bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling Spongy Moth Forest Mortality in Rhode Island Temperate Deciduous Forest
Authors: Dumarevskaya Liubov , Parent Jason R. 
Published: May 2024

Abstract

Invasive pests cause major ecological and economic damages to forests around the world. Satellite imagery is an important tool for monitoring defoliation at broad scales, but environmental conditions can affect whether defoliation leads to mortality. In this study, we modeled forest mortality resulting from a 2015-2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). We used Landsat-based defoliation mapping and geospatial environmental data with Random Forest to model mortality severity of canopy trees at a 100 m spatial resolution.
Defoliation was mapped based on declines in Normalized Difference Vegetation Index (NDVI) in outbreak years compared to baseline NDVI from pre-outbreak years. Other predictors included geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. The Random Forest tool in Python Sklearn was used to model forest mortality with 2 classes (low/high) and 3 classes (low/med/high). The best models had overall accuracies of 82% and 65% for the 2-class and 3-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Soils and topography had minimal importance in the models possibly due to limitations of the data and limited variability within our study area. Repeated defoliations were relatively rare during the outbreak. Model performance improved only slightly with the inclusion of more than 3 variables. The models classified 35% of forests as having canopy mortality > 5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality.
Estimation of mutual information via quantum kernel methods
Authors: Maeda Yota , Tezuka Hiroyuki , Kawaguchi Hideaki 
Published: May 2024

Abstract

Recently, the importance of analyzing data and collecting valuable insight efficiently has been increasing in various fields.
Estimating mutual information~(MI) plays a critical role in investigating the relationship among multiple random variables with a nonlinear correlation.
Particularly, the task to determine whether they are independent or not is called the independence test, whose core subroutine is estimating MI from given data.
It is a fundamental tool in statistics and data analysis that can be applied in a wide range of applications such as hypothesis testing, causal discovery and more.
In this paper, we propose a method for estimating mutual information using the quantum kernel.
We investigate the performance under various problem settings, such as different sample sizes or the shape of the probability distribution.
As a result, the quantum kernel method showed higher performance than the classical one under the situation that the number of samples is small, the variance is large or the variables possess highly non-linear relationships.
We discuss this behavior in terms of the central limit theorem and the structure of the corresponding quantum reproducing kernel Hilbert space.
Effect of pore characteristics on the dielectric constant of wood: considering the Moisture content and Frequency change
Authors: Qin Ruixia , Xu Huadong , Guo Jiaxing , Wang Peng , Yang Kang 
Published: May 2024

Abstract

Electromagnetic waves are gradually used in wood inspection and treatment. Three important parameters need to be considered in the application of this method, including moisture content (MC), porosity, and dielectric constant (ε), which affect the effectiveness of the application of electromagnetic waves, and these parameters can interact with each other. This study’s objective is to elucidate the effect and mechanism of porosity on the ε of wood with different MCs (absolute dry to saturated) in the frequency(f) range of 1 Hz-107 Hz. The pore parameters of four wood species are obtained by mercury piezometer and optical microscope, and the ε of wood with different MCs is obtained by dielectric spectrometer. The results indicate that the effect of porosity on the ε of wood needs to consider both f and MC. Relationship between porosity and ε of absolute dry wood shows opposite patterns in different f bands. When f ≤ 200 Hz, the porosity is positively correlated with ε. When f > 200 Hz, the porosity is negatively correlated with ε. However, the effect of f on the ε with the increase of MC is complicated. When f is low, the correlation among MC, porosity and ε is not significant, and as the f increases, the relationship between MC and ε becomes linear, and the growth rate of ε is large for wood with small porosity. The results of this study provide a reference for use of ε to detect wood pore characteristics and MC.
Cylinder wake flow in confined channel and its active control by sweeping jets
Authors: Song Jinsheng , Qiu Zihao , Liu Yingzheng , Wen Xin 
Published: May 2024

Abstract

The wake dynamics of the flow past a confined circular cylinder and its active control by sweeping jets (SWJs) and steady jets (SJs) positioned at the front stagnation points were experimentally investigated using Particle Image Velocimetry (PIV) and pressure measurements. Experiments were conducted across a range of Reynolds numbers (Re) from 10,000 to 45,000 and blockage ratios (β) of \(1/2\), \(1/3\), \(1/4\), and \(1/5\). A comprehensive comparison between the current results and existing literature on natural flow dynamics fills the knowledge gap and reveals that confinement gradually reduces the time-average pressure coefficient (C_p) and increases the drag coefficient (\({C}_{D}\)) and Strouhal number (St). The interaction between the wake and wall shear layer gradually increased as \(\beta\) increased. Both SWJs and SJs effectively suppressed wake fluctuations, and the statistical characteristics of the flow field and Proper Orthogonal Decomposition (POD) analysis indicated a consistent flow control mechanism between the two methods. However, the SJs introduced external fluctuations and unbalanced forces in the forward flow field, resulting in a wake flow asymmetry. By contrast, SWJs provide more uniform control and superior flow control effectiveness and efficiency.
Factors affecting the innovativeness of new drugs: Analysis of first-in-class drugs
Authors: Jung Soon Kyu , Yang Dong-Wook , Lee Sang-Won 
Published: May 2024

Abstract

Purpose: Analyzing how the US Food and Drug Administration (FDA) designates first-in-class drugs, which is an indicator of the innovativeness of new drugs, is fundamental to promoting innovative research in the pharmaceutical industry. In this study, we identify the factors that influence the approval of first-in-class drugs by the US FDA.
Methods: Datasets of new drugs approved by the US FDA between 2010 and 2022 were constructed using the EvaluatePharma database and Cortellis Competitive Intelligence. Using the set of drugs (n = 484) approved by the US FDA, we performed logistics regression analyses to identify the factors associated with the innovativeness of the new molecular entities. Molecular type, orphan drug status, disease, pharmacological action, drug target, firm size, country of developer, and drug development strategy were used as independent variables in the analyses. In addition, we examined differences over time.
Results: We identified statistically significant differences relating to antibody biologics, orphan drugs, oncology drugs, protein targets, self-originated or in-licensed strategy of US companies, and the acquisition strategy of non-US companies in the approval of first-in-class drugs. These effects differed in the two periods studied: after 2017 and before 2016.
Conclusion: By identifying the factors that influence the innovativeness of new drugs, pharmaceutical companies can develop strategies for developing innovative new drugs, and policymakers can gain insights into how to promote innovation in the pharmaceutical industry.
TOPCOAT: Towards Practical Two-Party Crystals-Dilithium
Authors: Snetkov Nikita , Vakarjuk Jelizaveta , Laud Peeter 
Published: May 2024

Abstract

The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main research focus was towards protocols constructed for various variants of Crystals-Dilithium, future NIST digital signature standard known as ML-DSA. In this work, we propose TOPCOAT, a two-party lattice-based signature algorithm that embodies Dilithium's compression techniques. The aforesaid result is achieved by introducing a new hinting mechanism that allows parties to collaboratively calculate $\textsf{HighBits}$. Our hinting mechanism allows public key compression similar to Dilithium. Additionally, we suggest an optimization technique to minimize number of restarts both parties need to produce a valid signature. We prove security of our scheme under MLWE and MSIS assumptions in ROM, and provide implementation of our proposed scheme. As additional contribution, we present vulnerabilities and inconsistencies found in Liu et al. work which aimed to construct distributed lattice-based signature protocol.
Urbanization as a causal agent of changes in mutualistic networks between bees and plants
Authors: Graf Letícia Vanessa , Meyer Fabrício Schmitz , Jeronimo Fernando Fortunato , Gonçalves Rodrigo Barbosa 
Published: May 2024

Abstract

Urbanization is a major driver of species declines, which consequently also depletes the ecological interactions in which they participate. Plant-pollinator networks are the basis of ecosystem productivity, so the breakdown of these relationships has far-reaching ecological implications across multiple levels. Given the considerable potential of network studies to enhance our understanding of community ecology, here we aim to understand how urbanization affects plant-bee interactions and the topological structure of its network. Our study focused on a database of plant-bee interactions across ten sites within an urban gradient in southern Brazil. Data was collected by sampling bees in flowers with an entomological net over a period of ten months between 2017 and 2018. We examined two landscape variables associated with urbanization, vegetation cover and landscape diversity, to assess their influence on some species metrics (specialization – d'; among and within module connectivity – c and z) and network metrics (specialization – H2'; modularity – ΔQ; nestedness – NODF). We found no relationship between the urbanization variables and the species metrics. On the other hand, sites with lower landscape diversity presented more specialized, modular and nestedness networks. In these sites, bees shared fewer resources, showed more compartmentalized networks, and had fewer interactions among specialized species. This study highlights the importance of considering landscape diversity and its role in shaping ecological networks, particularly in urbanized environments.
A Case of Sarcoidosis Associated with Capecitabine
Authors: Birel Fatma Didem , Arslan Büşra Babahanoğlu , Argüder Emine 
Published: May 2024

Abstract

Sarcoidosis is a systemic disease of unknown cause with multiorgan involvement, characterized by a noncaseating granulomatous reaction. There are four groups of drugs that have been associated with the development of drug-induced sarcoid-like reactions. These are immune checkpoint inhibitors, highly active antiretroviral drugs, interferons and tumor necrosis factor-alpha antagonists. A case of sarcoidosis due to capecitabine in a patient with colorectal cancer has previously been reported in the literature. Here, we aimed to present a case of sarcoidosis that developed after adjuvant capecitabine treatment in a patient diagnosed with breast cancer.
Role of Local Level Stakeholders in Adapting to Emerging Natech Risks Due to Climate Change in the Selangor River Basin, Malaysia
Authors: Yahaya Nurul Syazwani , Pereira Joy Jacqueline , Taha Mohd Raihan , Yaacob Wan Zuhairi Wan 
Published: May 2024

Abstract

Climate change has been linked to the aggravation of environmental degradation but adaptation measures to address local emerging issues are limited. Developing countries with growing industrial activities may be especially vulnerable to emerging pollution risk due to climate change. This paper investigates adaptation plans and the role of local stakeholders in addressing industrial pollution under the impacts of climate change. National Adaptation Plans (NAPs) submitted by selected countries in the Asia Pacific to the UNFCCC were initially analysed to determine the status of local-level measures. This was followed by semi-structured interviews with stakeholders exposed to floods and coastal inundation in an industrial area of the Selangor River Basin, Malaysia, to determine preparedness towards emerging risks. The findings revealed that NAPs do not explicitly address adaptation towards emerging risks of industrial pollution due to climate change. However, there are several potential entry points that can be used to incorporate this dimension. Interviews with stakeholders in a small coastal industrial area within the Selangor River Basin established that although 30% of the respondents have already been affected by floods or coastal inundation, less than 10% are prepared for emerging risks. This study provides insights for formulating local-level adaptation strategies to emerging climate and pollution risks, especially in developing nations with limited resources and capacity.
Simulation of the impact of humidity on the species generated by a one-dimensional discharge of helium gas.
Authors: Mehrabifard Ramin , soltani zahra , rezaie fatemeh , hatami mohammad mohsen , soltani hamed 
Published: May 2024

Abstract

Dielectric barrier discharge (DBD) plasma has several applications in different fields. One of these fundamental applications is medical usages, where various methods are employed to improve the plasma treatment process. The combination of different gases is one of the important strategies to improve the performance of plasma in treatment. In this paper, the optimized plasma parameters for one-dimensional radiofrequency discharge produced at low pressures in a helium gas combination is studied. In this research, the optimal combination of H2O and He is identified to attain the highest amount of reactive oxygen species (ROS). Considered mixture are 5,10,15 and 20 precent of H2O for one dimensional helium gas discharge. The results show that the parameters of the output plasma are highly dependent on the composition of the input gases. It is found that the greatest concentrations of H+, He+, Hes (excited helium), and OH/densities are observed when the H2O percentage is 10%. Moreover, the density distributions of various species and the temperatures of electrons are numerically calculated during the electrical discharge process. These findings provide useful knowledge on how to optimize plasma parameters for biomedical applications, which may lead to improved treatment results in several therapeutic areas.
Alzheimer's Disease Investigated via Gene-Environment Interactions, Biochemical Pathways, Cellular Processes, and Disease Phenotype Variability
Authors: Shafi Viqas , Siddiqui Ifrah 
Published: May 2024

Abstract

Background:
Alzheimer's disease (AD) is a neurodegenerative disorder influenced by genetic and environmental factors. APOE, APP, PSEN1, PSEN2, CLU, SORL1, BIN1, CR1, PICALM, TREM2, ABCA7, and CD33 play key roles in AD pathogenesis, affecting biochemical pathways and cellular processes. However, the interaction between genetic predisposition and environmental factors, as well as the reasons for variability in disease phenotype, remain poorly understood. This study aims to investigate these interactions to improve our understanding of AD etiology and inform personalized interventions.
Methods:
A comprehensive search encompassing databases such as PubMed, MEDLINE, Google Scholar, and open access/subscription-based journals was conducted to retrieve relevant articles for the investigation of genes involved in Alzheimer's disease (AD) pathogenesis, including APOE, APP, PSEN1, PSEN2, CLU, SORL1, BIN1, CR1, PICALM, TREM2, ABCA7, and CD33. Articles were searched without any date restrictions. Utilizing the criteria delineated in the methodology section, studies were systematically reviewed to elucidate how environmental factors and genetics influence Alzheimer's disease onset, progression, symptom severity, and progression rates. This study adheres to relevant PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses).
Results:
Our investigation revealed the complicated interactions between genetic predisposition, environmental factors, biochemical pathways, and cellular processes in Alzheimer's disease (AD) pathogenesis. APOE, APP, PSEN1, PSEN2, CLU, SORL1, BIN1, CR1, PICALM, TREM2, ABCA7, and CD33 influence amyloid-beta production, tau pathology, lipid metabolism, and inflammation in AD. These genes interact with environmental factors such as diet, pollutants, head trauma, and lifestyle, modulating disease risk and progression. Additionally, we found variability in disease phenotype among individuals carrying similar genetic mutations, influenced by genetic modifiers, environmental factors, cognitive reserve, and neurobiological differences.
Conclusion:
Alzheimer's disease (AD) is a multifactorial disorder influenced by genetic and environmental factors. APOE, APP, PSEN1, PSEN2, CLU, SORL1, BIN1, CR1, PICALM, TREM2, ABCA7, and CD33 play critical roles in AD pathogenesis by affecting amyloid-beta production, tau pathology, lipid metabolism, and inflammation. These genes interact with environmental factors such as diet, pollutants, head trauma, and lifestyle, further modulating disease risk and progression. Understanding these complicated interactions is essential for developing personalized interventions to delay onset, reduce severity, and slow AD progression.
Patterns of health workforce turnover and retention in Aboriginal Community Controlled Health Services in remote communities of the Northern Territory and Western Australia, 2017-2019
Authors: Veginadu Prabhakar , Russell Deborah J , Zhao Yuejen , Guthridge Steve , Ramjan Mark , Jones Michael P , Mathew Supriya , Fitts Michelle S , Murakami-Gold Lorna , Campbell Narelle , Tangey Annie , Boffa John , Rossingh Bronwyn , Schultz Rosalie , Humphreys John , Wakerman John 
Published: May 2024

Abstract

Background
Aboriginal Community Controlled Health Services (ACCHSs) in Australia aim to optimise access to comprehensive and culturally safe primary health care (PHC) for Aboriginal populations. Central to quality service provision is the retention of staff. However, there is lack of published research reporting patterns of staff turnover and retention specific to ACCHSs. This study quantified staff turnover and retention in regional and remote ACCHSs in the Northern Territory (NT) and Western Australia (WA), and examined correlations between turnover and retention metrics, and ACCHSs’ geographical and demographic characteristics.
Methods
The study used 2017-2019 payroll data for health workers in 22 regional and remote PHC clinics managed by 11 ACCHSs. Primary outcome measures included annual turnover and 12-month stability rates, calculated at both clinic and organisation levels.
Results
There was a median of 5 client-facing (Aboriginal health practitioners, allied health professionals, doctors, nurses/midwives, and ‘other health workers’ combined) and 2 non-client-facing (administrative and physical) staff per remote clinic, at any time point. Mean annual turnover rates for staff were very high, with 151% turnover rates at the clinic-level and 81% turnover rates at the organisation-level. Mean annual turnover rates for client-facing staff were 164% and 75%, compared to 120% and 98% for non-client-facing staff, at clinic and organisational levels, respectively. Mean 12-month stability rates were low, with clinic-level stability rates of only 49% and organisation-level stability rates of 58%. Mean annual clinic-level turnover rates were 162% for non-Aboriginal staff and 81% for Aboriginal staff. Both workforce metrics were moderately to highly correlated with the relative remoteness of clinics, size of regular clients serviced, and average annual headcount of employees in each clinic (p values <0.01).
Conclusion
Participating ACCHSs in remote NT and WA have a very high turnover and low retention of healthcare staff. Overall, clinic-level turnover rates tend to be lower for Aboriginal staff and increase as distance from regional centres increases. These staffing patterns are likely to impose an untoward burden on ACCHSs’ resources, and may also affect quality of service delivery due to lack of continuity of care.
Higher Performance of Mistral Large on MMLU Benchmark through Two-Stage Knowledge Distillation
Authors: Wilkins Jessica , Rodriguez Michael 
Published: May 2024

Abstract

Large language models (LLM) have undergone significant transformations through the application of knowledge distillation techniques aimed at enhancing performance on complex benchmarks like the MMLU. The research detailed herein introduces a novel two-stage distillation process designed to refine the capabilities of Mistral Large, resulting in marked improvements in both accuracy and contextual understanding. Initially, the model undergoes a teacher-student training phase where a high-performing teacher model imparts its knowledge to a less complex student model, utilizing both soft and hard target training methods to optimize knowledge transfer. This is followed by a specialized refinement stage where the student model is further fine-tuned on tasks that require advanced cognitive skills, specifically tailored to the challenges presented by the MMLU benchmark. Quantitative results indicate a substantial increase in accuracy across various tasks within the benchmark, while qualitative analyses show enhanced linguistic sophistication and contextual relevance in the model's responses. Comparisons with baseline models confirm that the distilled Mistral Large significantly outperforms traditional training approaches, setting new performance standards for language models. The implications of our findings suggest that the structured application of knowledge distillation can fundamentally alter the development trajectory of language models, making them more efficient and effective across diverse applications. The study's approach offers a scalable framework for future enhancements and has the potential to influence a wide range of applications in artificial intelligence, from automated conversational systems to sophisticated analytical tools.