Search for: All records

Background: Hypertension is a major risk factor for cardiovascular disease and requires long-term health treatment and ongoing monitoring to the extent that traditional management approaches may be limited in providing. Adopting appropriate digital tools like mobile health technology (mHealth) could be an effective strategy for improving the control and management of this public health burden. This pilot studyevaluated the feasibility of the AHOMKA care model at two tertiary hospitals in Ghana. Outcome measures were changes in systolic (SBP) and diastolic (DBP) blood pressure model acceptance by patients and health care providers.Objective: This study sought to assess the overall pattern of home blood pressure self-monitoring among participants from two teaching hospitals in southern Ghana, using mHealth.Methods: Participants attending two (2) cardiology clinics were recruited for this mixed-method pilot study over a period of eight (8) weeks. Following a longitudinal single-group approach, we conducted structured interviews at the baseline and end-line and used exports of the AHOMKA mHealth application, in-depth interviews and focus group discussions with patients and healthcare providers. Repeated measuresanalysis of variance was adopted to assess differences in SBP and DBP between baseline and end line.Results: This pilot study involved 27 participants with a mean of 50.4 ± 11.0 years-approximately 1:1 male-female participation. Mean SBP decreased by 11.6 mm Hg (95% CI = 15.0 to -8.2), from an average of 138.6 mmHg at baseline to 126.2 mmHg at endline. Average DBP was also significantly reduced by 3.0 mmHg (95% CI = -5.5 to -0.5), from an average of 87.0 mmHg at baseline to 83.0 mmHg at endline. Patients and healthcare providers were satisfied and optimistic about the AHOMKA care model.Conclusion: The encouraging trend in BP outcomes and high response rate from this pilot study provides evidence for further investigation involving the assessment of the effectiveness of the AHOMKA care model while culturally adapting the model to the Ghanaian context. In the spectrum of hypertension interventions, AHOMKA has the potential to ease the burden on the public health system 

Local differential privacy (LDP) can be adopted to anonymize richer user data attributes that will be input to sophisticated machine learning (ML) tasks. However, today’s LDP approaches are largely task-agnostic and often lead to severe performance loss – they simply inject noise to all data attributes according to a given privacy budget, regardless of what features are most relevant for the ultimate task. In this paper, we address how to significantly improve the ultimate task performance with multi-dimensional user data by considering a task-aware privacy preservation problem. The key idea is to use an encoder-decoder framework to learn (and anonymize) a task-relevant latent representation of user data. We obtain an analytical near-optimal solution for the linear setting with mean-squared error (MSE) task loss. We also provide an approximate solution through a gradient-based learning algorithm for general nonlinear cases. Extensive experiments demonstrate that our task-aware approach significantly improves ultimate task accuracy compared to standard benchmark LDP approaches with the same level of privacy guarantee. 

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least 25% while transmitting 80% less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem. 

Abstract Atomic nuclei are self-organized, many-body quantum systems bound by strong nuclear forces within femtometre-scale space. These complex systems manifest a variety of shapes^1–3, traditionally explored using non-invasive spectroscopic techniques at low energies^4,5. However, at these energies, their instantaneous shapes are obscured by long-timescale quantum fluctuations, making direct observation challenging. Here we introduce the collective-flow-assisted nuclear shape-imaging method, which images the nuclear global shape by colliding them at ultrarelativistic speeds and analysing the collective response of outgoing debris. This technique captures a collision-specific snapshot of the spatial matter distribution within the nuclei, which, through the hydrodynamic expansion, imprints patterns on the particle momentum distribution observed in detectors^6,7. We benchmark this method in collisions of ground-state uranium-238 nuclei, known for their elongated, axial-symmetric shape. Our findings show a large deformation with a slight deviation from axial symmetry in the nuclear ground state, aligning broadly with previous low-energy experiments. This approach offers a new method for imaging nuclear shapes, enhances our understanding of the initial conditions in high-energy collisions and addresses the important issue of nuclear structure evolution across energy scales.