q-Analogue of Shock Soliton Solution

By using Jackson's q-exponential function we introduce the generating function, the recursive formulas and the second order q-differential equation for the q-Hermite polynomials. This allows us to solve the q-heat equation in terms of q-Kampe de Feriet polynomials with arbitrary N moving zeroes, and to find operator solution for the Initial Value Problem for the q-heat equation. By the q-analog of the Cole-Hopf transformation we construct the q-Burgers type nonlinear heat equation with quadratic dispersion and the cubic nonlinearity. In q -> 1 limit it reduces to the standard Burgers equation. Exact solutions for the q-Burgers equation in the form of moving poles, singular and regular q-shock soliton solutions are found.Comment: 13 pages, 5 figure

Comparison of Model Predictive Control performance using grey-box and white box controller models

Model predictive control (MPC) for building climate control has received increasing attention the last decade. Its large scale implementation is, however, still hampered by the difficulty of obtaining accurate but computationally efficient multi-zone building controller models. This paper compares an existing grey-box approach with a novel white-box approach to obtain a controller model of the building envelope and it compares the performance achieved by using these two approaches. The comparison is made for an existing office building, which is currently controlled using a grey-box MPC [1].  The building envelope and its heating, cooling and air conditioning systems  (HVAC) are modelled using the Modelica building energy simulation library IDEAS. The model is validated using measurement data from the real building. This detailed simulation model is composed of discretised partial differential equations, ordinary differential equations and algebraic equations. The model is therefore too complex to be used as controller model for MPC. Two MPC approaches are compared. On the one hand, the white-box controller model is obtained by linearizing the building envelope part of the simulation model and by pre-computing model inputs such as solar gains through each window [2]. The method generates a linear state space model, which produces very similar temperatures as the original non-linear model. On the other hand, the grey-box identification method that was used to obtain the current controller model, is also applied to the detailed simulation model. Both white-box and grey-box MPC are applied to the detailed simulation model. The dynamics of the HVAC systems are not included in the MPC model but the efficiencies, constraints, cost function and boundary conditions are included. The energy use, the achieved thermal zone comfort and the prediction performance are compared. Finally, a new grey-box model is identified with operation data of the real building and the multi-step ahead prediction performance of the white-box and of both the grey-box models obtained with the simulation data and obtained with the measured data is computed for the real building using the measurement data and the weather forecast, which are used by the current MPC implementation.  [1] Zdenek Vana, Jiri Cigler, Jan Siroky, Eva Zacekova, Lukas Ferkl. Model-based energy efficient control applied to an office building. J. Process Control (2014).  [2] Picard, D., Jorissen, F., and Helsen, L. 2015. Methodology for Obtaining Linear State Space Building Energy Simulation Models. In 11th International Modelica Conference, pages 51–58, Paris

Nanodiamond quantum sensors reveal temperature variation associated to hippocampal neurons firing

Temperature is one of the most relevant parameters for the regulation of intracellular processes. Measuring localized subcellular temperature gradients is fundamental for a deeper understanding of cell function, such as the genesis of action potentials, and cell metabolism. Here, we detect for the first time temperature variations (1{\deg}C) associated with potentiation and depletion of neuronal firing, exploiting a nanoscale thermometer based on optically detected magnetic resonance in nanodiamonds. Our results provide a tool for assessing neuronal spiking activity under physiological and pathological conditions and, conjugated with the high sensitivity of this technique (in perspective sensitive to < 0.1{\deg}C variations), pave the way to a systematic study of the generation of localized temperature gradients. Furthermore, they prompt further studies explaining in detail the physiological mechanism originating this effect.Comment: 27 pages, 5 figures, 3 table

Associations of clinical, psychological, and sociodemographic characteristics and ecological momentary assessment completion in the 10-week Hypo- METRICS study:Hypoglycaemia MEasurements ThResholds and ImpaCtS

Introduction: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1and insulin-treated type 2 diabetes in real-time using ecological momentary assessment(EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion.Methods: Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics.Results: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66)years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR)overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion.Discussion: High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time

Associations of clinical, psychological, and sociodemographic characteristics and ecological momentary assessment completion in the 10-week Hypo- METRICS study:Hypoglycaemia MEasurements ThResholds and ImpaCtS

Introduction: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1and insulin-treated type 2 diabetes in real-time using ecological momentary assessment(EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion.Methods: Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics.Results: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66)years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR)overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion.Discussion: High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time

Associations Between Hypoglycemia Awareness Status and Symptoms of Hypoglycemia Among Adults with Type 1 or Insulin-Treated Type 2 Diabetes Using the Hypo-METRICS Smartphone Application

Introduction: This study examined associations between hypoglycemia awareness status and hypoglycemia symptoms reported in real-time using the novel Hypoglycaemia-MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone application (app) among adults with insulin-treated type 1 (T1D) or type 2 diabetes (T2D). Methods: Adults who experienced at least one hypoglycemic episode in the previous 3 months were recruited to the Hypo-METRICS study. They prospectively reported hypoglycemia episodes using the app for 10 weeks. Any of eight hypoglycemia symptoms were considered present if intensity was rated between "A little bit" to "Very much" and absent if rated "Not at all." Associations between hypoglycemia awareness (as defined by Gold score) and hypoglycemia symptoms were modeled using mixed-effects binary logistic regression, adjusting for glucose monitoring method and diabetes duration. Results: Of 531 participants (48% T1D, 52% T2D), 45% were women, 91% white, and 59% used Flash or continuous glucose monitoring. Impaired awareness of hypoglycemia (IAH) was associated with lower odds of reporting autonomic symptoms than normal awareness of hypoglycemia (NAH) (T1D odds ratio [OR] 0.43 [95% confidence interval {CI} 0.25-0.73], P = 0.002); T2D OR 0.51 [95% CI 0.26-0.99], P = 0.048), with no differences in neuroglycopenic symptoms. In T1D, relative to NAH, IAH was associated with higher odds of reporting autonomic symptoms at a glucose concentration &lt;54 than &gt;70 mg/dL (OR 2.18 [95% CI 1.21-3.94], P = 0.010). Conclusion: The Hypo-METRICS app is sensitive to differences in hypoglycemia symptoms according to hypoglycemia awareness in both diabetes types. Given its high ecological validity and low recall bias, the app may be a useful tool in research and clinical settings. The clinical trial registration number is NCT04304963.</p