Electromagnetic induction imaging with a radio-frequency atomic magnetometer

We report on a compact, tunable, and scalable to large arrays imaging device, based on a radio-frequency optically pumped atomic magnetometer operating in magnetic induction tomography modality. Imaging of conductive objects is performed at room temperature, in an unshielded environment and without background subtraction. Conductivity maps of target objects exhibit not only excellent performance in terms of shape reconstruction but also demonstrate detection of sub-millimetric cracks and penetration of conductive barriers. The results presented here demonstrate the potential of a future generation of imaging instruments, which combine magnetic induction tomography and the unmatched performance of atomic magnetometers.Comment: 5 pages, 5 figure

European American Therapist Self-Disclosure in Cross-Cultural Counseling

Eleven European American psychotherapists\u27 use of self-disclosure in cross-cultural counseling was studied using consensual qualitative research. As reasons for self-disclosing, therapists reported the intent to enhance the counseling relationship, acknowledge the role of racism/oppression in clients\u27 lives, and acknowledge their own racist/oppressive attitudes. Results indicated that therapists typically shared their reactions to clients\u27 experiences of racism or oppression and that these self-disclosures typically had positive effects in therapy, often improving the counseling relationship by helping clients feel understood and enabling clients to advance to other important issues

Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution

In this article, we present a novel data assimilation strategy in pore-scale imaging and demonstrate that this makes it possible to robustly address reactive inverse problems incorporating Uncertainty Quantification (UQ). Pore-scale modeling of reactive flow offers a valuable opportunity to investigate the evolution of macro-scale properties subject to dynamic processes. Yet, they suffer from imaging limitations arising from the associated X-ray microtomography (X-ray microCT) process, which induces discrepancies in the properties estimates. Assessment of the kinetic parameters also raises challenges, as reactive coefficients are critical parameters that can cover a wide range of values. We account for these two issues and ensure reliable calibration of pore-scale modeling, based on dynamical microCT images, by integrating uncertainty quantification in the workflow. The present method is based on a multitasking formulation of reactive inverse problems combining data-driven and physics-informed techniques in calcite dissolution. This allows quantifying morphological uncertainties on the porosity field and estimating reactive parameter ranges through prescribed PDE models with a latent concentration field and dynamical microCT. The data assimilation strategy relies on sequential reinforcement incorporating successively additional PDE constraints. We guarantee robust and unbiased uncertainty quantification by straightforward adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), ensuring reliable micro-porosity changes during geochemical transformations. We demonstrate successful Bayesian Inference in 1D+Time and 2D+Time calcite dissolution based on synthetic microCT images with meaningful posterior distribution on the reactive parameters and dimensionless numbers

Undergraduate Kinesiology Student Involvement in the Department and Sense of Belonging by Employment Status

Many undergraduate college students find the need to work either on or off campus in order to pay for their tuition and expenses. It is unknown whether or not employment impacts a students ability to be involved in the department or feel like they belong and not typically reported for Kinesiology programs. PURPOSE: To assess the impact employment had on college students in relation to their sense of belonging and involvement with their home department and to investigate belonging and involvement in Kinesiology students. METHODS: Undergraduate college/university students (18+ years) at a public institution in Southern California were recruited via flyers, social media, announcements and word of mouth to participate in an IRB approved online research study. The cross-sectional Qualtrics survey consisted of a few demographic questions (age, gender, major, employment status, etc.), 9 questions specific to involvement in the department (6-point Likert scale: 1=strongly disagree to 6=strongly agree) and 11 questions for belonging divided into two domains; social acceptance and valued competence (same Likert scale as involvement). For the involvement and belonging questions an average and sum score were determined for each individual, which was used in the statistical analysis. For employment, participants were asked if they were employed (yes/no) during the Spring 2023 semester, which was then coded to form the groups. Statistical analysis software (IBM SPSS v.28) was used to assess the differences between those that indicated they were employed and not employed and a p-value of 0.05 was implemented for significance. RESULTS: College undergraduate participants (N=149, age; 24.3 土 5.7 years, gender identification; n=1 other, n=111 female, and n=37 male, and Kinesiology n=83, or other majors n=59) indicated they were employed (n=86) or unemployed (n=26) and Kinesiology students reported similar employment statistics (employed n=51 and unemployed n=11). An Independent T-Test found no significant differences (P\u3e0.05) in involvement or belonging for employment status in all students and for those who declared Kinesiology as their major during Spring 2023. CONCLUSION: Employment status had no significant impact on an undergraduate student\u27s involvement in their major department or their sense of belonging

Loading of Meiotic Cohesin by SCC-2 Is Required for Early Processing of DSBs and for the DNA Damage Checkpoint

SummaryBackgroundChromosome segregation and the repair of DNA double-strand breaks (DSBs) by homologous recombination require cohesin, the protein complex that mediates sister chromatid cohesion (SCC). In addition, cohesin is also required for the integrity of DNA damage checkpoints in somatic cells, where cohesin loading depends on a conserved complex containing the Scc2/Nipbl protein. Although cohesin is required for the completion of meiotic recombination, little is known about how cohesin promotes the repair of meiotic DSBs and about the factors that promote loading of cohesin during meiosis.ResultsHere we show that during Caenorhabditis elegans meiosis, loading of cohesin requires SCC-2, whereas the cohesin-related complexes condensin and SMC-5/6 can be loaded by mechanisms independent of both SCC-2 and cohesin. Although the lack of cohesin in scc-2 mutants impairs the repair of meiotic DSBs, surprisingly, the persistent DNA damage fails to trigger an apoptotic response of the conserved pachytene DNA damage checkpoint. Mutants carrying an scc-3 allele that abrogates loading of meiotic cohesin are also deficient in the apoptotic response of the pachytene checkpoint, and both scc-2 and scc-3 mutants fail to recruit the DNA damage sensor 9-1-1 complex onto persistent damage sites during meiosis. Furthermore, we show that meiotic cohesin is also required for the timely loading of the RAD-51 recombinase to irradiation-induced DSBs.ConclusionsWe propose that meiotic cohesin promotes DSB processing and recruitment of DNA damage checkpoint proteins, thus implicating cohesin in the earliest steps of the DNA damage response during meiosis

Composite Bonded Joint: Repair Development

This report documents the details of my work as a NASA KSC intern for the Fall Session from August 27th to December 14th, 2018. My efforts and contributions were with the Materials Science Branch, a staffed organization within the Laboratories, Development, & Testing (NE-L) Division of the Engineering Directorate. The principle responsibilities of the Materials Science group are to support the design, development, and operations activities for materials and processes with the purpose of providing unique solutions for flight hardware, ground support equipment, and customer requests. My role as an intern focused on assisting engineers in developing repair processes to mature bonded joint technology in support of SLS ? scale hardware. My primary goals for this internship were to become more familiar with composite materials and learn more about processes I was unfamiliar with, such as prepregs and out-of-autoclave processing. This project allowed me to learn new skills such as scarfing and curing composites. Additional goals I had were to learn more about NASA's laboratories and projects under development. This internship not only provided me with those experiences, but also allowed me to build relationships with inspiring engineers; a takeaway I will never forget

Understanding Violence and Prevention During a Pandemic: California news about guns, gun violence, and firearm suicide 2020-2021

A key component of stopping gun violence and firearm suicide in America is understanding the complete picture of these public health crises. Do journalists cover these issues thoroughly and effectively? How has coverage changed in recent years since nationwide protests against police brutality and structural racism have put some types of gun violence under more intense scrutiny? This research report sheds light on the coverage and how advocates can continue to shift the narrative on violence

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible to robustly address multi-objective and multi-scale problems. BPINNs are a popular framework for data assimilation, combining the constraints of Uncertainty Quantification (UQ) and Partial Differential Equation (PDE). The relative weights of the BPINN target distribution terms are directly related to the inherent uncertainty in the respective learning tasks. Yet, they are usually manually set a-priori, that can lead to pathological behavior, stability concerns, and to conflicts between tasks which are obstacles that have deterred the use of BPINNs for inverse problems with multi-scale dynamics. The present weighting strategy automatically tunes the weights by considering the multi-task nature of target posterior distribution. We show that this remedies the failure modes of BPINNs and provides efficient exploration of the optimal Pareto front. This leads to better convergence and stability of BPINN training while reducing sampling bias. The determined weights moreover carry information about task uncertainties, reflecting noise levels in the data and adequacy of the PDE model. We demonstrate this in numerical experiments in Sobolev training, and compare them to analytically $\epsilon$-optimal baseline, and in a multi-scale Lokta-Volterra inverse problem. We eventually apply this framework to an inpainting task and an inverse problem, involving latent field recovery for incompressible flow in complex geometries

Applied Learning Experiences, Belonging & Preparedness for Career in Undergraduate Kinesiology Students

Applied “hands-on\u27\u27 student learning experiences may help prepare them for future career success. Gaining knowledge on professional tasks, proficiency in job skills, or understanding workplace satisfaction are all factors to be explored prior to embarking on a career in the field/industry. In addition, a student\u27s sense of belonging to their home “major” department when completing their bachelor\u27s degree may be related to positive outcomes and the job market. Whether hands-on learning experiences are related to workforce/graduate school preparedness or a student\u27s sense of belonging are yet to be determined (in all majors or specific to Kinesiology, KINE). PURPOSE: To assess the relationships between undergraduate student hands-on experiences, the perception of workforce/graduate school preparedness and a sense of belonging. METHODS: Undergraduate students (18+ years) at a public institution in California were recruited to participate in an IRB approved cross-sectional survey. Students were asked to complete demographic questions (age, gender, college major), a rating for hands-on learning experiences and preparedness for career/graduate school using a 5-point Likert scale (1=strongly disagree to 5=strongly agree) and eleven sense of belonging to the department questions (two domains; social acceptance and valued competence) with a 6-point Likert scale (1=strongly disagree to 6=strongly agree). Statistical analysis software (IBM SPSS v.28) was used to investigate the relationships (Pearson Correlation) between the variables, pRESULTS: Undergraduate participants (N=149, age; 24.3土5.7 years, gender identification; n=1 other, n=111 female, and n=37 male, and Kinesiology, n=83 or other majors, n=59) reported highly significant correlations between their hands-on learning experiences (all students, n=32, PPPPCONCLUSION:Students engaging in applied learning experiences during their undergraduate education may feel more prepared when entering the workforce and a greater sense of valued competence in their department