Internal Structure Research Articles

Towards effective women’s movements against violence: the role of organizational conditions in promoting leadership capital

ABSTRACT Over recent years, increasing violence in Latin American countries has led women’s movements against violence to face challenging circumstances while advancing their goals. Attention has recently turned to the internal structures of these movements, particularly the aspect of leadership. While prior research recognizes a preference for shared leadership among women’s movements, where responsibilities are commonly distributed among many members, it also points to its potential for conflict, which can impede collaborative action. However, there is still a limited understanding of the organizational conditions of movement leadership, especially within women’s movements facing adverse contexts that require enhancing their capacities for goal attainment. This study follows the concept of leadership capital, which posits that movement leaders use their skills and knowledge to undertake decision-making that is crucial for the movement’s goals. Conceptually, it explores potential relationships between organizational conditions that might foster elements to enhance leadership capital. Employing inductive research through a cross-case study of two Mexican women’s movements against violence, the Olimpia and the Anti-femicide movements, performed between 2017 and 2021, the study focuses on two main organizational conditions: the presence of a common agenda and effective communication. By comparing these movements, which had varying degrees of success in meeting their legislative objectives, the study reveals how differences in these organizational conditions can potentially relate to promoting leadership capital. This study thus advances the social movement literature by proposing a new framework to study movement leadership.

Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire.

Predictions are complex, multisensory, and dynamic processes involving real-time adjustments based on environmental inputs. Disruptions to prediction abilities have been proposed to underlie characteristics associated with autism. While there is substantial empirical literature related to prediction, the field lacks a self-assessment measure of prediction skills related to daily tasks. Such a measure would be useful to better understand the nature of day-to-day prediction-related activities and characterize these abilities in individuals who struggle with prediction. An interdisciplinary mixed-methods approach was utilized to develop and validate a self-report questionnaire of prediction skills for adults, the Prediction-Related Experiences Questionnaire (PRE-Q). Two rounds of online field testing were completed in samples of autistic and neurotypical (NT) adults. Qualitative feedback from a subset of these participants regarding question content and quality was integrated and Rasch modeling of the item responses was applied. The final PRE-Q includes 19 items across 3 domains (Sensory, Motor, Social), with evidence supporting the validity of the measure's 4-point response categories, internal structure, and relationship to other outcome measures associated with prediction. Consistent with models of prediction challenges in autism, autistic participants indicated more prediction-related difficulties than the NT group. This study provides evidence for the validity of a novel self-report questionnaire designed to measure the day-to-day prediction skills of autistic and non-autistic adults. Future research should focus on characterizing the relationship between the PRE-Q and lab-based measures of prediction, and understanding how the PRE-Q may be used to identify potential areas for clinical supports for individuals with prediction-related challenges.

A Feasible Dual Modification Strategy of Internal Anion Redox Chemistry and Surface Engineering on P2 Layer-Structured Cathodes in Sodium-Ion Batteries.

Boosting the anion redox reaction opens up a possibility of further capacity enhancement on transition-metal-ion redox-only layer-structured cathodes for sodium-ion batteries. To mitigate the deteriorating impact on the internal and surface structure of the cathode caused by the inevitable increase in the operation voltage, probing a solution to promote the bulk-phase crystal structure stability and surface chemistry environment to further facilitate the electrochemical performance enhancement is a key issue. A dual modification strategy of establishing an anion redox hybrid activation trigger agent inside the crystal structure in combination with surface oxide coating is successfully developed. P2-type layer structure cathode materials with Zn/Li (Na-O-Zn@Na-O-Li) anion redox hybrid triggers and a ZnO coating layer possess superior capacity and cycle performance, along with outstanding structural stability, decreased Mn-ion dissolution effect, and less crystal particle cracking during the cycling process. This study represents a facile modification solution to perform structure optimization and property enhancement toward high-performance layered structure cathode materials with anion redox features in sodium-ion batteries.

Quantum model of galactic halos with an Navarro-Frenk-White dark matter profile

A quantum model of a cold dark matter galactic halo is developed. The model requires specifying the mass and radius of the halo as well as its density profile. The structure of the halo resulting from the theory is predicted and its physical properties are determined. Verification of these theoretical predictions by observations is proposed and discussed. The model is constructed by analytically solving the governing equation and using its time-independent solutions to determine the internal structure of a galactic halo with an Navarro-Frenk-White cold dark matter density profile. The governing equation that is the basis of the developed theory is derived from the irreducible representations of the extended Galilean group. The method of finding the solutions is analytical, even though an Navarro-Frenk-White density profile is used in the calculations. The theory predicts a halo with a core composed of free dark matter particles that move randomly with frequent collisions. It also predicts an envelope in which the particles are confined to their orbits, which are quantized. Except in the close vicinity of the core, the population of the orbits remains fixed, and physical reasons for the nonexistence of quantum jumps between these orbits are presented. A quantum model of a galactic cold dark matter halo with a given Navarro-Frenk-White density profile is constructed. It predicts a quantum structure of the halo that is significantly different than any previously known dark matter model. The quantum model naturally accounts for dark matter being collisionless, and it predicts that dark matter can only emit radiation of one fixed frequency. The values of this frequency are computed for dark matter particles of different masses. A potential observational verification of the theory is also discussed.

Effects of component ratios on the properties of sweet potato-oat composite dough and the quality of its steamed cake.

To enhance the value proposition of sweet potato and oat while broadening their applicability in further processing, this study systematically investigated the impact of oat flour incorporation ratios (5%-25% of sweet potato dry weight) on the quality attributes of sweet potato-oat composite dough and its resulting steamed cake products. The results showed that the addition of oat flour could promote the rheological, water retention, and thermomechanical properties of the composite dough and improve the internal microstructure, specific volume, texture, and other processing properties of the steamed cake products. The rheology, water retention, and protein stability of the dough were maximized when the proportion of oat flour was 25%. The textural properties of steamed cakes, hardness, elasticity, cohesion, adhesion, chewiness, and recovery significantly increased (p<0.05) and viscosity significantly decreased (p<0.05) with the addition of oat flour. It is noteworthy that thermodynamic properties, internal structure of the dough, and air holding capacity, which are critical for processing, showed the best results at 20% oat flour addition. Therefore, the addition of 20%-25% oats is recommended to produce composite doughs with optimal quality and processing characteristics. PRACTICAL APPLICATION: As living standards improve, traditional cereals may no longer able to meet people's health needs. Therefore, there is an urgent consumer demand for nutritious, tasty alternatives to staple foods. In this study, oat flour and sweet potato mash were mixed to make sweet potato-oat cake, and the effect of ingredient ratio on the performance and quality of composite dough containing sweet potato-oat flour was analyzed, thus proposing an innovative approach to the research, development, and industrial production of sweet potato and oat food products.

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Software testing is a process created to detect anomalies in the operation of a program or system in order to achieve the expected results. White box testing is a software testing method that tests the internal structure, design and program code. This research aims to produce an optimization method for white box testing in Java programs by utilizing branching and repetition structures using the basis path method, as well as analyzing the effectiveness of the proposed method in generating test cases. The Java program tested in this research includes input function calls, loops, branching, and exception handling. Test case design is carried out by applying the basis path method to achieve comprehensive coverage. The test results show that the base path method is able to produce effective test cases for testing control structures without redundancy. Test case design is assisted by flowgraph and matrix graph modeling. It is hoped that this research can contribute to the optimization of white box testing techniques.

Financial Well-Being Scale: Evidence of Validity and Reliability in Peruvian Context

The objective of the study is to analyze the reliability and validity evidence based on the internal structure of the InCharge Finance Distress / Financial Wellbeing (IFDFW). It was administered to 545 citizens from the three regions of Peru (coast, the Andes and the rainforest). There were 296 women and 243 men, with an average age of 33.45 years (SD= 9.65). Confirmatory factor analysis provided adequate support for the 8-item one-dimensional model with the presence of correlated errors (CMIN= 58.635, DF = 17.00; CMIN/DF =3.449; CFI = 0.979; RMSEA= 0.067; SRMR= 0.032; and PClose = 0.061), also showed a good convergent internal validity based on the value of AVE = 0.554. Regarding reliability, a Cronbach's alpha = 0.881 and a CR= 0.91 are observed, which indicates adequate reliability of the one-dimensional model of the InCharge. The results suggest that the Peruvian version of the InCharge presents adequate psychometric evidence to measure the financial well-being of employees in a Peruvian context. Thus, it seeks to contribute to the advancement of the scientific study of financial well-being in Latin America.   Received: 1 September 2023 / Accepted: 16 March 2024 / Published: 5 May 2024

Parametric optimization of selected auxetic structures

AbstractAuxetic materials exhibit an interesting, counterintuitive behavior—when subjected to uniaxial tension, they stretch laterally, and when uniaxially compressed, they shrink laterally. In contrast to conventional materials, in auxetics, the value of Poisson’s ratio is negative. Behavior of auxetic materials is an effect of their internal structures. The auxetic effect depends mostly on the geometry of their internal unit cells and not on the properties of the bulk material. This paper presents the results of parametric optimization of selected two-dimensional auxetic unit cells with the aim to identify the geometrical parameters which exhibit the strongest influence on the value of Poisson’s ratio in each unit cell, and to identify geometries which exhibit the strongest auxetic effect. The optimization was conducted through numerical simulation with the use of the finite element method in commercial software. Response surface optimization and multi-objective genetic algorithm (MOGA) were applied. Obtained candidate geometries were verified via additional FEM analyses and confirmed to have improved auxetic effect and reduced equivalent stress. 5 × 5 structures composed of reference and optimized geometries of analyzed unit cells were subjected to similar analyses and it was confirmed that the optimization of singular unit cells caused an improvement of auxetic effect and reduction in equivalent stress in regular structures composed of multiple unit cells.

Visualizing the Internal Nanocrystallinity of Calcite Due to Nonclassical Crystallization by 3D Coherent X-Ray Diffraction Imaging.

The internal crystallinity of calcite is investigated for samples synthesized using two approaches: precipitation from solution and the ammonium carbonate diffusion method. Scanning electron microscopy (SEM) analyses reveal that the calcite products precipitated using both approaches have a well-defined rhombohedron shape, consistent with the euhedral crystal habit of the mineral. The internal structure of these calcite crystals is characterized using Bragg coherent diffraction imaging (BCDI) to determine the 3D electron density and the atomic displacement field. BCDI reconstructions for crystals synthesized using the ammonium carbonate diffusion approach have the expected euhedral shape, with internal strain fields and few internal defects. In contrast, the crystals synthesized by precipitation from solution have very complex external shapes and defective internal structures, presenting null electron density regions and pronounced displacement field distributions. These heterogeneities are interpreted as multiple crystalline domains, created by a nonclassical crystallization mechanism, where smaller nanoparticles coalescence into the final euhedral particles. The combined use of SEM, X-ray diffraction (XRD), and BCDI allows for structurally differentiating calcite crystals grown with different approaches, opening new opportunities to understand how grain boundaries and internal defects alter calcite reactivity.

Advancing Lip Augmentation: State-of-the-Art 2D and 3D Analysis for Assessing Volume Enhancement and Lip Line Redefinition

Over the preceding five decades, there has been a noticeable surge in the pursuit of achieving voluminous and well-defined lips. This trend has prompted an escalating number of individuals to undergo lip augmentation procedures, aiming for a natural three-dimensional enhancement of lip volume and distinct vermilion borders. Despite the proliferation of lip augmentation techniques, there remains a dearth of comprehensive investigations into their precise effects on the three-dimensional structural integrity of the lips. This research endeavors to address this gap by employing stereophotogrammetry as a quantitative tool to scrutinize lip augmentation outcomes and to appraise the efficacy of lip plumpers. The study methodology involves a comparative analysis of lip dimensions among subjects treated with a commercial lip plumper using multi-spectral imaging for lip dimension assessment, coupled with markerless tracking technology and 3D interpolating surface methodology to analyze lip volume and shape. Additionally, the study evaluated lip youth state, including moisture level, softness, firmness, and tissue density. The demand for lip augmentation procedures is driven by perceived advantages such as quick recovery and minimal risk. Therefore, it is crucial to substantiate their efficacy with robust findings. The investigation suggests that both 3D and 2D stereophotogrammetry techniques are reliable for evaluating lip size before and after augmentation, whether through cosmetic or aesthetic approaches. Overall, the study provides a comprehensive analysis of a lip treatment aimed at enhancing volume and redesigning lip lines. It demonstrates that stereophotogrammetry is effective for assessing 3D lip dimensions and their correlation with internal lip structure. This research could be particularly valuable for evaluating the efficacy and duration of various lip enhancement techniques, including dermal fillers, implants, and topical cosmetic formulations, offering quantitative and reproducible assessments over time.

Seismic Tomography 2024

ABSTRACT Seismic tomography is the most abundant source of information about the internal structure of the Earth at scales ranging from a few meters to thousands of kilometers. It constrains the properties of active volcanoes, earthquake fault zones, deep reservoirs and storage sites, glaciers and ice sheets, or the entire globe. It contributes to outstanding societal problems related to natural hazards, resource exploration, underground storage, and many more. The recent advances in seismic tomography are being translated to nondestructive testing, medical ultrasound, and helioseismology. Nearly 50 yr after its first successful applications, this article offers a snapshot of modern seismic tomography. Focused on major challenges and particularly promising research directions, it is intended to guide both Earth science professionals and early-career scientists. The individual contributions by the coauthors provide diverse perspectives on topics that may at first seem disconnected but are closely tied together by a few coherent threads: multiparameter inversion for properties related to dynamic processes, data quality, and geographic coverage, uncertainty quantification that is useful for geologic interpretation, new formulations of tomographic inverse problems that address concrete geologic questions more directly, and the presentation and quantitative comparison of tomographic models. It remains to be seen which of these problems will be considered solved, solved to some extent, or practically unsolvable over the next decade.

A comparison of univariate and meta-analytic structural equation modeling approaches to reliability generalization applied to the Maslach Burnout Inventory

IntroductionReliability is a property of tests scores that varies from sample to sample. One way of generalizing reliability of a test is to perform a meta-analysis on some reliability estimator. In 2011, a reliability generalization meta-analysis on the Maslach Burnout Inventory (MBI) was conducted, concluding that average alpha values for the MBI dimensions ranged from 0.71 to 0.88. In the present study, we aimed to update the average reliability values of the MBI by conducting a literature search from 2010 until now and comparing to statistical procedures of meta-analysis: the Univariate approach, that were used in the previous study, and a novel meta-analytic approach based on structural equation modeling.MethodAn estimation of average reliability was done based on 69 independent primary reliability coefficients for the Univariate approach. The average reliability was based on 9 independent studies in the case of the Meta-analytic Structural Equation Modeling (MASEM) approach. Given that MASEM has the additional capability of testing the internal structure of a test, we also fitted several models.ResultsThe data was well-suited to the bifactor model, revealing the dominance of the general factor over the domain-specific ones. Acceptable overall alpha and omega coefficients were achieved for the two of the MBI dimensions, having depersonalization reliability estimates below recommendations.DiscussionIn general, the MBI can be viewed as a highly interconnected three-factor scale, being its appropriate for research purposes.

Probing the broad line region geometry and size of the gravitationally lensed quasar Q2237+0305 with microlensing time series

Lensed quasars are powerful cosmic laboratories; they are used to simultaneously probe various astrophysical phenomena. Microlensing by stars within distant galaxies acts as strong gravitational lenses of multiply imaged quasars, and provides a unique and direct measurement of the lensed quasar internal structure. Microlensing of the continuum emitting region as well as the broad-line region (BLR) is well characterized by four observable indices, $ cont BLR $, $WCI$ (wing-core), and $RBI$ (red-blue), measured directly from the spectra. During the 2004--2007 monitoring period, image A of the quadruply lensed system Q2237+0305 underwent a strong microlensing amplification, while image D remained unaffected. We used 35 epochs of archival spectrophotometric data of Q2237+0305 obtained with the Very Large Telescope of the European Southern Observatory to develop an independent microlensing method for estimating the geometry and size of the BLR. We measured the index time series for the C iv line and the continuum emission at AA . We built a library of the simulated microlensing index time series that reproduce the observed times series based on three representative BLR models: Keplerian disk (KD), polar wind (PW), and equatorial wind (EW). After sampling the model parameter space, we find that KD is the predominant model, while PW and EW are less likely. We infer that the system is viewed at an intermediate viewing angle $i $, and we estimate the most likely C iv BLR half-light radius $r_ $ light days. Our results are in good agreement with previous findings in the literature and extend the validity of the index-based approach to a temporal domain.

Internal structure optimization for noise reduction in next-generation blower silencers

Internal structure optimization for noise reduction in next-generation blower silencers

Evaluation of the Internal Structure and Reliability of the School Violence Questionnaire (CEVEIP) in Peruvian Children from 9 to 13 Years of Age

This study is of psychometric and instrumental design, aimed at examining the psychometric evidence of the CEVEIP school violence questionnaire in children aged 9 to 13 in Lima and Callao. It consists of 27 items and 3 dimensions: witnessed, perpetrated, and experienced situations. The instrument was administered virtually via a form, with 458 children participating in the final sample. Additionally, 10 expert judges were consulted, achieving a 100% agreement through Aiken's V. Explained variances of 68%, 63%, and 61% were obtained in the EFA through the indicators composing each dimension; in the CFA across the 3 dimensions, the obtained indices were CFI = .97, TLI = .96, GFI = .99, RMSEA = .04, and SRMR = .06, indicating acceptable values. Reliability assessed through ordinal alpha was higher and acceptable across the three dimensions, with values of .86, .82, and .79. Consequently, the CEVEIP questionnaire is valid and reliable for its application.  

Atomic force microscopy simulations for CO-functionalized tips with deep learning

Atomic force microscopy (AFM) operating in the frequency modulation mode with a metal tip functionalized with a CO molecule is able to image the internal structure of molecules with an unprecedented resolution. The interpretation of these images is often difficult, making the support of theoretical simulations important. Current simulation methods, particularly the most accurate ones, require expertise and resources to perform ab initio calculations for the necessary inputs (i.e charge density and electrostatic potential of the molecule). Here, we propose a computationally inexpensive and fast alternative to the physical simulation of these AFM images based on a conditional generative adversarial network (CGAN), that avoids all force calculations, and uses as the only input a 2D ball–and–stick depiction of the molecule. We discuss the performance of the model when trained with different subsets extracted from the previously published QUAM-AFM database. Our CGAN reproduces accurately the intramolecular contrast observed in the simulated images for quasi–planar molecules, but has limitations for molecules with a substantial internal corrugation, due to the strictly 2D character of the input.

Effect of Processing Routes on Physical and Mechanical Properties of Advanced Cermet System

The present research focuses on the effects of different processing routes on the physical and mechanical properties of nano Ti(CN)-based cermets with metallic binders. Tungsten carbide (WC) is added as a secondary carbide and Ni-Co is added as a metallic binder to nano Ti(CN)-based cermet processed via conventional and spark plasma sintering (SPS). A systematic comparison of the composition and sintering conditions for different cermets’ systems was carried out to design novel composition and sintering conditions. Nano TiCN powder was prepared by 30 h of ball milling. The highest density of &gt;98.5% was achieved for the SPS-processed cermets sintered at 1200 °C and 1250 °C for 3 min at 60 MPa of pressure in comparison to the conventionally sintered cermets at 1400 °C for 1 h with a two-stage compaction process—uniaxially at 150 MPa and isostatically at 300 MPa of pressure. Comparative X-ray diffraction (XRD) analysis of the milled powders at different time intervals was performed to understand the characteristics of the as-received and milled powders. Peak broadening was observed after 5 h of ball milling, and it increased to 30 hr. Also, peak broadening and a refined carbide size was observed in the XRD and scanning electron microscope (SEM) micrographs of the SPS-processed cermet. Transmission electron microscope (TEM) analysis of the milled powder showed that its internal structure had a regular periodic arrangement of planes. SEM base scattered electron (BSE) images of all the cermets primarily showed three major microstructural phases of the core–rim–binder with black, grey, and white contrast, respectively. With the present sintering conditions, a high hardness of ~16 GPa and a fracture toughness of ~9 MPa m1/2 were obtained for SPS-processed cermets sintered at higher temperatures.

Psychometric properties and cultural adaptation of the Polish Version of the Gay Affirmative Practice Scale

IntroductionThe lesbian, gay, bisexual, and transgender (LGBT) people often face unique medical disparities, including obstacles to accessing adequate and respectful care. The purpose of this study was to test the psychometric properties(internal consistency, reliability, and factor structure) of the Polish-language version of the Gay Affirmative Practice Scale (GAP-PL).MaterialThe study was conducted over a 6-month period in 2023, from February to June, involving 329 medical students and professionals who evaluated the GAP-PL.MethodsBefore testing the psychometric properties of the original Gay Affirmative Practice Scale (GAP), it was translated and adapted from the original English language version into the Polish language. Authors then tested the psychometric properties of the tool on a sample of 329 participants. The internal coherence of the questionnaire was tested with the analysis of verifying factors (Confirmatory Factor Analysis). Cronbach alpha and the discriminatory power index were used as internal consistency measures.ResultsThere were more female than male participants (55.32%). More than 53% of the participants were heterosexual, and the average age of the respondents was ~30 years. The internal consistency of the Polish-language version and its domains was strong with the overall Cronbach's alpha ranges for each subscale domains ranging between 0.936 and 0.949. The McDonald's omega coefficient was 0.963.ConclusionThe GAP-PL has excellent properties of factorial validity and can be used in research and clinical practice in Polish-speaking populations.

Surface-Area Enhanced Raman Spectroscopy of DNA in Porous Silica: A Quantitative and Reproducible Alternative to Plasmonic-Based SERS.

Despite the success of surface-enhanced Raman spectroscopy (SERS) for detecting DNA immobilized on plasmonic metal surfaces, its quantitative response is limited by the rapid falloff of enhancement with distance from the metal surface and variations in sensitivity that depend on orientation and proximity to plasmonic "hot spots". In this work, we assess an alternative approach for enhancing detection by immobilizing DNA on the interior surfaces of porous silica particles. These substrates provide over a 1000-fold greater surface area for detection compared to a planar support. The porous silica substrate is a purely dielectric material with randomly oriented internal surfaces, where scattering is independent of proximity and orientation of oligonucleotides relative to the silica surface. We characterize the quantitative response of Raman scattering from DNA in porous silica particles with sequences used in previous SERS investigations of DNA for comparison. The results show that Raman scattering of DNA in porous silica is independent of distance of nucleotides from the silica surface, allowing detection of longer DNA strands with constant sensitivity. The surface area enhancement within particles is reproducible (<4% particle-to-particle variation) owing to the uniform internal pore structure and surface chemistry of the silica support. DNA immobilization with a bis-thiosuccinimide linker provides a Raman-active internal standard for quantitative interpretation of Raman scattering results. Despite the high (30 mM) concentrations of immobilized DNA within porous silica particles, they can be used to measure nanomolar binding affinities of target molecules to DNA by equilibrating a very small number of particles with a sufficiently large volume of low-concentration solution of target molecules.

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.