Temperature Data Research Articles

Were Late Jurassic climatic fluctuations responses to Pangea breakup? Evidence from isotopic analyses of belemnite rostra from the eastern Tethyan Ocean

Late Jurassic paleogeographic patterns were influenced by the growth of the Pacific plate and the breakup of Pangea, impacting the global paleoclimate during this warm and equable greenhouse period. However, the use of different proxies may introduce bias in seawater temperature reconstructions. In this study, we reconstructed midlatitude Tethyan seawater paleotemperatures in the Northern Hemisphere using carbonate clumped isotopes (Δ47) in well-preserved parts of belemnite rostra from the Upper Jurassic Hongqilafu Formation in the Taxkorgan Basin, southwest Xinjiang, China. Throughout the entire studied section, an increase in the 87Sr/86Sr ratio was observed. Comparison of this ratio with the published global seawater 87Sr/86Sr curve suggests a good correlation with the biostratigraphic and U-Pb age assignments of the Oxfordian−Tithonian interval. The Δ47-derived paleotemperature estimates indicate a long-term, relatively stable, and warm seawater temperature of ∼27 °C within the epipelagic zone of the eastern Tethyan Ocean during the Late Jurassic. Cooler seawater temperatures were recorded at the Oxfordian−Kimmeridgian boundary and in the late Tithonian, likely reflecting changes in paleogeography and paleoceanography resulting from the breakup of Pangea and/or variations in belemnite habitat depth. When compared with global seawater temperature data from different proxies, the results suggest that elevated atmospheric pCO2 levels during the Late Jurassic may have caused warmer conditions in midlatitude and polar regions. The reconstructed δ18Osea values exhibit a remarkably modern midlatitude seawater character and are more positive than values traditionally assumed for the Jurassic ice-free world. Given that the Taxkorgan Basin was located in a semi-enclosed basin and was dominated by evaporation in the Late Jurassic, the reconstructed δ18Osea values may represent regional seawater signals. Consequently, they cannot be used to support the existence of ice sheets in both polar regions. Our results suggest that climatic changes in the Jurassic greenhouse world may have been overprinted by local factors, potentially masking broader climatic trends.

Machine Learning-Based Univariate Time Series Imputation Method for Estimating Missing Values in Non-Stationary Data

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.

Sources and transport pathways of trace metals to the outer continental shelf off South Carolina and Georgia, USA revealed from the otoliths of moray eels

Trace metal concentrations in otoliths of spotted moray eels (Gymnothorax moringa), non-migratory residents of hard bottom reefs at depths of ∼30–70m along the outer shelf adjacent to South Carolina and Georgia, were analyzed to determine if results provide insights into trace metal sources and transport processes in this dynamic region of the shelf. Li and Mg appear to reflect exposure to local sea water circulated through adjacent porous rock outcrops where the eels reside. Concentrations of Mn, V, Cu, and Zn in otoliths appear to be associated with deep water upwelled along the shelf break. Based on 30 years of water temperature data from fishery surveys, Scamp Ridge, located at 32.30 N at a depth of ∼50m, has significantly lower summertime bottom temperature than elsewhere along the shelf edge, indicating locally enhanced upwelling at that location. The highest levels of Cu and Zn and the lowest of Mn and V were also found in otoliths of eels collected in the vicinity of Scamp Ridge. These results indicate that otolith chemistry in eels can reveal fine-scale structure of water mass inputs to the outer shelf of the southeastern US coast.

PSIV-14 Short-term heat stress affects the plasma metabolome of Florida Cracker sheep

Abstract Florida Cracker sheep is a heritage sheep breed and an endangered species that was developed under natural selection in Florida, US. The objective of this study was to evaluate the effect of short-term heat stress on the plasma metabolome of Florida Cracker ewes (2 yr). A total of fourteen ewes were allocated to heat stress (HT, n = 7) or cooling (CTL, n = 7) conditions for 6 wk during the summer season. Treatment groups were grouped in covered concrete floor pens with similar dimensions and characteristics and had access to shelter, feed, and freshwater ad libitum. The HT pen was exposed to regular ambient temperatures, and for the cooling pen, an evaporative cooling fan was used. Data for ambient temperature and relative humidity of HT and CTL pens were recorded every week to estimate the temperature humidity index (THI). Blood samples were collected from each experimental ewe 1 wk before the start of the experiment (baseline) and at wk 1, 2, 3, 4, 5, and 6 post-treatment. Plasma was extracted from the blood and processed for targeted metabolomics. Targeted metabolomics was performed using Nuclear Magnetic Resonance spectroscopy to quantify a total of 50 metabolites, including organic acids, amino acids, hexoses, lipids, and carnitines. Statistical analysis for biomarker and pathway enrichment was performed using Metaboanalyst 5.0. The THI values for the HT and THI groups ranged from 81.3 to 89.5 and from 73.5 to 75.6, respectively. A total of 19 differentially abundant metabolites were identified between HT and CTL. The pathway enrichment analysis showed upregulation of fatty acid and primary bile biosynthesis, and glutathione, porphyrin, glycine, serine, and threonine metabolism (P ≤ 0.05) in HT compared with CTL sheep. These metabolic pathways could have a role in the response of the ewes to short-term HT. Our findings highlight that short-term HT alters the plasma metabolome of Florida Cracker ewes. Understanding these changes can contribute to the development of effective management strategies to mitigate the detrimental impact of HT on sheep operations in the Southern US.

PSX-16 The effect of short-term heat stress on physiological, hematological, and immunological parameters of Florida Cracker Sheep

Abstract Florida Cracker sheep are a heritage sheep breed that was developed under natural selection in Florida, US. The objective of this study was to evaluate the effect of short-term heat stress on rectal temperature, respiration rate, hematological parameters, and IgA concentrations of Florida Cracker ewes (2 yr of age). A total of fourteen ewes were allocated to heat stress (HT, n = 7) or cooling (CTL, n = 7) conditions for 6 wk during the summer season. Treatment groups were grouped in covered concrete floor pens with similar dimensions and characteristics and had access to shelter, feed, and freshwater ad libitum. The HT pen was exposed to regular ambient temperatures, and for the cooling pen, an evaporative cooling fan was used. Data for ambient temperature and relative humidity of HT and CTL pens were recorded every week to estimate the temperature humidity index (THI). Blood samples were collected from each experimental ewe 1 wk before the start of the experiment (baseline) and at wk 1, wk 2, wk 3, wk 4, wk 5, and wk 6 post-treatment. Blood samples were analyzed using a hematology analyzer (Vetscan HM5, Zoetis). Plasma was extracted from the blood and was used to evaluate IgA concentrations with a commercial kit. For the statistical analysis of the physiological, hematological, and immunological parameters, a mixed model with repeated measures was used. Fixed effects included treatment (HT or CTL), wk (1 to 6), and the treatment by week interaction. Animal was fitted as a random effect and the baseline data was used as a covariate. The THI for the HT and CTL groups ranged from 81.3 to 89.5 and from 73.5 to 75.6, respectively. The HT group presented a significant (P < 0.05) increase in respiratory rate when compared with the CTL group. No significant changes were observed for rectal temperature and IgA concentrations in the experimental groups. However, for wk 3, a significant (P < 0.05) increase of neutrophils and a decrease in lymphocyte % was observed for the HT group. In conclusion, short-term heat stress increased respiratory rate and neutrophil % and decreased the lymphocyte % in Florida Cracker ewes. It is possible that heat stress could have a detrimental impact on immune mechanisms in Florida Cracker ewes. Further studies are required to investigate the effects of long-term heat stress.

Deep learning-based minute-scale digital prediction model for temperature induced deflection of a multi-tower double-layer steel truss bridge

Bridge deflection serves as a vital and intuitive index for the evaluation of bridge safety. Temperature load has the greatest influence on the bridge deformation and studies on the temperature-induced deformation prediction of long-span bridge are in limited numbers. A digital prediction model based on deep learning in minute scale is established to study the bridge deflection caused by temperature. The wavelet transform (WT) is adopted to filter the high-frequency signals of the original deflection caused by the related load factors. Three different networks, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Transformer variant, are studied and compared in the prediction process. Two different learning strategies considering different input data are also considered to optimize the prediction performance. The proposed prediction model is applied to the temperature induced deflection prediction of a multi-tower double-layer steel truss bridge. The results show that strategy A, which employs temperature time series data as input, is less effective than strategy B. Incorporating both temperature and deflection data as inputs is essential for predicting temperature-induced deflections. Moreover, the Transformer-variant network generally exhibits superior prediction performance compared to the LSTM and Bi-LSTM. The self-attention mechanism of the Transformer allows it to focus on key historical temperature points, thereby enhancing prediction accuracy.

Future energy landscapes: Analyzing the cost-effectiveness of nuclear-renewable integrated energy Systems in Retrofitting of coal power plants

Coal Power Plants (CPPs), despite their substantial contribution to global energy needs, pose significant environmental concerns due to Greenhouse Gas (GHG) emissions. Thus, the world has started thinking of alternative generation sources to replace CPPs. To replace CPPs, some energy generation resources must come into the scenario that can outshine the advantages of CPPs, such as easy availability of fuel, operational safety, and cost effectiveness. Concerning this matter, nuclear-renewable integrated systems can play a vital role as a potential replacement for CPPs. In this study, we systematically explore the transitional approach from CPPs to advanced energy systems and conduct an exhaustive comparative analysis focusing on three proposed energy system models: Greenfield, Coal-to-Nuclear (C2N), and Coal-to-Integrated Energy Systems (C2IES). Before conducting the comparative analysis, we determine the most feasible coal sites from Alaska, our surrogate location for this study, using a GIS-based nuclear reactor siting tool named “Siting Tool for Advanced Nuclear Development (STAND).” To carry out the comparative analysis among the proposed energy models for the selected coal site, we find out the optimal configuration of each system using a robust and recent nature-based metaheuristic optimization algorithm, Mountain Gazelle Optimization (MGO), complemented by another nature-based metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), for validation. The key data used in this study include solar irradiance, temperature, wind speed, load profiles, and comprehensive cost data for each system component. Since the proposed energy models are highly complex and consider several assumptions, the key research findings are strengthened by performing a comprehensive sensitivity analysis. The base case results show that C2IES can reduce the Cost of Energy (COE) by roughly 65 % compared to Greenfield and C2N and ensure the utmost reliability of the energy system. Although this cost-saving margin contrasts in the sensitivity analysis across a range of scenarios, C2IES consistently offers the most cost-effective solution, highlighting its potential for sustainable energy transition.

Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network.

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network's perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson's correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.

A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941.

Can temperature be a low-cost tracer for modelling water age distributions in a karst catchment?

To investigate the feasibility of using temperature for tracking rainfall-runoff processes in karst catchments, this study developed a tracer-aided conceptual model using temperature as a tracer by coupling water and heat transport processes at the catchment scale. The model was calibrated and validated using hourly hydrometeorological and temperature data from a 1.25 km2 karst catchment in south-western China. The results showed that the model was able to capture the water flux and temperature dynamics of different landscape units in the karst catchment. Utilizing this framework, the model delineated the flux age distribution within different landscape units, as well as the overall water transit times through the catchment. The average flux ages were determined to be approximately 80 days for the hillslope unit, 452 days for the slow flow system, and 260 days for the fast flow regime within the depression areas. These estimations align broadly with those acquired using stable isotopes as tracers. Comparative analysis revealed that the flux age distributions derived from both temperature and isotopic tracers exhibited analogous patterns at the catchment outlet and across the hillslope compartments. However, the simulations based on temperature hinted at a heightened proportion of exceedingly young and decidedly old water in the outflow, alluding to a potential overestimation of these extreme age classes by the temperature-tracer model. From the temperature-simulated transit time distribution, about 31 % of the precipitation entering during the study period have left the catchment within 3 years, and a notable proportion of rain water was either stored in the aquifer or lost through evapotranspiration. The general characteristics of the transit time distribution simulated using temperature was similar with that simulated using isotopes, though a higher proportion of precipitation being drained by fast flows was inferred from the transit time distribution simulated using temperature. Collectively, our study demonstrated that temperature can serve as a cost-effective tracer for modelling of water age distributions and associated hydrological processes in karst catchments.

Assessing the influence of temperature on slope stability in a temperate climate: A nationwide spatial probability analysis in Italy

Among landslide controls, the role of temperature in temperate regions remains poorly understood. Experiments revealed thermo-hydro-mechanical effects in geomaterials; however, field evidence of temperature-controlled landsliding is scarce. This complexity hinders the formulation of a temperature-related variable, useable in modelling across scales. Here, we identified spatial correlations between temperature and shallow landslides in gentle clay slopes. Notably, the temperature in the shallow underground is controlled by that of the atmosphere, and clays are the most sensitive to temperature among all geomaterials. Exploiting the Italian Landslide Inventory, we constructed a slope unit-based Generalised Additive Model and utilised Land Surface Temperature (LST) data from MODIS, accessible in Google Earth Engine. Interestingly, we observed a stronger positive correlation between landslides and LST, particularly in southern Italy, where categorised widespread shallow instabilities are common. Although more experiments and site-specific studies are warranted, the observed pattern appears consistent with thermal soil weakening, which may enhance landslide mobility.

A Geostatistics‐Based Tool to Characterize Spatio‐Temporal Patterns of Remotely Sensed Land Surface Temperature Fields Over the Contiguous United States

AbstractSurface fluxes and states can recur and remain consistent across various spatial and temporal scales, forming space‐time patterns. Quantifying and understanding the observed patterns is desirable, as they provide information about the dynamics of the processes involved. This study introduces the empirical spatio‐temporal covariance function and a corresponding parametric covariance function as tools to identify and characterize spatio‐temporal patterns in remotely sensed fields. The method is demonstrated using 2 km hourly GOES‐16/17 land surface temperature (LST) data over the Contiguous United States by splitting the area into 1.0° × 1.0° domains. The summer day‐time LST ESTCFs for 2018 to 2022 are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is applied to detect areas with similar spatio‐temporal LST patterns. Six main zones within CONUS are identified and characterized based on their variance and temporal and spatial characteristic length scales (i.e., scales for which the temperature variations are temporally and spatially related), respectively: (a) Eastern plains with 3 K2, ∼6 hr, and 0.15°, (b) Gulf of California with 60 K2, ∼8 hr, and 0.34°, (c) mountains and coasts transition 1 with 16 K2, ∼11 hr, and 0.25°, (d) central US, Midwest, and South cities with 5.5 K2, ∼8 hr, and ∼0.2°, (e) mountains and coasts transition 2 with ∼10 K2, ∼8 hr, and 0.2°, and (f) largest mountains and coastlines with ∼19 K2, ∼13 hr, and 0.3°. The tools introduced provide a pathway to formally identify and summarize the spatio‐temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil‐Vegetation‐Atmosphere System.

Using neural network models to estimate stellar ages from lithium equivalent widths: an eagles expansion

ABSTRACT We present an artificial neural network (ANN) model of photospheric lithium depletion in cool stars ($3000\lt T_{\rm eff}/{\rm K} \lt 6500$), producing estimates and probability distributions of age from $^7$Li 6708 Å equivalent width (LiEW) and effective temperature data inputs. The model is trained on the same sample of 6200 stars from 52 open clusters, observed in the Gaia-ESO spectroscopic survey, and used to calibrate the previously published analytical eagles model, with ages 2–6000 Myr and $-0.3 \lt $ [Fe/H] $\lt 0.2$. The additional flexibility of the ANN provides some improvements, including better modelling of the ‘lithium dip’ at ages $\lt 50$ Myr and $T_{\rm eff}\sim 3500$ K, and of the intrinsic dispersion in LiEW at all ages. Poor age discrimination is still an issue at ages >1 Gyr, confirming that additional modelling flexibility is not sufficient to fully represent the LiEW–age–T$_{\text{eff}}$ relationship, and suggesting the involvement of further astrophysical parameters. Expansion to include such parameters–rotation, accretion, and surface gravity–is discussed, and the use of an ANN means these can be more easily included in future iterations, alongside more flexible functional forms for the LiEW dispersion. Our methods and ANN model are provided in an updated version 2.0 of the eagles software.

Consistency of climatic changes at different time scales in Central England and Greenland

Characteristic variations in the Greenland isotope temperature data over the last 1000 years and in the meteorological temperature measurements collected from Central England during the past four centuries have been analyzed. We take advantage of the continuous wavelet transform to analyze the simultaneous occurrence of temperature variations of different time scales. We assess the extent to which these phenomena can be compared when examining two different northern hemisphere locations at different time scales. Among the long-term variations, we focus on the cooling at the turn of the 18th century, which occurred slightly later in Greenland than in central England, and the warming observed at present. On the short time scale, the range under study is limited to times of the order of 5-10 years. It has been found that it is on these scales that temperature variations in the two locations are relatively consistent, with a cross-correlation coefficient as high as 0.6 for timescales of the order of 9 years. The main solar activity cycle also falls within the interval of significant correlations. It is shown that despite the absence of direct correlation between temperature and solar activity, the time dependence of the wavelet cross-correlation coefficient of the two temperature series on the scale of 11 years reproduces the long-term variations of solar activity.

Shock equation of state experiments in MgO up to 1.5 TPa and the effects of optical depth on temperature determination

Laser-driven shock compression enables an experimental study of phase transitions at unprecedented pressures and temperatures. One example is the shock Hugoniot of magnesium oxide (MgO), which crosses the B1–B2-liquid triple point at 400–600 GPa, 10 000–13 000 K (0.86–1.12 eV). MgO is a major component within the mantles of terrestrial planets and has long been a focus of high-pressure research. Here, we combine time-resolved velocimetry and pyrometry measurements with a decaying shock platform to obtain pressure–temperature data on MgO from 300 to 1500 GPa and 9000 to 50 000 K. Pressure–temperature–density Hugoniot data are reported at 1500 GPa. These data represent the near-instantaneous response of an MgO [100] single crystal to shock compression. We report on a prominent temperature anomaly between 400 and 460 GPa, in general agreement with previous shock studies, and draw comparison with equation-of-state models. We provide a detailed analysis of the decaying shock compression platform, including a treatment of a pressure-dependent optical depth near the shock front. We show that if the optical depth of the shocked material is larger than 1 μm, treating the shock front as an optically thick gray body will lead to a noticeable overestimation of the shock temperature.

On atmospheric pressure and temperature correlation across various terrain types

Temperature (T), pressure (p), and density (ρ) are fundamental variables describing the atmospheric behavior. This study investigates the interdependence of these variables near Earth's surface in real-world conditions, by evaluating data from different European weather stations. It has been found that the correlation between pressure and temperature is strongly related to the dynamics of the PBL that facilitates a two-group classification. The first group includes all the stations in the plain or in the valley-floor and exhibits a weak correlation, R(p,T). 2D density plots representing hourly pressure against temperature have a distinctive triangular shape at these stations. Regardless of location, the upper boundary of this triangle consistently fits a linear equation with a constant slope and an intercept that scales with the average pressure of the station. This finding holds promising implications for enhancing the quality check of pressure and temperature data, enabling the identification of implausible measurements using a unified equation. In contrast, the second group includes stations with a strongly positive correlation R(p,T) and a more linear density plot; it includes all stations near a mountain-top. Their correlations exhibit identical features when compared to radiosounding data extracted at corresponding heights. The study concludes that: i) the first group of stations is significantly influenced by non-hydrostatic processes such as turbulence, friction and surface radiative heating/cooling in the PBL, resulting in weakly negative R(p,T) for shorter timescales that become null over longer durations; ii) the second group of stations has R(p,T) characteristics similar to the free atmosphere, predominantly regulated by hydrostatic balance and the advection of sensible heat.

Enhancing predictive monitoring of ethylene oxychlorination reactor states through spatiotemporal coupling analysis

The production of polyvinyl chloride (PVC) encounters challenges stemming from the temporal and spatial coupling characteristics inherent in the fixed bed ethylene oxychlorination process. Consequently, the implementation of enhanced safety measures and risk reduction strategies becomes imperative. This study introduces a pioneering methodology leveraging a spectral temporal graph neural network. By leveraging reactor temperature data, spatial variable decoupling facilitated by the Fourier transform, and a self-attentive mechanism within graph neural networks, the proposed approach adeptly forecasts future reactor states. The model's seamless alignment with the physical knowledge of reaction processes, validated through the adjacency matrix and hotspot region identification, underscores its efficacy in ensuring process safety and mitigating operational risks in PVC production. Empirical findings further validate the effectiveness of the approach, with predictions demonstrating an error margin of less than 0.5°C in forecasting future reactor temperatures.

Use of SLSTR Sea Surface Temperature Data in OSTIA as a Reference Sensor: Implementation and Validation

Use of SLSTR Sea Surface Temperature Data in OSTIA as a Reference Sensor: Implementation and Validation

Interannual variation of summer southwest monsoon rainfall over the monsoon core regions of the eastern Bay of Bengal and its relationship with oceans

The study looked at how summer monsoon rainfall in the eastern Bay of Bengal area changes from year to year due to Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Study used rainfall data and sea surface temperature data to see these variations. It's found that during ENSO positive phase, rainfall decreased in the eastern coastal region of the Bay of Bengal but increased in the northern Indo-Myanmar region. The opposite happened during ENSO negative phase. The study used a special analysis method called EOF and Morlet wavelet power-spectrum analysis to look for important patterns in the rainfall data and did correlation analysis to understand what causes abnormal rainfall in these regions. The study also found that the local convection and water vapor flux during ENSO positive phase are related to the anomalous rainfall in the Monsoon Core region. Rainfall is made stronger by the unusual anticyclone circulation in the upper troposphere. A strong/weak Mainland Indochina southwest monsoon (MSwM) in the positive or negative phase of ENSO can bring excess/less moisture to wet/dry the local southwest summer rainfall. In northern Indo-Myanmar, the anomalous rainfall is not only relied on the intensity of the MSwM but also the frequency of western disturbances also influences the regional rainfall, and further study need to develop.

On the use of vibrations and temperatures for the monitoring of plastic chain conveyor systems

In industrial automation, the transportation of unit loads plays a crucial role. This paper addresses the under-explored area of plastic chain conveyors, a versatile and efficient means of transport within production processes. Despite their widespread use, these conveyors suffer from wear and tear due to continuous contact between the chain and guide rails, often leading to unscheduled shutdowns and increased costs. The literature on monitoring solutions for such systems is sparse, particularly for plastic chains. This paper aims to bridge this gap by surveying the most common wear mechanisms for these systems and presenting a physical model that describes the interaction between the chain links and guide rails, based on chain of masses, springs and dampers and the Archard wear model. This model allows to relate the variations due to wear with other physical quantities, such as passage frequencies of the links and heat generated due to friction. The comprehensive model provides analytical formulas to understand the vibrations induced by the chain on the platform and the guide-rail temperatures. To validate the model and evaluate how the measurements are affected by the degradation of the system, an extensive experimental campaign was conducted on a conveyor test rig, acquiring vibration and temperature data. The end goal is to identify and discuss methodologies suitable for estimating the wear mechanisms of conveyor systems, with a focus on the constraints of industrial environments. Hence, this study aims to lay the groundwork for defining monitoring solutions that enhance the maintenance efficiency of plastic chain conveyor systems. The results suggest that the power Spectral Densities (PSD) of the platform’s accelerations are sufficient to estimate the average links pitch, which in turn could be used to assess the chain elongation. Besides, the local temperatures confirmed the model observations, making them suitable for estimating the slide-rail thinning.