Author: Latest Results

Modelling foreign exchange rate co-movement and its spatial dependence in emerging markets: a spatial econometrics approach

Latest Results

Abstract

This paper studies the impact of macroeconomic factors on co-movement in the foreign exchange rate markets. Data of foreign exchange rates from 24 merging markets is used to this end along with a dynamic spatial Durbin model as to examine spatial dependencies among markets. Our empirical findings show no evidence that cultural ties exert a role in spreading macroeconomic shocks in the exchange rate of a country to the exchange rates of other countries. Moreover, we show that economic closeness through foreign direct investment (FDI) and international bilateral trade is the most prominent channel in spreading macroeconomic shocks and spatial effects in emerging markets through the foreign exchange rates. In addition, geographical proximity reinforces the interdependence relationship of emerging markets. Our findings show that the co-movement of foreign exchange rate markets across the selected emerging markets is positively influenced by their gross domestic product (GDP) and interest rate differential and negatively affected by the terms of trade and remittance. In addition, we reveal that terms of trade, the inflation differential, and remittance are the most prominent fundamental factors affecting foreign exchange rate movements.

Ecological niche modelling of Tecomella undulata (Sm.) Seem: an endangered (A2a) tree species from arid and semi-arid environment imparts multiple ecosystem services

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Abstract

The objective of this study was to utilize niche modelling techniques and predictors, including bioclimatic, soil, habitat heterogeneity indices, and land-use land cover (LULC), to ascertain the present and potential distribution of Tecomella undulata in India. The bio-climatic variables of 2050 and 2070 timeframes were employed to forecast future occurrences. The study also examined the level of indigeneity of T. undulata and analysed the factors that impact its fundamental and realized niche. The Maxent model utilized for forecasting the distribution of T. undulata demonstrated a high level of precision, incorporating both bioclimatic and non-bioclimatic variables. The study highlights the significance of mean and maximum temperatures during the warmest quarter and month, as well as the wettest months and years’ worth of precipitation. In addition, threshold values for these predictors were calculated. In contrast to the limiting effects of climatic factors, the species in question was found to exhibit a greater degree of facilitation in response to soil conditions (including rooting conditions, nutrient availability, and salt excess), habitat heterogeneity indices (such as range, maximum, and coefficient of variance of diversity), and lLULC predictors (including urban areas, residential and infrastructure development, forested regions, and sparsely vegetated areas). As a result, this species was able to expand its range across a wider expanse of India. The Churu and Jhunjhunu districts and a transact region including Pali, Jalor, Jodhpur, Sanchor, and Barmer have been identified as the best possible locations for its occurrences. Shrinkage would begin around 2050 in all of these areas. By 2070, the Churu and Jhunjhunu regions had become significantly more fragmented, while the Jodhpur region and the surrounding areas of Barmer, Sanchor, Jalor, and Vav had grown. Specific coordinates were also identified pertains to zone of extinction, zone of re-occurrence and zone of maximum occurrence. The aforementioned discoveries enable us to ascertain the extent of land that is conducive to the growth of T. undulata across diverse ecological niches, as well as the underlying factors and critical points that impact its dispersion dynamics both presently and prospectively. This shall aid us in determining the necessity of extensive captive cultivation for the preservation of the species and its consequential ecological advantages.

Experimental measuring techniques for industrial-scale multiphase flow problems

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Abstract

Industrial-scale measuring techniques on multiphase flow behaviours are essential in understanding realistic industrial problems and investigating onsite multiphase flow problems. Over the past decades, several experimental measuring techniques (e.g., laser Doppler velocimetry, particle image velocimetry, and particle/droplet image analysis) were developed or successfully applied by Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia, on many collaborated industrial projects. Most of these valuable measuring techniques are presented in this review, followed by demonstrating case studies on multiphase flow problems using these techniques. The outcomes from the case studies revealed strong compatibility of the presented techniques on measuring a wide variety of multiphase flow scenarios. The applications could widely differ from highly-agitated industrial mixing cell to densely occupied commercial airliner cabin. The great potential on the future of industrial-scale measuring techniques for multiphase flow researches has been clearly revealed.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

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Abstract

This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September (JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa, particularly in providing improved forecast products which are essential for end users.

Hydrologic Extremes in a Changing Climate: a Review of Extremes in East Africa

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Abstract

Purpose

Eastern Africa has a complex hydroclimate and socio-economic context, making it vulnerable to climate change-induced hydrological extremes. This review presents recent research on drivers and typologies of extremes across different geographies and highlights challenges and improvements in forecasting hydrological extremes at various timescales.

Recent Findings

Droughts and floods remain the major challenges of the region. Recently, frequent alterations between droughts and floods have been a common occurrence and concern. Research underlines the heterogeneity of extremes and the impact of climate change as increased intensity and duration of extremes. Moreover, the importance of local and antecedent conditions in changing the characteristics of extremes is emphasized.

Summary

A better understanding of these drivers and how they interact is required. Observational and modeling tools must capture these relationships and extremes on short timescales. Although there are improvements in forecasting these extremes, providing relevant information beyond meteorological variables requires further research.

The Uncanny Capital: Mapping the Historical Spatial Evolution of Windhoek

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Abstract

This paper has traced the historic spatial development of Windhoek through five distinct socio-political epochs. These different periods’ spatial, aesthetic, and representational effects on the city’s urban landscape are presented in original maps, allowing a spatial-to-scale comparison and analysis of the city’s development. Rather than the discrete chapters in Windhoek’s urban development, successive occupations’ spatial compositions are shown to have been assembled from and grounded in the geomorphological, spatial, social, and administrative conditions preceding them. The paper expands the concept of the uncanny, the colonial drive to recreate home in foreign lands and local resistance to this, from the architectural to the urban. It describes how infrastructure, residential typologies and neighbourhood morphologies, memorials, places of memory, and public space were designed to segregate and subjugate Windhoek’s population and how this spatial legacy continues to inform city-making in Windhoek today. In doing so, the paper challenges the notion that the city’s spatial structure, layout, and urban planning are neutral quantitative entities.

Contextual Word Embedding for Biomedical Knowledge Extraction: a Rapid Review and Case Study

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Abstract

Recent advancements in natural language processing (NLP), particularly contextual word embedding models, have improved knowledge extraction from biomedical and healthcare texts. However, limited comprehensive research compares these models. This study conducts a scoping review and compares the performance of the major contextual word embedding models for biomedical knowledge extraction. From 26 articles identified from Scopus, PubMed, PubMed Central, and Google Scholar between 2017 and 2021, 18 notable contextual word embedding models were identified. These include ELMo, BERT, BioBERT, BlueBERT, CancerBERT, DDS-BERT, RuBERT, LABSE, EhrBERT, MedBERT, Clinical BERT, Clinical BioBERT, Discharge Summary BERT, Discharge Summary BioBERT, GPT, GPT-2, GPT-3, and GPT2-Bio-Pt. A case study compared the performance of six representative models—ELMo, BERT, BioBERT, BlueBERT, Clinical BioBERT, and GPT-3—across text classification, named entity recognition, and question answering. The evaluation utilized datasets comprising biomedical text from tweets, NCBI, PubMed, and clinical notes sourced from two electronic health record datasets. Performance metrics, including accuracy and F1 score, were used. The results of this case study reveal that BioBERT performs the best in analyzing biomedical text, while Clinical BioBERT excels in analyzing clinical notes. These findings offer crucial insights into word embedding models for researchers, practitioners, and stakeholders utilizing NLP in biomedical and clinical document analysis.

Systematic review of wetland ecosystem services valuation in India: assessing economic approaches, knowledge gaps, and management implications

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Abstract

India boasts a wealth of wetland ecosystems that support diverse and unique habitats and provide numerous ecological goods and services but are under tremendous stress. To ensure the sustainability of these ecosystems and the provision of various ecosystem services (ES), a systematic understanding of wetland ecosystem services (WES) and their economic value is highly important for guiding WES research and management. This systematic review provides an in-depth assessment of existing knowledge on WES and summarize key interdisciplinary approaches for measuring and valuing them. The review deals with the economic valuation approaches adopted in India for the WES and addresses the pressing need for reliable economic valuation methods that quantify trade-offs across various spatial–temporal scales and can assess the efficiency of alternative wetland management scenarios. By meticulously examining the available scientific literature related to WES and analyzing a diverse range of research papers that explicitly quantify these services, this paper seeks to identify gaps, advancements, management approaches, and future requirements in the field of WES valuation in India. It emphasizes the need for a pluralistic approach that includes a wider range of social perspectives and valuation techniques to better understand the relationship between ecosystem functioning and human well-being. After describing the specificity of knowledge gaps, we conclude with lessons for future research on wetland valuation in India.

Unravelling cross-country regulatory intricacies of data governance: the relevance of legal insights for digitalization and international business

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Abstract

In an era of digital transformation, where data is often referred to as the ‘new oil’ of business, with data privacy and cybersecurity incidents recurrently making the headlines, international business (IB) scholars are increasingly grappling with the challenges posed by disparate data governance regulations. Recognizing the growing importance of this topic for IB research and policymaking, our paper seeks to offer a comprehensive examination of cross-country regulatory intricacies of data governance, frequently described by IB scholars as ‘complex’ and ‘pluralistic’ institutional contexts. This allows us to explore the various implications of diverse data governance regulations on international business, thus laying the groundwork for rigorous IB policy studies in this area. As a preliminary finding, we highlight a greater need for international cooperation, where both policymakers and multinational enterprises play a pivotal role. Using the EU data governance framework as an illustrative example, we structure our discussion around four policy areas of data governance: data use; data transfers; data storage; and data flows. We aim for this categorization to serve as a foundational basis for future IB research, aiding in tackling one of the most pressing digital challenges of this day and age: reconciling data privacy and security with data-driven innovation.

The influence of societal nationalist sentiment on trade flows

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Abstract

In recent years, the world has witnessed a backlash against globalization and a rise in populist and nationalist movements around the world. However, there appears to be little empirical research concerning how these movements, and especially nationalist sentiment, actually influence trade. Therefore, we explore how and when nationalist sentiment within a country influences trade. Our results indicate that the effect of nationalist sentiment on imports is mediated by lower participation in free trade agreements (FTAs) but not via tariffs. Furthermore, we are unable to confirm support for a direct effect of nationalist sentiment on imports, as predicted by the consumer ethnocentricity literature. However, we do find a strong and negative impact of nationalist sentiment on exports. It would appear that nationalist sentiments tend to blunt the desire to export. Psychic distance between the countries appears to magnify the effects of nationalist sentiment on tariffs and FTAs, but not the direct effects on trade. Finally, we also find that custom union membership, such as the EU, negates the effect of nationalist sentiment on tariffs but this cannot be confirmed for FTAs. Overall, our model enriches our understanding of how nationalist sentiment in society affects trade and offers guidance to policymakers.