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Huiting Ma

Publications and source records attributed to Huiting Ma.

4 recordsLinked to original sources

Generation of dense relativistic electron beams via vortex laser-driven self-generated magnetic pinching

In multi-petawatt laser plasma accelerators, achieving high-density relativistic electron beams is typically accompanied by large transverse divergence, limiting the attainable effective electron density needed for high-flux interaction regimes relevant to laboratory astrophysics. Here we report experimental demonstration of self-generated magnetic pinching (SMP), a collective mechanism that actively regulates transverse beam dynamics using a Laguerre-Gaussian laser at strong relativistic intensity (~8 x 10^19 W/cm^2) interacting with an underdense plasma. The electron beam evolves from a two-lobe high-charge injection structure into a compressed, high-density profile, yielding a threefold reduction in divergence and nearly an order-of-magnitude enhancement in effective beam density compared with a Gaussian driver. Particle-in-cell simulations agree with the experimental observations and reveal that a self-generated azimuthal magnetic field governs the electron dynamics within the SMP regime, which is defined by the forming condition S = 0.717 l a0 [ne(10^18 cm^-3)]^-3/4 = 1, where l, a0, and ne are topological charge, laser amplitude, and plasma density, respectively. A transient kick from a dense inner sheath electron population drives collective magnetic pinching, transforming an initially separated electron distribution into a compressed and well-collimated beam. For higher-power laser systems, the forming condition can be extended to higher plasma densities and larger orbital angular momentum modes, potentially enabling electron beams with charges exceeding several nC and effective densities above 10^19 cm^-3. This mechanism provides a route to overcoming transverse expansion and enhancing rare interaction processes relevant to high-flux particle sources.

physics.plasm-ph

Contribution of high risk groups' unmet needs may be underestimated in epidemic models without risk turnover: a mechanistic modelling analysis

BACKGROUND. Epidemic models of STIs are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction (tPAF) of unmet prevention and treatment needs within risk groups. However, evidence suggests that STI risk is dynamic over an individual's sexual life course, which manifests as turnover between risk groups. We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups. METHODS. We developed a unifying, data-guided framework to simulate risk group turnover in deterministic, compartmental transmission models. We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups. We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models. RESULTS. The influence of turnover on group-specific prevalence was mediated by three main phenomena: movement of infectious individuals between risk groups; changes to herd immunity; and changes in discordant partnerships. Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups. Compared to the fitted model without turnover, the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time. IMPLICATIONS. If turnover is not captured in epidemic models, the projected contribution of high risk groups, and thus, the potential impact of prioritizing interventions to address their needs, could be underestimated. To aid the next generation of tPAF models, data collection efforts to parameterize risk group turnover should be prioritized.

q-bio.PE

Venue-based HIV testing at sex work hotspots to reach adolescent girls and young women living with HIV: a cross-sectional study in Mombasa, Kenya

Background: We estimated the potential number of newly diagnosed HIV infections among adolescent girls and young women (AGYW) using a venue-based approach to HIV testing at sex work hotspots. Methods: We used hotspot enumeration and cross-sectional bio-behavioural survey data from the 2015 Transitions Study of AGYW aged 14-24 years who frequented hotspots in Mombasa, Kenya. We compared the HIV cascade among AGYW who sell sex (YSW, N=408) versus those who do not (NSW, N=891); and triangulated the potential (100% test acceptance and accuracy) and feasible (accounting for test acceptance and sensitivity) number of AGYW that could be newly diagnosed via hotspot-based HIV rapid testing in Mombasa. We identified the profile of AGYW recently tested for HIV (in the past year) using multivariable logistic regression. Results: N=37/365 (10.1%) YSW and N=30/828 (3.6%) NSW were living with HIV, of whom 27.0% (N=10/37) and 30.0% (N=9/30) were diagnosed and aware (p=0.79). Rapid test acceptance was 89.3% and sensitivity was 80.4%. Hotspot enumeration estimated 15,635 (range: 12,172-19,097) AGYW in hotspots in Mombasa. The potential and feasible number of new diagnosis were 627 (310-1,081), and 450 (223-776), respectively. Thus, hotspot-based testing could feasibly reduce the undiagnosed fraction from 71.6% to 20.2%. The profile of AGYW who recently tested was similar among YSW and NSW. YSW were two-fold more likely to report a recent HIV test after adjusting for other determinants [odds ratio (95% CI): 2.1 (1.6-3.1)]. Conclusion: Reaching AGYW via hotspot-based HIV testing could fill gaps left by traditional, clinic-based HIV prevention and testing services.

q-bio.OT

A flexible integer linear programming formulation for scheduling clinician on-call service in hospitals

Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions. We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.

cs.AI