bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health promotion intervention to educate mothers of preschool children to promote wellness
Authors: Palavesam Perianaayagi , Rajendran Shankar Shanmugam , Venkatachalam Gomathi Priya , Narayanasamy Vanitha , Tamilselvi Palani , Sheriff Dhastagir Sultan 
Published: July 2024

Preschoolers are delicate individuals with higher rates of mortality and morbidity. Nowadays, the focus is to reduce mortality rates by focusing on risk factors, including preventable causes and elements that affect children's health. Creating an awareness among mothers in a particular community will help prevent and promote health among such children. Therefore, a study is designed to find out the effectiveness of focused intervention on health promotion among mothers of preschool children in selected urban area of Chennai. To validate the effectiveness of focused intervention a pre-test and posttest outcomes were measured to enable the wellness of preschool children. A quantitative research approach and pre-experimental design were used to gather data from 100 mothers of preschool children using convenience sampling technique. Self-structured knowledge questionnaires were used during pretest and post-test. After pretest, a focused intervention on health promotion among mothers of pre-school children was given. The outcome of the study was measured through a post-test. The data collected was analyzed, interpreted and the outcomes were reported. The average pre-test level of knowledge score of mothers was 8.22 and the average post-test knowledge score was 16.05, a significant difference (7.83). Mothers in nuclear family in the urban area having more than one child showed better outcome. Statistical significance was calculated using the chi-square test. The preliminary study’s findings support that focused intervention helps mothers to gain more knowledge that may help to maintain the health of their children. Such studies may be recommended to educate the mothers help reduce under five child mortality.
Generative AI for Academic Publishing? Some Thoughts About Epistemic Diversity and the Pursuit of Truth
Authors: Ma Lai 
Published: July 2024

The uses of generative AI have prompted both positive and negative responses. This short commentary contemplates potential issues concerning bibliodiversity, epistemic diversity, and data surveillance. It also cautions the potential erosion of public trust in academic publishing in the age of generative AI. Invoking the Sokal hoax, the commentary sheds light on what it means when knowledge, experience, expertise, and the pursuit of truth are on the line.
This commentary is part of the section "AI and Academic Publishing."
Control de calidad de las observaciones GPS de la Red Geodésica Nacional Activa en México del periodo 2020-2023
Authors: Llanes-Hernández Rafaela Mirasol , Romero-Andrade Rosendo , Guzmán-Galindo Tiojari Dagoberto , Santiago-Sánchez Lizbeth Guadalupe , Serrano-Agila Richard Germán 
Published: July 2024

Se realizó la propuesta de nuevos valores para el efecto multitrayectoria, relación señal-ruido, saltos de ciclo e integridad a través de estadística descriptiva con la finalidad de realizar el control de calidad de la Red Geodésica Nacional Activa en México para la constelación GPS. El análisis fue llevado a cabo mediante el software TEQC considerando los valores propuestos por el IGS, RVM y mediante la implementación de estadística descriptiva. Se analizaron las observaciones GPS de 32 estaciones de monitoreo continuo en el periodo 2020 al 2023. Los resultados demuestran que para el efecto multitrayectoria en la L1, 17 estaciones cumplen con el criterio, en el caso contrario para la L2 donde 27 estaciones superan el valor recomendado. Para la relación señal-ruido, solo dos estaciones presentan una potencia baja en ambas frecuencias. En relación con la integridad, solo 5 estaciones no cumplen con el valor propuesto, por su parte, para los saltos de ciclo, solo 5 estaciones no cumplen con el valor recomendado. Finalmente, se demuestra que las estaciones que pertenecen a la Red Geodésica Nacional Activa cumplen con los valores recomendados, por lo que es posible utilizar las estaciones para monitoreo de la corteza terrestre y trabajos topógrafo-geodésicos conforme a la nueva actualización masiva de equipos y receptores derivado de los valores presentados.

Descriptive statistics were used to propose new values for quality control of Mexico's Active National Geodetic Network, specifically for the multipath effect, signal-to-noise ratio, cycle slips, and integrity of the GPS constellation. The analysis was carried out using the TEQC software considering the values proposed by the IGS, RVM and by implementing descriptive statistics. Based on the results, the proposed criteria are met by 17 stations for the multipath effect in L1, while for L2, the recommended value is exceeded by 27 stations. For the signal-to-noise ratio, only two stations have low power on both frequencies. Regarding integrity, only 5 stations do not comply with the proposed value. Meanwhile, for cycle slips, only 5 stations do not comply with the recommended value. Finally, it is demonstrated that the stations that belong to the Active National Geodetic Network can be used for monitoring the Earth's crust and topographic-geodetic work in accordance with the new massive update of equipment and receivers derived from the values presented.
Trade openness, poverty, and sustainable development: Testing for causality using Dumitrescu-Hurlin approach
Authors: Balogun Adewale Musliudeen , Adelowokan Oluwaseyi Adedayo , Ajayi Felix Odunayo , Ogede Jimoh Sina 
Published: July 2024

Purpose — This research paper explores the causal links between trade openness, poverty, and sustainable development, shedding light on the potential impact of trade policies on poverty reduction and sustainable development in the Economic Community of West African States (ECOWAS) region.Method — We utilize the Dumitrescu-Hurlin (DH) panel causality test, a robust econometric approach capable of discerning the direction and magnitude of causal relationships among variables. We employ a comprehensive dataset spanning from 1986 to 2020, covering ECOWAS countries, to conduct a rigorous empirical analysis.Result — The empirical findings from the DH causality analysis reveal a unidirectional relationship between trade openness, human capital investment, and both sustainable development and poverty. Additionally, bidirectional causality relationships are observed between human capital investment and poverty. The results also highlight the absence of a consistent and uniform pattern of Granger causality between poverty and sustainability across individual West African economies. This heterogeneity underscores the need for customized policy approaches based on empirical evidence derived from country-specific causality analyses, rather than adopting one-size-fits-all solutions.Novelty — This research stands out by exploring the causal connections among trade openness, poverty, and sustainable development within the Economic Community of West African States (ECOWAS) region. The adoption of the Dumitrescu-Hurlin (DH) panel causality test enhances the empirical analysis, offering a comprehensive understanding of both the direction and magnitude of these relationships.
Productive zakat distribution in improving mustahik welfare: CIBEST model approach
Authors: Karunia Sinta Octavia Fabber , Amir Faizal 
Published: July 2024

Purpose — This research aims to determine the positive impact of the productive zakat program on Mustahik's welfare through increasing income and spiritual well-being and reducing poverty levels.Method — We employed a purposive sampling method in our sampling technique. The criteria for respondents selected as samples in this study were mustahiq, who received zakat from BAZNAS and were Muslim. The data for this research were obtained by distributing questionnaires directly through visits to Mustahiq's homes. The collected data were then processed using the Wilcoxon test and CIBEST model analysis. The Wilcoxon test determined the average difference in income before and after receiving zakat. Meanwhile, the CIBEST model was used to evaluate Mustahik's welfare.Result — The findings of this research show that mustahik recipients of productive zakat assistance can improve their financial conditions and materially enhance their circumstances. Overall, the research results confirm that the BAZNAS effective zakat program has had a positive impact on Mustahik's welfare, by increasing income, spiritual well-being, and reducing poverty levels.Practical implications — The distribution of productive zakat must continue to be improved, both in terms of the quality of its management and the utilization of abundant zakat funds, to achieve a more optimal positive impact on Mustahik's welfare.
Analisis faktor-faktor yang mempengaruhi Produk Domestik Regional Bruto (PDRB) di Kabupaten Kota Provinsi Lampung tahun 2017 – 2021
Authors: Wijayanti Diana , Laksono Ega Nanda 
Published: July 2024

Purpose – This research aims to analyze the effect of population, Local Original Income (PAD), General Allocation Fund (DAU), and Regional Expenditure on Gross Regional Domestic Product (PDRB) in Lampung Province for the period 2017-2021.
Method – This research uses panel data regression analysis with a Fixed Effect model. This research uses four independent variables, namely population, PAD, DAU, and Regional Expenditures, while the dependent variable is PDRB.
Findings – The research results explain that population, PAD, and DAU affect GRDP in Lampung Province, but regional expenditure does not affect GRDP.
Implications – This research implies that it can contribute to the development of literature and provide appropriate policy recommendations for local governments to increase GRDP, especially in Lampung Province.
Originality – The results of this study contribute to providing insight and identifying specific factors that influence PDRB, especially in areas in Regencies/Cities in Lampung Province.

Abstrak
Tujuan – Penelitian ini bertujuan untuk menganalisis pengaruh dari jumlah penduduk, Pendapatan Asli Daerah (PAD), Dana Alokasi Umum (DAU), dan Belanja Daerah terhadap Produk Domestik Regional Bruto (PDRB) di Provinsi Lampung untuk periode 2017-2021.
Metode – Penelitian ini menggunakan analisis regresi data panel dengan model Fixed Effect. Penelitian ini menggunakan empat variabel bebas yaitu jumlah penduduk, PAD, DAU dan Belanja Daerah, sedangkan variabel terikatnya adalah PDRB.
Temuan – Hasil penelitian menjelaskan bahwa jumlah penduduk, PAD, dan DAU berpengaruh terhadap PDRB di Provinsi Lampung, namun Belanja Daerah tidak berpengaruh terhadap PDRB.
Implikasi – Implikasi dari hasil penelitian ini adalah dapat memberikan kontribusi dalam pengembangan literatur dan memberikan rekomendasi kebijakan yang tepat bagi pemerintah daerah dalam upaya meningkatkan PDRB khususnya di Propinsi Lampung.
Orisinalitas – Hasil penelitian ini berkontribusi dalam memberikan wawasan dan mengidentifikasi faktor-faktor spesifik yang mempengaruhi PDRB khususnya di wilayah Kabupaten/Kota di Propinsi Lampung.
Upaya Pencegahan Diare Anak Melalui Edukasi Menggunakan Hand-Wash
Authors: Ardila Mita , Humolungo Dwi Tiva Widyanti S. , Amukti Danang Prasetyaning , Bachri Moch Saiful 
Published: July 2024

Penyakit yang sering terjadi di negara berkembang tak terkecuali Indonesia adalah penyakit diare. Diare merupakan keluarnya cairan atau feses encer minimal terjadi 3 kali Di Indonesia, penyakit diare termasuk Kejadian Luar Biasa (KLB) yang sering disertai kematian. Tujuannya secara umum dilakukan kegiatan pembekalan mengenai edukasi pencegahan diare melalui hand wash untuk menciptakan pengetahuan tentang hygiene dan sanitasi pada anak-anak SD Muhammadiyah Sukonandin untuk mencegah terjadinya diare. Metode Participatory Action Research (PAR) digunakan untuk menekankan partisipasi dan kolaborasi dari berbagai pihak dalam mengelola penelitian action research. Dalam metode ini menerapkan 3 kegiatan yakni sosialisasi, diskusi, dan pendampingan. Hasil pelaksanaan kegiatan PRODAMAT, siswa SD Muhammadiyah Sukonandin memperoleh nilai rata-rata pre-test sebesar 60% dan nilai rata-rata post-test sebesar 90%. Dengan demikian, dapat disimpulkan bahwa edukasi dapat meningkatkan pengetahuan dan pemahaman siswa tentang pencegahan diare melalui hand-wash
Intelligent Traffic Light Time Cycle Simulation Model using Fuzzy Mamdani
Authors: Zulfa Mulki Indana , Aryanto Andreas Sahir , Fadli Ari 
Published: July 2024

The growth of motorized vehicles in Indonesia has increased significantly. According to data from the Central Bureau of Statistics, the number of motorized vehicles in Indonesia has increased by around 10% each year in the last five years. One of the negative impacts of the increasing number of motorized vehicles is traffic congestion. Traffic congestion has become a serious problem in several cities in Indonesia. One of the causes is the increase in the number of vehicles at road intersections, which has an impact on congestion and the safety of road users. The rapid growth in the number of vehicles requires a more comprehensive strategy to reduce congestion and accidents at road intersections. Therefore, the need for Intelligent Transportation System, especially on the time-cycle configuration of intelligent red light is very important. This research aims to model the time-cycle of the red light using the Mamdani Fuzzy Inference System to simulate the green light time configuration so as to reduce the waiting time of road users at highway intersections. The simulation results show that the time-cycle configuration and green light time length of the Mamdani Fuzzy calculation are more varied relative to the number of vehicles. The values are relatively smaller than 6 to 54 seconds from the time configuration set by the local Department of Transportation. This shows a time efficiency for road users of up to 27%, which means that road users can complete trips 6 to 13 seconds faster.
Perbandingan Analisis Komponen Utama Robust Minimum Covarian Determinant dengan Least Trimmed Square pada Data Produk Domestik Regional Bruto
Authors: Amni Wa Ode Sitti Amni , Jaya Andi Kresna , Ilyas Nirwan 
Published: July 2024

Regression analysis is a method to examine the relationship between variables and determine their influence. However, the problem of multicollinearity often arises in linear regression analysis and can cause interpretation problems. To handle multicollinearity, Principal Component Analysis (PCA) is used. However, this method has a weakness when the data contains outliers. Therefore, it was developed into robust PCA using the Minimum Covariance Determinant (MCD) method and the Least Trimmed Square (LTS) estimation method. This study uses Gross Regional Domestic Product data in Indonesia in 2020, which has problems with multicollinearity and outliers. This data is modeled using two robust PCA methods, namely MCD and LTS. The robust PCA model with MCD has an adjusted value of 87.87% and an MSE value of 0.0700. However, in the robust PCA regression model with LTS, the adjusted value is 98.93% and the MSE value is 0.0325. Thus, the effective method in handling multicollinearity and outliers is the LTS method because it shows better results.
FORECASTING CHANGE IN THE LEVEL OF FOREST COVERAGE USING THE GLOBAL FOREST WATCH SERVICE AND THE PROGRAMMING AND DATA ANALYSIS LANGUAGE R
Authors: Melnykov Oleksandr , Denysenko Viktoriia 
Published: July 2024

The problem of calculating the level of forest cover is considered, including forecasting changes in forest cover in individual forestry. It is stated that the authors previously developed software for calculating forest cover and processing information about forest plantations using the example of the village of Spivakivka in the Izyum district of the Kharkiv region. A comparison of forest cover over a number of years was also made using the Global Forest Watch resource. From this resource, images of Prydonetsk Forestry were taken with conventional designations: areas where new forest plantations are being planted are shown in blue, and areas where cutting is taking place are shown in pink. It is proposed to divide each of the uploaded images of the selected forestry into squares, and then analyze the data for each square. The pink color saturation was calculated and stored in the table. It is noted that forecasting the change in forest stands on the selected site, that is, the change in the percentage of felling, can be done in different ways. First, use regression analysis - apply the regression equation separately to the values of each square, as well as to the entire forestry. Secondly, to form a list of input factors containing indicators on the selected plot in the two previous years and the same indicators on neighboring plots. Thus, the number of factors will be equal to 27: 26 input and 1 output (values on the studied square). Such a forecasting problem can be solved either by the method of multivariate linear regression or by the method of artificial neural networks. The R programming and data analysis language was used to perform calculations using both methods. A script was created that performs calculations by constructing regression lines and an artificial neural network, and also allows determining the best architecture of a neural network and a more effective method of its training for a certain data set. The calculation of the felling dynamics in the entire forestry (the forecast for the last year provides an error of 1 %) and the calculation of the felling dynamics in the selected square (the forecast for the last year provides an error of 3.5 %) are given. After many runs of the script, it was found that the best result is provided by a perceptron with two hidden layers and two neurons in each layer. The results of the calculations indicate a high correlation of the data for determining the percentage of forest that will be cut down in a certain square. Application of this perceptron for forecasting for the last year showed an error of 3 %.
Identification of Priorities for Colorectal Cancer Screening Interventions Among US Hispanic/Latino Populations
Authors: Buchalter R. Blake , Stern Mariana C. , Mendez Joel Sanchez , Kim Michelle K. , Rose Johnie , Meade Cathy D. , Gwede Clement K. , Figueiredo Jane C. , Schmit Stephanie L. 
Published: July 2024

Objectives. To identify nationwide census tract‒level areas where improving colorectal cancer (CRC) screening uptake via targeted local preventive intervention may benefit Hispanic or Latino/a (H/L) groups defined by region or country of origin. Methods. Using 2021 Centers for Disease Control and Prevention PLACES and American Community Survey data, we applied geographically weighted regression and Getis-Ord Gi* hot spot procedures to identify CRC screening priority zones for H/L groups in the United States. Priority zones can be conceptualized as census tracts with strong inverse associations between percentage of a particular H/L group in the population and CRC screening rate, after adjusting for socioeconomic deprivation and lack of insurance. Results. We identified 6519, 3477, 3522, 1069, and 1424 census tract CRC screening priority zones for H/L communities of Mexican, Puerto Rican, Central/South American, Dominican, and Cuban heritage, respectively. Priority zones for H/L groups had strong spatial heterogeneity, and overlap of geographic patterns among H/L groups varied by region. Conclusions. Our findings and interactive web map may serve as a translational tool for public health authorities, policymakers, clinicians, and other stakeholders to target investment and interventions to increase guideline-concordant CRC screening uptake benefitting specific H/L communities in the United States. ( Am J Public Health. 2024;114(S6):S515–S524. https://doi.org/10.2105/AJPH.2024.307733 ) [Formula: see text]
Subcellular Systems Observe Onsager Reciprocity
Authors: Horowitz Arie 
Published: July 2024

Abstract

The complexity of prokaryotic and, exceedingly, of eukaryotic cells, challenges the implemen-tation of methodologies to determine causality and quantify intracellular dynamics. Although stochastic thermodynamics can be used to effectively quantify the dynamics of single proteins or complexes, there is no statistical approach to generalize subcellular microstates to deter-mine the macrostates of a whole cell. Onsager reciprocity, a coupling between thermody-namic flows, has not been used systematically to quantify and establish causality among cel-lular dynamics. A prototypical thermodynamic profile of the cell is formulated as a model sys-tem to enable representation of its dynamics as force-driven thermodynamic flows. The ex-planatory potential of the methodology of thermodynamic reciprocity is demonstrated on mo-lecular mechanisms in which coupling between internal fluxes has been evident. Its predictive potential is then tested against mechanisms wherein coupling is unacknowledged, ruling out reciprocity between the contractile ring of the bacterial ortholog of tubulin that facilitates the separation of dividing Escherichia coli cells, and the bacterial actin ortholog, which polymer-izes along the cell walls. In contrast, reciprocity is identified, albeit with a large margin of error, between the actomyosin ring that facilitates the division of fission yeast cells and treadmilling longitudinal microtubules. Directly pertinent to the definition of the cell's prototypical thermody-namic profile, the reciprocity that was suggested between AMP-regulated kinase (AMPK) and mechanistic target of rapamycin (mTORC1) in the regulation of the cell cycle is confirmed. These findings support wider implementation of thermodynamic reciprocity to quantify and es-tablish causal relationships among cellular dynamics.
A Multilingual BERT-based classification of reviews for enhanced visitors’ experience analysis
Authors: RICCIARDI RICCARDO , Manisera Marica 
Published: July 2024

Abstract

Cultural organizations today can rely on online platforms to study users’ opinions and the most discussed topics related to both general and specific cultural offerings. Despite data acquisition tools, managing unstructured databases remains a hurdle. To overcome this, we propose a classification model that transforms unorganized data into a structured thematic database.
The specific case pertains to the Italian city of Brescia. We build a language model that classifies online reviews into four semantic areas defined by the key attractions of the city. We fine-tuned the pre-trained Multilingual BERT model in a multiclassification task. The model shows promising results based on traditional performance metrics. Additionally, clusters of reviews have been detected by applying the HDBSCAN algorithm on their vector representations produced by the model. As a transformation of the chi-square statistic, the Keyness statistic has been employed to extract cluster-specific keywords, which have proven to be highly consistent with the characteristics and offerings of the key cultural attractions, further confirming the good performance of the model. Results show that the proposed model can be profitably employed by policymakers and managers of cultural tourism institutions to understand textual data and derive relevant insights about visitors’ experience at specific attractions of interest.