Use of thermal signal for the investigation of near-surface turbulence

Organised motion of air in the roughness sublayer of the atmosphere was investigated using novel temperature sensing and data science methods. Despite accuracy drawbacks, current fibre-optic distributed temperature sensing (DTS) and thermal imaging (TIR) instruments offer frequent, moderately precise and highly localised observations of thermal signal in a domain geometry suitable for micrometeorological applications near the surface. The goal of this study was to combine DTS and TIR for the investigation of temperature and wind field statistics. Horizontal and vertical cross-sections allowed a tomographic investigation of the spanwise and streamwise evolution of organised motion, opening avenues for analysis without assumptions on scale relationships. Events in the temperature signal on the order of seconds to minutes could be identified, localised, and classified using signal decomposition and machine learning techniques. However, small-scale turbulence patterns at the surface appeared difficult to resolve due to the heterogeneity of the thermal properties of the vegetation canopy, which are not immediately evident visually. The results highlight a need for physics-aware data science techniques that treat scale and shape of temperature structures in combination, rather than as separate features.

The Roles of Temperature-Related Post-Transcriptional Regulation in Cereal Floral Development

Temperature is a critical environmental signal in the regulation of plant growth and development. The temperature signal varies across a daily 24 h period, between seasons and stochastically depending on local environmental events. Extracting important information from these complex signals has led plants to evolve multiple temperature responsive regulatory mechanisms at the molecular level. In temperate cereals, we are starting to identify and understand these molecular mechanisms. In addition, we are developing an understanding of how this knowledge can be used to increase the robustness of crop yield in response to significant changes in local and global temperature patterns. To enable this, it is becoming apparent that gene regulation, regarding expression and post-transcriptional regulation, is crucial. Large transcriptomic studies are identifying global changes in spliced transcript variants and regulatory non-coding RNAs in response to seasonal and stress temperature signals in many of the cereal crops. Understanding the functions of these variants and targets of the non-coding RNAs will greatly increase how we enable the adaptation of crops. This review considers our current understanding and areas for future development.

Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

On the relationship between mechanical energy rate and heat dissipated rate during fatigue for a C45 steel depending on stress ratio

This work deals with the analysis in the frequency domain of the temperature signal and mechanical energy rate of C45 steel under two different fatigue stepwise loading series at stress ratios of 0.1 and -1. It was first investigated the energy distribution among the harmonic components of the signals to understand possible variations caused by a different stress ratio. In addition, the second amplitude harmonic (SAH) of heat dissipated and mechanical energy rates have been considered in the analysis and their relationship was investigated. It has been shown as it depends only on the material, hence it is valid whatever the kind of the test is without any assumption on the energy supplied to the material or material hysteresis loop stabilisation. The adopted approach allows the analysis of intrinsic dissipations by means of rapid, full-field and contactless techniques without any specific requirement on loading condition or temperature signal stabilisation.

Light Quality and Intensity Modulate Cold Acclimation in Arabidopsis

Plant survival in temperate zones requires efficient cold acclimation, which is strongly affected by light and temperature signal crosstalk, which converge in modulation of hormonal responses. Cold under low light conditions affected Arabidopsis responses predominantly in apices, possibly because energy supplies were too limited for requirements of these meristematic tissues, despite a relatively high steady-state quantum yield. Comparing cold responses at optimal light intensity and low light, we found activation of similar defence mechanisms—apart from CBF1–3 and CRF3–4 pathways, also transient stimulation of cytokinin type-A response regulators, accompanied by fast transient increase of trans-zeatin in roots. Upregulated expression of components of strigolactone (and karrikin) signalling pathway indicated involvement of these phytohormones in cold responses. Impaired response of phyA, phyB, cry1 and cry2 mutants reflected participation of these photoreceptors in acquiring freezing tolerance (especially cryptochrome CRY1 at optimal light intensity and phytochrome PHYA at low light). Efficient cold acclimation at optimal light was associated with upregulation of trans-zeatin in leaves and roots, while at low light, cytokinin (except cis-zeatin) content remained diminished. Cold stresses induced elevation of jasmonic acid and salicylic acid (in roots). Low light at optimal conditions resulted in strong suppression of cytokinins, jasmonic and salicylic acid.

Continental-scale analysis of shallow and deep groundwater contributions to streams

Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.