bluuebunny/crossref_metadata_2025_split huggingface dataset:
Going a Step Deeper Down the Rabbit Hole: Deep Learning Model to Measure the Size of the Unregistered Economy Activity
Authors: Lazebnik Teddy 
Published: April 2024

AbstractAccurately estimating the size of unregistered economies is crucial for informed policymaking and economic analysis. However, many studies seem to overfit partial data as these use simple linear regression models. Recent studies adopted a more advanced approach, using non-linear models obtained using machine learning techniques. In this study, we take a step forward on the road of data-driven models for the unregistered economy activity’s (UEA) size prediction using a novel deep-learning approach. The proposed two-phase deep learning model combines an AutoEncoder for feature representation and a Long Short-Term Memory (LSTM) for time-series prediction. We show it outperforms traditional linear regression models and current state-of-the-art machine learning-based models, offering a more accurate and reliable estimation. Moreover, we show that the proposed model is better in generalizing UEA’s dynamics across countries and timeframes, providing policymakers with a more profound group to design socio-economic policies to tackle UEA.
Accepting video conferencing technology as an in-service training tool for health professionals
Authors: Ünal Rabia , Yilmaz M. Betül 
Published: April 2024

AbstractThis study investigates healthcare professionals’ acceptance of video conferences for in-service training in terms of performance expectancy and social influence. Furthermore, it attempts to determine which properties of video conferences influenced and predicted the adoption of video conferencing technology. We employed the cross-sectional survey research design, one of the descriptive research designs. The participants consisted of 181 physicians from a medical specialty society. To collect data, we used the Unified Theory of Acceptance and Use of Technology-2 (UTAUT-2) scale. Findings revealed that being able to ask questions during the video conferences, not paying for participation, timing problems, and lack of social interaction predicted the acceptance of video conferences for in-service training regarding performance expectancy and social influence among physicians. This article offers practical recommendations for professionals to adopt and maximize the use of videoconferencing for in-service training. The findings of this study will shed light on future practices and studies regarding the use of video conferencing systems for in-service training by revealing the preferences of physicians and the factors affecting their acceptance behavior.
Global mercury concentrations in biota: their use as a basis for a global biomonitoring framework
Authors: Evers David C. , Ackerman Joshua T. , Åkerblom Staffan , Bally Dominique , Basu Nil , Bishop Kevin , Bodin Nathalie , Braaten Hans Fredrik Veiteberg , Burton Mark E. H. , Bustamante Paco , Chen Celia , Chételat John , Christian Linroy , Dietz Rune , Drevnick Paul , Eagles-Smith Collin , Fernandez Luis E. , Hammerschlag Neil , Harmelin-Vivien Mireille , Harte Agustin , Krümmel Eva M. , Brito José Lailson , Medina Gabriela , Barrios Rodriguez Cesar Augusto , Stenhouse Iain , Sunderland Elsie , Takeuchi Akinori , Tear Tim , Vega Claudia , Wilson Simon , Wu Pianpian 
Published: April 2024

AbstractAn important provision of the Minamata Convention on Mercury is to monitor and evaluate the effectiveness of the adopted measures and its implementation. Here, we describe for the first time currently available biotic mercury (Hg) data on a global scale to improve the understanding of global efforts to reduce the impact of Hg pollution on people and the environment. Data from the peer-reviewed literature were compiled in the Global Biotic Mercury Synthesis (GBMS) database (>550,000 data points). These data provide a foundation for establishing a biomonitoring framework needed to track Hg concentrations in biota globally. We describe Hg exposure in the taxa identified by the Minamata Convention: fish, sea turtles, birds, and marine mammals. Based on the GBMS database, Hg concentrations are presented at relevant geographic scales for continents and oceanic basins. We identify some effective regional templates for monitoring methylmercury (MeHg) availability in the environment, but overall illustrate that there is a general lack of regional biomonitoring initiatives around the world, especially in Africa, Australia, Indo-Pacific, Middle East, and South Atlantic and Pacific Oceans. Temporal trend data for Hg in biota are generally limited. Ecologically sensitive sites (where biota have above average MeHg tissue concentrations) have been identified throughout the world. Efforts to model and quantify ecosystem sensitivity locally, regionally, and globally could help establish effective and efficient biomonitoring programs. We present a framework for a global Hg biomonitoring network that includes a three-step continental and oceanic approach to integrate existing biomonitoring efforts and prioritize filling regional data gaps linked with key Hg sources. We describe a standardized approach that builds on an evidence-based evaluation to assess the Minamata Convention’s progress to reduce the impact of global Hg pollution on people and the environment.
Explaining Siamese networks in few-shot learning
Authors: Fedele Andrea , Guidotti Riccardo , Pedreschi Dino 
Published: April 2024

AbstractMachine learning models often struggle to generalize accurately when tested on new class distributions that were not present in their training data. This is a significant challenge for real-world applications that require quick adaptation without the need for retraining. To address this issue, few-shot learning frameworks, which includes models such as Siamese Networks, have been proposed. Siamese Networks learn similarity between pairs of records through a metric that can be easily extended to new, unseen classes. However, these systems lack interpretability, which can hinder their use in certain applications. To address this, we propose a data-agnostic method to explain the outcomes of Siamese Networks in the context of few-shot learning. Our explanation method is based on a post-hoc perturbation-based procedure that evaluates the contribution of individual input features to the final outcome. As such, it falls under the category of post-hoc explanation methods. We present two variants, one that considers each input feature independently, and another that evaluates the interplay between features. Additionally, we propose two perturbation procedures to evaluate feature contributions. Qualitative and quantitative results demonstrate that our method is able to identify highly discriminant intra-class and inter-class characteristics, as well as predictive behaviors that lead to misclassification by relying on incorrect features.
Coupled fluid-structure simulation of a flapping wing using free multibody dynamics software
Authors: Caccia Claudio , Guerrero Joel , Masarati Pierangelo 
Published: April 2024

AbstractComputer simulations offer invaluable insights into fluid-structure interaction phenomena, increasing our understanding of complex behaviors within fluid flows and enabling predictions of consequential effects. This paper explores flapping wing simulation using an original toolchain based on free software. The structural domain is modeled using multibody dynamics, interfaced with arbitrary fluid dynamics solvers through a general-purpose multiphysics coupling library. The proposed toolchain is validated against benchmark models, demonstrating its effectiveness in various applications. Our study, inspired by experimental ones, applies this coupling to investigate the hydroelastic behavior of a flexible wing. Wing motion characteristics, structural properties, and convergence criteria are analyzed through numerical simulations. While achieving appreciable agreement with experimental data on wing motion ratios, challenges in dealing with large displacements have been identified. Nonetheless, the present study provides valuable insights into fluid-structure interactions, laying the groundwork for future refinements in computational modeling techniques and advancing the understanding of bio-inspired flight mechanisms.
Insights into metastatic roadmap of head and neck cancer squamous cell carcinoma based on clinical, histopathological and molecular profiles
Authors: Mastronikolis Nicholas S. , Delides Alexander , Kyrodimos Efthymios , Piperigkou Zoi , Spyropoulou Despoina , Giotakis Evangelos , Tsiambas Evangelos , Karamanos Nikos K. 
Published: April 2024

Abstract
The incidence of head and neck cancer (HNC), constituting approximately one in ten cancer cases worldwide, affects approximately 644,000 individuals annually. Managing this complex disease involves various treatment modalities such as systemic therapy, radiation, and surgery, particularly for patients with locally advanced disease. HNC treatment necessitates a multidisciplinary approach due to alterations in patients’ genomes affecting their functionality. Predominantly, squamous cell carcinomas (SCCs), the majority of HNCs, arise from the upper aerodigestive tract epithelium. The epidemiology, staging, diagnosis, and management techniques of head and neck squamous cell carcinoma (HNSCC), encompassing clinical, image-based, histopathological and molecular profiling, have been extensively reviewed. Lymph node metastasis (LNM) is a well-known predictive factor for HNSCC that initiates metastasis and significantly impacts HNSCC prognosis. Distant metastasis (DM) in HNSCC has been correlated to aberrant expression of cancer cell-derived cytokines and growth factors triggering abnormal activation of several signaling pathways that boost cancer cell aggressiveness. Recent advances in genetic profiling, understanding tumor microenvironment, oligometastatic disease, and immunotherapy have revolutionized treatment strategies and disease control. Future research may leverage genomics and proteomics to identify biomarkers aiding individualized HNSCC treatment. Understanding the molecular basis, genetic landscape, atypical signaling pathways, and tumor microenvironment have enhanced the comprehension of HNSCC molecular etiology. This critical review sheds light on regional and distant metastases in HNSCC, presenting major clinical and laboratory features, predictive biomarkers, and available therapeutic approaches.
Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye’s history
Authors: Eker Remzi , Çınar Tunahan , Baysal İsmail , Aydın Abdurrahim 
Published: April 2024

AbstractIn the summer of 2021, Türkiye experienced unprecedented forest fire events. Throughout that fire season, a total of 291 fire incidents, covering an area of 202,361 hectares, dominated the public agenda. This study aimed to document and analyze the 30 large fires (affecting over 100 hectares) of 2021 using remote sensing and GIS techniques. A comprehensive fire database was established, encompassing information on burned areas, fire severity, and fuel types, determined from forest-stand types and topographical properties including slope, elevation, and aspect (in eight directions). Sentinel-2 satellite images were utilized to calculate dNBR values for assessing fire severity, analyzed in the Google Earth Engine platform. Three GIS-integrated Python scripts were developed to construct the fire database. In total, 164,658 hectares were affected by these large fires, occurring solely in three regions of Türkiye: the Mediterranean, Aegean, and Eastern Anatolian. The majority of the burned area was situated in the Mediterranean region (59%), with only 3% in Eastern Anatolia. The burned areas ranged from a minimum of 150 hectares to a maximum of 58,798 hectares. Additionally, 679 hectares of residential areas and 22,601 hectares of agricultural land were impacted by the fire events. For each fire, 21 fuel types and their distribution were determined. The most prevalent fire-prone class, “Pure Turkish pine species (Pr-Çz),” accounted for 59.56% of the total affected area (99,516 hectares). Another significant fire-prone pine species, the “Pure Black pine species (Pr-Çk),” covered 7.67% (12,811 hectares) of the affected area. Fuel types were evaluated by considering both forest-stand development stages and canopy closure. Regarding forest-stand development stages, the largest area percentage burned belonged to the “Mature” class (26.48%).
Adaptive strategies of high and low nucleic acid prokaryotes in response to declining resource availability and selective grazing by protozoa
Authors: Hu Chen , Yu Liuqian , Chen Xiaowei , Liu Jihua , Zhang Yao , Batt John , Xiao Xilin , Shi Qiang , Zhang Rui , Luo Tingwei , Jiao Nianzhi , Xu Dapeng 
Published: April 2024

AbstractProkaryotes play a fundamental role in global ocean biogeochemical cycles. However, how the abundance and metabolic activity of ecologically distinct subgroups (i.e., high nucleic acid (HNA) and low nucleic acid (LNA) cells), and their regulating factors, change in response to changing marine environmental conditions remains poorly understood. Here, we delved into the time-evolving dynamic responses of the HNA and LNA prokaryotic subgroups to declining resource availability and selective grazing by protozoa by conducting a 73-day incubation experiment in a large-volume (117,000 L) macrocosm that facilitates community-level exploration. We found that the metabolic activity of the HNA subgroup was higher than that of the LNA subgroup when the macrocosm was resource replete but that the HNA subgroup declined more rapidly than the LNA subgroup as the resources became increasingly scarce, leading to a steadily increasing contribution of LNA cells to prokaryotic activity. Meanwhile, as resources in the macrocosm became limited, protozoan grazing preference shifted from the HNA to the LNA subgroup and the contributions of the LNA subgroup to the carbon flow within the macrocosm increased. The findings highlight the resilience of LNA cells in resource-limited environments, illuminate the critical role of selective grazing by protozoa in balancing distinct prokaryotic subgroups under changing resource conditions, and demonstrate the complex and adaptive interactions between protozoa and prokaryotes across diverse environmental contexts.
Information-entropy enabled identifying topological photonic phase in real space
Authors: Ma Rui , Yan Qiuchen , Luo Yihao , Li Yandong , Wang Xingyuan , Lu Cuicui , Hu Xiaoyong , Gong Qihuang 
Published: April 2024

AbstractThe topological photonics plays an important role in the fields of fundamental physics and photonic devices. The traditional method of designing topological system is based on the momentum space, which is not a direct and convenient way to grasp the topological properties, especially for the perturbative structures or coupled systems. Here, we propose an interdisciplinary approach to study the topological systems in real space through combining the information entropy and topological photonics. As a proof of concept, the Kagome model has been analyzed with information entropy. We reveal that the bandgap closing does not correspond to the topological edge state disappearing. This method can be used to identify the topological phase conveniently and directly, even the systems with perturbations or couplings. As a promotional validation, Su–Schrieffer–Heeger model and the valley-Hall photonic crystal have also been studied based on the information entropy method. This work provides a method to study topological photonic phase based on information theory, and brings inspiration to analyze the physical properties by taking advantage of interdisciplinarity.
Graphical Abstract
The application of plant-exosome-like nanovesicles as improved drug delivery systems for cancer vaccines
Authors: Hillman Tatiana 
Published: April 2024

AbstractThe use of cancer immunotherapeutics is currently increasing. Cancer vaccines, as a form of immunotherapy, are gaining much attention in the medical community since specific tumor-antigens can activate immune cells to induce an anti-tumor immune response. However, the delivery of cancer vaccines presents many issues for research scientists when designing cancer treatments and requires further investigation. Nanoparticles, synthetic liposomes, bacterial vectors, viral particles, and mammalian exosomes have delivered cancer vaccines. In contrast, the use of many of these nanotechnologies produces many issues of cytotoxicity, immunogenicity, and rapid clearance by the mononuclear phagocyte system (MPS). Plant-exosome-like nanovesicles (PELNVs) can provide solutions for many of these challenges because they are innocuous and nonimmunogenic when delivering nanomedicines. Hence, this review will describe the potential use of PELNVs to deliver cancer vaccines. In this review, different approaches of cancer vaccine delivery will be detailed, the mechanism of oral vaccination for delivering cancer vaccines will be described, and the review will discuss the use of PELNVs as improved drug delivery systems for cancer vaccines via oral administration while also addressing the subsequent challenges for advancing their usage into the clinical setting.
Modelling and analysis of strength and durability properties of internal curing concrete using PEG 400 and artificial neural network
Authors: Gowdra Virupakshappa Sowjanya , Shrishail Basappa Anadinni , Thimmarayappa Mahadevaiah , Narayana Channa Keshava Naik , Buradi Abdulrajak , Emma Addisu Frinjo 
Published: April 2024

AbstractThe traditional curing processes necessitate a large amount of water. This is especially difficult in locations where there is a lack of water and for construction of high-rise structures. In this research article, we provide a remedy by inventing concrete that will not require additional water for curing. In the present work, polyethylene glycol was utilized as an internal curing agent in varying percentages. The internally cured concrete with polyethylene glycol was cured at ambient conditions, whereas the conventional concrete without polyethylene glycol was kept in water for curing. Fresh and hardened characteristics of concrete with and without polyethylene glycol are compared. The results revealed that 1.5% polyethylene glycol is an optimum percentage where maximum strength and durable properties are achieved. The microstructure of internal curing concrete indicates that the pore sizes are small compared to conventional concrete. The crystallite size is smaller in internal curing compared to conventional concrete mixtures, resulting in an acceleration of the hydration process in concrete with smaller crystallites.Further experimental results are compared with ANN. Predicted results are very close to experimental values.
Risk, perception and COVID-19 impact on food security: evidence from Bundelkhand region, India
Authors: Jatav Surendra Singh 
Published: April 2024

AbstractThis study assessed the implications of the COVID-19 pandemic on household food security in the Bundelkhand region of Uttar Pradesh, India. Macro data on 29 indicators was collected to identify food insecure districts, and after identification of most food insecure region, i.e. Bundelkhand region, micro data was collected from an intensive field survey. A multi-stage sampling technique was adopted to select study sites and respondents. A total of 240 sample households of various land sizes and income groups were contacted to collect data using a well-structured and pre-tested schedule. The study findings revealed that districts belonging to the Bundelkhand region are highly food insecure compared with other regions of Uttar Pradesh, India. Micro-level findings indicate that households in the Lalitpur district are relatively food insecure compared to those in the Jhansi district. The results suggest that ongoing future government responses should focus on structural changes in food security by developing responsive packages to cushion members pushed into food insecurity by such pandemics.
Towards human-AI collaborative urban science research enabled by pre-trained large language models
Authors: Fu Jiayi , Han Haoying , Su Xing , Fan Chao 
Published: April 2024

AbstractPre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, sense public sentiment and so on. They have expanded the breadth of urban research in terms of content, increased the depth and efficiency of the application of multi-source big data in urban research, and enhanced the interaction between urban research and other disciplines. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.
COVID-19 outbreak and genomic investigation in an inpatient behavioral health unit
Authors: Rios-Guzman Estefany , Stancovici Alina G. , Simons Lacy M. , Barajas Grace , Glenn Katia , Weber Rachel T. , Ozer Egon A. , Lorenzo-Redondo Ramon , Hultquist Judd F. , Bolon Maureen K. 
Published: April 2024

Abstract

Background:
Inpatient behavioral health units (BHUs) had unique challenges in implementing interventions to mitigate coronavirus disease 2019 (COVID-19) transmission, in part due to socialization in BHU settings. The objective of this study was to identify the transmission routes and the efficacy of the mitigation strategies employed during a COVID-19 outbreak in an inpatient BHU during the Omicron surge from December 2021 to January 2022.

Methods:
An outbreak investigation was performed after identifying 2 COVID-19-positive BHU inpatients on December 16 and 20, 2021. Mitigation measures involved weekly point prevalence testing for all inpatients, healthcare workers (HCWs), and staff, followed by infection prevention mitigation measures and molecular surveillance. Whole-genome sequencing on a subset of COVID-19-positive individuals was performed to identify the outbreak source. Finally, an outbreak control sustainability plan was formulated for future BHU outbreak resurgences.

Results:
We identified 35 HCWs and 8 inpatients who tested positive in the BHU between December 16, 2021, and January 17, 2022. We generated severe acute respiratory coronavirus virus 2 (SARS-CoV-2) genomes from 15 HCWs and all inpatients. Phylogenetic analyses revealed 3 distinct but genetically related clusters: (1) an HCW and inpatient outbreak likely initiated by staff, (2) an HCW and inpatient outbreak likely initiated by an inpatient visitor, and (3) an HCW-only cluster initiated by staff.

Conclusions:
Distinct transmission clusters are consistent with multiple, independent SARS-CoV-2 introductions with further inpatient transmission occurring in communal settings. The implemented outbreak control plan comprised of enhanced personal protective equipment requirements, limited socialization, and molecular surveillance likely minimized disruptions to patient care as a model for future pandemics.