bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hubungan Usia dengan Kejadian Preeklamsia pada Ibu Hamil di RSUD Kabupaten Kediri Tahun 2018
Authors: Ertiana Dwi , Wulan Suci Retno 
Published: August 2024

The objective of this study was to determine the relationship of age with the incidence of preeclampsia in pregnant women in Kediri District Hospital. The study was conducted on April 8 to April 25, 2019 in Kediri District Hospital using instruments in the form of document sheets, namely the patient's medical record. The research method used was an analytical survey with a retrospective cohort approach, with independent variables of age in pregnant women and dependent variables for the incidence of preeclampsia. The total population of 3,096 respondents with a Simple Random Sampling sampling technique obtained as many as 179 respondents. The results showed almost half of the respondents (32.4%) 58 respondents were at risk of age who suffered preeclampsia in pregnancy and a small proportion of respondents (14%) 25 respondents were of non-risk age who suffered preeclampsia in pregnancy. Analysis using Chi Square obtained p value = 0,000 <α 0,05, CC value = 0,376, CI value = 2,962 - 10,718 and OR = 5.6, so Ho was rejected and H1 was accepted means there was a relationship between age and the incidence of preeclampsia in pregnant women in Kediri District Hospital. Mothers with an age at risk of being prone to preeclampsia because of an oxidative increase so that the production of trophoblast apoptosis and necrotic debris also increases, this causes a systemic inflammatory reaction. To prevent the occurrence of preeclampsia in pregnant women can make early detection in pregnancy.
Employability of forestry graduates: evidence from a state university in the Philippines
Authors: Cortado Jay Mark G. 
Published: August 2024

Employability is a measure of the effectiveness of an academic program in the quality of its graduates.
However, insufficient data on employability of forestry graduates are still observed, particularly in northern Philippines. Hence, the study aimed to trace the employment of BS Forestry (BSF) graduates in DMMMSU from 2015 to 2022. The study used descriptive research design to describe the BSF graduates' socio-demographic characteristics, educational background, and initial employment profile. Survey was employed as data collection method, and quantitative techniques were used for analysis. Results revealed that majority of graduates were female and had been awarded college scholarships; some graduated with honours and awards. Majority were licensed foresters, while very few had other professional eligibilities. Most were employed at least once after graduation and were professional contractual employees of companies, mainly in agriculture, hunting, and forestry. Salaries and benefits were the primary reason for acceptance of jobs, while most secured forestry-related jobs within six months after their graduation. The gross monthly income for a great majority of the initial forestry-related jobs reported was less than PhP 15,000.00. Most employed graduates agreed that their program and curriculum were related and relevant to their jobs. However, regular updating of curriculum and instruction is the major suggestion of graduates to improve the program. The likelihood of gaining employment after graduation significantly increases when school support services are provided for career and job placement. Honours and awards in college were seen to be significant factors contributing to the employability of forestry graduates.
ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms
Authors: Diwakar  , Raj Prof. Deepa 
Published: August 2024

Abstract

Cardiovascular diseases (CVDs), including abnormal arrhythmias and congestive heart failure, are a leading cause of mortality worldwide, with electrocardiogram (ECG) signals serving as a critical diagnostic tool. This study introduces a novel approach for classifying diseases in ECG signals into three categories: normal sinus rhythm (NSR), abnormal arrhythmia (ARR), and congestive heart failure (CHF). The classification is based on a combination of features extracted from both the time domain (mean and standard deviation) and the frequency domain (power spectral density and spectral centroid) of the ECG signals. Additionally, energy values from selected frequency bands are utilized. To enhance the model's robustness, incorporate data augmentation techniques, including time-shifting and flipping of the signals. These augmented datasets are then employed with various classifiers, and an optimization process using grid search is applied to enhance the classification performance. This methodology presents a promising framework for automated ECG signal analysis, The results of the proposed work have been exceptionally promising, showcasing a remarkable specificity rate of 99.7% and achieving an accuracy level of 99.58%. These findings hold significant promise for advancing early detection methods and enhancing patient outcomes in the realm of CVDs.
Targeted genotyping (90K-SPET) facilitates genome-wide association studies and the prediction of yield-related traits in faba bean (Vicia faba L.)
Authors: Lippolis Antonio , Gezan Salvador A. , Zuidgeest Jorrit , Cafaro Valeria , Dinter Bert-Jan , Elzes Geert , Paulo Maria-João , Trindade Luisa M. 
Published: August 2024

Abstract

Background
Establishing faba bean (Vicia faba L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These traits can be improved by exploiting the extensive but largely uncharacterized faba bean germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per pod (SP), and single seed weight (SSW) in 245 faba bean accessions (CGN population) via a Genome-Wide Association Study (GWAS). The accessions were grown in 2021 and 2022 in the Netherlands. Additionally, we developed genomic selection (GS) models to predict the genetic merit within large germplasm collections.
Results
The CGN population was an optimal panel for performing high-resolution GWAS, showing large phenotypic variation, high narrow-sense heritability for all traits, and minimal genetic relatedness among accessions. Population structure analysis revealed the presence of four genetic groups. GWAS uncovered 33 SNP-trait associations in 2021 and 17 in 2022. We identified one stable QTL for FT and four for SSW over the two years, representing key molecular markers for testing in breeding applications. Short linkage disequilibrium decay (~268 Kbp) facilitated the identification of several important candidate genes with interesting homologs in other crops. Ten SNPs in 2021 and five in 2022 were predicted to be intra-genic missense variants, potentially altering protein function. Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. Predictive ability ranged from 0.58 to 0.81 in 2021, and 0.47 to 0.85 in 2022 for different traits. Additionally, across-year predictions showed stable PA.
Conclusion
GWAS revealed promising QTLs for use in molecular breeding and highlighted new candidate genes. Interestingly, the prediction of intra-genic SNPs categorized 15 SNPs as putatively affecting protein function. Moreover, we demonstrated for the first time in faba bean that GS has the potential to unlock untapped diversity in genebank collections and accelerate trait integration into faba bean breeding programs.
Diversity-enhanced Conversational Recommendation via Multi-agent Reinforcement Learning
Authors: Wang Zihan , Feng Shi , Wang Daling , Song Kaisong , Wu Gang , Zhang Yifei , Zhao Han , Yu Ge 
Published: August 2024

Abstract

Multi-round Conversational Recommendation (MRCR) system assists users in finding the items they need with the fewest dialogue rounds by inquiring about desired features or making tailored recommendations. Numerous models employ single-agent Reinforcement Learning (RL) to accomplish MRCR and improve recommendation accuracy. However, they overlook the diversity of conversational recommendations and primarily focus on popular features or items. It impacts the fair visibility of the items and results in an unbalanced user experience. We propose a diversity-enhanced conversational recommendation model (DECREC), which is built on our proposed multi-agent RL framework. Three agents col-laboratively determine the actions at each round of the MRCR and each agent autonomously explores and learns distinct facets of the task. Compared to a single agent, their collaboration fosters the exploration of a more extensive array of actions to improve diversity. Furthermore, we introduce a dynamic experience replay method that balances long-tail and head data ensuring each learning batch includes long-tail samples, keeping the model attentive to these less common but important data. Moreover, we integrate feature entropy into the feature value estimation process during training to encourage the model to explore a broader spectrum of features, thereby indirectly enhancing the diversity of recommendation results. Extensive experiments on four public datasets demonstrate that DECREC reduces bias in MRCR and achieves optimal recommendation diversity and accuracy. Our code is available at https://github.com/wzhwzhwzh0921/ DECREC.
Antifungal activity of essential oils in Colletotrichum lindemuthianum and alternative control of bean anthracnose
Authors: Hoyos Julián Mauricio Agredo , Dorigan Adriano Francis , Silveira Patrícia Ricardino da , Labory Claudia Regina Gontijo , Júnior Pedro Martins Ribeiro , Fernandes Rafael , Alves Eduardo 
Published: August 2024

Abstract

Anthracnose, caused by Colletotrichum lindemuthianum, is the major disease of the common bean and causes serious economic losses. In the present study, we evaluated: (i) the antifungal activity of 26 essential oils (EOs) in the germination and mycelial growth of pathogen races 65 and 73, (ii) ultra structural changes of Cymbopogon citratus, Eugenia caryophyllata and Cymbopogon martini EOs in fungal conidia, (iii) the effect of these EOs on anthracnose control, applied before and after inoculation, and (iv) the effect of C. citratus EOs on the activity of defense enzymes on bean plants. Among the 26 EOs, C. citratus, C. martini, Cinnamomum sp., T. vulgaris, E. caryophyllata and M. recutita inhibited 95% conidial germination of fungal strains in concentrations lower than 0.1%. Mycelial growth was completely inhibited by C. citratus, E. caryophyllata and C. martini at 0.1% for 65 and 73 races. There was little mycelial growth in the fungicide treatment 4 DAI. Transmission electron microscopy (TEM) ultrastructure showed that C. citratus, C. martini and E. caryophyllata had direct antifungal action against C. lindemuthianum. These EOs caused serious ultrastructural damage to conidia, such as vacuolization, cytoplasm leakage and invagination of plasma membrane. Cymbopogon citratus and E. caryophyllata reduced anthracnose severity in both preventive and curative experiments. The activity of plant defense enzymes increased in the treatment with C. citratus. Essential oils show potential for the management of bean crop disease, especially anthracnose caused by C. lindemuthianum.
Frontiers and hotspots in induction chemotherapy of oral squamous cell carcinoma: a bibliometric and visualized study from 2000 to 2022 by Bioinformatics and Cancer Database Analysis
Authors: Xu Wei , Wu Yifan , Lu Hao , Xu Wanlin , Liu Shengwen , Yang Wenjun 
Published: August 2024

Abstract

Objectives: Oral squamous cell carcinoma (OSCC) is a prevalent malignant tumor worldwide, posing a significant threat to patient survival. Induction chemotherapy (IC) is an established therapeutic approach for advanced, unresectable OSCC, proven effective in controlling tumor progression and preventing distant metastasis. Despite its clinical significance, there is a lack of bibliometric studies in this research domain. This article aims to address this gap by conducting a comprehensive bibliometric analysis, which will shed light on the key areas of focus and provide insights into the research landscape of OSCC induction chemotherapy.
Methods: The relevant articles were identified using the Web of Science Core Collection (WoSCC). To examine the co-occurrence relationships across countries/regions, institutes, authors (cited authors), journals (cited journals), references, and keywords in this research field, bibliometric analysis was conducted with the assistance of two software tools, VOSviewer and CiteSpace. This approach enables a comprehensive exploration of the frontiers and hotspots within the field.
Results: A comprehensive collection of 227 publications was assembled between 2000 and 2022. Overall, both the number of publications and citations display a steady annual increase, with the peak observed in 2021 and 2022. China and the United States jointly occupy the first rank in terms of total publications (n = 46), with Japan following closely behind (n = 32). Among the academic institutions, Shanghai Jiao Tong University has made the most substantial contribution, with 26 publications. In the realm of authors, Zhong Laiping secures the top position in the list of the top 10 authors (n = 13), whereas Posner MR and Vermorken JB are equally distinguished as the most co-cited authors (n = 104). The research field's most prolific journal is Oral Oncology, which boasts the highest number of publications (n = 24), while the Journal of Clinical Oncology claims the greatest number of co-citations. Conducting a co-occurrence and cluster analysis on the references cited, it was discovered that the recent research focus has shifted from radiochemotherapy and oral cancer to paclitaxel. Keyword analysis unveils that current research on induction chemotherapy for OSCC underscores the significance of chemotherapeutic drugs such as cisplatin, docetaxel, and fluorouracil.
Conclusion: Despite the relatively modest number of publications, research on induction chemotherapy for OSCC is steadily growing. Currently, the primary focus of investigation revolves around drug selection for this therapeutic approach. This article provides an overview of the prevailing trends in this field and identifies potential directions and hotspots for future research.
Generalized Similarity Distance based Canonical CorrelationAnalysis (GSDCCA) for Multiview Data Representation
Authors: Gupta Surendra 
Published: August 2024

Abstract

Data representation has a significant impact on how well the standard multi-viewmachine learning algorithms perform. Existing data representation methods commonly construct a latent subspace such that transformed projections of sample datafrom multiple views are maximized in the common subspace. Different views aregenerally heterogeneous and may contain either similar or complementary information. To be able to remove common information from multiple views such that thedata contains unique information, an extension of the Canonical Correlation Analysis is presented in this paper as the Generalized Similarity Distance based CanonicalCorrelation Analysis (GSDCCA). The proposed approach exploits complementaryand coherent information between the views and intrinsic structural informationwithin the view to build a comprehensive latent space for multi-view machine learning algorithms. The Bhattacharya similarity distance approach is used to exploit thecomplementary and coherent information between the views to project the sampledata onto a common subspace for correlation analysis. Moreover, local neighbouring information is exploit as to obtain the view that preserves the local structureinformation of sample data while performing global dimensionality reduction. Experiments on real-world datasets and synthetic datasets demonstrate that the GSDCCAapproach can build a vast latent space that consistently captures the complementaryand coherent information between views and intrinsic structure information withinthe view. The performance of the proposed approach was compared to other relatedapproaches, and the results show that it is an effective and promising approachfor real-world applications and superior to other state-of-the-art approaches. It isalso observed that the method is highly suitable where views contain lots of similarinformation.
A new counting method for measuring and evaluating dynamic loads
Authors: Mahler Michael , Mahler Kerstin 
Published: August 2024

Abstract

The evaluation of dynamic loads could be complex but there exists well implemented evaluation and analyses approaches in an industry context such as counting methods and Fourier transformation. Multiple use cases such as in acoustics, electronics, magnetics, mechanics (durability, CAM machines), … exists. Currently, such long-time standards in evaluation and analyses will be well known. However, new requirements will be caused for example by upcoming big data approaches or long-time data evaluation. In addition, often multiple evaluation methods will be required and often alternative methods with different benefits have to be implemented. Thus, place for evaluation is given to reduce multiple methods used parallel or alternatively and to address new requirements. In this paper, the stage of technology will be discussed, an introduction into a new counting approach will be provided and benefits of the new counting method will be presented. The benefits will be summarized to potential of reduced energy consumption (green technology), cost reduction (economy optimization), further and new evaluation potential.
Energy consumption estimation of a local blockchain implementation for an energy community
Authors: Dhorbani Amira , Abbes Dhaker , Stephant Matthieu , Labrunie Antoine , Robyns Benoît , Hassam-Ouari Kahina 
Published: August 2024

Abstract

An innovative approach to optimizing energy exchanges within an energy community is introduced through the implementation of a local blockchain. User privacy is protected, and comprehensive distributed optimization is ensured without the need for third-party intervention. Optimization is conducted using an Alternating Direction Method of Multipliers (ADMM) algorithm. This algorithm operates within the blockchain environment to facilitate consensus among users. Python clients, working within the blockchain, are employed for the local optimization of power profiles. Furthermore, a smart contract is deployed on the blockchain, acting as a critical tool to ensure that global constraints are adhered to. This approach underscores the pivotal role of blockchain in enabling distributed optimization and consensus within an energy community.
Simulations are conducted to compare two distinct consensus mechanisms: Proof of Work and Proof of Authority. The energy consumption of the overall blockchain environment is measured and compared to a centralized situation. Findings indicate that for a limited number of nodes, both Proof of Work and Proof of Authority exhibit similar energy consumption levels. Compared to a centralized solution and specifically, for eight users, energy consumption of the blockchain is found to be 47% more.
The research suggests that for a larger number of nodes, a local blockchain could potentially be more energy-efficient than a centralized solution for optimization. This insight presents a promising direction for the large-scale development of blockchain-based energy exchange mechanisms.
The spillover effects of top-income inequality on innovation
Authors: Antonelli Cristiano , Pialli Guido , Tubiana Matteo 
Published: August 2024

Abstract

The existing literature has widely explored the impact of technological change on income inequality. The reverse relationship – from income inequality to innovation – has received considerably less attention. This paper contributes to fill this gap by advancing and testing the hypothesis that higher levels of top-income inequality enhance innovation and entrepreneurship. Our empirical analysis confirms that top-income inequality increases the formation of new establishments and startups across USA cities over the past two decades. Moreover, higher top-income inequality is associated with a greater proportion of new high-tech and high-growth firms. We support these findings by showing that households in the top-tail of the income distribution keep larger shares of risk assets, exhibit a greater propensity to save, and display higher levels of propensity to risk.
JEL classification: D31, D63, G24, L26, O33.
Evaluation of Accuracy of Three-Dimensional Printing and Three- Dimensional Miniplates in Treatment of Anterior Mandibular Fractures: Clinical Study
Authors: Abouelkhier Mazen , Saber Mohamed Elsayed , Sweedan Ahmed Ossama , Elashwah Adham 
Published: August 2024

Abstract

Background
There are strong torsional forces acting on the anterior mandible fractures. Maxillofacial surgery makes extensive use of digital technology, and three-dimensional printing is now an integral element of the workflow in several areas of oral and maxillofacial surgery. Three-dimensional (3D) miniplates had been proposed by several researchers as a good option for mandibular fracture fixation.
Methods
15 Patients with anterior mandibular fractures were included in the study. All patients undergone computer tomography (CT) scan and the data were imported in Mimics software. The unaffected healthy side was mirrored to the fractured side. Bone fixation three-dimensional plates were prebent and adapted on the model printed by three-dimensional printing machine, submitted to sterilization, and were used for bone reduction and fixation. An immediate postoperative CT scan was taken to evaluate the accuracy of virtual planning and after 3 months for evaluation of bone healing.
Results
Clinical observation revealed good stable occlusion and there was no significant difference between the postoperative three-dimensional image of the mandible and the virtually reduced mandible in the preoperative plan. The Hounsfield units (HU) measured on CT images after 3 months revealed good bone healing.