bluuebunny/crossref_metadata_2025_split huggingface dataset:
Results of testing potato varieties in the European North
Authors: Karavaeva E. S. 
Published: December 2023

Relevance. Potato varieties Eurasia and Onezhskiy were developed as a result of cooperation between the Murmansk State Agricultural Experiment Station – branch of the VIR and the Leningrad Scientific Research Institute of Agriculture "Belogorka" and were included in the State Register of the Russian Federation in 2014 (Onezhsky) and in 2017 (Eurasia). To determine the prospects for cultivation in the Kola North, the varieties were tested in the conditions of the Murmansk region.Materials and methods. The research was carried out on the experimental field of the Murmansk State Agricultural Experiment Station – branch of the VIR in 2018-2020. Potato varieties Eurasia and Onezhskiy were obtained from the Leningrad Research Institute of Agriculture "Belogorka". The plot in the experiment included 4 rows of 15 tubers in each row. The planting pattern is 70x35 cm. Repetition – four times. The location of the plots is systematic with the plots being shifted along tiers. Early maturity was assessed using one test coping on the 70th day after planting. The Elizabeth variety was used as a standard. The results were processed using the method of analysis of variance according to Dospekhov.Results. The research results showed that the Onezhsky variety is distinguished by high starchiness, non-darkening flesh and good taste, is resistant to pathogens: Synchytrium endobioticum (Schilbersky) Percival, Streptomyces scabies (Thaxter), Globodera rostochiensis (Wollenweber) Behrens, relatively resistant to Phytophthora infestans (Mont . De Bary), is weakly affected by Potato virus Y (PVY), Potato leaf roll virus (PLRV). The Onezhsky variety, on average, over three years of research exceeded the standard sample in terms of yield and plant productivity. The Eurasia variety has high starchy content, good taste, culinary type B, is resistant to pathogens: Potato virus Y (PVY), Potato leaf roll virus (PLRV), Globodera rostochiensis (Wollenweber) Behrens, Synchytrium endobioticum (Schilbersky) Percival, moderately susceptible to Phytophthora infestans (Mont. De Bary). The Eurasia variety also exceeded the standard sample in terms of yield and plant productivity. Thus, the Onezhsky and Eurasia potato varieties are recommended for cultivation in the Kola North.
Ideologi Pengarang dalam Sampul Novel Ranah 3 Warna Karya Ahmad Fuadi
Authors: Nisa  , Ramadhanti Dina , Samsiarni  
Published: December 2023

Analisis wacana multimodal salah satu pendekatan dalam kajian wacana. Wacana yang muncul mengkombinasi teks, warna, dan gambar dikaji dalam wacana multimodal. Wacana multimodal dapat ditemukan salah satunya dalam sampul novel Ranah 3 Warna karya Ahmad Fuadi. Penelitian ini menggunakan jenis penelitian kualitatif, dengan metode analysis content (analisis isi). Penelitian ini bertujuan untuk membongkar ideologi yang dipijak pengarang. Hasil penelitian ini menunjukkan kebudaayaan merantau anak laki-laki orang Minangkbau. Jadi, dapat disimpulkan “merantau” sebagai ideologi yang dipijak Ahmad Fuadi dalam karya novelnya.
Kata Kunci: Wacana, multimodal, sampul novel, Ahmad Fuadi
Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province
Authors: Hasibuan Devi Octaviani , Pau Teku Heribertin , Drostela Putri Maria Fatima , Setyawan Yudi , Dwi Bekti Rokhana 
Published: December 2023

Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed. The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020. The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.
Espiritualidade e religião a serviço da saúde integral na sociedade paliativa
Authors: Lustosa Lopes Antonio de Lisboa , Renan Mascarenhas Santos  
Published: December 2023

A saúde é um tema que afeta a todos, crentes ou não. Muito se discute em linhas de políticas públicas em vista de garantir o acesso aos serviços necessários para toda a população. No entanto, a saúde deve vir a ser pauta nos âmbitos religiosos. Saúde e salvação advêm da mesma raiz latina e, a partir disso, pode-se compreender melhor a relação saúde e espiritualidade. Em meio a sociedade paliativa, isto é, que repele toda forma de dor, a religião pode ser compreendida como simples amuleto, sendo procurada apenas para alcançar curas e milagres; de fato, há experiências bonitas nesse campo. No entanto, a religião desempenha importante missão ao colaborar com seus adeptos na busca de uma melhor qualidade de vida e na promoção do sentido de vida mais profundo que os leva a não desistir frente às crises e situações-limites. Desse modo, nas três grandes dimensões que compõe o ser humano (corpo, alma e espírito) podem a religião e a espiritualidade contribuir eficazmente para que o ser humano alcance não apenas a saúde eterna, mas já, no presente, possa sentir-se são e ajudar outros a experimentarem essa mesma saúde integral.
Finance Digitization in Low- and Lower-Middle-Income Countries: An Empirical Investigation and Prediction of Future Outcomes
Authors: Daodu Ladi Busuyi , Bhaumik Amiya 
Published: December 2023

Previous studies have suggested that digitization of finance leads to growth in the economy in several middle- and high-income countries, but research in low- and lower-middle-income countries (LLMICs) remains limited. This research aimed to examine the trends, barriers, relationships, and prospects of finance digitization in LLMICs.
This study used analytical and descriptive cross-sectional study designs. Sixty-six countries were purposively selected based on the available data on the Global Findex and the World Development Indicators databases. Descriptive analysis was used to identify the trends in the finance digitization indicators, correlation analysis was used to assess the nature of the association between finance digitization and the economy, and exponential smoothing was used to predict future outcomes in digital finance.
The results indicated that the adoption and utilization of finance digitization increased in these countries, but the growth rates varied by region and income group. Low-income countries reported the highest rates of subscriptions to digital finance accounts and storing money in digital accounts, while lower-middle-income countries (LMICs) reported higher rates of inbound/outbound digital transactions, borrowings, and savings using digital channels. Sub-Saharan Africa reported the highest rates of digital finance account subscriptions, digital savings, and digital borrowings, while Europe and Central Asia reported the highest rates of in/out digital transactions. Barriers to digitization in these countries included poor access to the internet, high cost of digital finance products, and lack of a mobile phone, with low-income countries facing more challenges. The analysis revealed a negative significant relationship between finance digitization and Gross National Income growth and a positive significant correlation between finance digitization and other economic growth indicators (gross savings and Gross Domestic Product). Finance digitization was predicted to continue to rise in these countries for the next decade. The study recommended that policymakers should promote the adoption of digital finance and address the barriers and risks of finance digitization in LLMICs, as it had positive effects on economic growth.
ASSESSING AND FORECASTING THE STATE OF DETERIORATING SYSTEMS WITH THE USE OF MODIFIED REGRESSION POLYNOMIALS ON THE BASIS OF FUNCTIONAL APPROXIMATION OF THEIR COEFFICIENTS
Authors: Raskin Lev , Sukhomlyn Larysa , Sokolov Dmytro , Vlasenko Vitalii 
Published: December 2023

Object of research is technical state of deteriorating systems whose operating conditions depend on a large number of interacting factors. The caused inhomogeneity of the sample of initial data on the technical state leads to impossibility of correct use of traditional methods of assessing the state of a system (meaning methods using mathematical tools of regression analysis). Subject of research is developing a method for constructing a regression polynomial based on the results of processing a set of controlled system parameters. Non-linearity of the polynomial describing the evolution of the technical state of real systems leads to an increase in the number of regression polynomial coefficients subject to estimation. The problem is further complicated by the growing number of factors affecting the technical state of the system. In these circumstances, the so-called <small sample effect> occurs. Goal the research consists in developing a method for constructing an approximation polynomial that describes evolution of the system state in a situation where the volume of the initial data sample is insufficient for correct estimating coefficients of this polynomial. The results obtained. The paper proposes a method for solving the given problem, based on implementation of a two-stage procedure. At the first stage a functional description of the approximation polynomial coefficients is performed; and this radically reduces the number of regression polynomial parameters to be estimated. This polynomial is used for preliminary estimation of its coefficients with the aim of filtering out insignificant factors and their interactions. At the second stage, parameters of the truncated polynomial are estimated by means of using standard technologies of mathematical statistics. Two approaches to constructing a modified polynomial have been studied: the additive one and the multiplicative one. It has been shown that the additive approach is, on average, an order of magnitude more effective than the multiplicative one.
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction
Authors: Mitra Suranjana , Majumder Annwesha Banerjee , Saha Tanusree 
Published: December 2023

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately. The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.
Experimental study of homogeneous charge compression ignition in a light-duty diesel engine operated with commercial gasoline-ethanol
Authors: Guzmán Juan José Milón , Braga Sergio Leal 
Published: December 2023

Abstract
An experimental bench was designed and developed to carry out experimental tests of a Diesel cycle engine using gasoline as fuel. The engine (originally a diesel cycle) was fitted with different devices to run in HCCI mode. These modifications did not affect the engine's original condition, which can be returned to diesel mode at any time. Instrumentation was set up to measurement the temperature (exhaust gases, inlet charge inlet, lubricating oil), pressure (intake air, gasoline inlet, combustion chamber), torque, rpm, air flow and fuel flow. The engine was subjected to different operating conditions and control parameters to study and analyze the effects of engine speed, air-fuel ratio and mixture temperature (air-gasoline). The results indicate that higher temperatures or amounts of fuel cause an earlier ignition phase, which would have a direct influence on the beginning of combustion. The results also indicate that, by increasing the amount of fuel and varying the inlet temperature, more useful energy is generated until reaching the limit of the detonation zone. Small gains in combustion efficiency translate into significant energy savings, also reducing exhaust gas pollution levels.
The effect of intrahepatic cholestasis in pregnancy combined with different stages of hepatitis B virus infection on pregnancy outcomes: a retrospective study
Authors: Gao Qian , Li Xuejiao , Wang Li , Tan Xiaozhi , Li Zhe , Xu Chengfang 
Published: December 2023

Abstract
Background and Aims: To investigate the impact of intrahepatic cholestasis of pregnancy (ICP) with hepatitis B virus (HBV) infection on pregnancy outcomes.
Methods: We selected 512 pregnant women, collected the data including maternal demographics, main adverse pregnancy outcomes and maternal HBV infected markers HBeAg and HBV-DNA loads status, then have a comparative analysis.
Results: There were 319 cases of isolated ICP, 113 cases of chronic hepatitis B (CHB) combined with ICP, 80 cases of asymptomatic HBV carriers combined with ICP. All HBV-infected pregnant women with ICP were divided into hepatitis B e antigen (HBeAg)-positive group (102 cases) and HBeAg-negative group (91 cases), according to the level of the serum HBeAg status; and into high viral load group (92 cases), moderate viral load group (46 cases) and low viral load group (55 cases) according to the maternal HBV-DNA level. CHB+ICP group had a higher level of serum total bile acids, transaminase, bilirubin as well as a higher percentage of premature delivery, neonatal transfer and meconium-stained amniotic fluid (MSAF) compared with the other two groups(P<0.05), but there were no significant differences in the the above indicators between the group of isolated ICP and asymptomatic HBV carriers with ICP.Among the HBV-infected patients with ICP, HBeAg-positive group had a higher level of serum transaminase, bilirubin and bile acid as well as shorter gestational weeks of delivery, lower birth weight of newborns and a higher rate of neonatal intensive care unit (NICU) admission than HBeAg-negative group (P<0.05); Those with a high viral load (HBV-DNA>106IU/ml) had a higher level of transaminase, bilirubin, and bile acid as well as shorter gestational weeks of delivery, lower birth weight of newborns and a higher rate of NICU admission compared with those with a low or moderate viral load (P<0.05).
Conclusion: ICP combined with CHB patients have more severe liver damage, a higher percentage of preterm birth and NICU admission. HBeAg-positive status and a high HBV-DNA load will increase the severity of conditions in HBV-infected pregnant women with ICP. Patients who have CHB combined with ICP, HBeAg-positive or a high viral load should be treated more actively.
Dynamic modeling of three-dimensional muscle wrapping based on absolute nodal coordinate formulation
Authors: Tang Yimin , Guo Jianqiao , Tian Qiang , Hu Haiyan 
Published: December 2023

Abstract

Muscle wrapping influences its moment arm and alters the dynamic response of the musculoskeletal system. Conventional musculoskeletal multibody models often simplify muscle wrapping as the shortest path problem, while these models cannot consider the three-dimensional muscular geometry. In comparison, the finite element description of skeletal muscles using three-dimensional solid element discretization leads to expensive computational costs. This paper established a novel flexible multibody modeling method of muscle wrapping based on absolute nodal coordinate formulation (ANCF), which can simultaneously deal with its fusiform geometry, distributed inertia, and active contractions. The skeletal muscle is discretized by full-parameterized ANCF beam elements with variable cross section. The three-dimensional shape of the muscle belly can be expressed by an explicit function, and its time-dependent change can be captured via the surface-to-surface contact detection algorithm. The muscle stress contains the effect of Hill-type active-passive contractions, muscular fascia hyperelasticity, and volume constancy based on continuum mechanics description. As a validation, a forward dynamics simulation of the sagittal elbow rotation driven by surface electromyography (sEMG) data was performed and compared with literature data and experimental results. The established numerical method provides an efficient analysis tool to describe muscle-bone force transmissions, presenting an approach for understanding the hidden biomechanics during human locomotion and human-machine interactions.
Unlock Tigrigna NLP - Design and Development of Morphological Analyzer for Tigrigna Verbs Using Hybrid Approach
Authors: Gebremeskel Hagos Gebremedhin , Chong Feng , Heyan Huang 
Published: December 2023

Abstract
Morphological analyzer is the basis for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for the analysis of Tigrigna verbs morphological analyzer using the hybrid of memory learning and rule based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross-validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.
Neural networks for the approximation of Euler's elastica
Authors: Celledoni Elena , Çokaj Ergys , Leone Andrea , Leyendecker Sigrid , Murari Davide , Owren Brynjulf , de Almagro Rodrigo T. Sato Martín , Stavole Martina 
Published: December 2023

Abstract
Euler's elastica is a classical model of flexible slender structures, relevant in many industrial applications. Static equilibrium equations can be derived via a variational principle. The accurate approximation of solutions of this problem can be challenging due to nonlinearity and constraints. We here present two neural network based approaches for the simulation of this Euler's elastica. Starting from a data set of solutions of the discretised static equilibria, we train the neural networks to produce solutions for unseen boundary conditions. We present a \textit{discrete} approach learning discrete solutions from the discrete data. We then consider a \textit{continuous} approach using the same training data set, but learning continuous solutions to the problem. We present numerical evidence that the proposed neural networks can effectively approximate configurations of the planar Euler's elastica for a range of different boundary conditions.
Fragment Molecular Orbital-based Variational Quantum Eigensolver for Quantum Chemistry in the Age of Quantum Computing
Authors: Lim Hocheol , Kang Doo Hyung , Kim Jeonghoon , Pellow-Jarman Aidan , McFarthing Shane , Pellow-Jarman Rowan , Jeon Hyeon-Nae , Oh Byungdu , Rhee June-Koo Kevin , No Kyoung Tai 
Published: December 2023

Abstract
Despite the potential of quantum computers in analyzing complex systems, their applications for larger systems are hindered by the limited qubit availability. Addressing this, our study presents the novel FMO/VQE algorithm that combines fragment molecular orbitals and variational quantum eigensolver methods, strategically designed for optimal qubit utilization in fragment-based quantum chemistry simulations. The FMO/VQE/UCCSD achieved an absolute error of just 0.053 mHa with 8 qubits in a H24 system using the STO-3G basis set, whereas traditional VQE methods require 48 qubits. In a H20 system with the 6-31G basis set, the FMO/VQE/UCCSD showed an error of 1.376 mHa with 16 qubits, as opposed to the 80 qubits required for the VQE/UCCSD. Consequently, the FMO/VQE not only outperforms in accuracy and scalability compared to conventional VQE, but also would facilitate quantum chemistry simulations of increasingly complex systems, as the field of quantum computing progresses and the availability of qubits expands.
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese
Authors: Tran Vinh Khiem , Phan Phu Hao , Nguyen Van Kiet , Nguyen Luu Thuy Ngan 
Published: December 2023

Abstract
In recent years, Visual Question Answering (VQA) has gained significant attention for its diverse applications, including intelligent car assistance, aiding visually impaired individuals, and document image information retrieval using natural language queries. VQA requires effective integration of information from questions and images to generate accurate answers. Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English.To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. The dataset comprises over 26,000 images and 30,000 question-answer pairs (QAs), each question annotated to specify the type of reasoning involved. Leveraging this dataset, we conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations.Furthermore, we present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions. The architecture effectively employs transformers to enable simultaneous reasoning over textual and visual data, merging both modalities at an early model stage. The experimental findings demonstrate that our proposed model achieves state-of-the-art performance across seven evaluation metrics.