Geographic Diversity Research Articles

Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies.

Genetic diversity, variation and recombination among the human papillomaviruses (HPVs) genomes isolated in China: a comparative genomic and phylogenetic analysis

ABSTRACT Human papillomaviruses (HPVs) are widespread, sexually transmitted group of viruses that infect most individuals at some stage, causing genital warts and cancers. They are members of the Papillomaviridae family, which contains about 400 HPV types. China is among the high HPV burden countries with reported infections of multiple HPV types, accounting for 17.3% of global deaths and 18.2% of global new cases. Thus, understanding the genetic variation and geographic diversity characteristics of HPVs isolated in China is critical for global HPV prevention strategies. Thus, we analyzed the available HPV genome sequences isolated in China that grouped into two categories (alpha- and gamma-papillomaviruses) based on full-length genomes. The most common were HPV-16, −6, −58, and −52 respectively. In addition, four of the novel strains isolated in China, e.g. TG550, JDFY01, CH2, and L55 clustered with the HPV-mSK 159, 244, 201, and 200 respectively. Our phylogeographic network analysis indicated that the L55, TG550, and CH2 are genetically identical to the mSK 200, 046, and 201 respectively, while JDFY01 appeared separately, connected to the mSK-040 following five mutational steps. Also, we found ten recombination events among HPV-6/11 types within their E1, E2, E7, L1/L2 proteins, and Long Control Region ORFs. We achieved the consensus amino acid sequences of HPV proteins and found a conserved stretch of amino acids within E5A of all HPVs circulating in China. These findings offer valued insights into the genetic relationships, distribution, and evolution of the HPVs in China that may assist in adapting effective HPV preventive measures.

A study of the morphological and geographical diversity of Korean indigenous buckwheat landraces for breeding

This study was carried out to explore the significance of morphology-based selection for breeding, using a case study of indigenous buckwheat landrace from Korea. Although many studies have utilized molecular methods to investigate germplasm diversity, this research underscores the often-overlooked importance of morphology-based selection in breeding strategies. Furthermore, it highlights the necessity of considering geographical information only when it accurately reflects the actual environment. The study collected and analyzed five traits from 96 samples across 27 varieties. The hypothesis was that these 27 samples might have been grouped by region due to similar environmental conditions. This hypothesis was tested using a dendrogram, clustering, correlation analysis of each trait, and PCA analysis. The results showed that morphological traits are a crucial factor in germplasm or landrace selection and that they should be considered the basic information for breeding purposes. Morphological traits were found to be random regardless of the locations where they were collected, indicating that they should be used to determine breeding targets rather than geographic locations. The study suggested that understanding the physiological traits of specific germplasm groups can accelerate breeding efforts. Overall, this study provided important insights into the selection of germplasm or landrace for breeding purposes, highlighting the importance of morphology-based selection and the need to consider environmental factors.

Fish size structures in lakes of the Lower Mississippi River floodplain

Abstract The Lower Mississippi River has a floodplain that includes >1350 perennial lakes carved by shifts in river courses and other hydro‐fluvial processes over eons. Notwithstanding their similar provenances, these waterbodies exhibit an immense variety of morphologies and successional stages that illustrate their natural trajectory from aquatic to forested wetlands. A result of this geographical, morphological and temporal diversity is dynamic and varied fish communities. We examined how size structures of fish communities in these floodplain lakes were associated with key in‐lake and off‐lake environmental drivers. Fish lengths were collected with standardised procedures in a sample of 30 of these lakes to construct a lake‐by‐length group matrix. Likewise, in‐lake and off‐lake environmental descriptors were collected to construct a lake‐by‐covariate matrix. Distance‐based linear models were used to assess associations between fish size structure and environmental descriptors. Smaller fish were typically associated with increasing levels of turbidity, chlorophyll‐a, phycocyanin and surrounding agriculture. Shallow, hypereutrophic floodplain lakes associated with agricultural landscapes and reduced connectivity experience harsh physicochemical environments. These conditions appeared to hinder the formation of sustained fish communities but may confer a survival advantage to juveniles or small short‐lived species. Conversely, larger fish were associated with increasing lake depth, water clarity, connectivity, and extent of surrounding forests‐wetlands. Enhanced stability and size structure were observed in communities residing in deeper and clearer lakes, suggesting that these conditions facilitated the development of longer‐lived species spanning multiple age groups. The enhanced connectivity that facilitated this increased stability also permitted the presence of larger itinerant species. Size‐structure assessments can serve as a valuable ecological and biodiversity indicator in floodplain lakes. Size‐structure assessments could supplement and, depending on objectives, even supplant conventional taxonomic analyses, and enhance surveillance of this vast and important natural resource.

Clinical Effects of Glucagon-Like Peptide-1 Agonist Use for Weight Loss in Women With Polycystic Ovary Syndrome: A Scoping Review.

Glucagon-like peptide-1 (GLP-1) is a gastrointestinal regulatory hormone that stimulates insulin release from the pancreas. While GLP-1 receptor agonists (GLP-1 RAs) have traditionally been utilized to address insulin resistance, their potential application in treating polycystic ovary syndrome (PCOS) has recently garnered attention. This study aimed to investigate the therapeutic efficacy of GLP-1 RAs use for weight loss in women diagnosed with PCOS. We conducted a scoping review following the Joanna Briggs Institute (JBI) methodology and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our investigation delved into the clinical effects experienced by women of diverse racial and ethnic backgrounds with PCOS who were prescribed GLP-1 RAs for weight loss. Peer-reviewed articles from Ovid Medline, Web of Science, CINAHL, Cochrane CENTRAL, SCOPUS, and ClinicalTrials.gov spanning from 2012 to 2023 were scrutinized. After eliminating duplicates, 811 articles were identified, and ultimately, eight met the eligibility criteria for inclusion. All studies were published in English and exhibited wide geographic diversity. The included studies uniformly reported reductions in weight and body mass index (BMI) among patients who were prescribed GLP-1 RAs, specifically liraglutide or exenatide. Additionally, evidence pointed towards improvements in anthropometric parameters (MF1) (including total body weight, BMI, reduction in waist circumference, and total fat percentage), glucose homeostasis, cardiovascular inflammatory markers (midregional pro-atrial natriuretic peptide (MR-proANP) and mid-regional pro-adrenomedullin (MR-proADM)), rates of pregnancy, and menstrual regulation. However, findings regarding the impact of GLP-1 RAs on lipid profiles were inconsistent. Although some short-term adverse effects were noted, long-term effects of GLP-1 RAs use remain undetermined. GLP-1 RA use demonstrated promising clinical outcomes for women with PCOS, including reduced BMI, improved metabolic parameters, menstrual regularity, and increased rates of natural pregnancy. While the current evidence is encouraging, further research is warranted to elucidate both short- and long-term adverse effects of GLP-1 RAtherapy for PCOS.

Early Poplar (Populus) Leaf-Based Disease Detection through Computer Vision, YOLOv8, and Contrast Stretching Technique.

Poplar (Populus) trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential. Convolutional Neural Networks (CNNs), particularly adept at processing visual information, are crucial for the accurate detection and classification of plant diseases. This study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, enhancing the geographic diversity and application of the dataset. The disease classes consist of "Parsha (Scab)", "Brown-spotting", "White-Gray spotting", and "Rust", reflecting common afflictions in these regions. This dataset will be made publicly available to support ongoing research efforts. Employing the advanced YOLOv8 model, a state-of-the-art CNN architecture, we applied a Contrast Stretching technique prior to model training in order to enhance disease detection accuracy. This approach not only improves the model's diagnostic capabilities but also offers a scalable tool for monitoring and treating poplar diseases, thereby supporting the health and sustainability of these critical resources. This dataset, to our knowledge, will be the first of its kind to be publicly available, offering a valuable resource for researchers and practitioners worldwide.

Prevalence of human papilloma virus among cervical cancer patients in India: A systematic review and meta-analysis.

Cervical cancer, predominantly caused by the human papillomavirus (HPV), is a major health challenge in India, with high morbidity and mortality rates. Given India's vast geographic and socio-economic diversity, understanding regional variations in HPV prevalence is crucial for developing targeted and effective public health interventions. This systematic review and meta-analysis were conducted to elucidate the prevalence of HPV among cervical cancer patients in India. A literature search was executed across PubMed, EMBASE, and Web of Science up to December 07, 2023. Observational studies reporting HPV prevalence among cervical cancer patients in India are included. A Modified Newcastle-Ottawa scale was used for quality assessment. A random-effects meta-analysis was used to determine pooled HPV prevalence, and heterogeneity was evaluated using the I² statistic. Subgroup and sensitivity analyses were performed to assess result stability and investigate heterogeneity sources. All statistical analyses were performed using R software version 4.3. The meta-analysis included 17 studies with a total of 2529 cervical cancer cases, of which 1977 were HPV-positive. The pooled HPV prevalence was 85% (95% CI: 71-92%), with substantial heterogeneity (I² = 94%). Subgroup analysis by geographic zones showed notable differences: South (88%, 95% CI: 76-95%), North (73%, 95% CI: 1-100%), East (99%, 95% CI: 1-100%), Central (71%, 95% CI: 54-84%), and West (77%, 95% CI: 0-100%). Sensitivity analysis demonstrated the consistency of the results, and a reanalysis, excluding influential studies, yielded a prevalence of 82% (95% CI: 67-91%). Our analysis reveals a high prevalence of HPV in cervical cancer patients in India, with significant regional variations. The observed heterogeneity highlights the complexity of HPV epidemiology in India and necessitates further research to explore underlying causes and regional characteristics. Future studies should aim to expand geographic representation and deepen understanding of the factors contributing to the variability in HPV prevalence.

Entomophagy in the Benin republic, new opportunities for mass-rearing of edible insects

Abstract Despite the long-standing tradition of insect consumption in Africa, there are few publications describing their geographical distribution, diversity, availability and consumption practices in the Benin republic. The present study investigated the geographical distribution of edible insects, but also the frequencies of insect citation and consumption across ten phytogeographical zones (PGZ) of the Benin republic. A survey involving 1,267 participants was conducted through focus group discussions (FGDs) and individual interviews, covering one district in each PGZ. The study identified 22 edible species from six different orders: Coleoptera, Hymenoptera, Isoptera, Lepidoptera, Mantodea, and Orthoptera. These species are primarily available during the rainy season and are typically collected using traditional methods. They are consumed in various forms, including roasted, fried, boiled, or raw, depending on the developmental stages. Based on the frequencies of cited edible species, the most cited was established for each PGZ, and a shortlist of 12 species was identified for all PGZs. For each five most cited edible species, the frequency of insect consumption was associated, allowing for the selection of species to promote for mass rearing. This frequency, which is generally low for most edible species, varies according to the species and the PGZ concerned. Brachytrupes membranaceus, Cybister spp., Gryllus bimaculatus, Gymnoproctus sculpturatus, Ornithacris turbida cavroisi, Rhynchophorus phoenicis and Zonocerus variegatus were identified as candidates for promotion in the Benin republic. This study showed that a variety of edible species exist in the Benin republic, but most are rarely consumed. For better promotion of these insects as food, future studies are needed to investigate the mass rearing and/or consumer perception of these promotable edible species.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms—the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

Articulation of design processes, computing, Cybernetics, and BIM: A retrospective

This article explores the historical and theoretical developments of Building Information Modeling (BIM) and the associated design processes. The study stems from ongoing doctoral research and aims to build an expanded understanding of the informatization process of architectural thought that led to BIM, considering contributions from the Cybernetics and Computing fields. The paper looks into the historical context of the 20th century and highlights the parallel and interdependent developments in Architecture, Cybernetics, and Computer Science. Based on a bibliographic review, retrospective, and theoretical analysis, this article emphasizes the contributions of works that systematized design processes and introduced systemic methodological approaches. Those works acknowledge the value of declaration and structuring of design information and contribute to incorporating computing into design processes and their development. The work presents the contributions of research institutions from the United Kingdom and the United States in developing computer programs for architecture, emphasizing that the geographical diversity of advancements in the field goes beyond these two lines. These computer programs were influenced by cybernetic theories, which were crucial for BIM development, and were created to produce models and systems to describe information. Finally, the importance of translating architectural thought into structured data that might be processable through computer programs, including BIM software, is highlighted, concluding that although BIM and developments of the current digital design processes followed the goal of assisting the production of North Atlantic architectural models, they originated from a broad and diverse historical and geopolitical context of the 20th century, involving theoretical, military, and professional research.

Understanding participation and corporatization in service of diversity in free/libre and open source software development projects

Issues associated with a lack of diversity and inclusivity persist in the domain of free/libre and open source software (FLOSS) development and in software development generally. Researchers have suggested that the corporatization of FLOSS affords opportunities for creating an inclusive workforce. To understand the potential for firms to increase diversity, we conducted a mixed-methods study of diversity and corporate engagement in FLOSS projects. We integrate the results of a qualitative survey and a big data analysis to understand developer perceptions of corporate engagement and its association with gender and geographic diversity. In the qualitative component, we collected responses from 64 FLOSS contributors to elicit their perspectives on corporate engagement. In the quantitative component, we analyzed GitHub data from 38 projects and 9,990 contributors to investigate differences in participation and diversity based on corporate engagement. We find that contributors vary across dimensions that can inform diversity interventions: stances toward corporations and orientation towards individual/collective benefits. Our results suggest that corporate engagement may limit a project's contributor base and geographic diversity. Yet, organizations subsidizing FLOSS have opportunities to increase access to projects which would benefit diversity. This research serves to identify individual and organizational factors which may harm and help diversity initiatives.

When does international knowledge connectivity of global cities attract R&D investments? The role of concentrated ownership through organizational pipelines

We argue that the degree of concentrated ownership of international knowledge connections of a city in the hands of a small number of MNEs reduces the potential for knowledge spillovers and has a negative influence on the attractiveness of a city for new R&D investments. Ownership concentration in international knowledge connections reduces the positive influence of two complementary characteristics of international knowledge connectivity: the international connectedness (“depth”) and the geographical diversity (“breadth”) of the cities' international knowledge networks. Our analysis of the location decisions for 3235 new cross-border R&D investments made by 1599 firms distributed across 71 global cities (2003–2016) provides support for these hypotheses.

Transfer learning with convolutional neural networks for hydrological streamline delineation

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven image-based pre-trained models and a baseline model using datasets from Rowan County, North Carolina, and Covington River, Virginia in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the pre-trained model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.

A Cross-Sectional Analysis of Interventional Clinical Trials in High-Grade Glioma Therapy.

High-grade glioma is the most frequent and lethal primary tumor of the central nervous system. Despite advances in surgical, pharmacological, and cell-directed therapies, there have been no updates to the standard of care in over a decade. This cross-sectional study analyzes patient and trial data from 201 interventional trials completed between 2010 and 2023, encompassing 18,563 participants. Although we found that all trials reported participant age and sex, only 52% of trials reported participant demographics, resulting in 51% of total participant demographics being unreported. The majority of studies did not report ethnicity, with approximately 60% of participants unreported. Additionally, males were significantly underrepresented in trials, comprising 60% of participants despite representing 75% of glioblastoma patients. Improved demographic reporting has been observed since 2011; however, it is inconsistent. Furthermore, we cataloged the geographic diversity of trials across the United States and found significant coverage deserts in relatively rural, but highly affected, areas such as Montana and Maine. We found a wider distribution of trials in both urban and wealthier regions, which indicates extensive coverage gaps and decreased access to participation for patients of a lower socioeconomic status.

Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.

Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.

Geographical distribution, disease association and diversity of Klebsiella pneumoniae K/L and O antigens in India: roadmap for vaccine development.

Klebsiella pneumoniae poses a significant healthcare challenge due to its multidrug resistance and diverse serotype landscape. This study aimed to explore the serotype diversity of 1072 K. pneumoniae and its association with geographical distribution, disease severity and antimicrobial/virulence patterns in India. Whole-genome sequencing was performed on the Illumina platform, and genomic analysis was carried out using the Kleborate tool. The analysis revealed a total of 78 different KL types, among which KL64 (n=274/1072, 26 %), KL51 (n=249/1072, 24 %), and KL2 (n=88/1072, 8 %) were the most prevalent. In contrast, only 13 distinct O types were identified, with O1/O2v1 (n=471/1072, 44 %), O1/O2v2 (n=353/1072, 33 %), and OL101 (n=66/1072, 6 %) being the predominant serotypes. The study identified 114 different sequence types (STs) with varying serotypes, with ST231 being the most predominant. O serotypes were strongly linked with STs, with O1/O2v1 predominantly associated with ST231. Simpson's diversity index and Fisher's exact test revealed higher serotype diversity in the north and east regions, along with intriguing associations between specific serotypes and resistance profiles. No significant association between KL or O types and disease severity was observed. Furthermore, we found the specific association of virulence factors yersiniabactin and aerobactin (P<0.05) with KL types but no association with O antigen types (P>0.05). Conventionally described hypervirulent clones (i.e. KL1 and KL2) in India lacked typical virulent markers (i.e. aerobactin), contrasting with other regional serotypes (KL51). The cumulative distribution of KL and O serotypes suggests that future vaccines may have to include either ~20 KL or four O types to cover >85 % of the carbapenemase-producing Indian K. pneumoniae population. The results highlight the necessity for comprehensive strategies to manage the diverse landscape of K. pneumoniae strains across different regions in India. Understanding regional serotype dynamics is pivotal for targeted surveillance, interventions, and tailored vaccine strategies to tackle the diverse landscape of K. pneumoniae infections across India. This article contains data hosted by Microreact.

Financing and cost-effectiveness of emergency medical services in low- and middle-income countries

In 2023, the 76th World Health Assembly declared coordinated emergency, critical, and operative care services fundamental for comprehensive universal health coverage in low- and middle-income countries. With increasing mortality from noncommunicable diseases, an organized emergency care system has the capacity to treat a variety of conditions with a common set of resources, optimizing per-unit cost efficiency by applying economies of scope and increasing cost-effectiveness. However, the financing and cost-effectiveness of emergency medical services remain poorly understood despite affordability and financial barriers comprising some of the most significant obstacles to development. Cost-effectiveness analyses generate incremental cost-effectiveness ratios for comparison against per-capita gross domestic product thresholds to determine cost-effectiveness, promoted by the World Health Organization’s Choosing Interventions that are Cost-Effective program. Incremental cost-effectiveness ratios may be used as context-specific indicators of value alongside budget impact and feasibility considerations. Currently, there are few high-quality cost-effectiveness studies of emergency care in low- and middle-income countries, demonstrating significant methodologic heterogeneity, little geographic diversity, neglecting descriptions of assumptions used in cost-effectiveness calculations and comparators used, and lacking incremental cost-effectiveness ratios for comparison. The assessment of emergency care cost-effectiveness is challenging, given the significant breadth of conditions encountered and difficulty in projecting subsequent impact. Without improved epidemiologic surveillance and data-collection infrastructure, data inputs for cost-effectiveness calculations will remain limited. Future efforts should practice standard cost-effectiveness methodologies to permit comparison of incremental cost-effectiveness ratios across interventions and settings while incorporating trauma registry data to longitudinally track patient outcomes over sufficient time horizons to determine impact. New indices that expand the scope of analysis to capture broader secondary impacts of emergency care for future cost-effectiveness studies are needed. In this article, we summarize the key steps for economic evaluations for prehospital care systems and recommend considerations for future prehospital emergency care cost-effectiveness analyses, determining the optimal structure for financing mechanisms well-suited to resource-limited settings are critical for future investigation.

Rural Social Work Practice in Turkey

Rural social work continues to grow in importance as a unique field of practice with unique opportunities and challenges. This qualitative research explores explore different aspects of rural social work in Turkey. To this end, the study explores social work practice in rural communities in depth, recognizing the distinctive contexts and characteristics that influence service delivery. In-depth interviews were conducted with rural social workers (n = 30) with significant knowledge and experience in addressing the social work needs of rural communities. Participants were selected through purposive sampling to ensure a broad representation of expertise and geographic diversity. Thematic analysis was used to identify recurring themes and patterns in the participants’ responses. The findings support previous studies that have argued that rural social work is a distinct field of practice. At the same time, the study shows that social work practices are shaped in an urban-centered manner, and local culture is ignored. The study concludes that rural social work should be integrated into graduate social work education, and even start during undergraduate education.

Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks.

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.

Challenges for ticks and tick-borne diseases research in Southeast Asia: Insight from the first international symposium in Cambodia.

Ticks, as critical vectors of a variety of pathogens, pose a significant public health challenge globally. In Southeast Asia (SEA), ticks are responsible for transmitting a diverse array of pathogens affecting humans and animals. The geographical and ecological diversity of SEA provides a unique environment that supports a wide range of tick species, which complicates the management and study of tick-borne diseases (TBDs). This article synthesizes findings from the first international symposium on ticks and TBDs in Southeast Asia, held in Phnom Penh on June 22 and 23, 2023. It highlights regional efforts to understand tick ecology and pathogen transmission. This paper proposes to present a summary of the various presentations given during the symposium following 3 main parts. The first one is devoted to the state of knowledge regarding ticks and TBDs in SEA countries, with presentations from 6 different countries, namely Cambodia, Indonesia, Laos, Malaysia, Thailand, and Vietnam. The second part focuses on the development of new research approaches on tick-borne pathogens (TBPs) and TBDs. The last part is a summary of the round table discussion held on the final day, with the aim of defining the most important challenges and recommendations for researches on TBP and TBD in the SEA region. Key topics discussed include advancements in diagnostic tools, such as MALDI-TOF MS and proteomics, and the development of sustainable strategies for tick management and disease prevention. The symposium facilitated the exchange of knowledge and collaborative networks among experts from various disciplines, promoting a unified approach to tackling TBDs in the region. The symposium underscored the need for enhanced surveillance, diagnostics, and inter-regional cooperation to manage the threat of TBDs effectively. Recommendations include the establishment of a regional database for tick identification and the expansion of vector competence studies. These initiatives are crucial for developing targeted interventions and understanding the broader implications of climate change and urbanization on the prevalence of TBDs.