bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Religiosity of Polish Roman Catholics and the Meanings They Attribute to Homosexuality: Evidence from Network Analysis
Authors: Borowik Irena , Grygiel Paweł , Zwierżdżyński Marcin 
Published: August 2023

Abstract
Introduction
Previous research suggests that negative views on homosexuality are fostered by higher levels of religiosity. Little is known, however, about what meanings religious people attribute to homosexuality, how these meanings connect with each other, what kind of network they form, and which of them are central.

Methods
The research was conducted in 2020, based on a representative sample of Polish Catholics (N = 874). Religiosity was determined using latent class analysis. Based on an investigation of press discourse in Poland, six meanings of homosexuality were identified as positive (orientation, love, fulfillment) and negative (sin, deviation, disease). Network analysis was used to determine the relationship between religiosity and the meanings attributed to homosexuality.

Results
The analyses showed that greater compliance of religious orientations with the Roman Catholic Church’s expectations translates into increased acceptance of negative meanings of homosexuality (primarily sin). Weaker (negative) correlations were noted between religiosity and positive meanings, which we interpret as resulting from the absence of the negation of positive meanings in Roman Catholic teaching.

Conclusions
The relationships between religiosity and positive and negative meanings of homosexuality were found to be asymmetrical, unbalanced, and designated by negativity bias. The weakening meaning of sin may play an important role in changing religious people’s attitude toward homosexuality.

Policy Implications
The absolution of homosexuality would reduce the likelihood of negative meanings such as deviance or disease. This could lead to a reduction in exclusionary perceptions of homosexuality.
Evaluation of reservoir subsidence due to hydrocarbon production based on seismic data
Authors: Sharifi Javad 
Published: August 2023

AbstractEnvironmental problems associated with depleted oil and gas reservoirs upon long-term production from them are likely to become important challenges in future decades. With the increasing trend of production from hydrocarbon reservoirs, more and more reservoirs across the world are reaching the second half of their life—a fact that places an emphasis on the necessity of investigating what is known as reservoir subsidence. Different analytical and numerical approaches have been introduced for analyzing the subsidence on the basis of the elasticity theory but in the form of case studies, leaving a comprehensive model yet to be proposed. In this work, a formulation was introduced for estimating reservoir subsidence by integrating the rock physics, rock mechanics, and thermo-poroelasticity theories. Then, a modified version of this formulation was developed to calculate compaction in an actively producing reservoir that is suspect of subsidence, as a case study. For this purpose, triaxial hydrostatic tests were carried out on core plugs obtained from the considered reservoir, and then, compaction parameters (i.e., compression index and coefficient of deformation) were obtained at a laboratory scale. In order to evaluate the subsidence at a reservoir scale, the laboratory-scale results and in situ reservoir properties were integrated with well-logging and 3D seismic data at well location to come up with 3D cubes of compaction information. Continuing with the research, time-dependent inelastic deformation was modeled considering continued production for different future periods. The field observations showed that the estimated compaction is not visible at the surface in the form of subsidence due to the high depth and stiffness of the studied reservoir. However, collapse of casing at some of wells drilled into the studied reservoir could be attributed to the reservoir subsidence. Finally, variations of compaction with pore pressure were investigated to propose a model for predicting the subsidence in future periods. Findings of this research can be used to forecast subsidence at well location to take the required measures for avoiding possible casing collapse and/or relevant environmental issues.
A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests
Authors: Rabie Asmaa H. , Saleh Ahmed I. 
Published: August 2023

AbstractAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disease that impacts a child’s way of behavior and social communication. In early childhood, children with ASD typically exhibit symptoms such as difficulty in social interaction, limited interests, and repetitive behavior. Although there are symptoms of ASD disease, most people do not understand these symptoms and therefore do not have enough knowledge to determine whether or not a child has ASD. Thus, early detection of ASD children based on accurate diagnosis model based on Artificial Intelligence (AI) techniques is a critical process to reduce the spread of the disease and control it early. Through this paper, a new Diagnostic Autism Spectrum Disorder (DASD) strategy is presented to quickly and accurately detect ASD children. DASD contains two layers called Data Filter Layer (DFL) and Diagnostic Layer (DL). Feature selection and outlier rejection processes are performed in DFL to filter the ASD dataset from less important features and incorrect data before using the diagnostic or detection method in DL to accurately diagnose the patients. In DFL, Binary Gray Wolf Optimization (BGWO) technique is used to select the most significant set of features while Binary Genetic Algorithm (BGA) technique is used to eliminate invalid training data. Then, Ensemble Diagnosis Methodology (EDM) as a new diagnostic technique is used in DL to quickly and precisely diagnose ASD children. In this paper, the main contribution is EDM that consists of several diagnostic models including Enhanced K-Nearest Neighbors (EKNN) as one of them. EKNN represents a hybrid technique consisting of three methods called K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Chimp Optimization Algorithm (COA). NB is used as a weighed method to convert data from feature space to weight space. Then, COA is used as a data generation method to reduce the size of training dataset. Finally, KNN is applied on the reduced data in weight space to quickly and accurately diagnose ASD children based on new training dataset with small size. ASD blood tests dataset is used to test the proposed DASD strategy against other recent strategies [1]. It is concluded that the DASD strategy is superior to other strategies based on many performance measures including accuracy, error, recall, precision, micro_average precision, macro_average precision, micro_average recall, macro_average recall, F1-measure, and implementation-time with values equal to 0.93, 0.07, 0.83, 0.82, 0.80, 0.83, 0.79, 0.81, 0.79, and 1.5 s respectively.
Parameter sharing and multi-granularity feature learning for cross-modality person re-identification
Authors: Chan Sixian , Du Feng , Tang Tinglong , Zhang Guodao , Jiang Xiaoliang , Guan Qiu 
Published: August 2023

AbstractVisible-infrared person re-identification aims to match pedestrian images between visible and infrared modalities, and its two main challenges are intra-modality differences and cross-modality differences between visible and infrared images. To address these issues, many advanced methods attempt to design new network structures to extract modality-sharing features, mitigate modality differences, or learn part-level features to overcome background interference. However, they ignore the parameter sharing of the convolutional layers to obtain more modality-sharing features. At the same time, only using part-level features lack discriminative pedestrian representations such as body structure and contours. To handle these problems, a parameter sharing and feature learning network is proposed in this paper to mitigate modality differences and further enhance feature discrimination. Firstly, a new two-stream parameter sharing network is proposed, by sharing the convolutional layers parameters to obtain more modality-sharing features. Secondly, a multi-granularity feature learning module is designed to reduce modality differences at both coarse and fine-grained levels while further enhancing feature discriminability. In addition, a center alignment loss is proposed to learn relationships between identities and to reduce modality differences by clustering features into their centers. For the part-level feature learning, the hetero-center triplet loss is adopted to alleviate the strict constraints of triplet loss. Finally, extensive experiments are conducted to validate our method outperforms state-of-the-art methods on two challenging datasets. In the SYSU-MM01 dataset, the Rank-1 and mAP reach $$74.0\%$$

74.0
%

and $$70.51\%$$

70.51
%

in the all-search mode, which is an increase of $$3.4\%$$

3.4
%

and $$3.61\%$$

3.61
%

to baseline, respectively.
EU Responses to the Democratic Deficit and the Rule of Law Crisis: Is It Time for a (New) European Exceptionalism?
Authors: Mavrouli Roila , Van Waeyenberge Arnaud 
Published: August 2023

AbstractThe European 'messianic' project was not particularly concerned with democracy or human rights, but rather sought 'legitimacy' in the nobility of its cause. However, when failure struck during the Euro-crisis, many sources of legitimacy suddenly collapsed. Similarly, failure struck the rule of law principle, demonstrating its precariousness and weak source of legitimation. The strong waves of de-europeanisation and the rise of illiberal democracies not only bolster the existing preoccupations of problematic democratic procedures, but further bring into question the continuity of the EU as a supranational entity. Interestingly, the European Union’s answer to these issues furnishes a solution that, on the one hand, focuses on the enhancement of democracy, while focusing the safeguarding of rule of law on the other. Such an enhancement of democracy could be the result of the Conference on the Future of Europe, whereas, the rule of law crisis is meant to be addressed through financial and techno-managerial mechanisms. However, if the first mechanism aims to palliate or even mitigate the democratic deficit, the second one risks further alienating Union citizens by seeing in it another instance of European Union ‘technocracy’. The solution to be foreseen is to reconnect democracy with rule of law as they have always been the foreign implants of European integration. Filling in these empty gaps of political messianism through an outright pairing of democracy and rule of law will rejuvenate the social legitimacy of European exceptionalism.
Toward the Reconstruction of Sacred Medieval Spatialities: Multiscale Analysis of the System of Eremitical Landscapes in Southern Portugal from Historical Records
Authors: Volzone Rolando , Fontes João Luís Inglês , da Conceição Parreira Carapinha Aurora 
Published: August 2023

AbstractReconstruction of the landscape of late medieval southern Portugal is hampered by the scarceness of physical evidence after the subsequent centuries of evolution. The aim of this article is to understand how the spread of eremitical communities in this region interacted with the existing landscape, contributing to its transformation. A multiscale analysis is carried out through the examination of historical records containing scattered data about the landscape system. This methodology is applied to a specific case study, the Portuguese eremitical congregation of São Paulo da Serra de Ossa, in southern Portugal. Although fragmentary and dispersed, primary sources from between 1366 and 1578 are useful in reconstructing the components of this landscape. Initial results show the definition of a typical Mediterranean form of settlement: “agro-sylvo-pastoral.”
Surrogate models for dynamic load factor and ductility ratio in blast response determination
Authors: Aleyaasin Majid 
Published: August 2023

AbstractIn this article, the blast response of structures by the Single Degree of Freedom (SDOF) method is revisited. The existing literature in which the Biggs’ chart is used to determine the ductility ratio is examined in detail. The numerical method determines the SDOF response by considering the elastic perfectly plastic behaviour of the structure. The numerical values of the ductility ratio in various plastic Dynamic Load Factors (DLF) in each td/T ratio are collected. Such verifiable data can represent the whole chart in the elastic and plastic regions. The only available implicit formula suitable for sharp pulses is tested. It is shown that substantial inaccuracy exists in the formula. Since the error in the existing formula is up to 100%, an alternative formula is suggested to reduce the error. The new nonlinear surrogate model describes the chart by introducing 3 regions. In each region, an approximate formula is developed. The model computes the ductility ratio with less than 2% error. The newly suggested functions are nonlinear quadratic types and have been developed by using high-order polynomial optimization. In the numerical example, the result of the new surrogate model is commented on in comparison with SDOF and FEM. It is shown that it can be used in the computational design of protective structures without using Biggs’ chart. It is concluded that similar surrogate models can be developed for unsymmetrical blast pulses.
Surface tension of steel at high temperatures
Authors: Volpp Joerg 
Published: August 2023

AbstractSurface tension is a material property that is needed to describe fluid behaviour, which impacts industrial processes, in which molten material is created, such as thermal cutting, welding and Additive Manufacturing. In particular when using metals, the material properties at high temperatures are often not known. This is partly because of limited possibilities to measure those properties, limitations of temperature measurement methods and a lack of theoretical models that describe the circumstances at such high temperatures sufficiently. When using beam heat sources, such as a laser beam, temperatures far above the melting temperature are reached. Therefore, it is mandatory to know the material properties at such high temperatures in order to describe the material behaviour in models and gain understanding of the occurring effects. Therefore, in this work, an experimental surface wave evaluation method is suggested in combination with thermal measurements in order to derive surface tension values of steel at higher temperatures than reported in literature. The evaluation of gravity-capillary waves in high-speed video recordings shows a steeper decrease of surface tension values than the extrapolation of literature values would predict, while the surface tension values seem not to decrease further above boiling temperature. Using a simplified molecular dynamic model based on pair correlation, a similar tendency of surface values was observed, which indicates that the surface tension is an effect requiring at least two atomic layers. The observed and calculated decreasing trend of the surface tension indicates an exponential relation between surface tension and temperature.
Morpho-physiological and Biochemical Effects of Palm Pollen Extract in Alleviation of Cadmium and Cobalt Toxicity in Beans (Phaseolus vulgaris L.)
Authors: Nessem Afaf A. , Ebrahim Mohsen K. H. , Saad-Allah Khalil M. , Gad Dina 
Published: August 2023

AbstractPalm pollen extract (PPE) is a viable source of osmoregulators, phytohormones, vitamins, minerals, enzymes, and cofactors supporting plant growth in normal and challenging environments. In this study, we explored the competency of methanolic-aqueous PPE to amend cadmium (Cd) and cobalt (Co) toxicity in beans (Phaseolus vulgaris L.). Bean seeds were germinated in clay-sandy soil containing plastic containers, thinned, and then administered 50 mM CdCl2, CoCl2, or a combination of both. Treated seedlings were foliar sprayed with PPE or distilled water (10 ml PPE pot-1). To assess their response, seedlings were harvested at 21 days old. Cd and Co, individually or combined, impaired beans growth, chlorophyll content, soluble sugars, amino acids, ascorbate peroxidase (APX), peroxidase (POD), glutathione reductase (GR) activities, flavonoids, macronutrients (N, P, and K), and yield components, particularly when combined. Metal treatments, however, upraised electrolyte leakage and catalase (CAT), superoxide dismutase (SOD), and glutathione S-transferase (GST) activities, beside increasing carotenoids, proline, hydrogen peroxide (H2O2), malondialdehyde (MDA), and phenolic levels. However, PPE contributed to lessening Cd and Co toxicity by restoring the growth, chlorophyll concentration, membrane stability, metabolic activity, and redox and ionic equilibrium. Moreover, PPE restored the metal-induced declines in carotenoids, flavonoids, antioxidant potential, and grain yield. PPE played a valuable role in metal-stressed beans by increasing proteins, amino acids, and phenols; improving APX, POD, and GST activities; and decreasing Cd and Co uptake. Overall, PPE could be employed as a feasible approach to support Cd- and Co-stressed bean growth, photosynthetic activity, osmotic and ionic homeostasis, and yield performance.
Learning Biometric Representations with Mutually Independent Features Using Convolutional Autoencoders
Authors: Musto Riccardo , Kuzu Ridvan Salih , Maiorana Emanuele , Hine Gabriel Emile , Campisi Patrizio 
Published: August 2023

AbstractRepresentations of biometric traits to be used in automatic recognition systems are typically learned with the goal of obtaining significant discriminative capabilities, that is, generating features that are notably different when produced by traits of different subjects, while maintaining an appropriate consistency for a given user. Nonetheless, discriminability is not the only desirable property of a biometric representation. For instance, the mutual independence of the coefficients in the employed templates is a valuable property when designing biometric template protection schemes. In fact, managing representations with independent coefficients allows to maximize the achievable security. In this paper we propose different learning strategies to obtain biometric representations with the property of statistical independence among coefficients, while preserving discriminability. In order to achieve this goal, different strategies are employed to train convolutional autoencoders. As a proof of concept, the effectiveness of the proposed approaches is tested by considering biometric recognition systems using both finger-vein and palm-vein patterns.
A Cellular Senescence-Related Signature Predicts Cervical Cancer Patient Outcome and Immunotherapy Sensitivity
Authors: Shao Huijing , Li Xia , Wu Pengfei , Chen Zixi , Zhang Caihong , Gu Hang 
Published: August 2023

AbstractCervical cancer (CC) is one of the most prevalent gynecological malignancies. The rate of mortality and morbidity among patients with CC is high. Cellular senescence is involved in tumorigenesis as well as in the cancer progression. However, the involvement of cellular senescence in CC development is still unclear and requires further investigation. In this study, we retrieved data on cellular senescence-related genes (CSRGs) from the "CellAge" Database. We used the TCGA-CESC and CGCI-HTMCP-CC datasets as the training and validation sets, respectively. Finally, a signature was constructed using "univariate" and "Least Absolute Shrinkage and Selection Operator" (LASSO) Cox regression analysis, which contains eight CSRGs. Using this signature, we calculated the risk scores of all patients in the training and validation cohorts and categorized them into the low-risk group (LR-G) and the high-risk group (HR-G). Results showed that, compared to patients in the HR-G, those in the LR-G demonstrated a more positive clinical prognosis, more abundant immune cell infiltrations, and a more active immune response. The signature could also modulate the expression of SASP factors. In vitro studies showed an increased expression of SERPINE1 and IL-1α genes included in the signature in CC cells and tissues. Our findings help to deepen our insights into the etiology of CC, which could be beneficial for prognostic prediction and immunotherapy in clinical practice.
Calycosin Alleviates Lupus Nephritis by Activating the Nrf2/HO-1 Signaling Pathway
Authors: Yu Yang , Sun Peng 
Published: August 2023

AbstractLupus nephritis is a serious condition, for which treatments are limited; hence, there is a need for new cure approaches. The aim of this study was to evaluate the therapeutic effects of calycosin against lupus nephritis induced by lipopolysaccharide (LPS) in human renal cortex proximal convoluted tubule epithelial cells (HK-2). HK-2 cells were stimulated with 1 μg/ml LPS to create a lupus nephritis cell model; the cells were pretreated with calycosin. Cell viability and apoptosis rate were determined using the cell counting kit-8 assay and flow cytometry, respectively. A caspase-3 activity detection kit was used to determine caspase-3 activity. Interleukin (IL)-6, IL-1β, and tumor necrosis factor alpha (TNF-α) levels were determined using enzyme-linked immunosorbent assay kits. Lactate dehydrogenase (LDH) level was determined using an LDH assay kit. Finally, western blotting and reverse transcription-quantitative polymerase chain reaction were performed to determine apoptosis-related protein levels and nuclear factor erythroid 2–related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) signaling. Calycosin had no cytotoxic effects on HK-2 cells. Lipopolysaccharide stimulation significantly inhibited cell viability; increased the IL-6, IL-1β, and TNF-α levels; and elevated apoptosis rate, caspase3 activity, and LDH level in HK-2 cells. The protein level of cleaved caspase3 was also increased in LPS-treated HK-2 cells. In addition, the pattern of Nrf2/HO-1 signaling was disturbed by LPS. These effects were reversed by calycosin treatment. Calycosin could alleviate LPS-induced lupus nephritis and may thus be a novel agent for its treatment.
Graphical Abstract