bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leptin receptor gene polymorphisms in patients with post-transplant diabetes mellitus
Authors: Dziedziejko Violetta , Safranow Krzysztof , Kijko-Nowak Mirosława , Malinowski Damian , Pawlik Andrzej 
Published: February 2023

Abstract
Post-transplant diabetes mellitus (PTDM) is a metabolic complication that often occurs after kidney transplantation. Factors that increase the risk of this complication are currently being researched, including polymorphisms in genes affecting carbohydrate-lipid metabolism. Leptin is a hormone that affects appetite and adipose tissue and plays an important role in regulating insulin secretion as well as glucose and lipid metabolism. The aim of this study was to examine the association between leptin receptor gene polymorphisms and the development of post-transplant diabetes mellitus. The study was carried out in a group of 201 patients who underwent kidney transplantation. The follow-up period was 12 months. PTDM was diagnosed in 35 patients. There were no statistically significant differences in the distribution of the LEPR rs1137100 and LEPR rs1805094 polymorphisms between patients with and without PTDM. Analysing the LEPR gene rs1137101 polymorphism, we observed in patients with PTDM an increased frequency of GG allele carriers (GG vs AA; OR 3.36; 95% CI (0.99–11.46), p = 0.044). Multivariate regression analysis confirmed that female sex, advanced age, increased BMI and a higher number of LEPR rs1137101 G alleles were independent risk factors for PTDM development. The risk of PTDM development was almost 3.5 times greater in LEPR rs1137101 G allele carriers than in AA homozygotes (GG + AG vs AA; OR 3.48; 95%CI (1.09–11.18), p = 0.035). The results suggest that patients after kidney transplantation with the LEPR gene rs1137101 G allele have an increased risk of post-transplant diabetes development.
Influence of dust extreme and mud adhesion on the power networks over southwest Iran: case study Ahvaz metropolitan
Authors: Nasry Maede , Rahimi Mohammad , Zalzadeh Saeed 
Published: February 2023

Abstract

Changes in climate have a significant effect on dust extremes. Occurring dust extremes in humid ambient air simultaneously or successively can form wet mud deposition on the surface of urban areas. The mud deposition on the power network systems devices causes irreversible damage to the devices and significantly influences system performance and efficiency in southwest Iran. This often resulted in blackouts that have caused problems in the operation of urban infrastructure and people's daily activities for up to several days. In this study, the climatic conditions and risk assessment of mud formation were investigated in Ahvaz. Data on a diurnal and monthly timescale of dust and humidity relative used for assessing extreme dust and wet conditions and were taken from Ahvaz synoptic station over 11 years (2009-2019). The multivariate copula-based framework is used to calculate univariate and bivariate return periods of mud deposition hazard. The results imply that dust anomalies increase the probability of dust extreme coincidence with wet extreme and occurrence of wet mud hazards in the cold seasons of the year. In addition, limited adaptive capacity, shortage of information, and poor stakeholder coordination and cooperation caused the large-scale impact of wet mud hazard in Ahvaz. By considering only relative humidity data, the return period of the 2017 Ahvaz mud adhesion hazard, is approximately 12 to 43 years. If we consider only dust the return period of the 2017 Ahvaz mud adhesion hazard is estimated at 80 to 700 years. However, for both dust and relative humidity extreme, the joint return periods for TDR (Dust and Relative humidity) and T'DR (Dust or Relative humidity) are Respectively estimated greater than 200 and lower than 20 years.
Data Augmentation and Transfer Learning for Cross-lingual Named Entity Recognition in the Biomedical Domain
Authors: Lancheros Brayan Stiven , Corpas-Pastor Gloria , Mitkov Ruslan 
Published: February 2023

Abstract

Given the increase in production of data for the biomedical field and the unstoppable growth of the internet, the need for Information Extraction (IE) techniques has skyrocketed. Named Entity Recognition (NER) is one of such IE tasks useful for professionals in different areas. There are several settings where biomedical NER is needed, for instance, extraction and analysis of biomedical literature, relation extraction, organisation of biomedical documents, and knowledge-base completion. However, the computational treatment of entities in the biomedical domain has faced a number of challenges including its high cost of annotation, ambiguity, and lack of biomedical NER datasets in languages other than English. These difficulties have hampered data development, affecting both the domain itself and its multilingual coverage. The purpose of this study is to overcome the scarcity of biomedical data for NER in Spanish, for which only two datasets exist, by developing a robust bilingual NER model. Inspired by back-translation, this paper leverages the progress in Neural Machine Translation (NMT) to create a synthetic version of the CRAFT (Colorado Richly Annotated Full-Text) dataset in Spanish. Additionally, a new CRAFT dataset is constructed by replacing 20% of the entities in the original dataset generating a new augmented dataset. Further, we evaluate two training methods: concatenation of datasets and continuous training to assess the transfer learning capabilities of transformers using the newly obtained datasets. The best performing NER system in the development set achieved an F-1 score of 86.39%. The novel methodology proposed in this paper presents the first bilingual NER system and it has the potential to improve applications across under-resourced languages.
Comparative toxicity study of aqueous, ethanol and methanol leaf extracts of Simarouba glauca on hematological indices in adult normotensive male Wistar rats
Authors: Osagie-Eweka SammyDavies E. , Orhue Noghayin E.J. , Moke Emuesiri G. , Omogbai Eric K.I. 
Published: February 2023

Abstract
Background
The study focused on the toxicological evaluation of leaf extracts of S. glauca on some hematological indices of male Wistar rats.
Methods
Thirty (30) male Wistar rats were divided into ten groups of three rats each. Test rats were given AESG, EESG or MESG at doses of 500, 1000 and 2000 mg/kg body weight respectively; the control group was provided with food and water ad libitum daily for thirty (30) days. At the end of the study, the fasted rats were sacrificed and haematological assessment was conducted.
Results
The data obtained indicates elevated (P ˂ 0.05) RBC levels of experimental rat administered EESG and MESG at 500, 1000 and 2000 mg respectively. Reduced (P ˂ 0.05) haemoglobin concentration at AESG and MESG 500 mg, AESG, EESG and MESG 1000 mg; EESG 2000 mg. The haematocrit/PCV levels where only slightly reduced (P ˂ 0.05) and elevated (P ˂ 0.05) at EESG 1000 and 2000 mg respectively. Platelets count was elevated (P ˂ 0.05) at MESG 500 mg, lowered at AESG 1000 mg and EESG 2000 mg. The WBC count was elevated (P ˂ 0.05) at AESG and EESG 1000 mg; elevated (P ˂ 0.05) at EESG and MESG 2000 mg. The lymphocytes count was elevated (P ˂ 0.05) at EESG 500, 1000 & 2000 mg; at AESG 1000 mg. The monocytes count was elevated (P ˂ 0.05) at AESG 500, 1000 & 2000 mg, elevated (P ˂ 0.05) AESG and EESG 1000 & 2000 mg respectively, and elevated (P ˂ 0.05) at MESG 2000 mg.
Conclusion
Oral administration of leaf extracts of S. glauca, especially at higher doses elicit disturbances in haematological indices.
Psychometric Properties of Maslach Burnout Inventory-EFL (MBI-EFL) among Foreign Language Students of Pakistan
Authors: Tian Mao , Mohsin Muhammad , Ashraf Rabbia 
Published: February 2023

Abstract

Students participate in and manage structured activities like attending classes, doing projects by the deadline, and taking tests. The goal-oriented nature of these activities might make students anxious, stressed, and exhausted, demanding the use of coping mechanisms on their behalf to maintain their learning thus, experiencing academic burnout. Particularly in Pakistan, where students' admittance to institutions or prospects are determined by intensely competitive, high-stakes exams This study was intended to explore the psychometric properties of the modified 10- item Maslach Burnout Inventory-EFL Student survey among (N = 1750) foreign language students of Pakistan. A cross-sectional validation survey design was used in the current study. A modified version of the Maslach Burnout Inventory-Student Survey (MBI-SS) was designed and assessed in 1718 Pakistani students in China. Based on the findings of several reliability and validity tests, a final 10-item Maslach Burnout Inventory-EFL Student Survey was created to meet the context of learning English as a foreign language (EFL). CFA extracted three factors comprising Reduced Efficacy, Cynicism, and Exhaustion, demonstrating the MBI-original SS's tri-factorial structure's invariance. The Cronbach's alphas for Exhaustion, Cynicism, and Reduced Efficacy were 0.78, 0.82, and 0.86, respectively, demonstrating good internal consistency reliability for the subscales. Pearson coefficients between 10 items and the scale varied from 0.55–0.81. An acceptable model fit (χ2 = 345.23, RMSEA = 0.05, TLI = 0.92, GFI = 0.93, SRMR = 0.03) was achieved. Thus, results demonstrate adequate validity and reliability of MBI-EFL student survey for measuring burnout among English as a foreign language student.
Approximation algorithms for covering vertices by long paths
Authors: Gong Mingyang , Edgar Brett , Fan Jing , Lin Guohui , Miyano Eiji 
Published: February 2023

Abstract

Given a graph, the general problem to cover the maximum number of vertices by a collection of vertex-disjoint long paths seemingly escapes from the literature. A path containing at least k vertices is considered long. When k ≤ 3, the problem is polynomial time solvable; when k is the total number of vertices, the problem reduces to the Hamiltonian path problem, which is NP-complete. For a fixed k ≥ 4, the problem is NP-hard and the best known approximation algorithm for the weighted set packing problem implies a k-approximation algorithm. To the best of our knowledge, there is no approximation algorithm directly designed for the general problem; when k = 4, the problem admits a 4-approximation algorithm which was presented recently. We propose the first (0.4394k +O(1))-approximation algorithm for the general problem and an improved 2-approximation algorithm when k = 4. Both algorithms are based on local improvement, and their theoretical performance analyses are done via amortization and their practical performance is examined through simulation studies.
2012 ACM Subject Classi cation Theory of computation ! Packing and covering problems
Enhanced visual multi-modal fusion framework for dense video captioning
Authors: Zhong Ruizhe , Zhang Qingchuan , Zuo Min 
Published: February 2023

Abstract
Dense video captioning is a machine translation task that aims to localize events from full video and describe them separately. Human observers who parse the video are also impressed by frames containing a high amount of information and duplication, but video often appears unrelated frames in the subject. However, existing works have largely ignored these details. To fully incorporate human visual perception into the process of understanding video, we propose an enhanced visual multi-modal fusion framework (Evmff), which utilizes the captions of video keyframes to improve dense video captioning performance. We first extract video keyframes through time stamps and then apply the recently proposed image captioning method DLCT to obtain a temporally aligned caption of the keyframe. Evmff fuses the textual information of speech, the caption of image keyframes, video features, and audio features, applying transformer architecture to convert the data into text descriptions. The performance of our model is verified using the ActivityNet Captions dataset with four different indicators, Bleu@N, METEOR, Rouge_L, and CIDEr-D. Ablation experiments show that employing the video keyframe description as the input of the multi-modal model compensates for the deficiency in visual information understanding. Our code will be released.
Effects of Savings on Depressive Symptoms among Homeless People in Korea: Using Propensity Score Matching
Authors: Choi Jungtae , Han Chang-Keun , Kang Sion 
Published: February 2023

Abstract

OBJECTIVE:In Korea, the financial crisis that occurred in 1997 increased the homeless population. Since then, mental health issues, particularly depressive symptoms, among the homeless have become a substantial social issue. Prior research documented that asset ownership positively influenced people’s mental health. However, the largely under-researched area is whether asset accumulation has an impact on depressive symptoms along with homeless persons.METHODS:This study investigates the relationship between the saving status and depressive symptoms of homeless people using the 2016 National Survey on Homeless People. To reduce the selection bias, this study employed the propensity score matching method. Further, multivariate logistic regression was performed to determine how the savings level affected depressive symptoms.RESULTS:This study found a negative relationship between the savings level and depressive symptoms after controlling for covariates.CONCLUSION:Our findings suggest that an asset-based policy targeting the homeless may play a crucial role in reducing depressive symptoms and facilitating their self-reliance.
Offline Kurdish Character Handwritten Recognition (OKCHR) Using CNN With Various Preprocessing Techniques
Authors: Majeed Hamssa D. , Nariman Goran Saman 
Published: February 2023

Abstract
Offline optical-character-recognition is considered part of the significant pattern-recognition applications. Handwriting recognition though is an active and challenging area of research in this field. Nowadays, many algorithms are introduced with the aim of achieving the highest accuracy. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step becomes essential for any image identification task. As for the classification phase, some researchers focused their research on the utilization of CNN (Convolutional Neural Networks). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this study suggests that the preprocessing mechanism is also another important factor that should be considered in order to increase the accuracy of classification. This study utilized seven stages for preprocessing mechanisms applied to the selected dataset and the measurement of the accuracy as the result of the utilization of the CNN for the classification was acquired. The study exposed that the utilization of using preprocessing steps indicated the most heightened accuracy with Barbarized-KDS as an input achieving training, testing, and validation accuracy of 0.992, 0.97, and 0.972 respectively after only 35 iterations. Furthermore, another significant finding is that using different steps in the processing of input images to trian the recognition model affects the recognition-rate within the same classifiers. Besides, the outcome reveals that each technique applied with the specific classifier may require a certain preprocess to obtain its optimal accuracy recognition rate.
Water, climate change and uncertainty in the Great Lakes and Rio Grande/Bravo Regions
Authors: Bassone-Quashie Yena , VanNijnatten Debora , Johns Carolyn 
Published: February 2023

AbstractUncertainty is inherent in transboundary water governance, yet climate change is deepening the uncertainties faced by those who manage shared water resources. This paper identifies and assesses uncertainties in the transboundary water governance context by applying an analytical framework which integrates insights from the uncertainty, adaptive governance, and public policy literatures to analyze policy documents in two complex transboundary cases: the Great Lakes and Rio Grande/Bravo basins. Findings from the analysis indicate that: first, deep uncertainties exist in both cases but the two basins face different combinations of, and interactions between, uncertainties; second, the system of scarcity assessed in this analysis (the Rio Grande/Bravo Basin) indicates more conflict-based uncertainties which aggravate natural and technical system uncertainties; and third, the governance system itself is a significant source of uncertainty, or exacerbates existing uncertainties, in both basins. The case studies reveal that governance systems need to focus on different sources, types and levels of uncertainty, and that policy responses need to be designed to move to a ‘monitor-and-adapt’ governance approach to reflect different uncertainties across systems of abundance and scarcity. An analysis of the preparedness of governance systems to respond and adapt to uncertainties is also needed and highly recommended.
A survey on applications of machine learning algorithms in water quality assessment and water supply and management
Authors: Oğuz Abdulhalık , Ertuğrul Ömer Faruk 
Published: February 2023

AbstractManaging water resources and determining the quality of surface and groundwater is one of the most significant issues fundamental to human and societal well-being. The process of maintaining water quality and managing water resources well involves complications due to human-induced errors. Therefore, applications that facilitate and enhance these processes have gained importance. In recent years, machine learning techniques have been applied successfully in the preservation of water quality and the management and planning of water resources. Water researchers have effectively used these techniques to integrate them into public management systems. In this study, data sources, pre-processing, and machine learning methods used in water research are briefly mentioned, and algorithms are categorized. Then, a general summary of the literature is presented on water quality determination and applications in water resources management. Lastly, the study was detailed using machine learning investigations on two publicly shared datasets.
Applying conditional optimization to determine the allowable loads on flexurally rigid threads
Authors: Tarasov D. A. 
Published: February 2023

Objective.Development of a methodology for determining the allowable loads on existing structural elements, the design model of which is a flexural-rigid thread, based on the requirements of strength and rigidity due to the changed operating conditions of structures.Method.This study is based on mathematical modeling of the problem of conditional optimization. The objective function is the total potential energy of deformation of a thread with some flexural rigidity. In this case, restrictions are imposed on strength, rigidity, and the condition of continuity of deformations. At the same time, to achieve this goal, the provisions of the strength of materials, as well as the apparatus of integral and differential calculation of functions of one or more variables, were used.Result.An assessment of the adequacy of the results obtained using the developed methodology was carried out. A numerical experiment was performed to determine the value of the allowable uniformly distributed load and the corresponding distribution of stresses and strains along the length of a thread of finite stiffness with given physical and geometric parameters. It has been established that the discrepancies in the values of the results obtained using the proposed modeling technology and the commercial computer-aided design and calculation system LIRA 10.8, implemented on the generally recognized finite element method, are no more than 5%.Conclusion.The developed technique allows setting and solving problems of determining the ultimate loads on flexural-rigid threads, based on the allowable stresses and strains, in contrast to existing commercial computer simulation systems that solve exclusively direct problems of determining the stress-strain state of various types of structures. It can be used at the stage of inspection of large-span coatings in the reconstruction of public buildings and engineering structures.
Protection against attacks using social engineering tools and methods
Authors: Repenko V. A. , Reznichenko S. A. 
Published: February 2023

Objective. In the modern world, innovative technologies are actively developing, communications are moving into the Internet space. Nevertheless, new technologies turned out to be in demand in the segment of organizations, individuals, cyber fraudsters and hackers. The purpose of the study is to analyze the essence and structure of modern social engineering, including reverse social engineering in the context of the digitalization of society.Method. Adapting the study to the current moment, it is clarified that technologies, developments and manipulative sociological tactics are picked up not just by hackers, but also by followers of radical ideas.Result. The optimal definition of the term "social engineering" is proposed - the identification and exploitation of incompetence, insufficient professional level or negligence of employees of an organization or individuals to obtain unauthorized access to confidential data; a set of technologies based on the use of the psychological specifics of a person.Conclusion. The novelty of the study lies in an in-depth study of the classification of types of attacks using social engineering methods and recommendations for countering and preventing them.