bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sector-Level Economic Effects of Regulatory Complexity: Evidence from Spain
Authors: Mora-Sanguinetti Juan S. , González Javier Quintana , Albaladejo Isabel Soler , Spruk Rok 
Published: April 2023

Abstract
This paper studies for first time the impact of the regulatory complexity by sector in Spain on various measures of economic efficiency. We base our analysis on an innovative database that classifies 206,777 regulations by sector of activity and by region which highlights the growing volume of regulation, as well as its diversity by sector, by region and by business cycle stage.
This analysis first looks at the aggregate impacts of sectoral regulatory complexity on the employment-to-population ratio, total working hours, sectoral GDP shares, labor intensity or capital intensity, but it delves, in the second place, into the heterogenous impacts observed over firms with different sizes and ages, making use of a rich database at the enterprise level, the MCVL - Continuous Work History Sample-.
On the first front, we estimate a set of multiple fixed-effects model specifications across 13 economic sectors, 23 regulatory sectors and 17 Spanish regions over the period 1995-2020. Our results suggest that greater regulatory complexity has a negative effect on employment rate and has a negative impact on value added. The effect on employment is consistent with previous findings for the U.S. In particular, each additional increase in regulatory complexity index is associated with 0.7 percent drop in the sector-level employment share, ceteris paribus. Furthermore, our results suggest that several distortionary sector-level effects of increasing regulatory complexity are taking place. Markedly lower labor intensity and decreased sector-level investment rates, which confirm that expansive regulatory complexity entails non-trivial sector-level costs. Distortionary effects of regulatory complexity materialize through compositional differences mainly through reduced wages and investment rate.
On the second front, using data on employment by firms’ characteristics, we show that the negative impact of regulatory complexity concentrates on smaller and younger firms. This finding supports the hypothesis that a more complex regulation imposes a burden than small and less experienced firms are less capable to handle. At the sector level, the group of sectors more negatively affected is manufacturing. This may be related to the higher investment required by these sectors.
JEL CODES: K2; R11; J00; E02.
Application of Artificial Neural Network in Daily Prediction of Bleeding in ICU Patients Treated With Anti-Thrombotic Therapy
Authors: Chen Daonan , Wang Rui , Jiang Yihan , Xing Zijian , Liu Xiaoqing , Zhao Lina , Xie Hui 
Published: April 2023

Abstract
Objectives Anti-thrombotic therapy is the basis of thrombosis prevention and treatment. Bleeding is the main adverse event of anti-thrombosis. Existing laboratory indicators cannot accurately reflect the real-time coagulation function. It is necessary to develop tools to dynamically evaluate the risk and benefits of anti-thrombosis to prescribe accurate anti-thrombotic therapy.Methods The prediction model,daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy, was built using deep learning algorithm recurrent neural networks, and the model results and performance were compared with clinicians.Results There was no significant statistical discrepancy in the baseline. ROC curves of the four models in the validation and test set were drawn, respectively. One-layer GRU of the validation set had a larger AUC (0.9462; 95%CI, 0.9147–0.9778). Analysis was conducted in the test set, and the ROC curve showed the superiority of two layers LSTM over one-layer GRU, while the former AUC was 0.8391(95%CI, 0.7786–0.8997). One-layer GRU in the test set possessed a better specificity (sensitivity 0.5942; specificity 0.9300). The Fleiss’ k of junior clinicians, senior clinicians, and machine learning classifiers is 0.0984, 0.4562, and 0.8012, respectively.Conclusions Recurrent neural networks were first applied for daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy. Deep learning classifiers are more reliable and consistent than human classifiers. The machine learning classifier suggested strong reliability. The deep learning algorithm significantly outperformed human classifiers in prediction time.
Identification of common factors among Fibrosarcoma, Rhabdomyosarcoma, and Osteosarcoma by network analysis
Authors: Radak Mehran , Ghamari Nakisa , Fallahi Hossein 
Published: April 2023

Abstract
Sarcoma cancers are uncommon malignant tumors, and there are many subgroups, including fibrosarcoma (FS), which mainly affects middle-aged and older adults in deep soft tissues. Rhabdomyosarcoma (RMS), on the other hand, is the most common soft-tissue sarcoma in children and is located in the head and neck area. Osteosarcomas (OS) is the predominant form of primary bone cancer among young adults, primarily resulting from sporadically random mutations. This frequently results in the dissemination of cancer cells to the lungs, commonly known as metastasis. Mesodermal cells are the origin of sarcoma cancers. In this study, a rather radical approach has been applied. Instead of comparing homogenous cancer types, we focus on three main subtypes of sarcoma: fibrosarcoma, rhabdomyosarcoma, and osteosarcoma, and compare their gene expression with normal cell groups to identify the differentially expressed genes (DEGs). Next, by applying protein-protein interaction (PPI) network analysis, we determine the hub genes and crucial factors, such as transcription factors (TFs), affected by these types of cancer. Our findings indicate a modification in a range of pathways associated with cell cycle, extracellular matrix, and DNA repair in these three malignancies.
Results showed that fibrosarcoma (FS), rhabdomyosarcoma (RMS), and osteosarcoma (OS) had 653, 1270, and 2823 down-regulated genes (DEGs), respectively. Interestingly, there were 24 DEGs common to all three types. Network analysis showed that the fibrosarcoma (FS) network had two sub-networks identified in FS that contributed to the catabolic process of collagen via the G-protein coupled receptor signaling pathway. The rhabdomyosarcoma (RMS) network included nine sub-networks associated with cell division, extracellular matrix organization, mRNA splicing via spliceosome, and others. The osteosarcoma (OS) network has 13 sub-networks, including mRNA splicing, sister chromatid cohesion, DNA repair, etc. In conclusion, the common DEGs identified in this study have been shown to play significant and multiple roles in various other cancers based on the literature review, indicating their significance.
GNSS data analysis and updated geodetic velocity field in Greece
Authors: Bitharis Stylianos , Pikridas Christos , Fotiou Aristeidis , Rossikopoulos Dimitrios 
Published: April 2023

Abstract
In this study an updated crustal velocity field for the Greece area was estimated using a time series analysis that covers a duration of 16 years from 227 Global Navigation Satellite System (GNSS) permanent stations. The GNSS processing was carried out using GNSS Analysis software at MIT (GAMIT) and the velocity field expressed with respect to Eurasian plate. For the time-series analysis, we applied a robust Median Interannual Difference Adjusted for Skewness (MIDAS) trend estimator to mitigate the effects of discontinuities due to geophysical phenomena on the estimation of geodetic velocities and their uncertainties. We also compare our geodetic velocities with five previous publications, where we found consistency at the mm/year level, leading to reliable results for the geodynamic behavior of the Greek area, providing a dense velocity field.
Coupling a void growth model with an elastoplastic material model for a simultaneous prediction of the borehole plastic zone and critical collapse pressure
Authors: Oyedokun Femi 
Published: April 2023

Abstract
The elastoplastic shear stability of highly inclined boreholes is explored in this study. First, the stress concentrations in the elastic and plastic zones around a highly inclined borehole are derived. Unlike other previously published elastoplastic wellbore stability solutions, this work considered the contributions of the anti-plane shear stresses. Furthermore, a new method for estimating the collapse pressure and extent of the plastic zone around the borehole is presented. In this approach an elastoplastic material model is coupled with a void-growth model to simultaneously predict the collapse pressure and plastic zone. The predicted collapse mud weights from the proposed approach compared very well with observed field data. The pure shear strength of the formation is a key material property that determines the contributions of the anti-plane shear stress in the stability of the borehole. If the pure shear strength is disregarded, as is the case with other previously published solutions, the predicted collapse pressure will be overly optimistic, underestimating the collapse pressure, if the pure shear strength of the rock is not negligible compared to the loading conditions. After conducting several sensitivity studies, the elastic brittle-plastic model performs better in predicting the shear stability of boreholes than the elastic perfectly-plastic model.
Building use-inspired species distribution models: using multiple data types to examine and improve model performance
Authors: Braun Camrin D. , Arostegui Martin C. , Farchadi Nima , Alexander Michael , Afonso Pedro , Allyn Andrew , Bograd Steven J. , Brodie Stephanie , Crear Daniel P. , Culhane Emmett F. , Curtis Tobey H. , Hazen Elliott L. , Kerney Alex , Lezama-Ochoa Nerea , Mills Katherine E. , Pugh Dylan , Queiroz Nuno , Scott James D. , Skomal Gregory B. , Sims David W. , Thorrold Simon R. , Welch Heather , Young-Morse Riley , Lewison Rebecca 
Published: April 2023

Abstract

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions
than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.
Enantio- and Diastereoenriched Enzymatic Synthesis of 1,2,3-Polysubstituted Cyclopropanes from (Z/E)-Trisubstituted Enol Acetates
Authors: Mao Runze , Wackelin Daniel J. , Jamieson Cooper S. , Rogge Torben , Gao Shilong , Das Anuvab , Taylor Doris Mia , Houk K. N. , Arnold Frances H. 
Published: April 2023

Abstract

In nature and synthetic chemistry, stereoselective [2+1] cyclopropanation is the most prevalent strategy for the synthesis of chiral cyclopropanes, a class of key pharmacophores in pharmaceuticals and bioactive natural products. One of the most extensively studied reactions in the organic chemist’s arsenal, stereoselective [2+1] cyclopropanation, largely relies on the use of stereodefined olefins, which require elaborate laboratory synthesis or tedious separation to ensure high stereoselectivity. Here we report engineered hemoproteins derived from a bacterial cytochrome P450 that catalyze the synthesis of chiral 1,2,3-polysubstituted cyclopropanes, regardless of the stereopurity of the olefin substrates used. Cytochrome P450BM3 variant IC-G3 exclusively converts (Z)-enol acetates to enantio- and diastereoenriched cyclopropanes and in our model reaction delivers a leftover (E)-enol acetate with 98% stereopurity, using whole Escherichia coli cells. IC-G3 was further engineered with a single mutation to enable the biotransformation of (E)-enol acetates to α-branched ketones with high levels of enantioselectivity while simultaneously catalyzing the cyclopropanation of (Z)-enol acetates with excellent activities and selectivities. We conducted docking studies and molecular dynamics simulations to understand how active-site residues distinguish between the substrate isomers and enable the enzyme to perform these distinct transformations with such high selectivities. Computational studies suggest the observed enantio- and diastereoselectivities are achieved through a stepwise pathway. These biotransformations streamline the synthesis of chiral 1,2,3-polysubstituted cyclopropanes from readily available mixtures of (Z/E)-olefins, adding a new dimension to classical cyclopropanation methods.
Efektivitas Pendidikan Gizi Metode Ceramah dan Audio Visual terhadap Pengetahuan dan Sikap tentang Pencegahan Stunting pada Wanita Usia Subur Pranikah
Authors: Hartanti Dwi 
Published: April 2023

The purpose of this study was to determine the effectiveness of the lecture and audio-visual nutrition education methods on knowledge and attitudes related to stunting prevention in women of childbearing age. This research is true experimental design with One Group Pretest Posttest Design. The sample was 77 female students of the Psychology Study Program, Faculty of Psychology and Health, UIN Walisongo, ranging in age from 18-25 years. The intervention was using nutrition education with combination of lecture and audiovisual methods. Knowledge and attitudes were measured using a questionnaire that has been assessed validity and reliability. Statistic Test was using Paired t-test. There was a significant difference between the average scores of knowledge and attitudes of subjects before the intervention (pretest) and after the intervention (posttest). The maverage score of the subject's knowledge before the intervention was 8.97 (59.8% of the questions were answered correctly) and the maverage score of the subject's knowledge after the intervention was 12.57 (83.8% of the questions were answered correctly). The average score of the subject's attitude before the intervention was 43.2% and 81.1% after the intervention. The conclusion is that there is a significant influence on the average differences of knowledge and attitudes score before and after the intervention. Penelitian ini bertujuan untuk mengetahui efektivitas pendidikan gizi metode ceramah dan audio visual terhadap pengetahuan dan sikap terkait pencegahan stunting pada wanita usia subur. Penelitian dengan desain true experimental dengan rancangan one group pretes-postest design. Sampel adalah mahasiswi perempuan Prodi Psikologi Fakultas Psikologi dan Kesehatan UIN Walisongo sebanyak 77 subyek dengan rentang usia 18-25 tahun. Intervensi berupa pendidikan gizi dengan metode ceramah dan audiovisual. Pengetahuan dan sikap diukur menggunakan kuesioner yang telah diuji validitas dan reliabilitas. Uji beda dilakukan menggunakan Uji paired t-test. Hasil statistik menunjukkan bahwa ada perbedaan yang signifikan antara rerata skor pengetahuan dan sikap subjek sebelum intervensi (pretest) dan setelah intervensi (posttest). Rerata skor pengetahuan subjek sebelum intervensi adalah 8,97 (59,8% pertanyaan dijawab benar) dan rerata skor pengetahuan subjek setelah intervensi adalah 12,57 (83,8% pertanyaan dijawab benar). Rerata skor sikap subjek sebelum intervensi adalah 49,2% dan 81,1% setelah dilakukan intervensi. Simpulan penelitian ini adalah terdapat pengaruh yang bermakna terhadap perbedaan rerata skor pengetahuan dan sikap sebelum dan setelah intervensi.
Snack Bar Suweg (Amorphophallus campanulatus B) dan Kacang Merah (Phaseolus vulgaris L) sebagai Camilan Sumber Serat Pangan
Authors: Arzaqina Aulia Adha , Ilmi Ibnu Malkan Bakhrul , Nasrullah Nanang 
Published: April 2023

This study aims to utilize local food products and determine the nutritional content, dietary fiber content, and sensory acceptance of snack bars made of elephant foot yam flour and red bean flour. In order to obtain the best formulation, the Completely Randomized Design (CRD) method with three different formulations was used. Determination of the best formulation was carried out by organoleptic test on 30 nutrition students and analyzed by Kruskal Wallis test followed by Mann-Whitney test. The organoleptic test results showed that the variations in the mixture of both elephant foot yam and red bean had a significant effect (p 0.05) on the taste level of snack bars’ texture. The snack bar F2 (50:50) was the best formulation with the highest level of preference, which contains 22.79% water, 2.93% ash, 10.66% protein, 11.42% fat, 52.21% carbohydrate, and 17.73% dietary fiber. The dietary fiber content can meet 59% of the adequacy of dietary fiber for the general public, so that it can be used as an alternative snack source of dietary fiber, which is good for people with diabetes mellitus and people who want to control their weight. Penelitian ini bertujuan untuk memanfaatkan produk pangan lokal dan mengetahui kandungan gizi, kadar serat pangan, serta daya terima konsumen terhadap snack bar tepung suweg dan tepung kacang merah. Penelitian ini menggunakan metodologi penelitian eksperimental dengan menggunakan metode Rancangan Acak Lengkap (RAL) dengan tiga perlakuan untuk mendapatkan formulasi terbaik. Penentuan formulasi terbaik dilakukan dengan uji organoleptik terhadap 30 orang mahasiswa gizi dan dianalisis dengan uji Kruskal Wallis dilanjutkan dengan uji Mann-Whitney. Hasil uji organoleptik menunjukkan variasi campuran suweg dan kacang merah berpengaruh nyata (p 0,05) terhadap tingkat kesukaan tekstur snack bar. Snack bar F2 (50:50) merupakan formulasi terbaik yang mempunyai tingkat kesukaan tertinggi, yang mengandung 22,79% air, 2,93% abu, 10,66% protein, 11,42% lemak, 52,21% karbohidrat, dan 17,73% serat pangan. Kandungan serat pangan tersebut dapat memenuhi 59% kecukupan serat pangan masyarakat umum, sehingga dapat dijadikan alternatif camilan sumber serat pangan, yang baik bagi penderita diabetes melitus dan orang yang ingin mengontrol berat badannya.
Hubungan Body Image dan Pengetahuan Gizi dengan Perilaku Makan Mahasiswa pada Masa Pandemi Covid-19 di Stikes Mitra Keluarga
Authors: Yusniar Ayu Sri , Noerfitri Noerfitri 
Published: April 2023

This study aims to determine the relationship between body image and nutritional knowledge with the eating behavior of students during the COVID-19 pandemic at STIKes Mitra Keluarga. This study used a cross-sectional design. The sampling technique used was a total sampling, with sampels as many as 306 students at STIKes Mitra Keluarga. Data were collected by giving questionnaires to sampels which included body image, nutritional knowledge, and eating behavior. There were 54.4% of students with negative body image have bad eating behavior and 41.9% of students with positive body image have bad eating behavior. Total of 141 students (51.5%) with poor nutritional knowledge had bad eating behaviors and as many as 10 students (31.3%) with good nutritional knowledge had bad eating behaviors. The statistical test used was the multiple logistic regression test. The results showed that the p-value for each research variable body image with eating behavior (0.032) and nutritional knowledge with eating behavior (0.033). The conclusion is there was a significant relationship between body image and nutritional knowledge with the eating behavior of students. Penelitian ini bertujuan untuk mengetahui hubungan body image dan pengetahuan gizi dengan perilaku makan mahasiswa pada masa pandemi COVID-19 di STIKes Mitra Keluarga. Penelitian ini menggunakan cross-sectional design. Teknik sampling yang digunakan adalah teknik sampling total, dengan sampel sebanyak 306 mahasiswa di STIKes Mitra Keluarga. Pengambilan data dilakukan dengan memberikan kuesioner pada sampel yang meliputi body image, pengetahuan gizi, dan perilaku makan. Terdapat 54,4% mahasiswa dengan body image negatif memiliki perilaku makan buruk dan 41,9% mahasiswa dengan body image positif memiliki perilaku makan buruk. Sebanyak 141 mahasiswa (51,5%) dengan pengetahuan gizi kurang memiliki perilaku makan buruk dan sebanyak 10 mahasiwa (31,3%) dengan pengetahuan gizi baik memiliki perilaku makan buruk. Uji statistik yang digunakan adalah uji regresi logistik berganda. Hasil penelitan menunjukkan bahwa nilai p-value pada masing-masing variabel penelitian yaitu body image dengan perilaku makan (0,032) dan pengetahuan gizi dengan perilaku makan (0,033). Kesimpulannya yaitu terdapat hubungan yang signifikan antara body image dan pengetahuan gizi dengan perilaku makan mahasiswa.
Calibration of hydrological models for ungauged catchments by automatic clustering using a differential evolution algorithm: The Gorganrood river basin case study
Authors: Alizadeh Zahra , Yazdi Jafar 
Published: April 2023

Abstract
The hydrological model calibration is a challenging task, especially in ungauged catchments. The regionalization calibration methods can be used to estimate the parameters of the model in ungauged sub-catchments. In this article, the model of ungauged sub-catchments is calibrated by a regionalization approach based on automatic clustering. Under the clustering procedure, gauged and ungauged sub-catchments are grouped based on their physical characteristics and similarity. The optimal number of clusters is determined using an automatic differential evolution algorithm-based clustering. Considering obtained five clusters, the value of the silhouette measure is equal to 0.56, which is an acceptable value for goodness of clustering. The calibration process is conducted according to minimizing errors in simulated peak flow and total flow volume. The Storm Water Management Model is applied to calibrate a set of 53 sub-catchments in the Gorganrood river basin. Comparing graphically and statistically simulated and observed runoff values and also calculating the value of the silhouette coefficient demonstrate that the proposed methodology is a promising approach for hydrological model calibration in ungauged catchments.