Multilevel security and concurrency control for distributed computer systems

Multilevel security deals with the problem of controlling the flow of classified information. We present multilevel information flow control mechanisms for distributed systems that allow concurrent accesses to shared data. In a distributed computing environment, the different sites communicate through message passing. Our security mechanisms check the security of information flows caused by computations within individual sites as well as ones caused by communications among the sites. The correct behavior of the security mechanisms cannot be guaranteed if the allowed concurrency is left uncontrolled in the system. We present concurrency control mechanisms for the security mechanisms. In the presence of such concurrency control mechanisms, the consistency of the security data, which the security mechanisms rely upon, is preserved. Finally, we also present schemes to increase the efficiency and the precision of the security mechanisms

In-beam internal conversion electron spectroscopy with the SPICE detector

The SPectrometer for Internal Conversion Electrons (SPICE) has been commissioned for use in conjunction with the TIGRESS $\gamma$-ray spectrometer at TRIUMF's ISAC-II facility. SPICE features a permanent rare-earth magnetic lens to collect and direct internal conversion electrons emitted from nuclear reactions to a thick, highly segmented, lithium-drifted silicon detector. This arrangement, combined with TIGRESS, enables in-beam $\gamma$-ray and internal conversion electron spectroscopy to be performed with stable and radioactive ion beams. Technical aspects of the device, capabilities, and initial performance are presented

Turning data into better mental health: Past, present, and future

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered “ground truth” for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions

“I Have Nothing More To Give”: Disparities in Burnout and the Protective Role of Immigrant Status During the COVID-19 Pandemic

Burnout is an epidemic, with deleterious effects on individuals, patient care, and healthcare systems. The Coronavirus Disease 2019 (COVID-19) pandemic may be exacerbating this problem. We aimed to explore socio-cultural and gender norms that modulate burnout development in physicians during the pandemic and analyze any disparities associated with gender, marital and immigration status and work-life balance. We conducted an online cross-sectional survey of physicians (August-November, 2021): The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) was used to measure burnout, combined with a validated survey assessing work-life balance. Demographic data was obtained for each participant. MBI-HSS subscales were measured, along with work and home related changes due to COVID-19. The association between life changes due to COVID-19 and odds of burnout was estimated by logistic regression. Complementary analysis was performed to determine factors most associated with burnout. 352 respondents were analyzed. There was a high prevalence of burnout. Over half of individuals reported a high degree of emotional exhaustion (EE) (56%). 83% of individuals reported at least one life factor changed due to COVID-19. Home-related life changes due to COVID-19 were associated with 143% higher odds of emotional burnout [adjusted odds ratio (aOR) 2.43; 95% confidence interval (CI) 1.49, 3.98] after covariate adjusted analysis. High EE was most evident when there were three or more life changes, suggesting a cumulative effect. First-generation immigrants, older physicians, and trainees were identified as protective factors. Although female gender was identified as a factor related to EE through forward selection, this was not statistically significant (aOR 1.34; 95% CI 0.80, 2.24). Burnout remains pervasive among physicians. We highlight new risk factors for EE (home-life changes due to COVID-19), and protective factors (first-generation immigrants) not previously explored. Understanding burnout and its disparities allows for improved mitigation strategies, decreasing its deleterious effects

Scattering of the Halo Nucleus Be 11 on Au 197 at Energies around the Coulomb Barrier

Angular distributions of the elastic, inelastic, and breakup cross sections of the halo nucleus Be11 on Au197 were measured at energies below (Elab=31.9 MeV) and around (39.6 MeV) the Coulomb barrier. These three channels were unambiguously separated for the first time for reactions of Be11 on a high-Z target at low energies. The experiment was performed at TRIUMF (Vancouver, Canada). The differential cross sections were compared with three different calculations: semiclassical, inert-core continuum-coupled-channels and continuum-coupled-channels ones with including core deformation. These results show conclusively that the elastic and inelastic differential cross sections can only be accounted for if core-excited admixtures are taken into account. The cross sections for these channels strongly depend on the B(E1) distribution in Be11, and the reaction mechanism is sensitive to the entanglement of core and halo degrees of freedom in Be11

RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews

Semi-structured interviews (SSIs) are a commonly employed data-collection method in healthcare research, offering in-depth qualitative insights into subject experiences. Despite their value, the manual analysis of SSIs is notoriously time-consuming and labor-intensive, in part due to the difficulty of extracting and categorizing emotional responses, and challenges in scaling human evaluation for large populations. In this study, we develop RACER, a Large Language Model (LLM) based expert-guided automated pipeline that efficiently converts raw interview transcripts into insightful domain-relevant themes and sub-themes. We used RACER to analyze SSIs conducted with 93 healthcare professionals and trainees to assess the broad personal and professional mental health impacts of the COVID-19 crisis. RACER achieves moderately high agreement with two human evaluators (72%), which approaches the human inter-rater agreement (77%). Interestingly, LLMs and humans struggle with similar content involving nuanced emotional, ambivalent/dialectical, and psychological statements. Our study highlights the opportunities and challenges in using LLMs to improve research efficiency and opens new avenues for scalable analysis of SSIs in healthcare research

Privacy-Preserving Social Ambiance Measure From Free-Living Speech Associates With Chronic Depressive and Psychotic Disorders

A social interaction consists of contributions by the individual, the environment and the interaction between the two. Ideally, to enable effective assessment and interventions for social isolation, an issue inherent to depressive and psychotic illnesses, the isolation must be identified in real-time and at an individual level. However, research addressing sociability deficits is largely focused on determining loneliness, rather than isolation, and lacks focus on the richness of the social environment the individual revolves in. In this paper, We describe the development of an automated, objective and privacy-preserving Social Ambiance Measure (SAM) that converts unconstrained audio recordings collected from wrist-worn audio-bands into four levels, ranging from none to active. The ambiance levels are based on the number of simultaneous speakers, which is a proxy for overall social activity in the environment. Results show that social ambiance patterns and time spent at each ambiance level differed between participants with depressive or psychotic disorders and healthy controls. Individuals with depression/psychosis spent less time in diverse environments and less time in moderate/active ambiance levels. Moreover, social ambiance patterns are found associated with the severity of self-reported depression, anxiety symptoms and personality traits. The results in this paper suggest that objectively measured social ambiance can be used as a marker of sociability, and holds potential to be leveraged to better understand social isolation and develop effective interventions for sociability challenges, thus improving mental health outcomes