New Releases by Hoang Nguyen

In response to escalating environmental crises, this paper introduces Nature Quotient (NQ) as a distinct and essential form of intelligence that enables humans to comprehend, adapt to, and harmonize with complex natural systems. Grounded in Granular Interaction Thinking Theory (GITT), NQ is defined as the capacity to perceive, process, and organize information about ecological interconnections—thereby fostering deeper ecological consciousness and guiding sustainable behavior. The paper posits that cultivating high NQ can counteract the anthropocentric biases inherent in conventional intelligence models and catalyze a socio-cultural shift from an eco-deficit paradigm to an eco-surplus culture. The conceptualization of NQ opens promising directions for interdisciplinary research, environmental education, and policy-making aimed at biodiversity conservation, climate change mitigation, and ecological resilience. Developing robust theoretical and empirical tools to measure NQ remains an urgent and necessary endeavor. *Keywords: Nature Quotient; NQ; ecological intelligence; environmental sustainability; biodiversity conservation; human-nature interactions; eco-surplus culture

Following the Paris Agreement, global actors intensified climate action through ambitious pledges, financial alliances, and investments in green technologies. However, despite continued rises in atmospheric carbon concentrations and global temperatures, a series of structural regressions have emerged, exposing the deep vulnerabilities and inherent flaws of artificial environmental protection systems—systems predominantly shaped by mainstream economics’ growth-oriented and technology-centric paradigms. This paper, grounded in Granular Interaction Thinking Theory (GITT), seeks to uncover the logical flaws embedded in such systems. It explains how they perpetuate the illusion that perpetual economic growth is compatible with environmental protection through prioritizing investment in advanced—yet often costly, low-impact, and highly uncertain—technological solutions. This bias also results in an overdependence on a limited range of technologies, heightening the risk of economic bubble formation and immiserizing growth. To address these systemic shortcomings, we advocate for the adoption of the semiconducting principle of monetary and environmental value exchange. This principle ensures that environmental values can be translated into monetary values, but not vice versa—thereby preventing monetary valuation from undermining ecological sustainability. Operationalizing this principle effectively requires the cultivation of Nature Quotient (NQ) across society and a broader socio-cultural shift toward an eco-surplus culture, in which environmental protection, restoration, and regeneration are not peripheral trade-offs, but foundational preconditions for long-term economic resilience, political stability, and social well-being. Such a profound transition will inevitably increase systemic entropy—manifesting as uncertainty and disruption across established socio-cultural and political patterns, norms, and structures. Accordingly, this paper also explores the major challenges that may hinder the emergence and diffusion of an eco-surplus culture, as well as strategies for addressing them.may hinder the emergence and diffusion of an eco-surplus culture, as well as strategies for addressing them.

Climate change, primarily driven by human activities, is becoming one of the most urgent global challenges of our time. Over the past decade, significant efforts have been made to address the climate change crisis, resulting in certain impacts in combating climate change and raising awareness about its consequences. This paper critically assesses global climate mitigation efforts, highlighting both achievements and persistent shortcomings. Despite growth in renewable energy, international agreements, and technological innovation, as well as a carbon market, greenhouse gas emissions remain at record highs, and climate impacts continue to accelerate. Key strategies that predominantly rely on a growth-oriented paradigm and technological solutions have fallen short, often reinforcing existing exploitation paradigms rather than transforming them. Drawing on Granular Interaction Thinking Theory (GITT), the paper proposes transitioning from an eco-deficit to an eco-surplus culture—one that views ecological restoration and protection as fundamental for socio-economic well-being and development. Mitigating climate change requires not only scaling proven solutions but also reimagining socio-economic and governance structures to align with planetary boundaries and long-term sustainability.

The major global challenges of our time, like climate and environmental crises, rising inequality, the emergence of disruptive technologies, etc., demand interdisciplinary research for effective solutions. A clear understanding of value is essential for guiding socio-cultural and economic transitions to address these issues. Despite numerous attempts to define value, existing approaches remain inconsistent across disciplines and lack a comprehensive framework. This paper introduces a novel perspective on value through the lens of granular interaction thinking theory, proposing an informational entropy-based notion of value. Grounded on quantum mechanics, Shannon’s information theory, and the mindsponge theory, this framework integrates both subjective and objective considerations and is highly compatible with interdisciplinary research. The informational entropy-based notion of value effectively bridges diverse concepts of value, including use and exchange value in economics, personal values in psychology, and cultural, moral, ethical, and aesthetic values in society. By offering a unifying perspective, granular interaction thinking theory provides a valuable framework for translating insights from quantum mechanics into socio-cultural, economic, and psychological contexts, enriching theoretical discourse and enhancing analytical effectiveness.

This data collection comprises of Amazon reviews of the book titled “Wild Wise Weird.” These reviews will be analyzed and updated to identify factors driving people to love a fiction book. Wild Wise Weird is a collection of 42 fables that blends traditional storytelling with modern sensibilities, weaving together life lessons, humor, and social commentary.

With wit, wisdom, and a “weird” approach through absurdity, Wild Wise Weird fosters readers’ awareness, self-reflection, informational connectivity, and even inspires them to confront stupidity to uncover wisdom. It may even inspire some readers to take up the pen. This book has the potential to resonate with readers, especially younger ones, embedding ecological sustainability in their humanistic values through the humor, vibrancy, and absurdity of its bird characters, as well as the wisdom woven throughout.

This discussion shows that a new approach to an enlarged definition (or view or notion) will likely empower us to more effectively consider different notions of value across research or professional disciplines--economic, socio-cultural, or humanistic--under a more enabling theoretical paradigm. It is because now the key ingredients, i.e., information, quantum states, interactions, information entropy, and probabilistic assignments, will help build productive thinking apparatuses from basic granules. ~~~ Please cite it as: Vuong, Q. H., & Nguyen, M. H. (2024). Further on informational quanta, interactions, and entropy under the granular view of value formation. https://ssrn.com/abstract=4922461

The contribution of the data paper publishing paradigm to the knowledge generation and validation processes is becoming substantial and pivotal. In this paper, through the information-processing perspective of Mindsponge Theory, we discuss how the data article publishing system serves as a filtering mechanism for quality control of the increasingly chaotic datasphere. The overemphasis on machine-actionality and technical standards presents some shortcomings and limitations of the data article publishing system, such as the lack of consideration of humanistic values, radical race for big data, and inadequate use of expertise in data evaluation. Without addressing the shortcomings and limitations, the reusability of data will be hindered, and scientific investment to facilitate data sharing will be wasted. Thus, we suggest that the current data paper publishing paradigm needs to be updated with a new philosophy of data.

Learning from the quantum and information theories, we provide a new definition of value, which is expected to help economists relax assumptions and loosen established economic principles in order to change and evolve. The new definition is also expected to enable social scientists to address newly arising phenomena or events that are not accounted for by existing value systems, such as environmental crises, artificial intelligence (AI), and interdisciplinary information, more proactively and productively. We hope that, by incorporating the quantum physics perspective into the interpretation of information and value, social scientists, particularly economists, will offer more reliable predictions and advice for societal transitions toward sustainability. Hanoi, July 21, 2024

"This timely book delves into the intricate relationship between tourism and poverty with a specific focus on nature conservation areas, using case studies of island economies in a developed country, Japan, and a developing one, Vietnam. This volume is a pivotal resource for scholars in tourism, particularly those focused on teaching and researching tourism geographies and sustainable development. It holds particular significance for scholars examining emerging nations across Asia"--

Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain expertise in identifying and modeling patterns that generate success Explore the concepts with Python using important libraries such as stats models Learn how to build models on real-world data sets and find solutions to practical challenges Book DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python''s rich ecosystem for data analysis.What you will learn Explore the use of statistics to make decisions under uncertainty Answer questions about data using hypothesis tests Understand the difference between regression and classification models Build models with stats models in Python Analyze time series data and provide forecasts Discover Survival Analysis and the problems it can solve Who this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

Introduction: Acute pesticide poisoning (APP) continues to affect farm workers, especially in low-and middle- income countries (LMIC). The dose-response relationship between exposure and APP is well-researched, but pesticide exposure assessment in a practical environment can be difficult to perform considering various work practices and protections in place. It is well known that inadequate PPE use is a risk factor of APP, but it is unknown which types of inadequate PPE use, such as face (face mask and eye protection) or other types of general protection (such as gloves, boots, and long clothing), is most harmful.Methods: This study aimed to identify if inadequate PPE is an indicator of APP risk following established specifications for meta-analysis of epidemiological studies. Included studies reported an odds ratio (OR) between PPE use to APP in agricultural workers. Data extracted from selected articles included: authors, publication year, country of origin, farm type, population size, method of data collection and time frame of reported symptoms, job task, type of PPE and pesticides used, adjustments made in analysis, OR for APP, and 95% confidence intervals (CI). Meta-analysis was done using a random effects model where ORs were pooled to assess an overall estimate for poisoning odds. Results: Our findings suggested that the inadequate PPE use was associated with increased odds (OR = 1.57, 95% CI=1.16-2.12) of having APP. Failure to use general protection and inadequate face protection increased odds of APP by 1.29 times (95% CI=0.88-1.90) and 1.92 times (95% CI=1.23-3.00), respectively. Conclusions: The meta-analysis results indicate that improper facial protection and general protection are not differently associated with APP odds. Our study concludes that more secured protection against inhalation and dermal contact are critical because any gaps in comprehensive full-body PPE would put workers at APP risk.

In the paper we consider the optimal portfolio choice problem under parameter uncertainty when the covariance matrix of asset returns is singular. Very useful stochastic representations are deduced for the characteristics of the expected utility optimal portfolio. Using these stochastic representations, we derive the moments of higher order of the estimated expected return and the estimated variance of the expected utility optimal portfolio. Another line of applications leads to their asymptotic distributions obtained in the high-dimensional setting. Via a simulation study, it is shown that the derived high-dimensional asymptotic distributions provide good approximations of the exact ones even for moderate sample sizes.

Air pollution is a considerable environmental stressor for urban residents in developing countries. Perceived health risks of air pollution might induce migration intention among inhabitants. The current study employed the Bayesian Mindsponge Framework (BMF) to investigate the rationale behind the domestic and international migration intentions among 475 inhabitants in Hanoi, Vietnam - one of the most polluted capital cities worldwide. We found that people perceiving more negative impacts of air pollution in their daily life are more likely to have migration intentions. The effect of perceived air pollution impact on international migration intention is stronger than that of domestic migration. Acknowledging a family member''s air pollution-induced sickness moderated the association between perceived air pollution impact and domestic migration intention, while the personal experience of air pollution-induced sickness did not. In contrast, the moderation effect of personal experience of sickness became significant in the international migration circumstance, but the effect of information about a family member''s sickness was negligible. The findings suggest that urban inhabitants'' consideration of air pollution averting strategies reflects some characteristics of Homo Oeconomicus . Although an individual''s socio-economic decision may seem insignificant on a collective scale, through environmental stressors as catalysts, such decisions might result in considerable social tendencies (e.g., internal migration and emigration).

The book examines the psycho-religious mechanism behind the violent extremism of suicide attacks in the post-9/11 world. It employs the mindsponge concept, an original dataset, and original research results obtained from the authors'' statistical investigations using the Hamiltonian Markov chain Monte Carlo technique. It provides insights and implications for policymakers and strategists in their efforts to engage in peace talks and reduce violent conflicts worldwide.

Masterarbeit aus dem Jahr 2018 im Fachbereich BWL - Rechnungswesen, Bilanzierung, Steuern, Note: 2,0, Universität Osnabrück (Fachgebiet Bilanz-, Steuer- und Prüfungswesen), Sprache: Deutsch, Abstract: Das Ziel der vorliegenden Arbeit ist es, die Besteuerung von (Publikums-)Investmentfonds und ihrer Anleger nach dem neuen Investmentsteuergesetz darzustellen. Mithilfe eines Fallbeispiels soll darüber hinaus aufgezeigt werden, wie sich die neue Investmentbesteuerung auf die Steuerlast des (Publikums-)Investmentfonds sowie von deren Anlegern auswirkt. Die vorliegende Arbeit besteht aus insgesamt acht Kapiteln. In Kapitel 2 werden zunächst eine Kurzcharakterisierung sowie die zivilrechtliche und steuerrechtliche Grundkonzeption eines Investmentfonds beschrieben. Anschließend befasst sich Kapitel 3 mit den Grundlagen der Investmentbesteuerung. Dazu wird zunächst im Abschnitt 3.1 das Verhältnis zum Kapitalanlagegesetzbuch dargestellt. Im nächsten Schritt beschäftigt sich Abschnitt 3.2 mit den Grundprinzipien des neuen Investmentsteuergesetzes. In Abschnitt 3.3 werden die Gründe und die Ziele der Investmentsteuerreform nahegelegt und abschließend wird in Abschnitt 3.4 der Anwendungsbereich des neuen Investmentsteuergesetzes erläutert. In Kapitel 4 findet die Darstellung der intransparenten Besteuerung von (Publikums-)Investmentfonds statt. Dazu wird in Abschnitt 4.1 zunächst ein Überblick gegeben. Darauf folgt Abschnitt 4.2 mit der Untersuchung der Besteuerung des (Publikums-)Investmentfonds mit der Körperschaftsteuer. Abschnitt 4.3 zeigt die Besonderheiten bei einer Beteiligung von steuerbegünstigten Anlegern auf, bevor in Abschnitt 4.4 die Besteuerung des (Publikums-)Investmentfonds mit der Gewerbesteuer dargestellt wird. Die Besteuerung der Anleger eines (Publikums-)Investmentfonds wird in Kapitel 5 widergespiegelt. Abschnitt 5.1 gibt zunächst einen Überblick über das Kapitel, bevor sich Abschnitt 5.2 mit den zu besteuernden Investmenterträgen aus dem (Publikums-)Investmentfonds auseinandersetzt. Abschließend wird in Abschnitt 5.3 die Erläuterung der Teilfreistellung für die jeweiligen Anleger vorgenommen. Im sechsten Kapitel wird die Besteuerung des (Publikums-)Investmentfonds sowie von deren Anlegern an einem konkreten Fallbeispiel illustriert. In Kapitel 7 wird das neue Investmentsteuergesetz kritisch gewürdigt, bevor im achten und damit letzten Kapitel die Ergebnisse der vorliegenden Arbeit zusammengefasst werden.

This dissertation contains three essays exploring how macroeconomists can identify and estimate dynamic causal effects in models where researchers have doubts about identifying assumptions. Chapter 1 proposes a new Markov Chain Monte Carlo algorithm to estimate a sign-restricted structural vector autoregression on time series that are subject to regime shifts. My approach can incorporate useful prior information about both model parameters and hidden states while transparently imposing sign restrictions. I illustrate my method by revisiting the literature on asymmetric effects of conventional monetary policy during recessions and expansions. My evidence suggests that previous empirical research found asymmetric effects by questionable identification schemes and neglecting changes in the variances of structural shocks. I find little difference in the structural parameters, and thus I do not find evidence of asymmetry. Chapter 2 studies the method of instrumental variables in set-identified models. I develop a proxy structural vector autoregression in which prior information from both theory and the empirical literature is incorporated about signs and magnitudes of certain parameters and equilibrium impacts. I use my method to investigate the relevance and validity of three popular instruments for monetary policy shocks, developed by Romer and Romer (2004), Sims and Zha (2006), and Smets and Wouters (2007). I find that all of them are strongly relevant but only that of Smets and Wouters is valid. Furthermore, the empirical analysis demonstrates that my framework can combine information from a relevant and valid instrument with prior information about sign restrictions to improve inference about structural impulse-response functions. Chapter 3 develops new methods to study dynamic causal effects in a data-rich environment. Current development in high-dimensional statistics fails to address the main interest of economists: causal inference with credible assumptions. I first review the literature on high-dimensional linear regression models and dynamic factor models. Then, I develop several new Bayesian numerical algorithms that combine the techniques in high-dimensional statistics with recent advances in dynamic causal inference. In particular, I discuss how to make causal statements from a high-dimensional structural model when researchers have doubts about identifying assumptions. Finally, I extend those algorithms to the case of Markov-switching models to accommodate nonlinearities in economic time series.

Financing issues play essential roles in the survival and development of entrepreneurial firms. The current study, employing the bibliometric analysis of 6,903 articles from 1970 to 2019, extracted from Web of Science database, aims to provide an overview of the discipline''s landscape and major scientific domains to facilitate scientific development within the field. Entrepreneurial finance is a young and growing field with exponential growth in the number of publications (with 19.54% per year) and rising collaboration tendency among authors. Journal of Business Venturing is the most prestigious journal, while Sustainability is noteworthy for its rapid contribution to the field. We also note a sign of Western ideological homogeneity from the collaboration networks and lists of top authors, institutions, and countries. Besides, using keyword co-occurrence analysis, seven major research domains are identified: “venture capital”, “crowdfunding”, “SMEs finance”, “social entrepreneurship finance”, “financial risk”, “microfinance”, and “human-social-financial capital”. Based on these findings, we raise the concern of lacking diversity in entrepreneurial finance research and provide several recommendations for future potential research directions.

Alzheimer''s Disease (AD) is the most common form of dementia. Chronic stress has been suggested to influence the progression of AD. Moreover, corticotropin-releasing factor (CRF) signaling in AD has been shown to be upregulated by stress and influence AD pathology such as amyloid-beta (A[beta]) accumulation. In rodent models, similar AD-related pathology and cognitive impairments are produced when exposed to either stress or exogenous CRF. Previous studies have shown that PSAPP (AD) mice treated with CRFR1 antagonist (R121919) resulted in reduced A[beta] accumulation and improvements in behavioral tests. However, the influence of stress while underdoing drug treatment is unknown. In this comparison study, we subjected cohorts of AD mice to R121919 in the presence and absence of restraint stress treatment. AD mice were aged to 6 and 12 months to investigate the pre-clinical and mature timepoints of AD pathology. Daily drug/vehicle treatment began at 2 months of age. Stress for the 6 and 12-month groups started at 2 and 6 months, respectively. Overall, R121919 treated AD mice resulted in a significant reduction of A[beta] levels. Additionally, it was shown that chronic restraint stress exacerbated A[beta] levels. This study suggests that CRFR1 antagonist treatment provides a potential preventative and disease-modifying therapy for populations subjugated to stress and at risk for AD development. Our data provides an important relationship between stress signaling in the CNS and the neuropathology of AD.