Month: January 2025

Estimation of the Spatiotemporal Variability of Surface soil Moisture Using Machine Learning Methods Integrating Satellite and Ground-based Soil Moisture and Environmental Data

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Abstract

Monitoring and quantifying the development of drought extremes is important to agriculture, water, and land management. For this, soil moisture (SM) is an effective indicator. However, currently, real-time monitoring and forecasting of SM is challenging. Thus, this study develops and tests a methodology based on machine learning methods that integrates ground-based data, Sentinel-1 satellite soil moisture (S1SSM) data, meteorological data, and relevant environmental parameters to improve the estimation of the spatiotemporal changes in SM. It also evaluates the relevance of the applied parameters and the applicability and limitations of S1SSM data in SM monitoring. Specifically, the performances of four machine learning methods (multiple linear regression, support vector machine regression, extreme gradient boosting, and a deep neural network) were evaluated in an area increasingly exposed to hydrological extremes. Overall, the extreme gradient boosting model provided the best result (R2 = 0.92). In this case, the difference between the modeled and observed SM values at ground-based stations was below 3%, with only five stations reporting differences above 5%, indicating the effectiveness of this model for SM monitoring in larger areas. Additionally, the spatial pattern of the observed S1SSM values and the modeled values showed good agreement (with a difference below 10%) in the larger part (45.5%) of the area, while more than 20% difference occurred in 27.1% of the area, demonstrating the application potential of S1SSM data in areas with less heterogeneous land use. However, the results also suggest that the S1SSM data can be affected by land use and/or soil types.

Anarchic Souls in the Leviacene Age

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Abstract

This article contends that Western iterations of sovereignty and its apparatuses produce, in part, souls and selves that have contributed to destructive, depersonalizing relations between human beings, other species, and the Earth. Given this, it is necessary to reimagine the notion of “soul” in relation to the idea of anarchy with the aim of creating more life-enhancing and inclusive eco-social-political relations. The underlying premise of this article is that anarchic souls are an existential reality that, in the West, have mostly been hidden, denied, or suppressed as a result of persons internalizing the philosophical and theological ideas, beliefs, and values associated with varied apparatuses of domination.

Anarchic Souls in the Leviacene Age

Latest Results

Abstract

This article contends that Western iterations of sovereignty and its apparatuses produce, in part, souls and selves that have contributed to destructive, depersonalizing relations between human beings, other species, and the Earth. Given this, it is necessary to reimagine the notion of “soul” in relation to the idea of anarchy with the aim of creating more life-enhancing and inclusive eco-social-political relations. The underlying premise of this article is that anarchic souls are an existential reality that, in the West, have mostly been hidden, denied, or suppressed as a result of persons internalizing the philosophical and theological ideas, beliefs, and values associated with varied apparatuses of domination.

Routine malaria vaccination in Africa: a step toward malaria eradication?

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Abstract

Malaria remains a significant global health challenge, with nearly half of the world's population at risk of infection. In 2022 alone, malaria claimed approximately 608,000 lives, with 76% of these fatalities occurring in children under the age of five, underscoring the disease’s disproportionate impact on vulnerable populations. Africa bears the highest burden, accounting for 94% of global malaria cases. For over 60 years, the development of a malaria vaccine has been a critical objective for scientists and governments, with substantial efforts directed toward this goal. Recent progress has led to the approval of the first malaria vaccines, RTS,S/AS01 (Mosquirix®) and the R21/Matrix-M vaccine. Inspired by the promise of these vaccines, the global malaria community has renewed its focus on malaria eradication, 50 years after flawed earlier eradication efforts in the mid-twentieth century. Since the World Health Organization’s endorsement of RTS,S in 2021 and R21 in 2023, several African countries, beginning with Cameroon, have integrated these vaccines into routine immunization programmes. This review examines the role of routine malaria vaccination in Africa as a key strategy toward malaria elimination, explores challenges and solutions for widespread vaccine implementation, and discusses future directions in the ongoing fight to eliminate malaria on the continent.

Fake news detection: comparative evaluation of BERT-like models and large language models with generative AI-annotated data

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Abstract

Fake news poses a significant threat to public opinion and social stability in modern society. This study presents a comparative evaluation of BERT-like encoder-only models and autoregressive decoder-only large language models (LLMs) for fake news detection. We introduce a dataset of news articles labeled with GPT-4 assistance (an AI-labeling method) and verified by human experts to ensure reliability. Both BERT-like encoder-only models and LLMs were fine-tuned on this dataset. Additionally, we developed an instruction-tuned LLM approach with majority voting during inference for label generation. Our analysis reveals that BERT-like models generally outperform LLMs in classification tasks, while LLMs demonstrate superior robustness against text perturbations. Compared to weak labels (distant supervision) data, the results show that AI labels with human supervision achieve better classification results. This study highlights the effectiveness of combining AI-based annotation with human oversight and demonstrates the performance of different families of machine learning models for fake news detection.

Mapping the research landscape of blockchain and crowdfunding

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Abstract

Blockchain technology and crowdfunding (CF) have emerged as disruptive forces in the finance and entrepreneurship landscape, potentially transforming traditional modes of capital raising and investment. This study investigates the intersection of blockchain technology and CF to provide a comprehensive overview of the current state of research through a systematic literature review and bibliometric analysis. By examining 219 publications sourced from Scopus, this study employed descriptive statistics, article co-citations, and keyword co-occurrence to identify key bibliometric indicators, themes, and trends. The findings reveal a surge in research activity related to blockchain and CF, emphasizing initial cryptocurrency offerings, financial technology (Fintech), and the role of blockchain in improving transactional efficiency, disintermediation, and venture capital CF. Keyword co-occurrence analysis reveals diverse research themes, including smart contracts, fundraising campaigns, sustainable entrepreneurship, and Islamic Fintech. Based on the findings of this analysis, several implications and directions for further investigation are highlighted. To the best of our knowledge, this is the first attempt to analyze the intersection of blockchain technology and CF using scientometric techniques systematically.

Multistage emplacement of a composite intrusion: magnetic fabric and zircon U–Pb age of the Parnamirim Pluton, NE Brazil

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Abstract

The advanced stages of the Neoproterozoic Brasiliano orogeny in western Gondwana led to significant crustal reworking and extensive peraluminous magmatism, with many plutons spatially associated with shear zones. This study applies a multi-methodological approach (airborne magnetic surveys, structural analysis, anisotropy of magnetic susceptibility analyses, and U–Pb zircon dating) to the Parnamirim Pluton (Central Domain of the Borborema Province, northeastern Brazil). The pluton is located between the West Pernambuco (E–W, dextral) and Parnamirim (NE–SW, sinistral) shear zones and consists of three main pulses (PP1, PP2, and CV) of peraluminous syenogranites to monzogranites, with magnetic susceptibility controlled by paramagnetic minerals. The NE–SW-striking magmatic, solid-state and magnetic foliations are steeply dipping to subvertical, reflecting syn- to post-emplacement deformation in response to strain caused by the regional deformation field. Magmatic to high-temperature solid-state microstructures dominate in the eastern PP1 unit, while high-strain and high-temperature solid-state deformation microstructures, characterized primarily by grain boundary migration recrystallization, show increasing strain in the western PP2 unit. Elongate country rock xenoliths indicate that pluton emplacement occurred initially by fracture propagation and evolved into sheet amalgamation. Pluton growth was controlled by NE–SW horizontal extension resulting from the combined motion of the West Pernambuco and Parnamirim shear zones, progressing westward, as constrained by the U–Pb zircon ages of PP1 (569 ± 1 Ma) and PP2 (559 ± 2 Ma). The age of the Parnamirim Pluton is similar to those of other granitoids in the Borborema Province and the  Benino-Nigerian Shield, indicating a large-scale magmatic and tectonic event.

Graphical abstract

Simplified model of the multi-stage intrusion and deformation of the Parnamirim Pluton. The schematic drawing illustrates: the sheet-like intrusion, consistent with the vertical foliation of the host rock (Salgueiro Complex); the initial intrusion took advantage of the foliation planes of the host rocks, facilitating the initial accommodation of the magmatic sheets which provides space and favorable thermal anisotropy for the advancement of the intrusion through the amalgamation of sheets; and the displacement of younger and hotter sheets to the west, favoring strain partitioning during the deformation of the pluton and allowing magmatic textures to be preserved in the eastern part. Additionally, the growth of the pluton was controlled by NE-SW horizontal extension resulting from the combined opposite motion of the West Pernambuco and Parnamirim shear zones, in response to NNW-SSE regional shortening. White arrows represent the transpressive context

The heterogeneous effect of digital economy on ecological resilience: considering the mediating role of technological innovation

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Abstract

Considering the high priority given to the digital economy and ecological resilience, it is crucial to study the heterogeneous impacts of the digital economy on ecological resilience for enhancing environmental protection and environmental policy design. This paper explores the heterogeneous impacts of the digital economy on ecological resilience and the nonlinear relationship between them using Bayesian non-parametric generalized quantile regression based on panel data from 2011 to 2020 for 30 provinces (municipalities and autonomous regions) in China. Moreover, the moderating effect of technological innovation is examined. The findings suggest that the digital economy exerts a notably heterogeneous effect on ecological resilience. Specifically, there is a decreasing and then increasing trend between them. At 0.3, 0.5, and 0.7 quantiles of ecological resilience, the elasticity coefficients of the digital economy follow similar trends, with smaller variations. At the 0.1 quantiles of the ecological resilience, the utility of the digital economy is not significant. At the 0.9 quantiles of the ecological resilience level, the elasticity coefficient has a large fluctuation. Lower and higher digital economy can effectively promote ecological resilience. However, medium digital economy inhibits ecological resilience. Moreover, the digital economy mainly affects ecological resilience through technological innovation. Finally, policy recommendations are given to promote environmental protection and environmental governance.

Comprehensive review of edge and contour detection: from traditional methods to recent advances

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Abstract

Edge and contour detection plays critical roles in computer vision and image processing, with extensive applications in advanced tasks including object recognition, shape matching, visual saliency, image segmentation, and inpainting. In recent decades, this field has attracted significant attention, leading to the development of numerous sophisticated methods that approximate human visual performance. Despite these advances, notable gaps remain. This review offers a comprehensive analysis of representative techniques, categorizing them into traditional and learning-based approaches, and examines their strengths and limitations to identify the underlying reasons for these gaps. Traditional methods are further divided into four sub-categories: local pattern, edge grouping, active contour, and bio-inspired techniques, with a specific emphasis on the promising potential of bio-inspired methods. Learning-based approaches, on the other hand, are classified into two types: classical learning, which typically relies on handcrafted features designed from empirical knowledge, and deep learning, which autonomously extracts features from large-scale datasets without human intervention. Additionally, benchmarks and evaluation metrics related to edge and contour detection are discussed, with potential issues identified within these frameworks. A quantitative assessment of representative methods is conducted across three popular benchmarks. Lastly, challenges and future prospects in edge and contour detection are explored, focusing on five key aspects: model architecture, learning strategies, feature extraction and fusion, method integration, and cross-domain applications. These considerations aim to bridge the gap with human visual perception. Overall, this work is expected to benefit researchers and advance progress in the field.

Becoming bilingual (or not): A look into the public’s intersecting orientations towards Bilingual 2030 in multilingual Taiwan

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Abstract

The Bilingual 2030 policy in Taiwan has attracted both support and criticism since it was introduced in 2018. Those who embrace the policy consider it an opportunity for Taiwan to become more globally connected, while those who are skeptical question whether the policy could actually lead Taiwan to a better place in the longer run—not just linguistically, but also socially, economically, and politically. Driven by this concern, this paper seeks to systematically examine the public’s response towards Bilingual 2030—through the analysis of opinion pieces and polls collected between 2019 and 2022, in addition to a series of comments gathered from a policy petition filed in 2023. With Ruiz’s (NABE J 8:15–34, 1984) theory as a guiding framework, this paper shows that the public’s language orientations are relational, intersecting, and multilayered, implicated in wider language power dynamics. In particular, the findings unveil a disconnect between what is projected and what is received, moving from an English-as-resource focus to a more dynamic discussion of resource, right, and problem orientations associated with different languages in Taiwan. While there is ample discussion around the economic benefits of English, issues related to national identity and de/colonization are also very much alive and kicking in interpretations of the policy. The main argument of this paper is that the “bilingual” label in policy discourse needs to be taken more critically and in ways that address the language concerns of the people. The paper ends with some suggestions to advance the vision of enhancing multilingual Taiwan.