Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Gender Gap in the Conceptual Survey of Electricity Exploring the Gender Gap in the Conceptual Survey of Electricity and Magnetism and Magnetism

The “ gender gap ” on various physics conceptual evaluations has been extensively studied. Men ’ s average pretest scores on the Force Concept Inventory and Force and Motion Conceptual Evaluation are 13% higher than women ’ s, and post-test scores are on average 12% higher than women ’ s. This study analyzed the gender differences within the Conceptual Survey of Electricity and Magnetism (CSEM) in which the gender gap has been less well studied and is less consistent. In the current study, data collected from 1407 students (77% men, 23% women) in a calculus-based physics course over ten semesters showed that male students outperformed female students on the CSEM pretest (5%) and post-test (6%). Separate analyses were conducted for qualitative and quantitative problems on lab quizzes and course exams and showed that male students outperformed female students by 3% on qualitative quiz and exam problems. Male and female students performed equally on the quantitative course exam problems. The gender gaps within CSEM post-test scores, qualitative lab quiz scores, and qualitative exam scores were insignificant for students with a CSEM pretest score of 25% or less but grew as pretest scores increased. Structural equation modeling demonstrated that a latent variable, called Conceptual Physics Performance/Non-Quantitative (CPP/NonQnt), orthogonal to quantitative test performance was useful in explaining the differences observed in qualitative performance; this variable was most strongly related to CSEM post-test scores. The CPP/NonQnt of male students was 0.44 standard deviations higher than female students. The CSEM pretest measured CPP/NonQnt much less accurately for women ( R 2 ¼ 4% ) than for men ( R 2 ¼ 17% ). The failure to detect a gender gap for students scoring 25% or less on the pretest suggests that the CSEM instrument itself is not gender biased. The failure to find a performance difference in quantitative test performance while detecting a gap in qualitative performance suggests the qualitative differences do not result from psychological factors such as science anxiety or stereotype threat. DOI: 10.1103/PhysRevPhysEducRes.13. 020114
Systematic Review of the Application of Computational Fluid Dynamics for Adult Aortic Diseases

Background : Computational fluid dynamics (CFD) is a new medical method combining medicine and science. The aim of this study is to summarize and analyze the application of CFD in adult aortic diseases. Methods : This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search in the PubMed, Cochrane Library and Chinese databases identified 47 highly relevant articles. Studies were included if they assessed biomechanical markers and their potential association with progression or rupture of aortic aneurysms or dissections. Results : There are no randomized controlled trials to examine the direct relationship between all biomechanical parameters and aortic disease progression or rupture. Wall stress and peak wall rupture risk can predict the risk of aortic aneurysm rupture using biomechanics, which is more accurate than the prediction based on “diameter” alone. Areas with lower time averaged wall shear stress (TAWSS) and higher oscillatory shear index (OSI) are at risk for further aortic expansion or dissection. Higher relative residence time (RRT) area can predict platelet activation and thrombosis. In addition, pressure, flow field and other indicators can also roughly predict the risk of aortic disease progression. Conclusions : Contemporary evidence suggests that CFD can provide additional hemodynamic parameters, which have the potential to predict the progression of aortic lesions, the effect of surgical intervention, and prognosis.
Length-weight relationship and condition factor of Tade gray mullet, Chelon planiceps (Valenciennes, 1836) from Hooghly-Matlah Estuary, West Bengal, India

Tade gray mullet (Chelon planiceps) forms a lucrative fishery in the Hooghly-Matlah estuarine system. During eight months of investigation 232 specimens were examined to study length-weight relationship and relative condition factor (Kn). The length and weight of fish was varied from 41 to 283 mm and 0.81 to 208 g respectively. Two length-weight relationship equations were obtained from the analysis, W=0.0000397L2. 952 for <187 mm size group and W=0.0000406L2. 834 for >187 mm size group. The size at first maturity was recorded as 187 mm. The b value was not significantly different from 3 indicating isometric growth. The monthly mean Kn for the male and female were 1.041–1.125 and 1.009–1.236 respectively. The highest Kn value was recorded in July for both the sexes, which may be due to advance maturity stages. The present study will be useful in the context of biological management of mullet fishery as well as to find out aqua-farming potential of Tade gray mullet.
DigitalCommons@UNO DigitalCommons@UNO

— Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and biological importance can be seen in/between structurally defined nodes, such as hub nodes and driver nodes, within a network and within clusters. This work proposes data mining approaches for identification and examination of relationships between hub and driver nodes within human, yeast, rat, and mouse PPI networks. Relationships with other types of significant nodes, with direct neighbors, and with the rest of the network were analyzed to determine if the model can be characterized biologically by its structural makeup. We performed numerous tests on structure with a data-driven mentality, looking for properties that were potentially significant on a network level and then comparing those properties to biological significance. Our results showed that identifying and cross-referencing different types of topologically significant nodes can exemplify properties such as transcription factor enrichment, lethality, clustering, and Gene Ontology (GO) enrichment. Mining the biological networks, we discovered a key relationship between network properties and how sparse/dense a network is — a property we described as “sparseness”. Overall, structurally important nodes were found to have significant biological relevance.
The mitigating consequences of perceived organisational support on the influence of worker morale in a distributed work landscape

The object of this research is the influence of a distributed work methodology on worker morale in the milieu of perceived organizational support. This paper aims to explore the influence of a distributed work methodology on worker morale in the milieu of perceived organizational support. One of the most challenging aspects of distributed work methodology is recognized and adapting to an unanticipated and novel work approach which is unchartered territory for many. During this research, a mixed methodology is used with a hypothetico-deductive approach, which enabled the testing of a working hypothesis through a sample in the administrative cadre in the public health sector, Cape Town, South Africa. As a result of the research, it is shown that mental health with continued well-being was confirmed as primarily well supported and mitigated by numerous organizational support incentives and therefore, work outcomes are found to be positively related to the influence of worker morale. As worker morale influences worker commitment and engagement levels, which has organizational behavior and productivity implications, it is clear from the findings that conceptualizing multi-dimensional well-being at work is vital to an in-depth comprehension of the relationship between living meaningfully and being content at work. Consequently, in terms of application, it befits management cadres to look at the evidence and ponder the best approach for the future of work. The supposition of a shifted and adapted management-subordinate control that relies primarily on the monitoring of remote outputs is supported, and therefore a proposed approach for future research should be focused on the role of organizational control in flexible work practices. More diverse sampling, representative of various corporate entities and inclusive of private entities, is recommended.
Influence of 2 Weeks of Mango Ingestion on Inflammation Resolution after Vigorous Exercise

Mangoes have a unique nutrient profile (carotenoids, polyphenols, sugars, and vitamins) that we hypothesized would mitigate post-exercise inflammation. This study examined the effects of mango ingestion on moderating exercise-induced inflammation in a randomized crossover trial with 22 cyclists. In random order with trials separated by a 2-week washout period, the cyclists ingested 330 g mango/day with 0.5 L water or 0.5 L of water alone for 2 weeks, followed by a 2.25 h cycling bout challenge. Blood and urine samples were collected pre- and post-2 weeks of supplementation, with additional blood samples collected immediately post-exercise and 1.5-h, 3-h, and 24 h post-exercise. Urine samples were analyzed for targeted mango-related metabolites. The blood samples were analyzed for 67 oxylipins, which are upstream regulators of inflammation and other physiological processes. After 2 weeks of mango ingestion, three targeted urine mango-related phenolic metabolites were significantly elevated compared to water alone (interaction effects, p ≤ 0.003). Significant post-exercise increases were measured for 49 oxylipins, but various subgroup analyses showed no differences in the pattern of change between trials (all interaction effects, p > 0.150). The 2.25 h cycling bouts induced significant inflammation, but no countermeasure effect was found after 2 weeks of mango ingestion despite the elevation of mango gut-derived phenolic metabolites.
Simulating multi-hazard event sets for life cycle consequence analysis

In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates “Level I” (or occurrence) interactions from “Level II” (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., heavy rain and flooding; liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, 5 which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard Life Cycle Consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, down-time, casualty rates) throughout a system’s service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qual-10 itatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method 15 outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method.
Deep Support Vector Machines for Regression Problems

Machine learning is one of the more actual topics within modern Artificial Intelligence. One of the most popular algorithms used for machine learning is the support vector machine (SVM) [3, 10, 4, 2]. SVMs provide a robust method to solve classification and regression problems. In order to project data on the featurespace SVMs make use of kernels [4, 8]. Traditionally only single kernels were used, but in the past decade several methods have been proposed to apply multiple kernel learning [1, 7] to support vector machines. However, all of these kernels are designed a priori, and can not be decomposed into subkernels. This paper will propose a new method for support vector machines using a new layer of support vector machines instead of a-priori defined kernels. This new method will be dubbed the ’Deep Support Vector Machine’ (DSVM). The deep support vector machine will use an extra layer of support vector machines in order to learn to extract features from a set of inputs. These features will be propagated to the ’main’ SVM, which will use those to construct a decision function. Once this decision function has been created, the outcome values produced by the main SVM, will be used to adapt to the different features. This method may be used for both classification and regression problems, however the focus of this paper will only be on regression problems. Through the essence of the structure of the deep support vector machine it is also possible to concatenate several layers of support vector machines, so that not only the main SVM will use support vector machines to learn its features, but also feature SVMs might make use of SVMs that extract
Design of a Flexible, Wearable Interdigitated Capacitive Sensor for Monitoring Biomarkers of Atopic Dermatitis

The emergence of wearable and remote sensing devices in e-health has revolutionized personalized healthcare monitoring, but the field of dermatology has yet to witness significant progress in this area. The diagnosis and monitoring of atopic dermatitis (AD), a common skin disease causing inflammation and rashes, depend on in-person assessment, leading to subjective evaluations and treatment procedures. This work presents a novel interdigitated capacitive sensor, specifically designed to detect biomarkers of AD and to empirically estimate the severity of the condition. The design was proven by finite-element analysis software to detect only biomarkers of AD—dehydration of the outermost skin layer. The IDC sensor has a compact and flexible footprint (sensing area of around 40 mm2); it is compatible with a wearable and noninvasive system, and can be used to remotely gather localized data about the condition. To calibrate its measurements, a novel hydration-gradient skin-mimicking phantom was developed, which simulates varying skin hydration conditions on the same substrate. The sensor is compared against a commercial skin hydration device, and its sensitivity is mapped against the arbitrary units (A.U.s) of the commercial device, resulting in a normalized capacitance sensitivity of 13.1 pF/cm2 per 1 A.U. This novel IDC sensor is the first sensor to accurately measure the permittivity of specific layers of the skin. Its bespoke design is the main enabler of the outermost layer’s permittivity to be isolated from the skin. This presents promising prospects for remote patient care, enabling accurate diagnosis, treatment monitoring, and personalized interventions for AD.
Aprendizaje Basado en Proyectos (ABP): evaluación desde la perspectiva de alumnos de Educación Primaria

Given the consolidation of project based learning (PBL) and its ever more frequent use in schools as a mechanism to develop curricular competences, increase student motivation and self-esteem, foster research and promote collaboration (Railsback, 2002), we designed an assessment scale for project based learning from the perspective of the students, guaranteeing its validity and reliability. The scale was administered to 364 students of Primary Education to analyze the results of different collaborative learning projects and to see whether there were differences in the assessments in terms of gender and type of project. Among the main results it can be highlighted that in students’ opinion, the projects proposed in class were well-organized, the tasks were well-defined, and the group size were adequate; moreover, the teachers provided the necessary help and feedback regarding the students’ achievements while they stimulated student interaction and collaborative learning in which a good working climate was shared and fostered. Inferential analyses showed that there were significant differences in terms of project type, but not as a function of gender.
Effects of four plants and solarization on bacterial wilt of tomato caused by Ralstonia solanacearum E. F. Smith in Burkina Faso

Bacterial wilt caused by Ralstonia solanacearum is a major constraint in tomato production. Experiments were carried out using four sanitised plants and solarization in a semi-controlled environment and in the field to reduce the infectious potential of the soil in R. solanacearum . The experimental design used is a randomized Fisher block with eight (8) treatments composed of Ocimum basilicom , Ocimum gratissimum , Allium cepa , Crotalaria retusa , solarization, untreated control, bactericide (IDEFIX) and the biocontrol indicator (Rossol). Seventy days after the implementation in the field, the initial infectious potential of 1.07×10 8 CFU g -1 of dry soil increased to 4.11×10 7 CFU g -1 of dry soil, an average reduction of 55.63%. O. gratissimum is the best sanitizing plant with 68.18% reduction in the infectious potential of the soil. In a semi-controlled environment C. retusa recorded the greatest reduction (73.96%) of the infectious potential of the soil among the sanitizing plants. The greatest reductions in disease incidence in the field were observed with solarization (60%) followed by C. retusa (58%).
Late Cenozoic - Quaternary thermo-tectonic history of Higher Himalayan Crystalline (HHC) in Kishtwar-Padar-Zanskar region, NW Himalaya: evidence from fission track ages

Fission Track (m) ages of apatite and zircon from the Higher Himalayan Crystalline (HHC), SE - Jammu and Kashmiralong Chenab-Suru-Dodarivers and their tributaries provide constraints on the cooling (<250°C) and exhumation history of these rocks. FT ages of apatite/zircon versus the topographic elevations of the host samples from different traverses provide linear relationships, indicating differential and secular nature of exhumation in the Himalaya. The HHC belt is exhumedat a rate of about 0.27 mm/a during Middle to Late Miocene. However, regional exhumation rate near the base along the Main Central Thrust and in central parts of the HHC from FT apatite ages is faster upto 0.35 mm/a since Late Miocene. Exhumation has considerably slowed down to 0.11 mm/a along its northern boundary and 0.02 mm/a along the Zanskar Shear Zone. No apparent faster exhurnationis discernible either along thee MCT or the Zanskar ShearZone (ZSZ). On the other hand, three large fold structures namely the Suru Dome, the Chisoti Dome and the Kishtwaranti formal window have indicated very young and fast exhumation of 0.33 mm/a, 1.1 mm/a and 3.6 mm/a respectively during the last 1 to 5 Ma.
Bushfires and Mothers’ Mental Health in Pregnancy and Recent Post-Partum

Background: The compounding effects of climate change catastrophes such as bushfires and pandemics impose significant burden on individuals, societies, and their economies. The enduring effects of such syndemics on mental health remain poorly understood, particularly for at-risk populations (e.g., pregnant women and newborns). The aim of this study was to investigate the impact of direct and indirect exposure to the 2019/20 Australian Capital Territory and South-Eastern New South Wales bushfires followed by COVID-19 on the mental health and wellbeing of pregnant women and mothers with newborn babies. Methods: All women who were pregnant, had given birth, or were within three months of conceiving during the 2019/2020 bushfires, lived within the catchment area, and provided consent were invited to participate. Those who consented were asked to complete three online surveys. Mental health was assessed with the DASS-21 and the WHO-5. Bushfire, smoke, and COVID-19 exposures were assessed by self-report. Cross-sectional associations between exposures and mental health measures were tested with hierarchical regression models. Results: Of the women who participated, and had minimum data (n = 919), most (>75%) reported at least one acute bushfire exposure and 63% reported severe smoke exposure. Compared to Australian norms, participants had higher depression (+12%), anxiety (+35%), and stress (+43%) scores. Women with greater exposure to bushfires/smoke but not COVID-19 had poorer scores on all mental health measures. Conclusions: These findings provide novel evidence that the mental health of pregnant women and mothers of newborn babies is vulnerable to major climate catastrophes such as bushfires.