Continuous Parameter Research Articles

Differences in Somatosensory Function Related to Hand Dominance: Results of a Quantitative Sensory Testing Study in Healthy Volunteers.

Quantitative sensory testing commonly utilizes the unaffected, contralateral side as a control to detect somatosensory dysfunction. There is scant evidence that somatosensory function for the volar dominant and non-dominant hands is equivalent, therefore intra-patient comparisons are unwarranted. This study aimed to identify dominance-related differences in palmar hand somatosensation, thereby determining if the unaffected contralateral hand is a valid comparator in clinical populations. With ethical approval (IREC_13_1_10) and informed consent, 110 healthy adult volunteers' participated in this clinical measurement study. Somatosensory function was assessed with the German Research Network on Neuropathic Pain (DFNS) quantitative sensory testing (QST) protocol. Half of the participants were tested on the dominant hand. Thirteen parameters of thermal and mechanical detection and pain threshold were evaluated at both the dorsal and volar hand (distal middle finger). Tests were performed in the same order and instructions were read from a standardized script. Results for dorsal hand tests were compared to DFNS normative data to confirm participants met study inclusion criteria. Between-group differences for age and sex were investigated with the independent samples t-test and Chi-square test of independence, respectively. Group differences for dominant and non-dominant hands for all 13 continuous QST parameters were investigated with the Mann-Whitney U-test. Data for 106 participants were included in statistical analysis. Fifty percent of participants were tested on the dominant hand [n=53]; there were no differences for age or sex between groups (dominant or non-dominant hand test group). The dominant volar hand was significantly more sensitive to vibration detection threshold than the non-dominant hand (P=0.001). There were no significant differences related to dominance for other DFNS QST measures. For quantitative sensory testing with the DFNS protocol in healthy cohorts, the contralateral, unaffected hand is a valid control, with the exception of vibration detection threshold.

Low-carbohydrate diet in children and young people with type 1 diabetes: A randomized controlled trial with cross-over design

AimsWe investigated whether a short period of tightly controlled low-carbohydrate diet (LCD) leads to higher time in range without increasing the associated risks in children and young people with diabetes (CYPwD). MethodsThirty-five (CYPwD) were recruited into this randomized controlled cross-over study (20 female; 20 CSII; age 14.5 ± 2.9 years; HbA1c 48.9 ± 9.4 mmol/mol). The interventions were five and five weeks of ready-made food box deliveries of isocaloric diets in random order: either LCD (94.5 ± 4.7 g/day) or recommended carbohydrate diet (RCD) (191 ± 19.2 g/day). The outcomes were continuous glucose monitoring parameters, anthropometric, laboratory and quality of life (QoL) data. ResultsTime in range was significantly higher in the LCD than in the RCD period (77.1 % vs. 73.8 %, P=0.008). Times in hyperglycemia and average glycaemia were significantly lower in the LCD. There was no difference between the diets in time in hypoglycemia or glycemic variability. The subjects’ body weight and BMI were significantly lower during the LCD. There was no significant difference in the LDL-cholesterol levels. No significant differences were observed in the self-assessed QoL. ConclusionsShort-term LCD led to an improvement of glycemic parameters without increasing time in hypoglycemia, disturbing the lipid profile or negatively affecting the quality of life of CYPwD.

Simulation of Novel Nano Low-Dimensional FETs at the Scaling Limit.

The scaling of bulk Si-based transistors has reached its limits, while novel architectures such as FinFETs and GAAFETs face challenges in sub-10 nm nodes due to complex fabrication processes and severe drain-induced barrier lowering (DIBL) effects. An effective strategy to avoid short-channel effects (SCEs) is the integration of low-dimensional materials into novel device architectures, leveraging the coupling between multiple gates to achieve efficient electrostatic control of the channel. We employed TCAD simulations to model multi-gate FETs based on various dimensional systems and comprehensively investigated electric fields, potentials, current densities, and electron densities within the devices. Through continuous parameter scaling and extracting the sub-threshold swing (SS) and DIBL from the electrical outputs, we offered optimal MoS2 layer numbers and single-walled carbon nanotube (SWCNT) diameters, as well as designed structures for multi-gate FETs based on monolayer MoS2, identifying dual-gate transistors as suitable for high-speed switching applications. Comparing the switching performance of two device types at the same node revealed CNT's advantages as a channel material in mitigating SCEs at sub-3 nm nodes. We validated the performance enhancement of 2D materials in the novel device architecture and reduced the complexity of the related experimental processes. Consequently, our research provides crucial insights for designing next-generation high-performance transistors based on low-dimensional materials at the scaling limit.

Inactivation of Highly Pathogenic Avian Influenza Virus with High-temperature Short Time Continuous Flow Pasteurization and Virus Detection in Bulk Milk Tanks

Infections of dairy cattle with clade 2.3.4.4b H5N1 highly pathogenic avian influenza virus (HPAIV) were reported in March 2024 in the U.S. and viable virus was detected at high levels in raw milk from infected cows. This study aimed to determine the potential quantities of infectious HPAIV in raw milk in affected states where herds were confirmed positive by USDA for HPAIV (and therefore were not representative of the entire population), and to confirm that the commonly used continuous flow pasteurization using the FDA approved 72 °C (161°F) for 15 s conditions for high−temperature short time (HTST) processing, will inactivate the virus. Double-blinded raw milk samples from bulk storage tanks from farms (n = 275) were collected in four affected states. Samples were screened for influenza A using quantitative real-time RT-PCR (qrRT-PCR) of which 158 (57.5%) were positive and were subsequently quantified in embryonating chicken eggs. Thirty-nine qrRT-PCR positive samples (24.8%) were positive for infectious virus with a median titer of 3.5 log10 50% egg infectious doses (EID50) per mL. To closely simulate commercial milk pasteurization processing systems, a pilot-scale continuous flow pasteurizer was used to evaluate HPAIV inactivation in artificially contaminated raw milk using the most common legal conditions in the US: 72 °C (161°F) for 15 s. Among all replicates at two flow rates (n = 5 at 0.5 L/min; n = 4 at 1 L/min), no viable virus was detected. A mean reduction of ≥5.8 ± 0.2 log10 EID50/mL occurred during the heating phase where the milk is brought to 72.5 °C before the holding tube. Estimates from heat-transfer analysis support that standard U.S. continuous flow HTST pasteurization parameters will inactivate >12 log10 EID50/mL of HPAIV, which is ∼9 log10 EID50/mL greater than the median quantity of infectious virus detected in raw milk from bulk storage tank samples. These findings demonstrate that the US milk supply is safe when pasteurized.

Leveraging long short‐term memory networks and transfer learning for the soft‐measurement of flue gas flowrate from coal‐fired boilers

AbstractThe dynamic operation and deep peak‐shaving of power‐generating units cause significant fluctuations in flue gas flowrate, thus affecting the accuracy of CO2 emissions measured by continuous emission monitoring systems (CEMS). This study established a long short‐term memory network with an attention mechanism (LSTM‐AM) for the soft measurement of the flue gas flowrate in real‐time. First, flue gas flowrate data and continuous operation parameters over 25 days were sampled from a typical 660 MW coal‐fired boiler in China. Then, a carbon balance model was established to verify the data reliability. The LSTM‐AM model was trained and testified at the 660 MW coal‐fired boiler. Results show that the LSTM‐AM model significantly surpassed the pristine LSTM model without attention, the convolutional neural network (CNN) with LSTM, and the static support vector regression (SVR) model in the real‐time prediction of flue gas flowrate. Finally, the LSTM‐AM model was generalized to a 630 MW coal‐fired power unit via transfer learning, which was further demonstrated to outperform the model re‐trained from scratch. This work manifests the feasibility of deep learning for the soft measurement of flue gas flowrate, which is promising to solve data‐lagging issues when measuring CO2 emissions from coal‐fired power plants.

Lattice Realization of Complex Conformal Field Theories: Two-Dimensional Potts Model with Q>4 States.

The two-dimensional Q-state Potts model with real couplings has a first-order transition for Q>4. We study a loop-model realization in which Q is a continuous parameter. This model allows for the collision of a critical and a tricritical fixed point at Q=4, which then emerge as complex conformally invariant theories at Q>4, or even complex Q, for suitable complex coupling constants. All critical exponents can be obtained as analytic continuation of known exact results for Q≤4. We verify this scenario in detail for Q=5 using transfer-matrix computations.

Constraints on the spectrum of field theories with non-integer $O(N)$ symmetry from quantum evanescence

We identify constraints in the energy spectra of quantum theories that have a global O(N)O(N) symmetry, where NN is treated as a continuous parameter. We point out that a class of evanescent states fall out of the spectrum at integer values of NN in pairs, via an annihilation mechanism. This forces the energies of the states in such a pair to approach equality as NN approaches a certain integer, with both states disappearing at precisely integer NN and the point of would-be degeneracy. These constraints occur between different irreducible representations of the analytic continuation of O(N)O(N) and hold non-perturbatively. We give examples in the spectra of the critical O(N)O(N) model.

Autonomous stabilization with programmable stabilized state

Reservoir engineering is a powerful technique to autonomously stabilize a quantum state. Traditional schemes involving multi-body states typically function for discrete entangled states. In this work, we enhance the stabilization capability to a continuous manifold of states with programmable stabilized state selection using multiple continuous tuning parameters. We experimentally achieve 84.6% and 82.5% stabilization fidelity for the odd and even-parity Bell states as two special points in the manifold. We also perform fast dissipative switching between these opposite parity states within 1.8 μs and 0.9 μs by sequentially applying different stabilization drives. Our result is a precursor for new reservoir engineering-based error correction schemes.

The weekend warrior effect: Consistent intermittent exercise induces persistent cognitive benefits

Exercise provides a range of cognitive benefits, including improved memory performance. Previously, we demonstrated that 14 days of continuous voluntary wheel-running exercise enables learning in a hippocampus-dependent Object Location Memory (OLM) task under insufficient, subthreshold training conditions in adult mice. Whether similar exercise benefits can be obtained from consistent intermittent exercise as continuous exercise is unknown. Here, we examine whether intermittent exercise (the weekend warrior effect: 2 days of exercise a week for 7 weeks) displays similar or distinct cognitive benefits as previously examined with 14 days of continuous exercise. We find that both continuous and intermittent exercise parameters similarly enable hippocampus-dependent OLM compared to the 2-day exercise control group. Mice receiving intermittent exercise maintained cognitive benefits following a 7-day sedentary delay, whereas mice that underwent 14 continuous days of exercise showed diminished cognitive benefits as previously reported. Further, compared to continuous exercise, intermittent exercise mice exhibited persistently elevated levels of the genes Acvr1c and Bdnf which we know to be critically involved in hippocampus-dependent long-term memory in the dorsal hippocampus. Together findings suggest that consistent intermittent exercise persistently enables hippocampal-dependent long-term memory. Understanding the optimal parameters for persistent cognitive function and the mechanisms mediating persistent effects will aid in therapeutic pursuits investigating the mitigation of cognitive ailments.

Holographic fluids from 5D dilaton gravity

We study a solvable class of five-dimensional dilaton gravity models that continuously interpolate between anti-de Sitter (AdS5), linear dilaton (LD5) and positively curved spacetimes as a function of a continuous parameter ν. The dilaton vacuum expectation value is set by a potential localized on a flat brane. We chart the elementary properties of these backgrounds for any admissible ν, and determine stability conditions of the brane-dilaton system. We find that the spectrum of metric fluctuations can be either continuous or discrete. It features a massless graviton mode confined between the brane and the curvature singularity, and a massive radion mode tied to brane-dilaton stability. We show that, in the presence of a bulk black hole, the holographic theory living on the brane features a perfect fluid. The equation of state of the holographic fluid interpolates between radiation, pressureless matter and vacuum energy as a function of ν. This extends earlier findings on holographic fluids. Our results suggest that the thermodynamics of the fluid mirrors precisely the thermodynamics of the bulk black hole.

The components of arginine and methylarginine metabolism are indicative of altered kidney function in intrauterine growth-restricted neonates.

Intrauterine fetal growth restriction (IUGR) affects up to 10% of all pregnancies. Severe IUGR is associated with impaired kidney development, reduced nephron endowment, and chronic kidney disease later in life. Currently, no early predictive biomarker exists for detecting altered kidney function in neonates with IUGR. Because nephrons produce key enzymes for the metabolism of arginine and methylarginine components, we quantified and compared the concentrations of arginine and methylarginine metabolites between IUGR and non-IUGR neonates to identify potential biomarkers for the early detection of altered kidney function in IUGR neonates. Seventy-one IUGR and 123 non IUGR neonates were examined. Serum and Urine samples were obtained between 30 h and 5 days of life and between 5 and 70 days of life. Serum concentrations of creatinine, urea, symmetric and asymmetric-dimethylarginine metabolites (SDGV, SDMA, ADGV, and ADMA), guanidino-2-oxo-caproic acid (GOCA), citrulline, homocitrulline, arginine, and homoarginine were quantified using LC-MS/MS and standard clinical laboratory methods. Datasets were compared by Mann-Whitney--Wilcoxon or Chi-square tests for continuous and discrete parameters. P values were corrected for multiple comparisons using the Bonferroni method. After Bonferroni correction, we found that serum creatinine, urea, SDGV, ADGV, and GOCA levels were significantly lower in neonates with IUGR. Consequently, the ratios of SDGV/SDMA, ADGV/ADMA, and GOCA/homoarginine were significantly lower in IUGR neonates. Our study suggests that arginine and methylarginine are possible early biomarkers for detecting altered kidney function in IUGR neonates.

A new strategy optimisation method for underwater flapping foil propulsion based on Twin-Delayed Deep Deterministic and Gaussian process regression

In complex sea environments, underwater bionic flapping foils exhibit significant nonlinear characteristics across a high-dimensional parameter space. This study introduces a novel optimisation paradigm, G-TD3, integrating Twin-Delayed Deep Deterministic (TD3) reinforcement learning with Gaussian process regression (GPR). An automatic experimental device was developed, using which a series of flapping foil propulsion experiments were conducted, exploring both symmetric and asymmetric flutter modes. Utilizing GPR, we characterized the flapping foil's operational environment from experimental data. In addition, the TD3 algorithm was deployed to exploit this environment, enabling interactive learning in high-dimensional, continuous parameter spaces. Our integrated approach facilitates comprehensive exploration-exploitation dynamics. The optimized propulsion strategy, fine-tuned through maximizing acquisition rewards, demonstrates G-TD3's superiority over traditional brute-force methods, requiring less data for optimising both symmetric and asymmetric flutter strategies. This research provides a new optimisation method for nonlinear systems and offers valuable insights into the practical applications of bionic flapping foils.

Calibrating the Discrete Boundary Conditions of a Dynamic Simulation: A Combinatorial Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) Approach.

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence.

Continuous Casting Preparation Process of Helical Fiber-Reinforced Metal Matrix Composites

To improve the strength of the metal while maintaining good plasticity, helical fibers are added to the metal matrix. How to form helical fiber and control its parameters in the preparation process are urgent problems to be solved in the study of helical fiber-reinforced metal matrix composites. In this paper, the continuous casting process of helical fiber-reinforced metal matrix composites was proposed. To reduce the difficulty of the experiment, the formation process of helical fiber on metal matrix and the relationship between the continuous casting process parameters and helical shape fiber parameters were studied by preparing helical carbon fiber-reinforced lead matrix composites with a low-melting-point metal matrix. The results show that this process can produce helical fiber-reinforced metal matrix composite stably and continuously, and the helical shape parameters of the composite can be controlled by changing the process parameters of continuous casting. To further improve the practical application of this process, helical carbon fiber-reinforced aluminum matrix composites were prepared. The test result in terms of mechanical property shows that the tensile strength and elongation of the composite were improved. This indicates that the reinforced phase of the helical structure of the metal matrix composite has higher strength and toughness compared with the matrix metal.

Vortex loop operators and quantum M2-branes

We study M2-branes in AdS_4× S^7/\mathbb{Z}_kAdS4×S7/ℤk dual to 1/2 and 1/3 BPS vortex loop operators in ABJM theory and compute their one-loop correction beyond the classical M2-brane action. The correction depends only on the parity of kk and is independent of all continuous parameters in the definition of the vortex loops. The result for odd kk agrees with the answers for the 1/2 BPS Wilson loop in the k=1k=1 theory and for even kk with the one in the k = 2k=2 theory. Combining with the classical part, we find that the natural expansion parameter seems to be 1/\sqrt{kN}1/kN rather than 1/\sqrt{N}1/N. This provides a further setting where semiclassical quantisation can be applied to M2-branes and produces new results inaccessible by other methods.

Landau free energy of small clusters beyond mean-field approach.

Landau free energy determines the landscape of order parameter fluctuations that occur in a physical system at thermal equilibrium and, in particular, characterizes the critical phenomena. We propose a semianalytical approach based on the fluctuating local field method, which allows us to estimate Landau free energy for small clusters with discrete (Ising model) and continuous (Heisenberg model) order parameter.

Deep learning CT reconstruction improves liver metastases detection

ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.MethodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.ResultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2–10) (median (Q₁–Q₃); total 733) versus 5 (2–10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.ConclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.Critical relevance statementDeep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.Key PointsDetection of liver metastases is crucial but limited with standard CT reconstructions.More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction.Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.Graphical

Wave propagation in a light-temperature neural network under adaptive local energy balance.

External electric and mechanical stimuli can induce shape deformation in excitable media because of its intrinsic flexible property. When the signals propagation in the media is described by a neural network, creation of heterogeneity or defect is considered as the effect of shape deformation due to accumulation or release of energy in the media. In this paper, a temperature-light sensitive neuron model is developed from a nonlinear circuit composed of a phototube and a thermistor, and the physical energy is kept in capacitive and inductive terms. Furthermore, the Hamilton energy for this function neuron is obtained in theoretical way. A regular neural network is built on a square array by activating electric synapse between adjacent neurons, and a few of neurons in local area is excited by noisy disturbance, which induces local energy diversity, and continuous coupling enables energy propagation and diffusion. Initially, the Hamilton energy function for a temperature-light sensitive neuron can be obtained. Then, the finite neurons are applied noise to obtain energy diversity to explore the energy spread between neurons in the network. For keeping local energy balance, one intrinsic parameter is regulated adaptively until energy diversity in this local area is decreased greatly. Regular pattern formation indicates that local energy balance creates heterogeneity or defects and a few of neurons show continuous parameter shift for keeping energy balance in a local area, which supports gradient energy distribution for propagating waves in the network.

Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units.

We present a mean-field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyze the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first-order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units f is smaller than some critical value f_{t}≲1/2, even for a finite system. Moreover, any solution for f<1/2 can be mapped to a solution for purely excitatory systems (f=0). In finite systems, when the system is dominated by inhibition (f>f_{t}), the first-order transition is replaced by a pseudocritical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behavior of a continuous phase transition order parameter. However, in the thermodynamic limit (i.e., infinite-system-size limit), we found that f_{t}→1/2 and the activity for the inhibition dominated case (f≥f_{t}) becomes negligible for any value of the parameters, while the first-order transition between low- and high-activity phases for f<f_{t} remains.

Predicting the dynamics of heart rate variability in patients with type 1 diabetes against improving glycemic control

The aim of the study: to analyze the effect of improvement of glycemic control on heart rate variability changes in patients with type 1 diabetes against the background of insulin therapy correction and to predict this dynamics based on the parameters of continuous glucose monitoring. We examined 49 patients with the level of glycosylated hemoglobin ≥7% and &lt;10% without late diabetic complications. The average age – 32.0 (21.5; 38.0) years, the average disease duration – 9.5 (5.0; 17.5) years. The study was based on the simultaneous registration of heart rate variability and continuous glucose monitoring before and 3 months after correction of insulin therapy. Logistic regression analysis and ROC-analysis were used to predict the changes. After 3 months, the patients had significant decrease in the level of glycosylated hemoglobin, glycose variability, and a decrease in the frequency of hypoglycemic episodes. Heart rate variability increased in 73.5% of persons. The presence of hypoglycemia, standard deviation of blood glucose levels and glomerular filtration rate after treatment turned out to be prognostic factors for the predicting improvement in heart rate variability (the proportion of correct prediction of the patient's actual belonging to one or another prognostic group was 76.39%). Based on the calculation of the theoretical values of the positive result probability using the logistic equation, a detailed scale for predicting changes in heart rhythm variability for type 1 diabetes patients was proposed: up to 0.07 – a low probability of a positive result; 0.07-0.29 – the probability of a positive result is below average; 0.29-0.51 – a moderate probability of a positive result; 0.51-0.90 – high probability of a positive result; more than 0.90 – a very high probability of a positive result. We found that improvement of glycemic control leads to an increase in both frequency and time characteristics of heart rate variability. The increase in the likelihood of improvement of heart rate variability in patients with type 1 diabetes was more likely to be associated with reduced glycose variability and fewer hypoglycemic episodes. We developed a predictive mathematical model of heart rate variability based on the continuous glucose monitoring parameters for type 1 diabetes with sensitivity of 88.0% and specificity 68.18%, AUC 0.739 (p=0.001).