Temperature Trajectory Research Articles

The Gut Microbiome Modulates Body Temperature Both in Sepsis and Health.

Rationale: Among patients with sepsis, variation in temperature trajectories predicts clinical outcomes. In healthy individuals, normal body temperature is variable and has decreased consistently since the 1860s. The biologic underpinnings of this temperature variation in disease and health are unknown. Objectives: To establish and interrogate the role of the gut microbiome in calibrating body temperature. Methods: We performed a series of translational analyses and experiments to determine whether and how variation in gut microbiota explains variation in body temperature in sepsis and in health. We studied patient temperature trajectories using electronic medical record data. We characterized gut microbiota in hospitalized patients using 16S ribosomal RNA gene sequencing. We modeled sepsis using intraperitoneal LPS in mice and modulated the microbiome using antibiotics, germ-free, and gnotobiotic animals. Measurements and Main Results: Consistent with prior work, we identified four temperature trajectories in patients hospitalized with sepsis that predicted clinical outcomes. In a separate cohort of 116 hospitalized patients, we found that the composition of patients' gut microbiota at admission predicted their temperature trajectories. Compared with conventional mice, germ-free mice had reduced temperature loss during experimental sepsis. Among conventional mice, heterogeneity of temperature response in sepsis was strongly explained by variation in gut microbiota. Healthy germ-free and antibiotic-treated mice both had lower basal body temperatures compared with control animals. The Lachnospiraceae family was consistently associated with temperature trajectories in hospitalized patients, experimental sepsis, and antibiotic-treated mice. Conclusions: The gut microbiome is a key modulator of body temperature variation in both health and critical illness and is thus a major, understudied target for modulating physiologic heterogeneity in sepsis.

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

Simulation of Combustion Performance of High Salinity Organic Waste Liquid Incinerator

High-salinity organic waste liquid is one of the important industrial pollution sources and the main by-product of petrochemical enterprises in China. Incineration is the mainstream technology for treating this kind of waste liquid. A three-dimensional numerical simulation of a practical 18t/h waste liquid incinerator was carried out using Fluent. The velocity, temperature, component concentration and particle trajectory distribution in the incinerator were obtained, proposing a corresponding incinerator performance optimization scheme according to the simulation results. The results demonstrate that the numerical combustion model established in this paper can accurately simulate the distribution of flow, combustion and heat transfer in the incinerator, verifying the reliability of the numerical model; The overall temperature and flow field organization of this type of waste liquid incinerator is relatively reasonable, with heat exchange reported only in the flue corner and some heat exchange surfaces. The flow field and temperature field distribution in the incinerator can be effectively modified so as to improve the boiler operation and maintenance cycle by optimizing the corner flow line and furnace structure.

Multi-objective optimal control of Docosahexaenoic Acid (DHA) production in fed-batch fermentation by Schizochytrium sp.

Docosahexaenoic Acid (DHA) production from fermentation process in a fed-batch reactor is suited solved using optimal control to obtain optimal temperature and feed flowrate trajectories. This fermentation process, which involves numerous conflicting objectives, necessitates the solution of a multi-objective optimal control (MOOC). MOOC results, which include a variety of ideal solutions, are configured as Pareto Front (PF). The ε-constraint with hybrid strategy (HS), and elitist non-dominated sorting genetic algorithm (NSGA-II) have been implemented to tackle conflict bi-objectives: minimisation final time and maximizing DHA production. By computing performance measurements such as space (SP), hypervolume (HV), and pure diversity (PD), these MOOC techniques were compared to the characteristic of the Pareto solution. Due to the most precise, diverse, and desirable spread points along the PF, the ε-constraint technique is the most effective. Each Pareto solution point comprises a unique combination of optimal feed flowrate trajectories, resulting in a unique amount of final time and DHA concentration. These solutions provide a variety of options for evaluating trade-offs and establishing the best operating strategy.

Thermo-Electrical state and demand response potential estimation for power systems with building thermostats

Heating and cooling are major resources of Demand Response (DR) to enhance the flexibility and reliability of power grids. In order to maximize their potential, it is necessary to reliably keep track of their operating states in real-time operation. This paper presents a comprehensive state estimation framework for power systems with building thermostats, with a tractable thermodynamic building model and integration of multi-source information from weather, power grid, building systems. Based on the thermodynamic model and state of the buildings, the building temperature trajectories in the next few hours can be accurately predicted, such that the DR potential of the building can be precisely estimated. To jointly estimate the state variables of power grids and state variables in building thermostats with different time scales of dynamics, a holistic estimation framework is developed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system and estimate the DR potential in the presence of measurement noise and bad data.

Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors

Reliable prediction for crop yield is crucial for economic planning, food security monitoring, and agricultural risk management. This study aims to develop a crop yield forecasting model at large spatial scales using meteorological variables closely related to crop growth. The influence of climate patterns on agricultural productivity can be spatially inhomogeneous due to local soil and environmental conditions. We propose a Bayesian spatially varying functional model (BSVFM) to predict county-level corn yield for five Midwestern states, based on annual precipitation and daily maximum and minimum temperature trajectories modeled as multivariate functional predictors. The proposed model accommodates spatial correlation and measurement errors of functional predictors, and respects the spatially heterogeneous relationship between the response and associated predictors by allowing the functional coefficients to vary over space. The model also incorporates a Bayesian variable selection device to further expand its capacity to accommodate spatial heterogeneity. The proposed method is demonstrated to outperform other highly competitive methods in corn yield prediction, owing to the flexibility of allowing spatial heterogeneity with spatially varying coefficients in our model. Our study provides further insights into understanding the impact of climate change on crop yield. Supplementary materials for this article are available online.

Dynamic Multi‐Objective Optimization of Autocatalytic Esterification in a Semi‐Batch Reactor

AbstractThe catalytic esterification of ‐butyl propionate in a semi‐batch reactor is examined using dynamic, nonlinear programming‐based optimization. To solve the conflicting bi‐objective of minimizing the final time tf and maximizing the conversion Xc, the ε‐constraint, weighted sum, and NSGA‐II (non‐dominated sorting genetic algorithm II) techniques were implemented. By computing performance measurements like hypervolume, pure diversity, and spacing, these multi‐objective optimization techniques were compared to the characteristics of the Pareto solution. Each Pareto solution point comprises a unique combination of optimal feed flow rate and temperature trajectories. These solutions provide various options for evaluating trade‐offs while establishing the best operating strategy.

Numerical Analyses and a Nonlinear Composite Controller for a Real-Time Ground Aerodynamic Heating Simulation of a Hypersonic Flying Object

This paper contains two parts: numerical analyses and a control method. The numerical analyses of a hypersonic flying object’s aerodynamic heating environment are based on three different two-dimensional outflow fields via finite element calculations. Then, the reference temperature trajectories of a hypersonic flying object are obtained. The other one is an intelligent proportional-derivative (IPD) with a nonlinear global sliding mode control (NGSMC) based on a nonlinear extended state observer (NESO) for a real-time ground aerodynamic heating simulation of a hypersonic flying object, named a thermal-structural test with quartz lamp heaters. The composite controller is made of three sub-components: a model free frame that is independent of the system dynamic model along with an ultra-local model; a NESO for the lumped disturbances observation; and an integral sliding mode control with a nonlinear function for the observation errors compensation. The flight environment of the hypersonic flying object is from Mach number 0.6 to Mach number 5.0, with between flight altitude of 31,272 m and flight altitude of 13,577 m. The comparative results demonstrate some superiorities of the proposed composite controller in terms of tracking errors and robustness.

A Two-Stage Real-Time Optimized EV Battery Cooling Control Based on Hierarchical and Iterative Dynamic Programming and MPC

The battery thermal management (BTM) system plays an increasingly important role today in the safety of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as the battery capacity and power ratings keep growing. The BTM system is commonly coupled with the vehicle passenger cabin HVAC system. This integrated thermal system is a major onboard energy consumer, and its complexity brings challenges to its control. With the planned-ahead speed profile and the corresponding power trajectory obtained from the connected and automated vehicle (CAV) technology, predictive control makes a desirable option for the integrated system to maintain battery safety and passenger comfort while lowering energy consumption. However, in order to achieve both high accuracy and low cost in a wide control range, a very long prediction horizon and high sampling rate are both necessary. This will overwhelm the processing capacity of the onboard electronic control unit (ECU) if using conventional predictive control. In this paper, a two-stage predictive control strategy for the BTM and HVAC coupled system is proposed to solve this problem. In stage 1, based on the integrated cooling system efficiency features, a hierarchical and iterative dynamic programming (HIDP) scheme is designed to derive the optimal battery temperature trajectory to reach the set point at the end of the horizon with a modest computation burden. In stage 2, a control-oriented model is constructed for the cooling system and a model predictive controller (MPC) is accordingly built to track the trajectory from Stage 1 while enforcing the energy saving. A high set-point-tracking performance and as high as 10.61% energy saving for the cooling system in the UDDS cycle are verified by simulation results. The real-time implementation capability of the proposed strategy is demonstrated by the vehicle emulator experiments based on hardware in the loop (HIL) and a rapid control prototyping (RCP) platform.

Numerical simulation on the melting kinetics of steel scrap in iron-carbon bath

Steel scrap melting kinetics is a key factor that affects the scrap ratio and temperature trajectory of Basic Oxygen Furnace steelmaking process and the productivity and energy utilization of Electric Arc Furnace steelmaking process. In this paper, the interface between steel scrap and melt is analyzed and a novel theoretical model of steel scrap melting in iron-carbon bath is established. Moreover, the effects of bath temperature, bath carbon content, preheating temperature and characteristic length of steel scrap on the melting rate of steel scrap, mass transfer coefficient of carbon, heat transfer coefficient and interface carbon concentration were investigated. The results show that the ultimate steady melting rate increases from −2.04 × 10 −4 to −3.32 × 10 −3 m/s with the increases of bath temperature from 1793 K to 1873 K, the ultimate steady heat and mass transfer coefficient first increase and subsequently decrease, are in the range of 1.04 × 10 5 to 2.86 × 10 5 W/m 2 /K and 0 to 2.89 × 10 −4 m/s, respectively. As the bath carbon content increase from 1.0 wt% to 4.0 wt%, the ultimate steady melting rate increases from −8.85 × 10 −5 to −3.23 × 10 −4 m/s, the ultimate steady heat transfer coefficient first increases and subsequently decreases, is in the range of 7.59 × 10 4 to 1.01 × 10 5 W/m 2 /K. Furthermore, the ultimate steady mass transfer coefficient and the ultimate steady interface carbon content gradually decreases from 1.40 × 10 −4 to 5.21 × 10 −5 m/s and from 0.73 to 0.69 wt%, respectively. With the increase of preheating temperature and characteristic length of steel scrap, the ultimate steady melting rate, heat transfer coefficient between steel scrap and melt, mass transfer coefficient and interface carbon concentration remain almost constant, about −8.85 × 10 −5 m/s, 8.40 × 10 4 W/m 2 /K, 1.40 × 10 −4 m/s and 0.70 wt%, respectively.

Adaptive Neural Network Global Nonsingular Fast Terminal Sliding Mode Control for a Real Time Ground Simulation of Aerodynamic Heating Produced by Hypersonic Vehicles

This paper presents a strategy for a thermal-structural test with quartz lamp heaters (TSTQLH), combined with an ultra-local model, a closed-loop controller, a linear extended state observer (LESO), and an auxiliary controller. The TSTQLH is a real time ground simulation of aerodynamic heating for hypersonic vehicles to optimize their thermal protection systems (TPS). However, lack of a system dynamic model for the TSTQLH results in inaccurate tracking of aerodynamic heating. In addition, during the control process, the TSTQLH has internal uncertainties of resistance and external disturbances. Therefore, it is necessary to establish a mathematical model between controllable α(t) and measurable T1(t). An ultra-local model of model-free control plays a crucial role in simplifying system complexity and reducing high-order terms due to high nonlinearities and strong couplings in the system dynamic model, and a global nonsingular fast terminal sliding mode control (GNFTSMC) is added to an ultra-local model, which is used to guarantee great tracking performance in the sliding phase and fast convergence to the equilibrium state in finite time. Moreover, the LESO is used mainly to estimate all disturbances in real time, and an adaptive neural network (ANN) shows a good approximation property in compensation for estimation errors by using a cubic B-spline function. The fitted curve of the wall temperature in the time sequence represents a reference temperature trajectory from the surface contour of an X-43A’s wing. The comparative results validate that the proposed control strategy possesses strong robustness to track the reference temperature trajectory.

Nuclear Reactor Power Level Model Predictive Control: A Consideration of Coolant Outlet Temperature Relaxation Tracking Method

Nuclear reactor core power level control and the average coolant temperature usually adopt independent control strategies, but tracking only one single core power makes the coolant temperature control risky in a disturbance situation. To solve this problem, a reference trajectory calculation based on the coolant outlet temperature under the steady state of the reactor power is designed; then, a model predictive controller with a dual objective optimization function, which also includes a relaxation factor, is constructed. By setting the tracking weight matrix in the controller, the tendency setting of dual target trajectory tracking can be realized. Simulation experiments verify the effectiveness of core power level control considering the coolant outlet temperature trajectory tracking. Furthermore, the tracking effect of different weight matrices is simulated and analyzed. The results show that the developed control strategy is more flexible than a single control scheme.

Design and application of data driven economic model predictive control for a rotational molding process

The present manuscript addresses the problem of economically achieving a user specified set of product qualities in an industrial complex batch process, illustrated through a lab-scale uni-axial rotational molding (also known as Rotomolding) setup. To this end, a data driven Economic MPC (EMPC) formulation is developed and implemented to achieve product specification via constraints on the predicted quality variables. First, a state-space dynamic model of the rotomolding process is built using previous batch data generated in the lab using uni-axial rotomolding setup. The dynamic model captures the internal mold temperature trajectory for an input sequence (combination of two heaters and compressed air). This model is then supplemented by a partial-least-squares quality model, which relates with key quality variables (sinkhole area and impact energy) with the terminal (states) prediction. The complete model is then placed within the EMPC scheme which minimizes the cost associated with inputs and allows the user to specify required product quality via constraints on the quality variables. Results achieved from experimental studies illustrate the capability of the proposed EMPC scheme in lowering the process cost (energy requirements) for two manifestations of the economic objective.

Multiobjective temperature trajectory optimization for unseeded batch cooling crystallization of aspirin

Batch cooling crystallization is a type of crystallization wherein supersaturation is brought about by reducing the temperature of the crystallization system with time. It is commonly used in the chemical and pharmaceutical industries to manufacture a wide variety of crystalline products. This work deals with multiobjective optimization of unseeded batch cooling crystallization of Aspirin. A novel method involving temperature changes rather than temperatures of the crystallization mixture over time has been discussed in this study. Optimization studies were carried out to minimize the coefficient of variation and maximize mean size. Optimization was carried out using the benchmark NSGA-II and NSGA-II hybrid optimizers available in MATLAB. A standard algorithm to select a trade-off point on the Pareto front is also discussed. Rigorous simulation studies were carried out to determine the best temperature trajectory by inspecting the crystal size distributions generated using the method of characteristics.

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes.

Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019. Retrospective observational study. Four hospitals within an academic healthcare system from March 2020 to February 2021. All adult patients hospitalized with coronavirus disease 2019. Using a validated group-based trajectory model, we classified patients into four previously defined temperature trajectory subphenotypes using oral temperature measurements from the first 72 hours of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes. The 5,903 hospitalized coronavirus disease 2019 patients were classified into four subphenotypes: hyperthermic slow resolvers (n = 1,452, 25%), hyperthermic fast resolvers (1,469, 25%), normothermics (2,126, 36%), and hypothermics (856, 15%). Hypothermics had abnormal coagulation markers, with the highest d-dimer and fibrin monomers (p < 0.001) and the highest prevalence of cerebrovascular accidents (10%, p = 0.001). The prevalence of venous thromboembolism was significantly different between subphenotypes (p = 0.005), with the highest rate in hypothermics (8.5%) and lowest in hyperthermic slow resolvers (5.1%). Hyperthermic slow resolvers had abnormal inflammatory markers, with the highest C-reactive protein, ferritin, and interleukin-6 (p < 0.001). Hyperthermic slow resolvers had increased odds of mechanical ventilation, vasopressors, and 30-day inpatient mortality (odds ratio, 1.58; 95% CI, 1.13-2.19) compared with hyperthermic fast resolvers. Over the course of the pandemic, we observed a drastic decrease in the prevalence of hyperthermic slow resolvers, from representing 53% of admissions in March 2020 to less than 15% by 2021. We found that dexamethasone use was associated with significant reduction in probability of hyperthermic slow resolvers membership (27% reduction; 95% CI, 23-31%; p < 0.001). Hypothermics had abnormal coagulation markers, suggesting a hypercoagulable subphenotype. Hyperthermic slow resolvers had elevated inflammatory markers and the highest odds of mortality, suggesting a hyperinflammatory subphenotype. Future work should investigate whether temperature subphenotypes benefit from targeted antithrombotic and anti-inflammatory strategies.

Temperature Trajectory Sub-phenotypes and the Immuno-Inflammatory Response in Pediatric Sepsis.

Heterogeneity has hampered sepsis trials, and sub-phenotyping may assist with enrichment strategies. However, biomarker-based strategies are difficult to operationalize. Four sub-phenotypes defined by distinct temperature trajectories in the first 72 h have been reported in adult sepsis. Given the distinct epidemiology of pediatric sepsis, the existence and relevance of temperature trajectory-defined sub-phenotypes in children is unknown. We aimed to classify septic children into de novo sub-phenotypes derived from temperature trajectories in the first 72 h, and compare cytokine, immune function, and immunometabolic markers across subgroups. This was a secondary analysis of a prospective cohort of 191 critically ill septic children recruited from a single academic pediatric intensive care unit. We performed group-based trajectory modeling using temperatures over the first 72 h of sepsis to identify latent profiles. We then used mixed effects regression to determine if temperature trajectory-defined sub-phenotypes were associated with cytokine levels, immune function, and mitochondrial respiration. We identified four temperature trajectory-defined sub-phenotypes: hypothermic, normothermic, hyperthermic fast-resolvers, and hyperthermic slow-resolvers. Hypothermic patients were less often previously healthy and exhibited lower levels of pro- and anti-inflammatory cytokines and chemokines. Hospital mortality did not differ between hypothermic children (17%) and other sub-phenotypes (3-11%; P = 0.26). Critically ill septic children can be categorized into temperature trajectory-defined sub-phenotypes that parallel adult sepsis. Hypothermic children exhibit a blunted cytokine and chemokine profile. Group-based trajectory modeling has utility for identifying subtypes of clinical syndromes by incorporating readily available longitudinal data, rather than relying on inputs from a single timepoint.

Experimental and numerical simulation research on fire suppression efficiency of dry powder mediums containing molybdenum flame retardant additive

In order to cope with the frequent fire accidents in modern society, it is crucial to develop efficient fire extinguishing agents to stamp out fires in its early stage. In this work, a new composite ultrafine dry powder extinguishing agent containing ammonium molybdate ((NH4)2MoO4) is prepared, and its fire extinguishing efficiency is studied. The cup burner experimental results show that with the increase of the mass fraction of (NH4)2MoO4, the flame temperature drop and flame height variation rate show a trend of increasing and then decreasing, while the MEC and extinguishing time present a trend of a decrease and then an increase. It is indicated that the composite dry powder has the best fire extinguishing efficiency when the mass fraction of (NH4)2MoO4 is 7%. Besides, in terms of the pyrolysis results of TGA and DSC, it is found that the degree of thermal decomposition is significantly promoted under the condition of 7% addition. Based on the gas-solid two-phase flow theory and multiphase flow model, a simplified simulation model of the cup burner is established by the ANSYS-FLUENT software to analyze the variation of temperature and particle motion trajectory. The simulation results demonstrate that the ultra-fine dry powder mediums have a good dispersibility after entering the cup burner. This can achieve a full submerged state at a faster speed, and the flame temperature of the entire fire suppression stage is consistent with the experimental results. This article mainly develops the application of fire extinguishing media in the field of fire protection from the perspective of process safety.

Water stable isotopes in snow along a traverse of the West Antarctic Ice Sheet: insights into moisture origins, air-masses distillation history, and climatic value.

This study investigated the water isotopic content (δ18O, δD, d-excess) of the surface snow along a 995 km traverse over the West Antarctic Ice Sheet from the Möller Ice Stream - Institute Ice Stream to the upper reaches of the Pine Island Glacier drainage basin. The purpose of this study was to evaluate the climatic record preserved in the snow. We analyzed 92 surface samples (~0.15-0.20 m deep), retrieved during 2014/2015 austral summer from every ~10 km along the traverse route, using the laser spectroscopy technique. We computed the isotopic-geographical characteristics and spatial co-isotopic empirical relationships and compared the isotopic results with the tropospheric mean annual temperature and air mass trajectories. Our isotopic results were sensitive to capturing the well-known climatic asymmetry between the Amundsen-Bellingshausen Sea (ABS; which receives more influence from warmer (oceanic) air masses) and Weddell Sea (WS; more influenced by colder (continental) air masses) sectors. Further, the spatial distribution of δs and d-excess and the co-isotopic relationships reflect two preferential fractionation paths: one from the coast of the ABS sector to the WS sector, and another from the coast of the WS sector to the inland. The Pacific Ocean is confirmed as the primary source of moisture.

УПРАВЛЕНИЕ ТЕМПЕРАТУРНЫМ РЕЖИМОМ НИЖНИХ ЗОН МЕТОДИЧЕСКИХ ПЕЧЕЙ

Introduction. The problem of ensuring the identity of the heating trajectories of the upper and lower halves of the slab (slab heating symmetry) is considered for significantly different numerical values of the external heat transfer parameters for its upper and lower surfaces. This is due, in particular, to poor thermal insulation of the hearth pipes. Purpose of the study. To develop an algorithm for calculating the ratio of settings (tasks) required for symmetry for the temperature controllers of the working space of the upper and lower heating zones of continuous furnaces. Materials and methods. The analysis and generalization of literature data on the problem is carried out. A mathematical description of asymmetric heating is proposed, an optimization problem is posed and solved. Results. Algorithm variants provide for the possibility of describing external and internal heat transfer both in linearized and in the original non-linear form. For the case of representation of the heating process by a fully linearized heat conduction equation with a convective form of external heat transfer, the desired result is represented by an analytical formula. This, as is known, makes it possible to trace the influence of the initial process parameters on the optimal ratio of settings. Numerical research shows that observance of the calculated ratio of the settings ensures a satisfactory coincidence of the mass-average temperature trajectories of the upper and lower halves of the slab in the case of asymmetric heating itself (inequality of external heat transfer coefficients). A simplified solution of the problem under consideration is also proposed, when the dynamics of mass-average temperatures of the upper and lower halves of the slab is initially described by a first-order ordinary differential equation obtained from the heat conduction equation by integral temperature averaging over the thickness of the slab. The simplified version also provides, as a rule, practically acceptable proximity of the heating trajectories of the upper and lower halves of the slab and is even more “transparent” as to how the initial data affects the desired result. Conclusion. The use of the optimal ratio of settings allows, for example, when solving problems of optimal process control, to reduce the dimension of the desired control vector by half, which is very important in the development of algorithmic support for automated process control systems for heating furnaces.

A Data-Driven Recipe Simulation for Synthetic Rubber Production

To manufacture synthetic rubber, rubber manufacturers require optimal recipes to ensure that it satisfies the required quality standards. Several experiments are required to create the optimal recipe, which adversely affects not only the cost and time required but also the health of workers. Suppose the experimental results can be predicted in advance at the recipe design stage before direct experimentation. In that case, the cost of the experiment can be reduced, and the workers’ health can be significantly less impacted. For this purpose, a method called the prediction walk model using a machine learning model was developed to generate the temperature trajectory in a kneading machine. A cross-updating method to predict the quality of the kneading operation is also proposed. From the results of the experiment, it was confirmed that the performance of the proposed models was superior to that of the existing prediction models.