bluuebunny/crossref_metadata_2025_split huggingface dataset:
EU surveys insights: analytical tools, future directions, and the essential requirement for reference materials in wastewater monitoring of SARS-CoV-2, antimicrobial resistance and beyond
Authors: Paracchini Valentina , Petrillo Mauro , Rajashekar Anandasagari Arcot , Robuch Piotr , Vincent Ursula , Corbisier Philippe , Tavazzi Simona , Raffael Barbara , Suffredini Elisabetta , Rosa Giuseppina La , Gawlik Bernd Manfred , Marchini Antonio 
Published: March 2024

Abstract
Background
Wastewater surveillance (WWS) acts as a vigilant sentinel system for communities, analysing sewage to protect public health by detecting outbreaks and monitoring trends in pathogens and contaminants. To achieve a thorough comprehension of present and upcoming practices in WWS, two EU surveys were conducted targeting WWS laboratories across Europe and other regions. The first survey explored a diverse range of activities currently undertaken or planned by laboratories. The second survey specifically targeted methods and quality controls utilised for SARS-CoV-2 surveillance.
Results
The findings of the two surveys provide a comprehensive insight into the procedures and methodologies applied in WWS. In Europe, WWS primarily focuses on SARS-CoV-2 with 99% of the survey participants dedicated to this virus. However, the responses highlighted a lack of standardisation in the methodologies employed for monitoring SARS-CoV-2. The surveillance of other pathogens, including antimicrobial resistance, is currently fragmented and conducted by only a limited number of laboratories. Notably, these activities are anticipated to expand in the future. Survey replies emphasize the collective recognition of the need to enhance the accuracy of results in WWS practices, reflecting a shared commitment to advancing precision and effectiveness in WWS methodologies.
Conclusions
These surveys identified the need for quality standards and reference materials to enhance the accuracy and reliability of WWS methods in the future. In addition, it is important to broaden surveillance efforts beyond SARS-CoV-2 to include other emerging pathogens and antimicrobial resistance to ensure a comprehensive approach to protecting public health.
The role of non-timber forest products in reducing rural poverty in Burkina Faso
Authors: OUEDRAOGO Issoufou , MAIGA Eugenie , ESBJERG Lars 
Published: March 2024

Abstract
Using data collected in 2023 on five hundred and thirty (530) randomly selected rural households in two regions of Burkina Faso, this research investigates the contribution of Non-Timber Forest Products (NTFP) exploitation to rural poverty reduction using two different approaches. Firstly, it considers NTFP income as an "exogenous transfer", which is added to total household income, and compares the Foster, Greer and Thorbecke (FGT) poverty indices calculated with and without NTFP income. Secondly, it considers NTFP income as a "potential substitute" for other household income and simulates the counterfactual of what rural household income and poverty would be in the absence of access to NTFPs. The results show that in the absence of NTFPs, rural poverty would be much higher and deeper. These results suggest that rural development policies focused on the sustainable exploitation of these products are potentially feasible. This would require improving rural populations' access to NTFPs through the domestication of species providing the main NTFPs.
Static Analysis-based Rapid Fire-Following Earthquake Risk Assessment Method using Simple Building and GIS Information
Authors: Kang Jaedo , Kang Taewook , Lee Kihak , Kim Haewon , Shin Jiuk 
Published: March 2024

Abstract

After the occurrences of large-scale earthquakes, regional secondary damage (e.g., fire-following earthquake) can result in tremendous losses of life, properties, and buildings. To reduce these disaster risks, fire-following earthquake assessment method composed of ignition and fire-burned rate estimation models were utilized. However, the previous methods required complex building/GIS information and large amount of time to consider various regional characteristics. This paper proposed static analysis-based rapid fire-following earthquake assessment method using simple information and implemented it to Pohan City, South Korea. Based on the previous studies, the best-fit model for the ignition rate estimation was selected, and the cluster-based fire-burned rate model simulated using simple building information (e.g., construction year, building occupancy, story, and total floor area) in public building database was developed. For the fire-burned rate model, the fire-resistant structural types were defined using the public database, and this was utilized to group or cluster buildings in a regional level by comparison between fire-spread distance for each structural type and adjacent distance among the buildings. This rapid method was demonstrated as follows: (1) the selected ignition rate model predicted the same ignition numbers with the actual reported number, and (2) the fire-burned rate model estimated fire-spread areas with a marginal difference compared to the dynamic fire spread simulation.
Season of birth and schizotypy in a sample of undergraduate students
Authors: Szöke Andrei , Richard Jean-Romain , Ladea Maria , Ferchiou Aziz , Ouaknine Elie , Briciu Victor Alexandru , Pirlog Mihai , Bran Mihai , Pignon Baptiste , Schürhoff Franck 
Published: March 2024

Abstract
Purpose
In line with the psychotic continuum theory, the study of psychometric schizotypy in non-clinical samples has been proposed as a convenient yet powerful method for studying the etiology of psychosis. Based on this paradigm, several studies explored the association between season of birth (SoB) and schizotypy but led to inconsistent results. Building on the analysis of the previous studies, in the present study, we aimed to advance our understanding by improving the methodology (using a homogeneous group, eliminating unreliable respondents, taking into account potential confounders) and the reporting.
Methods
Subjects were recruited among undergraduate students from 3 Romanian Universities. To limit the potential influence of invalid response, we applied methods for detecting unreliable and/or biased questionnaires and excluded subjects with unreliable/ biased answers from the analyses. Schizotypal dimensions were measured using the Romanian translation of the 22-items Schizotypal Personality Questionnaire-Brief (SPQ-B). The association between schizotypy scores and season of birth was explored using linear regression.
Results
In a sample of 484 undergraduate students from Romania, we found that being born in late winter/early spring (February and March) was associated to higher total schizotypy score and disorganization. Furthermore, we found that restricting the sample to subjects born in an urban environment increased the strength of the association.
Conclusion
This study is consistent with an association between SoB and the risk of psychotic disorders.
Impact of the COVID-19 Epidemic on Death Among Critically Ill Patients
Authors: Jang Hye Jin , Han Soyul , Won Sungho , Lee Bora , Kim Young Sam 
Published: March 2024

Abstract
Backgrounds: The coronavirus disease 2019 (COVID-19) epidemic is a major public health problem worldwide. This study estimated the impact of SARS-CoV-2 in terms of the excess mortality from any cause during the COVID-19 epidemic in the Republic of Korea.
Methods: The excess mortality, ICU admissions and hospital admissions during the COVID-19 epidemic was analyzed using the number of deaths over the past 5 years (2015–2019) in Korea. The numbers of total deaths and COVID-19–related deaths were counted from January 2020 through May 2022, using public data from the Korean Statistical Information Service (KOSIS) of Statistics Korea. Data of hospital admissions were obtained from Health Insurance Review and Assessment (HIRA) service and KOSIS of Statistics Korea.
Results: These numbers were compared with nationwide mortality data from January 1, 2015, through December 2019. There were more than 24,000 reported deaths officially across Korea from January 2020 through May 2022. Excess mortality was observed nationwide in March, August, October, November, and December 2020; January, March, July, and August 2021; and October 2021 through May 2022, which was consistent with the epidemic waves in the country. There was a decline in ICU admissions, especially soon after the declaration of the COVID-19 epidemic.
Conclusions: After 2021, significant excess mortality occurred at the national level despite decreasing COVID-19 case fatality rates and decreased admissions to intensive care units, which means there might be an another relevant factors on all-cause mortality aside from the direct effect of deaths from COVID-19.
Clinical characteristics of 2,378 patients presenting with headache disorders to headache clinics in Japan: A clinic-based multicenter study
Authors: Imai Noboru , Takizawa Tsubasa , Watanabe Narumi , Matsumori Yasuhiko 
Published: March 2024

Abstract
Background
There are single-center reports regarding patients visiting headache clinics; however, few multicenter studies have been conducted in Japan. This study aims to fill the aforementioned gaps by providing a detailed analysis of the clinical characteristics, headache types, severity, and psychiatric comorbidities of patients presenting to headache clinics across three distinct healthcare settings in Japan: a university hospital, regional headache center, and private headache clinic.
Methods
We prospectively evaluated the clinical characteristics of 2,378 patients with headache disorders visiting three Japanese Headache Society–accredited headache educational facilities between March 2021 and March 2022. Baseline demographics, such as the visual analog scale (VAS), and psychiatric assessments, such as the Japanese versions of the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9), were evaluated. Headache types were classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgias (TACs), other primary headache disorders (OPHDs), and secondary headache. Parameters were compared between headache types using the Kruskal–Wallis test.
Results
The most common headache type was migraine (78.8%), followed by TTH (12.2%), secondary headache (5.5%), OPHDs (2.1%), and TACs (1.6%). Patients with migraine were significantly younger at first consultation (median age 32.0) than those with other headache types (TTH; 47.0, TACs; 39.0, OPHDs; 49.5, and secondary headache; 47.0). Patients with TACs exhibited the highest severity and psychiatric symptoms, with VAS (median 90.0), GAD-7 (7.0), and PHQ-9 (7.5) scores significantly higher than those of other headache type (migraine; 70.0, 5.0, 5.0, TTH; 50.0, 4.0, 4.0, OPHDs; 65.0, 4.0, 3.5, and secondary headache; 60.0, 3.0, 3.5: p < 0.001, p = 0.032, p < 0.001).
Conclusion
Most patients visiting headache clinics had migraine; patients with TACs showed significantly higher headache severity and psychiatric symptoms than those with other headache types.
Sense of Coherence, mental health, and hair cortisol concentrations among older people during the COVID -19 pandemic: a cross-sectional study
Authors: Koppner Jenny , Lindelöf Ann , Iredahl Fredrik , Nilsson Staffan , Thorsell Annika , Larsen Hanna Israelsson , Faresjö Åshild 
Published: March 2024

Abstract
Background
A person’s sense of coherence (SoC) is likely to affect coping when exposed to a life changing event like the COVID − 19 pandemic, which impacted the older population especially hard, an age group that already suffers from a lot of mental illness. Thus, the aim of this study is to investigate the associations between SoC and mental health in older adults using both screening scales and hair cortisol concentrations (HCC).
Method
A cross-sectional design studying a cohort of 70–80 years old, N = 260, set in Swedish primary care during the pandemic years 2021–2022. Instruments used are sense of coherence 13 (SoC-13), EQ-5D-3L, Geriatric depression scale 20 (GDS-20), Hospital anxiety and depression scale (HADS), and Perceived stress scale 10 (PSS-10). Sociodemography and factors concerning SoC, and mental health are explored. HCC are measured using radioimmunoassay. Outcome measures are factors independently associated to SoC. Linear regression models were performed with SoC as dependent variable, and priory path analyses explored whether associations with SoC were direct, or indirect via anxiety.
Results
SoC was significantly associated to anxiety (p < 0.001), perceived economic status (p = 0.003), belief in the future (p = 0.001), and perceived negative mental effect from the COVID − 19 pandemic (p = 0.002). The latter was 95% indirectly associated with SoC, and perceived economic status together with belief in the future was 82% directly associated with SoC. HCC and sex were not significantly associated to SoC, but women had somewhat higher HCC than men, though not significantly, and women reported significantly lower quality of life (p = 0.03), and more symptoms of anxiety (p = 0.001) and depression (p < 0.001).
Conclusion
Anxiety, belief in the future, perceived negative effect on mental health due to the pandemic, and perceived economic status were significantly associated with SoC. Anxiety is suggested to be important in explaining the association between perceived negative mental effect from the COVID-19 pandemic and SoC. Women reported significantly poorer mental health and life quality than men.
Lactobacillus delbrueckii ssp. indius AG1: Potential candidate for Degradation of agriculture wastes and production of biogas by orsat instrument method
Authors: Suvagiya Ankita 
Published: March 2024

Abstract
The anaerobic digestion of organic materials, which is necessary to add chemicals to the continuous biochemical process, is largely mediated by microorganisms. Through a sequence of metabolic events, the microorganisms efficiently break down complex organic compounds to produce simple compounds like methane. In this investigation, inoculums used for biogas production was isolated from agriculture waste sources—L. delbrueckii ssp. Indius AG1 at mesophilic temperature (35ºC)—to assess their suitability for anaerobic digestion of agricultural residues (AR) and fruit and vegetable waste (FVW). The highest biogas production of 215 ml/2 kg waste was achieved for a mixture of inoculums, and biogas characterization by orsat instrument method showed 47.7% of the methane content. The morphological, biochemical, and molecular techniques were used to identify the microbial flora present in the high-yield reactor. The abundance of L. delbrueckii ssp. indius AG1. To get a high methane yield from organic waste it is necessary to maintain the equilibrium and availability of efficient microbial communities like firmicutous, hydrogenotrophic, and acetoclastic methanogens.
A Version-aware Data Layout for Heterogeneous Workloads in In-Memory Database Systems
Authors: Zhang First Qian , Wei Second Jianhao , Zhang Third Shichen , Luan Fourth Hao , Gong Fifth Xueqing 
Published: March 2024

Abstract
Most modern in-memory database systems rely on multi-version concurrency control (MVCC) to support real-time data analysis without interfering with concurrent writes. While MVCC elegantly enables lightweight isolation of readers and writers, it also generates many short-lived versions and results in long version chains. Existing version storage designs employ traditional data layouts lacking version information. Consequently, as time elapses, the long-running query latency increases due to increased chain lengths. In this paper, we introduce a novel version-aware data layout for in-memory MVCC databases. This layout improves search performance for heterogeneous workloads. It differs from previous approaches by organizing data in a version-centric manner, which supports index-only searches and reduces costly version traversals. Specifically, the layout utilizes a version B+-tree to organize the latest tuple versions of a table, making the table index-organized. Additionally, it employs range partitioning and epoch partitioning techniques to group older tuple versions efficiently. This design allows for fast retrieval of the correct versions visible to transactions, minimizing redundant memory accesses. Compared to existing version storage schemes, the version-aware data layout considers workload access patterns and the opportunity to skip unnecessary version accesses. The experimental evaluation indicates that the version-aware data layout can outperform the state-of-the-art designs by 1.15× ∼ 2×.
ABWOA: Adaptive Boundary Whale Optimization Algorithm for Large-Scale Digital Twin Network Construction
Authors: Feng Hao , Cao Kun , Huang Gan , Liu Hao 
Published: March 2024

Abstract
Digital twin network (DTN) as an emerging network paradigm, have garnered growing attention. For large-scale networks, a crucial problem is how to effectively map physical networks onto the infrastructure platform of DTN. To address this issue, we propose a heuristic method of the adaptive boundary whale optimization algorithm (ABWOA) to solve the digital twin network construction problem, improving the efficiency and reducing operational costs of DTN. Extensive comparison experiments are conducted between ABWOA and various algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution algorithm, moth search algorithm and original whale optimization algorithm. The experimental results show that ABWOA is superior to other algorithms in terms of solution quality, convergence speed, and time cost. It can solve the digital twin network construction problem more effectively.
Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks
Authors: Alqarni Areej A. , Al-Harbi Sanad H. , Subhan Irshad A. 
Published: March 2024

Abstract
According to the World Health Organization, glaucoma is a leading cause of blindness, accounting for over 12% of global blindness as it affects one in every 100 people. In fact, 79.6 million people worldwide live with blindness caused by glaucoma. This is because the current method for diagnosing glaucoma is by examining retinal fundus images. However, it is considerably difficult to distinguish the lesions' features solely through manual observations by ophthalmologists, especially in the early phases. This study introduces a novel glaucoma detection method using attention-enhanced convolutional neural networks, achieving 98.9% accuracy and a swift 30-second detection time, vastly surpassing traditional diagnostic methods. The attention mechanism is utilized to learn pixel-wise features for accurate prediction. Several attention strategies have been developed to guide the networks in learning the important features and factors that affect localization accuracy. The algorithms were trained for glaucoma detection using Python 2.7, TensorFlow, Py Torch, and Keras Machine Learning-Based Applications. The methods were evaluated on Drishti-GS and RIM-ONE datasets with 361 training and 225 test sets, consisting of 344 healthy and 242 glaucomatous images. The proposed algorithms can achieve impressive results that show an increase in overall diagnostic efficiency, as the algorithm displays a 30-second detection time with 98.9% accuracy compared to the 72.3% accuracy of traditional testing methods. Finally, this algorithm has been implemented as a webpage, allowing patients to test for glaucoma. This webpage offers various services such as: connecting the patient to the nearest care setup; offering scientific articles regarding glaucoma; and a video game that supports eye-treatment yogic exercises to strengthen vision and focus. This early diagnostic method has the near future potential to decrease the percentage of irreversible vision loss due to glaucoma by 42.79% (the percentage was calculated using the mean absolute error function), which could prevent glaucoma from remaining the leading cause of blindness worldwide. Our glaucoma diagnostic webpage can be found at: Glaucoma Detector (glaucomadiagnosis.com)
Representing Network Frontiers based on Spatio-temporal Graph Transformations
Authors: Nazir Hafiz Muhammad Jamsheed , Han Weihong , Li Shudong 
Published: March 2024

Abstract
Purpose-Spatio-temporal prediction in cyber elements can be enhanced by incorporating geographic features into the estimation process for cyber elements. It highlights a deficiency in existing studies that do not incorporate geographic characteristics when estimating network security factors.
Methodology-This methodology is then assessed through benchmark experiments using ARIMA and LSTM models, with the spatiotemporal graph convolution prediction model demonstrating superior forecasting performance based on evaluation criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).
Findings-This leads to the creation of a spatiotemporal network vulnerability dataset, demonstrating the feasibility of integrating spatial and temporal dimensions into network vulnerability analysis.
Research Limitations -The identified gap in studies integrating geographic characteristics in estimating network security factors suggests a need for further research in this area. Future studies could explore different approaches, datasets, and applications to build upon the current findings and contribute to the evolving field of spatio-temporal prediction in cyberspace.
Practical Implications-The practical implications of incorporating geographic features and utilizing the proposed spatiotemporal graph convolution model include improved network security planning, enhanced predictive capabilities, informed data-driven decision-making, benchmarking against traditional models, adaptation to evolving threats, and resource optimization. These implications highlight the potential practical benefits for organizations seeking advanced and effective approaches to cybersecurity.
Originality-The originality of incorporating geographic features, addressing a gap in the literature, introducing an innovative methodology, creating a unique spatiotemporal dataset, and proposing a sophisticated spatiotemporal graph convolution model with demonstrated superior forecasting performance. These contributions collectively advance the understanding and application of spatio-temporal prediction in the field of cyberspace security.