bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physical activity and glioma: a case–control study with follow-up for survival
Authors: Basiri Zohreh , Yang Yi , Bruinsma Fiona J. , Nowak Anna K. , McDonald Kerrie L. , Drummond Katharine J. , Rosenthal Mark A. , Koh Eng-Siew , Harrup Rosemary , Hovey Elizabeth , Joseph David , Benke Geza , Leonard Robyn , MacInnis Robert J. , Milne Roger L. , Giles Graham G. , Vajdic Claire M. , Lynch Brigid M. 
Published: February 2022

Abstract
Purpose
High-grade disease accounts for ~ 70% of all glioma, and has a high mortality rate. Few modifiable exposures are known to be related to glioma risk or mortality.

Methods
We examined associations between lifetime physical activity and physical activity at different ages (15–18 years, 19–29 years, 30–39 years, last 10 years) with the risk of glioma diagnosis, using data from a hospital-based family case–control study (495 cases; 371 controls). We followed up cases over a median of 25 months to examine whether physical activity was associated with all-cause mortality. Physical activity and potential confounders were assessed by self-administered questionnaire. We examined associations between physical activity (metabolic equivalent [MET]-h/wk) and glioma risk using unconditional logistic regression and with all-cause mortality in cases using Cox regression.

Results
We noted a reduced risk of glioma for the highest (≥ 47 MET-h/wk) versus lowest (< 24 METh/wk) category of physical activity for lifetime activity (OR = 0.58, 95% CI: 0.38–0.89) and at 15–18 years (OR = 0.57, 95% CI: 0.39–0.83). We did not observe any association between physical activity and all-cause mortality (HR for lifetime physical activity = 0.91, 95% CI: 0.64–1.29).

Conclusion
Our findings are consistent with previous research that suggested physical activity during adolescence might be protective against glioma. Engaging in physical activity during adolescence has many health benefits; this health behavior may also offer protection against glioma.
Treebanking user-generated content: a UD based overview of guidelines, corpora and unified recommendations
Authors: Sanguinetti Manuela , Bosco Cristina , Cassidy Lauren , Çetinoğlu Özlem , Cignarella Alessandra Teresa , Lynn Teresa , Rehbein Ines , Ruppenhofer Josef , Seddah Djamé , Zeldes Amir 
Published: February 2022

AbstractThis article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.
Repeated quasi-integration on locally compact spaces
Authors: Butler Svetlana V. 
Published: February 2022

AbstractWhen X is locally compact, a quasi-integral (also called a quasi-linear functional) on $$ C_c(X)$$

C
c

(
X
)

is a homogeneous, positive functional that is only assumed to be linear on singly-generated subalgebras. We study simple and almost simple quasi-integrals, i.e., quasi-integrals whose corresponding compact-finite topological measures assume exactly two values. We present equivalent conditions for a quasi-integral to be simple or almost simple. We give a criterion for repeated quasi-integration (i.e., iterated integration with respect to topological measures) to yield a quasi-linear functional. We find a criterion for a double quasi-integral to be simple or almost simple. We describe how a product of topological measures acts on open and compact sets. We show that different orders of integration in repeated quasi-integrals give the same quasi-integral if and only if the corresponding topological measures are both measures or one of the corresponding topological measures is a positive scalar multiple of a point mass.
Monetary policy, financial shocks and economic activity
Authors: Evgenidis Anastasios , Malliaris Anastasios G. 
Published: February 2022

AbstractThis paper contributes to a fuller understanding of macroeconomic outcomes to financial market disturbances and the central bank’s role in financial stability. Our two major contributions are conceptual and econometric. Conceptually, we introduce phases of the business cycle and econometrically we employ Bayesian VARs. We document that a shock that increases credit to non-financial sector leads to a persistent decline in economic activity. In addition, we examine whether the behavior of financial variables is useful in signaling the 2007–2009 recession. The answer is positive as our BVAR generates early warning signals pointing to a sustained slowdown in growth. We propose that the expansion phase of the business cycle can be subdivided into an early and a late expansion. Based on this distinction, we show that if the Fed had raised the policy rate when the economy moved from the early to late expansion, it could have mitigated the severity of the 2007–2009 recession.
A pictorial essay on radiological changes after stereotactic body radiation therapy for lung tumors
Authors: Matsumoto Yasuo 
Published: February 2022

AbstractStereotactic body radiation therapy (SBRT) is a frequently used modality for the treatment of early stage non-small cell lung cancer and oligometastatic disease of the lung. The radiological changes observed in the lung after SBRT are likely to differ from those observed after conventional thoracic radiation therapy, primarily due to the small size of the target volume and highly conformal dose distributions with steep dose gradients from the target to surrounding normal lung tissues used in SBRT. Knowledge of the radiological changes that can occur after SBRT is required to correctly diagnose local failure. Herein, I report several radiological changes specific to SBRT that have been observed.
Bentonite Clay Liquid Crystals for High-Performance Supercapacitors
Authors: Neelamma M. K. , Holla Sowmya R. , Selvakumar M. , Chandran P. Akhil , De Shounak 
Published: February 2022

AbstractAs the human population increases, there is invariably excessive demand for energy sources, thus making it a fundamental need. The basic use of supercapacitors is the storage of energy. But self-discharge is caused when no external power or internal battery is charging them. This clearly reduces their efficiency. It has been found that self-discharge can be reduced to a great extent by employing liquid crystals (LC). But the effect of LC in enhancing the supercapacitor properties has not been discussed. So here, the naturally available bentonite clay is analyzed for its lyotropic liquid crystalline (LLC) properties. It showed liquid crystalline properties at 0.055 g/cm3 in 0.2 M Na2SO4 solvent. It is characterized by a polarised optical microscope (POM). These LCs are added as an additive electrolyte to the supercapacitors. The electrode materials used for supercapacitors are a composite of activated carbon and polyaniline. When compared, the supercapacitor properties were enhanced with a decrease in IR drop in supercapacitors containing bentonite lyotropic liquid crystal, producing a specific capacitance of 237.5 F/g at a current density of 0.5 mA/cm3. The electrochemical behavior of the supercapacitors is determined by cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge-discharge cycling techniques.
Graphical Abstract
Telework satisfaction, wellbeing and performance in the digital era. Lessons learned during COVID-19 lockdown in Spain
Authors: Blahopoulou Joanna , Ortiz-Bonnin Silvia , Montañez-Juan Maribel , Torrens Espinosa Gema , García-Buades M. Esther 
Published: February 2022

AbstractThis study used a prospective design to examine the effects of telework satisfaction (time 1) on subjective wellbeing and self-reported performance (time 2) during the COVID-19 lockdown. Data were collected from 111 teleworkers through an online survey the first weeks of strict lockdown in Spain. Telework satisfaction showed positive direct effects on both subjective wellbeing and self-reported performance. Further, subjective wellbeing partially mediated the relationship between telework satisfaction and self-reported performance. Interestingly, employees with children felt less telework satisfaction but higher subjective wellbeing. The novelty of this study is that we evaluate the level of satisfaction with telework using a specific set of items that assess the employees’ contentment with diverse telework facets. Given the spreading of telework and the increasing competitiveness of organizations, we discuss practical implications in times of crisis, both present and future.
Integral Operators Induced by Symbols with Non-negative Maclaurin Coefficients Mapping into $$H^\infty $$
Authors: Peláez José Ángel , Rättyä Jouni , Wu Fanglei 
Published: February 2022

AbstractFor analytic functions g on the unit disk with non-negative Maclaurin coefficients, we describe the boundedness and compactness of the integral operator $$T_g(f)(z)=\int _0^zf(\zeta )g'(\zeta )\,d\zeta $$

T
g

(
f
)

(
z
)

=

∫
0
z

f

(
ζ
)

g
′

(
ζ
)

d
ζ

from a space X of analytic functions in the unit disk to $$H^\infty $$

H
∞

, in terms of neat and useful conditions on the Maclaurin coefficients of g. The choices of X that will be considered contain the Hardy and the Hardy–Littlewood spaces, the Dirichlet-type spaces $$D^p_{p-1}$$

D

p
-
1

p

, as well as the classical Bloch and $$\mathord {\mathrm{BMOA}}$$
BMOA
spaces.
Antioxidant-biocompatible and stable catalase-based gelatin–alginate hydrogel scaffold with thermal wound healing capability: immobilization and delivery approach
Authors: Abdel-Mageed Heidi Mohamed , Abd El Aziz Amira Emad , Abdel Raouf Batoul Mohamed , Mohamed Saleh Ahmed , Nada Dina 
Published: February 2022

AbstractHydrogel-based matrix prepared using biopolymers is a new frontier of emerging platforms for enzyme immobilization for biomedical applications. Catalase (CAT) delivery can be effective in inhibiting reactive oxygen species (ROS)-mediated prolongation of the wound healing process. In this study, to improve CAT stability for effective application, gelatin(Gel)–alginate (Alg) biocompatible hydrogel (Gel–Alg), as immobilization support, was prepared using calcium chloride as an ionic cross-linker. High entrapment efficiency of 92% was obtained with 2% Gel and 1.5% Alg. Hydrogel immobilized CAT (CAT–Gel–Alg) showed a wide range of pH from 4 to 9 and temperature stability between 20 to 60 °C, compared to free CAT. CAT–Gel–Alg kinetic parameters revealed an increased Km (24.15 mM) and a decreased Vmax (1.39 µmol H2O2/mg protein min) × 104. CAT–Gel–Alg retained 52% of its original activity after 20 consecutive catalytic runs and displayed improved thermal stability with a higher t1/2 value (half-life of 100.43 vs. 46 min). In addition, 85% of the initial activity was maintained after 8 weeks’ storage at 4 °C. At 24 h after thermal injury, a statistically significant difference in lesion sizes between the treated group and the control group was reported. Finally, our findings suggest that the superior CAT–Gel–Alg stability and reusability are resonant features for efficient biomedical applications, and ROS scavenging by CAT in the post-burn phase offers protection for local treatment of burned tissues with encouraging wound healing kinetics.
A novel multi-objective bi-level programming problem under intuitionistic fuzzy environment and its application in production planning problem
Authors: Singh V. P. , Sharma Kirti , Chakraborty Debjani , Ebrahimnejad Ali 
Published: February 2022

AbstractThis paper presents an optimization method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The idea is based on TOPSIS (technique for order preference by similarity to ideal solution) method. TOPSIS method is a multiple criteria method that identifies a satisfactory solution from a given set of alternatives based on the minimization of distance from an ideal point and maximization of distance from the nadir point simultaneously. A new model of multi-objective bi-level programming problem in an intuitionistic fuzzy environment has been considered. The problem is first reduced to a conventional multi-objective bi-level linear programming problem using accuracy function. Then the modified TOPSIS method is proposed to solve the problem at both the leader and the follower level where various linear/non-linear membership functions are used to represent the flexibility in the approach of decision-makers (DMs). The problem is solved hierarchically, i.e., first the problem at the leader level is solved and then the feasible region is extended by relaxing the decision variables controlled by the leader. The feasible region is extended to obtain a satisfactory solution for the DMs at both levels. Finally, the application of the proposed approach in the production planning of a company has been presented. An illustrative numerical example is also given to explain the methodology defined in this paper.
Cooperative multi-population Harris Hawks optimization for many-objective optimization
Authors: Yang Na , Tang Zhenzhou , Cai Xuebing , Chen Long , Hu Qian 
Published: February 2022

AbstractThis paper presents an efficient cooperative multi-populations swarm intelligence algorithm based on the Harris Hawks optimization (HHO) algorithm, named CMPMO-HHO, to solve multi-/many-objective optimization problems. Specifically, this paper firstly proposes a novel cooperative multi-populations framework with dual elite selection named CMPMO/des. With four excellent strategies, namely the one-to-one correspondence framework between the optimization objectives and the subpopulations, the global archive for information exchange and cooperation among subpopulations, the logistic chaotic single-dimensional perturbation strategy, and the dual elite selection mechanism based on the fast non-dominated sorting and the reference point-based approach, CMPMO/des achieves considerably high performance on solutions convergence and diversity. Thereafter, in each subpopulation, HHO is used as the single objective optimizer for its impressive high performance. Notably, however, the proposed CMPMO/des framework can work with any other single objective optimizer without modification. We comprehensively evaluated the performance of CMPMO-HHO on 34 multi-objective and 19 many-objective benchmark problems and extensively compared it with 13 state-of-the-art multi/many-objective optimization algorithms, three variants of CMPMO-HHO, and a CMPMO/des based many-objective genetic algorithm named CMPMO-GA. The results show that by taking the advantages of the CMPMO/des framework, CMPMO-HHO achieves promising performance in solving multi/many-objective optimization problems.
Software defect prediction based on nested-stacking and heterogeneous feature selection
Authors: Chen Li-qiong , Wang Can , Song Shi-long 
Published: February 2022

AbstractSoftware testing guarantees the delivery of high-quality software products, and software defect prediction (SDP) has become an important part of software testing. Software defect prediction is divided into traditional software defect prediction and just-in-time software defect prediction (JIT-SDP). However, most of the existing software defect prediction frameworks are relatively simplified, which makes it extremely difficult to provide developers with more detailed reference information. To improve the effectiveness of software defect prediction and realize effective software testing resource allocation, this paper proposes a software defect prediction framework based on Nested-Stacking and heterogeneous feature selection. The framework includes three stages: data set preprocessing and feature selection, Nested-Stacking classifier, and model classification performance evaluation. The novel heterogeneous feature selection and nested custom classifiers in the framework can effectively improve the accuracy of software defect prediction. This paper conducts experiments on two software defect data sets (Kamei, PROMISE), and demonstrates the classification performance of the model through two comprehensive evaluation indicators, AUC, and F1-score. The experiment carried out large-scale within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). The results show that the framework proposed in this paper has an excellent classification performance on the two types of software defect data sets, and has been greatly improved compared with the baseline models.
Motion magnification multi-feature relation network for facial microexpression recognition
Authors: Zhang Jing , Yan Boyun , Du Xiaohui , Guo Quanhao , Hao Ruqian , Liu Juanxiu , Liu Lin , Ni Guangming , Weng Xiechuan , Liu Yong 
Published: February 2022

AbstractMicroexpressions cannot be observed easily due to their short duration and small-expression range. These properties pose considerable challenges for the recognition of microexpressions. Thus, video motion magnification techniques help us to see small motions previously invisible to the naked eye. This study aimed to enhance the microexpression features with the help of motion amplification technology. Also, a motion magnification multi-feature relation network (MMFRN) combining video motion amplification and two feature relation modules was proposed. The spatial feature is enlarged while completing the spatial feature extraction, which is used for classification. In addition, we transferred Resnet50 network to extract the global features and improve feature comprehensiveness. The magnification of the features is controlled through hyperparameter amplification factor α. The effects of different magnification factors on the results are compared, and the best is selected. The experiments have verified that the enlarged network can resolve the misclassification problem caused by the one-to-one correspondence between microexpressions and facial action coding units. On CASME II datasets, MMFRN outperforms the traditional recognition methods and other neural networks.
A novel feature based algorithm for soil type classification
Authors: Uddin Machbah , Hassan Md. Rakib 
Published: February 2022

AbstractAgriculture is the backbone of Bangladesh’s economy and it is one of the largest employment sectors. In Bangladesh, the population is increasing rapidly and at the same time, the total cultivable land is decreasing significantly. To ensure maximum crop production using the limited land resources, it is essential to identify and select the appropriate type of soil because different crops need different soil types. Currently, there are two types of methods available to determine the soil type, namely chemical and image analysis. Although the first one is accurate, it is expensive and time consuming. On the other hand, image based soil classification is much cheaper and faster but its accuracy level is low. In this study, we present a novel feature based algorithm that combines quartile histogram oriented gradients (Q-HOG), most frequent $$\varphi $$
φ
-Pixels and a new feature selection method for classifying soil types. We have used four machine learning algorithms and evaluated the performance with different sets of features. We have also compared our work with two prominent and recent works on image-based soil classification systems. The experimental results show that the performance of our proposed method in terms of four standard evaluation metrics, namely accuracy, precision, F1_score, and recall scores are higher than the existing image-based soil classification systems.
Failure analysis and control measures of deep roadway with composite roof: a case study
Authors: Li Yongliang , Yang Renshu , Fang Shizheng , Lin Hai , Lu Shaojie , Zhu Ye , Wang Maosheng 
Published: February 2022

AbstractThere is great variation in the lithology and lamination thickness of composite roof in coal-measure strata; thus, the roof is prone to delamination and falling, and it is difficult to control the surrounding rock when developing roadway in such rock strata. In deep mining, the stress environment of surrounding rock is complex, and the mechanical response of the rock mass is different from that of the shallow rock mass. For composite-roof roadway excavated in deep rock mass, the key to safe and efficient production of the mine is ensuring the stability of the roadway. The present paper obtains typical failure characteristics and deformation and failure mechanisms of composite-roof roadway with a buried depth of 650 m at Zhaozhuang Coal Mine (Shanxi Province, China). On the basis of determining a reasonable cross-section shape of the roadway and according to the failure characteristics of the composite roof in different regions, the roof is divided into an unstable layer, metastable layer, and stable layer. The controlled unstable layer and metastable layer are regarded as a small structure while the stable layer is regarded as a large structure. A superimposed coupling support technology of large and small structures with a multi-level prestressed bearing arch formed by strong rebar bolts and highly prestressed cable bolts is put forward. The support technology provides good application results in the field. The study thus provides theoretical support and technical guidance for ground control under similar geological conditions.
The Unemployment Imbalance Between Non-English-Speaking Migrant Women and Australian Born Women
Authors: Haque Tariq Hassan , Haque M Ohidul 
Published: February 2022

AbstractThis study compares Non-English-Speaking migrant women’s unemployment situation with that of Australian born women, using the currently available 1% sample from the Australian 2016 Census data. A new general probit model is used to estimate the probability of unemployment for both groups combined, as well as for each of these groups separately to show the difference in unemployment rates between the two groups. Our contribution is to show how to estimate and test the imbalance in unemployment rates between the two groups. We show that the unemployment rate for Non-English-Speaking migrant women (7.2%) is significantly higher than for Australian born women (5.1%), but the gap is narrowing over time. We show that the unemployment rate for Non-English-Speaking migrant women increases when they are married and have children and when they have poor English proficiency and lower education levels. This implies that there is a significant higher probability of unemployment for Non-English-Speaking migrant women across human capital and demographic controls. We have discussed other dimensions of unemployment such as discrimination and cultural activities and shown that these are the main factors behind the relatively higher unemployment rate for Non-English-Speaking migrant women.