Global Context Research Articles

Vaccine hesitancy: a structured review from a behavioral perspective (2015–2022)

ABSTRACT Vaccine hesitancy, a complex behavioral phenomenon, poses a significant global health threat and has gained renewed attention amidst the COVID-19 pandemic. This paper scrutinized peer-reviewed literature on vaccine hesitancy published from 2015 to 2022, with a specific focus on behavioral perspectives, utilizing a Theories-Constructs-Variables-Contexts-Methods (TCVCM) framework. The study highlighted prominent theoretical approaches, abstract concepts, research variables, global contexts and academic techniques employed across a selected sample of 138 studies. The result is a consolidated overview of research and schematization of the factors influencing vaccine hesitancy and vaccination behaviors. These include individual-level, contextual, vaccine-specific, organizational, and public-policy-related dynamics. The findings corroborated the complexity of vaccine hesitancy and emphasized the difficulties of pursuing vaccine advocacy. The analysis also identified several directions for future research, and the need to conduct more contextual studies in low- and middle-income nations to bring out the cross-cultural nuances of vaccine hesitancy.

Vehicular Mini-LED backlight display inspection based on residual global context mechanism

Mini-LED backlight has emerged as a promising technology for high performance LCDs, yet the massive detection of dead pixels and precise LEDs placement are constrained by the miniature scale of the Mini-LEDs. The high-resolution network (Hrnet) with mixed dilated convolution and dense upsampling convolution (MDC-DUC) module and a residual global context attention (RGCA) module has been proposed to detect the quality of vehicular Mini-LED backlights. The proposed model outperforms the baseline networks of Unet, Pspnet, Deeplabv3+, and Hrnet, with a mean intersection over union (Miou) of 86.91%. Furthermore, compared to the four baseline detection networks, our proposed model has a lower root-mean-square error (RMSE) when analyzing the position and defective count of Mini-LEDs in the prediction map by canny algorithm. This work incorporates deep learning to support production lines improve quality of Mini-LED backlights.Graphical abstract

It's not about building houses; it's about building people's power: Conversation with Carmen and Andrea from the Movimiento territorial de Liberación (MTL) Buenos Aires

This conversation offers a look into the practices of the Movimiento Territorial de Liberación (MTL), a political and social organization in Argentina. It highlights the organization’s history, values, and significant projects, such as housing developments and community initiatives. Founded in 1999 after a break from the Argentine Communist Party over ideological differences, the MTL has its roots in Marxist-Leninist thought. Their work include building neighborhoods and developing collective housing for those displaced by neoliberal policies. The MTL’s projects go beyond merely providing shelter; they aim to foster a way of life that embodies their values of self-management, solidarity, and resistance against capitalist exploitation. Carmen, a leader within MTL, shares their journey, the challenges faced, and the ideological foundations of their work. Andrea, another key figure, manages finances and emphasizes the importance of transparency and accountability in their operations. The conversation also touches on the broader Latin American and global context, situating MTL's struggle within a larger framework of resistance against neoliberalism and imperialism.

Introduction: Social Protection in a Time of Global Uncertainty

Social protection has established itself as a vibrant policy sector across the global South, and as a pillar of international development cooperation. Social protection features in numerous country policies and development agency strategies, as well as in several Sustainable Development Goals. However, the global context today is characterised by a range of emerging and intensifying challenges and uncertainties, including post-Covid-19 pandemic recovery; the cost-of-living crisis; unprecedented climate change; and rising numbers of protracted wars and political instability, leading to mass displacement and migration. What is the role of social protection in this shifting and uncertain global context? This article highlights challenges and opportunities for social protection at this crossroads moment, under the themes of politics, crises, innovation, and inclusion, as a way of introducing and framing the contributions to this IDS Bulletin. We argue that the need for social protection has never been greater, but its future trajectories need urgent reimagining.

Epidemiology and genetic diversity of human respiratory syncytial virus in Belgium between 2011 and 2019

BackgroundHuman respiratory syncytial virus (HRSV) is worldwide one of the leading causes of acute respiratory tract infections in young children and the elderly population. Two distinct subtypes of HRSV (A and B) and a multitude of genotypes have been described. The laboratory of Clinical and Epidemiological Virology (KU Leuven/University Hospitals Leuven) has a long-standing history of HRSV surveillance in Belgium.MethodsIn this study, the seasonal circulation of HRSV in Belgium was monitored during 8 consecutive seasons prior to the SARS-CoV-2 pandemic (2011–2012 until 2018–2019). By use of a multiplex quantitative real time PCR panel, 27,386 respiratory samples were tested for HRSV. Further subtyping and sequencing of the HRSV positive samples was performed by PCR and Sanger sequencing. The prevalence and positivity rate were estimated in 4 distinct age groups and the circulating strains of each subtype were situated in a global context and in reference to the described genotypes in literature.ResultsHRSV circulated in Belgium in a yearly re-occurring pattern during the winter months and both HRSV subtypes co-circulated simultaneously. All HRSV-B strains contained the 60 nt duplication in the HVR2 region of the G gene. Strains of subtype HRSV-A with a 72 nt duplication in the HVR2 region were first observed during the 2011–2012 season and replaced all other circulating strains from 2014 to 2015 onwards.

Developing an mpox risk assessment matrix for social listening and infodemic insights generation

Abstract Issue Any effective response to an infodemic, requires prioritizing public health response on identified narrative themes that pose highest risk to health and well-being of populations. High-risk narratives often require more swift and far-reaching action and low-risk narratives require little or no response. Description of the problem Different countries are assigned different levels of risk to specific narratives or concerns, recognizing that for some issues there may be a large gap between the true public health threat and what is generating lots of conversations, concerns and press coverage that indicate a high degree of risk perception that may be unwarranted. WHO developed a risk matrix to apply to global analysis of questions, concerns and narratives about mpox, which resulted in biweekly infodemic intelligence reports that risk-assessed identified infodemic themes and corresponding recommendations for action. Results Risk assessment criteria were defined in the context of WHO’s global infodemic insights analysis for low, medium and high risk and positive sentiment in mpox global outbreak context before conducting the integrated analysis so that insights were reproducible and infodemic response actions were prioritized. This implemented the WHO/UNICEF training manual on 6 steps to produce an infodemic insights report, which includes tools on how to develop and apply a risk matrix for a specific public health question of concern and apply it in analysis. Lessons The approach resulted in rapid, systematic, reproducible and evidence-driven decision-making to respond to the infodemic accompanying the mpox global outbreak. Risk matrices must be adapted to the context of the public health event and the mandate of the health authority taking action. An mpox risk matrix for narratives included adaptation to reflect high-risk from stigmatizing narratives, as well as narratives circulating around or within LGBTQI+ communities. Key messages • A risk matrix must be developed and adapted to the context of each health emergency and level of health authority action. • Prioritization for rapid infodemic response based on a risk assessment is that it facilitates more effective response in a health emergency.

The chosen few: Mycobacterium tuberculosis isolates for IMPAc-TB

The three programs that make up the Immune Mechanisms of Protection Against Mycobacterium tuberculosis Centers (IMPAc-TB) had to prioritize and select strains to be leveraged for this work. The CASCADE team based at Seattle Children’s Research Institute are leveraging M.tb H37Rv, M.tb CDC1551, and M.tb SA161. The HI-IMPACT team based at Harvard T.H. Chan School of Public Health, Boston, have selected M.tb Erdman as well as a novel clinical isolate recently characterized during a longitudinal study in Peru. The PHOENIX team also based at Seattle Children’s Research Institute have selected M.tb HN878 and M.tb Erdman as their isolates of choice. Here, we describe original source isolation, genomic references, key virulence characteristics, and relevant tools that make these isolates attractive for use. The global context for M.tb lineage 2 and 4 selection is reviewed including what is known about their relative abundance and acquisition of drug resistance. Host–pathogen interactions seem driven by genomic differences on each side, and these play an important role in pathogenesis and immunity. The few M.tb strains chosen for this work do not reflect the vast genomic diversity within this species. They do, however, provide specific virulence, pathology, and growth kinetics of interest to the consortium. The strains selected should not be considered as “representative” of the growing available array of M.tb isolates, but rather tools that are being used to address key outstanding questions in the field.

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

A New Paradigm for Assessing the Relationship Between Indigenous Wellbeing and Health: A Meta-analytical Study of Critical Factors in a Global Context

A New Paradigm for Assessing the Relationship Between Indigenous Wellbeing and Health: A Meta-analytical Study of Critical Factors in a Global Context

Exhibiting Colonialism and Nationalism: The Case of Egyptian Museums and Its Cultural Heritage

Abstract: Egypt is one of four ancient civilizations acknowledged by the international community as having a time-honored historical legacy and a profound cultural heritage. However, from the 19th century onward, Egypt's cultural development trajectory underwent a significant shift due to the influence of Western colonial powers. Subsequently, with the uninterrupted progression of the nationalist movement, the trajectory of Egyptian history and culture, in addition to the conservation and investigation of cultural heritage, underwent yet another transformation. Museums, as crucial instruments for the conservation and exhibition of cultural heritage, have played a pivotal role in the history of Egyptian cultural research, bearing witness to the transformation in the preponderance of Egyptian historical narratives. This paper examines how historical narratives and cultural developments in Egypt were shaped under the influence of colonialism, with a particular focus on the profound impact of Orientalist theories and practices on the construction of museums about the study of historical and cultural heritage. In the post-colonial period, Egyptian museums have served as conduits for disseminating the country's rich cultural heritage and as instruments for enhancing Egyptian national identity. These institutions have played a pivotal role in the formation of national identity and the "decolonization" of the country's cultural heritage, largely due to their irreplaceable cultural space and their capacity to adapt to changing needs. As Egyptian museums have evolved, they have played a significant role in shaping national identity and contributing to the processes of decolonization and de-Westernization. Furthermore, they symbolize the prospective trajectory of Egyptian national identity within the context of globalization and the emergence of a more interconnected world.

Political polarization and health.

In addition to social determinants of health, such as economic resources, education, access to care and various environmental factors, there is growing evidence that political polarization poses a substantial risk to individual and collective well-being. Here we review the impact of political polarization on public health. We describe the different forms of polarization and how they are connected to health outcomes, highlighting the COVID-19 pandemic as a case study of the health risks of polarization. We then offer strategies for mitigating potential harms associated with polarization, with an emphasis on building social trust. Finally, we propose future research directions on this topic, underscore the need for more work in a global context and encourage greater collaboration between social scientists and medical scientists. We conclude that polarization is a serious-if largely overlooked-determinant of health, whose impacts must be more thoroughly understood and mitigated.

Dispossession and beyond: Politics of rural land conversion in China's tourism villages

Across the peripheries of global South cities, projects to convert rural land for development purposes have brought dramatic impact on rural communities and environments. Many of these initiatives involve aggregating land, mobilizing investments, and resettling populations, with direct implications for villagers’ land rights and livelihoods. In existing studies, rural land conversions have often been examined through frameworks of “land grabbing” and “dispossession”. This paper argues for the need to go beyond these frameworks in conceptualizing the varied pathways and outcomes of rural land takings. It presents a comparative case study of two villages in China, whose land has been redeployed for tourism development. In one village, state-led expropriation led to the loss of land rights and the resettlement of villagers in new housing complexes. In another, villagers held onto land ownership and their property but saw intra-community inequalities amplified as residents were differentially incorporated into the tourism economy. By demonstrating how the nuanced mechanisms of land conversion could facilitate variegated livelihood and distributive outcomes between and within communities, this paper problematizes universalist conceptualizations of dispossession and calls for theorizing both “with” and “beyond” dispossession to account for the multifaceted dynamics of land development in global South contexts.

Radar signal modulation identification using global context vision transformer

Abstract The accurate identification of phase-coded radar waveforms is critical in electronic warfare (EW) systems, particularly with the increasing use of low probability of intercept (LPI) radars. However, current methods struggle to reliably recognize these waveforms at low signal-to-noise ratios (SNRs). To address this challenge, we propose an AI-based Global Context Vision Transformer (GC-ViT) model that leverages short-time Fourier transform (STFT) phase spectrum for feature extraction. The GC-ViT model enhances recognition accuracy by incorporating both local and global self-attention mechanisms, enabling more effective identification of phase-coded signals in noisy environments. Experimental results demonstrate that the proposed method achieves approximately 80\% recognition accuracy at an SNR of -12 dB, which significantly outperforms existing techniques. This advancement in radar waveform recognition enhances the situational awareness and decision-making capability of EW systems in complex electromagnetic environments.

Global induced local network for infrared: dim small target detection

Abstract It is challenging to detect infrared dim targets submerged in complicated backgrounds due to their small size and faint intensity. The previous attention-based detection networks frequently require global long-range dependence. Significant calculations are required to determine the target's sparse but meaningful position. To prevent wasting calculations on the background, this paper offers a detection network guided by global context for local feature learning, named GILNet (Global Induced Local Network). It designs a global location module (GLM) and a local feature interaction module (LFIM) to capture the global position and features of targets, respectively. More specifically, using global context interaction, the GLM finds the region that might contain dim small targets, that is, the coarse location. In the coarsely located regions, the LFIM further acquires feature information about targets. Next, we also design an eight-directional attention operation to obtain the contour information of targets in the low feature map. It is fused with the high feature map in the multi-directional feature fusion module (MFFM), which retains more semantic and spatial information about targets. Finally, quantitative and qualitative analysis show that the GILNet performs better than eight comparison methods on two public datasets.

Food Waste Biotransformation into Food Ingredients: A Brief Overview of Challenges and Opportunities

In today’s global context, challenges persist in preventing agri-food waste due to factors like limited consumer awareness and improper food-handling practices throughout the entire farm-to-fork continuum. Introducing a forward-thinking solution, the upcycling of renewable feedstock materials (i.e., agri-food waste and by-products) into value-added ingredients presents an opportunity for a more sustainable and circular food value chain. While multi-product cascade biorefining schemes show promise due to their greater techno-economic viability, several biotechnological hurdles remain to be overcome at many levels. This mini-review provides a succinct overview of the biotechnological and societal challenges requiring attention while highlighting valuable food-grade compounds derived from biotransformation processes. These bio-based ingredients include organic acids, phenolic compounds, bioactive peptides, and sugars and offer diverse applications as antioxidants, preservatives, flavorings, sweeteners, or prebiotics in foodstuffs and other consumer goods. Therefore, these upcycled products emerge as a sustainable alternative to certain potentially harmful artificial food additives that are still in use or have already been banned from the industry.

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists’ evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.

A qualitative study illustrating factors that enable and constrain academic advising practices in a Global South context

A qualitative study illustrating factors that enable and constrain academic advising practices in a Global South context

Transcultural practices and inter-generational dynamics among migrant youth

ABSTRACT This paper explores intergenerational dynamics affecting second-generation migrant youth transcultural identities within a global context of increasing mobility, diversity and interconnectedness. Drawing on in-depth interviews of first and second-generation migrant youth across three research sites, this paper explores how migrant families in Melbourne (Australia), Toronto (Canada) and Birmingham (UK) maintain and transmit their heritage culture and associated values, skills and knowledge – a critical component of transcultural capital – to the next generation. The young adults’ narratives illustrate the socio-cultural processes that enable opportunities for inter-generational relating and cross-cultural belonging. The ensuing critical awareness, cross-cultural knowledge, and social engagement with one’s own culture(s) enhance intercultural openness, an important orientation in today’s hyper-connected and super-diverse world. Importantly, within this ethno-culturally pluralist framing, migrant youth act less as passive recipients of culture(s) and more as agentic drivers of multi-dimensional cultural adaptation. In mobilising selectively and agentically transcultural capital, migrant youth are then able to negotiate and critically engage with aspects of their heritage culture. This forms enabling strategies conducive to individually driven social empowerment and intercultural engagement.

Optimization Study of Coal Gangue Detection in Intelligent Coal Selection Systems Based on the Improved Yolov8n Model

To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, Yolov8n-improvedGD(GD—Gangue Detection), based on Yolov8n. The optimization strategy includes integrating the GCBlock(Global Context Block) from GCNet(Global Context Network) into the backbone network to enhance the model’s ability to capture long-range dependencies in images and improve recognition performance. The CGFPN (Contextual Guidance Feature Pyramid Network) module is designed to optimize the feature fusion strategy and enhance the model’s feature expression capabilities. The GSConv-SlimNeck architecture is employed to optimize computational efficiency and enhance feature map fusion capabilities, thereby improving the model’s robustness. A 160 × 160 scale detection head is incorporated to enhance the sensitivity and accuracy of small coal and gangue detection, mitigate the effects of low-quality data, and improve target localization accuracy.

The influence of political factors on economic growth: A study of the interconnection between governance spheres in a global context

The influence of political factors on economic growth: A study of the interconnection between governance spheres in a global context