bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach
Authors: Faiem Nabid , Asuroglu Tunc , Acici Koray , Kallonen Antti , van Gils Mark 
Published: May 2024

AbstractThe ability to regularly assess Parkinson’s disease (PD) symptoms outside of complex laboratories supports remote monitoring and better treatment management. Multimodal sensors are beneficial for sensing different motor and non-motor symptoms, but simultaneous analysis is difficult due to complex dependencies between different modalities and their different format and data properties. Multimodal machine learning models can analyze such diverse modalities together, thereby enhancing holistic understanding of the data and overall patient state. The Unified Parkinson’s Disease Rating Scale (UPDRS) is commonly used for PD symptoms severity assessment. This study proposes a Perceiver-based multimodal machine learning framework to predict UPDRS scores.We selected a gait dataset of 93 PD patients and 73 control subjects from the PhysioNet repository. This dataset includes two-minute walks from each participant using 16 Ground Reaction Force (GRF) sensors, placing eight on each foot. This experiment used both raw gait timeseries signals and extracted features from these GRF sensors. The Perceiver architecture’s hyperparameters were selected manually and through Genetic Algorithms (GA). The performance of the framework was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and linear Correlation Coefficient (CC).Our multimodal approach achieved a MAE of 2.23 ± 1.31, a RMSE of 5.75 ± 4.16 and CC of 0.93 ± 0.08 in predicting UPDRS scores, outperforming previous studies in terms of MAE and CC.This multimodal framework effectively integrates different data modalities, in this case illustrating by predicting UPDRS scores using sensor data. It can be applied to diverse decision support applications of similar natures where multimodal analysis is needed.
Digital Services in the Welfare, Social and Health Sector Organizations of the South Ostrobothnia Region
Authors: Hoffrén-Mikkola Merja 
Published: May 2024

AbstractDigital services in healthcare and social services have increased due to national promotion and Covid19 pandemic. However, the regional differences may exist. Successful implementation and sustainability of digital services requires that attention is paid to addressing barriers and supporting facilitators at all levels in health care provision.The purpose of this study was to investigate the effects of employee status, form of organization and organizational size on the views related to current state and the role of digital services, development barriers, development plans and the support needed for development in welfare, social and health service organizations operating in the South Ostrobothnia region. The study was carried out in the era of exceptional circumstances created by the Covid19 pandemic in the summer of 2021. The study was a quantitative cross-sectional study using an electronic survey. Respondents (n = 121) were managers, entrepreneurs and employees of welfare, social and health service organizations operating in the South Ostrobothnia region.The results suggested that in more than four out of five welfare, social and health service organizations operating in the region of South Ostrobothnia, part of the services were already digital in the summer of 2021. These services had been extensively developed during the previous year, which was lived in exceptional circumstances caused by the Covid19 pandemic. Digital services were seen to function especially as enablers of customers in exceptional circumstances. However, managers or entrepreneurs also saw digital services as reaching new customers more important than employees. The acquisition of technology and human resources were felt to be the most significant barriers in the development of digital services, regardless of the employee status, form of organization and organization size. Regarding the use and development of digital services, information was felt to be necessary, especially about the characteristics of digital services, and financial support was also felt to be necessary for the development. However, the support needs were significant in many aspects related to digital service development. In particular, large organizations needed information on the cost-effectiveness of digital services.The results can be used to support welfare, social and health service organizations in digital service development.
Contractive Multivariate Zipper Fractal Interpolation Functions
Authors: Miculescu Radu , Pasupathi R. 
Published: May 2024

AbstractIn this paper we introduce a new general multivariate fractal interpolation scheme using elements of the zipper methodology. Under the assumption that the corresponding Read-Bajraktarevic operator is well-defined, we enlarge the previous frameworks occurring in the literature, considering the constitutive functions of the iterated function system whose attractor is the graph of the interpolant to be just contractive in the last variable (so, in particular, they can be Banach contractions, Matkowski contractions, or Meir-Keeler contractions in the last variable). The main difficulty that should be overcome in this multivariate framework is the well definedness of the above mentioned operator. We provide three instances when it is guaranteed. We also display some examples that emphasize the generality of our scheme.
Advancements in Continuum Mechanics and Electrodynamics by a spacetime geometric approach
Authors: Romano Giovanni , Barretta Raffaele 
Published: May 2024

AbstractResults achieved by the authors in the course of research activity on continuum mechanics and electrodynamics (CME) during the past twenty years are illustrated, revised and discussed. Adoption of a geometric approach leads to renewal of concepts and methods of classical CME and to formulation in Euclid (3+1)D ambient spacetime wherein innovation, clarity and depth of a geometric treatment naturally emerge. The dissemination of novel concepts and methods in CME is not delayable, with critical revisitation of problematic notions, analyses and results still currently on the scene. Material frame indifference, equilibrium in a reference configuration, extremality principles in Dynamics, finite elasticity, chain decomposition of finite strain in elasto-thermo-plasticity, variance of electro-magnetic induction laws under frame changes, action on electric charges moving in a magnetic field, are under the spotlight of innovation and advancement. Fostering basic knowledge of Differential Geometry and application of geometric notions and methods contribute effective tools in formulating meaningful rules, amending misstatements and dimming debates based on vague affirmations.
Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning
Authors: KU Spandana , Kaniyala Melanthota Sindhoora , U Raghavendra , Rai Sharada , Mahato K. K. , Mazumder Nirmal 
Published: May 2024

Abstract
Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which can play a crucial role in the early detection of abnormal tissue morphology. We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as Machine learning (ML) output images were analysed effectively. The support vector machine image classification model achieved 95.78% validation accuracy and 94.81% testing accuracy with polarization parameter dataset. The study's findings demonstrate the potential of Stokes-Mueller polarimetry in tissue characterization and diagnosis, providing a valuable tool for biomedical applications.
Big data in transportation: a systematic literature analysis and topic classification
Authors: Tzika-Kostopoulou Danai , Nathanail Eftihia , Kokkinos Konstantinos 
Published: May 2024

AbstractThis paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies were evaluated from a dataset of 3532 collected papers, and bibliometric techniques were applied to capture the evolution of research interest over the years and identify the most influential studies. The proposed unsupervised classification model defined categories and classified the relevant articles based on their particular scientific interest using representative keywords from the title, abstract, and keywords (referred to as top words). The model’s performance was verified with an accuracy of 91% using Naïve Bayesian and Convolutional Neural Networks approach. The analysis identified eight research topics, with urban transport planning and smart city applications being the dominant categories. This paper contributes to the literature by proposing a methodology for literature analysis, identifying emerging scientific areas, and highlighting potential directions for future research.
The role of taxation in an integrated economic-environmental model: a dynamical analysis
Authors: Cavalli Fausto , Mainini Alessandra , Visetti Daniela 
Published: May 2024

AbstractWe propose a model with economic and environmental domains that interact with each other. The economic sphere is described by a Solow growth model, in which productivity is not exogenous but negatively affected by the stock of pollution that stems from the production process. A regulator can charge a tax on production, and the resources collected from taxation are used to reduce pollution. The resulting model consists of a two dimensional discrete dynamical system, and we study the role of taxation from both a static and a dynamical point of view. The focus is on the determination of the conditions under which taxation has a positive effect on the environment and leads to economic growth. Moreover, we show that a suitable environmental policy can allow recovering both local and global stability of the steady states. On the contrary, we show that, if the policy is not adequate, the system can exhibit endogenous oscillating and chaotic behavior and multistability phenomena.
Assessing paddy methane emissions through the identification of rice and winter crop areas using Sentinel-2 imagery in Korea
Authors: Jang Seongju , Park Jinseok , Lee Hyeokjin , Gou Jaejun , Song Inhong 
Published: May 2024

AbstractThe global efforts on reducing methane (CH4) emissions was emphasized in COP 28 and the potential for improved estimation became feasible through bottom-up data acquisition with advanced remote sensing technology. The objectives of this study were to extract summer rice and winter crop cultivation areas based on satellite images and to incorporate into estimating CH4 emissions in South Korea for the year 2020. Satellite images of Sentinel-1 and Sentinel-2 were acquired from European Space Agency. Rice paddy was classified with backscattering coefficient from Sentinel-1 images, while the normalized difference vegetation index from Sentinel-2 images was used to identify winter cropping field. The equation of IPCC guidelines was used to estimate CH4 emissions by incorporating the areas of rice paddy and winter crop extracted with the respective satellite image. National farming statistics were used to determine the scaling factors for paddy organic matter and water management practices. The estimated areas for rice paddy and winter crop cultivation were 712,237 ha and 117,840 ha, respectively. The rice paddy areas were primarily concentrated in the western regions of the Korean peninsula, whereas winter crop cultivation was predominantly found in southern part of the country. The total amount of CH4 emissions was 6272 Gg CO2 eq./yr when considering rice straw and winter cropping practices into estimation (modified Tier 2 method). This represents a 7% increase compared to the method that considered solely the rice straw incorporation (current Tier 2 method). The CH4 emissions per unit area were also 8.82 tons CO2 eq./ha/yr with the modified Tier 2 method, indicating a 10% greater compared to the current Tier 2 method. Substantial CH4 emissions were primarily concentrated in western regions where extensive rice paddy cultivation occurs, while greater CH4 emissions per unit area were predominantly found in southern regions with substantial winter crop cultivation. The study findings hold importance for improving the accuracy of CH4 emissions estimation by employing bottom-up approach that utilizes satellite imagery to assess rice paddy and winter cropping areas. Further study would be needed to incorporate field-based data on rice crop management practices, such as rice straw and water management, to further refine CH4 emission estimation method.
Tense in Mathematical English
Authors: Inglis Matthew , Strauss Jacob 
Published: May 2024

AbstractMany authors have commented on the relative frequency of the present tense—and the relative infrequency of the past tense—in mathematical writing. However, none (to our knowledge) have provided an estimate for the size of this effect or explored how universal it is. In this short note we report an analysis of corpora of mathematical and day-to-day English. We conclude that the present-to-past ratio of tenses is at least 3:1 in mathematical English, compared to approximately 5:7 in day-to-day English. Further, we show that this tendency to favour the present tense is almost universally present in written mathematics.
Takotsubo cardiomyopathy mimicking an apical hypertrophic cardiomyopathy
Authors: Maurizi N. , Antiochos P. , Monney P. 
Published: May 2024

AbstractA 45-years old woman presented for dyspnea and cardiac chest pain. ECG showed deep T-wave inversion while CMR showed normal ejection fraction, hypertrophy and systolic obliteration of the apex suggesting apical HCM. Myocardial oedema was noted at the apex. Complete regression of hypertrophy and myocardial edema was observed after 2 months, and a final diagnosis of subacute Takotsubo was made.
Multi-objective generalized normal distribution optimization: a novel algorithm for multi-objective problems
Authors: Khodadadi Nima , Khodadadi Ehsan , Abdollahzadeh Benyamin , EI-Kenawy El-Sayed M. , Mardanpour Pezhman , Zhao Weiguo , Gharehchopogh Farhad Soleimanian , Mirjalili Seyedali 
Published: May 2024

AbstractThis study introduces the Multi-objective Generalized Normal Distribution Optimization (MOGNDO) algorithm, an advancement of the Generalized Normal Distribution Optimization (GNDO) algorithm, now adapted for multi-objective optimization tasks. The GNDO algorithm, previously known for its effectiveness in single-objective optimization, has been enhanced with two key features for multi-objective optimization. The first is the addition of an archival mechanism to store non-dominated Pareto optimal solutions, ensuring a detailed record of the best outcomes. The second enhancement is a new leader selection mechanism, designed to strategically identify and select the best solutions from the archive to guide the optimization process. This enhancement positions MOGNDO as a cutting-edge solution in multi-objective optimization, setting a new benchmark for evaluating its performance against leading algorithms in the field. The algorithm's effectiveness is rigorously tested across 35 varied case studies, encompassing both mathematical and engineering challenges, and benchmarked against prominent algorithms like MOPSO, MOGWO, MOHHO, MSSA, MOALO, MOMVO, and MOAOS. Utilizing metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the study underscores MOGNDO's ability to produce Pareto fronts of high quality, marked by exceptional precision and diversity. The results affirm MOGNDO's superior performance and versatility, not only in theoretical tests but also in addressing complex real-world engineering problems, showcasing its high convergence and coverage capabilities. The source codes of the MOGNDO algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.
Consideration of the Accuracy of Empirical and Indirect Laboratory Methods for the Characterisation of Tension Strength
Authors: Sainsbury B. , McDonald A. 
Published: May 2024

AbstractThis research explores the results of the most common laboratory based indirect tension strength methods over the range of UCS strengths 0.1 MPa (cemented paste backfill) to 100 MPa (concrete). The accuracy of each of the methods is considered through a comparison with direct strength measurements completed on the same material. The results of 241 individual tests suggest that the use of indirect tension methods to determine tension strength for all geo-material strength ranges may provide misleading results unless a correction-factor is applied. Correction factors that relate splitting and flexure results to direct tension results are provided. The correction factors are based on the characteristic UCS of the material. A comparison of the traditional empirical relationship to derive tension from UCS is presented and an updated relationship is proposed that is relevant over the UCS strength range 0.1–100 MPa.
The “Turning Point” for the Fall Goose Hunt in Eeyou Istchee: A Social-Ecological Regime Shift from an Indigenous Knowledge Perspective
Authors: Idrobo C. Julián , Leblanc Mélanie-Louise , O’Connor Mary I. 
Published: May 2024

AbstractWe present a perspective on how the Eeyou (James Bay Cree) from Eeyou Istchee (Eastern James Bay, Québec) understand the transformation of their traditional fall goose hunt system as a consequence of social and environmental changes across marine and terrestrial ecosystems with drivers operating at the local, regional and continental scales. Eeyou land users from the Chisasibi and Wemindji First Nations report that their traditional fall goose hunt underwent a “turning point” during the early 2000s. Not only did the abundance of Canadian geese reach a historical low, but their feeding and migratory behavior became unpredictable. Eeyou land users associate such abrupt changes with the massive eelgrass die-off of the late 1990s, the onset of the effects of climate change on coastal habitats experienced since the 1970s, and agricultural development along geese flyways. This manuscript is an outcome of the Eeyou Knowledge component of the Coastal Habitat Comprehensive Research Project (2016–2022) and followed a community-based case study approach that included 28 semi-structured interviews and 14 mapping interviews with Eeyou research contributors. The findings presented here underscore the capacity of Indigenous knowledge to make sense of the multifaceted impacts of environmental change across various dimensions and layers of their social-ecological system, including management strategies and values.
Do Financial Barrier Beliefs About Marriage Predict Building Wealth? Latent Growth Curves of Emerging Adults’ Financial Barrier Beliefs, Assets, and Debt
Authors: Saxey Matthew T. , LeBaron-Black Ashley B. , Willoughby Brian J. , Yorgason Jeremy B. 
Published: May 2024

AbstractScholars have found that among emerging adults, financial barrier beliefs about marriage—like believing financial independence should be achieved prior to marriage—appear to be common. Despite the prevalence of beliefs about finances regarding marriage readiness, scholars have not established whether and how these financial barrier beliefs about marriage predict emerging adults’ debt and assets over time. Through the lens of marital paradigms theory, we sought to address this gap by examining whether and how the initial level and changes in financial barrier beliefs about marriage predict changes in young emerging adults’ debt and assets across four waves of data from 1,033 young U.S. emerging adults. We found that, on average, financial barrier beliefs about marriage increased, assets were relatively stable, and debt increased over the early years of emerging adulthood. We also found that increases in emerging adults’ agreement with financial barrier beliefs like ‘finances are a barrier to marriage’ over time were associated with a decrease in assets over time. However, neither the initial level nor changes in financial barrier beliefs about marriage predicted changes in debt over time. In short, our findings provide some evidence that beliefs like a certain amount of money should be saved before marriage, somewhat paradoxically, do not appear to help young emerging adults build their assets.
Carbon stock increase during post-agricultural succession in central France: no change of the superficial soil stock and high variability within forest stages
Authors: Weissgerber Magali , Chanteloup Laine , Bonis Anne 
Published: May 2024

AbstractForest development following agricultural abandonment concerns extensive areas including the Massif Central region of France where this study was undertaken. This land-use and land-cover change is expected to have effects on biodiversity and ecosystem services, including an increase of carbon sequestration—a major concern in the face of climate change. Nevertheless, uncertainties about carbon stock changes during successions are remaining, especially as to the total stock and the contribution of the different carbon pools. Our work contributes to this field by studying carbon stocks in multiple plots of different successional stages. We measured and estimated carbon stocks in aboveground and belowground vegetation, deadwood, litter and superficial soil, and surveyed plant communities and plot conditions (slope, aspect, soil characteristics). The average total carbon stock increased along the succession from 70.60 at stage 0 to 314.19 tC ha−1 at stage 5. However, the total carbon stocks at the young forest stage (abandoned for 74 years maximum) and the older forest stage (forested for at least 74 years) were not significantly different, and probably reflected strong local heterogeneity in the older forest stage. An increase of the carbon stock was found in all pools, except the soil pool that did not vary significantly between the successional stages. The aboveground carbon stock was found strongly related to the woody species cover, especially the macrophanerophyte cover. This case study supports the view that the succession dynamics of former agricultural plots participates in carbon sequestration, sometimes with great local variations.