Temperature Estimates Research Articles

Optimal limits of continuously monitored thermometers and their Hamiltonian structure

We investigate the fundamental and practical precision limits of thermometry in bosonic and fermionic environments by coupling an N-level probe to them and continuously monitoring it. Our findings show that the ultimate precision limit, quantified by the Fisher information, scales linearly with N, offering an exponential improvement over equilibrium thermometry, where the scaling is only log2N. For a fixed Hamiltonian structure, we develop a maximum-likelihood estimation strategy that maps the observed continuously monitored trajectories of the probe into temperature estimates with minimal error. By optimizing over all possible Hamiltonian structures, we discover that the optimal configuration is an effective two-level system, with both levels exhibiting degeneracy that increases with N—a stark contrast to equilibrium thermometry, where the ground state remains nondegenerate. Our results have practical implications. First, continuous monitoring is experimentally feasible on several platforms and accounts for the preparation time of the probe, which is often overlooked in other approaches such as prepare and reset. Second, the linear scaling is robust against deviations from the effective two-level structure of the optimal Hamiltonian. Additionally, this robustness extends to cases of initial ignorance about the temperature. Thus, in global estimation problems, the linear scaling remains intact even without adaptive strategies. Published by the American Physical Society 2025

ALMA Observations of Massive Clouds in the Central Molecular Zone: External-pressure-confined Dense Cores and Salpeter-like Core Mass Functions

Abstract We present Atacama Large Millimeter/submillimeter Array Band 6 (1.3 mm) observations of dense cores in three massive molecular clouds within the central molecular zone (CMZ) of the Milky Way, including the Dust Ridge cloud e, Sgr C, and the 20 km s−1 cloud, at a spatial resolution of 2000 au. Among the 834 cores identified from the 1.3 mm continuum, we constrain temperatures and linewidths of 253 cores using local thermodynamic equilibrium methods to fit the H2CO and/or CH3CN spectra. We determine their masses using the 1.3 mm dust continuum and derived temperatures, and then evaluate their virial parameters using the H2CO and/or CH3CN linewidths and construct the core mass functions (CMFs). We find that the contribution of external pressure is crucial for the virial equilibrium of the dense cores in the three clouds, which contrasts with the environment in the Galactic disk where dense cores are already bound, even without the contribution of external pressure. With our new temperature estimates we also find that the CMFs show a Salpeter-like slope in the high-mass (≳3–6 M ⊙) end, a change from previous works. Combined with the possible top-heavy initial mass functions (IMFs) in the CMZ, our result suggests that gas accretion and further fragmentation may play important roles in transforming the CMF to the IMF.

Independent effects of long and short-term exposures to non-optimal increased temperature on mortality

Although the short-term heat effects are well-established, longer-term effects, beyond those, have recently received attention, in the context of climate change. Our study aims to investigate the potential effects of long-term exposure to non-optimal warm period temperatures on all-cause mortality in four large regions in the UK, Norway, Italy, and Greece. Daily all-cause mortality counts from 1996 to 2018 for four European NUTS2 regions including 52-662 small areas were collected and associated with spatiotemporal temperature estimates. A NUTS2 region–specific mixed quasi-Poisson over-dispersed model, with a random intercept per small area within NUTS2 regions, was applied to investigate the association between long-term temperature exposure and mortality during the warm period (May to September), adjusting for short-term temperature, seasonality, long-term trends, and small-area population characteristics. As long-term temperature exposure indices per small area, we considered: 1) the warm period annual average temperature, 2) the annual standard deviation (SD) of warm period temperature, and 3) the coefficient of variation of warm period temperature (CV). We found consistent results following short-term temperature exposure on mortality, with higher effects in southern areas. Results on the shape of the long-term association between average temperature and mortality differed by country, while the different temperature metrics produced inconsistent findings. Increased mortality was associated with increased annual warm season temperature, lower SD and increased CV in Greece, with higher SD and decreased CV in Italy and with decreased annual temperature and CV in Norway. Effects in the UK did not reach the nominal level of statistical significance. Although our study implies an impact on mortality resulting from longer-term temperature exposure, its direction varied across areas and on the temperature metric used. Further research is warranted, applying non-ecological study designs and covering various geographical areas to capture the impact of individual and area-specific characteristics.

Near-surface air temperature estimation for areas with sparse observations based on transfer learning

Near-surface air temperature estimation for areas with sparse observations based on transfer learning

Heat source and internal temperature estimation of an integrated modular motor drive in robotic application using inverse heat conduction problem

Heat source and internal temperature estimation of an integrated modular motor drive in robotic application using inverse heat conduction problem

Rotor temperature estimation for Oil-Cooled induction Machines by a parameter identification network with parallel differentiated branches

Rotor temperature estimation for Oil-Cooled induction Machines by a parameter identification network with parallel differentiated branches

A novel skin temperature estimation system for predicting pressure injury occurrence based on continuous body sensor data: A pilot study.

Pressure injury prevention is important in older patients with immobility. This requires an accurate and efficient prediction of the development of pressure injuries. We aimed to develop a method for estimating skin temperature changes due to ischemia and inflammation using temperature sensors placed under bedsheets to provide an objective, non-invasive, and non-constrained risk assessment tool. This study consisted of a thermal skin simulation study and a descriptive correlation study in healthy participants. A thermal skin simulation study was conducted using a model reproducing the body surface (underwear, diaper, or wet diaper conditions) and bed environment. In a descriptive-correlational study, the participants lay supine on a mattress with a temperature sensor attached to their sacral skin. The thermal skin simulation study showed that temperature changes in the skin can be estimated under the sheets by inputting time-shifted temperature data into machine learning (R2=0.9967 for underwear, 0.9950 for diapers, and 0.9869 for wet diapers). It was also demonstrated that the absolute skin temperature of a healthy individual (N=17) could be estimated with the best accuracy by inputting time-shifted data into an extra-tree regressor (R2=0.8145). A combination of interface pressure and temperature sensors can be used to estimate skin temperature changes. These findings contribute to the development of a skin temperature measurement method that can capture temperature changes over time in clinical settings.

The fate of solid-phase transfer in subduction zones: Evidence from Ti-oxides in Luobusa ophiolitic chromitite

The transport of hydrous components from a subducting oceanic plate to the supra-subduction lithospheric mantle and their influence on arc magmas is well-documented. Yet the transfer of solid-phase materials remains enigmatic, despite an increasing body of literature regarding inherited rutile in well-characterized arc magmas and ophiolitic chromitites. The mechanism governing the introduction of solid phases into the overlying mantle wedge as well as the duration of their residence are poorly understood. This study investigates mineralogical and geochemical characteristics of rutile inclusions in the Luobusa chromitite of southern Tibet.In the investigated chromitite, rutile, associated with ilmenite, corundum, ulvöspinel, zircon, and melt silicate inclusions, exhibits geochemical characteristics indicative of derivation from crustal mafic rocks. Estimated formation or recrystallization temperatures of rutile range from 570 to 675 °C at approximately 1.2 GPa. These temperature estimates illustrate the challenges of obtaining accurate dating, as the rutile UPb system resets near 600 °C. The Fe/Zr ratios in Luobusa rutile serve as indicators of H2O/Zr ratios, suggesting a significant role for hydrous components in the behavior of high field strength elements (HFSE) during subduction processes.A fluid-assisted transport mechanism likely facilitated the movement of HFSE, such as Ti, V, Zr, Nb, and Ta, within accessory mineral phases into the supra-subduction mantle wedge. The presence of rutile and polymorphs of TiO₂, including Ti0.82Si0.18O2 and TiO2-II, within chromite crystals underscores the extensive recycling and recrystallization processes occurring under diverse mantle conditions during the genesis and evolution of the Luobusa ophiolites.

Estimation of the mean radiant temperature in office buildings using an artificial neural network developed in a Phyton environment

ABSTRACT Thermal comfort describes an occupant’s state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, Türkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R² value of 0.94 compared to the globe thermometer measurement method. The model’s advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT.

BiSpeD: Binary Spectral Disentangling applied to binary low-mass star searches

Context. In a companion paper, we present a novel method for the spectroscopic detection of low-luminosity secondary companions of binary stars. An interesting application field is the identification of very low-mass stars as companions of late-type main-sequence stars. Aims. To provide a simple tool based on our method, we developed Binary Spectral Disentangling (BiSpeD), a PYTHON repository that provides a toolkit for processing and analyzing spectroscopic observations. The main task of BiSpeD is find2c, which aims to calculate the mass ratio (q) of a single-lined spectroscopic binary from the analysis of a sample of observed spectra. In the same process, an estimate of the secondary effective temperature is obtained. In addition, the toolkit includes other spectral measurement tasks, including spectral disentangling and radial velocity (RV) measurement via cross-correlation. Methods. The BiSpeD package was tested on a sample of 41 late-type stars observed by the High-Accuracy Radial Velocity Planetary Searcher (HARPS). For each star in the sample, we measured the RV for the primary companion and determined the orbital parameters. We then applied the find2c task, which defines a grid of mass ratios and generates the corresponding grid of solutions that reconstruct the secondary spectrum using iterative spectral disentangling. Finally, a modified cross-correlation algorithm compares these secondary spectra with synthetic templates to find the best solution. Results. Our analysis led to the detection of 12 faint companions from the 41 candidates, including five stars below 0.3 M⊙. Among our positive detections are two cases of binaries formed by a red giant primary and a main-sequence secondary. We show that reliable mass ratio values can be obtained even in cases where the orbital period is unknown and the RV amplitude is only a few km s−1.

How machine learning can revolutionize building comfort: Accessing the impact of occupancy prediction models on HVAC control system

Ventilation control systems globally constitute among the most significant energy demands in the building industry. Optimizing the energy usage of such systems is imperative for constructing sustainable buildings and is essential for achieving environmental sustainability. Occupancy factors of these buildings, particularly Heating, Ventilation and Air Conditioning (HVAC) optimization, are pivotal for energy optimization. In this work, we apply machine learning approaches to improve the efficiency of the HVAC systems of the Engineering Classroom and Research Building (ENCARB) at Prairie View A&M University. We focus on supporting real-time automated HVAC control through the accurate estimation of HVAC temperature based on occupancy patterns. Therefore, we introduce AIRFLO, an AI-powered Robust Framework for Learning to Optimize HVAC energy consumption. Our framework integrates several occupancy factors obtained from the class schedule to estimate ideal HVAC temperatures. Specifically, we incorporate user nonspecific behavior vis-a-vis energy HVAC service period within the ENCARB Building to gather useful matrices as the basis for the model design. To learn the complex patterns in data, we trained our framework using supervised machine learning. Specifically, we initially trained our framework using an ensemble of multilayer neural networks using our training data and observed the estimation performance on an independent validation set. To learn deeper representations and perform systematic comparative model analysis, we proposed our plan to incorporate both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). Overall, our proposed approach will offer a comprehensive and scalable framework for optimizing HVAC energy optimization, leading to improved sustainability.

Estimation of extreme temperatures in direct solar methane pyrolysis within a porous medium

Estimation of extreme temperatures in direct solar methane pyrolysis within a porous medium

Solar radiation estimation in West Africa: impact of dust conditions during the 2021 dry season

Abstract. The anticipated increase in solar energy production in West Africa requires high-quality solar irradiance estimates, which are affected by meteorological conditions and in particular the presence of desert dust aerosols. This study examines the impact of incorporating desert dust into solar irradiance and surface temperature estimations. The research focuses on a case study of a dust event in March 2021, which is characteristic of the dry season in West Africa. Significant desert aerosol emissions at the Bodélé Depression are associated with a Harmattan flow that transports the plume westwards. Simulations of this dust event were conducted using the meteorological Weather Research and Forecasting (WRF) Model alone, as well as coupling it with the CHIMERE chemistry transport model, using three different datasets for the dust aerosol initial and boundary conditions (CAMS, GOCART, and MERRA-2). Results show that considering desert dust reduces estimation errors in global horizontal irradiance (GHI) by about 75 %. The dust plume caused an average of 18 % reduction in surface solar irradiance during the event. Additionally, the simulations indicated a positive bias in aerosol optical depth (AOD) and PM10 surface concentrations. The choice of dataset for initial and boundary conditions minimally influenced GHI, surface temperature, and AOD estimates, whereas PM10 concentrations and aerosol size distribution were significantly affected. This study underscores the importance of incorporating dust aerosols into solar forecasting for better accuracy.

Analysis of errors in junction temperature estimated by temperature-sensitive electrical parameter for parallel-connected SBDs

Abstract Multi-chip topology is widely adopted for large rated current power modules. Thermal management of multi-chip power modules has a significant impact on their long-term reliability. The transient thermal network model used for thermal management relies on the time response of junction temperature, which is estimated using temperature-sensitive electrical parameters (TSEPs) derived from the temperature dependency of I-V characteristics in power devices. However, the parallel connection of power devices complicates the junction temperature estimation because the sensing current is shared unevenly due to differences in the I-V characteristics of individual devices and the temperature gradient within the power module package. Consequently, the temperature estimated using a unified TSEP does not accurately represent the temperature difference among parallel-connected power devices. This paper investigates an error in the junction temperature estimated by using unified static TSEP for parallel-connected silicon carbide (SiC) Schottky barrier diodes (SBDs) as the power device. The results reveal that the unified TSEP underestimates the maximum temperature in multi-chip power modules. However, the semi-physical model of power devices enables us to estimate the junction temperature of each SiC SBD when we measure shared currents for each device.

Modelling the thermal evolution of extensional basins through lithosphere stretching factors: application to the NW part of the Pannonian Basin

Abstract. The reconstruction of thermal evolution in sedimentary basins is a key input for constraining geodynamic processes and geo-energy resource potential. We present a methodology to reproduce the most important transient thermal footprints accompanying basin formation: lithosphere extension and sedimentation. The forward model solving the transient heat equation is extended with an inversion workflow to constrain models with temperature measurement, providing estimates on model parameters, most importantly the amount of lithosphere stretching. We apply the methodology to the NW part of Hungary. We test the effect of variations in model input parameters on the resulting temperature estimates and discuss the uncertainties and limitations of the modelling technique. Realistic past and present-day temperature predictions for the entire lithosphere are achieved for a carefully assessed set of input parameters, suggesting the strong attenuation of the mantle lithosphere through extension and relatively small variations in the present-day thermal lithosphere thickness. The new temperature model can be used to constrain geodynamic processes and lithosphere structure and rheology, and it can serve as a first-order boundary condition for geothermal exploration.

Assessing Thermal Gradients Across Archean Stratigraphy Using Raman Spectra of Carbonaceous Material Thermometry and Mineral Chemistry in the Western Dharwar Craton, India

ABSTRACTThis study investigates the metamorphic evolution of the Chitradurga Schist Belt (CSB) in the Western Dharwar Craton, India, emphasising its relationship with tectonic processes. Due to the limited availability of ideal mineral assemblages for calculating metamorphic temperatures, we selected metasedimentary rocks containing carbonaceous material (CM) from each stratigraphic unit in the CSB to understand the tectono‐metamorphic evolution. Raman spectra of carbonaceous material (RSCM) thermometry was integrated with mineral chemical analyses to elucidate the regional metamorphic conditions. These findings were then coupled with the microstructural evolution and deformation history of the CSB to clarify the tectonic evolution of the terrane. Our findings reveal a distinct metamorphic gradient, with the Bababudan Group exhibiting amphibolite‐facies metamorphism at temperatures exceeding 500°C. Other stratigraphic units in the study area recorded greenschist‐facies metamorphism at temperatures below 450°C. Detailed examinations of metamorphic mineral assemblages align with RSCM temperature estimates; hornblende is a major constituent in the Bababudan Group and is replaced by actinolite and chlorite during D2 or D3 deformation. Hydrous minerals such as muscovite and chlorite are distributed across all stratigraphic units, appearing along S2 or S3 foliation. The metamorphism in the Bababudan Group is likely linked to the early stages of collisional events/metamorphism of pre‐rift sequences. In contrast, the pervasive hydration and lower‐grade metamorphism are associated with the later stages of hinterland‐thrust belt formation. This study highlights the significant influence of plate tectonic processes on regional‐scale metamorphism and deformation in the Meso‐Neoarchean Dharwar Craton.

Estimation of Outlet Water Temperature of a Cooling Tower Based on Weather Data in Kuala Lumpur, Malaysia

The study centres on predicting the outlet water temperature of a cooling tower in Kuala Lumpur (KL) by evaluating weather data, with the goal of reducing energy usage through improved forecasting. The objective of the study is to develop an accurate prediction equation for obtaining the wet-bulb temperature (Tw) using the dry-bulb temperature (Td) and relative humidity (Rh). This study also aims to prove the coefficient of performance (COP) of refrigeration cycles that are cooled by water from cooling towers. Using a weather station equipped in the UTM-KL campus, the Td and Rh were measured for every minute. The Tw was calculated by applying the bisection method and Sprung's formula to obtain the prediction equation for Tw. The maximum prediction error of the proposed equation for Tw was 0.15 °C. The prediction error by the previously proposed Stull’s formula was 0.69 °C. The COP of the water Carnot cooling refrigeration cycle was 1.9 times higher than that of the air cooling refrigeration cycle.

Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes

Abstract Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here we use a 3-D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature estimates by 43.5% in comparison with observational data. Soil moisture has little effect on soil temperature during the wet season (-1.5 %) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and soil temperature, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

Reconstructing the experienced temperature during the larval oceanic migration of anguillid eels from otolith stable oxygen isotopes

Migration routes and the depth patterns of anguillid eel larvae migrating long distances from spawning grounds in the ocean remain poorly understood. We used otolith stable isotope analysis to study the oceanic migrations of anguillid eels by reconstructing experienced water temperature histories of larvae. The otolith stable oxygen isotopes (δ18Ootolith) of recruited Anguilla japonica glass eels were analyzed to assess the relationship with the experienced water temperature of the early larval stage in laboratory experiments. A negative linear relationship between rearing water temperature and δ18Ootolith values was observed for eel leptocephali reared at five different temperatures between 19 °C and 27 °C. Subsequently, the obtained equation between water temperature and δ18Ootolith was applied to estimate the water temperature experienced by wild-caught glass eels during their larval oceanic migration according to recruitment latitude, season, and species. The δ18Ootolith values of A. japonica glass eels were significantly higher at higher recruitment latitudes from Taiwan to Japan and later recruitment periods of 6 months, from November to March, indicating that the individuals were exposed to lower water temperatures along the Kuroshio flowing northward as they reached the higher latitudes in the later recruitment seasons. Furthermore, the δ18Ootolith values of the temperate A. japonica were higher than those of the tropical Anguilla marmorata, recruited at the same locations in southern Japan and Taiwan. This suggests that larval migratory behavior in the ocean may differ between the two eel species, although they have sympatric spawning areas in the western North Pacific. Collectively, these results suggest that δ18Ootolith provides a reasonable estimation of the experienced water temperature and may prove useful for reconstructing the early migratory history of anguillid eels in the ocean.

A Novel Ultrasound Thermometry Method Based on Thermal Strain and Short and Constant Acoustic Bursts: Preliminary Study in Phantoms.

In the field of ultrasound therapy, the estimation of temperature to monitor treatments is becoming essential. We hypothesize that it is possible to measure temperature directly using a constant acoustic power burst. Under the assumption that the acoustic attenuation does not change significantly with temperature, the thermal strain induced by such bursts presents a linear relation with temperature. A mathematical demonstration is given in the introduction. Then, simulations of ultrasound waves in a canine liver model were conducted at different temperatures (from 20 °C to 90 °C). Finally, experimental measurements on phantom samples were performed over the same temperature range. The simulation and experimental results both showed a linear relation between thermal strain and temperature. This relation may suggest the foundation of a new ultrasound-based thermometry method. The potential and limitations of the method are discussed.