Simulation of space weathering on asteroid spectra through hydrogen ion and laser irradiation of meteorites

Space weathering can be defined as the combination of physical and chemical changes that occur in material exposed to an interplanetary environment on the surface of airless bodies. This process produces amorphous surface layers often containing small opaque particles such as nanophase metallic iron (npFe0). This darkens the topmost layer resulting in alterations in material spectroscopic features.Eventually it can lead to misinterpretation of remotely sensed data in the visible- near-infrared (VIS-NIR) spectrum. The goal of this research is to simulate solar wind effects on asteroid spectra through low energy 1 keV hydrogen ion irradiation of meteorite powder samples and measure the changes in their reflectance spectra. This allows to understand how space weathering depends on the mineralogy of the material. We used Bjurböle (L/LL4), Avanhandava (H6) and Luotolax (Howardite) meteorites. H+ ion irradiation was carried out on powdered samples compressed into pellets. The pellets were placed into a vacuum chamber with pressure between 1.2 x 10 -7-2.4 x 10 -7 mbar for the whole experiment. To simulate solar wind irradiation, H+ ions were used with 1 keV under three fluences; 1 x 1017, 2 x 1017 and 5 x 1017 ions/cm2. Subsequently reflectance spectra of the samples were measured and processes using Modified Gaussian Model (MGM) to derive key spectral parameters. Both chondrites show significant reddening in the VIS region. Bjurböle being an LL, it is more oxidized than Avanhandava. The reddening in the NIR region is more significant in Avanhandava than in Bjurböle. My work indicates that even for low-energy solar wind conditions, the chondritic materials (Q/S-type asteroids) with high olivine content and/or higher fayalite (Fa) compositions are more susceptible to silicate absorption bands reduction. Luotolax meteorite being howardite rich in orthopyroxene and clinopyroxene, shows VIS reddening but not observable band depth changes with increasing exposure to H+ ion irradiation. The smaller change in Luotolax may be due to higher pyroxene resistance to low-energy ion irradiation. Overall, at short timescales and typical solar wind energies, VIS slope reddening is the most dominant factor in all three material compositions

Finding Street Gang Members on Twitter

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016

Impact Of Social Media Influencers’ Credibility on The Purchase Intention: Reference to The Beauty Industry

Purpose: The role of social media influencers is growing in importance, due to their ability to effectively influence. In addition, marketers have identified Instagram as the most important social media channel for influencer marketing, while the concept of credibility has always been significant in the field of influencer marketing. While a few studies have recently focused on how social media influencers influence consumer purchase behavior, research that specifically focuses on influencer credibility and its impact on purchase behavior is still scarce. Therefore, the purpose of this study is to investigate the impact of influencer credibility on the purchase intention of beauty products in Sri Lanka. Design/methodology/approach: Quantitative study was conducted using the survey method. The sample consists of 150 Instagram users between the ages of 18 and 34 who live in Colombo, Sri Lanka. To empirically test the conceptual model, single and multiple regression analyses were used. Findings: Results suggest that there’s a positive impact of credibility dimensions towards purchase intention and the most impactful credibility factor towards purchase intention is “trustworthiness” Originality: This research contributed to the current debates about the credibility of social media influencers. Furthermore, this study focuses on the impact of Instagram influencer credibility dimensions, which is still scarce. So, the current paper fills a gap in the limited existing literature on the credibility of social media influencers on purchase intention, with a focus on the beauty industry in the Sri Lankan context. Implications: The findings assist marketers and advertisers in the fashion industry in understanding how influencer marketing affects consumer purchase intent. Additionally, this provides important insights to influencers in order for them to be successful influencers. Keywords: Social media influencers, Influencer Marketing, Instagram Influencers, Influencer Credibility, Beauty Industry

On an entire function represented by multiple Dirichlet series

summary:Consider the space $L$ of entire functions represented by multiple Dirichlet series that becomes a non uniformly convex Banach space which is also proved to be dense, countable and separable. Continuing further, for the given space $L$ the characterization of bounded linear transformations in terms of matrix and characterization of linear functional has been obtained

Social Work in Community Mental Health Teams: An Ethnographic Study with Two Community Mental Health Teams

Within the Community Mental Health Teams (CMHTs) in UK, Approved Mental Health Practitioners (AMHPs) and Mental Health Social Workers (MHSWs) from Local Authority Social Services Department (LASSD) work alongside other mental health professionals from health service backgrounds, promoting a multi-disciplinary model of working. However little is known about the impact of this model on these professionals. This research endeavoured to understand mental health social work interventions in multidisciplinary CMHTs in respect of: 1.The practice of general mental health social work 2.The impact of 2007 MHA on social work practice. 3.Mental Health Act assessments (statutory role) 4.Contributions to interdisciplinary mental health teams 5.Barriers and difficulties in integrated working An ethnographic approach in two CMHTs was employed in drawing upon secondary data, observations and interviews with AMHPs, MHSWs, other mental health professionals and service users, facilitating a rich understanding of the social work role from different perspectives. Bronfenbrenner’s (1977) ‘Ecology of Human Development Theory’ provided the conceptual and theoretical framework for the study, by identifying the different systems social work professionals interact in their practice. The findings reveal tensions in the microsystem (CMHT) on role definition, losing professional identity, difficulties in care coordination and stigma and status of social work professionals. Tensions in the exosystem include: poor collaboration between LASSD and the Mental Health Trust, fragmented relationships between AMHPs/MHSWs and LASSD, difficulties in working in specialist teams and the medical dominance in CMHTs. Findings on the macrosystem reveal impact of policies and legislation on social work professionals’ roles. I intend that these results will contribute significantly to the development and profile of MHSWs and AMHPs, as a professional group, and in turn will improve and develop the quality of social work support within mental health services. This subsequently will improve outcomes for service users, carers and communities

Why is the ocean surface slightly warmer than the atmosphere?

How much warmer is the ocean surface than the atmosphere directly above it? The present study offers a means to quantify this temperature difference using a conceptual nonlinear one-dimensional global energy balance coupled ocean–atmosphere model ( Aqua Planet ). The significance of our idealized model, which is of intermediate complexity, is its ability to obtain an analytical solution for the global average temperatures. Our analytical model results show that, for the present climate, predicted global mean ocean temperature is 291.1 K whereas surface atmospheric temperature above the ocean surface is 287.4 K. Thus, the modeled surface ocean is 3.7 K warmer than the atmosphere above it. Temporal perturbation of the global mean solution obtained for Aqua Planet showed a stable system. Oscillation amplitude of the atmospheric temperature anomaly is greater in magnitude than those found in the ocean. There is a phase shift (a lag in the ocean), which is caused by oceanic thermal inertia. Climate feedbacks due to selected climate parameters such as incoming radiation, cloud cover, and CO2 are discussed. Warming obtained with our model compares well with Intergovernmental Panel on Climate Change\u27s (IPCC) estimations. Application of our model to local regions illuminates the importance of evaporative cooling in determining derived air–sea temperature offsets, where an increase in the latter increases the systems overall sensitivity to evaporative cooling

Nonparametric approach to reliability and its applications

Reliability concepts are used by reliability engineers in the industry to perform systematic reliability studies for the identification and possible elimination of failure causes, quantification of failure occurrences and for the reduction of failure consequences. Apart from applications to mechanical, electronic systems and software, reliability concepts are heavily used in biomedicine to model and understand biological processes such as aging. The standard approach in estimating reliability measures is to assume that the underlying lifetime distribution is known, even if only approximately. When the assumed parametric model is valid, the accuracy of corresponding inferences made based on the estimated function is usually sufficient. However, when this is in doubt, use of a parametric approach could lead to inaccurate inferences. In the literature, this issue has been studied extensively. In such circumstances, estimating these reliability measures using nonparametric techniques has the advantage of flexibility as they generally impose less restriction on the underlying distribution of the life time variable. This thesis considers three popular reliability measures, namely, Reversed Hazard Rate (RHR), Expected Inactivity Time (EIT) and Mean Residual Life (MRL) functions and introduces new estimation methods based on a nonparametric technique called the fixed-design local polynomial regression method. Investigations were undertaken on the theoretical properties of these estimators such as their asymptotic bias, variance and distribution. Extensive simulations were carried out to investigate their performances. The thesis also introduces some novel hypothesis testing procedures for comparing between reliability measures based on samples from two populations using nonparametric techniques. Finally, these methods were applied to address various interesting problems in biomedicine and reliability engineering to demonstrate their practical utility

LOCALLY PRIMITIVELY UNIVERSAL FORMS AND THE PRIMITIVE COUNTERPART TO THE FIFTEEN THEOREM

An n-dimensional integral quadratic form over Z is a polynomial of the form f = f(x1, … ,xn) =∑_(1≤i,j ≤n)▒a_ij x_i x_j, where a_ij=a_ji in Z. An integral quadratic form is called positive definite if f(α_1, …,α_n) \u3e 0 whenever (0, … , 0) ≠(α_1, …,α_n) in Z^n. A positive definite integral quadratic form is said to be almost (primitively) universal if it (primitively) represents all but at most finitely many positive integers. In general, almost primitive universality is a stronger property than almost universality. Main results of this study are: every primitively universal form non-trivially represents zero over every ring Z_p of p-adic integers, and every almost universal form in five or more variables is almost primitively universal. With use of these results and improving a result of G. Pall from 1946, we then provide criteria to determine whether a given integral quadratic lattice over a ring Z_p of p-adic integers is Z_p-universal or primitively Z_p-universal. The criteria are stated explicitly in terms of a Jordan splitting of the lattice. As an application of the local criteria, we complete the determination of the universal positive definite classically integral quaternary quadratic forms that are almost primitively universal, which was initiated in work of N. Budarina in 2010. Finally, with the use of these local results, we identify 28 positive definite classically integral primitively universal quaternary quadratic forms which were not known previously, introducing a conjecture obtained by a numerical approach, which could possibly be the primitive counterpart to the Fifteen Theorem proved by J.H. Conway and W.A. Schneeberger in 1993

Directed Genome Evolution to Identify Genes for Macrophage Survival by Staphylococcus agnetis

Bacterial Chondronecrosis with Osteomyelitis (BCO) is a debilitating infection that negatively impacts animal welfare and costs the broiler industry billions of dollars annually. We have previously isolated Staphylococcus agnetis 908 from BCO samples obtained from broilers at the University of Arkansas research farm. This isolate can induce BCO lameness at greater than 50% in broilers exposed to the pathogen in drinking water. We found that S. agnetis 908 is capable of surviving and escaping macrophages compared to a closely related cattle isolate,1379. Through Directed Genome Evolution (DGE) we identified that this difference is at least partially associated with an alanine to glutamate substitution for residue 164 of the enzyme deoxyribose phosphate aldolase (a.k.a. deoC, DERA). This study further explores whether A164E in deoC is responsible for enhanced survival and escape of S. agnetis 908 from macrophages. S. agnetis 1379 was transformed with the PCR products of the 908 deoC1 and deoC2 paralogs. The resulting transformants were cocultured with chicken macrophage-like cells in standard phagocytosis assays for DGE. The survivors were characterized for sequence changes in deoC through PCR sequencing. In addition, we investigated the effect of the A164E on host-cell damage by utilizing gentamicin protection assays in tandem with crystal violet staining. We evaluate the drawbacks of the gentamicin protection assay and evaluated an alternative enzyme protection assay. The crystal violet staining revealed that 908 and the transformant of 1379 inflicted more cellular damage. The enzyme protection assay was superior to gentamicin protection and indicated that there were no significant differences in the damage to HTCs caused by the different bacterial strains. Identification of the bacterial virulence factors important to the infection process, and how these interact with the host immune responses are important in devising management plans for mitigation of BCO which would help the broiler industry control this important and costly disease

Quantum Autoencoders for Learning Quantum Channel Codes

This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum channel codes and evaluate their effectiveness. We explore classical, entanglement-assisted, and quantum communication scenarios within our framework. Applying it to various quantum channel models as proof of concept, we demonstrate strong performance in each case. Our results highlight the potential of quantum machine learning in advancing research on quantum communication systems, enabling a better understanding of capacity bounds under modulation constraints, various communication settings, and diverse channel models.Comment: Submitted to IEEE GLOBECOM 2023 and is subject to licence chang