bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of maternal depression on mothers’ perceptions of the emotional and behavioural problems of their internationally adopted children
Authors: Liskola Krista , Raaska Hanna , Lapinleimu Helena , Lipsanen Jari , Sinkkonen Jari , Elovainio Marko 
Published: March 2021

Abstract
Background: Even though child psychopathology assessment guidelines emphasise comprehensive multi-method, multimodal, and multi-informant methodologies (Pelham, Fabiano, & Massetti, 2005), maternal report symptom-rating scales often serve as the predominant source of information. Research has shown that parental mood symptomatology affects their reports of their offspring’s psychopathology. For example, the depression-distortion hypothesis suggests that maternal depression promotes a negative bias in mothers’ perceptions of their children’s behavioural and emotional problems (Gartstein, Bridgett, Dishion, & Kaufman, 2009). We investigated this difference in perception between adoptive mothers and their internationally adopted children. Most previous studies have suffered from the potential bias caused by the fact that parents and children share genetic risks. Our study design allows the elimination of passive gene-environment correlation, which is a phenomenon that occurs when there is a shared genetic background between a rearing parent and a child (Harold et al., 2011; Jaffee & Price, 2007; Price & Jaffee, 2008).Methods: Data were derived from the Finnish Adoption (FinAdo) survey study (a subsample of adopted children aged between 9 and 12 years; N = 222). The Child Behavior Checklist (CBCL) (Achenbach & Ruffle, 2000) was used to assess the emotional and behavioural problems and competences of the adopted children. The CBCL was completed by the adoptive mothers and the adopted children. Maternal depressive symptoms were measured using the short version of the General Health Questionnaire (Goldberg & Hillier, 1979).Results: On average, mothers reported fewer total CBCL symptoms in their children than the children themselves (0.25 vs 0.38, p-value < 0.01 for difference). Mothers’ depressive symptoms moderated the discrepancy in reporting internalizing symptoms (β = -0.14 and p-value 0.01 for interaction), the total symptoms scores (β = -0.22 and p-value < 0.001 for interaction), and externalizing symptoms in girls in the CBCL.Limitations: The major limitation of our study was its cross-sectional design and the fact that we only collected data in the form of questionnaires.Conclusions: The results of our research support the depression-distortion hypothesis concerning the association between maternal depressive symptoms and child internalizing symptoms and externalizing symptoms in girls in a sample without genetic bias.
The Comparisons of Three Stapler Placement Methods for Intrathoracic Mechanistic Circular Stapling in Ivor Lewis Minimally Invasive Esophagectomy
Authors: Zhang Bo , Wu Zi xiang , Wang Qi , Pan Sai Bo , Wang Lian , Shen Gang , Chai Hui Ping , Wu Ming 
Published: March 2021

Abstract
Objectives: To analyze the impact of the reversal penetrating technique (RPT) for intrathoracic gastroesophageal mechanical anastomosis on the development of anastomotic complications in Ivor Lewis minimally invasive esophagectomy (ILMIE) and further identify the risk factors for the development of anastomotic leakage and stricture.Methods: A retrospective observational study was conducted using clinical data of 316 patients with esophageal carcinoma (EC) who underwent ILMIE from January 2012 to December 2019. The participants were divided into three groups of RPT, transoral Orvil technique (TOT), or purse-string technique (PST) according to the different stapler placenent methods for intrathoracic mechanistic circular stapling. Multivariable analysis was performed to investigate the association of risk factors with anastomotic leakage and stricture.Results: There were 154 patients with RPT, 78 with TOT and 84 with PST intrathoracic gastroesophageal circular stapling in ILMIE. There was no differences in intraoperative anastomosis related conditions inclouding conversion of open operations, ways of esophageal reconstruction, lymph nodes harvested between the three groups. Whereas, The mean total operative time, and gastroesophageal anastomosis time in the RPT group were significantly shorter than those in other groups (both p<0.05). The rates of anastomotic leakage and stricture showed no statistical differences between three groups, respectively (Leakage: p=0.941; Stricture: p=0.942). Multivariate analysis revealed that the PRT method of the anvil placement does not increase the probability of anastomotic leakage (PRT: reference; TOT: odds ratio(OR) 2.845, P=0.255; PST: OR 2.234, p=0.242) and stricture (PRT: reference; TOT: OR 1.976, P=0.556; PST: OR 1.872, p=0.284).Conclusions: The PRT method of the anvil placement for intrathoracic gastroesophageal circular stapling does not increase the risk of anastomotic complications in ILMIE, but had significantly shorter surgical time and anastomosis time.
Securing Against DoS/DDoS Attacks in Internet of Flying Things using Experience-based Deep Learning Algorithm
Authors: Khan Inam Ullah , Abdollahi Arsin , Khan Muhammad Asghar , Uddin Irfan , Ullah Insaf 
Published: March 2021

Abstract
Due to the limited computational resources of small unmanned aerial vehicles (UAVs), the Internet of flying things (IoFT) is vulnerable to cybersecurity attacks, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. In addition, the transfer of reliable information from source UAV to destination UAV is another big challenge in IoFT networks. Therefore, this article aims to address the security deficiency by proposing an experience-based deep learning algorithm to cater to the DoS, D-DoS and a special kind of threat covering ping-of-death attacks. The proposed scheme uses the notion of the intrusion detection system (IDS). In addition, for reliable communication, a nature-based control routing algorithm AntHocNet is investigated with other contemporary protocols. The proposed approach is implemented in a smart city environment as a case study. The result authenticates the superiority of the proposed schemes in terms of security and QoS requirement from its counterparts.
Development and validation of a risk index to predict kidney graft survival: the Kidney Transplant Risk Index
Authors: Senanayake Sameera , Kularatna Sanjeewa , Healy Helen , Graves Nicholas , Baboolal Keshwar , Sypek Matthew P. , Barnett Adrian 
Published: March 2021

Abstract
BackgroundKidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia. MethodsData included donor and recipient characteristics (n=98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model. ResultsTwo models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration). ConclusionThis index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.
Performance of a deep learning system for automatic diagnosis of protruding lesions in colon capsule endoscopy: a multicentric study
Authors: Saraiva Miguel José Mascarenhas , Ferreira João , Cardoso Helder , Afonso João , Ribeiro Tiago , Andrade Patricia , Parente Marco , Natal Renato , Saraiva Miguel Mascarenhas , Macedo Guilherme 
Published: March 2021

Abstract
Colon capsule endoscopy (CCE) represents a landmark in minimally invasive exploration of the colonic mucosa for patients with contraindications for a conventional colonoscopy, or for whom the latter exam is unwanted or unfeasible. Colorectal neoplasia is the most common lesion found in CCE. The widespread acceptance of CCE as a non-invasive diagnostic method is particularly important in the setting of colorectal cancer screening. However, reviewing these exams is a time-consuming process as they generate a large number of frames, with the risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for the automatic detection of colonic protruding lesions. A CNN was constructed using an anonymized database of CCE images collected from a total of 124 patients. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images (2410 protruding lesions, 3305 normal mucosa or other findings) were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUROC), sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively). The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for the screening of colorectal neoplasia.
The Impact of Nonresponse in Different Survey Phases on the Precision of Prevalence Estimates
Authors: Ma Ming 
Published: March 2021

Abstract
Objective

Survey research is widely used in social studies. Whereas it has been widely known that nonresponse might produce biased results and impair the precision, the pattern of the impact on the precision of the estimate due to the non-response in the different survey stages is historically overlooked, though such information is essential to guide the recruitment plan. This study proposed to examine and compare the effect of first and second level nonresponse on the precision of prevalence estimates in the multi-stage survey studies. Based on the benchmark dataset from a state level survey, we used simulation approach to create datasets with different first and second level nonresponse rates and then compare the margin of error (an indicator for the precision) for the 12 outcomes between datasets with first vs. second level nonresponse.
Results

At the same nonresponse rate, the mean margin of error was greater for the data with first level nonresponse, compared to the data with second level nonresponse. As the nonresponse rate increased, the loss of precision was more inflated with the data with first level nonresponse, suggesting that the effort for recruiting primary sampling units is more crucial to improve the estimate precision in survey studies.
Frozen vs. Fresh Cycles IVF Outcomes: Retrospective study from an Indonesian IVF centre
Authors: Permadi Wiryawan , Bayuaji Hartanto , Tjandraprawira Kevin , Tjahyadi Dian , Harlianto Harris , Achmad Yanni Melliandari , Astarto Nanang Winarto , Djuwantono Tono 
Published: March 2021

Abstract
Objective
to compare the live birth rates (LBR) and neonatal outcomes of frozen cycle in vitro fertilization (IVF) with fresh cycle IVF in the Indonesian population.
Results
This was retrospective study using secondary data of IVF patients at a private fertility centre. Study recruitment was between 3/8/2018−31/12/2019. Total sampling included all patients undergoing oocyte retrieval and embryo transfer within recruitment period. Patients undergoing fresh IVF cycles and frozen IVF cycles were compared. 351 patients were recruited: 68.1% (239/351) underwent fresh cycles and 31.9% (112/351) frozen cycles. AMH was significantly higher in frozen cycle group (p = 0.04). Ovulatory disorder was significantly higher in frozen cycle group (p = 0.001). Among patients aged ≤ 30, fresh cycle group had significantly higher LBR (p = 0.02). Among those with ovulatory disorder, LBR was significantly higher with frozen cycle. No significant LBR difference was noted with other infertility causes. When stratified according to pregnancy order, frozen cycle patients had significantly higher birth lengths (p = 0.03) but not length of gestation nor neonatal birthweights. There was no significant difference in the proportion of biochemical pregnancy resulting in LBR (p = 0.08).
Observed and projected changes in extreme drought and wet-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index
Authors: Jena Pravat , azad sarita 
Published: March 2021

Abstract
Past versions of vulnerability index have shown ability to detect susceptible region by assessing socio-economic parameters at local scales. However, due to variability of these vulnerability index respect to socio-economic parameters, cann’t be utilized to predict the susceptibility region. The present endeavor aims to develops a new vulnerable index which identify and predict the spatio-temporal imprint of extreme drought and wet events at various scales 1o×1o in India by analyzing monthly observed and Coupled Model Inter-Comparison Phase 5 (CMIP5) rainfall data at spatial scale of time period pertaining to 1901-2100. New vulnerability index is proposed by consolidating the outcomes of Standard Precipitation Index (SPI) at different time scales such as 3- and 12-month and along with weights of individual grids. The weights of individual grid is calculated through the occurrence of extreme drought and wet events in the recent past which is to include a climate change factor in the proposed index. Based on the spatial distribution of high index values, the expected vulnerable regions concerning extreme drought events will be in Northeast, Northeast Central, East Coast, West, Northwest, Northcentral, and some grids in South part of India. Similarly, vulnerable regions concerning extreme wet events are likely to be in the Northeast, West Coast, East Coast, and some grids in the Peninsular region.Further, a conceptual model is presented to quantify the severity of extreme events. The analyses reveal that on the CMIP5 model data, it is obtained that 2024, 2026-27, 2035, 2036-37, 2043-44, 2059-60, 2094 are likely to be the most prominent drought years in all-India monsoon rainfall and their impact will persist for a longer time. Similarly, the most prominent wet events are predicted to be 2076, 2079-80, 2085, 2090, 2092, and 2099.
Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach
Authors: Shaher Al-Gounmeein Remal , Ismail Mohd Tahir 
Published: March 2021

AbstractThe Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is the primary objective of this study. The other goal of this paper is to expand on the researchers’ previous work by examining long memory and volatilities simultaneously, by using the ARFIMA-sGARCH hybrid model and comparing it against the ARFIMA-fGARCH hybrid model. Consequently, the hybrid models were configured with the monthly Brent crude oil price series for the period from January 1979 to July 2019. These datasets were considered as the global economy is currently facing significant challenges resulting from noticeable volatilities, especially in terms of the Brent crude prices, due to the outbreak of COVID-19. To achieve these goals, an R/S analysis was performed and the aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our study. In conclusion, these models can be used to predict oil prices more accurately than others.
Measurement of enterprise mobility among size classes, taking into account business demography
Authors: Ferretti Camilla 
Published: March 2021

Abstract
A extensive body of literature is devoted to the production of mobility measures based on transition matrices. The applications often involve panel data, and yet the impact of demographic events on enterprise mobility is not considered. The article aims provide a definition of enterprise mobility, in terms of the capability to create or liquidate jobs. Moreover, some existing mobility measures are modified so that they also take into account newborn and exiting firms. The proposed index has all the relevant basic properties which make it a rigorous descriptive statistics. The mobility of Italian capital-owned enterprises in the years 2010 – 2017 is analysed in the case study.
What we propose may be an alternative tool for practitioners to measure the degree of mobility in the presence of demographic events. It may be considered an initial step in future research regarding its different applications (e.g. labour market flows or movements among income classes), also considering more complex theoretical backgrounds.
Estimation of the number of residents included in a population frame
Authors: Krapavickaitė Danutė 
Published: March 2021

Abstract
A high migration rate of a population causes the number of a country’s residents to become extremely volatile, which negatively affects the quality of population frames. The aim of the paper is to present a method for estimating the number of residents included in a study population frame. The method involves the cross-classification of a population register with other databases which contain information relating to the activities of the population elements in a given country. The estimates from an ongoing sample survey are applied to some of the cells.
A new family of robust regression estimators utilizing robust regression tools and supplementary attributes
Authors: Sajjad Irsa , Hanif Muhammad , Koyuncu Nursel , Shahzad Usman , Al-Noor Nadia H. 
Published: March 2021

Abstract
Zaman and Bulut (2018a) developed a class of estimators for a population mean utilising LMS robust regression and supplementary attributes. In this paper, a family of estimators is proposed, based on the adaptation of the estimators presented by Zaman (2019), followed by the introduction of a new family of regression-type estimators utilising robust regression tools (LAD, H-M, LMS, H-MM, Hampel-M, Tukey-M, LTS) and supplementary attributes. The mean square error expressions of the adapted and proposed families are determined through a general formula. The study demonstrates that the adapted class of the Zaman (2019) estimators is in every case more proficient than that of Zaman and Bulut (2018a). In addition, the proposed robust regression estimators based on robust regression tools and supplementary attributes are more efficient than those of Zaman and Bulut (2018a) and Zaman (2019).The theoretical findings are supported by real-life examples.
Analysing the impact of dependency on conditional survival functions using copulas
Authors: Safari-Katesari Hadi , Zaroudi Samira 
Published: March 2021

Abstract
Nowadays, insurance contract reserves for coupled lives are considered jointly, which has a significant influence on the process of determining actuarial reserves. In this paper, conditional survival distributions of life insurance reserves are computed using copulas. Subsequently, the results are compared with an independence case. These calculations are based on selected Archimedean copulas and apply when the ‘death of one individual’ condition exists. The estimation outcome indicates that the insurer reserves calculated by means of Archimedean copulas are far more effective than those resulting from an independence assumption. The study demonstrates that copula-based dependency modelling improves the calculations of reserves made for actuarial purposes.