Local Temperature Research Articles

Identifying physiological indicators of the cognitive, thermal, and combined (cognitive-thermal) stress conditions.

Physiologically based stress detection systems have proven to be effective in identifying different stress conditions in the body to determine the source of stress and be able to counteract it. However, some stress conditions have not been widely studied, including thermal stress, cognitive stress, and combined (thermal-cognitive) stress conditions, which are frequently encountered in work or school environments. In order to develop systems to detect and differentiate these conditions, it is necessary to identify the physiological indicators that characterize each of them. The present research aims to identify which physiological indicators (heart rate, respiratory rate, galvanic skin response, and local temperature) could differentiate different stress conditions (no-stress, cognitive stress, thermal stress, and combined (thermal-cognitive) stress conditions). Thirty participants were exposed to cognitive, thermal, and combined stress sources while recording their physiological signals. The findings indicate that both mean heart rate and mean galvanic skin response identify moderate thermal and cognitive stress conditions as distinct from a no-stress condition, yet they do not differentiate between the two stress conditions. Additionally, heart rate uniquely identifies the cognitive-thermal stress condition, effectively distinguishing this combined stress condition from the singular stress conditions and the no-stress condition. Mean local temperature specifically signals thermal stress conditions, whereas mean respiratory rate accurately identifies cognitive stress conditions, with both indicators effectively separating these conditions from each other and from the no-stress condition. This is the first basis for differentiating thermal and cognitive stress conditions through physiological indicators.

Latitudinal variation in thermal performance of the common coral, Pocillopora spp.

Understanding how tropical corals respond to temperatures is important to evaluating their capacity to persist in a warmer future. We studied the common Pacific coral Pocillopora over 44° of latitude, and used populations at three islands with different thermal regimes to compare their responses to temperature using thermal performance curves (TPCs) for respiration and gross photosynthesis. Corals were sampled in the local autumn from Moorea, Guam, and Okinawa where mean (± s.d.) annual seawater temperature is 28.0±0.9°C, 28.9±0.7°C, and 25.1±3.4°C, respectively. TPCs for respiration were similar among latitudes, the thermal optimum (Topt) was above the local maximum temperature at all three islands, and maximum respiration was lowest at Okinawa. TPCs for gross photosynthesis were wider, implying greater thermal eurytopy, with a higher Topt in Moorea versus Guam and Okinawa. Topt was above the maximum temperature in Moorea, but was similar to daily temperatures over 13% of the year in Okinawa, and 53% of the year in Guam. There was greater annual variation in daily temperatures in Okinawa than Guam or Moorea, which translated to large variation in the supply of metabolic energy and photosynthetically fixed carbon at higher latitudes. Despite these trends, the differences in TPCs for Pocillopora were not profoundly different across latitudes, reducing the likelihood that populations of these corals could better match their phenotypes to future more extreme temperatures through migration. Any such response would place a premium on high metabolic plasticity and tolerance of large seasonal variations in energy budgets.

The Subseasonal Feedback of Extreme Anomalous Tibetan Plateau Snow Cover Events on the Atmosphere

Abstract Tibetan Plateau snow cover exhibits notable subseasonal variability and plays a crucial role in influencing the atmosphere. This study employs numerical experiments to investigate the atmospheric feedback resulting from extreme anomalous snow cover events on the Tibetan Plateau, with a focus on both local and nonlocal atmospheric temperatures. The findings reveal that diabatic heating, directly induced by these events, leads to a local surface energy cooling response over the Tibetan Plateau, contributing to a reduction in local temperatures. This cooling effect amplifies local atmospheric temperature anomalies associated with extreme anomalous Tibetan Plateau snow cover events, constituting approximately 50% of the total final local surface air temperature anomalies. Furthermore, the Tibetan Plateau snow cover, through adiabatic processes, exerts a nonlocal influence on atmospheric temperature and circulation. The atmospheric temperature responses downstream of the Tibetan Plateau vary at different heights and regions, featuring both cold and warm anomaly responses. These variations depend on the relative contributions of horizontal advection and vertical advection in adiabatic heating.

3D-Printed Soft Temperature Sensors Based on Thermoelectric Effects for Fast Mapping of Localized Temperature Distributions.

We propose a novel design of thermoelectric (TE) effect-based soft temperature sensors for directly monitoring localized subtle temperature stimuli. This design integrates rheology-engineered three-dimensional (3D) printing of high-performance carbon-based TE materials and polymer-based viscoelastic materials with low thermal conductivity. Rheological engineering of carbon nanotube (CNT) TE inks ensures the 3D printing of highly sensitive TE sensing units on directly written 3D soft platforms. Additionally, we pre-dope CNT inks with p- and n-type organic dopants to achieve high sensitivity and a fast response to temperature changes. The introduced 3D soft platforms with low thermal conductivity lead to an efficient thermal gradient on TE sensing units in the out-of-plane direction. Furthermore, encapsulating the temperature sensor array with the same polymer-based materials as the 3D soft platforms facilitates independent detection of localized temperature stimuli by minimizing thermal interaction between sensing units, resulting in precise temperature mapping by localized detection. Our 3D-printed soft temperature sensors exhibit high sensitivity to relatively small temperature changes, with a minimum sensing resolution of 0.1 K within tens of milliseconds. Moreover, the temperature sensor array not only detects localized temperature stimuli by imaging the temperature distribution but also demonstrates remarkable mechanical reliability against repetitive deformation with high accuracy.

Nonequilibrium Fluctuations of Global Warming

Abstract While the warming trends of Earth’s mean temperature are evident at climatological scales, the local temperature at shorter time scales are highly fluctuating. Here we show that the probabilities of such fluctuations are characterized by a special symmetry typical of small systems out of equilibrium. Their nearly universal properties are linked to the fluctuation theorem and reveal that the progressive warming is accompanied by growing asymmetry of temperature distributions. These statistics allow us to project the global temperature variability in the near future, in line with predictions from climate models, providing original insight about future extremes.

Localized Oxygen Evolution and Transport Analysis in PEM Water Electrolysis on Local Static Pressure, Temperature and Current Density

Localized Oxygen Evolution and Transport Analysis in PEM Water Electrolysis on Local Static Pressure, Temperature and Current Density

Temperature Intermittent Structures in the Fast Solar Wind

In the solar wind turbulence, proton temperature fluctuations are highly intermittent, especially at small scales in the inertial range. This phenomenon may contain information about solar wind intermittent heating. However, the physical nature of the temperature intermittency is not yet clear. Based on the measurements from Solar Orbiter between 2020 September and 2022 September, we identify 185 temperature intermittent structures at the scale of 24 s with well-defined minimum variance direction in the fast solar wind and study the nature and kinetic effects of them. According to the variations of proton temperature, the intermittent structures are classified into four types, including temperature bump, step, dip, and chain. When considering the magnetic field configuration together with other plasma properties, we find that, among the bump cases, 46 of them are linear magnetic holes (LMHs) that are mainly related to mirror-mode (MM) instability, and 43 of them are current sheets (CSs) with local temperature enhancement. The step cases are found to be associated with shock-like structures (13 cases) and tangential discontinuities (eight cases) that separate two different parcels of plasma. For the dip cases, 21 of them could be associated with the MM instability, and the rest of the 16 ones may prefer soliton scenario. The 38 chain cases are identified as compressive vortex-like structures and a mixture of LMHs and CSs. These results will help to further understand the intermittent dissipation process in the solar wind turbulence.

Widespread breakdown in masting in European beech due to rising summer temperatures.

Climate change effects on tree reproduction are poorly understood, even though the resilience of populations relies on sufficient regeneration to balance increasing rates of mortality. Forest-forming tree species often mast, i.e. reproduce through synchronised year-to-year variation in seed production, which improves pollination and reduces seed predation. Recent observations in European beech show, however, that current climate change can dampen interannual variation and synchrony of seed production and that this masting breakdown drastically reduces the viability of seed crops. Importantly, it is unclear under which conditions masting breakdown occurs and how widespread breakdown is in this pan-European species. Here, we analysed 50 long-term datasets of population-level seed production, sampled across the distribution of European beech, and identified increasing summer temperatures as the general driver of masting breakdown. Specifically, increases in site-specific mean maximum temperatures during June and July were observed across most of the species range, while the interannual variability of population-level seed production (CVp) decreased. The declines in CVp were greatest, where temperatures increased most rapidly. Additionally, the occurrence of crop failures and low seed years has decreased during the last four decades, signalling altered starvation effects of masting on seed predators. Notably, CVp did not vary among sites according to site mean summer temperature. Instead, masting breakdown occurs in response to warming local temperatures (i.e. increasing relative temperatures), such that the risk is not restricted to populations growing in warm average conditions. As lowered CVp can reduce viable seed production despite the overall increase in seed count, our results warn that a covert mechanism is underway that may hinder the regeneration potential of European beech under climate change, with great potential to alter forest functioning and community dynamics.

Thermometry mapping during CT-guided thermal ablations: proof of feasibility and internal validation using spectral CT.

Conventional CT imaging does not provide quantitative information on local thermal changes during percutaneous ablative therapy of cancerous and benign tumors, aside from few qualitative, visual cues. In this study, we have investigated changes in CT signal across a wide range of temperatures and two physical phases for two different tissue mimicking materials, each. 
Approach: A series of experiments were conducted using an anthropomorphic phantom filled with water-based gel and olive oil, respectively. Multiple, clinically used ablation devices were applied to locally cool or heat the phantom material and were arranged in a configuration that produced thermal changes in regions with inconsequential amounts of metal artifact. Eight fiber optic thermal sensors were positioned in the region absent of metal artifact and were used to record local temperatures throughout the experiments. A spectral CT scanner was used to periodically acquire and generate Electron Density weighted images. Average electron density weighted values in 1 mm3 volumes of interest near the temperature sensors were computed and these data were then used to calculate thermal volumetric expansion coefficients for each material and phase. 
Main Results: The experimentally determined expansion coefficients well-matched existing published values and variations with temperature -maximally differing by 5% of the known value. As a proof of concept, a CT-generated temperature map was produced during a heating time point of the water-based gel phantom, demonstrating the capability to map changes in electron density weighted signal to temperature. This study has demonstrated that spectral CT can be used to estimate local temperature changes for different materials and phases across temperature ranges produced by thermal ablations.

Characteristics of mesoscale eddies in the Mozambique Channel.

The mesoscale eddy characteristics of the Mozambique Warm Current were investigated by detecting and tracking satellite altimetry data from 2010 to 2019. A total of 1,086 eddies were identified in the Mozambique Channel, comprising 509 cyclonic eddies and 577 anticyclonic eddies. The results revealed that the bay area on the northwest coast of Madagascar was the main hotspot of eddy generation, and the mean amplitude and radius of the anticyclonic eddies in the Mozambique Channel were 24.23 cm and 82.7 km, respectively, which are larger than those of the cyclonic eddies. Local wind forcing had a significant impact on the formation of mesoscale eddies in the Mozambique Channel. In winter, the wind stress in the northern and southern areas of the Mozambique Channel exhibited a strong correlation with the distribution of eddy kinetic energy (EKE), where both monsoonal winds in the north and trade winds in the south could facilitate mesoscale anticyclonic eddy formation. In addition, the variability in the number of anticyclonic and cyclonic eddies in the Mozambique Channel may have exerted a significant influence on the seasonal anomalous fluctuations in local sea surface temperatures (SSTs). This study presented a novel analysis of the mesoscale eddy characteristics in the Mozambique Channel.

A three-probe method for accurate nanoscale thermal transport measurements

Measurements of transport phenomena are constantly plagued by contact resistance, prohibiting the sample's intrinsic electrical or thermal conductivity from being accurately determined. This predicament is particularly severe in thermal transport measurements due to the inability to meet similar impedance requirements for a four-probe method used in electrical resistance measurements. Here, we invent a three-probe measurement method that makes an accurate determination of thermal conductivity possible for nanomaterials. Incorporating electron beam heating provided by a scanning electron microscope (SEM) on a diffusive thermal conductor not only quantifies the thermal contact resistance, which may introduce an error of more than 270% to a sample's thermal conductivity, but also eliminates several device uncertainties that may contribute an additional 17% error in a measurement. The method also enables local temperature measurements, revealing nanoscale structural variations unfound by SEM. The high accuracy of the technique would make standardization of nanoscale thermal transport measurement possible.

Seasonal meropenem resistance in Acinetobacter baumannii and influence of temperature-driven adaptation

BackgroundRecognition of seasonal trends in bacterial infection and drug resistance rates may enhance diagnosis, direct therapeutic strategies, and inform preventive measures. Limited data exist on the seasonal variability of Acinetobacter baumannii. We investigated the seasonality of A. baumannii, the correlation between temperature and meropenem resistance, and the impact of temperature on this bacterium.ResultsMeropenem resistance rates increased with lower temperatures, peaking in winter/colder months. Nonresistant strain detection exhibited temperature-dependent seasonality, rising in summer/warmer months and declining in winter/colder months. In contrast, resistant strains showed no seasonality. Variations in meropenem-resistant and nonresistant bacterial resilience to temperature changes were observed. Nonresistant strains displayed growth advantages at temperatures ≥ 25 °C, whereas meropenem-resistant A. baumannii with β-lactamase OXA-23 exhibited greater resistance to low-temperature (4 °C) stress. Furthermore, at 4 °C, A. baumannii upregulated carbapenem resistance-related genes (adeJ, oxa-51, and oxa-23) and increased meropenem stress tolerance.ConclusionsMeropenem resistance rates in A. baumannii display seasonality and are negatively correlated with local temperature, with rates peaking in winter, possibly linked to the differential adaptation of resistant and nonresistant isolates to temperature fluctuations. Furthermore, due to significant resistance rate variations between quarters, compiling monthly or quarterly reports might enhance comprehension of antibiotic resistance trends. Consequently, this could assist in formulating strategies to control and prevent resistance within healthcare facilities.

A Simple Artificial Neural Model to Predict Dambovita River Temperature Affected by Urban Heat Islands in Bucharest City

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures.

Tree-ring based summer temperature variability since 1790 CE in the Hindu Kush region of northern Pakistan.

The Hindu Kush high-altitude regions of Pakistan are currently experiencing severe consequences as a result of global warming. In this sense, increasing soil erosion and the quick melting of glaciers are two particularly evident effects. In such a scenario, understanding long-term temperature changes is crucial for making accurate forecasts about how the Hindu Kush region may experience regional temperature changes in the future. In this study, the climate tree-ring width (TRW) analysis designated a positive and significant correlation (r = 0.622, p < 0.001) between the TRW chronology and the June to September (summer) mean maximum temperature (MMT). Using the tree-ring width of Pinus wallichiana A. B. Jackson, we reconstructed summer temperatures in the Hindu Kush region from 1790 CE. Statistical analysis showed that the reconstruction model has explained 38.7% of the climate variance during the instrumental period of 1967 to 2018 CE. Five extremely warm summer periods (≥ 4 years; before the instrumental period 1967-2018 CE) of 1804-1830, 1839-1862, 1876-1879, 1905-1910, 1923-1935 CE, and six cold summer periods of 1790-1803, 1832-1838, 1863-1875, 1880-1904, 1911-1922, and 1936-1945 CE have been observed during the past 229 years. Individually, the year 1856 CE experienced severe warmth (31.85°C), whereas 1794 CE was relatively cooler (29.60°C). The spectral multi-taper method (MTM) shows significant (p < 0.05) cycles, which take place about every 9.3, 5.7, 4.2, and 3.6 years. In particular, the 9.3-year cycle, which closely aligns with the 11-year solar activity cycle, suggests a potential correlation between solar activity and local temperature fluctuations. Moreover, our reconstruction demonstrates a significant degree of consistency when compared to actual climate data and regional temperature reconstruction series, reporting a strong logic of trust in the reliability and accuracy of our findings. This evidence reaffirms that our reconstruction shows significant and dependable regional temperature signals, notably being representative for the Hindu Kush region.

3D printed titanium carbide MXene-coated polycaprolactone scaffolds for guided neuronal growth and photothermal stimulation

The exploration of neural circuitry is paramount for comprehending the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in 3D remains a formidable challenge. Here, we introduce a method for engineering the growth of 3D neural circuits with the capability for optical stimulation. We fabricate bioactive interfaces by melt electrospinning writing (MEW) 3D polycaprolactone (PCL) scaffolds followed by coating with titanium carbide (Ti3C2Tx MXene). Beyond enhancing hydrophilicity, cell adhesion, and electrical conductivity, the Ti3C2Tx MXene coating enables optocapacitance-based neuronal stimulation, induced by localized temperature increases upon illumination. This approach offers a pathway for additive manufacturing of neural tissues endowed with optical control, facilitating functional tissue engineering and neural circuit computation.

Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001−2020)

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001–2020). Meanwhile, the mean local latent heat increased by 4.65Wm−2, following the mild reduction of emitted longwave radiation (−0.8Wm−2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001–2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Real-time swelling-collapse kinetics of nanogels driven by XFEL pulses.

Stimuli-responsive polymers are an important class of materials with many applications in nanotechnology and drug delivery. The most prominent one is poly-N-isopropylacrylamide (PNIPAm). The characterization of the kinetics of its change after a temperature jump is still a lively research topic, especially at nanometer-length scales where it is not possible to rely on conventional microscopic techniques. Here, we measured in real time the collapse of a PNIPAm shell on silica nanoparticles with megahertz x-ray photon correlation spectroscopy at the European XFEL. We characterize the changes of the particles diffusion constant as a function of time and consequently local temperature on sub-microsecond timescales. We developed a phenomenological model to describe the observed data and extract the characteristic times associated to the swelling and collapse processes. Different from previous studies tracking the turbidity of PNIPAm dispersions and using laser heating, we find collapse times below microsecond timescales and two to three orders of magnitude slower swelling times.

Near-infrared spectroscopy to quantify overall thermal process intensity during high-moisture extrusion of soy protein concentrate

High-moisture extrusion (HME) is widely used to produce meat analogues. During HME the plant-based materials experience thermal and mechanical stresses. It is complicated to separate their effects on the final products because these effects are interrelated. In this study we hypothesize that the intensity of the thermal treatment can explain a large part of the physicochemical changes that occur during extrusion. For this reason, near–infrared (NIR) spectroscopy was used as a novel method to quantify the thermal process intensity during HME. High–temperature shear cell (HTSC) processing was used to create a partial least squares (PLS) regression curve for processing temperature under controlled processing conditions (root mean standard error of cross-validation (RMSECV) = 4.00 °C, coefficient of determination of cross-validation (R2CV) = 0.97). This PLS regression model was then applied to HME extrudates produced at different screw speeds (200–1200 rpm) and barrel temperatures (100–160 °C) with two different screw profiles to calculate the equivalent shear cell temperature as a measure for thermal process intensity. This equivalent shear cell temperature reflects the effects of changes in local temperature conditions, residence time and thermal stresses. Furthermore, it can be related to the degree of texturization of the extrudates. This information can be used to gain new insights into the effect of various process parameters during HME on the thermal process intensity and extrudate quality.

Thermometric Analysis of Nanoaperture-Trapped Erbium-Containing Nanocrystals.

Temperature changes in plasmonic traps can affect biomolecules and quantum emitters; therefore, several works have sought out the capability of measuring the local temperature. Those works used ionic nanopore currents, fluorescence emission variations, and fluorescence-based diffusion tracking to measure the temperature dependence of shaped nanoapertures in metal films. Here, we make use of a stable erbium-containing NaYF4 nanocrystal that gives local temperature dependence while trapped in the nanoaperture hot spot. Ratiometric analysis of the emission at different wavelengths gives local temperature variation. Since the gold film dominates the thermal characteristic, we find that films of thickness 70, 100, and 130 nm give 0.64, 0.37, and 0.25 K/mW temperature change with laser power. Therefore, using thicker films can be effective in reducing the heating when it is not desired.