilizing parameterized quantum circuits (PQCs) designed to directly classify quantum phases of matter from raw many-body wavefunctions without intermediate classical reconstruction. By encoding spin-system Hamiltonians onto noisy intermediate-scale quantum (NISQ) hardware simulators, we train variational quantum classifiers to distinguish topological phase transitions in the transverse-field Ising and Heisenberg models. Our benchmarking results demonstrate that quantum kernel estimation methods achieve superior classification accuracy with significantly fewer training epochs compared to classical convolutional neural networks when handling high-entanglement superposition states. This work establishes a scalable methodological foundation for leveraging quantum computing to accelerate discovery in theoretical condensed matter physics and materials science.

DOI: 10.1668/ev.2017.2.2.05

Original Research Article Pages: 200–213

Socio-Economic Determinants of Renewable Energy Adoption in Rural Agrarian Cooperatives

Dr. Chinyere Okafor | Department of Agricultural Economics and Rural Development, University of Ibadan, Nigeria
Dr. Mateo Fernandez | Faculty of Economics, Universidad Nacional de Córdoba, Argentina

Keywords: Renewable Energy Adoption, Rural Cooperatives, Agrarian Economics, Solar Irrigation, Sustainable Development

Abstract

The widespread adoption of decentralised renewable energy technologies—particularly photovoltaic solar irrigation pumps and biomass gasifiers—is vital for enhancing agricultural productivity and climate resilience among smallholder farming communities in developing agrarian economies. Despite demonstrated long-term cost savings and ecological benefits, initial capital cost barriers and institutional financing bottlenecks continue to constrain widespread adoption among rural cooperatives. This empirical study investigates the primary socio-economic, institutional, and behavioral determinants influencing renewable energy technology adoption among 850 rural agricultural cooperatives across Nigeria and Argentina. Utilizing a multivariate probit econometric model, we evaluate how cooperative membership size, credit accessibility, technical literacy training, and local government subsidy schemes impact technology uptake decisions. Our empirical findings indicate that cooperative-led aggregation models significantly overcome individual capital constraints by pooling financial risk and facilitating collective bargaining with renewable energy equipment vendors. Furthermore, technical training programs organized by extension services substantially increase technology retention and operational efficiency. The study concludes with policy recommendations emphasizing public-private micro-financing partnerships and decentralized cooperative capacity building to accelerate green energy transitions in global rural sectors.

DOI: 10.1668/ev.2017.2.2.06

Original Research Article Pages: 214–225

Semantic Network Analysis of Interdisciplinary Citations in Open-Access Repositories

Dr. Aris Thorne | Department of Computer Science, National University of Singapore
Dr. Elena Vance | Stockholm Institute of Advanced Studies, Sweden

Keywords: Semantic Networks, Citation Analysis, Interdisciplinary Research, Scientometrics, Open Access

Abstract

The quantification and mapping of interdisciplinary knowledge transfer across traditional academic publishing boundaries remain central methodological pursuits within scientometrics and digital scholarship. Traditional citation analysis relies heavily on categorical journal subject classifications, which frequently fail to capture the nuanced conceptual bridges forged by multidisciplinary research communities. In this study, we construct a large-scale semantic citation network analyzing 1.2 million peer-reviewed articles published across open-access repositories between 2010 and 2017. Applying natural language processing (NLP) embeddings and graph clustering algorithms (Louvain community detection), we map semantic conceptual distance against physical citation links to identify emergent interdisciplinary bridge domains. Our empirical findings reveal that open-access publishing significantly accelerates interdisciplinary citation velocity compared to subscription-walled journals, particularly within computational social sciences, environmental engineering, and bioethics. Furthermore, we identify structural "hub" concepts that act as translational catalysts across disparate scientific vocabularies. This scientometric contribution provides academic institutions and funding agencies with advanced computational tools for evaluating interdisciplinary research impact and optimizing science policy allocation.

DOI: 10.1668/ev.2017.2.2.07

Everba Archive: Volume 2

Volume 2, Issue 1 (2017)

Neuroplasticity in Bilingual Cognitive Control: Functional MRI Evidence from Simultaneous Interpreters

Abstract

Data Sovereignty and Algorithmic Governance in the Age of Transnational Cloud Computing

Abstract

Microalgal Bioremediation of Heavy Metal Contaminated Effluents in Industrial Wastewater

Abstract

Rhetoric, Ritual, and Resistance: Commemorative Monuments in Post-Conflict Landscapes

Abstract

Bayesian Hierarchical Modeling of Macroeconomic Shocks in Emerging Market Economies

Abstract

Epistemic Authority and Public Trust in Science Communication via Social Media Platforms

Abstract

Thermodynamic Optimization of Organic Rankine Cycles for Low-Temperature Waste Heat Recovery

Abstract

Volume 2, Issue 2 (2017)

Epigenetic Inheritance of Environmental Stress Responses in Plant Model Systems

Abstract

Urban Resilience and Climate Adaptation Infrastructures in Coastal Megacities

Abstract

Algorithmic Transparency and Consumer Trust in Automated Financial Advisory Systems

Abstract

Multilingualism and Cultural Translation in Post-Modern Diaspora Literature

Abstract

Quantum Machine Learning Architectures for Phase Classification in Condensed Matter Physics

Abstract

Socio-Economic Determinants of Renewable Energy Adoption in Rural Agrarian Cooperatives

Abstract

Semantic Network Analysis of Interdisciplinary Citations in Open-Access Repositories

Abstract

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