quasi-Newton Method Research Articles

Monitoring autonomic responses in Parkinson’s disease individuals: non-linear and chaotic global metrics of heart rate variability

Aim To examine and compare the autonomic responses, as assessed through the non-linear and chaotic global metrics of heart rate variability in two groups: the Parkinson’s Disease Group (PDG) and the Control Group (CG), both at rest and during an active tilt test. Methods The study encompassed 46 participants (PDG: n = 23; 73.73 ± 7.28 years old; CG: n = 23; 70.17 ± 8.20 years old). Initial data collection involved the acquisition of participant’s characteristics. The autonomic modulation was estimated both at rest and during the active tilt test. For this assessment, we computed non-linear indices derived from five entropies (Approximate, Sample, Shannon, Renyi, Tsallis), Detrended Fluctuation Analysis and the seven chaotic global metrics (hsCFP1-hsCFP7). Results At rest, the PDG exhibited lower values of hsCFP3 (0.818 ± 0.116 vs. 0.904 ± 0.065; p < 0.05) and Sample Entropy (0.720 ± 0.149 vs. 0.799 ± 0.171; p < 0.05). During the test, the PDG demonstrated lower values of ApEn, while the CG presented lower values of SampEn, hsCFP1, hsCFP3, hsCFP7, and higher values of hsCFP5. An interaction was observed, indicating that hsCFP1 and hsCFP3 exhibit differential behavior for the CG and PDG in response to the test. Conclusion subjects with PD exhibited reduced complexity of the RR interval series at rest, and a diminished autonomic response to the active tilt test when compared with the CG. The test, together with non-linear indices, may serve for assessing the Autonomic Nervous System in individuals with PD in a clinical setting. The interpretation of these data should be approached with caution, given the possible influences of pharmacotherapies and the inclusion of diabetic participants.

Correlation of fetal heart rate dynamics to inflammatory markers and brain-derived neurotrophic factor during pregnancy.

This study aims to show the relation between biomarkers in maternal and cord-blood samples and fetal heart rate variability (fHRV) metrics through a non-invasive fetal magnetocardiography (fMCG) technique. Twenty-three women were enrolled for collection of maternal serum and fMCG tracings immediately prior to their scheduled cesarean delivery. The umbilical cord blood was collected for measurement of biomarker levels. The fMCG metrics were then correlated to the biomarker levels from the maternal serum and cord blood. Brain-derived neurotrophic factor (BDNF) had a moderate correlation with fetal parasympathetic activity (0.416) and fetal sympathovagal ratios (-0.309;-0.356). Interleukin (IL)-6 also had moderate-sized correlations but with an inverse relationship as compared to BDNF. These correlations were primarily in cord-blood samples and not in the maternal blood. In this small sample-sized exploratory study, we observed a moderate correlation between fHRV and cord-blood BDNF and IL-6 immediately preceding scheduled cesarean delivery at term. These findings need to be validated in a larger population.

Large-eddy simulation-based reconstruction of turbulence in a neutral boundary layer using spectral-tensor regularization

We propose an efficient method to reconstruct the turbulent flow field in a neutrally stratified atmospheric boundary layer using large-eddy simulation (LES) and a series of lidar measurements. The reconstruction is formulated as a strong four-dimensional variational data assimilation problem, which involves optimizing two competing terms that contribute in the objective functional. The first term is a likelihood term, while the second contains the initial background distribution of turbulent velocity fluctuations and works as a regularization term. However, computing and storing the full background covariance tensor in turbulent flows is time consuming and resource intensive. In the current work, we investigate the possibility of replacing the complex background tensor by simple analytical approximations based on spectral tensors such as the Hunt–Graham–Wilson (HGW) model (Boundary-Layer Meteorol., vol. 85, 1997, pp. 35–52) or the Mann model (J. Fluid Mech., vol. 273, 1994, pp. 141–168). Afterwards, the problem is solved using a quasi-Newton algorithm and preconditioned to enhance the convergence rate. We test the method using virtual lidar measurements collected on a fine reference LES. Results show a super-linear convergence rate of the optimization algorithm to a local minimum and very good agreement between virtual lidar measurements and reconstruction in the scanning region. Furthermore, we demonstrate that incorporating the Saffman energy spectrum ( $E(k) \sim k^2$ where E is the energy spectrum and k is the magnitude of the wavenumber vector) at low wavenumbers into the Mann spectral tensor yields a longer streamwise correlation length, resulting in reduced reconstruction error when compared with the Batchelor spectrum ( $E(k) \sim k^4$ ). Finally, we observe that using the HGW model or Mann model with a Saffman spectrum yields similar results.

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet.

Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7-5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44-1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

Two new limited-memory preconditioned conjugate gradient algorithms for nonlinear optimization problems

The conjugate gradient (CG) techniques are a class of unconstrained optimization algorithms with strong local and global convergence qualities and minimal memory needs. While the quasi-Newton methods are reliable and efficient on a wide range of problems and these methods are converge faster than the conjugate gradient methods and require fewer function evaluations, however, they are request substantially more storage, and if the problem is ill-conditioned, they may require several iterations. There is another class, termed preconditioned conjugate gradient method, it is a technique that combines two methods conjugate gradient with quasi-Newton. In this work, we proposed a new two limited memory preconditioned conjugate gradient methods (New1 and New2), to solve nonlinear unconstrained minimization problems, by using new modified symmetric rank one (NMSR1) and new modified Davidon, Fletcher, Powell (NMDFP), and also using projected vectors. We proved that these modifications fulfill some conditions. Also, the descent condition of the new technique has been proved. The numerical results showed the efficiency of the proposed new algorithms compared with some standard nonlinear, unconstrained problems.

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

AbstractThe cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.

Increased Pain Variability in Patients With Chronic Pain: A Role for Pain Catastrophizing

Pain is an inherently negative perceptual and affective experience that acts as a warning system to protect the body from injury and illness. Pain unfolds over time and is influenced by myriad factors, making it highly dynamic. Despite this, statistical measures often treat any intraindividual variability in pain ratings as noise or error. This is consequential, especially for research on chronic pain, because pain variability is associated with greater pain severity and depression. Yet, differences in pain variability between patients with chronic pain and controls in response to acute pain has not been fully examined—and it is unknown if dispositional factors such as pain catastrophizing (negative cognitive-affective response to potential or actual pain in which attention cannot be diverted away from pain) relate to pain variability. In the current study, we recruited chronic-pain patients (N = 30) and pain-free controls (N = 22) to complete a 30-second thermal pain task where they continually rated a painful thermal stimulus. To quantify pain variability and capture potential dynamics, we used both a traditional intraindividual standard deviation (iSD) metric of variability and a novel derivatives approach. For both metrics, patients with chronic pain had higher variability in their pain ratings over time, and pain catastrophizing significantly mediated this relationship. This suggests patients with chronic pain experience pain stimuli differently over time, and pain catastrophizing may account for this differential experience. PerspectiveThe present study demonstrates (using multiple variability metrics) that chronic pain patients show more variability when rating experimental pain stimuli, and that pain catastrophizing helps explain this differential experience. These results provide preliminary evidence that short-term pain variability could have utility as a clinical marker in pain assessment and treatment.

Modification of Quasi-Newton Successive Substitution Method for Calculating Phase Equilibria of Hydrocarbon Mixtures Taking into Account the Capillary Pressure Jump

Modification of Quasi-Newton Successive Substitution Method for Calculating Phase Equilibria of Hydrocarbon Mixtures Taking into Account the Capillary Pressure Jump

Q&amp;A With Dennis Denney, JPT Technology Focus Creator

_ In 1997, JPT debuted a monthly feature that represented an unconventional approach in meeting SPE’s goal of disseminating technical information to its members. Soon titled “Technology Focus,” the feature brought together subject-matter experts (SMEs) in a range of important industry topics to review technical-paper abstracts gathered from the previous year of SPE meetings, as well as the Offshore Technology Conference. Its architect, engineer, and pilot was Technology Editor Dennis Denney, who would become a familiar presence to SPE members and staff alike over a 17-year career, although his industry roots extended to his college days. Dennis helped recruit SMEs for review service; sent them hundreds of abstracts and full papers (monthly, that is; the cumulative count of papers he handled is likely a six-digit figure); answered their questions and concerns with prompt, supportive guidance; and then, once papers had been selected, synopsized them for publication in the magazine with the longtime help of Assistant Technology Editor Karen Bybee. As this writer well knows, condensing a 10,000-word paper on, say, distributed quasi-Newton derivative-free optimization methods for field development optimization into a 1,500-word summary that captures the novelty and technical sophistication of its authors’ work can be a daunting task. Applying his own background as a petroleum engineer to methodically analyze and trim each chosen paper, Dennis did just that dozens of times a year, transforming these texts into svelte packages that included only one table or figure to deliver the highest possible quantity of quality to JPT readers. When Dennis retired in 2013, I initially split his eyebrow-raising workload with fellow Technology Editor Adam Wilson (now JPT’s Special Publications Editor). It was immediately clear from the way that reviewers spoke about Dennis, and his contributions to the magazine and SPE itself, that he was held in the same high regard outside of the organization as he was within it, a testament to the reputation he had earned while working with so many members and SMEs. It was entirely fitting that, for JPT’s 75th anniversary celebration, we caught up with Dennis to reflect on his achievements in the industry and with the groundbreaking Technology Focus feature he created. Dennis lives with his wife Linda in Rockwall, Texas—not too far from SPE headquarters in Dallas, but not too close, either!—enjoying his retirement and his family. (Note: For more on the development and history of the Technology Focus features, read the companion piece to this article, “Technology Focus Topics Reflect Industry Growth, Evolution Over 25+ Years.”) Why did you decide to go into the field of petroleum engineering? I worked several years as an engineering tech, testing gas wells, tracking well and reservoir pressures, and writing computer programs to analyze pressure data. The department manager encouraged me to finish my degree and become a petroleum engineer. So, after I received a scholarship from J. Harold Dunn (at age 28), my wife and I sold our house, packed our things into a U-Haul truck, and loaded two young kids into the car to move to Bryan/College Station to attend Texas A&amp;M University.

Convergence and Stability Improvement of Quasi-Newton Methods by Full-Rank Update of the Jacobian Approximates

A system of simultaneous multi-variable nonlinear equations can be solved by Newton’s method with local q-quadratic convergence if the Jacobian is analytically available. If this is not the case, then quasi-Newton methods with local q-superlinear convergence give solutions by approximating the Jacobian in some way. Unfortunately, the quasi-Newton condition (Secant equation) does not completely specify the Jacobian approximate in multi-dimensional cases, so its full-rank update is not possible with classic variants of the method. The suggested new iteration strategy (“T-Secant”) allows for a full-rank update of the Jacobian approximate in each iteration by determining two independent approximates for the solution. They are used to generate a set of new independent trial approximates; then, the Jacobian approximate can be fully updated. It is shown that the T-Secant approximate is in the vicinity of the classic quasi-Newton approximate, providing that the solution is evenly surrounded by the new trial approximates. The suggested procedure increases the superlinear convergence of the Secant method φS=1.618… to super-quadratic φT=φS+1=2.618… and the quadratic convergence of the Newton method φN=2 to cubic φT=φN+1=3 in one-dimensional cases. In multi-dimensional cases, the Broyden-type efficiency (mean convergence rate) of the suggested method is an order higher than the efficiency of other classic low-rank-update quasi-Newton methods, as shown by numerical examples on a Rosenbrock-type test function with up to 1000 variables. The geometrical representation (hyperbolic approximation) in single-variable cases helps explain the basic operations, and a vector-space description is also given in multi-variable cases.

Effects of three-week intake of Matcha green tea powder on heart rate variability metrics

Introduction: Compared to other green teas, higher intake of multiple phytochemicals is achieved with Matcha green tea consumption. Green tea consumption is known to have metabolic effects but is also consumed for supposed calming effects. The aim of the present study was to examine the effects of encapsulated Matcha green tea on heart rate variability metrics during supine rest, as well as on physiological and metabolic responses during both supine rest and moderate-intensity exercise. Methods: Healthy females (n=8, age: 22±3 yrs, body mass: 68±11 kg, height: 162±5 cm) volunteered. The study employed a placebo-controlled, randomised cross-over design. Time-domain heart rate variability metrics during supine rest (n=5) and physiological and metabolic responses using indirect calorimetry techniques during supine rest and 60-min of moderate-intensity (~ 4-METs) treadmill walking (speed: 4.4±0.5 km·h-1) were measured following 3 weeks of 3 g·day-1 of Matcha green tea or placebo. Results: During supine rest with Matcha green tea, all participants had lower heart rates by 13±7% (P=0.01, d= -1.45), higher mean beat-to-beat RR intervals by 16±9% (P=0.03, d=1.25), higher SDNN by 44±32% (P=0.01, d=0.76) and higher pNN50 by 139±139% (P&lt;0.01, d=1.28). Matcha green tea had no effects on the physiological and metabolic responses during supine rest and moderate-intensity treadmill walking (e.g. respiratory exchange ratio, placebo: 0.78±0.04; Matcha: 0.78±0.03, P=0.87). Fat oxidation during supine rest was correlated (r=0.75, P&lt;0.01) with the moderate-intensity walking induced fat oxidation. Conclusions: In young adult healthy females, Matcha green tea beneficially effects heart rate variability metrics during supine rest indicating an alteration in parasympathetic nervous activity and therefore suggestive of a relaxing effect. Matcha green tea did not change the metabolic responses during supine rest and exercise possibly due to the low respiratory exchange ratio in the female cohort. Future work should address the effectiveness of Matcha green tea during conditions of psychological stress.Keywords: Matcha green tea, heart rate variability, rest, walking, substrate oxidation

Transient analysis of energy-saving strategy for cognitive radio networks using G-queue with heterogeneity

Energy efficiency, high data speeds, and effective spectrum utilization stand as the primary factors governing 5G and beyond networks. A base station (BS) in a cognitive radio network (CRN) that conserves energy during sleep periods is a promising contender for achieving more effective spectrum allocation. An efficient energy-saving strategy is offered in this study to achieve greener communication in wireless cellular networks. This study aims to propose and analyze an energy-saving scheme for the BS, taking into account the heterogeneity and reliability of networks. The entire system is modeled as a three-dimensional Markov chain by establishing a discrete-time preemptive priority queueing model with single vacation, heterogeneous packets, and negative packets such as viruses. Then, the transient analysis of the Markov chain is performed using the recursive method. Utilizing the transient probability distribution, we present numerical results derived from reliability analysis and queueing analysis to substantiate the validity of the proposed model. Furthermore, results based on the energy-saving degree are showcased to affirm the effectiveness of the proposed scheme. Finally, a reward cost function is obtained depending on the successful transmission of secondary packets, and the Quasi-Newton method is used to determine the optimal reward cost.

Programa de aplicação para resolver sistemas de equações não lineares

Introduction: Application programs in mathematics have had a significant impact on solving nonlinear systems of equations and are impacting various areas. In a nonlinear equation, it is not always easy to determine its root or convergence point; one must analyze and restrict the behavior of the functions that comprise it. Objective: To develop a mathematical program to solve nonlinear systems of equations, selecting the most efficient method and presenting results that include the analysis of convergence and stability of the implemented iterative methods. Method: To solve the system of type V(X)=0, methods such as Simple Iteration, Gradient, Newton, Modified Newton, and Quasi-Newton were used. Visual C++ 6.0 programming language along with Matlab 6.5 libraries were used for the development of the application program for mathematical notations. Results: An application program named SMENLI (Mathematical Software for solving Nonlinear Equations) was developed, which implemented various iterative methods to solve 20 systems of nonlinear equations. Of these, 15 converged and 5 diverged. Some did not converge due to the initial point provided to the program, which utilizes a lexical analyzer. Additionally, it is important to remember that not all systems of nonlinear equations have a solution. Conclusions: It was found that the Newton and Modified Newton methods are the most efficient in terms of convergence, standing out for their shorter time and fewer iterations compared to other implemented methods. However, in exceptional cases with certain systems of nonlinear equations, the Quasi-Newton method may prove superior to others.

Normative High-Resolution Pharyngeal Manometry: Impact of Age, Size of System, and Sex on Primary Metrics and Pressure Stability.

There have been many reports of normative pharyngeal swallowing pressures using high-resolution pharyngeal manometry, but there is a fair amount of between-subject variance in reported pressure parameters. The purpose of this study was to put forward normative pharyngeal high-resolution manometry measures across the lifespan and investigate the effects of age, size of system, and sex. High-resolution pharyngeal manometry was performed on 98 healthy adults (43 males) between the ages 21 and 89. Pressure duration, maxima, integral, and within-individual variability metrics were averaged over 10 swallows of 10-ml thin liquid. Multiple linear and logistic regressions with model fitting were used to examine how pharyngeal pressures relate to age, pharyngeal size, and sex. Age was associated with tongue base maximum pressure, tongue base maximum variability, and upper esophageal sphincter-integrated relaxation pressure (F3,92 = 6.69; p < 0.001; adjusted R2 = 0.15). Pharyngeal area during bolus hold was associated with velopharynx integral (F1,89 = 5.362; p = 0.02; adjusted R2 = 0.05), and there was no significant model relating pharyngeal pressures to C2-C4 length (p < 0.05). Sex differences were best described by tongue base integral and hypopharynx maximum variability (χ2 = 10.27; p = 0.006; pseudo R2 = 0.14). Normative data reveal the distribution of swallow pressure metrics which need to be accounted for when addressing dysphagia patients, the importance of pressure interactions in normal swallow, and address the relative stability of swallow metrics with normal aging.

Effect of sodium-glucose cotransporter-2 inhibitors on continuous glucose monitoring metrics, as adjunctive to insulin in adults with type 1 diabetes mellitus: a meta-analysis of randomized controlled trials

AimsThis meta-analysis of randomized controlled trials (RCTs) aimed to evaluate the effect of sodium-glucose cotransporter-2 inhibitors (SGLT2is) on continuous glucose monitoring metrics as adjunctive to insulin in adults with type 1 diabetes mellitus (T1D). MethodsA systematic literature search was conducted through Medline (via PubMed), Cochrane Library and Google Scholar until October 25, 2023. Dual-independent study selection, data extraction and quality assessment were conducted. Results were summarized with random effects meta-analysis. ResultsEight RCTs were identified, involving a total of 2310 T1D patients. The use of SGLT2is on top of standard insulin therapy was associated with a significantly higher time in range (TIR) compared to placebo (mean difference (MD) 9.7 %; 95 % confidence interval (CI) [8.3, 1.11]; P < 0.001). The time above range was significantly lower in patients receiving SGLT2is (MD -8.71 %; 95 % CI [−11.62, −5.79]; P < 0.001), whereas no difference was observed regarding the time below range (TBR) (MD 0.34 %; 95 % CI [−0.17, 0.85]; P = 0.19). A significantly lower sensor-recorded mean daily glucose was noted in the group receiving SGLT2is (MD -16.55 mg/dL; 95 % CI [−19.82, −13.29]; P < 0.001). When considering the metrics of glucose variability, SGLT2is demonstrated a significant favorable effect on the mean amplitude of glucose excursions (MD -16.92 mg/dL; 95 % CI [−25.31, −8.13]; P < 0.001) and the mean standard deviation of weekly glucose levels (MD -7.67 mg/dL; 95 % CI [−11, −4.35]; P < 0.001). No significant effect was observed concerning the coefficient of variation (MD -1 %; 95 % CI [−2.39, 0.4]; P = 0.16). Regarding safety outcomes, SGLT2is were significantly linked to higher odds of diabetic ketoacidosis compared to insulin alone (OR 3.18; 95 % CI [1.79, 5.66]; P < 0.001), with no significant impact on severe hypoglycemia events (OR 1; 95 % CI [0.54, 1.85]; P = 0.1). ConclusionOur findings suggest that in individuals with T1D, adjunct therapy with SGLT2is provides a significant benefit in terms of TIR and reduced glucose variability, without an increase in TBR.

Can Pulse Rate Variability be Used to Monitor Compliance with a Breath Pacer?

Slow paced breathing has been demonstrated to provide significant health benefits for a person’s health, and, during breathing sessions, it is desirable to monitor that a person is actually compliant with the breath pacer. We explore the potential use of pulse rate variability to monitor compliance with a breath pacer during meditation sessions. The study involved 6 human subjects each participating in 2–3 trials, where they are asked to follow or not to follow the breath pacer, where we collected data on how the magnitude of pulse rate variability changed. Two methods, logistic regression and a running standard deviation technique, were developed to detect non-compliance with the breath pacer based on pulse rate variability metrics. Results indicate that using pulse rate variability alone may not reliably detect non-compliance with the breath pacer. Both models exhibited limitations in terms of false positives and false negatives, with accuracy ranging from 67 to 65%. Existing methods involving visual, audio, and motion signals currently perform better for monitoring compliance with the breath pacer.

Shape-changing trust-region methods using multipoint symmetric secant matrices

In this work, we consider methods for large-scale and nonconvex unconstrained optimization. We propose a new trust-region method whose subproblem is defined using a so-called ‘shape-changing’ norm together with densely-initialized multipoint symmetric secant (MSS) matrices to approximate the Hessian. Shape-changing norms and dense initializations have been successfully used in the context of traditional quasi-Newton methods, but have yet to be explored in the case of MSS methods. Numerical results suggest that trust-region methods that use densely-initialized MSS matrices together with shape-changing norms outperform MSS with other trust-region methods.

Practical considerations for the adoption of Anderson acceleration in nonlinear diffusion accelerated transport

The use of the Anderson Acceleration (AA) method within the context of the coarse mesh finite difference (CMFD)-accelerated neutron transport k-eigenvalue calculation is explored for practical problems. Current application of CMFD only has a linear convergence rate. The AA method is similar to a quasi-Newton method and can improve the convergence rate of fixed-point iterations. We therefore implement AA for the 2D calculation in MPACT and study its performance in pressurized water reactor (PWR) problems. The initial results show that the traditional AA method can improve the convergence rate in a trivial problem and improve the stability of the CMFD method for some problems. However, the success of AA in the practical problems, where simulations with symmetry or domain decomposition, relies on one essential factor–the treatment of iterative angular flux boundary conditions in the AA. Additionally, the efficiency of AA is also affected by the computational cost for solving the optimization problem, and the computational cost of the transport solver and the CMFD solver. To address these issues, we propose a new implementation of AA that considers only the mutligroup scalar flux in the optimization problem, but the angular flux and the scalar flux in the mixing. The new approach achieves better stability and efficiency in practical simulations, and can be explained by the theory of Randomized Numerical Linear Algebra (RandNLA).